Autonomous taxis (AT), which integrate automated vehicles with on-demand services to provide direct door-to-door transport, are transforming urban mobility systems by lowering operating costs and enabling more controlled fleet management. AT services can efficiently match passengers and increase system throughput (e.g., number of served trips). However, AT operations, including passenger matching and proactive relocation, introduce operational uncertainties that could increase costs and travel times due to additional empty vehicle kilometres travelled. We develop an agent-based model to represent vehicle relocations alongside passenger matching and routing for urban AT services and to investigate the AT relocation trade-off between operating costs and operational performance (e.g., wait times, vehicle utilisation). The model is applied to a case study of The Hague, The Netherlands, using detailed road network data and time-dependent origin–destination flows derived from private car trips. Simulation results show that increasing the AT fleet sizes of a company affects service quality of competing operators, resulting in longer average waiting times and up to four additional minutes of travel times due to added traffic. Moreover, relocation generates empty travel, but it occurs during less congested hours, thereby avoiding adding vehicles to road traffic in peak periods. Overall, AT relocation could improve service levels and transport more passengers due to increased availability. An operator using relocation reduces waiting times by about 11% compared to competitors that do not relocate. Profits can also rise by nearly 16% (more than 2,000 euros during morning hours) because more trips are served while the operating costs remain comparatively low.

Data assimilation (DA) combines numerical model simulations with observed data to obtain the best possible description of a dynamical system and its uncertainty. Incorrect modeling assumptions can lead to filter divergence, making model identification an important issue in the field of DA. Variations in dynamic model structures can result in differences in parameter dimensions, complicating the resampling step in PFs. To meet this challenge, the Sequential Hierarchical Bayesian Model (SHBM) is proposed in this paper, which integrates the evolution model along with observation model from the DA scheme, and the hierarchical parameter model. A two-step resampling method are also proposed to estimate the SHBM: the first step uses the resampling scheme in the bootstrap filter to resample new particles based on weights, which may produce some duplicate particles; the second step utilizes the Reversible Jump Markov Chain Monte Carlo (RJMCMC) methods to draw new particles from the target distribution. This approach ensures particle diversity, with the first step aiming at avoiding particle degeneracy, and the second step intends to prevent the sample impoverishment. The performance in the Advection Equation example and Lorenz 96 example demonstrates the effectiveness of the proposed method.

Abstract Atmospheric chemistry encapsulates the emission of various pollutants, the complex chemistry reactions, and the meteorology dominant transport, which form a dynamic system that governs air quality. While deep learning (DL) models have shown promise in capturing intricate patterns for forecasting individual atmospheric components—such as PM2.5 and ozone—the critical interactions among multiple pollutants and the combined influence of emissions and meteorology are often overlook. This study introduces a DL-based framework–Zeeman for atmospheric chemistry forecasting. Our model effectively captures the nuanced relationships among these constituents and while achieving a 68.5-fold increase in computational speed compared to traditional numerical model. Evaluations demonstrate that our approach rivals numerical model, offering an efficient solution for atmospheric chemistry forecasting. In the future, this model could be further integrated with data assimilation techniques to facilitate efficient and accurate atmospheric emission estimation and concentration forecast.

Non-methane volatile organic compounds (NMVOCs) are key precursors of ozone and secondary organic aerosols. As one of the world's largest NMVOC emitters, accurate emission inventories are essential for understanding and mitigating air pollution in China. Commonly-used inventories (e.g., MEIC) are largely based on bottom-up methods, which often fail to capture the spatiotemporal variability of NMVOC emissions, resulting in significant model-observation mismatches. This study evaluates the shape factor, filtered data volume, and monthly mean biases of OMI, OMPS, and TROPOMI formaldehyde products, with the latest OMPS and TROPOMI retrievals offering substantially higher effective spatiotemporal coverage. Monthly NMVOC emissions over China in 2020 are then optimized by independently assimilating formaldehyde retrievals either from OMPS or from TROPOMI, using a self-developed 4DEnVar assimilation emission inversion system. The OMPS- and TROPOMI-driven assimilation yields consistent seasonal and regional increments in NMVOC emissions in general, but distinctions are also notable. A consistency analysis is introduced to assess the reliability of these two posterior emissions. Highly consistent increments are obtained in the North China Plain (May-June), the Yangtze River Delta and Pearl River Delta (January-March, October-December), and the Sichuan Basin (January, June-December). These adjustments significantly improve surface ozone simulations, with 81.25 % of consistent cases demonstrating reduced biases and an average RMSE reduction of 24.7 %. These findings highlight the effectiveness of OMPS and TROPOMI formaldehyde assimilation, coupled with consistency analysis, in refining NMVOC emission estimates and enhancing ozone simulation accuracy. Similar promising results are achieved in the OMPS/TROPOMI-based NMVOC emission inversion in 2019.

In financial time series analysis, the dynamic conditional correlation model is the most popular method for estimating the conditional covariance matrix, which represents financial risk and is critical for risk management, portfolio optimization, and asset pricing. Traditional covariance matrix estimation is often constrained by the rigid parameter settings and the assumption of the normal distribution, leading to the estimation biases when the markets are not normally distributed. To address these limitations, this paper proposes a Bayesian Non-parametric Dynamic Conditional Angular Correlation model based on the Fractionally Integrated GARCH model (BNDCAC-FIGARCH) that incorporates the asymmetric parameter and the student’s t-distribution to increase the adaptability and flexibility. Simulation experiments demonstrate that under overall correlation paths shaped as the sine or ramp functions, our model provides more accurate estimates, showcasing its effectiveness and stability. Empirical studies apply real stock market data, which includes DAX 40, FTSE 100, SSE 50, and CSI 100, to construct the portfolio optimization. The results demonstrate the superiority of the proposed model in terms of both portfolio returns and the reduction of parameter uncertainty. Furthermore, the results indicate that CSI 100 exhibits the weaker asymmetry compared to the other indices, likely due to its higher liquidity and a more accurate reflection of improved economic conditions resulting from national policies.

Achieving transport decarbonization depends on electric vehicle (EV) and fuel cell vehicle (FCV) deployment, yet their material demands and impacts vary by vehicle type. This study explores how powertrain preferences in light-duty vehicles (LDVs) and heavy-duty vehicles (HDVs) shape future resource use and material-related environmental outcomes. Using dynamic material flow analysis and prospective life cycle assessment, we assess three scenarios. In the S3 EV-dominant scenario, 2050 lithium and cobalt demand rises by up to 11.9-fold and 1.8-fold relative to 2020, with higher global warming and human toxicity impacts. The S2 FCV-dominant scenario leads to a 21.7-fold increase in platinum-group metal demand, driving up freshwater ecotoxicity and particulate emissions. A balanced S1 scenario, EVs in LDVs and FCVs in HDVs, yields moderate material demand and environmental burdens. These findings demonstrate that no single pathway can fully resolve material-related impacts, while combining EVs and FCVs across LDVs and HDVs enables a more balanced and sustainable transition.

This paper assesses the capability of liquid cloud droplet effective radius (CER) and cloud effective variance (CEV) retrieval from space-borne multi-angular hyperspectral measurements. The capability and sensitivity study is based on a neural network (NN) retrieval approach which is developed for the Spectropolarimeter for Planetary EXploration - one (SPEXone), a multi-angular hyperspectral polarimeter onboard Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) satellite. The synthetic measurements used in NN training and the sensitivity experiments are generated by Remote sensing of Trace gas and Aerosol Products (RemoTAP) forward model, and include variations of cloud, surface and aerosol properties, as well as cloud fraction. On the basic validation set, the NN performs similar over ocean and land with a mean absolute error (MAE) around 2μm on CER and around 0.04 on CEV. The performance over different cloud fraction (CF) and cloud optical thickness (COT) is evaluated, and indicates that most accurate retrievals can be performed for cases where CF >0.6 and COT between 2 and 12. The sensitivity to above-cloud aerosols (both fine-mode-dominated and dust-mode-dominated cases) suggests the retrieval is more sensitive to absorbing fine mode aerosols (CER MAE <2.5μm up to AOT of 0.2 for fully cloudy scene), than to dust aerosols (CER MAE <2.5μm up to AOT of 0.5). Moreover, the retrievals are virtually insensitive to above cloud cirrus over fully cloudy scene, but shows large sensitivity over partly cloudy scenes over land. Finally, synthetic measurements from partly cloudy scenes are generated based on the 3D MYSTIC radiative transfer model. The retrieval on these measurements suggests no significant 3D cloud radiative effect artifacts.

Incorporating external knowledge has been shown to improve emotion understanding in dialogues by enriching contextual information, such as character motivations, psychological states, and causal relations between events. Filtering and categorizing this information can significantly enhance model performance. In this paper, we present an innovative Emotion Recognition in Conversation (ERC) framework, called the Scene-Speaker Emotion Awareness Network (SSEAN), which employs a dual-strategy modeling approach. SSEAN uniquely incorporates external commonsense knowledge describing speaker states into multimodal inputs. Using parallel recurrent networks to separately capture scene-level and speaker-level emotions, the model effectively reduces the accumulation of redundant information within the speaker’s emotional space. Additionally, we introduce an attention-based dynamic screening module to enhance the quality of integrated external commonsense knowledge through three levels: (1) speaker-listener-aware input structuring, (2) role-based segmentation, and (3) context-guided attention refinement. Experiments show that SSEAN outperforms existing state-of-the-art models on two well-adopted benchmark datasets in both single-text modality and multimodal settings.

Modelling and observational techniques are pivotal in aerosol research, yet each approach exhibits inherent limitations. Aerosol observation is constrained by its limited spatial and temporal coverage compared to that of models. On the other hand, models tend to possess higher uncertainties and biases compared to observations. Aerosol data assimilation has gained popularity as it combines the advantages of both methods. Despite numerous studies in this domain, few have addressed the challenges faced in assimilating aerosol data with significant differences in magnitude and degree of freedom between the model state and observations, especially in the vertical direction. These challenges can lead to the preservation - or even the exacerbation - of structural inaccuracies within the assimilation process. This study investigates the sensitivity of dust aerosol data assimilation to the vertical structure of the aerosol profile. We assimilate a variety of dust observations, encompassing ground-based particulate matter (PM10) measurements, and satellite-derived dust optical depth (DOD) data, using the ensemble Kalman filter (EnKF). The assimilation process is elucidated, detailing the assimilation of raw ground-based and satellite-based observations for an optimized three-dimensional (3D) posterior state. To demonstrate the impact of accurate vs. erroneous prior aerosol vertical profiles on the assimilation result, we select three cases of super dust storms for analysis. Our findings reveal that the assimilation of ground observations would optimize the dust field at the ground in general. However, the vertical structure presents a more complex challenge. When the prior profile accurately reflects the true vertical structure, the assimilation process can successfully preserve this structure. Conversely, if the prior profile introduces an incorrect structure, the assimilation can significantly deteriorate the integrity of the aerosol profile. This is also found in the assimilation of DOD, which exhibits a comparable pattern in its sensitivity to the initial aerosol profile's accuracy.

The Basel Convention Plastic Waste Amendments, implemented in 2021, have the potential to reshape traditional ‘North-to-South' plastic waste trade patterns and their environmental impacts. We analyze plastic waste trade among 21 countries before (2019–2020) and after (2021–2022) the amendments, quantifying environmental impacts from transport and waste treatment using life cycle assessment. We find that post-amendment trade among selected EU and non-EU OECD countries increased to 71 %, up 12 percentage points from pre-amendment period, when half of the trade flowed to non-OECD Asian countries. This shift yielded modest increases of 2 % in climate and 5 % in energy benefits. Further expanding intra-EU-OECD trade could boost climate benefits by up to 12 %, mainly by reducing open burning in non-OECD Asian countries. These findings offer environmental insights into the EU's upcoming ban on plastic waste exports to non-OECD countries, suggesting future trade will likely concentrate among countries with aligned waste shipment rules.

This paper describes an algorithm for above-cloud aerosol (ACA) retrievals from PARASOL (Polarisation and Anisotropy of Reflectances for Atmospheric Science coupled with Observations from a Lidar) Multi-Angle Polarimetric measurements. The algorithm, based on neural networks (NNs), has been trained on synthetic measurements and has been applied to the processing of one-year PARASOL data. The algorithm makes use of three subsequent NNs: (1) for the detection of liquid clouds, (2) for the retrieval of aerosol properties for ACA cases, and (3) an NN forward model to evaluate the goodness-of-fit of the retrieval. The NN’s theoretical capability of retrieval is investigated by several synthetic data studies. It is shown that the NNs retrieve ACAOT₅₅₀ (above cloud aerosol optical thickness, at 550 nm), AE_440–670 (Ångström exponent, between 440 and 670 nm), and SSA₅₅₀ (single scattering albedo, at 550 nm) with an RMSE (root mean squared error) of ∼ 0.1 on ACAOT₅₅₀, ∼ 0.4 on AE_440–670 and ∼ 0.04 on SSA₅₅₀ in synthetic experiments. Finally, comparison between the NN retrievals and adjacent PARASOL-RemoTAP clear-sky retrieval in 2008 shows good agreement within the range expected from the synthetic study.

In multimodal emotion recognition, disentangled representation learning method effectively address the inherent heterogeneity among modalities. To facilitate the flexible integration of enhanced disentangled features into multimodal emotional features, we propose a task-driven multimodal emotion recognition method TDMER. Its Cross-Modal Learning module promotes adaptive cross-modal learning of features disentangled into modality-invariant and modality-specific subspaces, based on their contributions to emotional classification probabilities. The Task-Contribution Fusion mechanism then assigns controllable weights to the enhanced features according to their task objectives, generating multimodal fusion features that improve the emotion classifier's discriminative ability. The proposed TDMER approach has been evaluated on two widely-used multimodal emotion recognition benchmarks and demonstrated significant performance improvements compared with other state-of the-art methods.

Speech signals contain rich information, such as textual content, emotion, and speaker identity. To extract these features more efficiently, researchers are investigating joint training across multiple tasks, like Speech Emotion Recognition (SER) and Speaker Verification (SV), aiming to improve performance by decoupling task-specific knowledge. Traditional multitask decoupling methods in SER typically use orthogonalization to increase the distance between parameter vectors in the feature space. In this paper, we introduce a novel Hybrid instance-level Contrastive Decoupling Loss. This method leverages supervised labels to effectively decouple SER and SV. Unlike previous approaches, it is not restricted to dual-stream models with identical architectures and can be easily integrated with leading models for each sub-task. Experimental results show that our proposed Hybrid Contrastive Learning Decoupling (HCLD) method significantly outperforms traditional orthogonal decoupling approaches.

For the enhancement of the transient stability of power systems, the key is to define a quantitative optimization formulation with system parameters as decision variables. In this paper, we model the disturbances by Gaussian noise and define a metric named Critical Escape Probability (CREP) based on the invariant probability measure of a linearized stochastic process. CREP characterizes the probability of the state escaping from a critical set. CREP involves all the system parameters and reflects the size of the basin of attraction of the nonlinear systems. An optimization framework that minimizes CREP with the system parameters as decision variables is presented. Simulations show that the mean of the first hitting time when the state hits the boundary of the critical set, that is often used to describe the stability of nonlinear systems, is dramatically increased by minimizing CREP. This indicates that the transient stability of the system is effectively enhanced. It is also shown that suppressing the state fluctuations only is insufficient for enhancing the transient stability. In addition, the famous Braess’ paradox which also exists in power systems is revisited. Surprisingly, it turned out that the paradoxes identified by the traditional metric may not exist according to CREP. This new metric opens a new avenue for the transient stability analysis of future power systems integrated with large amounts of renewable energy.

Tools and analysis for improving the small-signal stability of a stochastic power system by optimal power dispatch in each short time horizon, such as five-minute intervals, are provided in this paper. An objective function which characterizes the maximal exit probability from the static stability region (−π/2,π/2) across the phase-angle differences of all power lines is formulated. This objective function is proven to be Lipschitz continuous, nondifferentiable, and nonconvex, with a finite minimum defined over the region of power supply vectors. The formulas of the generalized subgradient and directional derivative of the objective function are provided, and based on these formulas, a two-step algorithm is designed to approximate a minimizer accompanied by the convergence proof: (1) using a projected generalized subgradient method to compute an effective initial vector, and (2) applying the steepest descent method to approximate a local minimizer. The algorithms have been verified using a synthesized power network, demonstrating computational validity and effectiveness in minimizing the maximal exit probability of all power lines.

Multimodal emotion recognition (MER) is essential for understanding human emotions from diverse sources such as speech, text, and video. However, modality heterogeneity and inconsistent expression pose challenges for effective feature fusion. To address this, we propose a novel MER framework combining a Dynamic Weighted Graph Convolutional Network (DW-GCN) for feature disentanglement and a Cross-Attention Consistency-Gated Fusion (CACG-Fusion) module for robust integration. DW-GCN models complex inter-modal relationships, enabling the extraction of both common and private features. The CACG-Fusion module subsequently enhances classification performance through dynamic alignment of cross-modal cues, employing attention-based coordination and consistency-preserving gating mechanisms to optimize feature integration. Experiments on the CMU-MOSI and CMU-MOSEI datasets demonstrate that our method achieves state-of-the-art performance, significantly improving the 𝐴𝐶𝐶7 , 𝐴𝐶𝐶2, and 𝐹1 scores.

Machine learning algorithms have demonstrated outstanding performance for disease diagnosis. Kernel function selection plays a crucial role in effectively transforming the nonlinear nature of input data. To enhance breast cancer diagnosis, we propose a novel ensemble algorithm, namely, AUC-Ada- (Formula presented.) MKL-WSVM, which integrates Weighted Support Vector Machines (WSVM), AdaBoost, and Multi-Kernel Learning (MKL). This ensemble algorithm introduces two main innovations. First, it simultaneously updates the weights of training samples and the combined kernel function during classification. Second, it incorporates an AUC-based approach to adjust training sample weights, effectively controlling the growth rate of misclassified sample weights in AdaBoost. Experimental results are provided to demonstrate the effectiveness of our method, which achieves an AUC score of 97.21% and an accuracy of 97.64% on the WDBC dataset, and an AUC of 97.53% and an accuracy of 97.46% on the WBC dataset. Comparative analysis further confirms that our ensemble algorithm outperforms four benchmark models in classification accuracy.

We discuss the various performance aspects of parallelizing our transient global-scale groundwater model at 30′′ resolution (30arcsec; °1/41km at the Equator) on large distributed memory parallel clusters. This model, referred to as GLOBGM, is the successor of our 5′ (5arcmin; °1/410km at the Equator) PCR-GLOBWB 2 (PCRaster Global Water Balance model) groundwater model, based on MODFLOW having two model layers. The current version of GLOBGM (v1.0) used in this study also has two model layers, is uncalibrated, and uses available 30′′ PCR-GLOBWB data. Increasing the model resolution from 5′ to 30′′ creates challenges, including increased runtime, memory usage, and data storage that exceed the capacity of a single computer. We show that our parallelization tackles these problems with relatively low parallel hardware requirements to meet the needs of users or modelers who do not have exclusive access to hundreds or thousands of nodes within a supercomputer. For our simulation, we use unstructured grids and a prototype version of MODFLOW 6 that we have parallelized using the message-passing interface. We construct independent unstructured grids with a total of 278 million active cells to cancel all redundant sea and land cells, while satisfying all necessary boundary conditions, and distribute them over three continental-scale groundwater models (168 million - Afro-Eurasia; 77 million - the Americas; 16 million - Australia) and one remaining model for the smaller islands (17 million). Each of the four groundwater models is partitioned into multiple non-overlapping submodels that are tightly coupled within the MODFLOW linear solver, where each submodel is uniquely assigned to one processor core, and associated submodel data are written in parallel during the pre-processing, using data tiles. For balancing the parallel workload in advance, we apply the widely used METIS graph partitioner in two ways: it is straightforwardly applied to all (lateral) model grid cells, and it is applied in an area-based manner to HydroBASINS catchments that are assigned to submodels for pre-sorting to a future coupling with surface water. We consider an experiment for simulating the years 1958-2015 with daily time steps and monthly input, including a 20-year spin-up, on the Dutch national supercomputer Snellius. Given that the serial simulation would require °1/44.5 months of runtime, we set a hypothetical target of a maximum of 16h of simulation runtime. We show that 12 nodes (32 cores per node; 384 cores in total) are sufficient to achieve this target, resulting in a speedup of 138 for the largest Afro-Eurasia model when using 7 nodes (224 cores) in parallel. A limited evaluation of the model output using the United States Geological Survey (USGS) National Water Information System (NWIS) head observations for the contiguous United States was conducted. This showed that increasing the resolution from 5′ to 30′′ results in a significant improvement with GLOBGM for the steady-state simulation when compared to the 5′ PCR-GLOBWB groundwater model. However, results for the transient simulation are quite similar, and there is much room for improvement. Monthly and multi-year total terrestrial water storage anomalies derived from the GLOBGM and PCR-GLOBWB models, however, compared favorably with observations from the GRACE satellite. For the next versions of GLOBGM, further improvements require a more detailed (hydro)geological schematization and better information on the locations, depths, and pumping rates of abstraction wells.

The environmental impact of traded plastic waste hinges on how it is treated. Existing studies often use domestic or scenario-based recycling rates for imported plastic waste, which is problematic due to differences in recyclability and the fact that importers pay for it. We estimate the minimum required recycling rate (RRR) needed to break even financially by analysing import prices, recycling costs, and the value of recycled plastics across 22 leading importing countries and four plastic waste types during 2013–2022. Here we show that at least 63% of imported plastic waste must be recycled, surpassing the average domestic recycling rate of 23% by 40 percentage points. This discrepancy suggests that recycled plastics volumes from the global North-to-South trade may be underestimated. The country-specific RRR provided could enhance research and policy efforts to better quantify and mitigate the environmental impact of plastic waste trade.

In data assimilation (DA) schemes, the form representing the processes in the evolution models are pre-determined except some parameters to be estimated. In some applications, such as the contaminant solute transport model and the gas reservoir model, the modes in the equations within the evolution model cannot be predetermined from the outset and may change with the time. We propose a framework of sequential DA method named Reversible Jump Ensemble Filter (RJEnF) to identify the governing modes of the evolution model over time. The main idea is to introduce the Reversible Jump Markov Chain Monte Carlo (RJMCMC) method to the DA schemes to fit the situation where the modes of the evolution model are unknown and the dimension of the parameters is changing. Our framework allows us to identify the modes in the evolution model and their changes, as well as estimate the parameters and states of the dynamic system. Numerical experiments are conducted and the results show that our framework can effectively identify the underlying evolution models and increase the predictive accuracy of DA methods.