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Text–image person re-identification (TIReID) has emerged as a versatile approach for retrieving target pedestrians using textual descriptions. However, current TIReID research has been overly idealistic and has overlooked the issues of data incompleteness and modal imbalance in real-world application scenarios. Therefore, in this paper, we propose imbalanced text–image person re-identification (ITIReID) to address these problems. In comparison to TIReID, ITIReID contains a larger proportion of unimodal data, which leads to modal imbalance. The setting of ITIReID is more aligned with real-world scenarios, and studying ITIReID can expand the application scalability of TIReID. We propose a Graph-based Consistent Reconstruction and Alignment framework (GCRA), for ITIReID, which achieves modal balance by completing missing modality features for training implementation. By treating the accessible modality features as graph nodes, GCRA firstly builds an adjacency graph where a new semantic distance that establishes semantic relevance between nodes by comprehensively measuring both intra-modality and inter-modality correlation, serves as the measurement of graph's edges. GCRA further reconstructs the missing nodes – thus re-establishing missing modality features – using existing nodes connected with high semantic relevance. To ensure the reliability and effectiveness of reconstructed features, we propose a proxy-based identity constraint and a reconstruction constraint. In addition, to enable effective semantic alignment using both the reconstructed features and original features, we introduce a cross-modal semantic constraint. Extensive experiments demonstrate that GCRA can effectively handle issues of data incompleteness and modal imbalance, exhibiting its effectiveness and superiority. • We propose the imbalanced text–image person re-identification (ITIReID). • We propose a new cross-modal semantic distance. • We propose a proxy-based identity constraint (PIC). • We propose a graph-based consistent reconstruction and alignment framework (GCRA). [ABSTRACT FROM AUTHOR]

Text-to-image person re-identification (TIReID) refers to identifying images of a person of interest from a large-scale person image database based on natural language descriptions. Most of existing methods generally rely heavily on color information when matching cross-modal data, which is a kind of overfitting and can be termed as the color over-reliance problem. This problem would distract the model from other tiny but discriminative clues (e.g. clothes details, structural information, etc.), which are essential for both semantic alignment and fine-grained matching, and thus leads to a sub-optimal retrieval performance. To this end, in this paper, we propose a novel Bottom-up Color-independent Alignment Learning Framework (BCALF) for text-based person retrieval to tackle this problem in two folds, decoupling color-independent discrete local features and aggregating multiple key discrete features. We employ color-confused images as an auxiliary modality and perform discrete fine-grained semantic alignment where the minimal semantic units interact within the joint feature space to focus solely on content information. Furthermore, the multiple discrete local features are aggregated into more discriminative non-local decisive features. BCALF achieves semantic alignment from minimal semantic units to non-local aggregation units, which can be understood as a bottom-up process. Experimental results demonstrate that BCALF consistently outperforms previous methods and achieves the state-of-the-art performance on the CUHK-PEDES, ICFG-PEDES and RSTPReid datasets. [ABSTRACT FROM AUTHOR]

Text–image person re-identification (TIReID) seeks to leverage textual descriptions for the retrieval of target pedestrians. Due to its versatility, TIReID has gained increasing attention. However, manual annotation of textual descriptions and identity labels can be time-consuming and costly, limiting its scalability in practical settings. Privacy concerns and poor data storage can lead to data loss or ineffectiveness, further exacerbating challenges in real-world scenarios. To address these limitations, we propose for the first time incomplete Text–image person re-identification (iTIReID), which comprises a small amount of complete pairwise data and a large amount of incomplete data, where all identity labels are unavailable. We introduce a novel Contrastive Completing Learning (CCL) framework for iTIReID, consisting of two stages: Pure Contrastive Learning (PCL) and Feature Completion Contrastive Learning (FCCL). In PCL, only complete pairwise data is utilized for training, which serves as a preliminary improvement of the model's capacity and prepares for the upcoming feature completion stage. In FCCL, available features are used to complete missing modality features and facilitate effective training with incomplete data. During this process, Cross-modal Semantic Measure (CSM) is proposed to leverage intra-modality similarity to measure cross-modal similarity and filter out features with the highest semantic similarity, thereby circumventing modality discrepancy. Semantic-Weighted Generation (SWG) is proposed to generate approximate features based on the semantic similarity weight of the similar features. To fully leverage pairwise data for label-free training, we introduce the contrastive CMPM (CCMPM) loss for contrastive learning to achieve weakly supervised training. Experimental results verify the effectiveness of our proposed methods and demonstrate competitive performance compared to fully supervised methods using complete data. • We propose the incomplete text–image person re-identification (iTIReID). • We propose a novel contrastive CMPM loss to facilitate weakly supervised training. • We introduce a cross-modal semantic measure and a semantic-weighted generation. • We propose a contrastive completing learning framework for iTIReID. [ABSTRACT FROM AUTHOR]

Multi-label learning tasks usually encounter the problem of the class-imbalance, where samples and their corresponding labels are non-uniformly distributed over multi-label data space. It has attracted increasing attention during the past decade, however, there is a lack of methods capable of handling the imbalanced problem in a semi-supervised setting. This study proposes a label propagation technique to settle the semi-supervised imbalanced multi-label issue. Specially, we first utilize a collaborative manner to exploit the correlations from labels and instances, and learn a label regularization matrix to overcome the imbalanced problem in the labeled instance. After that, we extend to semi-supervised learning and explore to represent the similarity of instances with weighted graphs on labeled and unlabeled data. Then, the data distribution information and label correlations are fully utilized to design the loss function under the consistency assumption manner. At last, we present an iterative scheme to settle the optimization issue, thereby achieving label propagation to address the imbalanced challenge. Experiments on a variety of multi-label data sets show the favorable performance of the proposed method against related comparing approaches. Notably, the proposed method is also validated to be robust with a limited number of training instances. • Provide a new method for semi-supervised imbalanced multi-label classification. • Propose a label regularization matrix to handle the imbalanced multi-label problem. • Leverage a collaborative manner to ensure the balanced outcomes. • Utilize weighted graph to exploit the representation of labeled and unlabeled data. • Experimental results demonstrate the superior performance of the proposed method. [ABSTRACT FROM AUTHOR]

Three-dimensional (3D) porous LiFePO 4 /graphene aerogel (LFP/GA) composite was successfully prepared by an in-situ hydrothermal process. In this composite, the LiFePO 4 microspheres assembled by nanoparticles were embedded in a three-dimensional framework intertwined with the graphene sheets, which acts as a bridge for transfer of electron and diffusion of lithium ion. The large specific surface of the composite structure enables the increased infiltration area and utilization of the active material. The content of the graphene sheet is analyzed and is found important for the Li-storage characteristics of LiFePO 4. An aerogel composite with 10% of graphene displays the best electrochemical performance, with the specific discharge capacities of 168 mAh g−1 and 155 mAh g−1 at respectively 0.1C and 1C, and the capacity retains 96.3% for up to 800 cycles. This novel 3D porous aerogel composite is identified as a promising cathode material for the rechargeable Li battery, and the simple strategy may be applied to construct other high performing composite structure and materials. [ABSTRACT FROM AUTHOR]

This paper examines the impact of variability in impact of air pollution on life satisfaction (LS). Previous studies have shown robust negative impact of air pollution on subjective well-being (SWB). However, empirical studies that consider variability in air pollution effects through comparative city study are limited. This study provides comparative evaluation of two major Chinese cities: Beijing and Shanghai. We apply a geo-statistical spatial interpolation technique on pollution data from monitoring sites to estimate the Sulfur Dioxide (SO 2 ), Nitrogen Dioxide (NO 2 ), coarse particles with a diameter between 2.5 and 10 μm (PM 10 ) and fine particles with a diameter of 2.5 μm or less (PM 2.5 ) pollution exposure of respondents of a survey conducted in 2016. The results show that all pollutants have robust negative impacts on LS for Beijing residents, whereas only SO 2 and NO 2 have significant negative impacts on LS for Shanghai residents; Per unit impact of SO 2 is greater in Shanghai, and that of NO 2 is greater in Beijing. Beijing and Shanghai residents have almost same monetary valuation for SO 2 reduction but Beijing residents place approximately 1.5 times valuation on NO 2 reduction compared to Shanghai residents. Moreover, the LS of Beijing residents is sensitive to temporal changes in the pollution level, whereas Shanghai residents are unaffected by such changes. [ABSTRACT FROM AUTHOR]

Abstract: This work presents the first in-situ neutron diffraction study of the MoS2 electrode, undertaken in a custom-built Li-ion battery during discharge. A review of custom-designed cells for in-situ neutron diffraction experiments is presented along with our optimised cell, which we use to show real-time information corresponding to Li-insertion into MoS2 via disappearance of the (103) reflection and increase in the d-spacing of the (002) reflection. The changes in the diffraction patterns begin at the 1.1V plateau and are complete during the 0.5V plateau. Sequential Rietveld-refinement indicates the presence of an intermediate lithiated phase (Li x MoS2) between MoS2 and LiMoS2. We observe the disappearance of all reflections for the MoS2, corresponding to the loss of long-range order, during the 0.5V plateau and no new diffraction peaks appear with further electrochemical cycling. This result is indicative of a transformation from long-range ordered MoS2 to short-range ordered LiMoS2, a result that we confirm using ex-situ synchrotron X-ray and neutron diffraction. [Copyright &y& Elsevier]

Abstract: Layer structured δ-MnO2 was synthesized by a microwave-assisted hydrothermal method. The morphology of the product consists of flower-like spheres that range from about 200nm to 3μm in diameter and are composed of sheets about 5–10nm in thickness. When tested in the voltage range of 2 to 4.5V vs. Li+/Li in coin cells, the separator is blocked, handicapping Li+ conductivity and leading to cell failure. When tested in the voltage range of 2 to 4V in ethylene carbonate/dimethyl carbonate (EC/DMC), the δ-MnO2 delivers an initial reversible capacity of 143.7mAhg−1 and can maintain 120mAhg−1 at the 60th cycle. The δ-MnO2 electrode shows good cycling stability at different current densities and delivers a discharge capacity of about 90mAhg−1 at 1C, indicating that it is a promising cathode material for lithium ion batteries. [Copyright &y& Elsevier]

Abstract: Novel TiO2(B)@anatase hybrid nanowires with a bicrystalline structure consisting of TiO2(B) core and anatase shell exhibit superior Li ion storage capacities, cycling stability and rate capability. Owing to the excellent electrochemical performance, TiO2(B)@anatase hybrid nanowires could be promising anode materials for lithium ion batteries. [Copyright &y& Elsevier]

Abstract: Under optimized synthesis conditions, very large area uniform SnO2 nanofibers consisting of orderly bonded nanoparticles have been obtained for the first time by thermal pyrolysis and oxidization of electrospun tin(II)2-ethylhexanoate/polyacrylonitrile (PAN) polymer nanofibers in air. The structure and morphology were elaborated by X-ray diffraction (XRD), transmission electron microscopy (TEM), and X-ray photoelectron spectroscopy (XPS). The SnO2 nanofibers delivered a reversible capacity of 446mAhg−1 after 50 cycles at the 100mAg−1 rate and excellent rate capability of 477.7mAhg−1 at 10.0C. Owing to the improved electrochemical performance, this electrospun SnO2 nanofiber could be one of the most promising candidate anode materials for the lithium-ion battery. [Copyright &y& Elsevier]

Abstract: In this paper, we have reported a simple and rapid approach for the large-scale synthesis of β-Co(OH)2 nanoplatelets via the microwave hydrothermal process using potassium hydroxide as mineralizer at 140°C for 3h. Calcining the β-Co(OH)2 nanoplatelets at 350°C for 2h, porous Co3O4 nanoplatelets with a 3D quasi-single-crystal framework were obtained. The process of converting the β-Co(OH)2 nanoplatelets into the Co3O4 nanoplatelets is a self-supported topotactic transformation, which is easily controlled by varying the calcining temperature. The textural characteristics of Co3O4 products have strong positive effects on their electrochemical properties as electrode materials in lithium-ion batteries. The obtained porous Co3O4 nanoplatelets exhibit a low initial irreversible loss (18.1%), ultrahigh capacity, and excellent cyclability. For example, a reversible capacity of 900mAhg−1 can be maintained after 100 cycles. [Copyright &y& Elsevier]

Abstract: SnO2/multi-walled carbon nanotube (MWCNT) composites were prepared by the solvothermal method and subsequent heat treatment at 360°C. The samples were characterized by field emission scanning electron microscopy (FESEM), X-ray diffraction (XRD), and transmission electron microscopy (TEM). Results on the higher SnO2 content composite sample indicate that a uniform layer of SnO2 nanocrystals with crystal size around 5nm was deposited on the surface of the carbon nanotubes. The composite demonstrates a reversible lithium storage capacity of 709.9mAhg−1 at the first cycle and excellent cyclic retention up to 100 cycles as anode for lithium ion batteries. [Copyright &y& Elsevier]

A novel deep reinforcement learning (DRL) control framework for the energy management strategy of the series hybrid electric tracked vehicle (SHETV) is proposed in this paper. Firstly, the powertrain model of the vehicle is established, and the formulation of the energy management problem is given. Then, an efficient deep reinforcement learning framework based on the double deep Q-learning (DDQL) algorithm is built for the optimal problem solving, which also contains a modified prioritized experience replay (MPER) and an adaptive optimization method of network weights called AMSGrad. The proposed framework is verified by the realistic driving cycle, then is compared to the dynamic programming (DP) method and the previous deep reinforcement learning method. Simulation results show that the newly constructed deep reinforcement learning framework achieves higher training efficiency and lower energy consumption than the previous deep reinforcement learning method does, and the fuel economy is proved to approach the global optimality. Besides, its adaptability and robustness are validated by different driving schedules. • The powertrain model of the series hybrid electric tracked vehicle is established. • A new control framework based on double deep Q-learning algorithm is constructed. • Modified prioritized experience replay is designed to improve training efficiency. • An adaptive optimization method is applied to update weights of the neural network. • The proposed deep reinforcement learning framework realizes better performance. [ABSTRACT FROM AUTHOR]

Abstract: Fluorine-doped 5V cathode materials LiNi0.5Mn1.5O4−x F x (0.05≤ x ≤0.2) have been prepared by sol–gel and post-annealing treatment method. The results from X-ray diffraction and scanning electron microscopy (SEM) indicate that the spinel structure changes little after fluorine doping, but the particle size varies with fluorine doping and the preparation conditions. The electrochemical measurements show that stable cycling performance can be obtained when the fluorine amount x is higher than 0.1, but the specific capacity is decreased and 4V plateau capacity resulting from a conversion of Mn4+/Mn3+ remains. Moreover, influence of the particle size on the reversible capacity of the electrode, especially on the kinetic property, has been examined. [Copyright &y& Elsevier]

• Provides a new optimization framework for solving the readmission prediction. • Propose a graph-based method to deal with the class-imbalanced problem. • Present an end-to-end trainable prediction model to improve the generalization. • Applied the proposed method on six real-world readmission datasets. • The method is proved to be effective in comparison to other methods. Predicting hospital readmission with effective machine learning techniques has attracted a great attention in recent years. The fundamental challenge of this task stems from characteristics of the data extracted from electronic health records (EHR), which are imbalanced class distributions. This challenge further leads to the failure of most existing models that only provide a partial understanding for the learning problem and result in a biased and inaccurate prediction. To address this challenge, we propose a new graph-based class-imbalance learning method by fully making use of the data from different classes. First, we conduct graph construction for learning the pattern discrimination from between-class and within-class data samples. Then we design an optimization framework to incorporate the constructed graphs to obtain a class-imbalance aware graph embedding and further alleviate performance degeneration. Finally, we design a neural network model as the classifier to conduct imbalanced classification, i.e., hospital readmission prediction. Comprehensive experiments on six real-world readmission datasets show that the proposed method outperforms state-of-the-art approaches in readmission prediction task. [ABSTRACT FROM AUTHOR]

This research proposes a reinforcement learning-based algorithm and a deep reinforcement learning-based algorithm for energy management of a series hybrid electric tracked vehicle. Firstly, the powertrain model of the series hybrid electric tracked vehicle (SHETV) is constructed, then the corresponding energy management formulation is established. Subsequently, a new variant of reinforcement learning (RL) method Dyna, namely Dyna-H, is developed by combining the heuristic planning step with the Dyna agent and is applied to energy management control for SHETV. Its rapidity and optimality are validated by comparing with DP and conventional Dyna method. Facing the problem of the "curse of dimensionality" in the reinforcement learning method, a novel deep reinforcement learning algorithm deep Q-learning (DQL) is designed for energy management control, which uses a new optimization method (AMSGrad) to update the weights of the neural network. Then the proposed deep reinforcement learning control system is trained and verified by the realistic driving condition with high-precision, and is compared with the benchmark method DP and the traditional DQL method. Results show that the proposed deep reinforcement learning method realizes faster training speed and lower fuel consumption than traditional DQL policy does, and its fuel economy quite approximates to global optimum. Furthermore, the adaptability of the proposed method is confirmed in another driving schedule. • The powertrain model of the series hybrid electric tracked vehicle is constructed. • A novel reinforcement learning-based energy management strategy is proposed. • The rapidity and optimality of the reinforcement learning method are validated. • A new optimization method is applied to update the weights of the neural network. • The proposed deep reinforcement learning method realizes better performance. [ABSTRACT FROM AUTHOR]

• The overall model for the hybrid electric tracked vehicle is built in detail. • Fast Q -learning algorithm is applied to derive energy management strategy. • An efficient online energy management strategy update framework is constructed. • Hardware-in-loop simulation experiment is conducted to validate the performance. • The strategy improves fuel economy and has potential for real-time applications. The energy management approach of hybrid electric vehicles has the potential to overcome the increasing energy crisis and environmental pollution by reducing the fuel consumption. This paper proposes an online updating energy management strategy to improve the fuel economy of hybrid electric tracked vehicles. As the basis of the research, the overall model for the hybrid electric tracked vehicle is built in detail and validated through the field experiment. To accelerate the convergence rate of the control policy calculation, a novel reinforcement learning algorithm called fast Q -learning is applied which improves the computational speed by 16%. The cloud-computation is presented to afford the main computation burden to realize the online updating energy management strategy in hardware-in-loop simulation bench. The Kullback-Leibler divergence rate to trigger the update of the control strategy is designed and realized in hardware-in-loop simulation bench. The simulation results show that the fuel consumption of the fast Q -learning based online updating strategy is 4.6% lower than that of stationary strategy, and is close to that of dynamic programming strategy. Besides, the computation time of the proposed method is only 1.35 s which is much shorter than that of dynamic programming based method. The results indicate that the proposed energy management strategy can greatly improve the fuel economy and have the potential to be applied in the real-time application. Moreover, the adaptability of the online energy management strategy is validated in three realistic driving schedules. [ABSTRACT FROM AUTHOR]

Abstract: Manganese pyrophosphate, Mn2P2O7 was synthesized by a simple solvothermal method using Mn metal powder and P2S5 in ethylene glycol medium at 190–220°C. Morphology and crystalline structure of the products were characterized by X-ray diffraction and scanning electron microscopy. The flower-like microspheres with diameters of about 2–5 μm are composed of a number of nanoplatelets with thickness of 20–40nm. The effect of reaction temperature and reaction time on the microstructure of Mn2P2O7 was investigated. The samples were used as active anode materials for lithium-ion battery and their electrochemical properties were examined by constant current charge–discharge cycling. The Mn2P2O7 electrodes exhibited initial reversible capacities of 440–330mAhg−1 depending on the synthetic conditions. From these results, a possible reaction mechanism of Mn2P2O7 with lithium was proposed. [Copyright &y& Elsevier]

Abstract: In this paper, an efficient microwave-assisted homogeneous synthesis approach by urea hydrolysis is used to synthesize cobalt-basic-carbonate compounds. The dimensions and morphology of the synthesized precursor compounds are tailored by changes in the incorporated anions (CO3 2− and OH−) under different conditions of temperature and time under microwave irradiation. The wire-like cobalt-basic-carbonate compound self-assembles into one-dimensional porous arrays of Co3O4 nanowires constructed of interconnected Co3O4 nanocrystals along the [110] axis after thermal decomposition at 350°C. The textural characteristics of the Co3O4 products have strong positive effects on their electrochemical properties as electrode materials in lithium-ion batteries. The obtained porous nanowire Co3O4 arrays exhibit excellent capacity retention and rate capability at higher current rates, and their reversible capacity of 600mAhg−1 can be maintained after 100 cycles at the high current rate of 400mAg−1. [Copyright &y& Elsevier]

Abstract: Ultra-fine, porous, tin oxide–carbon (SnO2/C) nanocomposites are fabricated by a molten salt method at 300°C, and malic acid is decomposed as the carbon source. In situ synthesis is favourable for the combination of carbon and SnO2. The structure and morphology are confirmed by X-ray diffraction analysis, specific surface-area measurements, and transmission electron microscopy (TEM). Examination of TEM images reveals that the SnO2 nanoparticles are embedded in the carbon matrix, with sizes between 2 and 5nm. The electrochemical measurements show that the nanocomposite delivers a high capacity with good capacity retention as an anode material for lithium-ion batteries, due to the combination of the ultra-fine porous structure and the carbon component. [Copyright &y& Elsevier]

Abstract: Thin films of lithium cobalt oxide were deposited on Pt or Pt/Ti/quartz glass substrates by radio frequency (RF) magnetron sputtering at the substrate temperatures from room temperature to 500°C. As the substrate temperature increased, the film structure changed from amorphous structure to crystallinity with a strong (003) texture as characterized by X-ray diffraction. The surface morphology and cross-section were observed using scanning electron microscopy. It was found that the films tended to crack at a high substrate temperature. Charge–discharge tests of these films were conducted and compared. The different electrochemical characteristics of these films were attributed to the modified crystallography, morphology, and thermal stress. The LiCoO2 film deposited at 400°C showed a well-defined 4.0V voltage plateau on charge and a 3.9V plateau on discharge, and delivered 54.5μAh/cm2 μm at the first discharge capacity, with good cycling performance, giving evidence that such films could be used as the thin film cathodes for lithium microbatteries. [Copyright &y& Elsevier]

• CGA reduces effects of methyl viologen (MV) stress in apple leaves. • CGA enhances the antioxidant capacity in apple leaves. • It is the first to confirm the in vivo antioxidant fuctions of CGA in plants. Considered to be an antioxidant, chlorogenic acid is involved in resistance to insects and pathogens, and beneficial effects on human health. However, little is known about the antioxidant functions of chlorogenic acid against oxidative stress in plants. In this study, we studied the potential role of exogenous chlorogenic acid in alleviating methyl viologen-mediated oxidative stress in detached leaves of apple (Malus domestica Borkh). The results showed that exogenous chlorogenic acid availably relieved the decreases in chlorophyll concentrations and maximum potential Photosystem II efficiency. Exogenous chlorogenic acid also effectively reduced membrane damage and lipid oxidation and stimulated activity of antioxidant enzymes, including peroxidase, catalase, and polyphenol oxidase. It is noteworthy that exogenous chlorogenic acid improves antioxidant activity of apple leaf under oxidative stress using DPPH, ABTS, and FRAP assays. In addition, the concentration of phenolics increased remarkably in response to chlorogenic acid application after methyl viologen exposure. Furthermore, exogenous chlorogenic acid also changed the transcription levels of related genes for antioxidant enzymes and phenolic metabolism. These results suggest that chlorogenic acid can be effective antioxidants protecting the plants against oxidative stress by enhancing antioxidant capacity. [ABSTRACT FROM AUTHOR]

Abstract: The first example of a ruthenium nitrido compound as hydrosilylation catalyst is described, using phenylsilane as reductant. A variety of ketones and aldehydes are reduced to alcohols with good to high isolated yields. Some mechanistic insight on this new system is provided on the basis of the available experimental findings. [Copyright &y& Elsevier]

As a hybrid electric vehicle (HEV) key control technology, intelligent energy management strategies (EMSs) directly affect fuel consumption. Investigating the robustness of EMSs to maximize the advantages of energy savings and emission reduction in different driving environments is necessary. This article proposes a soft actor-critic (SAC) deep reinforcement learning (DRL) EMS for hybrid electric tracked vehicles (HETVs). Munchausen reinforcement learning (MRL) is adopted in the SAC algorithm, and the Munchausen SAC (MSAC) algorithm is constructed to achieve lower fuel consumption than the traditional SAC method. The prioritized experience replay (PER) is proposed to achieve more reasonable experience sampling and improve the optimization effect. To enhance the "cold start" performance, a dynamic programming (DP)-assisted training method is proposed that substantially improves the training efficiency. The proposed method optimization result is compared with the traditional SAC and deep deterministic policy gradient (DDPG) with PER through the simulation. The result shows that the proposed strategy improves both fuel consumption and possesses excellent robustness under different driving cycles. • A robust energy management strategy is established based on the SAC algorithm. • Munchausen reinforcement learning method is adopted to the SAC algorithm. • Prioritized experience replay is applied to improve training efficiency. • DP-assisted training method is proposed to enhance the "cold start" performance. • The proposed framework realizes better performance in fuel-saving and robustness. [ABSTRACT FROM AUTHOR]

Highlights: [•] We investigated the fruit firmness and ethylene production as well as the expression of ripening related genes in ‘Nanguo’ pear fruits. [•] The ripening related genes responded differentially to 1-MCP treatments under 4°C or room temperature. [•] Distinct expression patterns of ripening related genes were observed in fruits harvested from different locations. [ABSTRACT FROM AUTHOR]

Combining eco-driving optimization and simultaneous proper energy management, this paper proposes an efficient co-optimization strategy of unmanned hybrid electric tracked vehicles (HETVs) based on a hierarchical control framework to achieve accurate path tracking and optimal energy management simultaneously. Constrained by a pre-known reference path, a deep Q-learning (DQL) algorithm with the AMSGrad optimizer is designed in the upper layer to optimize the velocity of both side tracks to find the best trade-off between energy economy and accurate path tracking. Based on the optimal velocity profile obtained from the upper layer, an explicit model predictive control method is designed in the lower layer to distribute the power between the engine generator and battery in real time to achieve approximate optimal fuel economy. Simulation results verify that the designed DQL method only requires 0.67 s on average for real-time velocity planning, which is markedly lower than the dynamic programming algorithm. In addition, the proposed method also exhibits higher rapidity and optimality for velocity planning than the traditional DQL algorithm. Compared with the model predictive control, dynamic programming and a process without velocity planning, the proposed co-optimization strategy achieves good fuel economy, accurate path tracking and high computational efficiency. • Co-optimization strategy is designed for path tracking and energy management. • Deep Q-learning algorithm with AMSGrad optimizer is developed to plan velocity. • EMPC controller is designed to distribute the power in real time. • The proposed strategy is compared with different methods and analyzed in depth. [ABSTRACT FROM AUTHOR]

[Display omitted] • Star-shaped poly(hydroxybutyrate)s are produced by ROP of β -butyrolactone via a core first approach. • The core structures can be bioderived. • The thermal properties and wettability can be correlated with the number of arms. The ring-opening polymerization (ROP) of cyclic esters using multifunctional initiators is an efficient methodology that allows the preparation of polyesters with well-defined architectures. Here a series of star-shaped poly(β-hydroxybutyrates) (PHBs) has been synthesized by ROP of β -butyrolactone (BBL) with an amido-oxazolinate zinc catalyst. The star-shaped structure is constructed via a core-first approach, in which several bio-based multifunctional alcohols serve as the initiator for ROP. Specifically, three-, four-, and multi-armed star polymers are obtained and characterized by various techniques including NMR, GPC, TGA, and DSC, and the incorporation of the core structure in the star-shaped PHBs has been confirmed. The thermal properties can be modified by changing the arm number and arm length of PHBs, and the thermal stability decreases as the number of arms increases when they have the same arm lengths. The wetting behavior determined from AFM studies demonstrates a minimum in the macroscopic contact angle as a function of the number of arms of a star-shaped PHB. [ABSTRACT FROM AUTHOR]

In this paper, an energy management strategy (EMS) based on model predictive control (MPC) is proposed to minimize fuel cost, electricity usage and battery ageing. To fulfil the MPC framework, a novel speed predictor with a variable horizon based on a K-means algorithm and a radius basis function neural network, which contains various predictive submodels, is designed to cope with different input drive states. In addition, a Q-learning algorithm is applied to construct an adaptive multimode state-of-charge (SOC) reference generator, which takes advantage of velocity forecasts for each prediction horizon. The algorithm fully considers the model nonlinearities and physical constraints and requires less computational effort. Based on the SOC reference and predictive velocity, the MPC problem is formulated to coordinate fuel consumption and battery degradation. Moreover, considering the influence of real-time traffic information, a traffic model that simulates actual road conditions is constructed in VISSIM to evaluate the performance of the proposed EMS. The simulation results show that the proposed speed predictor can effectively improve the predictive accuracy, and the multimode control laws based on drive condition classification present superior adaptability in SOC reference generation compared to single-mode law. With the aforementioned two improvements, the proposed EMS achieves desirable performance in fuel economy and battery lifetime extension. • Cost-optimal problem is built for coordinating fuel economy and battery lifetime. • A novel speed predictor with variable horizon is constructed. • Q-learning algorithm is applied as the adaptive multimode SOC reference generator. • A traffic model is constructed in VISSIM to evaluate the performance of the proposed EMS. • Influences of SOC reference and predictive speed accuracy are discussed in depth. [ABSTRACT FROM AUTHOR]

This paper proposes a real-time predictive energy management strategy (PEMS) of plug-in hybrid electric vehicles for coordination control of fuel economy and battery lifetime, including velocity predictor, state-of-charge (SOC) reference generator, and online optimization. In velocity predictor, the radial basis function neural network algorithm is adopted to accurately estimate the future drive velocity. Based on predictive velocity and current driven distance, the SOC reference in predictive horizon can be determined online by reference generator. To coordinate fuel consumption and battery degradation, a model predictive control problem of cost minimization including fuel consumption cost, electricity cost of battery charging/discharging, and equivalent cost of battery degradation, is formulated. To mitigate the huge calculation burden in optimization, the continuation/generalized minimal residual (C/GMRES) algorithm is delegated to find the expected engine power command in real time. Since original C/GMRES algorithm cannot directly handle inequality constraints, the external penalty method is employed to meet physical inequality limits of powertrain. Numerical simulations are carried out and yield the desirable performance of the proposed PEMS in fuel consumption minimization and battery aging restriction. More importantly, the proposed C/GMRES algorithm shows great solving quality and real-time applicability in PEMS by comparing with sequence quadratic programming and genetic algorithms. • Battery degradation is considered in predictive energy management. • RBF-NN algorithm is adopted to accurately predict future velocity. • SOC reference in predictive horizon is obtained by a modified method. • C/GMRES algorithm is applied to achieve real-time predictive energy management. • Performance and computational efficiency by proposed strategy are verified. [ABSTRACT FROM AUTHOR]