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.

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.

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.

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.

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 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.

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.

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.

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.

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.

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.

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.