A synthetic digital city dataset for robustness and generalisation of depth estimation models.

Existing monocular depth estimation driving datasets are limited in the number of images and the diversity of driving conditions. The images of datasets are commonly in a low resolution and the depth maps are sparse. To overcome these limitations, we produce a Synthetic Digital City Dataset (SDCD) which was collected under 6 different weather driving conditions, and 6 common adverse perturbations caused by the data transmission. SDCD provides a total of 930 K high-resolution RGB images and corresponding perfect observed depth maps. The evaluation shows that depth estimation models which are trained on SDCD provide a clearer, smoother, and more precise long-range depth estimation compared to those trained on one of the best-known driving datasets KITTI. Moreover, we provide a benchmark to investigate the performance of depth estimation models in different adverse driving conditions. Instead of collecting data from the real world, we generate the SDCD under severe driving conditions with perfect observed data in the digital world, enhancing depth estimation for autonomous driving.

Radiation polymerization for the preparation of universal coatings: remarkable anti-fogging and frost-resisting performance.

Hydrophilic anti-fogging coatings have attracted considerable attention due to their ease of preparation and excellent fog resistance. In this study, a hydrophilic anti-fogging coating based on the random copolymer p(AA-co-SAS) was prepared using acrylic acid (AA) and sodium allylsulfonate (SAS) as monomers through radiation polymerization. The introduction of SAS successfully transformed the random copolymer from a gel state into a film-forming polymer solution. The presence of AA structural units in p(AA-co-SAS) improved the film-forming properties of the polymer solution. Additionally, there was a positive correlation between the proportion of SAS structural units in the random copolymer and the scratch hardness and wetting properties of the coating. After coating polycarbonate (PC) sheets, the surface hydrophilicity was significantly enhanced, with the contact angle of PC-AA10/SAS5 decreasing from 100.1° to 18.8° within 50 seconds. The outstanding wetting properties endowed the coating with exceptional anti-fogging and frost-resisting performance. It exhibited optimal transparency under both testing conditions and demonstrated good stability during cyclic testing. Tape adhesion tests indicated that the adhesion between the coating and PC reached a 5B level. When AA10/SAS5 was applied to PET film, glass, and PMMA goggles, all samples showed excellent anti-fog performance. Even after being naturally placed for one year under ambient conditions, the PMMA goggles still maintained good performance in the anti-fog and frost resistance tests. The remarkable comprehensive properties of the polymer coating based on p(AA-co-SAS) suggest enormous potential applications in industries such as packaging, healthcare, and optical equipment.

Progress in Research on the Preparation of Graphene-Based Aerogels Using γ-ray Irradiation Technology.

Graphene aerogels (GAs) are of significant interest in the scientific community due to their unique attributes, including a three-dimensional porous structure, exceptional specific surface area, and remarkable chemical stability. Researchers have made notable breakthroughs in aerogel preparation, focusing on aspects like porous structures and chemical stability. This review explores product morphologies and properties developed between 2011 and 2023, particularly examining applications of graphene aerogels with amine or alcohol radical scavengers. It offers a roadmap for researchers, suggesting possibilities for radiation-based preparation and indicating broader applications. These findings contribute to a deeper understanding of aerogels and expand the potential applications of graphene aerogels across various fields.

Progress and summary of reinforcement learning on energy management of MPS-EV.

The escalating environmental concerns and energy crisis caused by internal combustion engines (ICE) have become unacceptable under environmental regulations and the energy crisis. As a promising alternative solution, multi-power source electric vehicles (MPS-EVs) integrate various clean energy systems to enhance the powertrain efficiency. The energy management strategy (EMS) is plays a pivotal role for MPS-EVs to maximize efficiency, fuel economy, and range. Reinforcement Learning (RL) has emerged as an effective methodology for EMS development, attracting continuous attention and research. However, a systematic analysis of the design elements of RL-based EMS is currently lacking. This paper addresses this gap by presenting a comprehensive analysis of current research on RL-based EMS (RL-EMS) and summarizing its design elements. This paper first summarizes the previous applications of RL in EMS from five aspects: algorithm, perception scheme, decision scheme, reward function, and innovative training method. It highlights the contributions of advanced algorithms to training effectiveness, provides a detailed analysis of perception and control schemes, classifies different reward function settings, and elucidates the roles of innovative training methods. Finally, by comparing the development routes of RL and RL-EMS, this paper identifies the gap between advanced RL solutions and existing RL-EMS. Potential development directions are suggested for implementing advanced artificial intelligence (AI) solutions in EMS.

pH-Responsive NIR-II phototheranostic agents for in situ tumor vascular monitoring and combined anti-vascular/photothermal therapy.

Developing nanoplatforms integrating superior fluorescence imaging ability in second near-infrared (NIR-II) window and tumor microenvironment responsive multi-modal therapy holds great potential for real-time feedback of therapeutic efficacy and optimizing tumor inhibition. Herein, we developed a pH-sensitive pyrrolopyrrole aza-BODIPY-based amphiphilic molecule (PTG), which has a balanced NIR-II fluorescence brightness and photothermal effect. PTG is further co-assembled with a vascular disrupting agent (known as DMXAA) to prepare PTDG nanoparticles for combined anti-vascular/photothermal therapy and real-time monitoring of the tumor vascular disruption. Each PTG molecule has an active PT-3 core which is linked to two PEG chains via pH-sensitive ester bonds. The cleavage of ester bonds in the acidic tumor environment would tricker releases of DMXAA for anti-vascular therapy and further assemble PT-3 cores into micrometer particles for long term monitoring of the tumor progression. Furthermore, benefiting from the high brightness in the NIR-II region (119.61 M-1 cm-1) and long blood circulation time (t1/2 = 235.6 min) of PTDG nanoparticles, the tumor vascular disrupting process can be in situ visualized in real time during treatment. Overall, this study demonstrates a self-assembly strategy to build a pH-responsive NIR-II nanoplatform for real-time monitoring of tumor vascular disruption, long-term tracking tumor progression and combined anti-vascular/photothermal therapy.

Potential auto-driving threat: Universal rain-removal attack.

Severe weather conditions pose a significant challenge for computer vision algorithms in autonomous driving applications, particularly regarding robustness. Image rain-removal algorithms have emerged as a potential solution by leveraging the power of neural networks to restore rain-free backgrounds in images. However, existing research overlooks the vulnerability concerns in neural networks, which exposes a potential threat to the intelligent perception of autonomous vehicles in rainy conditions. This paper proposes a universal rain-removal attack (URA) that exploits the vulnerability of image rain-removal algorithms. By generating a non-additive spatial perturbation, URA significantly diminishes scene restoration similarity and image quality. The imperceptible and generic perturbation employed by URA makes it a crucial tool for vulnerability detection in image rain-removal algorithms and a potential real-world AI attack method. Experimental results demonstrate that URA can reduce scene repair capability by 39.5% and image generation quality by 26.4%, effectively targeting state-of-the-art rain-removal algorithms.

Microstructures and Mechanical Properties of a Nanostructured Al-Zn-Mg-Cu-Zr-Sc Alloy under Natural Aging.

Nanocrystalline (NC) structure can lead to the considerable strengthening of metals and alloys. Obtaining appropriate comprehensive mechanical properties is always the goal of metallic materials. Here, a nanostructured Al-Zn-Mg-Cu-Zr-Sc alloy was successfully processed by high-pressure torsion (HPT) followed by natural aging. The microstructures and mechanical properties of the naturally aged HPT alloy were analyzed. The results show that the naturally aged HPT alloy primarily consists of nanoscale grains (~98.8 nm), nano-sized precipitates (20-28 nm in size), and dislocations (1.16 × 1015 m-2), and exhibits a high tensile strength of 851 ± 6 MPa and appropriate elongation of 6.8 ± 0.2%. In addition, the multiple strengthening modes that were activated and contributed to the yield strength of the alloy were evaluated according to grain refinement strengthening, precipitation strengthening, and dislocation strengthening, and it is shown that grain refinement strengthening and precipitation strengthening are the main strengthening mechanisms. The results of this study provide an effective pathway for achieving the optimal strength-ductility match of materials and guiding the subsequent annealing treatment.

Pyrrolopyrrole aza-BODIPY-based NIR-II fluorophores for in vivo dynamic vascular dysfunction visualization of vascular-targeted photodynamic therapy.

Real-time monitoring vascular responses is crucial for evaluating the therapeutic effects of vascular-targeted photodynamic therapy (V-PDT). Herein, we developed a highly-stable and bright aggregation induced emission (AIE) fluorophore (PTPE3 NP) for dynamic fluorescence (FL) imaging of vascular dysfunction beyond 1300 nm window during V-PDT. The superior brightness (ϵmaxΦf>1000 nm ≈ 180.05 M-1 cm-1) and high resolution of PTPE3 NP affords not only high-clarity images of whole-body and local vasculature (hindlimbs, mesentery, and tumor) but also high-speed video imaging for tracking blood circulation process. By virtue of the NPs' prolonged blood circulation time (t1/2 ≈ 86.5 min) and excellent photo/chemical (pH, RONS) stability, mesenteric and tumor vascular dysfunction (thrombosis formation, vessel occlusion, and hemorrhage) can be successfully visualized during V-PDT by FL imaging for the first time. Furthermore, the reduction of blood flow velocity (BFV) can be monitored in real time for precisely evaluating efficacy of V-PDT. These provide a powerful approach for assessing vascular responses during V-PDT and promote the development of advanced fluorophores for biological imaging.

Green preparation of silver nanocluster composite AgNCs@CF-g-PAA and its application: 4-NP catalytic reduction and hydrogen production.

4-Nitrophenol (4-NP) is a serious organic environmental pollutant. Conversion of 4-nitrophenol to 4-aminophenol (4-AP) by catalytic hydrogenation is an effective solution. In this work, a catalyst (AgNCs@CF-g-PAA) loaded with silver nanoclusters (AgNCs) was prepared by radiation technique. Firstly, the template polyacrylic acid (PAA) was grafted onto the cotton fiber (CF) by radiation grafting technique to obtain a solid template (CF-g-PAA). After that, AgNCs were synthesized in situ on CF-g-PAA by radiation reduction, and the composite material AgNCs@CF-g-PAA was obtained directly. AgNCs@CF-g-PAA has an obvious photoluminescence phenomenon, which is attributed to the stable AgNCs binding to the carboxyl on the PAA molecular chain. Due to the extremely small size of AgNCs, AgNCs@CF-g-PAA has good catalytic characteristics. The prepared AgNCs@CF-g-PAA catalyst has a very high catalytic rate for the hydrogenation of 4-NP. Even at high concentrations of 4-NP, AgNCs@CF-g-PAA can still maintain a high catalytic rate. At the same time, the AgNCs@CF-g-PAA catalyst can also be used to catalyze the rapid hydrolysis of sodium borohydride, which is conducive to hydrogen production. In summary, we have prepared a practical catalyst AgNCs@CF-g-PAA with high catalytic performance based on cheap raw materials and a simple synthesis route, which provides a catalyst candidate for the treatment of water contaminant 4-NP and the production of hydrogen from sodium borohydride.

A Novel A-ECMS Energy Management Strategy Based on Dragonfly Algorithm for Plug-in FCEVs.

The mechanical coupling of multiple powertrain components makes the energy management of 4-wheel-drive (4WD) plug-in fuel cell electric vehicles (PFCEVs) relatively complex. Optimizing energy management strategies (EMSs) for this complex system is essential, aiming at improving the vehicle economy and the adaptability of operating conditions. Accordingly, a novel adaptive equivalent consumption minimization strategy (A-ECMS) based on the dragonfly algorithm (DA) is proposed to achieve coordinated control of the powertrain components, front and rear motors, as well as the fuel cell system and the battery. To begin with, the equivalent consumption minimization strategy (ECMS) with extraordinary instantaneous optimization ability is used to distribute the vehicle demand power into the front and rear motor power, considering the different motor characteristics. Subsequently, under the proposed novel hierarchical energy management framework, the well-designed A-ECMS based on DA empowers PFCEVs with significant energy-saving advantages and adaptability to operating conditions, which are achieved by precise power distribution considering the operating characteristics of the fuel cell system and battery. These provide state-of-the-art energy-saving abilities for the multi-degree-of-freedom systems of PFCEVs. Lastly, a series of detailed evaluations are performed through simulations to validate the improved performance of A-ECMS. The corresponding results highlight the optimal control performance in the energy-saving performance of A-ECMS.

State of Health Prediction of Lithium-Ion Battery Based on Deep Dilated Convolution.

A battery's charging data include the timing information with respect to the charge. However, the existing State of Health (SOH) prediction methods rarely consider this information. This paper proposes a dilated convolution-based SOH prediction model to verify the influence of charging timing information on SOH prediction results. The model uses holes to fill in the standard convolutional kernel in order to expand the receptive field without adding parameters, thereby obtaining a wider range of charging timing information. Experimental data from six batteries of the same battery type were used to verify the model's effectiveness under different experimental conditions. The proposed method is able to accurately predict the battery SOH value in any range of voltage input through cross-validation, and the SDE (standard deviation of the error) is at least 0.28% lower than other methods. In addition, the influence of the position and length of the range of input voltage on the model's prediction ability is studied as well. The results of our analysis show that the proposed method is robust to different sampling positions and different sampling lengths of input data, which solves the problem of the original data being difficult to obtain due to the uncertainty of charging-discharging behaviour in actual operation.

SGGformer: Shifted Graph Convolutional Graph-Transformer for Traffic Prediction.

Accurate traffic prediction is significant in intelligent cities' safe and stable development. However, due to the complex spatiotemporal correlation of traffic flow data, establishing an accurate traffic prediction model is still challenging. Aiming to meet the challenge, this paper proposes SGGformer, an advanced traffic grade prediction model which combines a shifted window operation, a multi-channel graph convolution network, and a graph Transformer network. Firstly, the shifted window operation is used for coarsening the time series data, thus, the computational complexity can be reduced. Then, a multi-channel graph convolutional network is adopted to capture and aggregate the spatial correlations of the roads in multiple dimensions. Finally, the improved graph Transformer based on the advanced Transformer model is proposed to extract the long-term temporal correlation of traffic data effectively. The prediction performance is evaluated by using actual traffic datasets, and the test results show that the SGGformer proposed exceeds the state-of-the-art baseline.

Synthesis of silver nanoclusters by irradiation reduction and detection of Cr3+ ions.

In our work, a simple and fast synthesis method is provided to synthesize silver nanoclusters (AgNCs). In this method, with using polyacrylic acid (PAA) as a template, the silver ions were reduced to silver nanoclusters by irradiation reduction at room temperature. The prepared silver nanoclusters (PAA-AgNCs) with average particle size of 1.98 ± 0.79 nm have a fluorescence property, and their physical and chemical properties can be controlled by absorbed dose, PAA/Ag+ mole ratio and other factors. The fluorescence stability of the PAA-AgNCs is good, and it is unique in that the fluorescence emission of the s PAA-AgNCs depends on the excitation wavelength. In addition, based on the fluorescence quenching phenomenon of PAA-AgNCs in the presence of Cr3+ ion, we established a simple and efficient method for the detection of Cr3+ ion by using PAA-AgNCs as fluorescent probes.

Myotis fimbriatus Virome, a Window to Virus Diversity and Evolution in the Genus Myotis.

Significant efforts have been made to characterize viral diversity in bats from China. Many of these studies were prospective and focused mainly on Rhinolophus bats that could be related to zoonotic events. However, other species of bats that are part of ecosystems identified as virus diversity hotspots have not been studied in-depth. We analyzed the virome of a group of Myotis fimbriatus bats collected from the Yunnan Province during 2020. The virome of M. fimbriatus revealed the presence of families of pathogenic viruses such as Coronavirus, Astrovirus, Mastadenovirus, and Picornavirus, among others. The viral sequences identified in M. fimbriatus were characterized by significant divergence from other known viral sequences of bat origin. Complex phylogenetic landscapes implying a tendency of co-specificity and relationships with viruses from other mammals characterize these groups. The most prevalent and abundant virus in M. fimbriatus individuals was an alphacoronavirus. The genome of this virus shows evidence of recombination and is likely the product of ancestral host-switch. The close phylogenetic and ecological relationship of some species of the Myotis genus in China may have played an important role in the emergence of this alphacoronavirus.

Double Similarity Distillation for Semantic Image Segmentation.

The balance between high accuracy and high speed has always been a challenging task in semantic image segmentation. Compact segmentation networks are more widely used in the case of limited resources, while their performances are constrained. In this paper, motivated by the residual learning and global aggregation, we propose a simple yet general and effective knowledge distillation framework called double similarity distillation (DSD) to improve the classification accuracy of all existing compact networks by capturing the similarity knowledge in pixel and category dimensions, respectively. Specifically, we propose a pixel-wise similarity distillation (PSD) module that utilizes residual attention maps to capture more detailed spatial dependencies across multiple layers. Compared with exiting methods, the PSD module greatly reduces the amount of calculation and is easy to expand. Furthermore, considering the differences in characteristics between semantic segmentation task and other computer vision tasks, we propose a category-wise similarity distillation (CSD) module, which can help the compact segmentation network strengthen the global category correlation by constructing the correlation matrix. Combining these two modules, DSD framework has no extra parameters and only a minimal increase in FLOPs. Extensive experiments on four challenging datasets, including Cityscapes, CamVid, ADE20K, and Pascal VOC 2012, show that DSD outperforms current state-of-the-art methods, proving its effectiveness and generality. The code and models will be publicly available.

The synthesis of 3D graphene/Au composites via γ-ray irradiation and their use for catalytic reduction of 4-nitrophenol.

Graphene oxide (GO) and gold ions (Au3+) can be simultaneously reduced and self-assembled into a three-dimensional (3D) graphene/Au composite (GA/Au) porous structure at room temperature via one-step γ-ray irradiation. The microstructure of GA/Au composites were observed under different magnifications and the pores were observed to be uniform 3D porous structure. In addition, Au nanoparticles were homogeneously attached to graphene sheets and had a typical diameter of 6 nm. These GA/Au composites were analyzed and characterized by x-ray diffraction analysis, x-ray photoelectron spectroscopy, and thermal gravity analysis. Due to synergistic catalysis between graphene and Au nanoparticles, GA/Au composites catalyzed 4-nitrophenol with excellent catalytic performance, even at concentrations up to 6.48 × 10-3 M. When the concentration of 4-nitrophenol was 2.16 × 10-3 M and 4.22 × 10-3 M, the first-order kinetic constants were 2.00 and 1.43 min-1, respectively.

Edible quality of soft-boiled chicken processing with chilled carcass was better than that of hot-fresh carcass.

Soft-boiled chicken is widely popular with its flavor and texture. In a traditional view, the edible quality of soft-boiled chicken producing with hot-fresh carcass (without any chilled procedure after evisceration) was better than that of chilled carcass. Hot-fresh groups with 1, 2, or 4 hr and chilled groups with 24, 48, or 60 hr were used to clarify the view in this study. The results indicated that no significant difference in hardness, springiness, cohesiveness of texture profiles and b* value of skin color was observed between each group, although the highest L* value was obtained in hot-fresh 4 hr group. Higher contents of succinic acid were found in chilled groups when compared to that of hot-fresh groups, but there was no difference in lactic acid and pH values. Lower contents of adenosine 5'-monophosphate (AMP), guanosine 5'-monophosphate (GMP), inosine and hypoxanthine, and higher inosine-5'-monophosphate (IMP) (especially for hot-fresh 1 hr) were observed in hot-fresh groups. In addition, although no difference in umami amino acids and bitter amino acid was observed between each tested group, higher amounts of Asp, Met, Ile, Leu, Tyr, and Arg were observed in chilled groups, especially for chilled 60 hr. The finding indicated that the traditional view was lack of scientific evidence, and chilled carcass was suitable for soft-boiled chicken, substituting for the hot-fresh carcass.

Two-Dimensional MXene (Ti3C2)-Integrated Cellulose Hydrogels: Toward Smart Three-Dimensional Network Nanoplatforms Exhibiting Light-Induced Swelling and Bimodal Photothermal/Chemotherapy Anticancer Activity.

Two-dimensional (2D) MXenes have recently been shown to be promising for applications in anticancer photothermal therapy (PTT), owing to their outstanding photothermal performance. However, as with the other inorganic 2D nanomaterials, the MXene-based nanoplatforms lack the appropriate biocompatibility and stability in physiological conditions, targeting capability, and controlled release of drug, for cancer therapy. Fabricating a smart MXene-based nanoplatform for the treatment of cancer therefore remains a challenge. In this work, composite hydrogels based on cellulose and Ti3C2 MXene, were synthesized for the first time. We have shown that the cellulose/MXene composite hydrogels possess rapid response near-infrared-stimulated characteristics, which present as a continuous dynamic process in water. As a result, when loaded with the anticancer drug doxorubicin hydrochloride (DOX), the cellulose/MXene hydrogels are capable of significantly accelerating the DOX release. This behavior is attributed to the expansion of the pores within the three-dimensional cellulose-based networks, triggered by illumination with an 808 nm light. Capitalizing on their excellent photothermal performance and controlled, sustained release of DOX, the cellulose/MXene hydrogels are utilized as a multifunctional nanoplatform for tumor treatment by intratumoral injection. The results showed that the combination of PTT and prolonged adjuvant chemotherapy delivered using this nanoplatform was highly efficient for instant tumor destruction and for suppressing tumor relapse, demonstrating the potential of the nanoplatform for application in cancer therapy. Our work not only opens the door for the fabrication of smart MXene-based nanocomposites, along with their promising application against cancer, but also paves the way for the development of other inorganic 2D composites for applications in biomedicine.

The extraction of uranium using graphene aerogel loading organic solution.

A new approach for uranium extraction employing graphene aerogel (GA) as a skeleton loading organic solution (GA-LOS) is proposed and investigated. Firstly, the GA with super-hydrophobicity and high organic solution absorption capacity was fabricated by one-step reduction and self-assembly of graphene oxide with ethylenediamine. By adsorbing Tri-n-butyl phosphate (TBP)/n-dodecane solution to prepare GA-LOS, the extraction of U(VI) from nitric acid medium using GA-LOS was investigated and compared with conventional solvent extraction. It is found that the GA-LOS method can provide several advantages over conventional solvent extraction and adsorption due to the elimination of aqueous-organic mixing-separation procedures and easy solid-liquid separation. Furthermore, it also possesses higher extraction capacity (the saturated extraction capacity of GA loading TBP for U(VI) was 316.3mgg-1 ) and lower consumption of organic diluents, leading to less organic waste. Moreover, the stability of GA-LOS in aqueous solution and cycling test were also studied, and it shows a remarkable regeneration capability, making it an ideal candidate for metal extraction from aqueous solution.

High-Performance Perovskite Solar Cells Engineered by an Ammonia Modified Graphene Oxide Interfacial Layer.

The introduction of an ammonia modified graphene oxide (GO:NH3) layer into perovskite-based solar cells (PSCs) with a structure of indium-tin oxide (ITO)/poly(3,4-ethylene-dioxythiophene):poly(4-styrenesulfonate) ( PEDOT: PSS)-GO: NH3/CH3NH3PbI3-xClx/phenyl C61-butyric acid methyl ester (PCBM)/(solution Bphen) sBphen/Ag improves their performance and perovskite structure stability significantly. The fabricated devices with a champion PCE up to 16.11% are superior in all the performances in comparison with all the reference devices without the GO:NH3 layer. To understand the improved device performances, synchrotron-based grazing incidence X-ray diffraction (GIXRD), scanning electron microscopy (SEM), ultraviolet photoelectron spectroscopy (UPS), X-ray photoelectron spectroscopy (XPS), and UV-visible absorption measurements have been conducted on perovskite films on different substrates. It was found that these improvements should be partially attributed to the improved crystallization and preferred orientation order of peovskite structure, partially to the improved morphology with nearly complete coverage, partially to the enhanced optical absorption caused by the PEDOT: PSS-GO:NH3 layer, and partially to the better matched energy-level-alignment at the perovskite interface. Furthermore, the device was shown to be more stable in the ambient condition, which is clearly associated with the improved peovskite structure stability by the GO:NH3 layer observed by the GIXRD measurements. All these achievements will promote more applications of chemically modified graphene oxide interfacial layer in the PSCs as well as other organic multilayer devices.