Luminescence thermometry driven by a support vector machine: a strategy toward precise thermal sensing.

Luminescence thermometry is a promising non-contact temperature measurement technique, but improving the precision and reliability of this method remains a challenge. Herein, we propose a thermal sensing strategy based on a machine learning. By using Gd3Ga5O12: Er3+-Yb3+ as the sensing medium, a support vector machine (SVM) is preliminarily adopted to establish the relationship between temperature and upconversion emission spectra, and the sensing properties are discussed through the comparison with luminescence intensity ratio (LIR) and multiple linear regression (MLR) methods. Within a wide operating temperature range (303-853 K), the maximum and the mean measurement errors actualized by the SVM are just about 0.38 and 0.12 K, respectively, much better than the other two methods (3.75 and 1.37 K for LIR and 1.82 and 0.43 K for MLR). Besides, the luminescence thermometry driven by the SVM presents a high robustness, although the spectral profiles are distorted by the interferences within the testing environment, where, however, LIR and MLR approaches become ineffective. Results demonstrate that the SVM would be a powerful tool to be applied on the luminescence thermometry for achieving a high sensing performance.

Fast Helmet and License Plate Detection Based on Lightweight YOLOv5.

The integrated fast detection technology for electric bikes, riders, helmets, and license plates is of great significance for maintaining traffic safety. YOLOv5 is one of the most advanced single-stage object detection algorithms. However, it is difficult to deploy on embedded systems, such as unmanned aerial vehicles (UAV), with limited memory and computing resources because of high computational load and high memory requirements. In this paper, a lightweight YOLOv5 model (SG-YOLOv5) is proposed for the fast detection of the helmet and license plate of electric bikes, by introducing two mechanisms to improve the original YOLOv5. Firstly, the YOLOv5s backbone network and the Neck part are lightened by combining the two lightweight networks, ShuffleNetv2 and GhostNet, included. Secondly, by adopting an Add-based feature fusion method, the number of parameters and the floating-point operations (FLOPs) are effectively reduced. On this basis, a scene-based non-truth suppression method is proposed to eliminate the interference of pedestrian heads and license plates on parked vehicles, and then the license plates of the riders without helmets can be located through the inclusion relation of the target boxes and can be extracted. To verify the performance of the SG-YOLOv5, the experiments are conducted on a homemade RHNP dataset, which contains four categories: rider, helmet, no-helmet, and license plate. The results show that, the SG-YOLOv5 has the same mean average precision (mAP0.5) as the original; the number of model parameters, the FLOPs, and the model file size are reduced by 90.8%, 80.5%, and 88.8%, respectively. Additionally, the number of frames per second (FPS) is 2.7 times higher than that of the original. Therefore, the proposed SG-YOLOv5 can effectively achieve the purpose of lightweight and improve the detection speed while maintaining great detection accuracy.

An immediate-return reinforcement learning for the atypical Markov decision processes.

The atypical Markov decision processes (MDPs) are decision-making for maximizing the immediate returns in only one state transition. Many complex dynamic problems can be regarded as the atypical MDPs, e.g., football trajectory control, approximations of the compound Poincaré maps, and parameter identification. However, existing deep reinforcement learning (RL) algorithms are designed to maximize long-term returns, causing a waste of computing resources when applied in the atypical MDPs. These existing algorithms are also limited by the estimation error of the value function, leading to a poor policy. To solve such limitations, this paper proposes an immediate-return algorithm for the atypical MDPs with continuous action space by designing an unbiased and low variance target Q-value and a simplified network framework. Then, two examples of atypical MDPs considering the uncertainty are presented to illustrate the performance of the proposed algorithm, i.e., passing the football to a moving player and chipping the football over the human wall. Compared with the existing deep RL algorithms, such as deep deterministic policy gradient and proximal policy optimization, the proposed algorithm shows significant advantages in learning efficiency, the effective rate of control, and computing resource usage.

Isolation, molecular typing and antimicrobial resistance of Clostridium difficile in dogs and cats in Lanzhou city of Northwest China.

Clostridium difficile infection (CDI) in human and animals belonged usually to antibiotic-associated diarrhea, ranging in severity from mild to life-threatening intestinal tract illnesses. This study aimed to isolation and characterization, toxin genes test, molecular typing, and drug sensitivity of Clostridium difficile (C. difficile) which were isolated from clinical diseased dogs and cats. A total of 247 clinical samples were collected from five animal hospitals in Lanzhou City of Northwest China, of which dogs and cats accounted for 74.9% (185/247) and 25.1% (62/247), respectively. We successfully identified 24 C. difficile strains by 16S rRNA and Matrix-Assisted Laser Desorption/Ionization Time of Fight Mass Spectroscopy (MALDI-TOF-MS). 10.3% (19/185) of dogs and 8.1% (5/62) of cats were positive for C. difficile. Among them, 16 strains were toxic and 8 were non-toxic, with a toxic rate of 57.9% (11/19) in dogs and 100% (5/5) in cats. A total of 10 STs and 10 RTs were identified in this study. The percentages of ST42 (RT106) and ST2 (RT014/LW01) among 16 toxic strains were 41.7 and 12.5%, respectively. However, ST3 (RT001), ST1 (RT027), ST133 (LW04), and ST-UN (LW04) had only one strain. ST42 (RT106) was the most common genotype and RT027 strain was first isolated in China from pets. Antimicrobial susceptibility test showed that isolates were extremely sensitive to vancomycin and metronidazole but were resistant to erythromycin and ciprofloxacin. The drug resistant rates to clindamycin, levofloxacin, moxifloxacin and meropenem were 62.5, 20.8, 16.7, and 8.3%, respectively. In conclusion, C. difficile was quietly prevalent in dogs and cats in Lanzhou city with RT106 and RT014 as the main ribotypes. The CDI in pets should be paying more attention and further studies are needed.

Synthesis and shaping of metal-organic frameworks: a review.

Metal-organic frameworks (MOFs) possess excellent advantages, such as high porosity, large specific surface area, and an adjustable structure, showing good potential for applications in gas adsorption and separation, catalysis, conductivity, sensing, magnetism, etc. However, they still suffer from significant limitations in terms of the scale-up synthesis and shaping, hindering the realization of large-scale commercial applications. Despite some attempts having been devoted to addressing this, challenges remain. In this paper, we outline the advantages and drawbacks of existing synthetic routes such as electrochemistry, microwave, ultrasonic radiation, green solvent reflux, room temperature stirring, steam-assisted transformation, mechanochemistry, and fluid chemistry in terms of scale-up production. Then, the shaping methods of MOFs such as extrusion, mechanical compaction, rolling granulation, spray drying, gel technology, embedded granulation, phase inversion, 3D printing and other shaping methods for the preparation of membranes, coatings and nanoparticles are discussed. Finally, perspectives on the large-scale synthesis and shaping of MOFs are also proposed. This work helps provide in-depth insight into the scale-up production and shaping process of MOFs and boost commercial applications of MOFs.

Plasmonic metal nanostructures with extremely small features: new effects, fabrication and applications.

Surface plasmons in metals promise many fascinating properties and applications in optics, sensing, photonics and nonlinear fields. Plasmonic nanostructures with extremely small features especially demonstrate amazing new effects as the feature sizes scale down to the sub-nanometer scale, such as quantum size effects, quantum tunneling, spill-out of electrons and nonlocal states etc. The unusual physical, optical and photo-electronic properties observed in metallic structures with extreme feature sizes enable their unique applications in electromagnetic field focusing, spectra enhancing, imaging, quantum photonics, etc. In this review, we focus on the new effects, fabrication and applications of plasmonic metal nanostructures with extremely small features. For simplicity and consistency, we will focus our topic on the plasmonic metal nanostructures with feature sizes of sub-nanometers. Subsequently, we discussed four main and typical plasmonic metal nanostructures with extremely small features, including: (1) ultra-sharp plasmonic metal nanotips; (2) ultra-thin plasmonic metal films; (3) ultra-small plasmonic metal particles and (4) ultra-small plasmonic metal nanogaps. Additionally, the corresponding fascinating new effects (quantum nonlinear, non-locality, quantum size effect and quantum tunneling), applications (spectral enhancement, high-order harmonic wave generation, sensing and terahertz wave detection) and reliable fabrication methods will also be discussed. We end the discussion with a brief summary and outlook of the main challenges and possible breakthroughs in the field. We hope our discussion can inspire the broader design, fabrication and application of plasmonic metal nanostructures with extremely small feature sizes in the future.

Well-dispersed Co3Fe7 alloy nanoparticles wrapped in N-doped defect-rich carbon nanosheets as a highly efficient and methanol-resistant catalyst for oxygen-reduction reaction.

To explore alkaline fuel cells in practice, searching low-cost and efficient alternatives to Pt-based catalysts is urgent yet challenging for oxygen reduction reaction (ORR). Herein, Co3Fe7 alloy nanoparticles wrapped in N-doped defect-rich carbon nanosheets (Co3Fe7/CNs) were synthesized at 800 °C by a one-step pyrolysis of the mixture (dicyandiamide, iron (II) phthalocyanine (FePc), cobalt (II) phthalocyanine (CoPc) and Fe2O3), defined as Co3Fe7/CNs-800 for simplicity. The pyrolysis temperature and the dosages of dicyandiamide closely correlated to the ORR performance of the resultant catalysts in 0.1 M KOH solution. Significantly, the optimized Co3Fe7/CNs-800 exhibited encouraging onset potential (Eonset = 0.97 V) and half-wave potential (E1/2 = 0.85 V) in the alkaline media, surpassing commercial Pt/C (50 wt%, Eonset = 0.96 V, E1/2 = 0.84 V). This work provides a feasible strategy for developing efficient non-noble metal ORR electrocatalysts in the alkaline condition.

Flexible 3D carbon cloth as a high-performing electrode for energy storage and conversion.

High-performance energy storage and conversion devices with high energy density, power density and long-term cycling life are of great importance in current consumer electronics, portable electronics and electric vehicles. Carbon materials have been widely investigated and utilized in various energy storage and conversion devices due to their excellent conductivity, mechanical and chemical stability, and low cost. Abundant excellent reviews have summarized the most recent progress and future outlooks for most of the current prime carbon materials used in energy storage and conversion devices, such as carbon nanotubes, fullerene, graphene, porous carbon and carbon fibers. However, the significance of three-dimensional (3D) commercial carbon cloth (CC), one of the key carbon materials with outstanding mechanical stability, high conductivity and flexibility, in the energy storage and conversion field, especially in wearable electronics and flexible devices, has not been systematically summarized yet. In this review article, we present a careful investigation of flexible CC in the energy storage and conversion field. We first give a general introduction to the common properties of CC and the roles it has played in energy storage and conversion systems. Then, we meticulously investigate the crucial role of CC in typical electrochemical energy storage systems, including lithium-ion batteries, sodium-ion batteries, lithium-sulfur batteries and supercapacitors. Following a description of the wide application potential of CC in electrocatalytic hydrogen evolution, oxygen evolution/reduction, full-water splitting, etc., we will give a brief introduction to the application of CC in the areas of photocatalytically and photoelectrochemically induced solar energy conversion and storage. The review will end with a brief summary of the typical superiorities that CC has in current energy conversion and storage systems, as well as providing some perspectives and outlooks on its future applications in the field. Our main interest will be focused on CC-based flexible devices due to the inherent superiority of CC and the increasing demand for flexible and wearable electronics.

Topological Design of a Lightweight Sandwich Aircraft Spoiler.

In this study, a lightweight sandwich aircraft spoiler (AS) with a high stiffness-to-weight ratio was designed. Excellent mechanical properties were achieved by the synthetic use of topology optimization (TO), lattice structure techniques, and high-performance materials, i.e., titanium alloy and aluminum alloy. TO was first utilized to optimize the traditional aircraft spoiler to search for the stiffest structure with a limited material volume, where titanium alloy and aluminum alloy were used for key joints and other parts of the AS, respectively. We then empirically replaced the fine features inside the optimized AS with 3D kagome lattices to support the shell, resulting in a lightweight sandwich AS. Numerical simulations were conducted to show that the designed sandwich AS exhibited good mechanical properties, e.g., high bending rigidity, with a reduction in weight by approximately 80% when compared with that of the initial design model. Finally, we fabricated the designed model with photosensitive resin using a 3D printing technique.

Graphene wrapped Fe7C3 nanoparticles supported on N-doped graphene nanosheets for efficient and highly methanol-tolerant oxygen reduction reaction.

Green and efficient non-precious metal electrocatalysts for oxygen reduction reaction (ORR) are prepared to meet the increasing demand for clean, secure and sustainable energy. Herein, we report a novel and environmentally friendly strategy for synthesis of graphene-wrapped iron carbide (Fe7C3) nanoparticles supported on hierarchical fibrous N-doped graphene with open-mesoporous structures (Fe7C3/NG) by simply annealing the mixture of melamine, iron (II) phthalocyanine (FePc) and Fe2O3. The effects of the pyrolysis temperature and the molar ratio of FePc to melamine were critically examined in the controls. Remarkably, the Fe7C3/NG obtained at 800 °C (i.e. Fe7C3/NG-800) manifested the forward shifts in the onset potential (0.98 V) and half-wave potential (0.85 V) with respective to commercial Pt/C (50 wt%) in 0.1 M KOH, coupled with the great enhancement in the durability (still remained 92.11% of its initial current density even after 40,000 s) and strong methanol tolerance. This research presents a promising strategy for developing Pt-free non-precious efficient ORR electrocatalysts in fuel cells.

One-pot solvothermal synthesis of three-dimensional hollow PtCu alloyed dodecahedron nanoframes with excellent electrocatalytic performances for hydrogen evolution and oxygen reduction.

It is a great challenge to develop simple approach to construct three-dimensional (3D) bimetallic alloyed nanoframes (NFs) with tunable surface structures, albeit with the availability of noble metal NFs in catalysis. Herein, a one-pot solvothermal method was employed for scalable preparation of uniform hollow PtCu rhombic dodecahedron nanoframes (PtCu RDNFs) in the presence of cetyltrimethylammonium chloride (CTAC), where diglycolamine (DGA) served as the co-reductant and co-structure director. The above architectures had the larger electrochemically active surface area (ECSA, 36.85 m2 g-1Pt) than that of commercial Pt black (15.85 m2 g-1Pt), along with the improved catalytic characters for hydrogen evolution reaction (HER) and oxygen reduction reaction (ORR) in acid electrolytes alternative to those of Pt black. It demonstrates great potential applications of PtCu RDNFs in fuel cells and beyond.

Uric acid supported one-pot solvothermal fabrication of rhombic-like Pt35Cu65 hollow nanocages for highly efficient and stable electrocatalysis.

High activity and good durability of electrocatalysts are of significance in practical applications of fuel cells. Among them, multi-component metallic hollow nanocages/nanoframes show great potential as advanced catalysts because of their highly open structures, large surface area and good stability. Herein, we report a general uric acid-mediated solvothermal method for shape-controlled synthesis of rhombic-like Pt35Cu65 hollow nanocages (HNCs) with uric acid as co-reductant and co-structure-directing agent. Uric acid and cetyltrimethylammonium chloride (CTAC) played important roles in the hollow cages. The specific architectures showed remarkably enhanced catalytic properties towards glycerol oxidation reaction (GOR), ethylene glycol oxidation reaction (EGOR) and oxygen reduction reaction (ORR) with the enhanced specific activity, outperforming commercial Pt/C (20 wt%). This work provides a new avenue for rational design of novel bimetallic nanocatalysts with enhanced characters in energy storage and conversion.

A Novel Design Framework for Structures/Materials with Enhanced Mechanical Performance.

Abstract: Structure/material requires simultaneous consideration of both its design and manufacturing processes to dramatically enhance its manufacturability, assembly and maintainability. In this work, a novel design framework for structural/material with a desired mechanical performance and compelling topological design properties achieved using origami techniques is presented. The framework comprises four procedures, including topological design, unfold, reduction manufacturing, and fold. The topological design method, i.e., the solid isotropic material penalization (SIMP) method, serves to optimize the structure in order to achieve the preferred mechanical characteristics, and the origami technique is exploited to allow the structure to be rapidly and easily fabricated. Topological design and unfold procedures can be conveniently completed in a computer; then, reduction manufacturing, i.e., cutting, is performed to remove materials from the unfolded flat plate; the final structure is obtained by folding out the plate from the previous procedure. A series of cantilevers, consisting of origami parallel creases and Miura-ori (usually regarded as a metamaterial) and made of paperboard, are designed with the least weight and the required stiffness by using the proposed framework. The findings here furnish an alternative design framework for engineering structures that could be better than the 3D-printing technique, especially for large structures made of thin metal materials.

Criterion to identify Hopf bifurcations in maps of arbitrary dimension.

The classical Hopf bifurcation criterion is stated in terms of the properties of eigenvalues. In this paper, a criterion without using eigenvalues is proposed for maps of arbitrary dimension. The parameter mechanism of Hopf bifurcation may be explicitly formulated on the basis of the criterion. A numerical example demonstrates that the proposed criterion is preferable to the classical Hopf bifurcation criterion in theoretical analysis and practical applications.

Estimation of initial conditions and parameters of a chaotic evolution process from a short time series.

Tracing back to the initial state of a time-evolutionary process using a segment of historical time series may lead to many meaningful applications. In this paper, we present an estimation method that can detect the initial conditions, unobserved time-varying states and parameters of a dynamical (chaotic) system using a short scalar time series that may be contaminated by noise. The technique based on the Newton-Raphson method and the least-squares algorithm is tolerant to large mismatch between the initial guess and actual values. The feasibility and robustness of this method are illustrated via the numerical examples based on the Lorenz system and Rossler system corrupted with Gaussian noise.

Control of degenerate Hopf bifurcations in three-dimensional maps.

A feedback control method is proposed to create a degenerate Hopf bifurcation in three-dimensional maps at a desired parameter point. The particularity of this bifurcation is that the system admits a stable fixed point inside a stable Hopf circle, between which an unstable Hopf circle resides. The interest of this solution structure is that the asymptotic behavior of the system can be switched between stationary and quasi-periodic motions by only tuning the initial state conditions. A set of critical and stability conditions for the degenerate Hopf bifurcation are discussed. The washout-filter-based controller with a polynomial control law is utilized. The control gains are derived from the theory of Chenciner's degenerate Hopf bifurcation with the aid of the center manifold reduction and the normal form evolution.

On creation of Hopf bifurcations in discrete-time nonlinear systems.

Bifurcation characteristics of a nonlinear system can be manipulated by small controls. In this paper, we present a control method to create Hopf bifurcations in discrete-time nonlinear systems. The critical conditions for the Hopf bifurcations are discussed. The center manifold method, normal form technique and the Iooss's Hopf bifurcation theory are employed in the derivation of the control gain. Numerical demonstration is provided. (c) 2002 American Institute of Physics.