Time Slices Research Articles

Future Changes in Hydro-Climatic Extremes across Vietnam: Evidence from a Semi-Distributed Hydrological Model Forced by Downscaled CMIP6 Climate Data

Flood hazards have led to substantial fatalities and economic loss in the last five decades, making it essential to understand flood dynamics in a warming climate. This study reports the first comprehensive assessment of projected flood hazards across Vietnam. We used downscaled climate data from the CMIP6 initiative, involving a total of 20 climate models, and streamflow projection simulated using a semi-distributed hydrological model. The assessment covers seven near-natural catchments, each representing a climate zone of the country. To evaluate climate change impacts on floods, the study simultaneously analyzes changes in three indices: (i) the annual hottest day temperature, to represent temperature extremes; (ii) the maximum daily rainfall amount, to represent rainfall extremes; and (iii) the discharge value exceeding 5% in a year, to assess streamflow extremes. Changes in the selected indices (relative to the reference period from 1985 to 2014) are assessed under four emission scenarios (SSP1–2.6, SSP2–4.5, SSP3–7.0, and SSP5–8.5) and two future time slices (2036–2065 and 2070–2099). Although the robustness (as indicated by multi-model agreement) and significance (identified through the statistical test) of the changes vary substantially, depending on the selected indices and assessed time slices, an overall increase is consistently identified across all of the assessed hydro-climatic extremes (up to 4.8 °C for temperature extremes, 43 mm for rainfall extremes, and 31% for streamflow extremes). The findings suggest a potential increase in flood risk across Vietnam in a warming climate, highlighting the urgent need for improved flood preparedness and investment to reduce economic loss and mortality in an uncertain future.

Travelling away from home? Joining global change and recovery scenarios to anticipate the marine distribution of diadromous fish

Species Distribution Models (SDM) are useful tools providing results that can be extrapolated to anticipate species range shifts, under climate change scenarios. SDM studies integrating spatial constraints are significantly lacking in the marine environment, leading to optimistic predictions. This is particularly true for anadromous species in which marine distributions can be driven by their affinity to their natal rivers. Acipenser sturio is a critically endangered anadromous fish for which two stocked populations are currently maintained in the Gironde-Garonne-Dordogne (France) and Elbe (Germany) river systems. Benefiting from bycatch reports of A. sturio, we applied a SDM process that explicitly considers distance to home when evaluating habitat suitability. More precisely, we included the variable ‘distance to mouth of the natal river system’ into SDM inputs to test and characterize its influence on the marine distribution of A. sturio. We used this model to obtain the marine distribution under current climatic conditions with the two source populations and under population recovery scenarios (functional populations hypothesized to exist in ten currently unoccupied river systems). We projected the model under future conditions with two climatic scenarios (RCP 4.5 and 8.5) and three time slices over the 2023–2099 period. Constrained-ranges of both existing and hypothetical populations are projected to expand in the future. We observed an overall increase of habitat suitability, with new suitable sectors localized further from natal river mouths. By informing on the suitable marine surface that each hypothetical population holds and adds to the existing ones, our approach aims at informing about the feasibility of species recovery and marine habitats protection strategies. Our findings highlight the need for including dispersal information in marine SDM. The application of our dispersal-constrained approach may be considered for other less-well-known species for which dispersal point sources are identifiable, such as other diadromous species in different study areas.

A bi-level framework for real-time crash risk forecasting using artificial intelligence-based video analytics.

This study proposes a bi-level framework for real-time crash risk forecasting (RTCF) for signalised intersections, leveraging the temporal dependency among crash risks of contiguous time slices. At the first level of RTCF, a non-stationary generalised extreme value (GEV) model is developed to estimate the rear-end crash risk in real time (i.e., at a signal cycle level). Artificial intelligence techniques, like YOLO and DeepSort were used to extract traffic conflicts and time-varying covariates from traffic movement videos at three signalised intersections in Queensland, Australia. The estimated crash frequency from the non-stationary GEV model is compared against the historical crashes for the study locations (serving as ground truth), and the results indicate a close match between the estimated and observed crashes. Notably, the estimated mean crashes lie within the confidence intervals of observed crashes, further demonstrating the accuracy of the extreme value model. At the second level of RTCF, the estimated signal cycle crash risk is fed to a recurrent neural network to predict the crash risk of the subsequent signal cycles. Results reveal that the model can reasonably estimate crash risk for the next 20-25min. The RTCF framework provides new pathways for proactive safety management at signalised intersections.

3D SURVEY POINT CLOUD DATA AS DIRECT RENDERING ASSETS FOR VISUALISING COMPLEX HERITAGE IN VIRTUAL APPLICATIONS

Abstract. Digital technology provides methods to record and preserve cultural heritage, support conservation and restoration efforts, and share our collective past with a worldwide audience. Between 2011 and 2017, the 3D Survey Group from Politecnico di Milano operated an annual workshop in the medieval village of Ghesc in which photogrammetry and laser-scanner surveys were carried out. The point cloud data acquired in these activities has become “time slices” documenting different stages of the preservation interventions in Ghesc and the evolution of advanced survey techniques. The main objective of this research is to streamline the workflow of delivering immersive and interactive experiences for complex heritage by directly utilising the 3D survey point cloud data, whether derived from a photogrammetric survey, static laser scanner, or mobile mapping.A point cloud-based multiplatform application is designed and delivered with versatile functions. It runs on PC and VR devices to provide virtual access to the village and narrate its revitalisation story. Additionally, it operates on mobile devices with an AR feature that brings vibrancy to the on-site experience. This application integrates high-fidelity point cloud models, detailed information on vernacular architecture in the Ossola Valley, and information on the preservation project with gamified learning experiences. The unconventional approach of using points as rendering primitives in virtual applications offers a practical solution for visualising complex heritage, enabling an efficient transition from the data collection stage to the data sharing stage without the need for 3D reconstruction and intricate BIM modelling.

Optimizing Topology in Satellite–UAV Collaborative IoT: A Graph Partitioning Simulated Annealing Approach

Currently, space-based information networks, represented by satellite Internet, are rapidly developing. UAVs can serve as airborne mobile terminals, representing a novel node in satellite IoT, offering more accurate and robust data streaming for connecting global satellite–UAV collaborative IoT systems. It is characterized by high-speed dynamics, with node distances and visibility constantly changing over time. Therefore, there is a need for faster and higher-quality topology optimization research. A reliable, secure, and adaptable network topology optimization algorithm has been proposed to handle various complex scenarios. Additionally, considering the dynamic and time-varying nature of these types of networks, the concept of time slices has been introduced to accelerate the iterative efficiency of problem-solving. Experimental results demonstrate that the proposed algorithm is expected to exhibit better convergence and performance in subsequent iterations compared with traditional solutions. Besides being a solution for topology optimization, the proposed algorithm offers a new way of thinking, enabling the handling of larger satellite–UAV collaborative IoT systems.

Observational study of intermittent solar jets: p-mode modulation

Aims. Recurring jets are observed in the solar atmosphere. They can erupt intermittently over a long period of time. By the observation of intermittent jets, we wish to understand what causes the characteristics of the periodic eruptions. Methods. We report intermittent jets observed by the Goode Solar Telescope (GST) with the TiO Broadband Filter Imager (BFI), the Visible Imaging Spectrometer (VIS) in Hα, and the Near-InfraRed Imaging Spectropolarimeter (NIRIS). The analysis was aided and complemented by 1400 Å and 2796 Å data from the Interface Region Imaging Spectrograph (IRIS). These observational instruments allowed us to analyze the temporal characteristics of the jet events. By constructing the Hα dopplergrams, we found that the plasma first moves upward, but during the second phase of the jet, the plasma flows back. Working with time slice diagrams, we investigated the characteristics of the jet dynamics. Results. The jet continued for up to 4 h. The time-distance diagram shows that the peak of the jet has clear periodic-eruption characteristics (5 min) during 18:00 UT–18:50 UT. We also found a periodic brightening phenomenon (5 min) during the jet bursts in the observed bands in the transition region (1400 Å and 2796 Å), which may be a response to intermittent jets in the upper solar atmosphere. The time lag is 3 min. Evolutionary images in the TiO band revealed a horizontal movement of the granulation at the location of the jet. By comparison to the quiet region of the Sun, we found that the footpoint of the jet is enhanced at the center of the Hα spectral line profile, without significant changes in the line wings. This suggests prolonged heating at the footpoint of the jet. In the mixed-polarity magnetic field region of the jet, we observed the emergence of magnetic flux, its cancellation, and shear, indicating possible intermittent magnetic reconnection. This is confirmed by the nonlinear force-free field model, which was reconstructed using the magneto-friction method. Conclusions. The multiwavelength analysis indicates that the events we studied were triggered by magnetic reconnection that was caused by mixed-polarity magnetic fields. We suggest that the horizontal motion of the granulation in the photosphere drives the magnetic reconnection, which is modulated by p-mode oscillations.

Local Rényi entropy-based Gini index for measuring and optimizing sparse time-frequency distributions

This paper introduces a novel localized Gini index (GI) designed for the assessment and optimization of time-frequency distributions (TFDs). This approach employs the localized Rényi entropy (LRE) to quantify the local number of signal components, providing a unique perspective on interpreting time and frequency slice segments while considering the presence of auto-terms or cross-terms. The results demonstrate the effectiveness of the proposed LRE-based GI in overcoming the limitations of conventional concentration and sparsity measures, particularly in scenarios with varying component numbers and missing essential components that may arise during TFD reconstruction using the compressive sensing method. Furthermore, it proves as an efficient objective function in meta-heuristic optimization, ensuring the preservation of auto-terms and a significant enhancement in overall TFD quality. The performance of the proposed LRE-based GI is demonstrated on noisy synthetic and real-life signals.

Embedded Spatial–Temporal Convolutional Neural Network Based on Scattered Light Signals for Fire and Interferential Aerosol Classification

Photoelectric smoke detectors are the most cost-effective devices for very early warning fire alarms. However, due to the different light intensity response values of different kinds of fire smoke and interference from interferential aerosols, they have a high false-alarm rate, which limits their popularity in Chinese homes. To address these issues, an embedded spatial–temporal convolutional neural network (EST-CNN) model is proposed for real fire smoke identification and aerosol (fire smoke and interferential aerosols) classification. The EST-CNN consists of three modules, including information fusion, scattering feature extraction, and aerosol classification. Moreover, a two-dimensional spatial–temporal scattering (2D-TS) matrix is designed to fuse the scattered light intensities in different channels and adjacent time slices, which is the output of the information fusion module and the input for the scattering feature extraction module. The EST-CNN is trained and tested with experimental data measured on an established fire test platform using the developed dual-wavelength dual-angle photoelectric smoke detector. The optimal network parameters were selected through extensive experiments, resulting in an average classification accuracy of 98.96% for different aerosols, with only 67 kB network parameters. The experimental results demonstrate the feasibility of installing the designed EST-CNN model directly in existing commercial photoelectric smoke detectors to realize aerosol classification.

Visual Analysis Method for Traffic Trajectory with Dynamic Topic Movement Patterns Based on the Improved Markov Decision Process

The visual analysis of trajectory topics is helpful for mining potential trajectory patterns, but the traditional visual analysis method ignores the evolution of the temporal coherence of the topic. In this paper, a novel visual analysis method for dynamic topic analysis of traffic trajectory is proposed, which is used to explore and analyze the traffic trajectory topic and evolution. Firstly, the spatial information is integrated into trajectory words, calculating the dynamic trajectory topic model based on dynamic analysis modeling and, consequently, correlating the evolution of the trajectory topic between adjacent time slices. Secondly, in the trajectory topic, a representative trajectory sequence is generated to overcome the problem of the trajectory topic model not considering the word order, based on the improved Markov Decision Process. Subsequently, a set of meaningful visual codes is designed to analyze the trajectory topic and its evolution through the parallel window visual model from a spatial-temporal perspective. Finally, a case evaluation shows that the proposed method is effective in analyzing potential trajectory movement patterns.

Robust Multi-Dimensional Time Series Forecasting.

Large-scale and high-dimensional time series data are widely generated in modern applications such as intelligent transportation and environmental monitoring. However, such data contains much noise, outliers, and missing values due to interference during measurement or transmission. Directly forecasting such types of data (i.e., anomalous data) can be extremely challenging. The traditional method to deal with anomalies is to cut out the time series with anomalous value entries or replace the data. Both methods may lose important knowledge from the original data. In this paper, we propose a multidimensional time series forecasting framework that can better handle anomalous values: the robust temporal nonnegative matrix factorization forecasting model (RTNMFFM) for multi-dimensional time series. RTNMFFM integrates the autoregressive regularizer into nonnegative matrix factorization (NMF) with the application of the L2,1 norm in NMF. This approach improves robustness and alleviates overfitting compared to standard methods. In addition, to improve the accuracy of model forecasts on severely missing data, we propose a periodic smoothing penalty that keeps the sparse time slices as close as possible to the time slice with high confidence. Finally, we train the model using the alternating gradient descent algorithm. Numerous experiments demonstrate that RTNMFFM provides better robustness and better prediction accuracy.

Altered Weddell Sea warm- and dense-water pathways in response to 21st-century climate change

Abstract. The transport of water masses with ocean circulation is a key component of the global climate system. In this context, the Filchner Trough in the southern Weddell Sea is critical, as it is a hotspot for the cross-shelf-break exchange of Dense Shelf Water and Warm Deep Water. We present results from Lagrangian particle tracking experiments in a global-ocean–sea-ice model (FESOM-1.4) which includes ice-shelf cavities and has eddy-permitting resolution on the southern Weddell Sea continental shelf. With backward and forward experiments, we assess changes between a present-day and a future (SSP5-8.5) time slice in the origin of waters reaching the Filchner Ice Shelf front and the fate of waters leaving it. We show that particles reaching the ice-shelf front from the open ocean originate from 173 % greater depths by 2100 (median; 776 m as compared to 284 m for the present day), while waters leaving the cavity towards the open ocean end up at 35 % shallower depths (550 m as compared to 850 m for the present day). Pathways of water leaving the continental shelf increasingly occur in the upper ocean, while the on-shelf flow of waters that might reach the ice-shelf cavity, i.e., at deeper layers, becomes more important by 2100. Simultaneously, median transit times between the Filchner Ice Shelf front and the continental shelf break decrease (increase) by 6 (9.5) months in the backward (forward) experiments. In conclusion, our study demonstrates the sensitivity of regional circulation patterns in the southern Weddell Sea to ongoing climate change, with direct implications for ice-shelf basal melt rates and local ecosystems.

Enhancing understanding of healthy aging based on time-varying dependencies among multidimensional health, life satisfaction, and health behaviors of older adults aged 60 years and over.

Healthy aging is a process of not only achieving good health but also increasing the life satisfaction of older adults aged 60 years and over, in which health behaviors play an important role. There is a lack of research on the time-varying dependencies between health, life satisfaction, and health behaviors, impeding a deeper understanding of healthy aging. To develop an integrated framework for modeling the interrelationships among the components of healthy aging between multiple time slices. Based on the Chinese Longitudinal Healthy Living Survey (CLHLS) data in the three waves of 2011/2012, 2014, and 2017/2018, Bayesian network and dynamic Bayesian network are jointly employed to study the relationships among the components of healthy aging within one time slice, as well as to explore the time-varying dependencies among the components between time slices. The results of structure learning reveal the direction of effects between different dimensions of health, with mental health and social health affecting physical health and self-rated health affecting both physical and mental health. In addition, health behaviors are found to affect mental health and social health, while self-rated health can influence life satisfaction. The parameters learned from the data show the magnitude and direction of concurrent effects, one-period lagged effects and two-period lagged effects between the factors, which find that the time-varying dependencies vary but are generally positive, long-term, and accumulative over time. In addition, the results of autoregressive effects show the positive predictive effects of health and life satisfaction. It confirms the influence pathway from health behaviors to multidimensional health to life satisfaction, and the time-varying dependencies among the components of healthy aging, which facilitates a deeper understanding of healthy aging. Combining the results of autoregressive effects and descriptive statistics, it further indicates that healthy aging is a comprehensive result arising from interactions of multiple factors. Policymakers should guide older adults aged 60 years and over to adopt healthier behaviors and ensure the long-term sustainability and continuity of policies.

Monitoring of simulated clandestine graves of victims using UAVs, GPR, electrical tomography and conductivity over 4-8 years post-burial to aid forensic search investigators in Colombia, South America

In Colombia there are estimated to be over 121,000 missing people and victims of forced disappearances. Forensic investigators therefore need assistance in determining optimal detection techniques for buried victims, to give victims’ families closure and for the wider community to see that justice is being served. Previous research has created 12 controlled simulated clandestine graves of typical Colombian murder victim scenarios at 0.5 m – 1.2 m depths in savannah and rainforest sites in Colombia. The 0–3 years of geophysical monitoring results of were published, with this paper reporting on 4–8 years monitoring of both UAV drone results and geophysical data. The UAV results from the year 8 survey, published for first time from Colombia, showed that the simulated graves could still be located using NDVI and NIR multi-spectral data, but not using optical or other multi-spectral data. The 0–3 years of geophysical data found the simulated clandestine graves could be detected with electrical resistivity and GPR methods, with the 4–8 year surveys evidencing that they could still be detected using bulk ground conductivity surveys, GPR horizontal time slice datasets and 2D ERT profiles. Research implications suggest initial use of UAV remote sensing technology to pinpoint likely search areas, before subsequent ground reconnaissance, geophysical surveys and their interpretation, before intrusive investigation methods are employed for detecting missing and disappeared persons in Colombia.

Traffic matrix completion by weighted tensor nuclear norm minimization and time slicing

Traffic matrix completion by weighted tensor nuclear norm minimization and time slicing

Research on Simulation Approximate Solution Strategy for Complex Kinematic Models

Abstract In order to meet the needs of military, road construction, multimedia industry and other aspects, UAVs are gradually given more functions. As the basic function of UAV applications, the fixed-point delivery problem model has higher and higher accuracy requirements. However, in the actual scene, the UAV delivery problem is affected by the interaction of various factors such as flight height, air resistance, and dive angle, which makes it difficult to achieve high stability and high hit accuracy. This paper will analyze the complex motion model based on the fixed-point delivery of explosives by UAV, study the relationship between the stability of UAV delivery and the hit accuracy, and analyze the influence of relevant parameters on the problem by using modeling. In this paper, a multivariate nonlinear continuous time change model is proposed, and a continuous time slice discretization idea operation model is introduced to approximate the time slice splitting inside the UAV launch motion. Secondly, the design quantified evaluation index reaction the initial velocity of the explosive, the launch Angle, the height off the ground and other parameters to analyze the model. Finally, the best scheduling strategy is obtained and verified by using the idea of variable traversal and trial- and-error simulation. The experimental results show that the variation of UAV flying height, speed, depression and other interference factors is consistent with the prediction of score and hit accuracy, according to the environment setting of this model, when the UAV is 300 meters above the ground and 290 meters away from the target horizontal position, the delivery speed is 250m/s, and the pitch angle is about 27°, the fixed-point delivery of explosives is the best strategy.

Joint Design of Long-Term Base Station Activation and Short-Term Beamforming for Green Wireless Networks

Base station (BS) activation is a widely-used approach to alleviate the system power cost for device maintenance. However, frequently switching on/off BSs may also introduce extra power and signaling costs, which motivates us to limit the BS switching frequency when performing BS activation. To address this, we study a problem of joint long-term BS activation and short-term beamforming (J-LTBA-STBF) in a network where multiple multi-antenna BSs cooperatively serve multiple single-antenna users. LTBA means that each BS’s active/inactive switching frequency is properly controlled to yield relatively stable BS-user association and limited switching power, while STBF lies in that due to the low cost of refreshing beamformers, the transmit beamformers are allowed to be updated frequently to maximally lower the transmit power. Following the idea, two efficient algorithms are designed, according to the data amount they request to start running, to optimize the active BSs and the BS transmit beamformers in multiple adjacent time slices. Consequently, the transmit, maintenance and switching powers can be well balanced to facilitate power-efficient communications. Numerical results validate the efficacies of the algorithms.

Machine Learning for Dynamic Prognostication of Patients With Hepatocellular Carcinoma Using Time-Series Data: Survival Path Versus Dynamic-DeepHit HCC Model.

Patients with intermediate or advanced hepatocellular carcinoma (HCC) require repeated disease monitoring, prognosis assessment and treatment planning. In 2018, a novel machine learning methodology "survival path" (SP) was developed to facilitate dynamic prognosis prediction and treatment planning. One year after, a deep learning approach called Dynamic Deephit was developed. The performance of the two state-of-art models in dynamic prognostication have not been compared. We trained and tested the SP and Dynamic DeepHit models in a large cohort of 2511 HCC patients using time-series data. The time-series data were converted into data of time slices, with an interval of three months. The time-dependent c-index for OS at given prediction time (t = 1, 6, 12, 18 months) and evaluation time (∆t = 3, 6, 9, 12, 18, 24, 36, 48 months) were compared. The comparison between SP model and Dynamic DeepHit-HCC model showed the latter had significant better performance at the time of initial admission. The time-dependent c-index of Dynamic DeepHit-HCC model gradually decreased with the extension of time (from 0.756 to 0.639 in the training set; from 0.787 to 0.661 in internal testing set; from 0.725 to 0.668 in multicenter testing set); while the time-dependent c-index of SP model displayed an increased trend (from 0.665 to 0.748 in the training set; from 0.608 to 0.743 in internal testing set; from 0.643 to 0.720 in multicenter testing set). When the prediction time comes to 6 months or later since initial treatment, the survival path model outperformed the dynamic DeepHit model at late evaluation times (∆t > 12 months). This research highlighted the unique strengths of both models. The SP model had advantage in long term prediction while the Dynamic DeepHit-HCC model had advantages in prediction at near time points. Fine selection of models is needed in dealing with different scenarios.

Time-slice dynamic prediction and multiway serial PCA for batch industrial process monitoring

This paper introduces an innovative method for batch industrial process monitoring and fault detection. By combining time-slice dynamic prediction with an enhanced adaptive multiway principal component analysis (PCA) technique, it aims to balance the trade-off between high fault detection rates and low false alarm rates in monitoring batch industrial data. The method extracts dynamic features from 3D batch data time slices and utilizes a serial PCA framework with variable control limits for real-time monitoring. Comparative evaluations against traditional adaptive multiway PCA and dynamic time-slice canonical correlation analysis approaches demonstrate its superior performance. A case study using a novel cold rolling mill simulation model underscores its effectiveness for batch industrial process monitoring and fault detection.

Inferring Viral Transmission Time from Phylogenies for Known Transmission Pairs.

When the time of an HIV transmission event is unknown, methods to identify it from virus genetic data can reveal the circumstances that enable transmission. We developed a single-parameter Markov model to infer transmission time from an HIV phylogeny constructed of multiple virus sequences from people in a transmission pair. Our method finds the statistical support for transmission occurring in different possible time slices. We compared our time-slice model results to previously described methods: a tree-based logical transmission interval, a simple parsimony-like rules-based method, and a more complex coalescent model. Across simulations with multiple transmitted lineages, different transmission times relative to the source's infection, and different sampling times relative to transmission, we found that overall our time-slice model provided accurate and narrower estimates of the time of transmission. We also identified situations when transmission time or direction was difficult to estimate by any method, particularly when transmission occurred long after the source was infected and when sampling occurred long after transmission. Applying our model to real HIV transmission pairs showed some agreement with facts known from the case investigations. We also found, however, that uncertainty on the inferred transmission time was driven more by uncertainty from time calibration of the phylogeny than from the model inference itself. Encouragingly, comparable performance of the Markov time-slice model and the coalescent model-which make use of different information within a tree-suggests that a new method remains to be described that will make full use of the topology and node times for improved transmission time inference.

Climate warming worsens thermal resource utilization for practical rice cultivation in China.

Rice production is sensitive to climate change and significantly affected by warming in recent years. To what extent climate warming shifted rice phenology and varied thermal resource condition were explored across five agro-ecological zones in China, based on up-to-date observations of meteorology and rice cultivation in 1981-2020. It was clearly signaled that there was a general advance of 0.3-3.8days in observed sowing date and a delay of 0.4-3.5days in observed maturity date in 2001-2020 relative to 1981-2000 in major zones. A vacant time slice of 2.6-28.8days between observed sowing date and potential sowing date, and a lag of 15.4-56.7days in potential maturity date compared to observed maturity date were identified in 2001-2020. Within longer growing season, useful accumulated temperature increased by 76.7-117.6°C·d in 2001-2020 relative to 1981-2000, while disactive accumulated temperature also increased. In Northeast China, actual rice cultivation was undergoing earlier sowing date than potential sowing date and longer growing duration than potential duration, yet leading to upward disactive accumulated temperature. The decrease in the thermal resource utilization in 2001-2020 relative to 1981-2000 was highlighted at 55.3-78.3% stations in major zones, with a decrement of 0.006-0.018 in average magnitude. The changes in thermal resource utilization unveiled that the shifts in actual rice cultivation still could not compensate for the suitability in thermal resource utilization benefited from climate warming.