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Multi-robot systems have many potential applications; however, the available results for coordination were based on qualitative information. Fuzzy logic reasoning has a feature of human being thinking, so a novel coordinated algorithm is proposed. The algorithm utilizes sharing sensing information of rooms and semantic robots to coordinating robots in a structured environment exploration. The approach divides all teammate robots into two classes according to robot exploration performance, and divides rooms into large, medium and small ones according to estimations of the individual areas. On the purpose of minimizing exploration time of the system, the reasoning coordination assigns large room to good performance robot, and vice versa. A parameter update law is introduced for fuzzy membership functions. Finally, the results are validated by computer simulations for a structured environment.

Ultrasound (US)-induced sonodynamic therapy (SDT) is an efficient and precise method against tumor, and the integration of multiple cancer therapies has been proved as a promising strategy for better therapeutic effects. Herein, for the first time, a multifunctional nanoreactor has been fabricated by integrating Fe-MIL-88B-NH2, PFC-1, and glucose oxidase (GOx) to form urchin-like Fe-MIL-88B-NH2@PFC-1-GOx (MPG) nanoparticles as Fenton's reagent, a sonosensitizer, and a tumor microenvironment (TME) modulator. In detail, MPG can generate •OH for chemodynamic therapy (CDT) and deplete glutathione (GSH) to alleviate the antioxidant ability of cancer cells. Moreover, catalase (CAT)-like MPG can react with H2O2 to generate O2 for relieving hypoxia in TME, enhancing GOx-catalyzed glucose oxidation to produce H2O2 and gluconic acid. Then, the regenerated H2O2 can promote the Fenton reaction to achieve GOx catalysis-enhanced CDT. Owing to its large π-electron conjugated system, MPG also serves as an ideal sonosensitizer, realizing a burst generation of 1O2 under US irradiation for efficient SDT. Therefore, the tumor treatment will be notably enhanced by MPG-based synergetic CDT/SDT/starvation therapy via a series of cascade reactions. Overall, this work develops a versatile nanoreactor with improved tumor treatment effectiveness and broadens the application prospects of porous materials in the field of biomedical research.

A full analysis of 3-month Standardized Precipitation-Evapotranspiration index (SPEI-3) changes and attribution analyses are of significance for deeply understanding dryness/wetness evolutions and thus formulating specific measures to sustain regional development. In this study, we analyze monthly and annual SPEI-3 changes over Southwest China (SWC; including Sichuan (SC), Chongqing (CQ), Guizhou (GZ), Yunnan (YN), and west Guangxi (wGX)) during 1961–2012, using the SPEI model and routine meteorological measurements at 269 weather sites. For SWC and each subregion (excluding wGX), annual SPEI-3 during 1961–2012 tends to decrease, and drying is at most of months in January and September–December, but wetting is in February–August (excluding March for wGX). Additionally, more than 50% of sites show declined and increased SPEI-3 in January, April, June, and August–December and the remaining months, respectively. Except for wGX with dominant of ET0, annual SPEI-3 changes in SWC and other four subregions have dominant of precipitation. Spatially, annual SPEI-3 changes at 59% of sites are because of precipitation, generally located in southeast SC, south YN, CQ, GZ, and south and northeast wGX. Nevertheless, dominants at regional and site scales vary among months, e.g., SWC, SC, CQ, and GZ, having dominant of precipitation (ET0) during September–December (most of months during January–August), YN always with dominant of precipitation, and wGX with dominant of precipitation (ET0) in February–April and July–December (January, May, and June). Importantly, this study provides a reference for quantitatively evaluating spatiotemporal dryness/wetness variations with climate change, especially for regions with significant drying/wetting.

Assessing satellite-based precipitation product capacity for detecting precipitation and linear trends is fundamental for accurately knowing precipitation characteristics and changes, especially for regions with scarce and even no observations. In this study, we used daily gauge observations across the Huai River Basin (HRB) during 1983–2012 and four validation metrics to evaluate the Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR) capacity for detecting extreme precipitation and linear trends. The PERSIANN-CDR well captured climatologic characteristics of the precipitation amount- (PRCPTOT, R85p, R95p, and R99p), duration- (CDD and CWD), and frequency-based indices (R10mm, R20mm, and Rnnmm), followed by moderate performance for the intensity-based indices (Rx1day, R5xday, and SDII). Based on different validation metrics, the PERSIANN-CDR capacity to detect extreme precipitation varied spatially, and meanwhile the validation metric-based performance differed among these indices. Furthermore, evaluation of the PERSIANN-CDR linear trends indicated that this product had a much limited and even no capacity to represent extreme precipitation changes across the HRB. Briefly, this study provides a significant reference for PERSIANN-CDR developers to use to improve product accuracy from the perspective of extreme precipitation, and for potential users in the HRB.

Carbon-bonded carbon fiber (CBCF) composites are novel and important high-temperature insulation materials owing to their light weight, low thermal conductivity and high fracture tolerance. To further improve the mechanical property of CBCF composite, we propose a three-dimensional (3D) SiC nanowires structure, which is in situ grown on a CBCF matrix via directly annealing silicon oxycarbide (SiOC) ceramic precursor. The synthesized multiscale reinforcements including microscale SiOC ceramics and nanoscale SiC nanowires are mainly attributed to the initial phase separation of SiOC phase and subsequent solid-phase reaction of SiO and C phases. Compared to SiOC/CBCF composite, the resulting 3D SiC nanowires/SiOC/CBCF hybrid structure exhibited high flexural/tensile strength and fractured strain due to the pull-out and bridging behavior of SiC nanowires. This one-step process supplied a feasible way to synthesize 3D SiC nanowires to reinforce and toughen SiOC-modified CBCF composite.

Polarization measurement is often completed in the scenes with low signal-to-noise ratio (SNR) in some fields, such as biomedical sensing and remote sensing, resulting in the requirement for the polarimeter to conduct the real-time measurement of the polarization state at low light field. However, accurate measurement of the polarization state at low light field is challenging due to background-noise interference. Herein, we develop a Mueller-mapping star test polarimetry (MMSTP) method by quantitatively linking the Mueller matrix of a space-variant birefringence device and the polarization-dependent intensity distribution. Combined with a normalized least-squares method that accounts for the spatial distribution characteristics of photons at low light field, in addition to a low-pass fast-Fourier-transform filter, the proposed method can calculate polarization states from a single irradiance image at low light field with higher accuracy. Experimental results show that the proposed method can significantly improve the measurement accuracy of polarization states compared with the classical division-of-time Stokes polarimeters at low light field.

The resin-free YAG:Ce 3+ -Al 2 O 3 eutectic ceramic phosphor for white light emitting diodes (WLEDs) was successfully grown in vacuum by Horizontal Directional Solidification method (HDS). X-ray diffraction and scanning electron microscopy indicate that this material has a typical eutectic structure of interpenetrating sapphire and garnet phases. The excitation spectra, emission spectra and temperature characteristics of the eutectic show that it is characterized by a wide excitation band and it has good stability in high temperature. In X-ray photoelectron spectroscopy, annealing in an air atmosphere could eliminate the oxygen vacancies and didn’t change the Ce 3+ valence in the eutectic. The YAG:Ce 3+ -Al 2 O 3 eutectic ceramic with different thickness was fixed in COB (chip on board) element for researching the performance of the WLEDs with the phosphor. The electroluminescence characterization of the WLEDs show that the WLEDs with the eutectic ceramic are more excellent than the common commercial WLEDs.

Tin-based ternary oxides have obtained much attention for energy storage systems due to high capacity based on alloying/dealloying reactions and improved cycling performance resulted from the inactive buffer matrix. Herein, SnP2O7 particles are encapsulated into N/P dual-doped graphene (SnP2O7/NPG), which is served as an universal anode material for sodium-ion batteries (SIBs) and sodium-based dual-ion batteries (SDIBs). Benefiting from the unique structure of tin-based ternary oxides and synergy effect of multicomponent, the obtained SnP2O7/NPG delivers a good reversible capacity of 412.9 mAh g−1 at 0.05 A g−1, superior cycling performance of 86.2 mAh g−1 at 2 A g−1 after 1000 cycles, and exceptional rate performance of 182.3 mAh g−1 at 2 A g−1 in Na+ half cells. Especially, assembly of SnP2O7/NPG with the graphite cathode could yield a SnP2O7/NPG-graphite SDIBs, which shows high rate performance (up to 2 A g−1) and good cycling stability over 100 cycles with a capacity retention of 63.2%. Furthermore, the Na+ insertion/extraction mechanism is systematically investigated using the extensive kinetics investigation. This work provides inspirations as to extending the wider application prospect of tin-based ternary oxides in energy storage systems.

Performing time and speed in multiple robots search and rescue is the key, requiring multiple robots to quickly complete task assignments. When each robot bid for a task with multiple prices, the BP neural network algorithm is used to fuse these bid prices and determine the robot that wins the bid. However, the traditional BP neural network has the disadvantages that network training can easily fall into local minimums and training errors. It will lead to long time and slow speed for multiple robots to perform tasks. Based on the traditional BP neural network algorithm, this paper proposes a method based on genetic algorithm to optimize BP neural network. This method can effectively deal with the problem of redundant parameters, and the training error is small. The simulation results show that the proposed method can greatly improve the training times and errors of BP network. It also verified that multiple robots can save time when completing tasks.

Anisotropic fibrous networks, especially transverse isotropic fibrous networks, are widely used to model the microstructures of biological tissues, polymer gels, fibrous thermal insulations, and other fibrous materials. In this letter, we build a three-dimensional transverse isotropic fibrous network model and study its mechanical properties along the through-thickness direction. We propose a measurement of anisotropy for transverse isotropic fibrous networks and then study the influence of anisotropy on the networks' mechanical properties, including its elastic modulus, maximum elongation, and stress–strain curve, by means of finite-element simulation. We also study theoretically the influence of anisotropy on maximum elongation. We find that as the anisotropy of the networks becomes stronger, the elastic modulus decreases and the maximum elongation increases, indicating a transition in mechanical properties from brittle to ductile. We identify this transition as the “ductile–brittle transition.” This tr...