Compact Fluorescence Spectrometer with Built-In In-Line Calibration: Application to Detect Dissolved Organic Matter in Water.

Fluorescence spectrometer (FS) is widely used for component analysis because each fluorescing material has its own characteristic spectrum. However, the spectral calibration is complicated and bulky. Herein, an in-line spectral calibration sheet (ISCS) was proposed in which a narrow band-pass filter and a linear variable filter (LVF) were integrated on a metal plate. By moving the ISCS, the transmitted excitation light power (TEP) as well as fluorescence spectrum can be seamlessly scanned, and the TEP can be used for in-line spectral calibration. A compact FS apparatus based on UV-LED excitation, metal capillary (MC) and ISCS was fabricated (i.e., ISCS-FS), and the ISCS-FS apparatus was applied to detect sodium humate in water. By employing TEP calibration, both the primary inner filter effect (PIFE) and the drift in the optical power of UV-LED can be simultaneously compensated. The linear correlation coefficient of signal concentration was improved from 0.89 to 0.998, and the relative standard deviation (RSD) of replicated detection was improved from 3 to 0.7%. A detection limit of concentration (DLC) of 1.3 µg/L was realized, which is 15-fold lower than that of a commercial FS apparatus (20 µg/L). The DLC is even comparable with that (0.5-4 µg/L) of commercial total organic carbon (TOC) analyzers, which are bulky and expensive. The linear correlation between the measurement results of ISCS-FS and commercial TOC analyzers can reach a good value of 0.94.

Multiple Bioimaging Applications Based on the Excellent Properties of Nanodiamond: A Review.

Nanodiamonds (NDs) are emerging as a promising candidate for multimodal bioimaging on account of their optical and spectroscopic properties. NDs are extensively utilized for bioimaging probes due to their defects and admixtures in their crystal lattice. There are many optically active defects presented in NDs called color centers, which are highly photostable, extremely sensitive to bioimaging, and capable of electron leap in the forbidden band; further, they absorb or emit light when leaping, enabling the nanodiamond to fluoresce. Fluorescent imaging plays a significant role in bioscience research, but traditional fluorescent dyes have some drawbacks in physical, optical and toxicity aspects. As a novel fluorescent labeling tool, NDs have become the focus of research in the field of biomarkers in recent years because of their various irreplaceable advantages. This review primarily focuses on the recent application progress of nanodiamonds in the field of bioimaging. In this paper, we will summarize the progress of ND research from the following aspects (including fluorescence imaging, Raman imaging, X-ray imaging, magnetic modulation fluorescence imaging, magnetic resonance imaging, cathodoluminescence imaging, and optical coherence tomography imaging) and expect to supply an outlook contribution for future nanodiamond exploration in bioimaging.

A Review on the Progress of Optoelectronic Devices Based on TiO2 Thin Films and Nanomaterials.

Titanium dioxide (TiO2) is a kind of wide-bandgap semiconductor. Nano-TiO2 devices exhibit size-dependent and novel photoelectric performance due to their quantum limiting effect, high absorption coefficient, high surface-volume ratio, adjustable band gap, etc. Due to their excellent electronic performance, abundant presence, and high cost performance, they are widely used in various application fields such as memory, sensors, and photodiodes. This article provides an overview of the most recent developments in the application of nanostructured TiO2-based optoelectronic devices. Various complex devices are considered, such as sensors, photodetectors, light-emitting diodes (LEDs), storage applications, and field-effect transistors (FETs). This review of recent discoveries in TiO2-based optoelectronic devices, along with summary reviews and predictions, has important implications for the development of transitional metal oxides in optoelectronic applications for researchers.

A Review on Optoelectronical Properties of Non-Metal Oxide/Diamond-Based p-n Heterojunction.

Diamond holds promise for optoelectronic devices working in high-frequency, high-power and high-temperature environments, for example in some aspect of nuclear energetics industry processing and aerospace due to its wide bandgap (5.5 eV), ultimate thermal conductivity, high-pressure resistance, high radio frequency and high chemical stability. In the last several years, p-type B-doped diamond (BDD) has been fabricated to heterojunctions with all kinds of non-metal oxide (AlN, GaN, Si and carbon-based semiconductors) to form heterojunctions, which may be widely utilized in various optoelectronic device technology. This article discusses the application of diamond-based heterostructures and mainly writes about optoelectronic device fabrication, optoelectronic performance research, LEDs, photodetectors, and high-electron mobility transistor (HEMT) device applications based on diamond non-metal oxide (AlN, GaN, Si and carbon-based semiconductor) heterojunction. The discussion in this paper will provide a new scheme for the improvement of high-temperature diamond-based optoelectronics.

Photoluminescence and Electrical Properties of n-Ce-Doped ZnO Nanoleaf/p-Diamond Heterojunction.

The n-type Ce:ZnO (NL) grown using a hydrothermal method was deposited on a p-type boron-doped nanoleaf diamond (BDD) film to fabricate an n-Ce:ZnO NL/p-BDD heterojunction. It shows a significant enhancement in photoluminescence (PL) intensity and a more pronounced blue shift of the UV emission peak (from 385 nm to 365 nm) compared with the undoped heterojunction (n-ZnO/p-BDD). The prepared heterojunction devices demonstrate good thermal stability and excellent rectification characteristics at different temperatures. As the temperature increases, the turn-on voltage and ideal factor (n) of the device gradually decrease. The electronic transport behaviors depending on temperature of the heterojunction at different bias voltages are discussed using an equilibrium band diagram and semiconductor theoretical model.

Enhanced Photoluminescence and Electrical Properties of n-Al-Doped ZnO Nanorods/p-B-Doped Diamond Heterojunction.

The hydrothermal approach has been used to fabricate a heterojunction of n-aluminum-doped ZnO nanorods/p-B-doped diamond (n-Al:ZnO NRs/p-BDD). It exhibits a significant increase in photoluminescence (PL) intensity and a blue shift of the UV emission peak when compared to the n-ZnO NRs/p-BDD heterojunction. The current voltage (I-V) characteristics exhibit excellent rectifying behavior with a high rectification ratio of 838 at 5 V. The n-Al:ZnO NRs/p-BDD heterojunction shows a minimum turn-on voltage (0.27 V) and reverse leakage current (0.077 µA). The forward current of the n-Al:ZnO NRs/p-BDD heterojunction is more than 1300 times than that of the n-ZnO NRs/p-BDD heterojunction at 5 V. The ideality factor and the barrier height of the Al-doped device were found to decrease. The electrical transport behavior and carrier injection process of the n-Al:ZnO NRs/p-BDD heterojunction were analyzed through the equilibrium energy band diagrams and semiconductor theoretical models.

Detection of Oil Spill Using SAR Imagery Based on AlexNet Model.

Synthetic aperture radar (SAR) plays an irreplaceable role in the monitoring of marine oil spills. However, due to the limitation of its imaging characteristics, it is difficult to use traditional image processing methods to effectively extract oil spill information from SAR images with coherent speckle noise. In this paper, the convolutional neural network AlexNet model is used to extract the oil spill information from SAR images by taking advantage of its features of local connection, weight sharing, and learning for image representation. The existing remote sensing images of the oil spills in recent years in China are used to build a dataset. These images are enhanced by translation and flip of the dataset, and so on and then sent to the established deep convolutional neural network for training. The prediction model is obtained through optimization methods such as Adam. During the prediction, the predicted image is cut into several blocks, and the error information is removed by corrosion expansion and Gaussian filtering after the image is spliced again. Experiments based on actual oil spill SAR datasets demonstrate the effectiveness of the modified AlexNet model compared with other approaches.

Hybrid Regularization of Diffusion Process for Visual Re-Ranking.

To improve the retrieval result obtained from a pairwise dissimilarity, many variants of diffusion process have been applied in visual re-ranking. In the framework of diffusion process, various contextual similarities can be obtained by solving an optimization problem, and the objective function consists of a smoothness constraint and a fitting constraint. And many improvements on the smoothness constraint have been made to reveal the underlying manifold structure. However, little attention has been paid to the fitting constraint, and how to build an effective fitting constraint still remains unclear. In this article, by deeply analyzing the role of fitting constraint, we firstly propose a novel variant of diffusion process named Hybrid Regularization of Diffusion Process (HyRDP). In HyRDP, we introduce a hybrid regularization framework containing a two-part fitting constraint, and the contextual dissimilarities can be learned from either a closed-form solution or an iterative solution. Furthermore, this article indicates that the basic idea of HyRDP is closely related to the mechanism behind Generalized Mean First-passage Time (GMFPT). GMFPT denotes the mean time-steps for the state transition from one state to any one in the given state set, and is firstly introduced as the contextual dissimilarity in this article. Finally, based on the semi-supervised learning framework, an iterative re-ranking process is developed. With this approach, the relevant objects on the manifold can be iteratively retrieved and labeled within finite iterations. The proposed algorithms are validated on various challenging databases, and the experimental performances demonstrate that retrieval results obtained from different types of measures can be effectively improved by using our methods.

Identification of adhesion-associated DNA methylation patterns in the peripheral nervous system.

Schwann cells are unique glial cells in the peripheral nervous system. These cells provide a range of cytokines and nutritional factors to maintain axons and support axonal regeneration. However, little is known concerning adhesion-associated epigenetic changes that occur in Schwann cells after peripheral nerve injury (PNI). In the present study, adhesion-associated DNA methylation biomarkers were assessed between normal and injury peripheral nerve. Specifically, normal Schwann cells (NSCs) and activated Schwann cells (ASCs) were obtained from adult Wistar rats. After the Schwann cells were identified, proliferation and adhesion assays were used to assess differences between NSCs and ASCs. Methylated DNA immunoprecipitation-sequencing and bioinformatics analysis were used to identify and analyze the differentially methylated genes. Reverse transcription-quantitative PCR was performed to assess the expression levels of adhesion-associated genes. In the present study, the proliferation and adhesion assays demonstrated that ASCs had a more robust proliferative activity and adhesion compared with NSCs. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were performed to identify methylation-associated biological processes and signaling pathways. Protein-protein interaction network analysis revealed that Fyn, Efna1, Jak2, Vav3, Flt4, Epha7, Crk, Kitlg, Ctnnb1 and Ptpn11 were potential markers for Schwann cell adhesion. The expression levels of several adhesion-associated genes, such as vinculin, BCAR1 scaffold protein, collagen type XVIII α1 chain and integrin subunit ß6, in ASCs were altered compared with those in NSCs. The current study analyzed adhesion-associated DNA methylation patterns of Schwann cells and identified candidate genes that may potentially regulate Schwann cell adhesion in Wistar rats before and after PNI.

A Collective Neurodynamic Optimization Approach to Nonnegative Matrix Factorization.

Nonnegative matrix factorization (NMF) is an advanced method for nonnegative feature extraction, with widespread applications. However, the NMF solution often entails to solve a global optimization problem with a nonconvex objective function and nonnegativity constraints. This paper presents a collective neurodynamic optimization (CNO) approach to this challenging problem. The proposed collective neurodynamic system consists of a population of recurrent neural networks (RNNs) at the lower level and a particle swarm optimization (PSO) algorithm with wavelet mutation at the upper level. The RNNs act as search agents carrying out precise local searches according to their neurodynamics and initial conditions. The PSO algorithm coordinates and guides the RNNs with updated initial states toward global optimal solution(s). A wavelet mutation operator is added to enhance PSO exploration diversity. Through iterative interaction and improvement of the locally best solutions of RNNs and global best positions of the whole population, the population-based neurodynamic systems are almost sure able to achieve the global optimality for the NMF problem. It is proved that the convergence of the group-best state to the global optimal solution with probability one. The experimental results substantiate the efficacy and superiority of the CNO approach to bound-constrained global optimization with several benchmark nonconvex functions and NMF-based clustering with benchmark data sets in comparison with the state-of-the-art algorithms.

A Collective Neurodynamic Approach to Constrained Global Optimization.

Global optimization is a long-lasting research topic in the field of optimization, posting many challenging theoretic and computational issues. This paper presents a novel collective neurodynamic method for solving constrained global optimization problems. At first, a one-layer recurrent neural network (RNN) is presented for searching the Karush-Kuhn-Tucker points of the optimization problem under study. Next, a collective neuroydnamic optimization approach is developed by emulating the paradigm of brainstorming. Multiple RNNs are exploited cooperatively to search for the global optimal solutions in a framework of particle swarm optimization. Each RNN carries out a precise local search and converges to a candidate solution according to its own neurodynamics. The neuronal state of each neural network is repetitively reset by exchanging historical information of each individual network and the entire group. Wavelet mutation is performed to avoid prematurity, add diversity, and promote global convergence. It is proved in the framework of stochastic optimization that the proposed collective neurodynamic approach is capable of computing the global optimal solutions with probability one provided that a sufficiently large number of neural networks are utilized. The essence of the collective neurodynamic optimization approach lies in its potential to solve constrained global optimization problems in real time. The effectiveness and characteristics of the proposed approach are illustrated by using benchmark optimization problems.

Trace analysis of oil-in-water by using visible LED and metal waveguide capillary.

Trace analysis of oil-in-water (O/W) has wide applications in life science, industry and environmental monitoring (such as oil spilling). In this paper, with the aid of surfactant, diesel was dispersed in water as O/W emulsion, which can be detected by using visible LED and metal-waveguide-capillary (MWC). Due to the enhancement of optical-path and related light-droplet interaction in MWC, detecting diesel of a concentration as low as 2.14 ng/ml was realized with a 7cm long MWC. The detection limit was improved 125 fold compared with that of commercial spectrophotometer with 1 cm-cuvette. The detecting system features compact, low cost and high sensitivity.

A dynamic feedforward neural network based on gaussian particle swarm optimization and its application for predictive control.

A dynamic feedforward neural network (DFNN) is proposed for predictive control, whose adaptive parameters are adjusted by using Gaussian particle swarm optimization (GPSO) in the training process. Adaptive time-delay operators are added in the DFNN to improve its generalization for poorly known nonlinear dynamic systems with long time delays. Furthermore, GPSO adopts a chaotic map with Gaussian function to balance the exploration and exploitation capabilities of particles, which improves the computational efficiency without compromising the performance of the DFNN. The stability of the particle dynamics is analyzed, based on the robust stability theory, without any restrictive assumption. A stability condition for the GPSO+DFNN model is derived, which ensures a satisfactory global search and quick convergence, without the need for gradients. The particle velocity ranges could change adaptively during the optimization process. The results of a comparative study show that the performance of the proposed algorithm can compete with selected algorithms on benchmark problems. Additional simulation results demonstrate the effectiveness and accuracy of the proposed combination algorithm in identifying and controlling nonlinear systems with long time delays.