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Compared with only pursuing recommendation accuracy, the explainability of a recommendation model has drawn more attention in recent years. Many graph-based recommendations resort to informative paths with the attention mechanism for the explanation. Unfortunately, these attention weights are intentionally designed for model accuracy but not explainability. Recently, some researchers have started to question attention-based explainability because the attention weights are unstable for different reproductions, and they may not always align with human intuition. Inspired by the counterfactual reasoning from causality learning theory, we propose a novel explainable framework targeting path-based recommendations, wherein the explainable weights of paths are learned to replace attention weights. Specifically, we design two counterfactual reasoning algorithms from both path representation and path topological structure perspectives. Moreover, unlike traditional case studies, we also propose a package of explainability evaluation solutions with both qualitative and quantitative methods. We conduct extensive experiments on four real-world datasets, the results of which further demonstrate the effectiveness and reliability of our method.

Automatic detection of traffic signs is crucial for Advanced Driving Assistance Systems (ADAS). Current two-stage approaches consist of a preliminary object detection step, where the traffic signs are categorized within broader families (e.g., speed limits), and then sub-classes (e.g., speed limit 40). However, these cascading methods fail to achieve satisfying performance, especially in more realistic driving scenarios where images are acquired under more challenging conditions. Under such conditions, the first-stage detection step is likely to provide inaccurate predictions, making the subsequent classification step useless. In this paper, we propose a simple yet effective two-stage fusion framework for traffic sign detection. Different from the previous cascading method, our framework directly predicts categories in the first-stage detection and fuse the two-stage category predictions to improves overall robustness. Besides, in order to filter the false detection boxes under low-resolution inputs, we also propose an effective post-processing method called Surrounding-Aware Non-Maximum Suppression (SA-NMS) as an alternative technique for the first-stage detection. After combining the above proposed methods, our framework obtains good detection performance. Experimental results on the widely used Tsinghua-Tencent 100K (TT100K) traffic sign dataset, which contains images of traffic signs collected under a variety of challenging conditions, show that the proposed framework outperforms current approaches in both accuracy and inference speed, achieving 89.7 mAP and 65 FPS for ${608\times 608}$ low resolution images.

Core fucosylation, a special type of N-linked glycosylation, is important in tumor proliferation, invasion, metastatic potential, and therapy resistance. However, the core-fucosylated glycoproteome has not been extensively profiled due to the low abundance and poor ionization efficiency of glycosylated peptides. Here, a “one-step” strategy has been described for protein core-fucosylation characterization in biological samples. Core-fucosylated peptides can be selectively labeled with a glycosylated probe, which is linked with a temperature-sensitive poly(N-isopropylacrylamide) (PNIPAM) polymer, by mutant endoglycosidase (EndoF3-D165A). The labeled probe can be further removed by wild-type endoglycosidase (EndoF3) in a traceless manner for mass spectrometry (MS) analysis. The feasibility and effectiveness of the “one-step” strategy are evaluated in bovine serum albumin (BSA) spiked with standard core-fucosylated peptides, H1299, and Jurkat cell lines. The “one-step” strategy is then employed to characterize core-fucosylated sites in human lung adenocarcinoma, resulting in the identification of 2494 core-fucosylated sites distributed on 1176 glycoproteins. Further data analysis reveals that 196 core-fucosylated sites are significantly upregulated in tumors, which may serve as potential drug development targets or diagnostic biomarkers. Together, this “one-step” strategy has great potential for use in global and in-depth analysis of the core-fucosylated glycoproteome to promote its mechanism research.

Single-cell proteomics offers unparalleled insights into cellular diversity and molecular mechanisms, enabling a deeper understanding of complex biological processes at the individual cell level. Here, we develop an integrated sample processing on an active-matrix digital microfluidic chip for single-cell proteomics (AM-DMF-SCP). Employing the AM-DMF-SCP approach and data-independent acquisition (DIA), we identify an average of 2258 protein groups in single HeLa cells within 15 min of the liquid chromatography gradient. We performed comparative analyses of three tumor cell lines: HeLa, A549, and HepG2, and machine learning was utilized to identify the unique features of these cell lines. Applying the AM-DMF-SCP to characterize the proteomes of a third-generation EGFR inhibitor, ASK120067-resistant cells (67R) and their parental NCI-H1975 cells, we observed a potential correlation between elevated VIM expression and 67R resistance, which is consistent with the findings from bulk sample analyses. These results suggest that AM-DMF-SCP is an automated, robust, and sensitive platform for single-cell proteomics and demonstrate the potential for providing valuable insights into cellular mechanisms.

To address the issues of low photothermal conversion efficiency, salt deposition and biological contamination in solar-powered desalination, we have developed nanofibrous membranes composed of an optimized ratio of silver-loaded graphene oxide (GO-Ag) composites and polyvinyl alcohol/chitosan (PVA/CS), using a simple and productive electrospinning technique. The synergistic effect of GO and Ag significantly enhanced the photothermal conversion efficiency of the dual-photothermal PVA/CS/GO-Ag-4% electrospun nanofiber membranes (EFMs), which achieved the pure water evaporation rate of 1.67 kg m−2h−1and photothermal conversion efficiency of 93.3% under 1 sun (1 kW m−2). Notably, 97% of optical energy is converted to heat when the PVA/CS/GO-Ag-4% EFMs were placed on a homemade evaporation device, achieving up to a simulated seawater evaporation rate of 1.64 kg m−2h−1under 1sun, with a desalination rate of 99.99%. More importantly, larger area EFMs are easier to prepare compared to other morphologies of solar evaporators, and larger membranes can be placed on outdoor evaporation units for large-scale effective desalination of seawater used to collect fresh water under natural light. Furthermore, PVA/CS/GO-Ag-4% EFMs exhibited outstanding antibacterial performance against Escherichia coliand Staphylococcus aureus. Considering these strengths, the PVA/CS/GO-Ag-4% EFMs has considerable application potential in solar-driven desalination to solve water crises.

Background: Evaluating risk of poor outcome for Traumatic Brain Injury (TBI) in early stage is necessary to make treatment strategies and decide the need for intensive care. This study is designed to verify the prognostic value of serum cystatin C in TBI patients. Methods: 415 TBI patients admitted to West China hospital were included. Logistic regression was performed to explore risk factors of mortality and testify the correlation between cystatin C and mortality. Mediation analysis was conducted to test whether Acute Kidney Injury (AKI) and brain injury severity mediate the relationship between cystatin C level and mortality. Area under the receiver operating characteristic curve (AUC) was used to evaluate the prognostic value of cystatin C and the constructed model incorporating cystatin C. Results: The mortality rate of 415 TBI patients was 48.9%. Non-survivors had lower GCS (5 vs 8, p< 0.001) and higher cystatin C (0.92 vs 0.71, p< 0.001) than survivors. After adjusting confounding effects, multivariate logistic regression indicated GCS (p< 0.001), glucose (p< 0.001), albumin (p= 0.009), cystatin C (p< 0.001) and subdural hematoma (p= 0.042) were independent risk factors of mortality. Mediation analysis showed both AKI and brain injury severity exerted mediating effects on relationship between cystatin C and mortality of included TBI patients. The AUC of combining GCS with cystatin C was 0.862, which was higher than that of GCS alone (Z= 1.7354, p< 0.05). Conclusion: Both AKI and brain injury severity are mediating variables influencing the relationship between cystatin C and mortality of TBI patients. Serum cystatin C is an effective prognostic marker for TBI patients.

• ZnO and Zn2+ can be co-doped in the osteoinductive BCP bioceramics by one-step route. • BCP bioceramics with ZnO/Zn2+ co-doping possess strong antibacterial ability on E. coli and S. aureus as well as stimulate the osteogenic differentiation of BMSCs effectively. • The antibacterial mechanism of ZnO/Zn2+ co-doping mainly includes ROS generation, protein inactivation, and ZnO deposition. • The obtained bioceramics exhibit excellent anti-infective ability and bone regenerative ability in the repair of infected bone defects. How to endow bone grafts with long-term antibacterial activity and good bone regenerative ability to achieve the regenerative repair of infected bone defects has been the focus of the clinical treatment of osteomyelitis. The present study introduced a novel one-step route to realizing the co-doping of zinc oxide (ZnO) and zinc ion (Zn2+) in biphasic calcium phosphate (BCP) ceramics to utilize their synergistic antibacterial. Compared with the conventional BCP ceramics (BCP-Ca), the ZnO/Zn2+ co-doping ones (BCP-Zn) possessed strong antibacterial ability on E. coli and S. aureus as well as stimulated the osteogenic differentiation of bone marrow mesenchymal stem cells (BMSCs) effectively. The synergistic antibacterial mechanism of ZnO and Zn2+ was also investigated. BCP-Zn showed excellent osteoinductivity and angiogenesis at three months postoperatively in the canine intramuscular implantation model. Moreover, BCP-Zn exhibited excellent anti-infective ability and bone regenerative repair compared to BCP-Ca and control groups in the infected bone defect model of rat femur. Collectively, these findings suggest that the simultaneous introduction of ZnO/Zn2+ could have immense potential to expand the application of osteoinductive BCP ceramics in the regenerative repair of infected bone defects. [Display omitted] [ABSTRACT FROM AUTHOR]

Recent advances in path-based explainable recommendation systems have attracted increasing attention thanks to the rich information from knowledge graphs. Most existing explainable recommendations only utilize static knowledge graphs and ignore the dynamic user-item evolutions, leading to less convincing and inaccurate explanations. Although some works boost the performance and explainability of recommendations through modeling the user's temporal sequential behavior, most of them either only focus on modeling the user's sequential interactions within a path or independently and separately of the recommendation mechanism. Moreover, some path-based explainable recommendations use random selection or traditional machine learning methods to decrease the volume of explainable paths, which cannot guarantee high quality of the explainable paths for the recommendation. To deal with the problem, recent path exploration use reinforcement learning to improve diversity and quality. However, unsupervised training leads to low-efficiency path exploration. Therefore, we propose a novel Temporal Meta-path Guided Explainable Recommendation leveraging Reinforcement Learning (TMER-RL), which utilizes supervised reinforcement learning to explore item-item paths between consecutive items with attention mechanisms to sequentially model dynamic user-item evolutions on a dynamic knowledge graph for the explainable recommendation. Extensive evaluations of TMER-RL on two real-world datasets show state-of-the-art performance compared to recent strong baselines.

Recently, deep learning techniques have yielded immense success on recommender systems. However, one weakness of most deep methods is that, users/items mutual semantic relationships, which are latent in the user-item interactions, are not distilled out explicitly. Moreover, most methods have been primarily focused on representation learning in euclidean geometry. Since recent studies have shown that the bipartite graph structure has the non-euclidean latent anatomy, euclidean embeddings may suffer from a certain degree of distortion. In this work, we present Hyperbolic Neural Collaborative Recommender (HNCR), a deep hyperbolic representation learning method that exploits mutual semantic relationships among users/items for collaborative filtering tasks. HNCR first introduces a neighbor construction strategy to build user and item semantic neighborhoods. Then HNCR develops a framework based on deep learning and hyperbolic geometry to integrate constructed neighborhoods into recommendation. To evaluate our method, we conduct experiments on the four datasets. Experimental results show the superiority of HNCR compared with its euclidean counterpart and state-of-the-art recommendation baselines. The results also indicate that hyperbolic representations can reflect meaningful data insights.

Online social networks have greatly facilitated our lives but have also propagated the spreading of rumours. Traditional works mostly find rumors from content, but content can be strategically manipulated to evade such detection, making these methods brittle. To improve the accuracy and robustness of rumor detection, we propose to integrate and exploit the content, propagation structure, and temporal relations because information in the networks always spreads dynamically with significant structures. In this paper, we propose a novel rumor detection framework in online temporal networks via structure learning. Specifically, to exploit the propagation structure, we propose a novel hyperedge walking strategy on a meta-hyperedge graph to learn the representations of sub-structures in the networks. Then a hyperedge expansion method is proposed to generate more global structural features. The expanded hyperedges are more hierarchical, making the learned structural embeddings more expressive. To make full use of content, we design a hypergraph learning model using hyperedge expansion to fuse node content with structural features and generate comprehensive representations for the entire graph. To exploit temporal relations, we design a masked temporal attention unit for learning the evolving patterns of the network. Extensive evaluations with six state-of-the-art baselines on two real-world datasets demonstrate the superiority of our solution.

A series of waste diaper-derived heterogeneous magnetic catalysts for the synthesis of biodiesel was developed through the two-step method, i.e., first wet-impregnation in nickel nitrate solution and then calcination at 700 °C. The structure and catalytic ability of the catalyst were characterized by SEM, FTIR, XRD, BET, VSM, and acid–base titration techniques. The investigation results indicated that WDHMCs were mainly composed of Ni, Na2CO3, and carbon. With the increase in the ratio of nickel nitrate to waste diapers, the magnetization of the prepared catalysts increased, while the catalytic activity decreased. When the ratio of nickel nitrate to waste diaper was 2 mmol/g, the obtained catalyst exhibited relatively high catalytic activity with the biodiesel yield of 96.4% and high magnetic separation property in the transesterification reaction of waste cooking oil with methanol. Moreover, the prepared waste diaper-derived heterogeneous magnetic catalyst could be easily reused by simple magnetic separation and maintain high catalytic activity after being reused four times.

Signed link prediction in social networks aims to reveal the underlying relationships (i.e., links) among users (i.e., nodes) given their existing positive and negative interactions observed. Most of the prior efforts are devoted to learning node embeddings with graph neural networks (GNNs), which preserve the signed network topology by message-passing along edges to facilitate the downstream link prediction task. Nevertheless, the existing graph-based approaches could hardly provide human-intelligible explanations for the following three questions: (1) which neighbors to aggregate, (2) which path to propagate along, and (3) which social theory to follow in the learning process. To answer the aforementioned questions, in this paper, we investigate how to reconcile the balance and status social rules with information theory and develop a unified framework, termed as Signed Infomax Hyperbolic Graph (SIHG). By maximizing the mutual information between edge polarities and node embeddings, one can identify the most representative neighboring nodes that support the inference of edge sign. Different from existing GNNs that could only group features of friends in the subspace, the proposed SIHG incorporates the signed attention module, which is also capable of pushing hostile users far away from each other to preserve the geometry of antagonism. The polarity of the learned edge attention maps, in turn, provides interpretations of the social theories used in each aggregation. In order to model high-order user relations and complex hierarchies, the node embeddings are projected and measured in a hyperbolic space with a lower distortion. Extensive experiments on four signed network benchmarks demonstrate that the proposed SIHG framework significantly outperforms the state-of-the-arts in signed link prediction.

A flame-retardant polylactic acid composite added with piperazine pyrophosphate and melamine cyanurate was developed. Piperazine pyrophosphate and melamine cyanurate showed synergistic effect in the polylactic acid composites. When the weight ratio of piperazine pyrophosphate to melamine cyanurate was 4:1 and the loading of the piperazine pyrophosphate/melamine cyanurate flame retardant was 15 wt.%, the prepared polylactic acid composites passed V-0 rating in the UL-94 test and achieved the high limit oxygen index value of 34.9%. The cone calorimeter test result confirmed that the addition of piperazine pyrophosphate/melamine cyanurate flame retardant reduced the total heat release value of polylactic acid. The effect of polylactic acid biodegradation on the flame retardancy of polylactic acid composite was further discussed. The biodegraded polylactic acid, which was exposed to the air for 8 months, showed high flame retardancy and even passed V-0 rating in the UL-94 test without the addition of piperazine pyrophosphate/melamine cyanurate flame retardant, which proved that biodegradation could affect the flame retardancy of polylactic acid.

Asphalt pavement depression, e.g., cracking, rutting and bulges, are the main factors endangering transportation safety and capacity. Detection of these depression is a significant step for pavement management; to date several laser-scanning-based technologies have been implemented for this purpose. However, an automated solution remains a challenging task due to the complicated pavement conditions in real world such as illumination and shadows. In this paper, a vision-based automated detection method for pavement cracks is proposed using deep learning technology, wherein a convolutional neural network (CNN) is trained to learn the features of the cracks from images without any preprocessing. The designed CNN is trained on the image database containing 240 images, based on the open-source TensorFlow framework by Google Brain team, and consequently records with about 96% accuracy. The robustness and adaptability of the trained CNN are tested on 40 images taken from different roads under various crack types, which were not used in the training and validation process. Testing results show that the proposed method has satisfactory performance, and therefore, could be beneficial for providing an alternative solution to automated detection of pavement cracks.

The ion specificity effect on the water solubility of poly(N-isopropylacrylamide)-containing copolymers complies with the Hofmeister series, which is applicable to other copolymers or not need to be explored. In this work, effects of ionic strength under acidic conditions and ion specificity under alkaline conditions on the air/water interface behavior of two amphiphilic diblock copolymers poly(dimethylaminoethyl methacrylate)–poly(lauryl methacrylate) (PDMAEMA-PLMA) were systematically studied. Under acidic conditions, the surface pressure–area isotherms of a predominantly hydrophilic copolymer are insensitive to ionic strength. In contrast, the isotherms of a predominantly hydrophobic copolymer successively shift to the large, small, and large molecular area with the increase of ionic strength. Under alkaline conditions, the interfacial stretch degrees of PDMAEMA chains of two copolymers change with salt species and concentrations, which do not comply with the Hofmeister series. All of the Langmuir–Blodgett films of the former copolymer exhibit separate circular micelles. Nevertheless, those of the latter copolymer obtained under alkaline conditions exhibit various distinctive morphologies such as separate circular micelles, large separate PLMA cores within large PDMAEMA domains, and large PLMA domains/aggregates surrounded by short PDMAEMA shells. It can be attributed to the high deformability of PLMA chains, the ion specificity effect on the stretch degree of PDMAEMA blocks, and their underwater solubility upon compression.

At the air/water interface, amphiphilic block copolymers are well-known to form typical surface micelles composed of hydrophobic block cores and hydrophilic block coronas. However, the micelle/aggregate structures of the double hydrophilic block copolymers (DHBCs) need to be explored because of the lack of hydrophobic blocks. Influences of subphase pH and temperature on the interfacial aggregation behavior of a thermoresponsive DHBC of poly(N-isopropylacrylamide)-block-poly[oligo(ethylene glycol) acrylate] (PNIPAM-b-POEGA) and the structures of its Langmuir–Blodgett (LB) films were studied with the Langmuir monolayer technique and atomic force microscopy, respectively. All initial LB films of PNIPAM-b-POEGA show circular micelles with a sunflower-like structure composed of a small PNIPAM backbone core, a short amide group shell, and several large POEGA petals, the structure of which is proposed in the DHBCs for the first time. Two kinds of novel methods based on the methylene group number in a core or the core diameter were successfully used to evaluate the micelle structure. With the increase of subphase pH, the isotherms move toward the larger mean molecular areas due to the gradually increased stretching degrees of POEGA blocks at the air/water interface. With the rise in temperature, the limiting molecular areas of the isotherms initially increase and then decrease above the lower critical solution temperature. As compared to those of other copolymers, the maximum monolayer compression moduli of PNIPAM-b-POEGA are very low, which indicates that the DHBC monolayers are highly compressible. Furthermore, at low temperature, hysteresis levels in the continuous compression–expansion cycles decrease stage by stage, whereas those at high temperature change slightly.

The Langmuir film balance technique was used to investigate the effects of copolymer composition and subphase pH/temperature on the aggregation behaviors of two stimuli-responsive amphiphilic diblock copolymers poly(2-(dimethylamino)ethyl methacrylate)-block-poly(lauryl methacrylate) (PDMAEMA-b-PLMA) at the air/water interface. The morphologies of their Langmuir–Blodgett (LB) films were characterized by atomic force microscopy. With the rise in subphase pH, the corresponding surface pressure–molecular area isotherms of the two copolymers gradually move to large areas due to the decreased protonation degrees of PDMAEMA blocks. Almost all the LB films of the predominantly hydrophilic copolymer prepared under different subphase conditions exhibit isolated circular micelles. For the predominantly hydrophobic copolymer, however, the dense wormlike aggregates and the large domains appear in its LB films due to the connection of adjacent PLMA cores at low temperature and the local richness of PDMAEMA chains at high temperature, respectively.

The increasing utilization of polyvinylidene fluoride (PVDF) ultrafiltration membranes in water treatment has necessitated the development of membranes with superior separation, antifouling performance and high pure water permeability. As a hydrophilic and antibacterial modifier, quaternized graphene oxide (QGO) has been successfully applied to improve the antifouling and antibacterial properties of the membranes simultaneously. Herein, we proposed a simple and effective strategy to fabricate a nanocomposite ultrafiltration membrane by coating the PVDF membrane surface with QGO and using polydopamine (PDA) as a “bio-glue”. The filtration followed by cross-linking method was utilized to construct a “brick-mud” structure on the membrane surface. The prepared QGO/PDA-PVDF membrane exhibited favorable separation and antifouling properties (79.5% of BSA rejection rate, 15.7% of irreversible fouling rate when filtering 0.5 g/L BSA solution, 97.2% of E. coliand 96.3% of S. aureusantibacterial rate), while maintaining a high pure water permeability of 1105.4 L.m−2.h−1.bar−1. QGO nanosheets were stably coated on the membrane surface, and the QGO/PDA-PVDF membrane prepared by this novel strategy also demonstrated application potential in the field of seawater desalination pretreatment. Besides, the possible mechanisms during modifications were also discussed. This research could serve as a reference for improving the comprehensive performance of PVDF ultrafiltration membrane through surface coating.

Loose nanofiltration membrane exhibits significant advantages in removing organic pollutants due to its high selectivity at a lower operating pressure. Herein, a novel zwitterionic loose polyamide separation layer was designed and formed on the modified polyethersulfone substrate to obtain the zwitterionic loose nanofiltration (ZLNF) membrane with high water permeability and good antifouling properties. The BAPP-TMC polyamide layer was fabricated by the interfacial polymerization between 1,4-bis(3-aminopropyl)piperazine (BAPP) and trimesoyl chloride (TMC). Afterward, the BAPP-TMC polyamide layer was grafted with zwitterions by a facile surface modification. Owing to the fabrication of the relatively loose polyamide structure with zwitterions, the prepared ZLNF-0.2 membrane presented an ultra-high pure water permeability of 148.5 L m−2h−1bar−1while maintaining relatively good removal capacities for organic pollutants. The removal rates for humic acid (HA, 100 ppm) and Congo Red (CR, 100 ppm) were 98.1 % and 97.6 %, respectively. Furthermore, the good hydrophilicity and strong surface hydration effect of zwitterions endowed the ZLNF membranes with excellent anti-organic fouling and antibacterial adhesion properties. The good antibacterial abilities of ZLNF membranes were also proved by the fluorescence staining results. Due to the effective separation performance and good antifouling properties, the fabricated ZLNF membranes present great application potential in removing natural organic matter and dyes.

The performance of electric vehicles is affected by the shift quality of multi-gear transmission. The realization of dual-target tracking control requires the transmission control unit (TCU) to accurately measure and process the input signals of the gear-shifting control system and precisely control the drive motor torque and the position of shift motors. An electric-vehicle-dedicated TCU was designed to meet the above design requirements. Its function modules included a single-chip control circuit, shift position signal sampling circuit, signal conditioning circuit of the rotational speed and angle, controller area network communication circuit, and shift motor drive circuit. A hardware-in-the-loop simulation test system showed that the TCU design scheme met measurement accuracy requirements and coordinated the actions of the shift actuator and motor control unit to achieve fast and smooth shifting before the road test. The power interruption time of the shifting process was within 350 ms. The reliability of the TCU design was further verified in a 150,000-km vehicle road test.

Micro/nanomotors can effectively convert other forms of energy into mechanical energy, which have been widely used in microscopic fields. However, it is still challenging to integrate the micro/nanomotors to perform complex tasks for broad applications. Herein, a new mode for driving the collective motion behaviors of integrated micro/nanomotors in a liquid by plasmonic heating is reported. The integrated micro/nanomotors, constituted by gold hollow microcone array (AuHMA), are fabricated via colloidal lithography. Owing to the excellent plasmonic-heating property of the AuHMA, the integrated micro/nanomotors can generate vapor bubbles in the liquid as exposure to near-infrared (NIR) irradiation, therefore inducing versatile motions via on/off NIR irradiation. The floating–diving motions are reversible for at least 60 cycles without fatigue. In addition, precise manipulation of the coordinated motion behaviors, including bending, convex, and jellyfish-like floating motions, can be realized by adjusting the irradiated positions of incident NIR light together with the sizes and shapes of AuHMA films. Moreover, the AuHMA film can act as a robust motor to drive a foam craft over 57-folds of its own weight as exposure to NIR irradiation. Our investigation into the NIR-driven AuHMA film provides a facile approach for obtaining integrated micro/nanomotors with controllable collective motions, which holds promise in remotely controlled smart devices and soft robotics in liquids.

Aminoglycosides (AGs) are an important class of antibiotics that have a good bactericidal effect on Gram-positive and Gram-negative bacteria. Long-term consumption of AGs may cause many allergic reactions due to their high affinity to tissue. It is necessary to develop a sensitive and reliable analytical method for trace-level AGs in food. A capillary electrophoresis-electrospray ionization-tandem mass spectrometry (CE-ESI-MS/MS) method was established for the determination of AGs. High sensitivity was achieved by using a sheathless CE-MS interface and transient isotachophoresis. Under the optimized conditions, four AGs were separated by CE and quantified by MS detection with high sensitivity. The limit of quantitation for the four AGs in milk was 0.67 μg kg−1. The recoveries of the four AGs in spiked milk ranged from 76.20 to 110.00% with relative standard deviations of 7.25–13.73%. The results indicated that sheathless CE-ESI-MS/MS would be a sensitive and effective platform for AG detection in food.

Sapphire is an important material for optical applications, but the anisotropy of key properties makes it difficult to obtain uniform ground surface. In this work, cross-grinding experiments were first conducted on C- and M-planes of sapphire to investigate the dependence of the ground surface quality on crystal orientation. Then, scratching tests were performed along the specific crystal orientations to explain the formation mechanism of the anisotropic grinding patterns. The results showed that the surface roughness distribution on C-plane is of a threefold symmetry, while one axis of symmetry for M-plane. The damage pits on C-plane possess lower aspect ratio than that on M-plane. By combining the scratching-induced fracture features with the contact-induced deformation theory of sapphire, it was found that the propagation of the primary cracks in sapphire is accompanied by activating different deformation systems. The material removal on C-plane is dominated by the shallow lateral cracks, which propagate parallel to the ground surface and result in shallow and wide pits. However, the deep lateral cracks play an important role on M-plane, which intersect with the ground surface and cause deep and dense defects. The non-uniformity of the ground surface is attributed to the appearance of radial cracks for C-plane, while it is associated with the number of the available deformation systems for M-plane.

The formation of complete and well-functioning endothelium is critical for the success of tissue-engineered vascular grafts yet remaining a fundamental challenge. Endothelium remodeling onto the lumen of tissue-engineered vascular grafts is affected by their topographical, mechanical, and biochemical characteristics. For meeting multiple requirements, composite strategies have recently emerged for fabricating hybrid scaffolds, where the integrated properties are tuned by varying their compositions. However, the underlying principle how the integrated properties of hybrid scaffolds regulate vascular endothelium remodeling remains unclear. To uncover the regulation effects of hybrid scaffolds on vascular endothelium remodeling, we prepared different biomimetic hybrid scaffolds using gelatin methacrylamide (GelMA) and poly-ε-caprolactone (PCL) and then investigated vascular endothelial cell responses on them. GelMA and PCL, respectively, conferred the resulting scaffolds with biomimetic bioactivity and mechanical properties, which were tuned by varying GelMA/PCL mass ratios (3:1, 1:1, or 1:3). On different GelMA/PCL hybrid scaffolds, distinct vascular endothelial cell responses were observed. Firm cell–scaffold/cell–cell interactions were rapidly established on the hybrid scaffolds with the highest mass ratio of bioactive GelMA. However, they were mechanically insufficient as vascular grafts. On the contrary, the scaffolds with the highest mass ratio of PCL showed significantly reinforced mechanical properties but poor biological performance. Between the two extremes, the scaffolds with the same GelMA/PCL mass ratio balanced the pros and cons of two materials. Therefore, they could meet the mechanical requirements of vascular grafts and support the early-stage vascular endothelial cell remodeling by appropriate biological signaling and mechanotransduction. This investigation experimentally proves that scaffold bioactivity is the dominant factor affecting vascular endothelial cell adhesion and remodeling, whereas mechanical properties are crucial factors for the integrity of endothelium. This work offers a universal design strategy for desirable vascular grafts for improved endothelium remodeling.

Surfaces patterned with hydrophilic and hydrophobic regions provide robust and versatile means for investigating the wetting behaviors of liquids, surface properties analysis, and producing patterned arrays. However, the fabrication of integral and uniform arrays onto these open systems remains a challenge, thus restricting them from being used in practical applications. Here, we present a simple yet powerful approach for the fabrication of water droplet arrays and the assembly of photonic crystal bead arrays based on hydrophilic–hydrophobic patterned substrates. Various integral arrays are simply prepared in a high-quality output with a low cost, large scale, and uniform size control. By simply taking a breath, which brings moisture to the substrate surface, complex hydrophilic–hydrophobic outlined images can be revisualized in the discontinuous hydrophilic regions. Integration of hydrogel photonic crystal bead arrays into the “breath-taking” process results in breath-responsive photonic crystal beads, which can change their colors upon a mild exhalation. This state-of-the-art technology not only provides an effective methodology for the preparation of patterned arrays but also demonstrates intriguing applications in information storage and biochemical sensors.

Electrocatalytic hydrodechlorination (ECH) based on Pd-modified Ni foam electrodes offers a sustainable approach for decontaminating halogenated chlorinated organics, but the synthesis of Pd/Ni foam is always poorly controlled. In this work, with clofibric acid (emerging as a typical pharmaceutical) as a probe contaminant, we show that the ECH performance of Pd/Ni foam is highly dependent on the impregnation time during its preparation. During preparation, the dissolved Ni2+can combine with aqueous OH–to form in situ Ni(OH)2coatings on Pd/Ni foam. By prolonging the impregnation time, continuous deposition of Ni(OH)2gradually increases the anti-corrosive property and the electrochemically active surface area of the electrode. However, such covered Ni(OH)2coatings promote the interfacial charge transfer resistance and hinder the atomic H* adsorption by Pd, which is detrimental to ECH. This work provides a novel strategy for the synthesis of Pd/Ni foam electrodes with an enhanced ECH property for electroreductive remediation of chlorinated pharmaceutical compounds.

In this paper, Janus micropillar array (MPA) with fore–aft controllable wettability difference was demonstrated. With two-step modification process, we successfully decorate the Janus pillar skeletons with wettability-switchable polymer brush on one side and hydrophilic self-assembled monolayer on the other. Owing to the switchable wettability of the polymer brush, the patterned surface could switch between anisotropic wetting and isotropic wetting at different temperatures, which gives the possibility of coupling the well-designed surface with microfluidic channel to manipulate the microfluid motion. Additionally, a further photothermal control of microfluid was also established based on the thermal-responsive Janus MPA through introducing infrared light to adjust the temperature of the microfluidic system. We believe that the thermal-responsive Janus micropillar arrays would provide a new strategy to control the flow and motion of fluids in microfluidic channels and show potential applications in the future microfluidic chips.

To improve the contradiction between vehicle dynamic performance and ride comfort by using the motor active output torque compensation method to reduce the torsional vibration of the hybrid electric vehicle (HEV) powertrain, an active multiobjective real-time vibration control (AMRVC) algorithm based on model prediction control (MPC) is proposed in this paper, and the current mainstream parallel HEV is taken as the research object to verify the proposed algorithm. Firstly, the torsional vibration model of the parallel HEV powertrain is established, simplified, and verified according to the experimental data, which provides the basis for the multimode adaptability and the application of the controller. Then, based on the state space equation of the torsional vibration system, the MPC controller considering the balance between ride comfort and power performance is designed, which can meet the multiobjective and real-time control effect of the hybrid power system while realizing the active vibration control. Finally, the effectiveness and real-time performance of the AMRVC algorithm are verified by multimode simulation analysis and hardware-in-the-loop (HIL) experiment. The simulation results show that the AMRVC algorithm based on MPC has good real-time application conditions, which can well coordinate the multiobjective requirements of vehicle ride comfort and dynamic performance, and remarkably improve the torsional vibration response of the powertrain by more than 55% in all HEV typical operating modes.

An infrared (IR) dual band multi-element detector with the abilities of dual band IR counter–countermeasure (IRCCM) and spatial filtering is presented for effective target detection in a complex tactical environment. The detection elements of the detector are specially arranged like a conventional reticle pattern. With special design, the ratio of radiation intensity from two IR bands can be calculated to distinguish the target from the IR target-flare mixed signal and the two detection bands use a common aperture in the seeker. Without a reticle in the optical system of the IR seeker, the dual band detector can still perform spatial filtering to eliminate background noise effectively. The design details of the detector are presented. The performance of the detector’s dual band IRCCM and spatial filtering are analyzed. Simulation results are presented verifying validity of the presented method.

Dual-band IR imaging detector can be responsive to two correlated spectrum of the target and the flare. With the equivalent temperature of the target and the flare, the radiation characteristic of the target and the flare is simulated by MATLAB which is based on Plank's law of the blackbody radiation, and the SWIR/MWIR spectrum is chose where the IR radiation is the largest difference between the target and the flare. The theoretical calculation procedure is designed according to the transform flow of the IR radiation. First we calculate the responsive results of InSb IRFPA to the cavity blackbody and the extended area blackbody, because of the simulated results are consistent with the tested results, the simulation procedure is of practical .Then consideration of the HgCdTe 128×128 detector with pixels are stacked, the performance parameters of HgCdTe SW/MW dual-band IR imaging detector are simulated with MATLAB procedures, the purpose of distinguish the target from the flare with image and dual-band ratio can be reached, and the two benefit conclusions can be achieved.

In this paper, fibrous-shaped of V6O13 were successfully synthesized via V2O5 and ethanol as reactants through the method of magnetic stirring in the oil bath, Compared to the prepared sample without mechanical force-driven, the sample prepared by this method has a fibrous morphology with good dispersion. It can be seen that as the speed increases, the width of the fibers decreases, the fiber length increases after SEM tests. After the electrochemical tests, it can be seen that the attrition rate of capacity will be successfully inhibited in the process of charging and discharging when the fibrous-shaped V6O13 as a lithium ion batteries cathode materials. When the stirring rate was 0 rpm and possessed a reversible capacity of 72 mAh/g after 50 deep cycles. By contrast, the stirring rate was 1200 rpm and possessed a reversible capacity of 102 mAh/g after 50 deep cycles. It increased 41.6%.

A 20-year-old woman presented with a 2-day history of headache. She had a congenital grayish-blue pigmented nevus on her left side face, which involved ipsilateral sclera and oral mucosal membrane, diagnosed as nevus of Ota (figure 1, A and B). Neuroradiologic manifestation revealed a lesion in the left cerebellar hemisphere, the etiology of the presenting tumor apoplexy (figure 1, C–F). A left cerebellar hemisphere craniotomy was performed. A well-defined black lesion involved dura mater to the pia mater and was firmly attached to the cerebellar tentorium (figure 2, A and B). Histopathologic examination confirmed a meningeal melanocytoma. Postoperative CT demonstrated total tumor resection.1

Motor-transmission coupled drive system is attractive for battery and hybrid electric vehicles. In such a system, the motor rotor is directly connected to the transmission input shaft and the active-synchronization technique is implemented to assist the speed synchronization; therefore, the gear-shifting characteristics are different from those of traditional manual and automated mechanical transmissions. In this work, we present a methodology for modeling the gear-shifting process and analyzing its characteristics in a motor-transmission coupled drive system. We treat the engaging of sleeve and desired clutch gear as a two-phase process—sleeve first interacting with synchro ring and then with clutch gear, respectively, and investigate all possible interaction ways in each phase. The movement of each part is governed by multibody dynamics, and the speed jumps caused by shifting impacts are described using the Poisson coefficient of restitution. We then develop a hybrid automaton (HA) model to couple the continuous-time evolutions and the discrete transitions of state variables, which cover all interaction ways of sleeve, synchro ring, and clutch gear. Based on this model, we carry out simulations in matlab to analyze the effects of two control parameters—the relative rotational speed of sleeve and desired clutch gear, and the shifting force—on shifting performance. Simulation and bench test results show that the optimal control parameters are located in the domain where the relative rotational speed is negative with small absolute value, which means the sleeve will not be locked out by synchro ring and can engage with the desired clutch gear smoothly.

Currently, graph learning models are indispensable tools to help researchers explore graph-structured data. In academia, using sufficient training data to optimize a graph model on a single device is a typical approach for training a capable graph learning model. Due to privacy concerns, however, it is infeasible to do so in real-world scenarios. Federated learning provides a practical means of addressing this limitation by introducing various privacy-preserving mechanisms, such as differential privacy (DP) on the graph edges. However, although DP in federated graph learning can ensure the security of sensitive information represented in graphs, it usually causes the performance of graph learning models to degrade. In this paper, we investigate how DP can be implemented on graph edges and observe a performance decrease in our experiments. In addition, we note that DP on graph edges introduces noise that perturbs graph proximity, which is one of the graph augmentations in graph contrastive learning. Inspired by this, we propose leveraging graph contrastive learning to alleviate the performance drop resulting from DP. Extensive experiments conducted with four representative graph models on five widely used benchmark datasets show that contrastive learning indeed alleviates the models' DP-induced performance drops. [ABSTRACT FROM AUTHOR]