A Study on Dimensionality Reduction and Parameters for Hyperspectral Imagery Based on Manifold Learning.

With the rapid advancement of remote-sensing technology, the spectral information obtained from hyperspectral remote-sensing imagery has become increasingly rich, facilitating detailed spectral analysis of Earth's surface objects. However, the abundance of spectral information presents certain challenges for data processing, such as the "curse of dimensionality" leading to the "Hughes phenomenon", "strong correlation" due to high resolution, and "nonlinear characteristics" caused by varying surface reflectances. Consequently, dimensionality reduction of hyperspectral data emerges as a critical task. This paper begins by elucidating the principles and processes of hyperspectral image dimensionality reduction based on manifold theory and learning methods, in light of the nonlinear structures and features present in hyperspectral remote-sensing data, and formulates a dimensionality reduction process based on manifold learning. Subsequently, this study explores the capabilities of feature extraction and low-dimensional embedding for hyperspectral imagery using manifold learning approaches, including principal components analysis (PCA), multidimensional scaling (MDS), and linear discriminant analysis (LDA) for linear methods; and isometric mapping (Isomap), locally linear embedding (LLE), Laplacian eigenmaps (LE), Hessian locally linear embedding (HLLE), local tangent space alignment (LTSA), and maximum variance unfolding (MVU) for nonlinear methods, based on the Indian Pines hyperspectral dataset and Pavia University dataset. Furthermore, the paper investigates the optimal neighborhood computation time and overall algorithm runtime for feature extraction in hyperspectral imagery, varying by the choice of neighborhood k and intrinsic dimensionality d values across different manifold learning methods. Based on the outcomes of feature extraction, the study examines the classification experiments of various manifold learning methods, comparing and analyzing the variations in classification accuracy and Kappa coefficient with different selections of neighborhood k and intrinsic dimensionality d values. Building on this, the impact of selecting different bandwidths t for the Gaussian kernel in the LE method and different Lagrange multipliers λ for the MVU method on classification accuracy, given varying choices of neighborhood k and intrinsic dimensionality d, is explored. Through these experiments, the paper investigates the capability and effectiveness of different manifold learning methods in feature extraction and dimensionality reduction within hyperspectral imagery, as influenced by the selection of neighborhood k and intrinsic dimensionality d values, identifying the optimal neighborhood k and intrinsic dimensionality d value for each method. A comparison of classification accuracies reveals that the LTSA method yields superior classification results compared to other manifold learning approaches. The study demonstrates the advantages of manifold learning methods in processing hyperspectral image data, providing an experimental reference for subsequent research on hyperspectral image dimensionality reduction using manifold learning methods.

Point Cloud Denoising and Feature Preservation: An Adaptive Kernel Approach Based on Local Density and Global Statistics.

Noise removal is a critical stage in the preprocessing of point clouds, exerting a significant impact on subsequent processes such as point cloud classification, segmentation, feature extraction, and 3D reconstruction. The exploration of methods capable of adapting to and effectively handling the noise in point clouds from real-world outdoor scenes remains an open and practically significant issue. Addressing this issue, this study proposes an adaptive kernel approach based on local density and global statistics (AKA-LDGS). This method constructs the overall framework for point cloud denoising using Bayesian estimation theory. It dynamically sets the prior probabilities of real and noise points according to the spatial function relationship, which varies with the distance from the points to the center of the LiDAR. The probability density function (PDF) for real points is constructed using a multivariate Gaussian distribution, while the PDF for noise points is established using a data-driven, non-parametric adaptive kernel density estimation (KDE) approach. Experimental results demonstrate that this method can effectively remove noise from point clouds in real-world outdoor scenes while maintaining the overall structural features of the point cloud.

Vanadate-based Fe-MOFs as promising negative electrode for hybrid supercapacitor device.

In the supercapacitor field, negative electrodes are mainly concentrated in carbon-based materials, such as activated carbon, carbon nanotubes, graphene, and so forth. However, materials based on metal-organic frameworks (MOFs) as negative active components are relatively rare. Herein, a series of composite materials based on graphene oxide (GO) and vanadate-based Fe-organic frameworks have been prepared by hydrothermal method namely GO/Fe-VO4-BIPY. The deposition amount of polyoxometalate-based metal-organic frameworks (POMOFs) on the surface of graphene is adjusted by changing the content of POMOFs. Through the deposition, it can effectively reduce the accumulation between graphene, and increase the dispersion of POMOFs. As a result, the charge storage performance of the as-obtained materials is greatly improved. Among these materials, GO/Fe-VO4-BIPY-1 has the most prominent performance, with a specific capacitance of 190 F g-1at 0.5 A g-1, which is attributed to the excellent synergistic effect between the Faraday chemical reaction and electric double-layer capacitance. In comparison with pristine Fe-VO4-BIPY, GO/Fe-VO4-BIPY-1 delivers more excellent surface area and therefore exhibits abundant redox reaction sites, achieving better electrochemical performance the best. After assembly with the positive Ni(OH)2electrode, the maximum energy density of 46.84 W h kg-1at a power density of 850 W kg-1is achieved.

Non-reciprocal and non-Newtonian mechanical metamaterials.

Non-Newtonian liquids are characterized by stress and velocity-dependent dynamical response. In elasticity, and in particular, in the field of phononics, reciprocity in the equations acts against obtaining a directional response for passive media. Active stimuli-responsive materials have been conceived to overcome it. Significantly, Milton and Willis have shown theoretically in 2007 that quasi-rigid bodies containing masses at resonance can display a very rich dynamical behavior, hence opening a route toward the design of non-reciprocal and non-Newtonian metamaterials. In this paper, we design a solid structure that displays unidirectional shock resistance, thus going beyond Newton's second law in analogy to non-Newtonian fluids. We design the mechanical metamaterial with finite element analysis and fabricate it using three-dimensional printing at the centimetric scale (with fused deposition modeling) and at the micrometric scale (with two-photon lithography). The non-Newtonian elastic response is measured via dynamical velocity-dependent experiments. Reversing the direction of the impact, we further highlight the intrinsic non-reciprocal response.

Micro-Scale Mechanical Metamaterial with a Controllable Transition in the Poisson's Ratio and Band Gap Formation.

The ability to significantly change the mechanical and wave propagation properties of a structure without rebuilding it is currently one of the main challenges in the field of mechanical metamaterials. This stems from the enormous appeal that such tunable behavior may offer from the perspective of applications ranging from biomedical to protective devices, particularly in the case of micro-scale systems. In this work, a novel micro-scale mechanical metamaterial is proposed that can undergo a transition from one type of configuration to another, with one configuration having a very negative Poisson's ratio, corresponding to strong auxeticity, and the other having a highly positive Poisson's ratio. The formation of phononic band gaps can also be controlled concurrently which can be very useful for the design of vibration dampers and sensors. Finally, it is experimentally shown that the reconfiguration process can be induced and controlled remotely through application of a magnetic field by using appropriately distributed magnetic inclusions.

3D Auxetic Metamaterials with Elastically-Stable Continuous Phase Transition.

In solid state physics, phase transitions can influence material functionality and alter their properties. In mechanical metamaterials, structural-phase transitions can be achieved through instability or buckling of certain structural elements. However, these fast transitions in one mechanical parameter typically affect significantly the remaining parameters, hence, limiting their applications. Here, this limitation is addressed by designing a novel 3D mechanical metamaterial that is capable of undergoing a phase transition from positive to negative Poisson's ratio under compression, without significant degradation of Young's modulus (i.e. the phase transition is elastically-stable). The metamaterial is fabricated by two-photon lithography at the micro-scale and its mechanical behavior is assessed experimentally. For another choice of structural parameters, it is then shown that the auxetic behavior of the considered 3D metamaterial class can be maintained over a wide range of applied compressive strain.

Surface porous poly-ether-ether-ketone based on three-dimensional printing for load-bearing orthopedic implant.

Poly-ether-ether-ketone (PEEK) possesses excellent biocompatibility and similar elastic modulus as bones but yet suffers from poor osseointegration. In order to balance PEEK's mechanical and osseointegration properties, a novel surface porous PEEK (SP-PEEK) is successfully fabricated by fused deposition modelling three-dimensional printing (FDM 3DP) and characterized by mechanical and osteogenesis in vitro tests. Moreover, the effects of pore diameter and pore layer number on the mechanical behaviors of SP-PEEK are investigated by theoretical model and numerical simulation. Comparison among experimental, theoretical and simulation results show good agreement. As pore diameter decreases, the equivalent strength and modulus become more sensitive to the decrease of pore layer number. In addition, the SP-PEEK exhibits the mechanical properties within the range of human trabecular bone and cortical bone, and thus can be tailored to mimic human bone by adjusting the pore diameter and pore layer number, which is benefit to mitigate stress shielding. The effects of pore diameter on the cell proliferation and osteogenic differentiation of SP-PEEK are tested by the co-culture of osteoblast precursor cells (MC3T3-E1) and SP-PEEK round discs. Results showcase that porous surface improves the osteogenesis in vitro, and the SP-PEEK group that the pore diameter is 0.6 mm exhibits optimal-performance osteogenesis in vitro.

Modeling and interpreting the COVID-19 intervention strategy of China: A human mobility view.

The Coronavirus Disease 2019 (COVID-19) has proved a globally prevalent outbreak since December 2019. As a focused country to alleviate the epidemic impact, China implemented a range of public health interventions to prevent the disease from further transmission, including the pandemic lockdown in Wuhan and other cities. This paper establishes China's mobility network by a flight dataset and proposes a model without epidemiological parameters to indicate the spread risks through the network, which is termed as epidemic strength. By simply adjusting an intervention parameter, traffic volumes under different travel-restriction levels can be simulated to analyze how the containment strategy can mitigate the virus dissemination through traffic. This approach is successfully applied to a network of Chinese provinces and the epidemic strength is smoothly interpreted by flow maps. Through this node-to-node interpretation of transmission risks, both overall and detailed epidemic hazards are properly analyzed, which can provide valuable intervention advice during public health emergencies.

Assembling reduced graphene oxide with sulfur/nitrogen- "hooks" for electrochemical determination of Hg(II).

Herein the sulfur/nitrogen contained groups, serving as the "hooks" for electrochemical determination of Hg(II), were assembled on the reduced graphene oxide (hereafter SN-rGO) via a one-step facile hydrothermal method. The thiourea acts as a precursor for sulfur/nitrogen doping and partial reduction of graphene oxide. The SN-rGO was used to modify the glassy carbon electrode (GCE) for electrochemical detection of Hg(II) by square wave anodic stripping voltammetry (SWASV). The sulfur/nitrogen doping significantly improves the Hg(II) complexation by SN-rGO due to the creation of multifunctional groups on the graphene nanosheet. The SN-rGO modified electrode has excellent sensitivity (20.48 µA/µM) and limit of detection (LOD 8.93 nM) for Hg(II) detection. The newly developed Hg(II) sensing electrode possesses minimal interference for other ions typically found in natural waters. Therefore, it can be used for routine water quality monitoring. The fabrication of the SN-rGO electrode is rapid and low cost; hence, it offers a potential platform for environmental monitoring of toxic metal ions.

Construction of electrochemical sensing interface towards Cd(II) based on activated g-C3N4 nanosheets: considering the effects of exfoliation and protonation treatment.

There is an urgent need to construct highly selective low-cost sensors for fast detection of toxic metal ions such as cadmium. When compared with 3D bulk materials, 2D layered materials after activation treatments show superior performances for electrochemical metal ion detection. The bulk graphitic carbon nitride (hereafter b-g-C3N4) was prepared by thermal polymerization with urea as a precursor; it was then activated through ultrasonic liquid exfoliation and protonation which resulted in successful fabrication of activated ultrathin g-C3N4 nanosheets (hereafter a-g-C3N4). The a-g-C3N4-modified glassy carbon electrode demonstrates excellent electrochemical performances for Cd2+ detection with 22.668 µA/µM sensitivity and 3.9 nM LOD (S/N = 3) due to high specific surface area and active sites created on the 2D layered structure. The chemical interference of Pb2+, Cu2+, and Hg2+ on Cd2+ detection was minimal. We have also measured Cd2+ in natural water and rice samples using the newly developed a-g-C3N4-modified electrode with high spike recoveries. Our results demonstrate the potential applications of newly developed a-g-C3N4-modified electrode for rapid detection of toxic metal ions in different sample matrixes. Graphical Abstract The activated g-C3N4 nanosheets (a-g-C3N4) were synthesized and used to construct electrochemical sensors with high sensitivity and anti-interference performance.

Pd/MXene(Ti3C2Tx)/reduced graphene oxide hybrid catalyst for methanol electrooxidation.

A key challenge in developing direct methanol fuel cells is the fabrication of electrocatalysts with high activity and long durability. Herein, we report a performance enhanced electrocatalyst of nanoscale Pd on MXene (Ti3C2Tx) and reduced graphene oxide (rGO). The mass activity of Pd/Ti3C2Tx-rGO (1: 1) hybrid toward methanol oxidation reaction is 753 mA mg-1, which is 1.7 times than that of Pd/C (446 mA mg-1). Additionally, the current density of Pd/Ti3C2Tx-rGO (1:1) catalyst contains 212 mAmg-1 which is nine times higher than that of Pd/C (23 mA mg-1) after 7200 s. The Pd/Ti3C2Tx-rGO (1:1) catalyst exhibits excellent cycling stability and long-term life. These remarkable catalytic performances are attributed to the role of Ti3C2Tx and rGO in enhancing the catalytic activity surface area and rapid mass/charge transfer due to the synergistic effect between Pd and Ti3C2Tx/rGO.

Sensitive and anti-interference stripping voltammetry analysis of Pb(II) in water using flower-like MoS2/rGO composite with ultra-thin nanosheets.

Herein, combined with the outstanding adsorption ability of flower-like MoS2 with ultra-thin nanosheets and the good electrical conductivity of reduced graphene oxide (rGO), a sensitive and anti-interference electrochemical sensing interface for the analysis of Pb(II) was constructed by using MoS2/rGO nanocomposite modified glassy carbon electrode (MoS2/rGO-GCE). The stripping behavior of Pb(II) was estimated using square wave anodic stripping voltammetry (SWASV), a high sensitivity of 50.80 µA µM-1 was achieved, and the limit of detection (LOD) was determined to be 0.005 µM (3σ method). The reasons for the excellent electrochemical performance of MoS2/rGO-GCE toward Pb(II) is that the excellent adsorption capacity and electrical conductivity, and the redox reaction of Pb(II) occurs directly on the surface of the MoS2/rGO nanocomposite. Moreover, series interference experiments were systematically conducted, the sensitivity of Pb(II) has no obvious change in the presence of other coexist heavy metal ions. In addition, an intriguing interference phenomenon between Pb(II) and Cu(II) was found. When Cu(II) exists in the solution, two stripping peaks of Pb(II) were observed, this phenomenon can be explained that the form of PbCu alloy or intermetallic compounds during the preconcentration process. Further experiments indicated that the proposed method has good stability and reproducibility. Finally, the accurate detection result of Pb(II) in wastewater sample gathered from Wangxiaoying wastewater treatment plant were obtained.