Temporal Characteristics Research Articles

Synergistic regional emission reductions in China: Network evolution, spatial and temporal characteristics, and driving factor

Synergistic regional emission reduction (SRER) is an important part of the strategy to achieve carbon neutrality. In this paper, a system efficiency value-added model is proposed to measure the strength of SRER network relationships for 40,186 sets of city pairs and the SRER levels of 284 cities in China from 2011 to 2021. Subsequently, the evolution characteristics of the SRER network, the spatial and temporal characteristics of urban SRER levels, and the driving factors of the SRER network evolution are further analyzed. The results are as follows: SRER network is shifting from a fan-shaped network pattern of “three-region clustering and sporadic coverage” to a shuttle-shaped nested network pattern of “east-west linkage and multi-region support”. Time series analysis found that urban SRER level in China showed fluctuating growth characteristics during the study period. Spatial analysis reveals that the spatial autocorrelation of urban SRER level is decreasing, and the spatial distribution range is expanding towards the southwest. Emission reduction comparative advantages (CAER), green innovation division of labor (GIDL) and urban economic linkages (LCEL) are driver factors of SRER network evolution. This study proposes an evaluation method for the urban SRER level from network relationships perspective. Subsequently, network evolution characterization, spatial and temporal characterization, and driving factor analysis were conducted. The results of the study provide important theoretical guidance and inspiration for grasping the current status and enhancement paths of China's urban SRER level.

Assessing the impacts of ice penetration on monitoring water levels of high-latitude and -altitude lakes from CryoSat-2 altimetry

The CryoSat-2 satellite provides high-precision and long-term monitoring of global lake levels, offering advantages for water resource management, ecological protection, and disaster warning. However, the lake ice penetration by radar electromagnetic waves of CryoSat-2 altimetry tends to result in underestimation of water levels for lakes in high latitudes and altitudes. By contrast, the laser altimetry of ICESat-2 enables us to obtain the height measurements from lake ice surface and the ice penetration impacts on lake levels can be nearly ignored in the ICESat-2 elevation product. After converting the CryoSat-2 and ICESat-2 altimetry data to the same datum and height reference system, the water-level difference of (near-) synchronous observations between the two satellites could be mainly attributed to the influences by the lake ice penetration from CryoSat-2 data. In this study, totally 257 lakes in high-latitude (north of 50°N) and high-altitude (the Tibetan Plateau, TP) regions that are covered by both CryoSat-2 and ICESat-2 satellite tracks are examined to explore the characteristics of ice penetration impacts on water level measurements. The altimetry data during unfreezing seasons (June to October) are used to correct the systematic height biases of these two satellite measurements, thus their water level differences during freezing seasons (November to May of the following year) are deemed as the influences of ice penetration on lake level measurements by CryoSat-2 radar altimetry. The results show that the maximum underestimation depth of water level for each lake varies within the range of 0.01–3.47 m, with an average of 0.90 m. Particularly in North America with a dense distribution of lakes, the underestimation of lake levels by CryoSat-2 gradually ascends with increasing latitude. CryoSat-2 underestimates lake levels in Europe and the TP less than in North America. Among different months, the lake ice leads to the maximum underestimation on the water level by CryoSat-2 altimetry in March. The spatial and temporal characteristics of lake ice impacts on the water level measurements by CryoSat-2 altimetry would be an essential reference for quantifying and reducing the uncertainties of satellite radar altimetry for monitoring inland water dynamics.

Spatial and Temporal Characteristics and Mechanisms of Marine Heatwaves in the Changjiang River Estuary and Its Surrounding Coastal Regions

Marine heatwave (MHW) events have significant consequences for marine ecosystems and human society. This paper investigates a MHW’s spatial–temporal characteristics in the Changjiang River Estuary and its surrounding coastal regions (CRESs), as well as analyzes the drivers, using satellite and reanalysis data spanning from 1982–2021. The findings show that, during the last 40 years, all four of the MHW indicators have increased. The summer MHW is more severe than other seasons, showing a rising pattern from southeast to northwest. The rise of MHWs can be attributed to the increase in sea surface heat flux, weak wind speed, and powerful El Niño events. Additionally, two special MHW events were detected during the entire study period: Event A lasted for 191 days from 9 October 2006 to 17 April 2007; Event B had an average intensity of 4.93 °C from 5 July 1994 to 1 August 1994. For locations so close to each other, the characteristics of MHWs can also vary, and the mechanisms behind them are highly complex.

SAD-ARGRU: A Metro Passenger Flow Prediction Model for Graph Residual Gated Recurrent Networks

This paper proposes a graph residual gated recurrent network subway passenger flow prediction model considering the flat-peak characteristics, which firstly proposes the use of an adaptive density clustering method, which is capable of dynamically dividing the flat-peak time period of subway passenger flow. Secondly, this paper proposes graph residual gated recurrent network, which uses a graph convolutional network fused with a residual network and combined with a gated recurrent network, to simultaneously learn the temporal and spatial characteristics of passenger flow. Finally, this paper proposes to use the spatial attention mechanism to learn the spatial features around the subway stations, construct the spatial local feature components, and fully learn the spatial features around the stations to realize the local quantization of the spatial features around the subway stations. The experimental results show that the graph residual gated recurrent network considering the flat-peak characteristics can effectively improve the prediction performance of the model, and the method proposed in this paper has the highest prediction accuracy when compared with the traditional prediction model.

Research on the coordinated development of provincial urbanization and carbon emission efficiency of construction industry in China

BackgroundEnergy conservation and emission reduction policies restrict the economic and social development of all countries in the world, and the impact on China, which has low urbanization, is more serious. In the process of promoting urbanization, the pressure of carbon emission reduction in the construction industry has increased, and the high emissions of the construction industry have made the low-carbon development of cities face severe challenges. China is at a critical stage of urbanization development, and there is become a general consensus on how to improve the carbon emission efficiency of the construction industry. The interaction between urbanization and the carbon emission efficiency of the construction industry is a long-term and complex process. As one of the industries contributing to China’s urbanization process and carbon emissions, it is of great practical significance to explore the coordination relationship between urbanization and the carbon emission efficiency of the construction industry (CEECI) to realize the goal of “double carbon”, promoting urbanization construction and solving the problem of “green development”. Taking 30 provinces in China as the research target area, the double weighted summation method and the undesirable output superefficiency window-EBM-DEA model are used to measure the provincial urbanization level and CEECI, respectively. Then, the coupling coordination degree model of the relative development index is introduced, and the spatial autocorrelation model and the spatial and temporal differentiation characteristics of the coordination level of urbanization and the CEECI are analysed.ResultsFrom 2010 to 2021, China’s urbanization level increased steadily, but the growth rate gradually decreased. There were significant differences in urbanization levels among provinces. The eastern provinces have a higher level of urbanization but lack an impetus in the later period, while the western provinces have a lower level of urbanization but a faster growth rate. The low-carbon development trend of China’s construction industry is good, and the overall development of the CEECI shows an “N” type, and the inflection points appear in 2013 and 2018. The interprovincial urbanization level is significantly different from that of the CEECI, and the development form of the central and western provinces is better than that of the eastern provinces. The coordination level of urbanization and the CEECI in China is transitioning from the running-in stage to the coordination stage, and the coupling coordination degree between systems is on the rise, while the relative development degree is on the decline. The spatial distribution pattern is in a dynamic state of change, and the overall distribution pattern is “high in the east and low in the central and western regions”. The differences among provinces were significantly decreased, with 63.33% of provinces at the high running-in level. The provinces that entered the coordination stage were mainly located in the eastern region, and only Beijing was in the coordination stage by the end of the study. In addition, 90% of the provinces exhibited lagging efficiency, and only Jiangxi, Guangxi and Chongqing, which had low coordination levels maintained synchronous development of the two systems. The coordination level between provincial urbanization and the CEECI showed a positive spatial distribution, the global Moran index showed a “V” shape trend, and the spatial dependence of the coordination level between the two systems gradually weakened. In the local spatial distribution, there are two types of convergence: high and low.ConclusionsThe coordination degree of urbanization and the CEECI analysed in this study is an extension of the research on the relationship between the two. By integrating the two into a unified framework, the method of combining quantitative and qualitative analysis is used to further explore the coordination relationship between the two, which not only enhances the scientificity and accuracy of the research but also extends the breadth and depth of relevant theoretical research. At the same time, according to the coordination type between urbanization and the CEECI, China should propose corresponding targeted coordination and optimization paths from the perspective of urbanization and low-carbon development of the construction industry to achieve high-quality development of China’s economy and society.

Anthropogenic activities dominated the spatial and temporal changes of normalized difference vegetation index (NDVI) in the Hehuang valley in the northeastern Qinghai Province between 2000 and 2020

The Hehuang Valley (HV) is a key development area in the Qinghai Province; understanding changes in the vegetation within this area is of great significance if we are to maintain the ecological quality of this regional environment. Based on the 30 m spatial resolution Normalized difference vegetation index (NDVI) time series dataset, this paper analyzes the spatial and temporal characteristics and evolutionary trends of NDVI in the HV from 2001 to 2020 under the influences of climate change and human activities, by applying Mann-Kendall trend analysis, the Hurst index, and residual analysis. Analysis showed that firstly, high NDVI values (>0.5) were distributed in the low elevation areas of the HV except for towns and cropland, while the low NDVI values (<0.5) were mainly distributed in the high elevation regions; the NDVI exhibited an increasing trend over the study period. Second, human activities promoted NDVI growth in the HV by changing land-use types, although there is a risk of vegetation degradation in the future. Third, the proportion of NDVI changes affected by climate change and human activities was determined to be 87.24% of the HV; furthermore, the contribution of human activities was three-fold higher than that of climate change. Fourth, managers should scientifically manage grasslands and forests and implement specific anthropogenic interventions based on the characteristics of regional NDVI degradation, to improve ecosystem resilience. These results can be used to quantitatively analyze the relative contributions of natural and anthropogenic factors to the ecological changes in the HV, and provide reference guidelines for the management of ecological environments.

Study on spatial and temporal correlation characteristics for RIS-aided macrocell high-speed railway propagation systems

Study on spatial and temporal correlation characteristics for RIS-aided macrocell high-speed railway propagation systems

Reinforcing systems of the brain and quantification of their work

Background. The reinforcing systems of the brain are represented mainly by the ventral forbrain dopaminergic bundle, which innervates the emotiogenic structures of the limbic system. Their study comes down to the reproduction of unconditioned (self-stimulation, self-administration) and conditioned reflex (preference for place, temperature, color) reactions. Questions regarding the quantitative assessment of the brain's reinforcing systems remain unresolved. For self-stimulation of brain structures, the change of the pedal presses in the Skinner chamber and some calculated coefficients are used, for example, the “mismatch coefficient,” which characterizes the temporal characteristics of the pedal pressings.
 The aim of this study was to develop, test and substantiate an additional objective quantitative method for assessing the reinforcing systems of the brain, called the “addiction coefficient” based on an analysis of the effect of three psychoactive compounds (amphetamine, morphine and ethanol) in different doses on self-stimulation of the lateral hypothalamus in rats.
 Methods. The main method for studying the reinforcing systems of the brain was the reaction of self-stimulation of the lateral hypothalamus in Wistar rats, which was modulated by the administration of psychoactive substances. The psychomotor stimulant amphetamine (phenamine) hydrochloride (0.5; 1; 2; 4 mg/kg), the narcotic analgesic morphine hydrochloride (1; 2; 4; 8 mg/kg) and ethanol (0.5; 1; 2; 4 g/kg), administered intraperitoneally were used as inductors of reinforcing. The control was the administration of different doses of 0.9% NaCl solution (0.1; 0.2; 0.4; 0.8 ml/rat).
 Results. The use of different controls, characterized by an increase or decrease in the self-stimulation reaction in response to the introduction of a 0.9% NaCl solution, showed that calculated coefficients, such as the “mismatch coefficient,” can also change in different directions and do not objectively reflect the reinforcing effects of pharmacological substances. The “addiction coefficient” we propose, which reflects the component of psychic dependence, always changes unidirectionally towards an increase. The degree of this increase can be tens and hundreds of percent of the control, and is significantly independent of the initial values of self-stimulation. As expected, the “addiction coefficient” increases most clearly after the administration of the psychostimulant amphetamine and less significantly after injections of morphine and ethanol.
 Conclusion. It is concluded that the “addiction coefficient” of a psychoactive substance, calculated as the ratio of the increase in pedal presses to the value of the “mismatch coefficient,” is a clear quantitative indicator when assessing the reinforcing properties of psychoactive substances in the self-stimulation reaction of the lateral hypothalamus. The “addiction coefficient” does not significantly depend on the initial level of self-stimulation and is recommended for a comparative assessment of the reinforcing properties of primarily related psychoactive compounds.

Real Feeling and Fictional Time in Human-AI Interactions

AbstractAs technology improves, artificial systems are increasingly able to behave in human-like ways: holding a conversation; providing information, advice, and support; or taking on the role of therapist, teacher, or counsellor. This enhanced behavioural complexity, we argue, encourages deeper forms of affective engagement on the part of the human user, with the artificial agent helping to stabilise, subdue, prolong, or intensify a person’s emotional condition. Here, we defend a fictionalist account of human/AI interaction, according to which these encounters involve an elaborate practise of imaginative pretence: a make-believe in which the artificial agent is attributed a life of its own. We attend, specifically, to the temporal characteristics of these fictions, and to what we imagine artificial agents are doing when we are not looking at them.

Research on the Laws of Overlying Rock Fracture and Energy Release under Different Mining Speeds

Mining activities are key triggers for strong mine earthquakes and even rock bursts in coal mines. This study explores the impact of mining speed on the overlying strata’s deformation and energy release through theoretical analysis, numerical simulation, and the digital speckle method. The temporal and spatial evolution characteristics of the impact energy during mining are simulated. The digital speckle method illustrates a positive correlation between rapid mining and increased fracture block degree of overburden rock and roof separation, confirming that accelerated mining speed extends the fracture distance of the stope. Furthermore, numerical simulations establish that both the energy associated with overlying rock breaking and the frequency of energy occurrence events are amplified during rapid mining, in contrast to slow mining. This observation corroborates that escalating mining speed augments the energy dispensed by the breaking of the upper rock. Consequently, this escalation induces a transformation in the energy levels of mine earthquakes, culminating in a heightened incidence of large-energy mine earthquakes.

Spatial–temporal evolution and peer effects of urban green development efficiency in China

In the process of global urban development, there are urgent ecological security and environmental pollution problems, green development is the fundamental way for urban sustainable development, economic transformation and mitigation of ecological and environmental problems. Based on the panel data of 283 cities at prefecture level and above in China from 2003 to 2017, this paper analyzes spatial–temporal evolution characteristics of urban green development efficiency (UGDE) and the peer effects of UGDE between cities of different grades. It is found that during the study period, in terms of temporal evolution, the average UGDE in China increased from 0.47 in 2003 to 0.61 in 2017, with a cumulative growth rate of 29.79%, showing a rising trend in general. In terms of spatial evolution, the number of low-efficiency cities and medium-efficiency cities continued to decrease. The eastern region has always been the main distribution area of higher-efficiency cities and high-efficiency cities; in the central region, UGDE in most cities improved significantly; in the western region, UGDE has always lagged behind that in the eastern and central regions. In addition, the center of gravity of UGDE presented a trend of northwest migration in general, with a total displacement of 100.07 km, and UGDE showed a spatial dispersion trend. The empirical results indicate that the improvement of UGDE in large cities has a driving effect on that in neighboring medium cities and small cities through the positive peer effect, and the growth of UGDE in medium cities has a promoting effect on that in neighboring small cities through the positive peer effect; the increase of UGDE in medium cities has a positive peer effect on that in neighboring large cities, and the growth of UGDE in small cities has a positive peer effect on that in neighboring medium cities; UGDE promotes each other between large cities through the positive peer effect.

Research on Knowledge Tracing-Based Classroom Network Characteristic Learning Engagement and Temporal-Spatial Feature Fusion

To accurately assess students’ cognitive state of knowledge points in the learning process within the smart classroom, a knowledge tracing (KT) model based on classroom network characteristic learning engagement and temporal-spatial feature fusion (CL-TSKT) is proposed. First, a classroom network is constructed based on the information of the student ID, seating relationship, student–student interaction, head-up or head-down state, and classroom network characteristics obtained from a smart classroom video. Second, a learning engagement model is established by utilizing the student–student interactions, head-up or head-down state, and classroom network characteristics. Finally, according to the learning engagement model and the knowledge point test data, a parallel temporal attention GRU network is proposed. It is utilized to extract the temporal features of the knowledge points and learning engagement. They are fused to obtain the knowledge point-learning engagement temporal characteristics and their associated attributes. Meanwhile, a CNN is used to extract the knowledge point-knowledge point spatial features. We consider the associative properties of knowledge point-knowledge points from a spatial perspective and fuse the knowledge point-knowledge point spatial features with the knowledge point-learning engagement temporal features. To accurately characterize the cognitive state of the knowledge points and provide effective support for teachers’ accurate and sustainable interventions for learners in the teaching and learning process, this paper conducts extensive experiments on four real datasets. The CL-TSKT model in this paper shows superior performance in all four evaluation metrics, compared with the state-of-the-art KT models.

Ecological risk assessment and spatial–temporal differentiation of soil and water resources in the Hefei metropolitan area

There are important ways to solve the ecological risk problems of regional water resources and soil resources, and to promote the benign development of soil and water resources, involving scientific evaluation of the ecological risk of soil and water resources in Hefei metropolitan area, clarifying the intrinsic evolution law of ecological risk and identifying the characteristics of spatial and temporal variations. Based on the conceptual model of “ST-QS-RR”, the evaluation indicator system is constructed, the CRITIC method is used to assign weights, and the TOPSIS method, kernel density method, markov chain and resistance model are used to measure and analyse the spatial and temporal characteristics of ecological risk of soil and water resources, and to explore the main factors that cause ecological risk of soil and water resources. The results of the study show that: (1) Hefei metropolitan area and its cities show a steady decline and the characteristics of “high in the north and low in the south, high in the west and low in the east”. (2) Most of the subsystems in the Hefei metropolitan area and the cities show a decreasing trend, with its resistance factors mainly concentrated in the QS system. (3) There is club convergence in Hefei metropolitan area. When the type of adjacent domain is higher, the change of risk type is more sensitive.

Study of the probabilistic and temporal characteristics of wireless networks using the CSMA/CA access method

Objectives. The aim of this study is to develop analytical methods to evaluate the probabilistic and temporal characteristics and performance of wireless networks using the CSMA/CA access method. These methods enable the process of selecting rational operating modes to be automated and the impact of collisions in networks implementing the 802.11 protocols to be reduced.Methods. The methods employed herein include reliability theory, theory of random processes, queuing theory, and the Laplace–Stieltjes transform.Results. A problem statement is presented and developed, along with an analytical method for evaluating the probabilistic and temporal characteristics and performance of wireless networks using the CSMA/CA access method. This method considers time constraints on information transmission, thus expanding the applicability of previously proposed analytical approaches for studying Ethernet local area networks. The analysis of networks that use the CSMA/CA access method was carried out. An original mathematical model was developed that allows evaluating various characteristics of packet transmission processes in wireless networks under time constraints on the transmission. These characteristics include latency, packet transfer time, node load, and network performance. A software package was developed to simplify the analysis and evaluation of various operation modes of wireless networks using the CSMA/CA access method.Conclusions. We demonstrate the need for developing nested analytical models describing packet transmission processes in wireless networks under time constraints on link-layer transmission. This implies the development of more complex models for more exact description of packet transmission processes in such networks. The software package developed herein enables the various options for the functioning of the network to be studied and analytical calculations to be performed. Calculations were carried out, in order to assess the probabilistic and temporal characteristics of packet transmission processes and the wireless network performance. The research involved varying the number of workstations and the intensity of packet flows entering the network nodes under the time constraint on packet transmission. The application of the developed mathematical models will be useful in creating and optimizing wireless networks such as Wi-Fi networks, networks based on the IEEE 802.11 standard, and other data transmission systems using the CSMA/CA access method. Such models and the analysis based on them will be useful in optimizing network performance, adjusting parameters, as well as selecting the capacity and configuration of wireless networks.

An efficient Parkinson's disease detection framework: Leveraging time-frequency representation and AlexNet convolutional neural network

Parkinson's disease (PD) is a progressive neurodegenerative disorder affecting the quality of life of over 10 million individuals worldwide. Early diagnosis is crucial for timely intervention and better patient outcomes. Electroencephalogram (EEG) signals are commonly used for early PD diagnosis due to their potential in monitoring disease progression. But traditional EEG-based methods lack exploration of brain regions that provide essential information about PD, and their performance falls short for real-time applications. To address these limitations, this study proposes a novel approach using a Time-Frequency Representation (TFR) based AlexNet Convolutional Neural Network (CNN) model to explore EEG channel-based analysis and identify critical brain regions efficiently diagnosing PD from EEG data. The Wavelet Scattering Transform (WST) is employed to capture distinct temporal and spectral characteristics, while AlexNet CNN is utilized to detect complex spatial patterns at different scales, accurately identifying intricate EEG patterns associated with PD. The experiment results on two real-time EEG PD datasets: San Diego dataset and the Iowa dataset demonstrate that frontal and central brain regions, including AF4 and AFz electrodes, contribute significantly to providing more representative features compared to other regions for PD detection. The proposed architecture achieves an impressive accuracy of 99.84% for the San Diego dataset and 95.79% for the Iowa dataset, outperforming existing EEG-based PD detection methods. The findings of this research will assist to create an essential technology for efficient PD diagnosis, enhancing patient care and quality of life.

Promoting Sustainable Coal Mining: Investigating Multifractal Characteristics of Induced Charge Signals in Coal Damage and Failure Process

Monitoring and preventing coal–rock dynamic disasters are essential for ensuring sustainable and safe mining. Induced charge monitoring, as a geophysical method, enables sustainable monitoring of coal–rock deformation and failure. The induced charge signal contains crucial information regarding damage evolution, making it imperative and important to explore its temporal characteristics for effective monitoring and early warnings of dynamic disasters in deep mining. This paper conducted induced charge monitoring tests at different loading rates, investigating the multifractal characteristics of induced charge signals during the early and late stages of loading. It proposed the maximum generalized dimension D(q)max, multifractal spectrum width Δα, and height difference Δf as multifractal parameters for induced charge signals. Additionally, quantitative characterization of coal damage was performed, studying the variation patterns of signal multifractal characteristic parameters with coal damage evolution. This study revealed the induced charge signal of the coal body multifractal characteristics in the whole loading process. In the late loading stage, the double logarithmic curve demonstrated some nonlinearity compared to the previous period, indicating the higher non-uniformity of the induced charge time series. D(q)max and Δα in the late loading stage were higher than those in the early stage and increased with loading rates. As coal damage progressed, there were significant jumps of D(q)max in both the early and late stages of damage, with larger jumps indicating richer fracture events in the coal. The width Δα showed an overall trend of increase–decrease–increase with coal damage evolution, while the height difference Δf fluctuated around zero in the early stage of damage development but increased significantly during severe damage and destruction. By studying the multifractal characteristics of induced charge signals, this study provides insights for the early identification of coal–rock dynamic disasters.

EEG Microstate Associated with Trait Nostalgia.

Nostalgia, a self-related emotion characterized by its bittersweet yet predominantly positive nature, plays a vital role in shaping individual psychology and behavior. This includes impacts on mental and physical health, behavioral patterns, and cognitive functions. However, higher levels of trait nostalgia may be linked to potential adverse outcomes, such as increased loneliness, heightened neuroticism, and more intense experiences of grief. The specific electroencephalography (EEG) feature associated with individuals exhibiting trait nostalgia, and how it differs from others, remains an area of uncertainty. To address this, our study employs microstate analysis to investigate the differences in resting-state EEG between individuals with varying levels of trait nostalgia. We assessed trait nostalgia in 63 participants using the Personal Inventory of Nostalgia and collected their resting-state EEG signals with eyes closed. The results of the regression analysis indicate a significant correlation between trait nostalgia and the temporal characteristics of microstates A, B, and C. Further, the occurrence of microstate B was significantly more frequent in the high trait nostalgia group than in the low trait nostalgia group. Independent samples t-test results showed that the transition probability between microstates A and B was significantly higher in the high trait nostalgia group. These results support the hypothesis that trait nostalgia is reflected in the resting state brain activity. Furthermore, they reveal a deeper sensory immersion in nostalgia experiences among individuals with high levels of trait nostalgia, and highlight the critical role of self-referential and autobiographical memory processes in nostalgia.

Spatial–Temporal Pattern Analysis and Development Forecasting of Carbon Stock Based on Land Use Change Simulation: A Case Study of the Xiamen–Zhangzhou–Quanzhou Urban Agglomeration, China

The spatial–temporal distribution and evolution characteristics of carbon stock under the influence of land use changes are crucial to the scientific management of environmental resources and the optimization of land spatial layout. Taking the Xiamen–Zhangzhou–Quanzhou urban agglomeration in the southeastern coastal region of China as an example, based on seven land use types from 1990 to 2020, including cultivated land, woodland, and construction land, we quantitatively investigate the spatial–temporal patterns of carbon stock development and the spatial correlation of carbon stock distribution. Additionally, two scenarios for the development of urban and ecological priorities in 2060 are established to investigate the effects of land use changes on carbon stock. The results indicate that (1) the research area has formed a land use spatial pattern centered around urban construction in the eastern bay area, with the western forest area and coastal forest belt serving as ecological barriers. Carbon stock is influenced by land use type, and the distribution of total carbon stock exhibits a spatial aggregation phenomenon characterized by “low in the southeast, high in the north, and medium in the center”. (2) Distance of trunk and secondary roads, elevation, slope, watershed borders, population size, and gross domestic product (GDP) factors are the main drivers of the growth of land use types. The primary causes of the reduction in carbon stock are the widespread conversion of cultivated land, woodland, and grassland into construction land, as well as water and unused land. (3) In 2060, there will be a decrease of 41,712,443.35 Mg in the urban priority development scenario compared to 2020, and a decrease of 29,577,580.48 Mg in the ecological priority development scenario. The estimated carbon stock under the two scenarios varies by 12,134,862.88 Mg. The average carbon storage of Zhangpu County, Quangang County, and Jimei County is expected to rise by one level under the ecological protection scenario, indicating that the vast forest area can become a potential area to maintain carbon stock. It is crucial to encourage the coordinated development of peri-urban agroforestry and ecological barriers, as well as to establish a harmonious spatial pattern of land use and carbon stock at the scale of urban agglomerations.

Changes in Vegetation NDVI and Its Response to Climate Change and Human Activities in the Ferghana Basin from 1982 to 2015

Exploring the evolution of vegetation cover and its drivers in the Ferghana Basin helps to understand the current ecological status of the Ferghana Basin and to analyze the vegetation changes and drivers, with a view to providing a scientific basis for regional ecological and environmental management and planning. Based on GIMMS NDVI3g and meteorological data, the spatial and temporal evolution characteristics of NDVI were analyzed from multiple perspectives with the help of linear trend and Mann–Kendall (MK) test methods using arcgis and the R language spatial analysis module, combined with partial correlation coefficients and residual analysis methods to analyze the impacts of climate change and human activities on the regional vegetation cover of the Ferghana Basin from 1982 to 2015. NDVI driving forces. The results showed the following: (1) The growing season of vegetation NDVI in the Ferghana Basin showed an increasing trend in the 34-year period, with an increase rate of 0.0044/10a, and the spatial distribution was significantly different, which was high in the central part of the country and low in the northern and southern parts of the country. (2) Temperature and precipitation simultaneously co-influenced the vegetation NDVI growth season, with most of the temperature and precipitation contributing in the spring, most of the temperature in the summer being negatively phased and the precipitation positively correlated, and most of the temperature and precipitation in the fall inhibiting vegetation NDVI growth. (3) The combined effect of climate change and human activities was the main reason for the overall rapid increase and great spatial variations in vegetation NDVI in China, and the spatial distribution of drivers, namely human activities and climate change, contributed 44.6% to vegetation NDVI in the growing season. The contribution of climate change and human activities to vegetation NDVI in the Ferghana Basin was 62.32% and 93.29%, respectively. The study suggests that more attention should be paid to the role of human activities and climate change in vegetation restoration to inform ecosystem management and green development.

Optical solitons for 2D-NLSE in multimode fiber with Kerr nonlinearity and its modulation instability

This paper deals with the soliton solutions for beam movement within a multimode optical fiber featuring a parabolic index shape. It is considered that a Two-Dimensional Nonlinear Schrödinger Equation (2D-NLSE) with an instantaneous Kerr nonlinearity of the kind can represent the beam dynamics. Nonlinear Multimode Optical Fibers (MMFs) of this kind are gaining popularity because they provide novel approaches to control the spectral, temporal, and spatial characteristics of ultrashort light pulses. We gain the optical soliton solutions for the nonlinear evolution beam dynamics using the Jacobi Elliptic Function Expansion (JEFE) method. The exact analytical solution of Nonlinear Partial Differential Equations (NLPDEs) can be achieved with wide application using the effective JEFE approach. These solutions are obtained in the form of dark, bright, combined dark–bright, complex combo, periodic, and plane wave solutions. Additional solutions for Jacobi elliptic functions, encompassing both single and dual function solutions, have been acquired. This approach is based on Jacobi elliptic functions, which will provide us the exact soliton solutions to nonlinear problems. Additionally, we will analyze the Modulation Instability (MI) for the underlying model. Moreover, we show the physical behavior of the beam propagation in a multimode optical fiber the three-dimensional, two-dimensional, and their corresponding contour plots are dispatched using the different values of parameters.