Search

Waste plastics and rubber have always posed a threat to the environment and a great challenge for disposal. The use of these two waste materials as modifiers for road asphalt provides new road asphalt binders and at the same time provides a new way for waste material resource treatment. Rubber-modified asphalt and plastic-modified asphalt have received widespread attention due to their green and low-carbon characteristics and some performance advantages, but there are still some performance shortcomings that hinder their promotion and application. In order to continue to explore the application of waste materials in the field of road engineering, combined with the performance advantages of both, many scholars began to study the rubber-plastic composite modified asphalt (RPCMA). Therefore, this paper reviewed the different types of waste rubber and plastic, the comprehensive performance of different types of asphalt and its mixture performance, analyzed the modification mechanism of rubber-plastic composite modified asphalt and its performance changes. The results show that rubber-plastic composite modified asphalt is mostly prepared by directly adding modified materials. However, the pretreatment or melt granulation of rubber and plastic will make the performance of rubber-plastic modified asphalt has improved. There is a mutual promotion mechanism between rubber and plastic in asphalt, which makes the performance of rubber-plastic modified asphalt better than that of asphalt obtained by a single modification method (rubber modified asphalt or plastic modified asphalt). In some properties, it can be close to the commercial SBS modified asphalt. The performance of rubber and plastic composite modified asphalt at high temperature has been generally recognized by researchers, but its low-temperature performance is still controversial. Therefore, it is recommended to apply in high-temperature areas. Future work should focus on the development of composite modifiers with stable performance, low cost and ease of use, standardization of modification processes, improvement of low-temperature performance, and monitoring of road performance and environmental impact.

Abstract Exploiting thin Li metal anode is essential for high-energy-density battery, but is severely plagued by the poor processability of Li, as well as the uncontrollable Li plating/stripping behaviors and Li/electrolyte interface. Herein, a thickness/capacity-adjustable thin alloy-type Li/LiZn@Cu anode is fabricated for high-energy-density Li metal batteries. The as-formed lithophilic LiZn alloy in Li/LiZn@Cu anode can effectively regulate Li plating/stripping and stabilize the Li/electrolyte interface to deliver the hierarchical Li electrochemistry. Upon charging, the Li/LiZn@Cu anode firstly acts as Li source for homogeneous Li extraction. At the end of charging, the de-alloy of LiZn nanostructures further supplements the Li extraction, actually playing the Li compensation role in battery cycling. While upon discharging, the LiZn alloy forms just at the beginning, thereby regulating the following Li homogeneous deposition. The reversibility of such an interesting process is undoubtedly verified from the electrochemistry and in-situ XRD characterization. This work sheds light on the facile fabrication of practical Li metal anodes and useful Li compensation materials for high-energy-density Li metal batteries.

Abstract In this study, we perform online sea ice bias correction within a Geophysical Fluid Dynamics Laboratory global ice‐ocean model. For this, we use a convolutional neural network (CNN) which was developed in a previous study (Gregory et al., 2023, https://doi.org/10.1029/2023ms003757) for the purpose of predicting sea ice concentration (SIC) data assimilation (DA) increments. An initial implementation of the CNN shows systematic improvements in SIC biases relative to the free‐running model, however large summertime errors remain. We show that these residual errors can be significantly improved with a novel sea ice data augmentation approach. This approach applies sequential CNN and DA corrections to a new simulation over the training period, which then provides a new training data set to refine the weights of the initial network. We propose that this machine‐learned correction scheme could be utilized for generating improved initial conditions, and also for real‐time sea ice bias correction within seasonal‐to‐subseasonal sea ice forecasts.

Abstract Data assimilation is often viewed as a framework for correcting short‐term error growth in dynamical climate model forecasts. When viewed on the time scales of climate however, these short‐term corrections, or analysis increments, can closely mirror the systematic bias patterns of the dynamical model. In this study, we use convolutional neural networks (CNNs) to learn a mapping from model state variables to analysis increments, in order to showcase the feasibility of a data‐driven model parameterization which can predict state‐dependent model errors. We undertake this problem using an ice‐ocean data assimilation system within the Seamless system for Prediction and EArth system Research (SPEAR) model, developed at the Geophysical Fluid Dynamics Laboratory, which assimilates satellite observations of sea ice concentration every 5 days between 1982 and 2017. The CNN then takes inputs of data assimilation forecast states and tendencies, and makes predictions of the corresponding sea ice concentration increments. Specifically, the inputs are states and tendencies of sea ice concentration, sea‐surface temperature, ice velocities, ice thickness, net shortwave radiation, ice‐surface skin temperature, sea‐surface salinity, as well as a land‐sea mask. We find the CNN is able to make skillful predictions of the increments in both the Arctic and Antarctic and across all seasons, with skill that consistently exceeds that of a climatological increment prediction. This suggests that the CNN could be used to reduce sea ice biases in free‐running SPEAR simulations, either as a sea ice parameterization or an online bias correction tool for numerical sea ice forecasts.

Objective: We aimed to retrospectively analyze the toxicity profiles and predictors of immune-related adverse events (irAEs) as well as the correlation between irAEs and the clinical efficacy of multi-type immune checkpoint inhibitors (ICIs) in patients with advanced pan-cancer in a real-world setting. Methods: We retrospectively analyzed data from 105 patients with advanced pan-cancer treated with multi-type ICIs at the First Hospital of Jilin University between January 1, 2016 and August 1, 2020. We used logistic regression analyses to investigate the associations of irAEs with clinical baseline characteristics, blood count parameters, and biochemical indicators during treatment. Receiver operating characteristic curves were used to determine cutoff values for parameters and area under the curve values. Kaplan–Meier and Cox multivariate regression analyses were performed to estimate the relationships of baseline characteristics and irAEs with progression-free survival (PFS) and overall survival (OS). Results: A lower relative lymphocyte count (cutoff = 28.5%), higher albumin level (cutoff = 39.05 g/L), and higher absolute eosinophil count (AEC) (cutoff = 0.175 × 109/L) were significantly associated with the occurrence of irAEs, among which a higher AEC (cutoff = 0.205 × 109/L) was strongly associated with skin-related irAEs [odds ratios (ORs) = 0.163, P = 0.004]. Moreover, a higher lactate dehydrogenase level (cutoff = 237.5 U/L) was an independent predictor of irAEs of grade ≥ 3 (OR = 0.083, P = 0.023). In immune cell subgroup analysis, a lower absolute count of CD8+CD28− suppressor T cells (OR = 0.806; 95% confidence interval: 0.643–1.011; P = 0.062), which are regulatory T lymphocytes, was associated with the occurrence of irAEs, although the difference was not statistically significant. Furthermore, a higher percentage of CD19+ B cells was associated with the occurrence of irAEs of grade ≥ 3 (P = 0.02) and grade ≥ 2 (P = 0.051). In addition, patients with any grade of irAE had a significantly high PFS (8.37 vs. 3.77 months, hazard ratios (HR) = 2.02, P = 0.0038) and OS (24.77 vs. 13.83 months, HR = 1.84; P = 0.024). Conclusions: This retrospective study reports clinical profile data for irAEs in unselected patients in a real-world setting and explored some parameters that may be potential predictive markers of the occurrence, type, or grade of irAEs in clinical practice. Evidence of a correlation between safety and efficacy may facilitate a complete assessment of the risk-benefit ratio for patients treated with ICIs.

Alterations in the anaplastic lymphoma kinase (ALK) gene play a key role in the development of various human tumors, and targeted therapy has transformed the treatment paradigm for these oncogene-driven tumors. However, primary or acquired resistance remains a challenge. ALK gene variants (such as gene rearrangements and mutations) also play a key role in the tumor immune microenvironment. Immunotherapy targeting the ALK gene has potential clinical applications. Here, we review the results of recent studies on the immunological relevance of ALK-altered tumors, which provides important insights into the development of tumor immunotherapies targeting this large class of tumors.

Ionic liquid functional MCM-41 was synthesized, characterized and used as heterogeneous catalyst for the Knoevenagel condensation of isatins with malononitrile. A series of corresponding isatylidene malononitrile were obtained in high isolated yield (96–99%) at room temperature in a short time. The heterogeneous catalyst can be easily recovered by centrifugation and showed almost no loss of activity over 10 recycle experiments. At the same time, the gram-scale experiments showed excellent yields and provided a highly effective method for scale up applications.

After the iconic outburst of Zonag Lake in Hoh Xil of Tibetan Plateau (TP) in 2011, the causes of the collapse and its impact on the volume of water contained by the downstream lakes have highly been concerned, but the overall changes of the hydrological ecosystem and its impacts on the basin are yet not clear. This paper analyzes the hydrological system (lake area and hydrological connection) and ecosystem (sandstorm activity and vegetation growth status) changes and their impacts on the regional ecological environment after the outburst event, based on remotely sensed images from 2000 to 2020, meteorological observations, and field surveys. The results show that the outburst of Zonag Lake has not only changed the regional hydrology and ecosystem pattern but also caused a trend of degradation in the ecological environment of Zonag Lake Basin. Some lambing areas and active areas of Tibetan antelope in the area are being buried and eroded by the sandstorm. The downstream Yanhu Lake has risen to the highest level and overflowed through the emergency diversion engineering. These impacts are continuously threatening the health of the ecological environment of the world natural heritage site in Hoh Xil, the safety of Qinghai-Tibet highway, Qinghai-Tibet railway, and other engineering facilities, as well as the stability of fluvial geomorphology in the northern most sources of Yangtze River.

The treatment of organic pollutants in wastewater is becoming a great challenge for social development. Herein, a novel contact-piezoelectric bi-catalysis of a ZnO@ PVDF composite membrane was prepared by electrospinning technology. The obtained ZnO@PVDF composite membranes is superior to the pure PVDF membrane in decomposing methyl orange (MO) under ultrasonication at room temperature, which is mainly attributed to the synergy effect of the contact-electro-catalysis of dielectric PVDF, as well as the piezoelectric catalysis of tetrapodal ZnO and the β-phase of PVDF. The heterostructure of the piezoelectric-ZnO@dielectric-PVDF composite is beneficial in reducing the electron/hole pair recombination. As compared to the pure PVDF membrane, the catalytic degradation efficiency of the ZnO@PVDF composite membrane was improved by 444.23% under ultrasonication. Moreover, the reusability and stability of the composite membrane are comparable to those of the traditional powdered catalyst. This work offers a promising strategy for improving the pollutant degradation by combining contact-electro-catalysis with piezoelectric catalysis.

Background. High tibial osteotomy (HTO) is used to treat medial degeneration of the osteoarthritis (OA) knee. However, shortcomings still exist in the current procedure, like unprecise creation, inability to correct knee rotation, and internal fixed failure. Here, we reported a novel procedure: patient-specific 3D-printed plates for opening wedge high tibial osteotomy (OWHTO) combined with Taylor spatial frame (TSF). The detailed technique was described, and the clinical outcomes were evaluated. Methods. We prospectively evaluate outcomes of patient-specific 3D-printed plates for OWHTO with use of TSF in 25 patients with knee OA and varus alignment. Postoperative efficacy was evaluated using the HSS knee score, pain visual simulation score (VAS), and knee joint motion (ROM), and lower limb alignment was evaluated by measuring femorotibial angle (FTA) and hip-knee-ankle (HKA). Results and Conclusion. All patients did not experience complications such as wound infection, nerve damage, or bone amputation. 25 patients were followed up for 6–18 months. The bony union at bone amputation was achieved in 3 months after surgery, and the pain symptoms were significantly alleviated or disappeared. The VAS score was significantly reduced in 6 months after surgery compared with preoperative; the HSSS score was significantly added in 6 months after surgery compared with preoperative. The ROM of knee joint increased significantly 6 months after operation compared with that before operation, and the difference was statically significant (P

The geometric agreement, commonly hailed as load-transferring paths within bridge structures, is significantly crucial to the bridge structural mechanical performance, such as capacity, deformation, and collapse behavior. This paper presents a methodology dependent on alternative load paths to investigate the collapse behavior of a double-pylon cable-stayed bridge with steel truss girders subjected to excess vehicle loading. The cable-stayed bridge with steel truss girders is simplified using a series-parallel load-bearing system. This research manifests that the enforced vehicle loading can be transferred to alternative paths of cable-stayed bridges in different load-structure scenarios. A 3-D finite element model is established utilizing computer software ANSYS to explore the collapse path of cable-stayed bridge with steel truss girders, taking into account chord failure, loss of cables together with corrosion in steel truss girders. The results show that chord failures in the mid-portion of the mainspan result in brittle damage in truss girders or even sudden bridge collapse. Further,the loss of long cables leads to ductile damage with significant displacement.The corrosion in steel truss girders has a highly slight influence on the collapse behavior of cable-stayed bridge. The proposed methodology can be reliably used to assess and determine the vulnerability of cable-stayed bridge with steel truss girders during their service lifetime, thus preventing structural collapses in this type of bridge.

Abstract The next‐generation seasonal prediction system is built as part of the Seamless System for Prediction and EArth System Research (SPEAR) at the Geophysical Fluid Dynamics Laboratory (GFDL) of the National Oceanic and Atmospheric Administration (NOAA). SPEAR is an effort to develop a seamless system for prediction and research across time scales. The ensemble‐based ocean data assimilation (ODA) system is updated for Modular Ocean Model Version 6 (MOM6), the ocean component of SPEAR. Ocean initial conditions for seasonal predictions, as well as an ocean state estimation, are produced by the MOM6 ODA system in coupled SPEAR models. Initial conditions of the atmosphere, land, and sea ice components for seasonal predictions are constructed through additional nudging experiments in the same coupled SPEAR models. A bias correction scheme called ocean tendency adjustment (OTA) is applied to coupled model seasonal predictions to reduce model drift. OTA applies the climatological temperature and salinity increments obtained from ODA as three‐dimensional tendency terms to the MOM6 ocean component of the coupled SPEAR models. Based on preliminary retrospective seasonal forecasts, we demonstrate that OTA reduces model drift—especially sea surface temperature (SST) forecast drift—in coupled model predictions and improves seasonal prediction skill for applications such as El Niño–Southern Oscillation (ENSO).

Cement-fly ash mixture has been commonly used for the foundation treatment projects in the fly ash stratum, as it is effective in improving foundation bearing capacity and reducing settlement of stratum. In order to figure out the effect of dynamic and static load on the mechanical properties exhibited by the cement-fly ash and the reaction mechanism of cement-fly ash, a combination of the unconfined compressive test, impact test, scanning electron microscopy (SEM), and X-ray diffraction (XRD) method was adopted in this study to investigate the cement-fly ash test samples. As demonstrated by the results, the observed growth rate of 0–60 days (d) is higher than that in the later stages and the typical stress-strain curve can be divided into six sections under the unconfined compressive test. At the gas pressure of 0.2 MPa, the cement-fly ash samples exhibited obvious plastic properties in early curing time (0–60 d), and brittle failure was observed in the final stage (90 d). It is obvious that the value of dynamic compressive strength (DCS) is higher than that of unconfined compressive strength (UCS). The analysis of XRD has revealed that the hydration products are primarily derived from the hydration reaction of cement in the early stage and the pozzolanic reaction in the late stage. The pores of cement-fly ash are found to be filled with the hydration products, despite the presence of a mass of pores in the interior.

Barnyardgrass is a problematic agricultural weed. Here, via genomic analysis, Guo et al. identify genes potentially underlying its success, including a gene cluster activated in response to co-cultivation with rice that leads to synthesis of the allelochemical DIMBOA.

Properly initializing land snow conditions with multi-satellite data assimilation (DA) may help tackle the long-standing challenge of Asian monsoon seasonal forecasts. However, to what extent can snow DA help resolve the problem remains largely unexplored. Here we establish, for the first time, that improved springtime snow initializations assimilating the Moderate Spectral Imaging Satellite (MODIS) snow cover fraction and the Gravity Recovery and Climate Experiment (GRACE) terrestrial water storage data can improve the simulation accuracy of Asian monsoon seasonal anomalies. Focusing on the western Tibetan Plateau (TP) and mid- to high-latitude Eurasia (EA), two regions where multi-satellite snow DA is critical, we found that DA influences the monsoon circulation at different months depending on the regional snow–atmosphere coupling strengths. For the pre-monsoon season, accurate initialization of the TP snow is key, and assimilating MODIS data slightly outperforms jointly assimilating MODIS and GRACE data. For the peak-monsoon season, accurate initialization of the EA snow is more important due to its long memory, and assimilating GRACE data brings the most pronounced gains. Among all the Asian monsoon subregions, the most robust improvement is seen over central north India, a likely result of the region’s strong sensitivity to thermal forcing. While this study highlights complementary snow observations as promising new sources of the monsoon predictability, it also clarifies complexities in translating DA to useful monsoon forecast skill, which may help bridge the gap between land DA and dynamical climate forecasting studies.