Development of Multilayer Optics for the EUV, Soft X-ray and X-ray Regions in Tongji University

In EUV and X- ray regions, multilayer mirrors are the essential and necessary optics elements. The good prospects of the EUV and X- rays for next generation lithography system, microscopy in the “water windows”, astro- nomical telescope, spectroscopy, plasma diagnostics, and X- ray laser have impelled the development of multilayer optics. The paper introduces the recent results of the multilayer optics elements in Tongji University, including beam splitters, broadband/angular polarizers, supermirrors and high- reflectance mirrors. The product of reflectivity and transmissivity is above 4% for the Mo/Si multilayer beam splitter. Over the 15￣17 nm wavelength range, the s- re- flectivity of the non- periodic Mo/Si broadband multilayer polarizers is reasonably constant, as high as 36.6%, and the degree of polarization is more than 97.8%. At the fixed energy of 8 keV (Cu Kαline), the W/Si supermirror has the reflectivity of above 30% in the angle range of 0.4°￣0.85°, and a W/B4C supermirror has the reflectivity of about 20% in the angle range of 0.9°￣1.2°, and the reflectivity of W/C supermirror working in the grazing incident angle range of 0.9°￣1.2°is about 20%. The experimental results of some high- reflectance mirrors in our lab are also pre- sented, such as Mo/Si, Mo/Y, Cr/C, La/B4C, Si/C and Si/SiC. The reflectivity of Mo/Si multilayer is as high as 61.1% at wavelength of 13.4 nm.

Evolutionary Decision-Making and Planning for Autonomous Driving Based on Safe and Rational Exploration and Exploitation

Decision-making and motion planning are extremely important in autonomous driving to ensure safe driving in a real-world environment.This study proposes an online evolutionary decision-making and motion planning framework for autonomous driving based on a hybrid data-and model-driven method.First,a data-driven decision-making module based on deep reinforcement learning(DRL)is developed to pursue a rational driving performance as much as possible.Then,model predictive control(MPC)is employed to execute both longitudinal and lateral motion planning tasks.Multiple constraints are defined according to the vehicle’s physical limit to meet the driving task requirements.Finally,two principles of safety and rationality for the self-evolution of autonomous driving are proposed.A motion envelope is established and embedded into a rational exploration and exploitation scheme,which filters out unreasonable experiences by masking unsafe actions so as to collect high-quality training data for the DRL agent.Experiments with a high-fidelity vehicle model and MATLAB/Simulink co-simulation environment are conducted,and the results show that the proposed online-evolution framework is able to generate safer,more rational,and more efficient driving action in a real-world environment.

Predictive modeling for postoperative delirium in elderly patients with abdominal malignancies using synthetic minority oversampling technique

BACKGROUND Postoperative delirium,particularly prevalent in elderly patients after abdominal cancer surgery,presents significant challenges in clinical management.AIM To develop a synthetic minority oversampling technique(SMOTE)-based model for predicting postoperative delirium in elderly abdominal cancer patients.METHODS In this retrospective cohort study,we analyzed data from 611 elderly patients who underwent abdominal malignant tumor surgery at our hospital between September 2020 and October 2022.The incidence of postoperative delirium was recorded for 7 d post-surgery.Patients were divided into delirium and non-delirium groups based on the occurrence of postoperative delirium or not.A multivariate logistic regression model was used to identify risk factors and develop a predictive model for postoperative delirium.The SMOTE technique was applied to enhance the model by oversampling the delirium cases.The model’s predictive accuracy was then validated.RESULTS In our study involving 611 elderly patients with abdominal malignant tumors,multivariate logistic regression analysis identified significant risk factors for postoperative delirium.These included the Charlson comorbidity index,American Society of Anesthesiologists classification,history of cerebrovascular disease,surgical duration,perioperative blood transfusion,and postoperative pain score.The incidence rate of postoperative delirium in our study was 22.91%.The original predictive model(P1)exhibited an area under the receiver operating characteristic curve of 0.862.In comparison,the SMOTE-based logistic early warning model(P2),which utilized the SMOTE oversampling algorithm,showed a slightly lower but comparable area under the curve of 0.856,suggesting no significant difference in performance between the two predictive approaches.CONCLUSION This study confirms that the SMOTE-enhanced predictive model for postoperative delirium in elderly abdominal tumor patients shows performance equivalent to that of traditional methods,effectively addressing data imbalance.

Development of advanced anion exchange membrane from the view of the performance of water electrolysis cell

Green hydrogen produced by water electrolysis combined with renewable energy is a promising alternative to fossil fuels due to its high energy density with zero-carbon emissions.Among water electrolysis technologies,the anion exchange membrane(AEM) water electrolysis has gained intensive attention and is considered as the next-generation emerging technology due to its potential advantages,such as the use of low-cost non-noble metal catalysts,the relatively mature stack assembly process,etc.However,the AEM water electrolyzer is still in the early development stage of the kW-level stack,which is mainly attributed to severe performance decay caused by the core component,i.e.,AEM.Here,the review comprehensively presents the recent progress of advanced AEM from the view of the performance of water electrolysis cells.Herein,fundamental principles and critical components of AEM water electrolyzers are introduced,and work conditions of AEM water electrolyzers and AEM performance improvement strategies are discussed.The challenges and perspectives are also analyzed.

Pathologically successful conversion hepatectomy for advanced giant hepatocellular carcinoma after multidisciplinary therapy:A case report and review of literature

BACKGROUND Hepatocellular carcinoma(HCC)is one of the leading causes of death due to its complexity,heterogeneity,rapid metastasis and easy recurrence after surgical resection.We demonstrated that combination therapy with transcatheter arterial chemoembolization(TACE),hepatic arterial infusion chemotherapy(HAIC),Epclusa,Lenvatinib and Sintilimab is useful for patients with advanced HCC.CASE SUMMARY A 69-year-old man who was infected with hepatitis C virus(HCV)30 years previously was admitted to the hospital with abdominal pain.Enhanced computed tomography(CT)revealed a low-density mass in the right lobe of the liver,with a volume of 12.9 cm×9.4 cm×15 cm,and the mass exhibited a“fast-in/fast-out”pattern,with extensive filling defect areas in the right branch of the portal vein and an alpha-fetoprotein level as high as 657 ng/mL.Therefore,he was judged to have advanced HCC.During treatment,the patient received three months of Epclusa,three TACE treatments,two HAIC treatments,three courses of sintilimab,and twenty-one months of lenvatinib.In the third month of treatment,the patient developed severe side effects and had to stop immunotherapy,and the Lenvatinib dose had to be halved.Postoperative pathological diagnosis indicated a complete response.The patient recovered well after the operation,and no tumor recurrence was found.CONCLUSION Multidisciplinary conversion therapy for advanced enormous HCC caused by HCV infection has a significant effect.Individualized drug adjustments should be made during any treatment according to the patient's tolerance to treatment.

Predicting short-term major postoperative complications in intestinal resection for Crohn’s disease:A machine learning-based study

BACKGROUND Due to the complexity and numerous comorbidities associated with Crohn’s disease(CD),the incidence of postoperative complications is high,significantly impacting the recovery and prognosis of patients.Consequently,additional stu-dies are required to precisely predict short-term major complications following intestinal resection(IR),aiding surgical decision-making and optimizing patient care.AIM To construct novel models based on machine learning(ML)to predict short-term major postoperative complications in patients with CD following IR.METHODS A retrospective analysis was performed on clinical data derived from a patient cohort that underwent IR for CD from January 2017 to December 2022.The study participants were randomly allocated to either a training cohort or a validation cohort.The logistic regression and random forest(RF)were applied to construct models in the training cohort,with model discrimination evaluated using the area under the curves(AUC).The validation cohort assessed the performance of the constructed models.RESULTS Out of the 259 patients encompassed in the study,5.0%encountered major postoperative complications(Clavien-Dindo≥III)within 30 d following IR for CD.The AUC for the logistic model was 0.916,significantly lower than the AUC of 0.965 for the RF model.The logistic model incorporated a preoperative CD activity index(CDAI)of≥220,a diminished preoperative serum albumin level,conversion to laparotomy surgery,and an extended operation time.A nomogram for the logistic model was plotted.Except for the surgical approach,the other three variables ranked among the top four important variables in the novel ML model.CONCLUSION Both the nomogram and RF exhibited good performance in predicting short-term major postoperative complic-ations in patients with CD,with the RF model showing more superiority.A preoperative CDAI of≥220,a di-minished preoperative serum albumin level,and an extended operation time might be the most crucial variables.The findings of this study can assist clinicians in identifying patients at a higher risk for complications and offering personalized perioperative management to enhance patient outcomes.

Sustained release of vascular endothelial growth factor A and basic fibroblast growth factor from nanofiber membranes reduces oxygen/glucose deprivation-induced injury to neurovascular units

Upregulation of vascular endothelial growth factor A/basic fibroblast growth factor(VEGFA/b FGF)expression in the penumbra of cerebral ischemia can increase vascular volume,reduce lesion volume,and enhance neural cell proliferation and differentiation,thereby exerting neuroprotective effects.However,the beneficial effects of endogenous VEGFA/b FGF are limited as their expression is only transiently increased.In this study,we generated multilayered nanofiber membranes loaded with VEGFA/b FGF using layer-by-layer self-assembly and electrospinning techniques.We found that a membrane containing 10 layers had an ideal ultrastructure and could efficiently and stably release growth factors for more than 1 month.This 10-layered nanofiber membrane promoted brain microvascular endothelial cell tube formation and proliferation,inhibited neuronal apoptosis,upregulated the expression of tight junction proteins,and improved the viability of various cellular components of neurovascular units under conditions of oxygen/glucose deprivation.Furthermore,this nanofiber membrane decreased the expression of Janus kinase-2/signal transducer and activator of transcription-3(JAK2/STAT3),Bax/Bcl-2,and cleaved caspase-3.Therefore,this nanofiber membrane exhibits a neuroprotective effect on oxygen/glucose-deprived neurovascular units by inhibiting the JAK2/STAT3 pathway.

Probabilistic analysis of tunnel face seismic stability in layered rock masses using Polynomial Chaos Kriging metamodel

Face stability is an essential issue in tunnel design and construction.Layered rock masses are typical and ubiquitous;uncertainties in rock properties always exist.In view of this,a comprehensive method,which combines the Upper bound Limit analysis of Tunnel face stability,the Polynomial Chaos Kriging,the Monte-Carlo Simulation and Analysis of Covariance method(ULT-PCK-MA),is proposed to investigate the seismic stability of tunnel faces.A two-dimensional analytical model of ULT is developed to evaluate the virtual support force based on the upper bound limit analysis.An efficient probabilistic analysis method PCK-MA based on the adaptive Polynomial Chaos Kriging metamodel is then implemented to investigate the parameter uncertainty effects.Ten input parameters,including geological strength indices,uniaxial compressive strengths and constants for three rock formations,and the horizontal seismic coefficients,are treated as random variables.The effects of these parameter uncertainties on the failure probability and sensitivity indices are discussed.In addition,the effects of weak layer position,the middle layer thickness and quality,the tunnel diameter,the parameters correlation,and the seismic loadings are investigated,respectively.The results show that the layer distributions significantly influence the tunnel face probabilistic stability,particularly when the weak rock is present in the bottom layer.The efficiency of the proposed ULT-PCK-MA is validated,which is expected to facilitate the engineering design and construction.

Recent progress of self-supported air electrodes for flexible Zn-air batteries

Smart wearable devices are regarded to be the next prevailing technology product after smartphones and smart homes,and thus there has recently been rapid development in flexible electronic energy storage devices.Among them,flexible solid-state zinc-air batteries have received widespread attention because of their high energy density,good safety,and stability.Efficient bifunctional oxygen electrocatalysts are the primary consideration in the development of flexible solid-state zinc-air batteries,and self-supported air cathodes are strong candidates because of their advantages including simplified fabrication process,reduced interfacial resistance,accelerated electron transfer,and good flexibility.This review outlines the research progress in the design and construction of nanoarray bifunctional oxygen electrocatalysts.Starting from the configuration and basic principles of zinc-air batteries and the strategies for the design of bifunctional oxygen electrocatalysts,a detailed discussion of self-supported air cathodes on carbon and metal substrates and their uses in flexible zinc-air batteries will follow.Finally,the challenges and opportunities in the development of flexible zinc-air batteries will be discussed.

Neuroprotective effects of chaperone-mediated autophagy in neurodegenerative diseases

Chaperone-mediated autophagy is one of three types of autophagy and is characterized by the selective degradation of proteins.Chaperone-mediated autophagy contributes to energy balance and helps maintain cellular homeostasis,while providing nutrients and support for cell survival.Chaperone-mediated autophagy activity can be detected in almost all cells,including neurons.Owing to the extreme sensitivity of neurons to their environmental changes,maintaining neuronal homeostasis is critical for neuronal growth and survival.Chaperone-mediated autophagy dysfunction is closely related to central nervous system diseases.It has been shown that neuronal damage and cell death are accompanied by chaperone-mediated autophagy dysfunction.Under certain conditions,regulation of chaperone-mediated autophagy activity attenuates neurotoxicity.In this paper,we review the changes in chaperone-mediated autophagy in neurodegenerative diseases,brain injury,glioma,and autoimmune diseases.We also summarize the most recent research progress on chaperone-mediated autophagy regulation and discuss the potential of chaperone-mediated autophagy as a therapeutic target for central nervous system diseases.

Product quality prediction based on RBF optimized by firefly algorithm

With the development of information technology,a large number of product quality data in the entire manufacturing process is accumulated,but it is not explored and used effectively.The traditional product quality prediction models have many disadvantages,such as high complexity and low accuracy.To overcome the above problems,we propose an optimized data equalization method to pre-process dataset and design a simple but effective product quality prediction model:radial basis function model optimized by the firefly algorithm with Levy flight mechanism(RBFFALM).First,the new data equalization method is introduced to pre-process the dataset,which reduces the dimension of the data,removes redundant features,and improves the data distribution.Then the RBFFALFM is used to predict product quality.Comprehensive expe riments conducted on real-world product quality datasets validate that the new model RBFFALFM combining with the new data pre-processing method outperforms other previous me thods on predicting product quality.

基于文献回顾的有色金属冶炼废渣的特征、资源化途径及其安全利用评价

冶炼废渣作为冶炼过程中产生的工业副产物,由于其缺乏安全、合理的防护和管理措施可能导致环境问题。通过全面分析近年来发表的相关文献,本文对有色金属冶炼废渣的特性、资源化利用途径及安全利用评价等方面的最新研究进展进行了介绍。研究结果表明,不同的原生精矿、冶炼条件和冶炼类型决定了冶炼废渣的化学和矿物学特征。此外,冶炼废渣在各种资源化利用中表现出极高的灵活性,不仅可以用于金属回收和建筑材料,还可以作为农业肥料和修复剂。同时,本文强调了在各种利用情景下对废渣进行安全性评估的重要性,以降低其潜在环境风险。此外,本文还强调了未来的研究方向,包括建立一种综合且量化的土渣混合体中重金属环境风险评价方法,以及探索更多创新的冶炼废渣资源化利用方法。综上所述,本文对于推动冶炼废渣在环境保护和资源可持续利用方面的研究有重要意义。

Rock mass quality prediction on tunnel faces with incomplete multi-source dataset via tree-augmented naive Bayesian network

Rock mass quality serves as a vital index for predicting the stability and safety status of rock tunnel faces.In tunneling practice,the rock mass quality is often assessed via a combination of qualitative and quantitative parameters.However,due to the harsh on-site construction conditions,it is rather difficult to obtain some of the evaluation parameters which are essential for the rock mass quality prediction.In this study,a novel improved Swin Transformer is proposed to detect,segment,and quantify rock mass characteristic parameters such as water leakage,fractures,weak interlayers.The site experiment results demonstrate that the improved Swin Transformer achieves optimal segmentation results and achieving accuracies of 92%,81%,and 86%for water leakage,fractures,and weak interlayers,respectively.A multisource rock tunnel face characteristic(RTFC)dataset includes 11 parameters for predicting rock mass quality is established.Considering the limitations in predictive performance of incomplete evaluation parameters exist in this dataset,a novel tree-augmented naive Bayesian network(BN)is proposed to address the challenge of the incomplete dataset and achieved a prediction accuracy of 88%.In comparison with other commonly used Machine Learning models the proposed BN-based approach proved an improved performance on predicting the rock mass quality with the incomplete dataset.By utilizing the established BN,a further sensitivity analysis is conducted to quantitatively evaluate the importance of the various parameters,results indicate that the rock strength and fractures parameter exert the most significant influence on rock mass quality.

Recent advances and future prospects on Ni_(3)S_(2)-Based electrocatalysts for efficient alkaline water electrolysis

Green hydrogen(H_(2))produced by renewable energy powered alkaline water electrolysis is a promising alternative to fossil fuels due to its high energy density with zero-carbon emissions.However,efficient and economic H_(2) production by alkaline water electrolysis is hindered by the sluggish hydrogen evolution reaction(HER)and oxygen evolution reaction(OER).Therefore,it is imperative to design and fabricate high-active and low-cost non-precious metal catalysts to improve the HER and OER performance,which affects the energy efficiency of alkaline water electrolysis.Ni_(3)S_(2) with the heazlewoodite structure is a potential electrocatalyst with near-metal conductivity due to the Ni–Ni metal network.Here,the review comprehensively presents the recent progress of Ni_(3)S_(2)-based electrocatalysts for alkaline water electrocatalysis.Herein,the HER and OER mechanisms,performance evaluation criteria,preparation methods,and strategies for performance improvement of Ni_(3)S_(2)-based electrocatalysts are discussed.The challenges and perspectives are also analyzed.

Thermo-hydro-mechanical (THM) coupled simulation of the land subsidence due to aquifer thermal energy storage (ATES) system in soft soils

Aquifer thermal energy storage(ATES)system has received attention for heating or cooling buildings.However,it is well known that land subsidence becomes a major environmental concern for ATES projects.Yet,the effect of temperature on land subsidence has received practically no attention in the past.This paper presents a thermo-hydro-mechanical(THM)coupled numerical study on an ATES system in Shanghai,China.Four water wells were installed for seasonal heating and cooling of an agriculture greenhouse.The target aquifer at a depth of 74e104.5 m consisted of alternating layers of sand and silty sand and was covered with clay.Groundwater level,temperature,and land subsidence data from 2015 to 2017 were collected using field monitoring instruments.Constrained by data,we constructed a field scale three-dimensional(3D)model using TOUGH(Transport of Unsaturated Groundwater and Heat)and FLAC3D(Fast Lagrangian Analysis of Continua)equipped with a thermo-elastoplastic constitutive model.The effectiveness of the numerical model was validated by field data.The model was used to reproduce groundwater flow,heat transfer,and mechanical responses in porous media over three years and capture the thermo-and pressure-induced land subsidence.The results show that the maximum thermoinduced land subsidence accounts for about 60%of the total subsidence.The thermo-induced subsidence is slightly greater in winter than that in summer,and more pronounced near the cold well area than the hot well area.This study provides some valuable guidelines for controlling land subsidence caused by ATES systems installed in soft soils.

开挖补偿法防控深部地下岩爆灾害——引汉济渭工程秦岭输水隧洞案例分析

Rockburst disasters occur frequently during deep underground excavation,yet traditional concepts and methods can hardly meet the requirements for support under high geo-stress conditions.Consequently,rockburst control remains challenging in the engineering field.In this study,the mechanism of excavation-induced rockburst was briefly described,and it was proposed to apply the excavation compensation method(ECM)to rockburst control.Moreover,a field test was carried out on the Qinling Water Conveyance Tunnel.The following beneficial findings were obtained:Excavation leads to changes in the engineering stress state of surrounding rock and results in the generation of excess energy DE,which is the fundamental cause of rockburst.The ECM,which aims to offset the deep excavation effect and lower the risk of rockburst,is an active support strategy based on high pre-stress compensation.The new negative Poisson’s ratio(NPR)bolt developed has the mechanical characteristics of high strength,high toughness,and impact resistance,serving as the material basis for the ECM.The field test results reveal that the ECM and the NPR bolt succeed in controlling rockburst disasters effectively.The research results are expected to provide guidance for rockburst support in deep underground projects such as Sichuan-Xizang Railway.

Exploring deep learning for landslide mapping:A comprehensive review

A detailed and accurate inventory map of landslides is crucial for quantitative hazard assessment and land planning.Traditional methods relying on change detection and object-oriented approaches have been criticized for their dependence on expert knowledge and subjective factors.Recent advancements in highresolution satellite imagery,coupled with the rapid development of artificial intelligence,particularly datadriven deep learning algorithms(DL)such as convolutional neural networks(CNN),have provided rich feature indicators for landslide mapping,overcoming previous limitations.In this review paper,77representative DL-based landslide detection methods applied in various environments over the past seven years were examined.This study analyzed the structures of different DL networks,discussed five main application scenarios,and assessed both the advancements and limitations of DL in geological hazard analysis.The results indicated that the increasing number of articles per year reflects growing interest in landslide mapping by artificial intelligence,with U-Net-based structures gaining prominence due to their flexibility in feature extraction and generalization.Finally,we explored the hindrances of DL in landslide hazard research based on the above research content.Challenges such as black-box operations and sample dependence persist,warranting further theoretical research and future application of DL in landslide detection.

Thermal safety boundary of lithium-ion battery at different state of charge

Thermal runaway(TR)is a critical issue hindering the large-scale application of lithium-ion batteries(LIBs).Understanding the thermal safety behavior of LIBs at the cell and module level under different state of charges(SOCs)has significant implications for reinforcing the thermal safety design of the lithium-ion battery module.This study first investigates the thermal safety boundary(TSB)correspondence at the cells and modules level under the guidance of a newly proposed concept,safe electric quantity boundary(SEQB).A reasonable thermal runaway propagation(TRP)judgment indicator,peak heat transfer power(PHTP),is proposed to predict whether TRP occurs.Moreover,a validated 3D model is used to quantitatively clarify the TSB at different SOCs from the perspective of PHTP,TR trigger temperature,SOC,and the full cycle life.Besides,three different TRP transfer modes are discovered.The interconversion relationship of three different TRP modes is investigated from the perspective of PHTP.This paper explores the TSB of LIBs under different SOCs at both cell and module levels for the first time,which has great significance in guiding the thermal safety design of battery systems.

Reactor field reconstruction from sparse and movable sensors using Voronoi tessellation-assisted convolutional neural networks

The aging of operational reactors leads to increased mechanical vibrations in the reactor interior.The vibration of the incore sensors near their nominal locations is a new problem for neutronic field reconstruction.Current field-reconstruction methods fail to handle spatially moving sensors.In this study,we propose a Voronoi tessellation technique in combination with convolutional neural networks to handle this challenge.Observations from movable in-core sensors were projected onto the same global field structure using Voronoi tessellation,holding the magnitude and location information of the sensors.General convolutional neural networks were used to learn maps from observations to the global field.The proposed method reconstructed multi-physics fields(including fast flux,thermal flux,and power rate)using observations from a single field(such as thermal flux).Numerical tests based on the IAEA benchmark demonstrated the potential of the proposed method in practical engineering applications,particularly within an amplitude of 5 cm around the nominal locations,which led to average relative errors below 5% and 10% in the L_(2) and L_(∞)norms,respectively.

Wetting sub-nanochannels via ionic hydration effect for improving charging dynamics

The ionic transport in sub-nanochannels plays a key role in energy storage,yet suffers from a high energy barrier.Wetting sub-nanochannels is crucial to accelerate ionic transport,but the introduction of water is challenging because of the hydrophobic extreme confinement.We propose wetting the channels by the exothermic hydration process of pre-intercalated ions,the effect of which varies distinctly with different ionic hydration structures and energies.Compared to the failed pre-intercalation of SO_(4)^(2-),HSO_(4)^(-) with weak hydration energy results in a marginal effect on the HOMO(Highest Occupied Molecular Orbital)level of water to avoid water splitting during the electrochemical intercalation.Meanwhile,the ability of water introduction is reserved by the initial incomplete dissociation state of HSO_(4)^(-),so the consequent exothermic reionization and hydration processes of the intercalated HSO_(4)^(-) promote the water introduction into sub-nanochannels,finally forming the stable confined water through hydrogen bonding with functional groups.The wetted channels exhibit a significantly enhanced ionic diffusion coef-ficient by~9.4 times.