Spatiotemporal Coupling Research Articles

Shifts bidirectional dependency between vegetation greening and soil moisture over the past four decades in China

Soil moisture (SM) has changed significantly over the past 40 years in China, while NDVI has varied dramatically, leading to increasing regional conflict between vegetation growth and water resource use. Quantifying the bidirectional dependency between SM and NDVI is essential for understanding the balance between land vegetation and water resources. However, few studies have reported their mutual feedback and spatiotemporal bidirectional dependency. This paper aims to reveal the bidirectional dependency between SM and NDVI using Granger causality test to show spatiotemporal tendency coupling patterns through trend coupling analysis, wavelet transform, and lag correlation. The Results indicated that a coupling relationship existed between SM and NDVI over most of China. The unidirectional Granger effect between SM on NDVI was 58 %, the unidirectional Granger effect of NDVI on SM was 26 %, and the bidirectional Granger relationship between SM and NDVI was 16 %. The Granger relationship is different for different soil layers or land cover types. SM and NDVI increased together in 36 % of the land cover areas, but SM increased and NDVI decreased in 12 %, and the SM decreased and NDVI increased in 27 %. The trend coupling between SM and NDVI has spatial heterogeneity. There is no change rule of coupling relationship with drought variation, but SM and NDVI increased together with more overlapping ecological restoration projects. SM decreased with the increase of NDVI from 1982 to 2010 but has reversed since 2011. NDVI and SM co-increased significantly with the implementation of ecological restoration projects during 2011–2022. The coupling relationship has a time lag effect of 1–3 months, and the time lag of NDVI to SM of deep soil layers mainly occurred in Southern China. This study illustrated the coupling framework and feedback analysis between SM and vegetation greening, which is helpful for the scientific implementing ecological restoration projects and the management of ecosystem carbon and water cycles.

Spatiotemporal coupling analysis of land urbanization and carbon emissions: A case study of Zhejiang Province, China

AbstractUrban expansion is one of the most important drivers of carbon emissions. It not only causes the invasion of terrestrial carbon sinks but also aggravates carbon emissions through social and economic activities. Because the effect of urban expansion on carbon emissions varies at different stages of urban development, spatiotemporal analysis of the dynamic relationship between urban expansion and carbon emissions is critical for low‐carbon urban planning. By combining nighttime light remote sensing and panel data, this study explored the spatiotemporal coupling characteristics of urban expansion and carbon emissions in a case study of Zhejiang in China. Spatial overlap analysis was used to measure the consistency of the spatial movement trajectories, and the coupling coordination degree model was used to dynamically investigate the interaction between urban land expansion and carbon emission increase. In this way, the dynamic coupling is represented from both a temporal and spatial perspective. The results showed that both the carbon emission and urban expansion growth rates first increased and then decreased, and their spatial coupling became increasingly close. Significantly, the coordinated development level between carbon emissions and urban expansion presented an inverted U‐shaped curve, which is consistent with the Kuznets curve theory. Overall, our research revealed the regularity of carbon emissions associated with land urbanization, which can help policymakers and urban planners to achieve sustainable land management.

Integrated effects of meteorological factors, edaphic moisture, evapotranspiration, and leaf area index on the net primary productivity of Winter wheat − Summer maize rotation system

Net primary productivity (NPP) assumes a pivotal role in the realm of plant growth, organic matter production, and carbon cycling. Nevertheless, the mechanism underlying the integrated effects of meteorological factors, soil water content (SWC), evapotranspiration (ET), and leaf area index (LAI) on the NPP in agricultural systems remains unclear. Elucidating the evolution pattern of NPP in agricultural system and its responding mechanism to meteorological factors, SWC, ET, and LAI hold pronounced significant for sustainable agricultural operation, agroecological maintenance, and climate-adaptive strategy optimization. This study focused on the winter wheat - summer maize rotation, a predominant agricultural system, in the North China Plain (NCP). Spatiotemporal pattern of NPP was clarified using geostatistical technique, Mann-Kendall test, Sen's Slope, and Rescaled Range analysis. The mechanism underlying the integrated response of NPP to different meteorological factors, SWC, ET, and LAI was revealed through spatiotemporal coupling correlation analysis, Random Forest, and Structural Equation Modeling. The results demonstrated a widespread annual increase in NPP across the majority of rotation areas from 2000 to 2019, with a prominent geospatial heterogeneity. Meteorological factors, SWC, ET, and LAI collectively explained 70.27% of NPP variability. Of which, ET, FAPAR, P, LAI, Tmax, Tmin, and the SWC in 40–100 cm were predominant factors affecting NPP, explaining 63.59% of its variability. These factors exerted multidimensional effects on NPP, instead of a unilateral induction. Despite the limited direct effect, precipitation indirectly improved NPP by significantly increasing the SWC in 40–100 cm soil horizon. The latter not only directly enhanced NPP, but also indirectly promoted NPP by facilitating ET. In context of drought and water deficit, maintaining adequate soil moisture in the 40–100 cm layer through irrigation emerges as a potential strategy to ensure sustainable productivity. ET exerted the greatest comprehensive effect and the largest direct promotion on NPP. LAI, FAPAR, and SWC induced NPP variation indirectly through affecting ET. These consistently underscored the major relationship between productivity and water use. Future intensive agriculture in the NCP should prioritize the trade-off between carbon sequestration and water loss at plant physiological level. Improving the carbon sequestration per unit of water consumption through optimizing the photosynthesis-transpiration mechanism is an imperative climate-adaptative pathway for achieving sustainable food provision and mitigating water crisis.

Terrestrial ecological drought dynamics and its response to atmospheric circulation factors in the North China Plain

Ecological drought is a natural disaster caused by the shortage of water supply, which can lead to many problems such as the reduction of vegetation greenness, shrinkage of water area, and destruction of biological habitats. In view of the increasingly serious problem of ecological drought in the North China Plain (NCP), the remotely sensed vegetation health index (VHI) was adopted to characterize the ecological drought phenomenon. In this study, the modified Mann-Kendall (MMK) and Hurst index methods were used to analyze the spatio-temporal coupling dynamic variation of ecological drought. Subsequently, the Breaks for Additive Seasons and Trend (BFAST) algorithm was employed to explore the segmented trend characteristics and mutation point types. Furthermore, the cross wavelet transform and multiple wavelet coherence were applied to reveal the mutual effect of atmospheric circulation factors on ecological drought. The results indicated that: (1) the ecological drought showed an overall alleviation trend from 1982 to 2020 in the NCP; (2) the trend type of VHI was interrupted increase in Tianjin (TJ) and Hebei (HB) with a mitigated drought trend, and was decrease to increase in Beijing (BJ) and Henan (HN) with a slight increase and then significant decrease drought trend, and all the mutation points occurred before 2000 during the study period; (3) the most severe ecological drought occurred in winter with a minimal VHI of 0.519, and the least severe ecological drought appeared in summer with a maximal VHI of 0.581; and (4) the combination of atmospheric circulation factors, North Atlantic Oscillation (NAO)-Atlantic Multidecadal Oscillation (AMO)-Arctic Oscillation (AO), had the strongest driving effect on ecological drought, which can be adopted as input variables of drought pre-warning mechanism for improving the accuracy of drought prediction.

Building cooling load forecasting of IES considering spatiotemporal coupling based on hybrid deep learning model

Accurate short-term forecasting of building cooling load (CLF) in an integrated energy system (IES) is essential for effective building energy management. However, the existing CLF models for IES often treat each building as an independent entity and neglect the spatiotemporal correlation among buildings. To address this research gap and achieve accurate CLF, this paper proposes a new hybrid deep learning model that considers spatiotemporal coupling. First, the coupled spatial–temporal features among different buildings were analyzed, and the meteorological factors were screened based on the Spearman's rank order correlation coefficient (SROCC). Second, synchrosqueezing wavelet denoising (SWT) was adopted to denoise the historical cooling load (CL) data, remove high-frequency noise, and improve data quality. Third, the TTGAT-GTC model was constructed for the CLF of an IES. A temporal trend-aware graph attention network (TTGAT) captured the spatial correlation of CL between buildings. A gated temporal convolution layer (GTC) was constructed to extract the trend in the dynamic temporal variation in historical load. Residual and skip connections were applied to avoid gradient disappearance and increase the computational efficiency of the model. To validate the effectiveness of the proposed SWT-TTGAT-GTC model, this paper compared the proposed model with four benchmark models using MAPE, RMSE, MAE, and R2. The experimental results showed that the performance of the proposed CL forecasting model is superior and that the proposed model appropriately introduces the spatio-temporal coupling information between buildings.

Research on the Collaborative Optimization of the Power Distribution Network and Traffic Network Based on Dynamic Traffic Allocation

With the increasing penetration rate of electric vehicles, the spatiotemporal coupling relationship between the power distribution network and traffic network is stronger than ever before. Under the dynamic wireless charging mode, traffic jam charging is introduced and the dynamic loading process of traffic flow is described using a cellular transmission model. The charging load is related to traffic flow and serves as a bond between the power distribution network and traffic network. The traffic flow achieves balanced allocation under dynamic user equilibrium conditions, and cooperatively optimizes the power flow of the power distribution network in conjunction with charging loads. Numerical analysis shows that this model can accurately depict the congestion situation during peak travel periods, and alleviate traffic congestion and distribution network voltage out of range.

Four-dimensional dynamics of multirotational transition stimulated rotational Raman scattering in air

Stimulated rotational Raman scattering in air is a powerful parasitic process that degrades high intensity laser beams and pulses propagated over significant distances. Conversely, it is used beneficially in the context of Raman lasers. Through this inelastic scattering process, laser photons are converted to higher (anti-Stokes) or lower (Stokes) energies, according to rotational mode transitions in nitrogen and oxygen diatomic molecules. The full wave-mixing problem involves numerous frequencies, and it is consistently assumed that only one rotational mode contributes to the conversion process. We instead present a dynamic 4D multirotational model that is implemented in a parallelized manner within the Virtual Beamline++ optical modeling package allowing high-resolution 4D studies. We highlight the effect that spontaneous emission plays in large and small beam-width setups, even in the highly saturating regime. The weaker transition modes play a large role in the persistent dynamics and can lead to complex spatiotemporal coupling through nonlinear competition of the modes. We highlight how and why these weaker modes persist, how the size and shape of speckle patterns depends highly on the initial beam profile, and how weaker modes can transiently become stronger as a result of such competition.

Analyzing the spatiotemporal coupling relationship between public opinion and the epidemic during COVID-19

PurposeThe outbreak of COVID-19 has become a major public health emergency worldwide. How to effectively guide public opinion and implement precise prevention and control is a hot topic in current research. Mining the spatiotemporal coupling between online public opinion and offline epidemics can provide decision support for the precise management and control of future emergencies.Design/methodology/approachThis study focuses on analyzing the spatiotemporal coupling relationship between public opinion and the epidemic. First, based on Weibo information and confirmed case information, a field framework is constructed using field theory. Second, SnowNLP is used for sentiment mining and LDA is utilized for topic extraction to analyze the topic evolution and the sentiment evolution of public opinion in each coupling stage. Finally, the spatial model is used to explore the coupling relationship between public opinion and the epidemic in space.FindingsThe findings show that there is a certain coupling between online public opinion sentiment and offline epidemics, with a significant coupling relationship in the time dimension, while there is no remarkable coupling relationship in space. In addition, the core topics of public concern are different at different coupling stages.Originality/valueThis study deeply explores the spatiotemporal coupling relationship between online public opinion and offline epidemics, adding a new research perspective to related research. The result can help the government and relevant departments understand the dynamic development of epidemic events and achieve precise control while mastering the dynamics of online public opinion.

Tectonic Subsidence and Its Response to Geological Evolution in the Xisha Area, South China Sea

The evolution and mechanisms of tectonic subsidence in the Xisha area are poorly investigated, especially the spatiotemporal distribution features and reasons for the variations in tectonic subsidence. In this study, multi-channel seismic data and stratigraphic and lithologic features of wells are used to examine tectonic subsidence in the Xisha area from the Paleogene to Quaternary. The largest tectonic subsidence in the Xisha area is located in the Changchang Depression, with a maximum subsidence of 5.4 km, while the smallest tectonic subsidence is located on the Guangle Uplift and Xisha Uplift, which are close to 1.0 km and 1.5 km, respectively. Two rapid tectonic subsidence phases were mainly in the Oligocene, and from Middle to Late Miocene, with maximum subsidence rates of 0.45 m/ky and 0.32 m/ky, respectively. Five phases for the tectonic subsidence are proposed since the Paleogene based on our data. (1) The slow subsidence phase during the Eocene (53.5–32 Ma) was due to the transchronicity of the basement in the pro-rifted stage. (2) The rapid subsidence phase was common in the south and north margins of Qiongdongnan Basin, because of the faults triggered by the inherited stretched and thinned of crust in the Oligocene from 32 to 23.3 Ma. (3) The interim phase followed the rapid subsidence phase was in the Early Miocene (23.3–15.5 Ma) and marked the end of the rifted stage. (4) The accelerated rise phase started from the Middle Miocene (15.5 Ma) to the Late Miocene (5.5 Ma), and the reversal of the Red River Fault Zone may be tied to the acceleration of the tectonic subsidence. (5) The transitional phase started in the Pliocene (5.5 Ma) and lasts to the present. As the Red River Fault Zone changed from sinistral to dextral movement, the stress field of the study area has changed. Our results are helpful to better understand the spatiotemporal coupling relationship between tectonic subsidence and regional geological evolution in the Xisha area, South China Sea.

Spatio-temporal characteristics and coupling coordination relationship between urbanization and atmospheric particulate pollutants in the Bohai Rim in China

The coordinated development of urbanization and atmospheric environment is of great significance for improving the quality of urbanization and solving air pollution problems in the Bohai Rim region. We have constructed urbanization quality level system indicator system and atmospheric environment system indicator system. By combining the coupling coordination degree model and spatial autocorrelation method, we analyze the spatiotemporal changes of urbanization, PM2.5, and PM10 of 43 cities in the Bohai Rim region from 2015 to 2020 using the coupling coordination model and spatial autocorrelation method, revealing the spatiotemporal evolution law of the coupling coordination relationship between regional urbanization system and atmospheric environment system. The results show that: (1) The overall urbanization level is increasing and the concentration of atmospheric particulate matter pollution is decreasing from 2015 to 2020 in the Bohai Rim; the urbanization level shows a spatial distribution pattern of high in the northeast and low in the southwest, and the concentrations of PM2.5 and PM10 show a pattern of high in the southwest and low in the northeast, with obvious clustering characteristics; (2) The coupling and coordination relationship between urbanization and atmospheric environment is showing an increasing trend in the Bohai Rim region. During the research period, the lowest, highest, average, and coupling coordination levels of the coupling and coordination scheduling are continuously increasing. The spatial distribution pattern of the coupling coordination degree is low in the southwest and high in the northeast, and there is a strong regional convergence of the coupling coordination type transformation, with certain spatial and temporal dependence.

Evaluation of typical ecosystem services in Dabie Mountain area and its application in improving residents' well-being.

Research on ecosystem services and residents' well-being in old revolutionary base areas is an important task for China's ecological civilization construction and rural revitalization. Taking Jinzhai County, the core area of Dabie Mountains, China, as an example, based on InVEST model, the methods of spatial autocorrelation and coupling coordinated development degree, the spatiotemporal evolution, spatial heterogeneity and coupling association patterns of ecosystem services and multidimensional well-being in the study area from 2005 to 2020 were discussed. The major results are: In the past 15 years, in the core area of the Dabie Mountains, ecosystem services such as food supply, soil retention and water yield showed an upward trend, carbon sequestration and biodiversity maintenance showed a downward trend. The comprehensive index of multidimensional well-being in the core area of Dabie Mountain increased by 27.23% and the spatial difference in multidimensional well-being is gradually narrowing. By the analysis of coupling coordination, the number of units with the type of coupling disharmony between ecosystem services and multidimensional well-being in the study area decreased significantly from 56.85% in 2005 to 26.81% in 2020, respectively. The analysis of geographical detection showed that the habitat quality factor was the dominant controlling factor of coupling coordination spatial difference. By bivariate spatial autocorrelation analysis, in the past 15 years, the number of units with the "high ecology-high well-being" synergy type increased from 5.44% to 13.31%. The results can provide a reference for accurate identification, optimal regulation and synergistic improvement between ecosystem services and relative poverty in the Dabie Mountain area.

A cell-centered spatiotemporal coupled method for the compressible Euler equations

A cell-centered spatiotemporal coupled method is developed to solve the compressible Euler equations. The spatial discretization is performed using an improved weighted essentially non-oscillation scheme, where the Harten–Lax–van Leer–contact approximate Riemann solver is used for computing the numerical fluxes. A two-stage fourth-order scheme is adopted to carry out time advancement for unsteady problems. The proposed method is featured by spatiotemporal coupling time-stepping that can be generalized without using the case-dependent generalized Riemann problem solver. A number of one- and two-dimensional test cases are presented to demonstrate the performance of the proposed method for solving the compressible Euler equations on structured grids. The numerical results indicate that the novel method can achieve relatively large Courant–Friedrichs–Lewy (CFL) number compared to other studies that implement the two-stage fourth-order scheme, and that it is more capable of capturing small-scale flow structures than the Runge–Kutta (RK) method.

Measuring spatio-temporal couplings using modal spatio-spectral wavefront retrieval.

Knowledge of spatio-temporal couplings such as pulse-front tilt or curvature is important to determine the focused intensity of high-power lasers. Common techniques to diagnose these couplings are either qualitative or require hundreds of measurements. Here we present both a new algorithm for retrieving spatio-temporal couplings, as well as novel experimental implementations. Our method is based on the expression of the spatio-spectral phase in terms of a Zernike-Taylor basis, allowing us to directly quantify the coefficients for common spatio-temporal couplings. We take advantage of this method to perform quantitative measurements using a simple experimental setup, consisting of different bandpass filters in front of a Shack-Hartmann wavefront sensor. This fast acquisition of laser couplings using narrowband filters, abbreviated FALCON, is easy and cheap to implement in existing facilities. To this end, we present a measurement of spatio-temporal couplings at the ATLAS-3000 petawatt laser using our technique.

Two-layer management of HVAC-based Multi-energy buildings under proactive demand response of Fast/Slow-charging EVs

Building heating, ventilation, and air conditioning (HVAC) and associated energy consumption make up the more and more important part of the world, whose reduction provides a cost-effective path to the “dual carbon” goal. This paper proposes a two-layer management of HVAC-based multi-energy buildings under proactive demand response of fast/slow-charging electric vehicles (EVs). In this paper, the building HVAC is mathematically formulated via a R-C thermodynamic model, which coordinates with multi-energy converters and storages to form a 100% renewable building. The building management is a challenging optimization problem due to its severe constraints and strong spatio-temporal couplings. In the first layer, a day-ahead multi-energy dispatch is formulated to economically optimize the electrical, heat, gas energy carriers. In the second layer, alternating current (AC) slow charging and direct current (DC) fast charging types of EVs are considered and managed via a novel real-time supply–demand pricing mechanism. After acquiring the economical dispatch references in the first layer, the second layer implements a model predictive control (MPC)-based real-time scheduling to handle the multi-energy supply–demand fluctuations. The original two-layer optimization is further handled via mixed-integer linear program (MILP) reformulation for high-efficient solving. Comparisons have shown the advantageous performances of the proposed two-layer optimization over economics and practicability. Simulations results show that the overall system operating cost can be reduced by at most 3.01% with a higher operational flexibility in building management.

Spatiotemporal variation and coupling of grazing intensity and ecosystem based on four quadrant model on the Inner Mongolia

Scientific understanding of the coupling relationship between grazing activities and grassland ecosystem is an important foundation for protecting and improving the ecological quality of grassland. This study took the Inner Mongolia Autonomous Region (Inner Mongolia) as the research area, systematically evaluated the grassland ecosystem and grazing intensity status in Inner Mongolia, and evaluated the ecological quality of Inner Mongolia county based on the four quadrant model. The results showed that: (1) The types of landscape patterns in Inner Mongolia were mainly grassland, forest, and desert. During the study period, the overall pattern of the landscape was stable, but problems such as urban expansion needed to be given more attention. (2) The areas with good grassland ecosystem carrying capacity were principally distributed in the eastern and southeastern Inner Mongolia, while the western Inner Mongolia and the northwest region of the Xilingol League were relatively low, and in the period 2000–2010 the ecological quality of the Inner Mongolia grassland in general belonged to the fair, middle level, but had been in a stable state and showed a good direction to improve the trend of ascension. (3) The grazing overload degree (GOD) in Inner Mongolia was significantly different in the region. GOD decreased from northeast to southwest in both periods. And most of the grassland stocking pressure was alleviated during 2000–2010. (4) The results of the four quadrant model analysis showed that there were obvious spatial differences in ecological quality in Inner Mongolia. And about 97% of the research area was located in the first, second, and fourth quadrants, with only 3% located in the third quadrant. From 2000 to 2010, the counties with nearly unchangeable and non-significantly worse accounted for 91.76% and 4.33%, respectively, indicating that the overall grassland ecosystem in Inner Mongolia remained stable and showed a slight improvement trend. This study can provide the necessary reference basis for the scientific formulation of grassland resources-related policies and plans and the promotion of grassland ecological protection in Inner Mongolia.

The Spatiotemporal Coupling and Coordination of Rural Labor Transfer and Agricultural Ecological Environment of Counties in Anhui

From the perspective of spatiotemporal evolution, this article constructs an evaluation system for the coupling coordination between labor transfer and agricultural ecological environment. The coupling coordination model is used to explore the spatiotemporal coupling coordination between rural labor transfer and agricultural ecological environment in Anhui from 2015 to 2017. The results showed that the ecological environment level in Anhui is gradually increasing and showing a trend of shifting towards southern county towns; The spatial distribution of labor force transfer shows a stable state, with each urban area as the main agglomeration area, and forming agglomeration areas in surrounding counties; From 2015 to 2017, the annual average coupling degree was close to 0.9, indicating good coordination. However, the annual average coupling coordination was on the rise, but it has been in a moderate imbalance.

A novel spatio-temporal cellular automata model coupling partitioning with CNN-LSTM to urban land change simulation

Land use change (LUC) has gained attention as a core topic of global ecological environment change research. The cellular automata (CA) model affects the global layout through local changes, and is widely used in LUC. However, most previous studies are based on the assumption of the Markov model which ignores the temporal dependency of LUC. In addition, most researchers have used the identical transition rules when simulating LUC variation across a region, ignoring the spatial heterogeneity in LUC studies.Accordingly, we propose a novel CA model integrating K-means, convolutional neural networks (CNN), and long-short-term memory neural networks (LSTM) to solve temporal dependency and spatial heterogeneity, named K-means-CNN-LSTM-CA (KCL-CA). First, in order to resolve spatial heterogeneity, we divided the study area into homogeneous sub-regions using K-means clustering algorithm. We then extracted multi-year spatial neighbourhood features and assigned weights with Gaussian functions according to the time sequence order to realise the fusion of multi-year features. LSTM was used to extract the spatio-temporal dependency features of historical land use data and to calculate the transition probability maps for sub-regions. Finally, CA generated the dynamic simulation results for the whole region.The KCL-CA model was validated based on data collected in Hangzhou from 1995 to 2020 Traditional logistic regression (LR)-CA and artificial neural network (ANN)-CA were used for comparison. Comparing the traditional model with the results shows that the proposed KCL-CA model improves the FoM index by 9.86%–19.43%. Considering the temporal dependency, the FoM index increased by 0.98%–3.51%; when considering spatial heterogeneity, the FoM index increased by 1.08%–5.15%. KCL-CA can deal with the temporal dependency of spatial heterogeneity in urban land expansion simulations and can effectively predict future urban expansion. The simulation results can effectively monitor the future trend of urban LUC and help to provide policy support for urban planning and management for decision makers.

Quantifying the streamflow change and influencing factors with a spatio-temporal coupling analysis framework

AbstractStreamflow change and its influencing factors are synchronous and correlated in temporal and spatial scales. The aim of this study is to develop a spatio-temporal coupling analysis framework for quantifying streamflow change and its influencing factors was established. Specifically, the Mann–Kendall test, Pettitt's test, hierarchical cluster analysis, and Ripley's L-function were jointly used to study the spatial heterogeneity of the temporal evolution of streamflow; and the Soil and Water Assessment Tool (SWAT) model was employed to quantify the impacts of climate and human activities on streamflow change. The preliminary application in the Dawen River Basin (China) case has shown that (1) the natural streamflow change in the basin during 1953–2013 is mainly affected by climate change–human activities, followed by climate change and human activities, accounting for a total area of 52.04, 24.90, and 23.06%, respectively; and (2) the vast majority of sub-basins with relatively large natural streamflow change are mainly driven by climate change (i.e., precipitation). In general, the proposed framework can effectively reflect the spatio-temporal patterns of streamflow change and its influencing factors, which can provide a theoretical basis for water resources management in the context of global change.

Unveiling the complexity of spatiotemporal soliton molecules in real time

Observing the dynamics of 3D soliton molecules can hold great opportunities for unveiling the mechanism of molecular complexity and other nonlinear problems. In spite of this fantastic potential, real-time visualization of their dynamics occurring on femtosecond-to-picosecond time scales is still challenging, particularly when high-spatiotemporal-resolution and long-term observation are required. In this work, we observe the real-time speckle-resolved spectral-temporal dynamics of 3D soliton molecules for a long time interval using multispeckle spectral-temporal measurement technology. Diverse real-time dynamics of 3D soliton molecules are captured for the first time, including the speckle-resolved birth, spatiotemporal interaction, and internal vibration of 3D soliton molecules. Further studies show that nonlinear spatiotemporal coupling associated with a large average-chirp gradient over the speckled mode profile plays a significant role in these dynamics. These efforts may shed new light on decomposing the complexity of 3D soliton molecules, and create an analogy between 3D soliton molecules and chemical molecules.

A new version of membrane search algorithm for hybrid renewable energy systems dynamic scheduling

It is a consensus that hydropower should be clean and renewable. The economic emission dynamic scheduling of hydro hybrid combined heat and power (Hydro-CHP) systems allows for further reduction of greenhouse gas emissions from the power sector. A new version of our Membrane Search Algorithm, denoted as IMSA, is proposed to solve the complex dynamic scheduling problems of CHP and hydro-thermal systems with spatio-temporal coupling, which can obtain competitive solutions. Furthermore, based on IMSA, we construct a multi-objective algorithm, called the Multi-objective Improved Membrane Search Algorithm (MOIMSA), which can solve the complex economic emission dynamic scheduling problem of CHP systems with ramp limits, and provide a solution set with relatively less emissions. Finally, a solution to the dynamic scheduling problem of the Hydro-CHP system is proposed. IMSA obtains the maximum power output of the hydro unit, and MOIMSA is used to search for the Pareto front of the economic emission of CHP. Compared with the unimproved MOMSA, the compromise solution provided by MOIMSA is better. Experiments show that this study contributes to the efficient use of renewable energy and the reduction of emissions.