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We fabricated a full spectrum responsive photocatalytic antimicrobial agent (WS2 QDs) and applied it as an antimicrobial nanoplatform for photocatalytic hydrogen peroxide redox for enhanced photocatalytic antimicrobial therapy (PCAT).

As an important parameter of the Earth’s energy budget, the planetary albedo of Earth varies with the dynamics of atmospheric and surface variables. In this study, we investigated the spatiotemporal characteristics and driving factors of the global planetary albedo using the Clouds and the Earth’s Radiant Energy System (CERES) Energy Balanced and Filled (EBAF) dataset and the Geodetector method. The results revealed that the planetary albedo can be decomposed into atmospheric and surface contributions, and the planetary albedo in the middle and low latitudes was predominantly affected by the atmospheric contribution. The global planetary albedo and the atmospheric and surface contributions exhibited decreasing trends of − 0.0020, − 0.0015, and − 0.0004/decade from 2001 to 2018, respectively, which were closely related to the variations of atmospheric and surface variables. The cloud fraction was the driving factor of the atmospheric contribution in the middle and low latitudes, and its influence was further enhanced by the aerosol optical thickness (AOT), ice water path (IWP), and liquid water path (LWP). The snow/ice coverage and normalized difference vegetation index (NDVI) were the driving factors of the surface contribution in the snow/ice-covered and vegetated areas, respectively. The interaction relationships between the surface variables were mainly bi-enhanced and nonlinearly enhanced. These results provide useful information about the driving factors of the planetary albedo and are benefit for improving the parametrization of the planetary albedo.

Background: Metal-based nanomaterials with shifting or mixed redox states have been widely applied for combating against bacteria-infected diseases by virtue of their peroxidase (POD)-like catalytic activity, which can decompose endogenous overexpressed hydrogen peroxide (H2O2) into highly oxidized hydroxyl radical (·OH) via the Fenton or Fenton-like reaction. However, the compromised POD-like catalytic activity in biofilm microenvironment (BME) restrains their therapeutic efficiency. Results: Herein, a polyoxometalate-based heterojunction (GdP5W30@WS2) with shifting or mixed redox states (W6+/W5+) is devised to achieve boosted reactive oxygen species (ROS) generation by near-infrared (NIR) laser-facilitated W6+/W5+ redox cycling for effective biofilm eradication and accelerated bacteria-infected wound healing. On the one hand, under the exposure to NIR laser, WS2 as electron donor could be stimulated to transfer electrons to GdP5W30, which promotes the reduction from W6+ to W5+ active sites on the surface of the GdP5W30. Consequently, the regenerated W5+ active sites could catalyze endogenous overexpressed H2O2 to persistently generate ROS, which displays enhanced POD-like catalytic activity for chemodynamic therapy (CDT) by NIR-driven photocatalytic W6+/W5+ redox cycling. Moreover, GdP5W30@WS2 also displays favorable glutathione (GSH)-activated photothermal performance by W6+-triggered GSH exhaustion for sufficient ROS generation, thereby facilitating the synergistic therapeutic efficiency of photothermal therapy (PTT) and CDT. Conclusions: We expect that the present work will promote the development of the heterojunction with shifting or mixed redox states for biofilm elimination based on NIR-facilitated enzyme-like catalytic activity, which may act as a candidate for the BME-activated synergistic therapy.

Recently, various deep learning models, which are mainly based on data-driven algorithms, have received more and more attention in the field of intelligent fault diagnosis and prognostics. However, there are two major assumptions accepted by default in the existing studies: 1) The training (source domain) and testing (target domain) data sets obey the same feature distribution; 2) Sufficient labeled data with fault information is available for model training. In real industrial scenarios, especially for different machines, these assumptions are mostly invalid, which makes it a huge challenge to build reliable diagnostic model. Motivated by transfer learning, we present a novel intelligent method named deep transfer network (DTN) with multi-kernel dynamic distribution adaptation (MDDA) to address the problem of cross-machine fault diagnosis. In the proposed approach, the DTN has wide first-layer convolutional kernel and several small convolutional layers, which is utilized to extract transferable features across different machines and suppress high frequency noise. Then, the MDDA method constructs a weighted mixed kernel function to map different transferable features to a unified feature space, and the relative importance of the marginal and conditional distributions are also evaluated dynamically. The proposed method is verified by three transfer learning tasks of bearings, in which the health states of wind turbine bearings in real scenario are identified by using diagnosis knowledge from two different bearings in laboratories. The results show that the proposed method can achieve higher diagnosis accuracy and better transfer performance even under different noisy environment conditions than many other state-of-the-art methods. The presented framework offers a promising approach for cross-machine fault diagnosis.

Biofilm microenvironment (BME)-activated antimicrobial agents display great potential for improved biofilm-related infection therapy because of their superior specificities and sensitivities, effective eliminations, and minimal side effects. Herein, BME-activated Fe-doped polydiaminopyridine nanofusiform-mediated single-atom nanozyme (FePN SAzyme) is presented for photothermal/chemodynamic synergetic bacteria-infected wound therapy. The photothermal therapy (PTT) function of SAzyme can be specifically initiated by the high level of H2 O2 and further accelerated through mild acid within the inflammatory environment through "two-step rocket launching-like" process. Additionally, the enhanced chemodynamic therapy (CDT) for the FePN SAzyme can also be endowed by producing hydroxyl radicals through reacting with H2 O2 and consuming glutathione (GSH) of the BME, thereby contributing to more efficient synergistic therapeutic effect. Meanwhile, FePN SAzyme could catalyze biofilm-overexpressed H2 O2 decomposing into O2 and overcome the hypoxia of biofilm, which significantly enhances the susceptibility of biofilm and increases the synergistic efficacy. Most importantly, the synergistic therapy of bacterial-induced infection diseases can be switched on by the internal and external stimuli simultaneously, resulting in minimal nonspecific damage to healthy tissue. These remarkable characteristics of FePN SAzyme not only develop an innovative strategy for the BME-activated combination therapy but also open a new avenue to explore other nanozyme-involved nanoplatforms for bacterial biofilm infections.

Land surface albedo is a critical parameter of the Earth's energy budget and has been greatly altered by climate change and human activities. To improve our understanding of historical land surface albedo changes and their climatic effects before the satellite remote sensing era, we reconstructed land surface albedo in China from 850 to 2015 using the historical land use harmonization version 2 (LUH2) data set and albedo look‐up maps, and estimated the radiative forcing induced by land surface albedo changes using atmospheric radiative kernels. The reconstruction results showed that the annual‐mean land surface albedo in China increased by 0.00110 during 850–2015, and the rate of increase was accelerated by the expansion of croplands during recent centuries. The most significant increases in land surface albedo were found in the Huang‐Huai‐Hai (HHH; +0.00646) and Northeast China (NE; +0.00501), which were primarily driven by anthropogenic land cover transformations (e.g., land reclamation, deforestation, and urbanization) and can be enhanced by the vegetation masking effect on snow cover. The radiative forcing induced by land surface albedo changes in China during 850–2015 and 1750–2015 was −0.09 ± 0.04 and −0.06 ± 0.02 W m−2, respectively, which indicated that the land surface albedo changes produced a slight climate cooling effect, helping to offset the warming effect caused by greenhouse gas emissions.

Land surface albedo is an important parameter in surface energy budget, which determines the reflected/absorbed fraction of shortwave solar radiation of land surfaces. As the land surface albedo is highly spatio-temporal dynamic, it is important to understanding the spatial patterns of the leading factors of land surface albedo. In this study, we analyzed the driving factors of global land surface albedo from 1982 to 2015 with the global land surface satellite (GLASS) and the modern-era retrospective analysis for research and applications version 2 (Merra-2) datasets based on the multilinear regression and partial correlation methods. The results show that the variations of land surface albedo can be largely explained by the three major factors: snow cover extent, vegetation dynamics, and surface soil moisture. The driving factors of land surface albedo vary with different area, and the spatial distribution patterns for driving factors of land surface albedo were also explored by this study.

Nanomaterials with intrinsic enzyme-mimicking characteristics, refered to as nanozymes, have become a hot research topic owing to their unique advantages of comparative low cost, high stability and large-scale preparation. Among them, Single-atom nanozymes (SAzymes), as novel nanozymes with abundant atomically dispersed active sites, have caused specific attention in the development of nanozymes for their remarkable catalytic activities, maximum atomic utilization and excellent selectivity, the homogeneous catalytic sites and clear catalytic mechanisms. Herein, a novel single-atom nanozyme based on Fe(III)-doped polydiaminopyridine nanofusiforms (Fe-PDAP SAzyme) was successfully proposed via facile oxidation polymerization strategy. With well-defined coordination structure and abundant Fe-Nx active sites similar to natural metalloproteases, the Fe-PDAP SAzyme exhibits superior peroxidase-like activity by efficiently decomposing H2O2 for hydroxyl radical (.OH) species formation. Based on their superior peroxidase-like activity, colorimetric biosensing of H2O2 and glucose in vitro was performed by using a typical 3,3,5,5-tetramethylbenzidine through a multienzyme biocatalytic cascade platform, exhibiting the superior specificity and sensitivity. This work not only provides a novel promising SAzyme-based biosensor but also paves an avenue for evaluating enzyme activity and broadens the application of other nanozyme-based biosensors in the fields of biomedical diagnosis.

Background The oncogenic role of the newly identified lncRNA LUADT1 has been revealed in lung adenocarcinoma. It was reported that LUADT1 plays a critical role in multiple human diseases. This study was carried out to investigate the role of LUADT1 in sepsis. Methods Sixty patients with sepsis and sixty healthy volunteers were recruited for this study. Plasma samples were collected from all participants. Human primary coronary artery endothelial cells were also used in this study. The expression of Pim-1, miR-195 and LUADT1 were detected by RT-qPCR. The interaction between miR-195 and LUADT1 was determined by overexpression experiments and luciferase activity assay. Cell apoptosis was detected by flow cytometry. The expression of apoptosis-related protein was detected by Western blotting. Results Bioinformatics analysis revealed the potential interaction between LUADT1 and miR-195, which was confirmed by dual luciferase reporter assay. LUADT1 was downregulated in patients with sepsis. Moreover, LPS treatment downregulated the expression of LUADT1 in primary cardiac endothelial cells. Overexpression of LUADT1 and miR-195 did not affect the expression of each other in primary cardiac endothelial cells. Interestingly, overexpression of LUADT1 was found to upregulate the expression of Pim-1, a target of miR-195. In addition, it was found that overexpression of LUADT1 and Pim-1 reduced the enhancement effects of miR-195 on LPS-induced cardiac endothelial cell apoptosis. Conclusion In summary, LUADT1 may protect cardiac endothelial cells against apoptosis in sepsis by regulating the miR-195/Pim-1 axis.

In industrial applications, the vibration and temperature measurements of rolling element bearings are known as two popular condition monitoring methods. The previously published method for remaining useful life (RUL) prediction has been limited to using the vibration signal. However, a single signal source cannot fully reflect the degradation trend of bearings, influencing the RUL prediction precision. In this paper, a novel general log-linear Weibull (GLL-Weibull) model is constructed by considering vibration and temperature condition monitoring signals to estimate the model parameters. During the feature extraction stage, the relative root mean square (RRMS) is derived from the monitored vibration signal, and the relative temperature trend value is extracted from the monitored temperature signal to eliminate individual differences in bearings and random signal fluctuations. Then, a fuzzy operator is introduced to describe the degree of an “overheated bearing” and “excessive bearing vibrations.” During the RUL prediction stage, both the extracted vibration and temperature characteristics are used to create the GLL-Weibull model. The best parameters are attained by employing the maximum likelihood estimation approach. The algorithm performance is checked with criteria like the root mean square error (RMSE) and the mean absolute percentage error (MAPE). The effectiveness and superiority of the presented approach are validated by two real-life prognosis cases. According to the experimental results, the presented approach provides superior prediction precision and lower computational cost than other approaches for bearings under constant or variable operating conditions.

Eco-friendly phosphorus-doped carbon quantum dots (P-CQDs) have been successfully synthesized by facial hydrothermal treatment of phytic acid (PA) and sodium citrate. PA is selected because of its hexatomic ring structure with firm C-P bonds, abundant phosphate group and completely natural. In this case, the phosphorus content of the as synthesized P-CQDs achieves 8.52 wt.%. The P-CQDs show a narrow size distribution in the range of 2.5–4.5 nm and exhibit excellent sensitivity to Cu2+ with an ultra-low detection limit of 1 nM. Furthermore, the P-CQDs have been applied in water sample to detect Cu2+, and the results are highly consistent with the data measured by inductively coupled plasma optical emission spectrometry. Therefore, the as synthesized P-CQDs display a great potential for applications in the area of Cu2+ ion detection.

Aiming at the problem that the classification effect of support vector machine (SVM) is not satisfactory due to improper selection of penalty factor C and kernel parameter g, this paper proposes a new modified classifier that uses the improved particle swarm optimization (IPSO) to optimize the parameter of SVM (IPSO-SVM) by introducing the dynamic inertia weight, global neighborhood search, population shrinkage factor and particle mutation probability. The classification result is tested by Common data sets named BreastTissue、 Heart and Wine from the Libsvm toolbox, the results show that IPSO-SVM classifier is obviously superior to SVM and PSO-SVM classifier in terms of prediction accuracy and classification time. Then it is applied to the fault diagnosis in two classification problems and multiple classification problems of rolling bearings. The simulation results show that the IPSO-SVM classifier has stronger global convergence ability and faster convergence speed, and the ideal classification results can be obtained. Finally, the IPSO-SVM classifier is used to diagnose the fault of the actual bearing. The results show that the classifier has a better classification stability and is worthy of further promotion in engineering field.

In this paper, Lyapunov approach is applied to study stability and stabilization for linear periodically time-varying descriptor systems. First, a Lyapunov function is found, and a Lyapunov equation is established, then the necessary and sufficient conditions for strong asymptotical stability are obtained. Further, a corresponding Riccati equation is proposed, and then the stabilization problems of the systems are studied by using the Riccati equation. Finally, two numerical examples are used to illustrate the main results.

This paper presents the direct characterization and explicit criterion of impulsive controllability for a class of linear periodic descriptor systems. The operator expression of linear periodic descriptor systems is first given by differential operator. Then, it describes the direct characterization of impulsive controllability in terms of the original systems coefficients and computable matrices. Especially, a sufficient and necessary condition of impulsive controllability is established based on the rank of monodromy matrices and Gramian matrices, which are from operator expression of linear periodic descriptor systems.