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Abstract As a main carrier mode for the sustainable development of the construction industry in China, prefabricated building may lead to problems such as cost overruns, project delays, and waste of resources due to unreasonable selection of prefabricated components. Therefore, we quantitatively analyze the contribution rate of quality optimization of prefabricated components using QFD-SEM. Under the constraints of prefabrication rate, quality optimization contribution rate, and expected values of various sub-goals, we propose a multi-objective optimization method for prefabricated component combinations based on cost, duration, and carbon emissions. By using NSGA-II to solve the model, we can obtain a set of optimal Pareto solutions for prefabricated component combinations. Based on the optimal Pareto solution set, we establish a multi-objective evaluation model using simulated annealing optimization projection tracing method, and select the optimal prefabricated component combination solution according to the projected eigenvalues of the solutions. An empirical study is conducted using an eleven-story framed building in Shenzhen, Guangdong Province, China as a case study. The results show that: (1) Using this method, optimal solutions can be obtained in an unbounded solution space, with the optimal solution having advantages over both fully cast-in-place and fully prefabricated solutions. Compared to the fully cast-in-place solution, the duration and carbon emissions are reduced by 36.62% and 12.74% respectively, while compared to the fully prefabricated solution, costs are reduced by 4.15%. (2) There is a certain negative correlation between the cost of prefabricated component combinations and duration, carbon emissions, and quality optimization, while there is a certain positive correlation with the prefabrication rate. (3) The size of the optimal projection direction vector based on the optimization objectives indicates that carbon emissions have the greatest impact on the evaluation results of the solutions.

Abstract Background The presence of infarction in patients with unrecognized myocardial infarction (UMI) is a critical feature in predicting adverse cardiac events. This study aimed to compare the detection rate of UMI using conventional and deep learning reconstruction (DLR)-based late gadolinium enhancement (LGEO and LGEDL, respectively) and evaluate optimal quantification parameters to enhance diagnosis and management of suspected patients with UMI. Methods This prospective study included 98 patients (68 men; mean age: 55.8 ± 8.1 years) with suspected UMI treated at our hospital from April 2022 to August 2023. LGEO and LGEDL images were obtained using conventional and commercially available inline DLR algorithms. The myocardial signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and percentage of enhanced area (Parea) employing the signal threshold versus reference mean (STRM) approach, which correlates the signal intensity (SI) within areas of interest with the average SI of normal regions, were analyzed. Analysis was performed using the standard deviation (SD) threshold approach (2SD–5SD) and full width at half maximum (FWHM) method. The diagnostic efficacies based on LGEDL and LGEO images were calculated. Results The SNRDL and CNRDL were two times better than the SNRO and CNRO, respectively (P 0.05). The Parea−DL and Parea−O also differed except between the 2SD and 3SD and the 4SD/5SD and FWHM methods (P

Abstract Skyrmions in existing 2D van der Waals (vdW) materials have primarily been limited to cryogenic temperatures, and the underlying physical mechanism of the Dzyaloshinskii–Moriya interaction (DMI), a crucial ingredient for stabilizing chiral skyrmions, remains inadequately explored. Here, we report the observation of Néel-type skyrmions in a vdW ferromagnet Fe3−x GaTe2 above room temperature. Contrary to previous assumptions of centrosymmetry in Fe3−x GaTe2, the atomic-resolution scanning transmission electron microscopy reveals that the off-centered FeΙΙ atoms break the spatial inversion symmetry, rendering it a polar metal. First-principles calculations further elucidate that the DMI primarily stems from the Te sublayers through the Fert–Lévy mechanism. Remarkably, the chiral skyrmion lattice in Fe3−x GaTe2 can persist up to 330 K at zero magnetic field, demonstrating superior thermal stability compared to other known skyrmion vdW magnets. This work provides valuable insights into skyrmionics and presents promising prospects for 2D material-based skyrmion devices operating beyond room temperature.

Ti6Al4V lattice structures (LSs) have great potentials in medical implants due to their excellent biocompatibility and high strength-to-weight ratio. This paper investigates the manufacturability and performance of unique Ti6Al4V heterogeneous glass sponge (GS) LSs fabricated via laser powder bed fusion (LPBF). Compared with the commonly used LSs, including Cross, body-centered cubic (BCC), Diamond and octet-truss (OCT), Ti6Al4V GS LSs exhibit the strongest compressive strength and energy absorption ability of 58 MPa and 23.84 J/cm3, respectively. Finite element (FE) models are established to evaluate the macroscopic deformation, microscopic stress and strain evolution in the solid struts of five different LSs. Local plastic stresses are found to generate near the nodes of Cross, BCC, Diamond and OCT LSs, thus forming plastic hinges, while the GS LSs can evenly transfer local plastic stresses to the grid-like rods of each layer, eventually showing a uniform stress distribution. Moreover, all the LSs are featured with satisfactory biocompatibility concerning cytotoxicity, cell proliferation, and cell attachment. The results indicate that the Ti6Al4V GS LSs can address the conflict between lightness and strength simultaneously, thus achieving excellent energy absorption performance, compressive properties and great biocompatibility, endowing it with tremendous application potential in the field of medical implants.

Abstract The Holocene temperature conundrum, the discrepancy between proxy-based Holocene global cooling and simulated global annual warming trends, remains controversial. Meanwhile, reconstructions and simulations show inconsistent spatial patterns of terrestrial temperature changes. Here we report Holocene alkenone records to address spatial patterns over mid-latitude Eurasia. In contrast with long-term cooling trends in warm season temperatures in northeastern China, records from southwestern Siberia are characterized by colder conditions before ~6,000 years ago, thus long-term warming trends. Together with existing records from surrounding regions, we infer that colder airmass might have prevailed in the interior of mid-latitude Eurasian continent during the early to mid-Holocene, perhaps associated with atmospheric response to remnant ice sheets. Our results challenge the proposed seasonality bias in proxies and modeled spatial patterns in study region, highlighting that spatial patterns of Holocene temperature changes should be re-considered in record integrations and model simulations, with important implications for terrestrial hydroclimate changes.

Satellite remote sensing enables the extensive, long-term observation of oceanic changes. To achieve transparent ocean observation, artificial intelligence is innovatively used to reconstruct the three-dimensional ocean structure from remote sensing data with spatiotemporal attention. However, high-resolution satellite imagery imposes high computational demands. This study proposes Spatiotemporal Window Ocean (SWO), which is an efficient network that uses a special calculation strategy and spatiotemporal window attention to reduce complexity. The model integrates multiple satellite data sources, including sea surface temperature, absolute dynamic topography, and sea surface salinity. Our experimental results demonstrate that SWO achieves lower computational costs and superior performance compared with recent commonly used spatiotemporal sequence models. Specifically, SWO requires a training time equivalent to 1/3 of SimVP, 1/4 of PredRNN, and 1/5 of SA-ConvLSTM while achieving a root mean square error index that is 13.8%, 20.3%, and 13.2% better, respectively. The computational advantages of SWO offer an important technical means for the high-resolution reconstruction of ocean phenomena in the future.

Abstract Superhydrophobic surfaces demonstrate excellent anti-icing performance under static conditions. However, they show a marked decrease in icing time under real flight conditions. Here we develop an anti-icing strategy using ubiquitous wind field to improve the anti-icing efficiency of superhydrophobic surfaces during flight. We find that the icing mass on hierarchical superhydrophobic surface with a microstructure angle of 30° is at least 40% lower than that on the conventional superhydrophobic plate, which is attributed to the combined effects of microdroplet flow upwelling induced by interfacial airflow and microdroplet ejection driven by superhydrophobic characteristic. Meanwhile, the disordered arrangement of water molecules induced by the specific 30° angle also raises the energy barriers required for nucleation, resulting in an inhibition of the nucleation process. This strategy of microdroplet movement manipulation induced by interfacial airflow is expected to break through the anti-icing limitation of conventional superhydrophobic materials in service conditions and can further reduce the risk of icing on the aircraft surface.

Dockless bike-sharing (DBS) is an effective solution to the “first and last mile” problem in urban transportation. It can be integrated with urban rail transit (URT) to provide passengers with more convenient travel services. This study focuses on the integrated use of DBS and URT in Shenzhen, utilising a multi-buffer zone approach to identify DBS data within URT station catchment areas. By employing ordinary least squares (OLS), geographically weighted regression (GWR) and multiscale geographically weighted regression (MGWR) models, the spatiotemporal heterogeneity of integrated use and its relationship with environmental factors surrounding URT stations were examined. The empirical findings highlight the superiority of the MGWR model in accurately explaining spatial relationships compared to the OLS and GWR models. Furthermore, the study reveals that the impact of built environment factors on integrated use varies during morning and evening peak periods, as well as in terms of access and egress. Specifically, factors such as catering, shopping, companies, residential buildings, bus stops, minor roads, transfer stations and population density were found to influence the integrated use of DBS and URT. These findings not only contribute to the promotion of the DBS-URT integration but also promote the overall development of urban transportation.

It is fundamental to obtain accurate land surface temperature (LST) to study surface energy process. Infrared thermal imagers are commonly used for deriving LST on the basis of radiance measurements. However, when deriving LST from brightness temperature of a blackbody in thermal imagers, thermal imagers only allow setting a fixed land surface emissivity (LSE). This causes uncertainty in retrieving thermal infrared (TIR) temperature from heterogeneous surfaces with varied LSE, such as those covered in vegetation. Corrections can be made using the Normalized Vegetation Index (NDVI). However, commercial thermal imagers provide only red (R), green (G), and blue (B) bands without a near infrared band so NDVI cannot be calculated on the same instrument and is commonly not available. We propose an alternative method to estimate LSE using RGB-based vegetation index. Thereafter the estimated LSE was used to correct the TIR temperature derived from the fixed LSE. An experiment was conducted to validate the proposed correcting method. The results show that 1) the corrected LST values were closer to the ground truth, with a mean absolute error (MAE) of 0.41 ± 0.34 °C, whereas the MAE was 0.75 ± 0.56 °C for the uncorrected LST; 2) the more heterogeneous the surface, the greater the difference between the corrected and uncorrected LST values, indicating the necessity of LST correction when applying thermal imagers over heterogeneous surface.

In this study, we investigate the feasibility of using historical remote sensing data to predict the future three-dimensional subsurface ocean temperature structure. We also compare the performance differences between predictive models and real-time reconstruction models. Specifically, we propose a multi-scale residual spatiotemporal window ocean (MSWO) model based on a spatiotemporal attention mechanism, to predict changes in the subsurface ocean temperature structure over the next six months using satellite remote sensing data from the past 24 months. Our results indicate that predictions made using historical remote sensing data closely approximate those made using historical in situ data. This finding suggests that satellite remote sensing data can be used to predict future ocean structures without relying on valuable in situ measurements. Compared to future predictive models, real-time three-dimensional structure reconstruction models can learn more accurate inversion features from real-time satellite remote sensing data. This work provides a new perspective for the application of artificial intelligence in oceanography for ocean structure reconstruction.

Abstract The improvement of non-oxide ceramic plasticity while maintaining the high-temperature strength is a great challenge through the classical strategy, which generally includes decreasing grain size to several nanometers or adding ductile binder phase. Here, we report that the plasticity of fully dense boron carbide (B4C) is greatly enhanced due to the boundary non-stoichiometry induced by high-pressure sintering technology. The effect decreases the plastic deformation temperature of B4C by 200 °C compared to that of conventionally-sintered specimens. Promoted grain boundary diffusion is found to enhance grain boundary sliding, which dominate the lower-temperature plasticity. In addition, the as-produced specimen maintains extraordinary strength before the occurrence of plasticity. The study provides an efficient strategy by boundary chemical change to facilitate the plasticity of ceramic materials.

A porous high entropy alloy (HEA) coating was prepared on a steel surface by vacuum sintering. The coating was then used as a transition layer during dissimilar laser joining of Al to steel. Compared with the uncoated laser joints, the liquid alloy spread and infiltrated into the porous structure, the contact angle of the weld reduced from 65.8° to 56.7°, and the brazed width increased from 5.1 mm to 5.9 mm, which improved the wettability and spreadability of the molten filler wire on the substrate. In the case of the uncoated steel, the fusion zone/steel interfacial microstructure consisted of laminated Al7·2Fe1·8Si and Fe(Al,Si)3, while it changed to a composite-like structure containing a soft HEA skeleton and hard IMCs which included Al7·2Fe1·8Si, Al3Ni, and (Al,Si)2Cr. In addition, due to the sluggish diffusion effect of HEAs, a layer of gradient nanocrystalline composed of Al7·2Fe1·8Si was generated, which significantly strengthened the dissimilar laser joints with improvements in both the fracture load (∼26.5%) and the displacement (∼101.8%). The fracture mode changed from brittle to ductile failure when the porous HEA coating was applied, with fracture propagating through the HEA skeleton. This work provides a novel solution for the strengthening of hard-to-join dissimilar combinations.

The transboundary region along the Heilongjiang River, encompassing the Russian Far East and Northeast China, possesses abundant agricultural natural resources crucial for global food security. In the face of the challenge of disruptions in the global food supply chain, the precise monitoring and exploitation of agricultural resources in the Heilongjiang Basin becomes imperative. This study employed deep learning to classify crop status in 2023 in the Heilongjiang Basin using Sentinel-2 satellite remote sensing images at a 10 m resolution. Various vegetation indices, including the Normalized Difference Vegetation Index (NDVI), the Normalized Difference Water Index (NDWI), the Enhanced Vegetation Index (EVI), the Modified Soil Adjusted Vegetation Index (MSAVI), and others, were computed and analyzed for different crops. The Google Earth Engine (GEE) platform was utilized for validation point sampling based on plot objects. The random forest (RF) classification method was successfully employed to classify and identify major crops in the study area (wheat, maize, rice, and soybean), as well as wetlands, tree cover, grassland, water, and constructed land, with an overall classification accuracy of 86%. Tree cover dominated the land cover, constituting 62%, while wheat, maize, rice, and soybeans accounted for 7% of the total area. Of these, soybeans occupied the largest area (57,646.60 hectares), followed by rice (53,209.53 hectares), maize (39,998.37 hectares), and wheat (8782.31 hectares). This study demonstrated that sample selection based on plot objects facilitates efficient sample labeling, providing insights into crop classification in other, potentially larger, areas. This method simultaneously distinguishes wetland, cultivated land, and forest features, supporting further integrated investigations for more natural resources.

High-resolution land cover mapping is crucial in various disciplines but is often hindered by the lack of accurately matched labels. Our study introduces an innovative deep learning methodology for effective land cover mapping, independent of matched labels. The approach comprises three main components: (1) An advanced fully convolutional neural network, augmented with super-resolution features, to refine labels; (2) The application of an instance-batch normalization network (IBN), leveraging these enhanced labels from the source domain, to generate 2-m resolution land cover maps for test sites in the target domain; (3) Noise assessment tests to evaluate the impact of varying noise levels on the model’s mapping accuracy using external labels. The model achieved an overall accuracy of 83.40% in the target domain using endogenous super-resolution labels. In contrast, employing exogenous, high-precision labels from the National Land Cover Database in the source domain led to a notable accuracy increase of 2.55%, reaching 85.48%. This improvement highlights the model’s enhanced generalizability and performance during domain shifts, attributed significantly to the IBN layer. Our findings reveal that, despite the absence of native high-precision labels, the utilization of high-quality external labels can substantially benefit the development of precise land cover mapping, underscoring their potential in scenarios with unmatched labels.

Abstract The van der Waals (vdW) ferromagnet Fe3‐δGeTe2 has garnered significant research interest as a platform for skyrmionic spin configurations, that is, skyrmions and skyrmionic bubbles. However, despite extensive efforts, the origin of the Dzyaloshinskii–Moriya interaction (DMI) in Fe3‐δGeTe2 remains elusive, making it challenging to acquire these skyrmionic phases in a controlled manner. In this study, it is demonstrated that the Fe content in Fe3‐δGeTe2 has a profound effect on the crystal structure, DMI, and skyrmionic phase. For the first time, a marked increase in Fe atom displacement with decreasing Fe content is observed, transforming the original centrosymmetric crystal structure into a non‐centrosymmetric symmetry, leading to a considerable DMI. Additionally, by varying the Fe content and sample thickness, a controllable transition between Néel‐type skyrmions and Bloch‐type skyrmionic bubbles is achieved, governed by a delicate interplay between dipole–dipole interaction and the DMI. The findings offer novel insights into the variable skyrmionic phases in Fe3‐δGeTe2 and provide the impetus for developing vdW ferromagnet‐based spintronic devices.

Hyperspectral imaging is able to provide a finer delivery of various material properties than conventional imaging systems. Yet in reality, an optical system can only generate data with high spatial resolution but low spectral one, or vice versa, at video rates. As a result, an issue that fuses low-resolution hyperspectral and high-resolution multispectral images has gained great attention. However, most fusion approaches depend purely on hand-crafted regularizers or data-driven priors, leading to the issues of tricky parameter selection or poor interpretability. In this work, a subspace-based deep prior regularization is proposed to tackle these problems, which takes both hand-crafted regularizer and data-driven prior into account. Specifically, we leverage the spectral correlation of the images and transfer them from the original space to the subspace domain, within which a modified U-net-based deep prior learning network (SDPL-net) is designed for the fusion issue. Moreover, instead of taking the output of SDPL-net directly as the result, we further feed the output back to the model-based optimization. Under such prior regularization, the recovered high-resolution hyperspectral image holds a high consistency to its inherent structure and hence tends to present enhanced reliability and accuracy. Experimental results on simulated and real data reveal that the proposed method excels other state-of-the-art methods in both quantitative and qualitative metrics.

Fine fuel load (FFL) is a crucial variable influencing the occurrence of wildfire. Accurate knowledge of the distribution of FFL in mountainous forests is essential for ongoing wildfire risk management and the stability of mountain ecosystems. Traditional methods of estimating forest fuel load typically involve ground surveys combined with remote sensing, which can be costly and inefficient. Therefore, low-cost, large-scale FFL estimation remains challenging. In this study, Sentinel-2A satellite imagery from the Changsha forest region was used as the data source. Firstly, different feature variables were constructed based on false-color (B843), true-color (B432), four-band (B8432) combinations, and the Normalized Difference Water Index (NDWI). Subsequently, a machine learning approach based on random convolution was employed to estimate FFL. This study also included accuracy assessments of the estimation results and the creation of FFL maps for the study area. The results showed that the FFL estimation based on the B8432 band combination achieved the highest accuracy, with RMSE and R2 values of 5.847 t·hm−2 and 0.656, respectively. FFL estimation results based on false-color imagery followed, with true-color imagery and NDWI index-based estimation results exhibiting lower accuracy. This study offers critical FFL insights using random convolution techniques applied to Sentinel-2A imagery, enhancing the ability to monitor and manage forest fuel conditions effectively, thereby facilitating more informed regional wildfire risk management strategies.

Change detection is a crucial task in remote sensing that finds broad application in land resource planning, forest resource monitoring, natural disaster monitoring, and evaluation. In this paper, we propose a change detection model for cross-domain recognition, which we call CrossCDNet. Our model significantly improves the modeling ability of the change detection on one dataset and demonstrates good generalization on another dataset without any additional operations. To achieve this, we employ a Siamese neural network for change detection and design an IBNM (Instance Normalization and Batch Normalization Module) that utilizes instance normalization and batch normalization in order to serve as the encoder backbone in the Siamese neural network. The IBNM extracts feature maps for each layer, and the Siamese neural network fuses the feature maps of the two branches using a unique operation. Finally, a simple MLP decoder is used for end-to-end change detection. We train our model on the LEVIR-CD dataset and achieve competitive performance on the test set. In cross-domain dataset testing, CrossCDNet outperforms all the other compared models. Specifically, our model achieves an F1-score of 91.69% on the LEVIR-CD dataset and an F1-score of 77.09% on the WHU-CD dataset, where the training set was LEVIR-CD.

Objective To translate the original English version of the Spinal Instability Neoplastic Score (SINS) into simplified Chinese, adapt it cross‐culturally, validate its psychometric properties in measuring spinal instability in patients with metastatic spinal tumors in the Chinese mainland, examine the reliability and validity to demonstrate its accuracy and applicability in clinical practice. Methods Patients diagnosed with metastatic spinal disease between January 2016 and January 2020 were recruited. The number of participants was advised to be at least 50 for appropriate analysis of reliability, construct validity, as well as ceiling or floor effects, and recruitment of 100 patients was advised for internal consistency analysis. The study was conducted in two phases: first, the SINS was translated into simplified Chinese; second, the factor structure, internal consistency, test–retest reliability, validity, and floor and ceiling effects of the SC‐SINS were assessed. The internationally recognized cross‐cultural adaptation guidelines were followed. Internal consistency was evaluated with Cronbach's alpha. Test–retest reliability was examined among the patients with a 4‐week interval. The validity of the Chinese version of SINS (SC‐SINS) was assessed by examining its relationship with Kostuik classification. Principal component analysis was conducted to confirm the factor structure of each subscale. Results A total of 160 participants (88 males and 72 females) were enrolled. No major difficulties occurred in the forward and backward translations of SINS. The internal consistency of SC‐SINS was excellent (Cronbach's α =0.857, ranging from 0.68 to 0.85). Test–retest reliability was also excellent with a value of 0.89, ranging from 0.86 to 0.95. Validity analyses indicated that the SC‐SINS was positively and significantly correlated with Kostuik classification. The correlation between “Posterolateral Involvement of Spinal Elements” and “1‐2 Partial Damage” was the highest with a correlation value of 0.792. The correlation between “Pain” and “1–2 Partial Damage” was the lowest with a value of 0.341. All items showed principal component coefficients greater than 0.4. The values of Factor 1 ranged from 0.523 to 0.681; Factor 2 ranged from 0.591 to 0.731; Factor 3 ranged from 0.613 to 0.754; Factor 4 ranged from 0.461 to 0.711; Factor 5 ranged from 0.513 to 0.701; and Factor 6 ranged from 0.501 to 0.668. In addition, neither floor nor ceiling effects were seen in the SC‐SINS. Conclusion The SC‐SINS demonstrated high internal consistency and test–retest reliability, which has been proven valid and reliable to measure spinal stability in patients from the Chinese mainland with metastatic spinal tumor.

Camellia oleifera is a vital economic crop of southern China. Accurate mapping and monitoring of Camellia oleifera plantations are essential for promoting sustainable operations within the Camellia oleifera industry. However, traditional remote sensing interpretation methods are no longer feasible for the large-scale extraction of plantation areas. This study proposes a novel deep learning-based method that utilizes GF-2 remote sensing imagery to achieve precise mapping and efficient monitoring of Camellia oleifera plantations. First, we conducted a comparative analysis of the performance of various semantic segmentation models using a self-compiled dataset of Camellia oleifera plantations. Subsequently, we proceeded to validate the prediction results obtained from the most effective deep-learning network model for Camellia oleifera plantations in Hengyang City. Finally, we incorporated DEM data to analyze the spatial distribution patterns. The findings indicate that the U-Net++ network model outperforms other semantic segmentation methods when applied to our self-generated dataset of Camellia oleifera plantations. It achieves a recall rate of 0.89, a precision rate of 0.92, and an mIOU of 0.83, demonstrating the effectiveness of the proposed method in identifying and monitoring Camellia oleifera plantations. By combining the predicted results with the data from DEM, we discovered that these plantations are typically situated at elevations ranging from 50 to 200 m, with slopes below 25°, and facing south or southeast. Moreover, a significant positive spatial correlation and clustering phenomenon are observed among the townships in Hengyang City. The method proposed in this study facilitates rapid and precise identification and monitoring of Camellia oleifera plantations, offering significant theoretical support and a scientific foundation for the management and ecological conservation of Camellia oleifera plantations.

Fusion technology has been the core strategy to obtain a high-spatial-spectral resolution hyperspectral image (HSI). In recent years, few fusion models focused on exploiting the underlying manifold structure in the spatial dimension of the high-resolution HSI. We assume that image patches of high-resolution multispectral image (HR-MSI) and high-resolution hyperspectral image (HR-HSI) share similar manifold structures. Through this bridge, a local-nonlocal low-dimensional manifold defined from HR-MSI is built to favor the patch relationship in HR-HSI, which is further utilized to build a set of orthogonal bases. Subsequently, we introduce a truncation operation based on the adaptively constructed orthogonal bases, which can efficiently preserve the low-frequency information of the image and reduce the interference of noise. Finally, we combine the local-nonlocal manifold term and the truncation operation, coined as LNTM, into a variational super-resolution framework to regularize the latent HR-HSI, leading to a strongly convex function regarding the fusion problem. The cost function is solved by a carefully designed variant of alternating direction method of multipliers algorithm. Different experiments on three public benchmarks validate our algorithm outperforms the recent start-of-the-arts concerning both visual quality and numerical metrics.

Abstract Two stroke slow speed diesel engines are mainly used in deep sea going vessels such as container ships, bulk carriers, and tankers. In this article, a numerical model is developed and solved to relate impact occurrence and magnitude on engine structure to rigid body tribodynamics of the crosshead guide system for a two‐stroke marine engine. To achieve this, the reciprocating piston group is coupled to the crosshead guide shoes through the crosshead guide pin. The resulting second order nonlinear differential equations are solved for displacements, velocities, and friction characteristics of the system. The rigid body motions are important in identifying occurrences and the intensities of transient impacts between contacting surfaces. These impacts influence noise and vibration characteristics of the engine. A study of the coupled system at different engine running speeds as well as varying crosshead guide system clearances is performed. It is observed that secondary displacements, velocities, and friction characteristics increase with increase in speed and clearance on the crosshead guide system. Consequently, the impact on the engine structure increase with increase in these tribodynamic characteristics of the system. This impact is transferred to the engine surface as vibration and noise responses.

The processes of hydraulic fracturing to extract shale gas generate a large amount of wastewater, and the potential impacts of wastewater discharge after treatment are concerning. In this field study, we investigated the effects of the irrigation of paddy fields for 2 consecutive years by river water that has been influenced by shale gas wastewater discharge on soil physicochemical properties, microbial community structure and function, and rice grain quality. The results showed that conductivity, chloride and sulfate ions in paddy soils downstream of the outfall showed an accumulative trend after two years of irrigation, but these changes occurred on a small scale (

Abstract In the Mesozoic, the age of Paleo‐Pacific plate that flatly subducted beneath the South China was ∼80 Ma, resulting in a cold and dense oceanic slab. The transition process from flat to steep subduction of this old slab and factors that affect it are still unclear. Using 2‐D thermomechanical numerical models, we investigate the dependence of the transition mode on the size of TOC (length and thickness) and the subduction rate. The results indicate that under low subduction rates (e.g., 1 cm/yr), even a 100‐km long eclogitized TOC can induce slab delamination beneath continental lithosphere. The maximum subduction rate for slab delamination to occur increases with an increasing length or thickness of TOC. However, when the subduction rate is over a certain critical value (e.g., ≥4 cm/yr), oceanic slab rolls back. The presence of the TOC delays the initial rollback time, as compared to cases with no TOC. In addition, with a TOC of small size (e.g., 100‐km length and 20‐km thickness) under a certain range of subduction rate, an 80‐Ma old slab can maintain a long‐lasting flat subduction. The comparisons between the numerical results and global plate motion models indicate that the northwestward motion of South China Block since ca. 195 ± 5 Ma induced the delamination of oceanic slab. The arrival of the TOC in the Paleo‐Pacific Plate to the continental arc should be later than the previously estimated ca. 250 Ma. Determination of the TOC size requires three‐dimensional simulations and more detailed geological records.

The pushover method for underground structures is a seismic analysis method featured by high calculation accuracy and a simple implementation process. The method has been widely used in seismic design and other related scientific research; however, the influence of different soil-structure flexibility ratios on the accuracy of this method is still not well understood. In this study, we select the cross-section structures beneath the Daikai subway station as the research object and establish 12 finite element analysis models with different soil-structure flexibility ratios using ABAQUS. All models are computed by the dynamic time-history method or the pushover method. Furthermore, the dynamic time-history solution result is taken as the standard solution, and the precision and application of the pushover analysis method are discussed based on the parameters of peak interlayer displacement and peak internal force of the middle column section. The results show that the soil-structure flexibility ratio has a significant influence on the calculation accuracy of the pushover method, and the calculation accuracy of this method is the most ideal when the soil-structure flexibility is equal to 1. The research results can provide significant references for the seismic design of underground structures or the improvement of simplified seismic analysis methods.

PurposeTo compare the clinical outcomes and radiological parameters of patients undergoing percutaneous vertebroplasty (PVP) versus those undergoing percutaneous vertebral-disc plasty (PVDP) for back pain, segmental instability, and kyphosis due to thoracolumbar very severe osteoporotic vertebral compression fractures (vsOVCFs).MethodsThis prospective randomized controlled study included elderly patients with thoracolumbar vsOVCFs. All the patients were randomly allocated into the PVP group (who underwent conventional PVP) and the PVDP group (who underwent PVP combined percutaneous cement discoplasty). The visual analogue scale (VAS), Oswestry Disability Index (ODI), local kyphosis angle, and disc height were recorded preoperatively and postoperatively.ResultsSignificant postoperative improvements in the VAS, ODI, and the local kyphosis angle (LKA) were shown, compared with the preoperative values in both groups (p 0.05). The change was significantly lower in the PVDP group at the last follow-up in those parameters (p

The cliff ecosystem is one of the least human-disturbed ecosystems in nature, and its inaccessible and often extreme habitats are home to many ancient and unique plant species. Because of the harshness of cliff habitats, their high elevation, steepness of slopes, and inaccessibility to humans, surveying cliffs is incredibly challenging. Comprehensive and systematic information on cliff vegetation cover is not unavailable but obtaining such information on these cliffs is fundamentally important and of high priority for environmentalists. Traditional coverage survey methods—such as large-area normalized difference vegetation index (NDVI) statistics and small-area quadratic sampling surveys—are not suitable for cliffs that are close to vertical. This paper presents a semi-automatic systematic investigation and a three-dimensional reconstruction of karst cliffs for vegetation cover evaluation. High-resolution imagery with structure from motion (SFM) was captured by a smart unmanned aerial vehicle (UAV). Using approximately 13,000 records retrieved from high-resolution images of 16 cliffs in the karst region Guilin, China, 16 models of cliffs were reconstructed. The results show that this optimized UAV photogrammetry method greatly improves modeling efficiency and the vegetation cover from the bottom to the top of cliffs is high-low-high, and very few cliffs have high-low cover at the top. This study highlights the unique vegetation cover of karst cliffs, which warrants further research on the use of SFM to retrieve cliff vegetation cover at large and global scales.

As a cutting-edge research direction in the field of radar imaging, video SAR has the capability of high-resolution and persistent imaging at any time and under any weather. Video SAR requires high computational efficiency of the imaging algorithm, and PFA has become the preferred imaging algorithm because of its applicability to the spotlight mode and relatively high computational efficiency. However, traditional PFA also has problems, such as low efficiency and limited scene size. To address the above problems, a generalized persistent polar format algorithm, called GPPFA, is proposed for airborne video SAR imaging that is applicable to the persistent imaging requirements of airborne video SAR under multitasking conditions. Firstly, the wavenumber domain resampling characteristics of video SAR PFA are analyzed, and a generalized resampling method is proposed to obtain higher efficiency. Secondly, for the problem of scene size limitation caused by wavefront curvature error, an efficient compensation method applicable to different scene sizes is proposed. GPPFA is capable of video SAR imaging at different wavebands, different slant ranges, and arbitrary scene sizes. Point target and extended target experiments verify the effectiveness and efficiency of the proposed method.

This paper presents the results of an experimental study on the heat transfer and pressure drop characteristics of a novel spiral plate mini-channel gas cooler designed for use with supercritical CO2. The CO2 channel of the mini-channel spiral plate gas cooler has a circular spiral cross-section with a radius of 1 mm, while the water channel has an elliptical cross-section spiral channel with a long axis of 2.5 mm and a short axis of 1.3 mm. The results show that increasing the mass flux of CO2 can effectively enhance the overall heat transfer coefficient when the water side mass flow rate is 0.175 kg·s−1 and the CO2 side pressure is 7.9 MPa. Increasing the inlet water temperature can also improve the overall heat transfer coefficient. The overall heat transfer coefficient is higher when the gas cooler is vertically oriented compared to horizontally oriented. A Matlab program was developed to verify that the correlation based on Zhang’s method has the highest accuracy. The study found a suitable heat transfer correlation for the new spiral plate mini-channel gas cooler through experimental research, which can provide a reference for future designs.

The construction of the tunnels of Metro Line 4 and Gulou Station in central Nanjing poses a potential threat to the nearby historic building, Gulou Tower, due to the relatively small spacing and the deteriorated structure behaviors. Two aspects are important for the protection of Gulou Tower: (i) reducing the soil movement caused by the tunnel–station construction and (ii) increasing the total stiffness of this sensitive building. This research first presents the main features of two tunnels and the triple-arch tunnel as the connection between the two tunnels and Gulou Station. The details of the excavation means and construction procedures of the tunnels, together with the engineering measures that tend to reduce soil disturbance during tunnel excavation, are presented. Meanwhile, to improve the overall stiffness of Gulou Tower, additional support for the masonry terrace and the upper timber structure is also discussed. Moreover, the construction procedures of the tunnels and the station that could influence the settlement development of Gulou Tower are also suggested. The measured ground settlement and structure displacement are found to be limited to the allowable values, indicating that the aforementioned protective measures are adequate to protect hybrid timber–masonry historical structures nearby tunneling.

State-of-the-art performances for natural language processing tasks are achieved by supervised learning, specifically, by fine-tuning pre-trained language models such as BERT (Bidirectional Encoder Representation from Transformers). With increasingly accurate models, the size of the fine-tuned pre-training corpus is becoming larger and larger. However, very few studies have explored the selection of pre-training corpus. Therefore, this paper proposes a data enhancement-based domain pre-training method. At first, a pre-training task and a downstream fine-tuning task are jointly trained to alleviate the catastrophic forgetting problem generated by existing classical pre-training methods. Then, based on the hard-to-classify texts identified from downstream tasks’ feedback, the pre-training corpus can be reconstructed by selecting the similar texts from it. The learning of the reconstructed pre-training corpus can deepen the model’s understanding of undeterminable text expressions, thus enhancing the model’s feature extraction ability for domain texts. Without any pre-processing of the pre-training corpus, the experiments are conducted for two tasks, named entity recognition (NER) and text classification (CLS). The results show that learning the domain corpus selected by the proposed method can supplement the model’s understanding of domain-specific information and improve the performance of the basic pre-training model to achieve the best results compared with other benchmark methods.

The startup process is the crucial transition phase of the supercritical carbon-dioxide Brayton cycle, so it is essential to focus on and investigate the transient performance for the system’s safety and stability. The pressure in the buffer tank approaches the safety upper limit with different startup schemes during the joint commissioning of the compressor and heater in a MWe-scale experiment system, while the maximum temperature is 309 °C. Hence, dynamic simulations are carried out to explore the dynamic startup characteristics from a cold state or a warm state to the turbine pre-start condition, in which 60% of the rated mass flow rate and 67% of the rated compressor speed are reached in the end. The results show that, when starting from a cold state, the startup scheme of simultaneously heating and speeding up has a limited effective application scope. Two venting operations during the above process help the system establish heat regeneration and promote temperature uniformity in the system. Furthermore, when starting from a warm state with an existing temperature gradient in the system, the startup scheme of simultaneously heating and speeding up is more effective and has a more extensive range of control.

The low-dimensional manifold of image patches has been introduced as regularizer term, and shown effective in hyperspectral image inpainting. However, in this article, we find that using only the low-dimensional property of manifold may not always generate smooth results. In terms of this, we first present a higher order term to the low-dimensional manifold model, namely nonlocal second-order regularization (NSR), which provides better approximation to the real data distribution and manifests both the properties of low dimensionality and smoothness. Moreover, in order to balance the known and unknown sets, we further propose a weighted version of NSR, called WNSR. The generalized minimal residual algorithm is adopted to solve this unsymmetrical model, in which a semi-patch is applied for acceleration of the nearest neighbor search. Finally, we conduct intensive numerical experiments on five well-known datasets to verify the superiority of our method. The inpainting results show that our proposed (W)NSR significantly outperforms the state-of-the-art methods with respect to both visual and numerical quality.

Multiple phases sectionalized modulation (MPSM) jamming is a kind of blanket jamming method against linear frequency modulation (LFM) radar, which can produce range-controllable noise-like jamming effect by dividing the signal into multiple subsections in the time domain and modulating different phases on each subsection. However, the partial relationship between the jamming effect and jamming parameters is complicated, which decrease the controllability of MPSM jamming effect. By transforming the problem of the controllability of MPSM jamming effect into the problem of the optimization of MPSM jamming effect, an optimization algorithm based on genetic algorithm (GA) is proposed to solve the problem in this paper. In the optimization based on GA, first, the objective function is constructed according to the jamming evaluation, the parameters are coded, the population is initialized, and the fitness value of the initial population is calculated. Then, the selection operation, crossover operation and mutation operation are performed, the next population is generated and its fitness value is calculated. Finally, judge if reach the termination, continue or output the optimization result and its corresponding parameters. The optimization method proposed in this paper can not only effectively increase the controllability of MPSM jamming effect, but also optimize the jamming effect. The simulation results show that the optimization method is feasible and has strong stability.

Dynamic detection of forest change is the fundamental method of monitoring forest resources and an essential means of preserving the accuracy and timeliness of forest land resource data. This study focuses on a deep learning-based method for dynamic forest change detection using Sentinel-2 satellite data, especially within mountainous areas. First, the performance of various deep learning models (U-Net++, U-Net, LinkNet, DeepLabV3+, and STANet) and various loss functions (CrossEntropyLoss(CELoss), DiceLoss, FocalLoss, and their combinations) are compared on a self-made dataset. Next, the best model and loss function is used to predict the annual forest change in Hunan Province from 2017 to 2021, and the detection results are evaluated in 12 sample areas. Finally, forest changes are detected in Sentinel-2 images for each quarter of 2017–2021. In addition, a dynamic detection map of forest change in Hunan Province from 2017 to 2021 is drawn. The results reveal that the U-Net++ model and the CELoss performed the best on the self-made dataset, with a Precision of 0.795, a Recall of 0.748, and an F1-score of 0.771. The results of annual and quarterly forest change detection were consistent with the changes in the Sentinel-2 images with accurate boundaries. This result demonstrates the high practicality and generalizability of the method used in this paper. This paper achieves a rapid and accurate extraction of multi-temporal Sentinel-2 image forest change areas based on the U-Net++ model, which can be used as a benchmark for future large territorial areas monitoring and management of forest resources.

With the continuous improvement of machine learning methods, building the interatomic machine learning potential (MLP) based on the datasets from quantum mechanics calculations has become an effective technical approach to improving the accuracy of classical molecular dynamics simulation. The Spectral Neighbor Analysis Potential (SNAP) is one of the most commonly used machine learning potentials. It uses the bispectrum to encode the local environment of each atom in the lattice. The hyperparameter jmax controls the mapping complexity and precision between the local environment and the bispectrum descriptor. As the hyperparameter jmax increases, the description will become more accurate, but the number of parameters in the bispectrum descriptor will increase dramatically, increasing the computational complexity. In order to reduce the computational complexity without losing the computational accuracy, this paper proposes a two-level ensemble feature selection method (EFS) for a bispectrum descriptor, combining the perturbation method and the feature selector ensemble strategy. Based on the proposed method, the feature subset is selected from the original dataset of the bispectrum descriptor for building the dimension-reduced MLP. As a method application and validation, the data of Fe, Ni, Cu, Li, Mo, Si, and Ge metal elements are used to train the linear regression model based on SNAP for predicting these metals’ atomic energies and forces them to evaluate the performance of the feature subsets. The experimental results show that, compared to the features of SNAP and qSNAP, the training complexity improvement of our EFS method on the qSNAP feature is more effective than SNAP. Compared with the existing methods, when the feature subset size is 0.7 times that of the original features, the proposed EFS method based on the SSWRP ensemble strategy can achieve the best performance in terms of stability, achieving an average stability of 0.94 across all datasets. The training complexity of the linear regression model is reduced by about half, and the prediction complexity is reduced by about 30%.

The use of remote sensing images to detect forest changes is of great significance for forest resource management. With the development and implementation of deep learning algorithms in change detection, a large number of models have been designed to detect changes in multi-phase remote sensing images. Although synthetic aperture radar (SAR) data have strong potential for application in forest change detection tasks, most existing deep learning-based models have been designed for optical imagery. Therefore, to effectively combine optical and SAR data in forest change detection, this paper proposes a double Siamese branch-based change detection network called DSNUNet. DSNUNet uses two sets of feature branches to extract features from dual-phase optical and SAR images and employs shared weights to combine features into groups. In the proposed DSNUNet, different feature extraction branch widths were used to compensate for a difference in the amount of information between optical and SAR images. The proposed DSNUNet was validated by experiments on the manually annotated forest change detection dataset. According to the obtained results, the proposed method outperformed other change detection methods, achieving an F1-score of 76.40%. In addition, different combinations of width between feature extraction branches were analyzed in this study. The results revealed an optimal performance of the model at initial channel numbers of the optical imaging branch and SAR image branch of 32 and 8, respectively. The prediction results demonstrated the effectiveness of the proposed method in accurately predicting forest changes and suppressing cloud interferences to some extent.

Polyether ketone (PEK) plastics are linear thermoplastic polymers connected by at least one ether bond and at least one ketone bond on the aryl group. The reason for their excellent heat resistance, rigidity, and mechanical strength is that their main molecular chain contains plenty of aromatic rings and polar carbonyl groups, and their molecular chain presents a large rigidity and strong intermolecular force. In addition, the main chain contains a considerable number of ether bonds, resulting in a certain toughness. However, polyether ketone materials have the disadvantage of poor solubility because of their excellent rigidity. To improve the solubility of polyether ketone, the preparation method of a novel nitrogenous heterocyclic polyaromatic ether monomer, 2-(4-chlorophenyl)-2,3-dihydrophthalazine-1,4-dione (CDD), was proposed, and its activity of polymerization was studied. The average molecular weight of the poly(aryl ether ketone) containing a nitrogenous heterocyclic polyaromatic ether group obtained by self-polycondensation of CDD was 4.181 × 103 kg/mol, and the yield was 90.5%. In order to further explore the activity of monomers, novel copolymerized poly(aryl ether ketone) (PBCD) containing a nitrogenous heterocyclic polyaromatic ether structure was prepared by ternary copolymerization with 4,4-difluorobenzophenone (DFBP) and bisphenol fluorene (BHPF) with high activity. The average molecular weight of PBCD was 72.793 × 103 kg/mol, the molecular weight distribution was 2.344, and the yield was 88.1%. Fourier transform infrared spectroscopy (FT-IR) and 1H NMR were used to confirm the structure of the obtained polymer. Through thermogravimetric analysis (TGA), the determined weight loss temperature of 5% under nitrogen was higher than 500 °C, indicating excellent thermal stability. Compared with the solubility of the binary copolymer containing fluorenyl poly(aryl ether ketone) (PBD), the polymer showed reasonable solubility in selective solvents such as chloroform and N,N-dimethylacetamide.

Background: Roxadustat, a hypoxia-inducible factor prolyl-hydroxylase inhibitor (HIF-PHI), has been used to treat anemia in patients with chronic kidney disease (CKD). However, its safety and efficacy remain controversial.Methods: The PubMed, EMBASE, Science Citation Index, Cochrane Central Register of Controlled Trials, and Clinical Trial Registries databases were searched for relevant studies published up to April 2021. We identified randomized controlled trials (RCTs) comparing roxadustat with placebo or erythropoiesis-stimulating agents (ESAs) in anemia patients with CKD with or without dialysis.Results: Eleven studies including 6,631 patients met the inclusion criteria. In non-dialysis-dependent (NDD-) and dialysis-dependent (DD-) CKD patients, the total adverse events were not significantly different between the roxadustat and control (placebo for NDD-CKD patients and ESA for DD-CKD patients) groups [relative risk (RR) = 1.02, 95% confidence interval (CI) = 1.00, 1.04, P = 0.08, and RR = 1.22, 95% CI = 0.91, 1.64, P = 0.18, respectively], and the trial sequential analysis (TSA) confirmed the result in the NDD-CKD groups. No significant differences in hyperkalemia and infection incidences were found between roxadustat and placebo in the DD-CKD groups. The pooled results showed that roxadustat significantly increased the hemoglobin response rate compared with placebo in the NDD-CKD group and had an effect similar to that of ESA in the DD-CKD group. However, iron metabolism parameters did not seem to be obviously optimized by roxadustat.Conclusion: Roxadustat can be safely used in CKD patients. Oral roxadustat was more effective than placebo as a therapy for anemia in NDD-CKD patients and non-inferior to ESA in correcting anemia in DD-CKD patients. However, additional clinical trials are still needed to further prove whether roxadustat can optimize iron metabolism.

A gas cooler is one of the important parts of a carbon dioxide (CO2) heat pump water heater, and it must meet the needs of not only pressurization but also heat transfer. It is important to study gas coolers. In this paper, a heat exchanger with a spiral channel is studied. ANSYS CFX software was used to analyze the flow and heat transfer characteristics of the heat exchanger (single-plate model). The influences of the cooling pressure of CO2, the mass flux of CO2, the mass flux of water and the channel radius of CO2 are discussed. In this paper, the results show that the cooling pressure of CO2, the mass flux of CO2 and the channel radius of CO2 all have a large influence on the local heat transfer coefficient: with an increase in the cooling pressure of CO2, the peak value of the heat transfer coefficient of CO2 decreases and the average heat transfer coefficient decreases; with an increase in the mass flux of CO2, the peak value of the heat transfer coefficient of CO2 increases and the average heat transfer coefficient increases; and with a decrease in the channel radius of CO2, the peak value of the heat transfer coefficient of CO2 increases. The water mass flux has only a slight effect on heat transfer, and the lower cooling pressure of CO2 corresponds to a higher peak heat transfer coefficient, which can reach 27.5 kW∙m−2∙K−1 at 9 MPa.

To alleviate the impact of fake news on our society, predicting the popularity of fake news posts on social media is a crucial problem worthy of study. However, most related studies on fake news emphasize detection only. In this paper, we focus on the issue of fake news influence prediction, i.e., inferring how popular a fake news post might become on social platforms. To achieve our goal, we propose a comprehensive framework, MUFFLE, which captures multi-modal dynamics by encoding the representation of news-related social networks, user characteristics, and content in text. The attention mechanism developed in the model can provide explainability for social or psychological analysis. To examine the effectiveness of MUFFLE, we conducted extensive experiments on real-world datasets. The experimental results show that our proposed method outperforms both state-of-the-art methods of popularity prediction and machine-based baselines in top-k NDCG and hit rate. Through the experiments, we also analyze the feature importance for predicting fake news influence via the explainability provided by MUFFLE.

High-frequency vibration error of a moving radar platform easily introduces a non-negligible phase of periodic modulation in radar echoes and greatly degrades terahertz synthetic aperture radar (THz-SAR) image quality. For solving the problem of THz-SAR image-quality degradation, the paper proposes a multi-component high-frequency vibration error estimation and compensation approach based on the short-time Fourier transform (STFT). To improve the robustness of the method against noise effects, STFT is used to extract the instantaneous frequency (IF) of a high-frequency vibration error signal, and the vibration parameters are coarsely obtained by the least square (LS) method. To reduce the influence of the STFT window widths, a method based on the maximum likelihood function (MLF) is developed for determining the optimal window width by a one-dimensional search of the window widths. In the case of high noise, many IF estimation values seriously deviate from the true ones. To avoid the singular values of IF estimation in the LS regression, the random sample consensus (RANSAC) is introduced to improve estimation accuracy. Then, performing the STFT with the optimal window width, the accurate vibration parameters are estimated by LS regression, where the singular values of IF estimation are excluded. Finally, the vibration error is reconstructed to compensate for the non-negligible phase of the platform-induced periodic modulation. The simulation results prove that the error compensation method can meet THz-SAR imaging requirements, even at a low signal-to-noise ratio (SNR).

Electric control of topological magnetic phases has attracted extensive attention due to its potential applications in energy-efficient spintronic devices. Here, using first-principles calculations and atomistic spin model simulations, we demonstrate that electric control of topological magnetic phases can be realized in Janus-magnet-based multiferroic heterostructure, i.e., MnBi_{2}Se_{2}Te_{2}/In_{2}Se_{3}. The loops of vortices and antivortices can be transformed into skyrmions with diameter of only 4 nm via ferroelectricity reversal, which is originated from the change of magnetic anisotropy. For heterostructure with up polarization, loops of vortices and antivortices are further tuned to bimeron solitons by applying in-plane magnetic field. Our results thus pave the way for achieving highly tunable topological magnetism in atomic-thickness heterostructure, which can be useful in nonvolatile data encoding and storage with low-energy consumption.

Hexaphenoxycyclotriphosphazene (HPCP) is a common flame retardant for epoxy resin (EP). To improve the thermostability and fire safety of HPCP-containing EP, we combined UiO66-NH2 (a kind of metal-organic frame, MOF) with halloysite nanotubes (HNTs) by hydrothermal reaction to create a novel synergistic flame retardant (H-U) of HPCP for EP. For the EP containing HPCP and H-U, the initial decomposition temperature (T5%) and the temperature of maximum decomposition rate (Tmax) increased by 11 and 17 °C under nitrogen atmosphere compared with those of the EP containing only HPCP. Meanwhile, the EP containing HPCP and H-U exhibited better tensile and flexural properties due to the addition of rigid nanoparticles. Notably, the EP containing HPCP and H-U reached a V-0 rating in UL-94 test and a limited oxygen index (LOI) of 35.2%. However, with the introduction of H-U, the flame retardant performances of EP composites were weakened in the cone calorimeter test, which was probably due to the decreased height of intumescent residual char.

Research on flourishing public bike-sharing systems has been widely discussed in recent years. In these studies, many existing works focus on accurately predicting individual stations in a short time. This work, therefore, aims to predict long-term bike rental/drop-off demands at given bike station locations in the expansion areas. The real-world bike stations are mainly built-in batches for expansion areas. To address the problem, we propose LDA (Long-Term Demand Advisor), a framework to estimate the long-term characteristics of newly established stations. In LDA, several engineering strategies are proposed to extract discriminative and representative features for long-term demands. Moreover, for original and newly established stations, we propose several feature extraction methods and an algorithm to model the correlations between urban dynamics and long-term demands. Our work is the first to address the long-term demand of new stations, providing the government with a tool to pre-evaluate the bike flow of new stations before deployment; this can avoid wasting resources such as personnel expense or budget. We evaluate real-world data from New York City’s bike-sharing system, and show that our LDA framework outperforms baseline approaches.

Abstract (S)-carbonyl reductase II (SCRII) from Candida parapsilosis is a short-chain alcohol dehydrogenase/reductase. It catalyses the conversion of 2-hydroxyacetophenone to (S)-1-phenyl-1,2-ethanediol with low efficiency. Sortase was reported as a molecular “stapler” for site-specific protein conjugation to strengthen or add protein functionality. Here, we describe Staphylococcus aureus sortase A-mediated crosslinking of SCRII to produce stable catalysts for efficient biotransformation. Via a native N-terminal glycine and an added GGGGSLPETGG peptide at C-terminus of SCRII, SCRII subunits were conjugated by sortase A to form crosslinked SCRII, mainly dimers and trimers. The crosslinked SCRII showed over 6-fold and 4-fold increases, respectively, in activity and k cat/K m values toward 2-hydroxyacetophenone compared with wild-type SCRII. Moreover, crosslinked SCRII was much more thermostable with its denaturation temperature (Tm) increased to 60 °C. Biotransformation result showed that crosslinked SCRII gave a product optical purity of 100% and a yield of >99.9% within 3 h, a 16-fold decrease in transformation duration with respect to Escherichia coli/pET-SCRII. Sortase A-catalysed ligation also obviously improved Tms and product yields of eight other short-chain alcohol dehydrogenases/reductases. This work demonstrates a generic technology to improve enzyme function and thermostability through sortase A-mediated crosslinking of oxidoreductases.