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A new signal-amplified photothermometric sensor of Ag+ was explored based on a simple yet effective integration of inorganic and organic photothermal probes, mainly depending on the successful exploitation of a dual-signal transduction channel originating from the inherent photothermal property and the peroxidase-like activity of Prussian blue nanocubes (PB NCs).

Two fluorescent signal indicators were simply converted from one organic precursor system by using the superior oxidation capability of manganese dioxide (MnO2) nanosheets for the first time, finally resulting in the successful fabrication of a ratio fluorometric bioassay of alkaline phosphatase (ALP).

For the feature tensor of multi-sensor signals classification problem in gear intelligent fault diagnosis, a new tensor classifier named nearest neighbor convex hull tensor classification (NNCHTC) is proposed in this paper. First, the convex hull distance from a test tensor sample to the convex hull is taken as the similarity measure for classification. Then, the convex hull distance calculation is transformed into the feature tensor inner product, and CANDECOMP/PARAFAC (CP) decomposition is applied to the calculation process to capture the intrinsic information of the feature tensor. Furthermore, the reduction factor is introduced into NNCHTC to enhance its robustness. Finally, feature tensors are obtained from multi-sensor signals by wavelet packet transform (WPT) and used to identify gear working condition by NNCHTC. The experimental results show that NNCHTC not only can be effectively applied to the gear intelligent fault diagnosis based on multi-sensor signals but also has better robustness.

With an objective to harvest ambient mechanical power for self-powered systems, triboelectric nanogenerators (TENGs) based on paper have been currently put forward as an extensively effective, affordable as well as a sturdy methodology for the generation of power from mechanical vibrations, which are based on coupling between triboelectrification and electrostatic induction. Nevertheless, previous paper TENGs are built by vertical disintegration of the triboelectric-charged planes, requiring intricate structures of the device for ensuring that they are sufficiently resilient towards the charge disintegration; or else, no output current is observed. In this work, we firstly proposed a sliding paper TENG (SP-TENG) that was built based on in-plane charge disintegration mechanism, which utilizes related sliding between two interacting facades. Paper and Teflon tape served as the triboelectric pairs, and carbon ink was used as the conductive electrode. The proposed SP-TENG brings forth an open-circuit voltage of approximately 1000 V, a short-circuit current of approximately 42 μA and a peak power density of approximately 18.4 W/m2. The output performance can instantaneously drive 200 high-powered light-emitting diodes (LEDs). In comparison with the prevailing vertical-touching based TENGs, the advantages of elevated effectiveness, simple manufacture and appropriateness for different kinds of mechanical triggering are exhibited by SP-TENG. Moreover, a stacked SP-TENG is proposed to improve output performance. The proposed SP-TENG has potential applications as self-powered velocity sensor and force sensor.

In recent years, paper-based triboelectric nanogenerator (TENG) was proposed. In order to reduce cost, a cost-effective and high output performance TENG can be developed by utilizing commercial and cheap materials. In this study, TENG based on paper is proposed by creating arch structure with the materials that can be commercially obtained, for example, teflon tape or conductive copper foil tape. Silicone paper on the conductive copper foil tape is adopted as both triboelectric component and supporting frame. The market available LEDs have been lit up by the electronic outputs. This work will promote the development of paper-based TENG in the electronics industry.

Flexible electronics based on the paper have attracted significant attention. A paper-based triboelectric nanogenerator (TENG) consisting of commercial and low-cost materials, e.g., printer paper, polytetrafluoroethylene tape, and conductive ink, was proposed in this study. This TENG unit can directly illuminate 24 blue commercial LEDs. Furthermore, compound paper-based TENGs were developed and a considerable enhancement of the electrical output performance was thereby realized. These compound TENGs can also serve as a height sensor.

Paper-based triboelectric nanogenerators (TENGs) represent an affordable, green, and eco-friendly energy harvesting methodology. In this work, we propose a novel X-shaped paper TENG (XP-TENG) formed from a ballpoint ink layer coated by painting with a commercial brush pen. The paper and Teflon tape serve as the triboelectric pairs, with the paper serving as the supporting component. Moreover, the XP-TENG is capable of providing two working patterns that can yield an expanded scope of practical applications. A stacked XP-TENG was proposed for increasing the output performance and harvesting the mechanical energy generated by motion of the human body. This staked XP-TENG can directly light up 101 blue high-power LEDs with working voltage of 3.4 V. The fabricated XP-TENG can be used for harvesting mechanical energy from human walking.

T-type network inverter is a topology that can realize three-phase output from two single-phase output inverters through transformer. The purpose of this paper is to design the circuit topology and controller to make the inverter output voltage meet. Aviation power requirements. Firstly, the DC power input circuit and transformer structure are designed according to the principle of voltage vector synthesis. Then the controller is designed by state space average modeling method and generalized predictive control method. Finally, the system model is built by MATLAB/Simulink simulation software. The 115V/400Hz three-phase power required to convert 27V DC power to aviation power is realized, which verifies the effectiveness of the design content of this paper.

As an adaptive signal decomposition method, symplectic geometry mode decomposition (SGMD) method is suitable for dealing with non-stationary signals However, the decomposition effect is not ideal when dealing with rolling bearing fault signals with strong background noise. On the one hand, this noise reduction method of SGMD is not suitable for fault signals with strong background noise. On the other hand, SGMD uses QR decomposition method, which results in decomposition error diffusion in the decomposition of singular matrix. Therefore, an enhanced symplectic characteristics mode decomposition (ESCMD) method is proposed in this paper. ESCMD enhances fault features through the calculus operator to make fault features easier to extract, and replaces QR decomposition with eigenvalue decomposition (EVD) to avoid error diffusion during matrix decomposition. Emulational and experimental results show that ESCMD has excellent noise robustness and feature enhancement performance.

Maximum margin classifier with flexible convex hulls (MMC-FCH) is an adaptive pattern recognition method based on convex hull vector and shrinkage factor, which can effectively identify different fault states. However, MMC-FCH is a shallow learning algorithm that cannot effectively diagnose complex signals. Meanwhile, MMC-FCH is essentially a binary classifier. For multi-classification, MMC-FCH can only perform multiple binary classifications. To overcome the shortcomings of MMC-FCH, we propose a deep stacking center configuration convex hull (DSC3H), which combines the convex hull with the idea of stacking-based representation learning (SRL). In DSC3H, the output of all previous modules combine with the original signal as the input of the next module to fully learn the information in the original signal. At the same time, the concept of center configuration is used to construct the multi-classification objective function of center configuration convex hull (C3H). However, redundant information and noise information may still exist in the proposed DSC3H method. Therefore, we further propose a deep stacking l1-norm center configuration convex hull (DSl1C3H) method, which makes the prediction model more robust and sparser under the constraint of l1-norm distance. The experiments of rolling bearings show that the proposed DSl1C3H method has better classification performance.

This paper presents a back propagation artificial neural network (BP ANN) prediction model of the gate freeze time (tgf) for injection molded polypropylenes. An orthogonal design method was applied to enhance the BP ANN performance. The test results on the performance of the BP ANN prediction model showed that it can predict tgf with reasonable accuracy. Utilizing the BP ANN prediction model, the effects of the process factors, melt temperature (Tme), fill time (tf), gate area (Ain), packing pressure (Pp), and mold temperature (Tmo) on tgf were investigated. The simulation results showed that the most important process factor affecting tgf was Tme, followed by tf, Ain, and Pp, with Tmo having the least effect. The gate freeze time increased with elevated Tme, tf, Ain, and Pp.

The effects of process parameters, mold temperature (T mo), melt temperature (T me), cooling time (tc ), fill pressure (Pf ), packing pressure (Pp ), and packing time (tp ) on the shrinkage of injection molded polypropylene were investigated by utilizing a combination of the Artificial Neural Network (ANN) method and Moldflow software. An ANN model is developed to understand the relationship between plastic injection molding process parameters and shrinkage. The test results on the performance of the ANN model show that it can predict the shrinkage with reasonable accuracy. The simulation results show that the most important process parameter affecting shrinkage is Pp , followed by T me and T mo, with tc , Pf , and tp having the least effect. Shrinkage increases with the elevated T mo and tc . In contrast, the increases in Pp , Tme , tp , and Pf cause shrinkage to decrease. The strongest effect on the shrinkage is the amount of material forced into the mold, followed by the crystallinity and orientation o...

Low-pressure die-cast (LPDC) is widely used in manufacturing thin-walled aluminum alloy products. Since the quality of LPDC parts are mostly influenced by process conditions, how to determine the optimum process conditions becomes the key to improve the part quality. In this paper, a combining artificial neural network and genetic algorithm (ANN/GA) method is proposed to optimize the LPDC process. In this method, considering the more complicated preparation process of thin-walled casting, an ANN model combining learning vector quantization and back-propagation (BP) algorithm is proposed to map the complex relationship between process conditions and quality indexes of LPDC. Meanwhile, the orthogonal array design and numerical simulation is applied to obtain the training samples instead of carrying out a real experiment for the sake of cost saving. The genetic algorithm is employed to optimize the process parameters with the fitness function based on the trained ANN model. Then, by applying the optimized parameters, a thin-walled component of 300 mm in length, 100 mm in width, and 1.5 mm in thickness is successfully prepared. The results indicate that the proposed intelligent system is an effective tool for the process optimization of LPDC.

To study the essence of N-terminal amino acid sequence of 12 000-protein in gingival crevicular fluid (GCF).GCF samples from patients with RPP and AP were collected. 12 000-protein was separated by SDS-PAGE and transformed to PVDF by electronic transformation. The aim band was cut to be analyzed in 491 Protein Sequencer.The first ten of N-terminal amino acid sequence of 12 000-protein in GCF was Met, Leu, Thr, Glu, Leu, Glu, Lys, Ala, Leu, Asn. Through checking up in MS-Edman, the sequence was similar to "Ca binding protein, MRP8" which is the light subunit of Calprotectin.Calprotectin is a major protein in granulocytes and monocytes, and is related to many inflammatory diseases, maybe served as a effective marker for evaluating the inflammation of periodontium.