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The rapid development of the global economy and population growth are accompanied by the production of numerous waste textiles. This leads to a waste of limited resources and serious environmental pollution problems caused by improper disposal. The rational recycling of wasted textiles and their transformation into high-value-added emerging products, such as smart wearable devices, is fascinating. Here, we propose a novel roadmap for turning waste cotton fabrics into three-dimensional elastic fiber-based thermoelectric aerogels by a one-step lyophilization process with decoupled self-powered temperature–compression strain dual-parameter sensing properties. The thermoelectric aerogel exhibits a fast compression response time of 0.2 s, a relatively high Seebeck coefficient of 43 μV·K−1, and an ultralow thermal conductivity of less than 0.04 W·m−1·K−1. The cross-linking of trimethoxy(methyl)silane (MTMS) and cellulose endowed the aerogel with excellent elasticity, allowing it to be used as a compressive strain sensor for guessing games and facial expression recognition. In addition, based on the thermoelectric effect, the aerogel can perform temperature detection and differentiation in self-powered mode with the output thermal voltage as the stimulus signal. Furthermore, the wearable system, prepared by connecting the aerogel-prepared array device with a wireless transmission module, allows for temperature alerts in a mobile phone application without signal interference due to the compressive strains generated during gripping. Hence, our strategy is significant for reducing global environmental pollution and provides a revelatory path for transforming waste textiles into high-value-added smart wearable devices.

Abstract Background Mediation analysis is a powerful tool to identify factors mediating the causal pathway of exposure to health outcomes. Mediation analysis has been extended to study a large number of potential mediators in high-dimensional data settings. The presence of confounding in observational studies is inevitable. Hence, it’s an essential part of high-dimensional mediation analysis (HDMA) to adjust for the potential confounders. Although the propensity score (PS) related method such as propensity score regression adjustment (PSR) and inverse probability weighting (IPW) has been proposed to tackle this problem, the characteristics with extreme propensity score distribution of the PS-based method would result in the biased estimation. Methods In this article, we integrated the overlapping weighting (OW) technique into HDMA workflow and proposed a concise and powerful high-dimensional mediation analysis procedure consisting of OW confounding adjustment, sure independence screening (SIS), de-biased Lasso penalization, and joint-significance testing underlying the mixture null distribution. We compared the proposed method with the existing method consisting of PS-based confounding adjustment, SIS, minimax concave penalty (MCP) variable selection, and classical joint-significance testing. Results Simulation studies demonstrate the proposed procedure has the best performance in mediator selection and estimation. The proposed procedure yielded the highest true positive rate, acceptable false discovery proportion level, and lower mean square error. In the empirical study based on the GSE117859 dataset in the Gene Expression Omnibus database using the proposed method, we found that smoking history may lead to the estimated natural killer (NK) cell level reduction through the mediation effect of some methylation markers, mainly including methylation sites cg13917614 in CNP gene and cg16893868 in LILRA2 gene. Conclusions The proposed method has higher power, sufficient false discovery rate control, and precise mediation effect estimation. Meanwhile, it is feasible to be implemented with the presence of confounders. Hence, our method is worth considering in HDMA studies.

Flexible thermoelectric materials play an important role in smart wearables, such as wearable power generation, self-powered sensing, and personal thermal management. However, with the rapid development of Internet of Things (IoT) and artificial intelligence (AI), higher standards for comfort, multifunctionality, and sustainable operation of wearable electronics have been proposed, and it remains challenging to meet all the requirements of currently reported thermoelectric devices. Herein, we present a multifunctional, wearable, and wireless sensing system based on a thermoelectric knitted fabric with over 600 mm·s−1 air permeability and a stretchability of 120%. The device coupled with a wireless transmission system realizes self-powered monitoring of human respiration through an mobile phone application (APP). Furthermore, an integrated thermoelectric system was designed to combine photothermal conversion and passive radiative cooling, enabling the characteristics of being powered by solar-driven in-plane temperature differences and monitoring outdoor sunlight intensity through the APP. Additionally, we decoupled the complex signals of resistance and thermal voltage during deformation under solar irradiation based on the anisotropy of the knitted fabrics to enable the device to monitor and optimize the outdoor physical activity of the athlete via the APP. This novel thermoelectric fabric-based wearable and wireless sensing platform has promising applications in next-generation smart textiles.

Abstract Background Outliers, data points that significantly deviate from the norm, can have a substantial impact on statistical inference and provide valuable insights in data analysis. Multiple methods have been developed for outlier detection, however, almost all available approaches fail to consider the spatial dependence and heterogeneity in spatial data. Spatial data has diverse formats and semantics, requiring specialized outlier detection methodology to handle these unique properties. For now, there is limited research exists on robust spatial outlier detection methods designed specifically under the spatial error model (SEM) structure. Method We propose the Spatial-Θ-Iterative Procedure for Outlier Detection (Spatial-Θ-IPOD), which utilizes a mean-shift vector to identify outliers within the SEM. Our method enables an effective detection of spatial outliers while also providing robust coefficient estimates. To assess the performance of our approach, we conducted extensive simulations and applied it to a real-world empirical study using life expectancy data from multiple countries. Results Simulation results showed that the masking and JD (Joint Detection) indicators of our Spatial-Θ-IPOD method outperformed several commonly used methods, even in high-dimensional scenarios, demonstrating stable performance. Conversely, the Θ-IPOD method proved to be ineffective in detecting outliers when spatial correlation was present. Moreover, our model successfully provided reliable coefficient estimation alongside outlier detection. The proposed method consistently outperformed other models (both robust and non-robust) in most cases. In the empirical study, our proposed model successfully detected outliers and provided valuable insights in the modeling process. Conclusions Our proposed Spatial-Θ-IPOD offers an effective solution for detecting spatial outliers for SEM while providing robust coefficient estimates. Notably, our approach showcases its relative superiority even in the presence of high leverage points. By successfully identifying outliers, our method enhances the overall understanding of the data and provides valuable insights for further analysis.

IntroductionLung image segmentation plays an important role in computer-aid pulmonary disease diagnosis and treatment.MethodsThis paper explores the lung CT image segmentation method by generative adversarial networks. We employ a variety of generative adversarial networks and used their capability of image translation to perform image segmentation. The generative adversarial network is employed to translate the original lung image into the segmented image.ResultsThe generative adversarial networks-based segmentation method is tested on real lung image data set. Experimental results show that the proposed method outperforms the state-of-the-art method.DiscussionThe generative adversarial networks-based method is effective for lung image segmentation.

Abstract Thermoelectric sensors have attracted increasing attention in smart wearables due to the recognition of multiple signals in self‐powered mode. However, present thermoelectric devices show disadvantages of low durability, weak wearability, and complex preparation processes and are susceptible to moisture in the microenvironment of the human body, which hinders their further application in wearable electronics. Herein, we prepared a new thermoelectric fabric with thermoplastic polyurethane/carbon nanotubes (TPU/CNTs) by combining vacuum filtration and electrospraying techniques. Electrospraying TPU microsphere coating with good biocompatibility and environmental friendliness made the fabric worn directly and exhibits preferred water resistance, mechanical durability, and stability even after being bent 4000 times, stretched 1000 times, and washed 1000 times. Moreover, this fabric showed a Seebeck coefficient of 49 μV K−1 and strain range of 250% and could collect signals well and avoided interference from moisture. Based on the biocompatibility and safety of the fabric, it can be fabricated into devices and mounted on the human face and elbow for long‐term and continuous collection of data on the body's motion and breathing simultaneously to provide collaborative support information. This thermoelectric fabric‐based sensor will show great potential in advanced smart wearables for health monitoring, motion detection, and human–computer interaction.

Pseudomonas fragi (P. fragi) is usually detected in low-temperature meat products, and seriously threatens food safety and human health. Therefore, the study investigated the antibacterial mechanism of linalool against P. fragi from membrane damage and metabolic disruption. Results from field-emission transmission electron microscopy (FETEM) and atomic force microscopy (AFM) showed that linalool damage membrane integrity increases surface shrinkage and roughness. According to Fourier transform infrared (FTIR) spectra results, the components in the membrane underwent significant changes, including nucleic acid leakage, carbohydrate production, protein denaturation and modification, and fatty acid content reduction. The data obtained from amino acid metabolomics indicated that linalool caused excessive synthesis and metabolism of specific amino acids, particularly tryptophan metabolism and arginine biosynthesis. The reduced activities of glucose 6-phosphate dehydrogenase (G6PDH), malate dehydrogenase (MDH), and phosphofructokinase (PFK) suggested that linalool impair the respiratory chain and energy metabolism. Meanwhile, genes encoding the above enzymes were differentially expressed, with pfkB overexpression and zwf and mqo downregulation. Furthermore, molecular docking revealed that linalool can interact with the amino acid residues of G6DPH, MDH and PFK through hydrogen bonds. Therefore, it is hypothesized that the mechanism of linalool against P. fragi may involve cell membrane damage (structure and morphology), disturbance of energy metabolism (TCA cycle, EMP and HMP pathway) and amino acid metabolism (cysteine, glutamic acid and citrulline). These findings contribute to the development of linalool as a promising antibacterial agent in response to the food security challenge.

Deuterium labelling techniques have shown widespread applications in organic synthesis, analytic chemistry, life science and material science. The design of practical deuteration reactions which is environmentally friendly is highly desired for pharmaceutical development and industrial processes while remaining underexplored. We herein report a convenient, transition metal-free strategy for reductive deuteration of ketone derivatives to α-deuterated alcohols by employing a conjugated aryl amine-based organophotocatalyst, a dipropyl disulfide co-catalyst and an inorganic reductant. Upon irradiation with visible light and under an air atmosphere, a variety of diaryl ketones, heteroaryl ketones, cyclic ketones, α-ketoesters, benzil derivatives, aliphatic ketones and drug-like molecules were converted into corresponding α-deuterated aryl alcohols in good to excellent yields and with high deuterium incorporation (up to 99% D-incorporation). Only low toxic side-products, such as sodium salts and carbon dioxide, were generated. Consequently, the synthetic utility of this method is highlighted by the preparation of several D-labeled drug-like molecules, including orphenadrine, carbinoxamine and modafinil.

Abstract Stretchable thermoelectrics have recently attracted widespread attention in the field of self‐powered wearable electronics due to their unique capability of harvesting body heat. However, it remains challenging to develop thermoelectric materials with excellent stretchability, durable thermoelectric properties, wearable comfort, and multifunctional sensing properties simultaneously. Herein, an advanced preparation strategy combining electrospinning and spraying technology is proposed to prepare carbon nanotube (CNT)/polyvinyl pyrrolidone (PVP)/polyurethane (PU) composite thermoelectric fabrics that have high air permeability and stretchability (~250%) close to those of pure PU nanofiber fabrics. Furthermore, PVP can not only improve the dispersion of CNTs but also act as interfacial binders between the CNT and the elastic PU skeleton. Consequently, both the electrical conductivity and the Seebeck coefficient remain unchanged even after bending 1000 times. In addition, self‐powered sensors for the mutual conversion of finger temperature and language and detection of the movement of joints to optimize an athlete's movement state were successfully fabricated. This study paves the way for stretchable thermoelectric fabrics with fascinating applications in smart wearable fields such as power generation, health monitoring, and human–computer interaction.

Background: Predicting cognition decline in patients with mild cognitive impairment (MCI) is crucial for identifying high-risk individuals and implementing effective management. To improve predicting MCI-to-AD conversion, it is necessary to consider various factors using explainable machine learning (XAI) models which provide interpretability while maintaining predictive accuracy. This study used the Explainable Boosting Machine (EBM) model with multimodal features to predict the conversion of MCI to AD during different follow-up periods while providing interpretability. Methods: This retrospective case-control study is conducted with data obtained from the ADNI database, with records of 1042 MCI patients from 2006 to 2022 included. The exposures included in this study were MRI biomarkers, cognitive scores, demographics, and clinical features. The main outcome was AD conversion from aMCI during follow-up. The EBM model was utilized to predict aMCI converting to AD based on three feature combinations, obtaining interpretability while ensuring accuracy. Meanwhile, the interaction effect was considered in the model. The three feature combinations were compared in different follow-up periods with accuracy, sensitivity, specificity, and AUC-ROC. The global and local explanations are displayed by importance ranking and feature interpretability plots. Results: The five-years prediction accuracy reached 85% (AUC = 0.92) using both cognitive scores and MRI markers. Apart from accuracies, we obtained features’ importance in different follow-up periods. In early stage of AD, the MRI markers play a major role, while for middle-term, the cognitive scores are more important. Feature risk scoring plots demonstrated insightful nonlinear interactive associations between selected factors and outcome. In one-year prediction, lower right inferior temporal volume (4) is the most important. During four-year prediction, APOE4 is the most critical. For five-year prediction, lower right entorhinal volume (

Background: High dimensional mediation analysis is frequently conducted to explore the role of epigenetic modifiers between exposure and health outcome. However, the issue of high dimensional mediation analysis with unmeasured confounders for survival analysis in observational study has not been well solved.Methods: In this study, we proposed an instrumental variable based approach for high dimensional mediation analysis with unmeasured confounders in survival analysis for epigenetic study. We used the Sobel‘s test, the Joint test, and the Bootstrap method to test the mediation effect. A comprehensive simulation study was conducted to decide the best test strategy. An empirical study based on DNA methylation data of lung cancer patients was conducted to illustrate the performance of the proposed method.Results: Simulation study suggested that the proposed method performed well in the identifying mediating factors. The estimation of the mediation effect by the proposed approach is also reliable with less bias compared with the classical approach. In the empirical study, we identified two DNA methylation signatures including cg21926276 and cg26387355 with a mediation effect of 0.226 (95%CI: 0.108-0.344) and 0.158 (95%CI: 0.065-0.251) between smoking and lung cancer using the proposed approach.Conclusion: The proposed method obtained good performance in simulation and empirical studies, it could be an effective statistical tool for high dimensional mediation analysis.

The gravity recovery and climate experiment (GRACE) provides an unprecedented opportunity to detect the spatial and temporal variation of the terrestrial water storage (TWS) for regional to continental scales. However, the GRACE system's coarse temporal resolution (∼monthly) and data discontinuity missing perplexed the TWS research during the operation. In this article, the data assimilation (DA) method was employed to integrate the vertical deformation obtained from the small baseline subset (SBAS) InSAR processing into the NASA catchment land surface model (CLSM), which improved the estimation of the TWS. First, we used a one-dimensional ensemble Kalman filter for DA research to estimate the TWS in Dali Prefecture, southwestern China. Finally, we compared the estimated TWS with the GRACE-based TWS from December 2, 2018 to January 21, 2021. The unbiased root-mean-square of the open loop (OL; without DA) method and the SBAS-InSAR DA method are 61 mm and 30 mm in Dali Prefecture, respectively. Results revealed that the numerical difference between the estimated TWS and the GRACE TWS retrievals was significantly decreased by the SBAS-InSAR DA method than the OL method. In addition, the temporal resolution of the SBAS-InSAR DA-based TWS was improved to 12 days compared with GRACE-based TWS. Furthermore, we recovered the discontinuous deletion and blank of GRACE-based TWS from 2015 to 2018 by the SBAS-InSAR DA method.

Diet plays a crucial role in regulating individuals’ lifestyles and is closely related to health. The intake of animal-sourced foods (ASF) provides the human body with high-quality protein and various micronutrients. This study aimed to investigate whether the diversity of animal foods has a positive impact on the health-related quality of life (HRQoL) among residents. The data came from the Shaanxi baseline survey of the Northwest Chinese Regional Ethnic Cohort Study, which recruited more than 100 thousand participants aged 35 to 74 from five provinces between June 2018 and May 2019. A total of 39,997 participants in Shaanxi (mean age: 50 years; 64% women) were finally included in this current study. The animal source food diet diversity score (ASFDDS) was established based on the frequency of consuming pork, mutton, beef, poultry, seafood, eggs, pure milk, and yogurt. The physical component score (PCS) and mental component score (MCS), ranging from 0 to 100 on the 12-Item Short Form Survey (SF-12), were used to assess participants’ HRQoL. Better PCS/MCS was defined as scores higher than the 90th percentile. The results showed that men had a higher intake of ASF and ASFDDS than women. After adjusting for potential confounders, compared with those who never or rarely consumed animal foods, the likelihood of having better PCS and MCS increased by 16% (OR = 1.16, 95%CI: 1.01–1.34) and 24% (OR = 1.24, 95%CI: 1.03–1.448), respectively, in men with an ASFDDS ≥ 2. In women, a 34% increase (OR = l.34, 95%CI: 116–l.54) likelihood for better PCS was observed for an ASFDDS ≥ 2, but no association was observed for MCS. Increasing each specific animal source’s food intake was associated with better PCS after adjusting for all covariates. However, for MCS, positive associations were only observed in seafood consumption among men and eggs among women. Restricted cubic splines showed a substantial dose-response association between intake frequency of animal-source foods and PCS, both in men and women. The study suggests that a diverse intake of animal-sourced foods can potentially improve the HRQoL of Chinese adults.

Pixel Offset Tracking (POT) for optical imagery is a widely used method for extracting large-scale ground deformation. However, the influence of imaging parameters on the measurement accuracy of POT is still unclear. In this study, based on 16 pairs of Sentinel-2 images covering the period before and after the Ms6.9 Menyuan earthquake in 2022, we quantitatively assessed the effects of imaging bands, time intervals between image pairs, and differences in solar zenith angles on the measurement accuracy of optical POT. The results showed that the quality of ground deformation extracted using the near-infrared band was superior to other bands. The accuracy of optical POT measurements exhibited a negative correlation with both the time interval between image pairs and the differences in solar zenith angles. The maximum difference in optical POT measurement accuracy for the near-infrared band between image pairs with different time intervals (5/10/15 days) reached 30.3%, while the maximum difference in deformation measurement accuracy for pairs with different solar zenith angle differences was 30.56%. Utilizing the optimal POT image pair, the accuracy of co-seismic deformation measurement for the Menyuan earthquake improved by 48.3% compared to the worst image pair. The maximum co-seismic horizontal displacement caused by the earthquake was estimated to be 3.00 ± 0.51 m.

Abstract Alzheimer's disease (AD) is one of the most common neurodegenerative causes of dementia, the pathology of which is still not much clear. It′s challenging to discover the disease modifying agents for the prevention and treatment of AD over the years. Emerging evidence has been accumulated to reveal the crucial role of up‐regulated glutaminyl cyclase (QC) in the initiation of AD. In the current study, the QC inhibitory potency of a library consisting of 1621 FDA‐approved compounds was assessed. A total of 54 hits, 3.33 % of the pool, exhibited QC inhibitory activities. The Ki of the top 5 compounds with the highest QC inhibitory activities were measured. Among these selected hits, compounds affecting neuronal signaling pathways and other mechanisms were recognized. Moreover, several polyphenol derivatives with QC inhibitory activities were also identified. Frameworks and subsets contained in these hits were analyzed. Taken together, our results may contribute to the discovery and development of novel QC inhibitors as potential anti‐AD agents.

Since most patients with heart failure are re-admitted to the hospital, accurately identifying the risk of re-admission of patients with heart failure is important for clinical decision making and management. This study plans to develop an interpretable predictive model based on a Chinese population for predicting six-month re-admission rates in heart failure patients. Research data were obtained from the PhysioNet portal. To ensure robustness, we used three approaches for variable selection. Six different machine learning models were estimated based on selected variables. The ROC curve, prediction accuracy, sensitivity, and specificity were used to evaluate the performance of the established models. In addition, we visualized the optimized model with a nomogram. In all, 2002 patients with heart failure were included in this study. Of these, 773 patients experienced re-admission and a six-month re-admission incidence of 38.61%. Based on evaluation metrics, the logistic regression model performed best in the validation cohort, with an AUC of 0.634 (95%CI: 0.599–0.646) and an accuracy of 0.652. A nomogram was also generated. The established prediction model has good discrimination ability in predicting. Our findings are helpful and could provide useful information for the allocation of healthcare resources and for improving the quality of survival of heart failure patients.

Litchi polysaccharides are a kind of macromolecular polymers with various biological activities and a wide range of molecular weights. In this study, two separate fractions, with average molecular weights of 378.67 kDa (67.33%) and 16.96 kDa (6.95%), which were referred to as LP1 and LP2, respectively, were separated using an ultrafiltration membrane. Their physicochemical properties, and immunomodulatory and prebiotic activity were compared. The results revealed that LP2 contained more neutral sugar, arabinose, galactose and rhamnose, but less uronic acid, protein, mannose and glucose than LP1. Compared with LP1, LP2 possessed higher solubility and lower apparent viscosity. LP2 exhibited stronger stimulation on macrophage secretion of NO, TNF-α and IL-6, as well as better proliferation of Lactobacillus plantarum, Leuconostoc mesenteroides, Lactobacillus casei and Bifidobacterium adolescentis. These results suggest that an ultrafiltration membrane might be used to prepare a highly-active polysaccharide fraction from litchi pulp that may be used for food or drug development.

Abstract Mesenchymal stem cells (MSCs) were known to have excellent properties in cell therapy. However, the risk of immune rejection associated with cell transplant therapy hampers its use. Extracellular vesicles secreted by MSCs derived from different sources that contain therapeutic molecules such as RNA and proteins, which is a novel strategy for cell-free therapy. Recently, researches show EVs from MSCs (MSC-EVs) of different sources have special functions and effects on different diseases. Here, we collected these researches and compared them to each other. In addition, their potential and possible application in clinical treatment are described.

Introduction: With an increasing number of China's aging population, Parkinson's disease (PD) increases year by year. Persons with PD exhibit abnormal balance functions, leading to motor skills difficulties, such as unstable walking or even falling. Therefore, activities of daily living and quality of life are affected. This study aims to explore the effectiveness of Tai Chi training based on the mobile phone app in improving the balance ability of persons with PD.Methods and Analysis: A randomized, single-blind, parallel controlled trial will be conducted in this study. One hundred forty-four persons with PD who meet the inclusion criteria will be randomly divided into a 1:1:1 ratio: (1) control group, (2) basic experimental group (basic app with no Tai Chi training features), and (3) balanced-enhanced experimental group (basic app with Tai Chi training features). Individuals with PD will be evaluated on balance and motor function outcomes. The primary outcome measure is the limits of stability (including the maximum excursion and direction control); the secondary outcome measures include the Unified Parkinson's Disease Rating Scale III (UPDRS-III), Berg Balance Scale (BBS), Functional Reach Test (FRT), Timed Up & Go (TUG), 6-Minute Walk Test (6MWT), and 39-item Parkinson's Disease Questionnaire (PDQ-39). Each group of patients will go through an assessment at baseline, 17 and 33 weeks.Discussion: This study will evaluate the effectiveness of the mobile phone app Tai Chi training on the balance function of persons with PD. We assume that a challenging Tai Chi project based on a mobile phone app will improve balance in the short and long term. As walking stability progresses, it is expected that daily activities and quality of life improve. These findings will be used to improve the effectiveness of future home management measures for persons with PD.Ethics and Dissemination: This study has been approved by the ethical review committee of the Shanghai University of Sport (approval number: 102772019RT056). Informed consent will be obtained from all participants or their guardians. The authors intend to submit the study findings to peer-reviewed journals or academic conferences to be published.Clinical Trial Registration: Chinese Clinical Trial Registry (ChiCTR2000029135).

Active rock glaciers (ARGs) are important permafrost landforms in alpine regions. Identifying ARGs has mainly relied on visual interpretation of their geomorphic characteristics with optical remote sensing images, while mapping ARGs from their kinematic features has also become popular in recent years. However, a thorough comparison of geomorphic- and kinematic-based inventories of ARGs has not been carried out. In this study, we employed a multi-temporal interferometric synthetic aperture radar (InSAR) technique to derive the mean annual surface displacement velocity over the Daxue Shan, Southeast Tibet Plateau. We then compiled a rock glacier inventory by synergistically interpreting the InSAR-derived surface displacements and geomorphic features based on Google Earth images. Our InSAR-assist kinematic-based inventory (KBI) was further compared with a pre-existing geomorphic-based inventory (GBI) of rock glaciers in Daxue Shan. The results show that our InSAR-assist inventory consists of 344 ARGs, 36% (i.e., 125) more than that derived from the geomorphic-based method (i.e., 251). Only 32 ARGs in the GBI are not included in the KBI. Among the 219 ARGs detected by both approaches, the ones with area differences of more than 20% account for about 32% (i.e., 70 ARGs). The mean downslope velocities of ARGs calculated from InSAR are between 2.8 and 107.4 mm∙a−1. Our comparative analyses show that ARGs mapping from the InSAR-based kinematic approach is more efficient and accurate than the geomorphic-based approach. Nonetheless, the completeness of the InSAR-assist KBI is affected by the SAR data acquisition time, signal decorrelation, geometric distortion of SAR images, and the sensitivity of the InSAR measurement to ground deformation. We suggest that the kinematic-based approach should be utilized in future ARGs-based studies such as regional permafrost distribution assessment and water storage estimates.

In order to reduce the total cost of empty container repositioning, a multi-period empty container repositioning optimization model of the China Railway Express was established by using distributed robust chance-constrained programming based on partial information such as mean and variance of demand. This established model considered sea-land intermodal transportation, uncertain empty container demand and foldable containers. To simplify the model, the distributed robust chance constraints were transformed into equivalent ones that could be easily solved, and the empty container demands were determined. Numerical experiments were carried out to analyze the influence of different parameters on the total cost. The results showed that the total cost could be greatly reduced by sea-land intermodal transportation. Using foldable containers could reduce the total cost of empty container repositioning. With the improvement of service level, the numbers of empty container repositioning increased owing to the distributional robust chance constraints. When standard and foldable containers were used simultaneously, the total cost could be greatly reduced by appropriately using foldable containers under three different supply–demand relationships of containers. The optimization results may provide a greatly feasible reference for the decision makers of the China Railway Express.