Adaptive fixed-time synchronization of Lorenz systems with application in chaotic finance systems

Nonlinear Dynamics - This paper concerns the problem of fixed-time synchronization of master–slave Lorenz systems. The adaptive control and fixed-time control strategies are successfully...     

微区X射线荧光成像技术在岩心分析中的应用

微区X射线荧光（Micro-XRF）分析技术是通过微小的X射线光束照射样品，对样品进行原位成分观测的无损分析手段之一，具有灵敏度高、速度快和准确性高的特点。采用微区X射线荧光光谱仪（M6 JETSTREAM）对安徽铜陵冬瓜山铜矿床四段岩心样品进行面扫描，分析不同矿层共17种元素区域分布特征、空间分布规律及组合关系等，结果表明：（1）Cu和Fe两种成矿元素高值空间分布区域基本不重叠，S与Fe分布范围高度重叠，关系密切，微量元素Ni，Bi，Pb，Zn，Si，Na与Cu密切相关，而Ti，Al，K与Fe具有弱相关性；（2）垂向上，Fe元素含量随深度增加逐步增大，而Cu元素含量呈降低趋势，其他元素也随深度呈下降趋势；（3）元素分布受石炭纪中期海底喷流沉积成矿作用和岩浆热液成矿作用叠加改造作用明显；（4）该钻孔矿石矿物以磁黄铁矿、黄铜矿和黄铁矿为主，垂向上组合规律明显，脉石矿物以石英、石榴子石和透辉石为主。该技术通过分析元素空间分布规律、相关性以及矿物组合和分配关系等可对元素富集和运移以及对矿床的成矿机制、成因模式等地质环境和地质过程提供新认识和新证据。结合矿床地球化学特征的分布模式，微量元素可作为寻找主矿种的指示元素，为深部找矿提供依据。此外，该技术能作为预分析技术快速筛选出感兴趣的信息和位置，为后期各种更高精度的微区分析提供不同尺度、不同层次的元素分布信息。     

Polymeric Carbon Nitride-based Single Atom Photocatalysts for CO2 Reduction to C1 Products

Photocatalytic CO₂ reduction to C₁ fuels is considered to be an important way for alleviating increasingly serious energy crisis and environmental pollution. Due to the environment-friendly, simple preparation, easy formation of highly-stable metal-nitrogen(M-N_x) coordination bonds, and suitable band structure, polymeric carbon nitride-based single-atom catalysts(C₃N₄-based SACs) are expected to become a potential for CO₂ reduction under visible-light irradiation. In this review, we summarize the recent advancement on C₃N₄-based SACs for photocatalytic CO₂ reduction to C₁ products, including the reaction mechanism for photocatalytic CO₂ reduction to C₁ products, the structure and synthesis methods of C₃N₄-based SACs and their applications toward photocatalytic CO₂ reduction reaction(CO₂RR) for C₁ production. The current challenges and future opportunities of C₃N₄-based SACs for photoreduction of CO₂ are also discussed.     

Correction to: Theoretical modeling and experimental evaluation of a magnetostrictive actuator with radial-nested stacked configuration

Nonlinear Dynamics -     

Amorphous High-Entropy Hydroxides of Tunable Wide Solar Absorption for Solar Water Evaporation

The high-entropy materials have raised much attention in recent years due to their extraordinary performances in mechanical, catalysis, energy storage fields. Herein, a new type of high-entropy hydroxides (e.g., NiFeCoMnAl(OH)_x) that are amorphous and capable of broad solar absorption is reported. A facile one-pot co-precipitation method is employed to synthesize these amorphous high-entropy hydroxides (a-HEHOs) under ambient conditions. The a-HEHOs thus obtained display widely tunable bandgap (e.g., from 2.6 to 1.1 eV) due to their high-entropy and amorphous characteristics, enabling efficient light absorbance and photothermal conversion in the solar regime. Further solar water evaporation measurements show that the a-HEHOs delivered a considerable energy conversion efficiency of 55%, comparable to black titanium oxides that are synthesized using more complex and expensive methods.     

A Criterion of Nonparabolicity by the Ricci Curvature

A complete manifold is said to be nonparabolic if it does admit a positive Green’s function. To ?nd a sharp geometric criterion for the parabolicity/nonparbolicity is an attractive question inside the function theory on Riemannian manifolds. This paper devotes to proving a criterion for nonparabolicity of a complete manifold weakened by the Ricci curvature. For this purpose, we shall apply the new Laplacian comparison theorem established by the ?rst author to show the existence of a non-constant bounded subharmonic function.     

Sentinel-2卫星的多光谱轻量级船舶目标检测算法

近年来深度卷积神经网络在可见光船舶检测方面取得了显著的进展，然而，大多数相关研究是通过改进大型的网络结构来提高检测性能，因此加大了对更高计算机性能的需求。此外，可见光图像难以在云、雾、海杂波、黑夜等复杂场景检测到船舶。针对以上问题，提出了一种融合红（red, R）、绿（green, G）、蓝（blue, B）和近红外（NIR）4个波段光谱信息的由粗到精细的轻量型船舶检测算法。与现有的方法中根据光谱特性利用水体检测算法提取水体区域不同之处是该算法是利用改进的水体检测算法来提取船舶候选区域。为获取更准确的候选区域，对船舶、厚云、薄云、平静海面、杂波海面5种场景中4个波段的像素值进行了统计分析，选取近红外大于阈值作为辅助判断，并以其中心点获取候选区域32×32大小的切片，并对切片进行非极大值抑制，由此获得了船舶粗检测结果。随后构建了轻量级LSGFNet网络对船舶候选区域切片进行精细识别。构建的网络融合了1×1卷积提取的波谱特征与3×3的提取几何特征，为防止光谱特征与几何特征的信息在融合时“信息不流通”，在LSGFNet网络中引入了ShuffleNet中的通道打乱机制，并减小了模型结构，与典型的轻量级网络相比具有更好的效果且模型较小。最后，利用Sentinel-2卫星多光谱10 m分辨率数据构建了512×512大小的1 120组数据进行粗检测，以及32×32大小的6 014组数据进行精细网络训练，其中候选区域粗提取的查全率为98.99%，精细识别网络精确度为96.04%，不同场景下的平均精确度为92.98%。实验表明该算法在抑制云层、海浪杂波等干扰的复杂背景下具有较高的检测效率，且训练时间短、计算机性能需求低。