Broadband wavelength designable achromatic grating based on a cholesteric liquid crystal template.

Liquid crystal (LC) gratings have played important roles in light field control due to the advantages of being lightweight, low cost, having no moving parts, and low power consumption. However, the chromatic aberration limits the bandwidth of the LC device and affects the efficiency of the grating. To solve the chromatic aberration issue, a broadband wavelength designable achromatic grating is proposed. Different grating structures are integrated into a single-layer templated cholesteric liquid crystal (CLC) device, and the achromatic diffraction wavelength of the grating can be freely designed from the visible spectral region to the infrared range within the Bragg reflection band of the CLCs. The diffraction intensity of different orders can be changed with the electric field applied to meet the need for dynamic modulation. This grating shows suitable potential applications in optical communication and displays.

Impact of the comprehensive remediation project on hydrological conditions in the lower reaches of the Ganjiang River.

This study undertakes a systematic analysis of the hydrological changes before and after the implementation of the Comprehensive Remediation Project in the lower reaches of the Ganjiang River. It focuses on changes in downstream inflow, ratios of flow distribution, and water levels, as well as water velocity near the gates. The results indicate a significant improvement in the spatial distribution of water resources in the lower reaches of the Ganjiang River. The project enhances the inflow from the northern and southern branches, positively influencing downstream water usage and the ecological environment. Building upon these findings, the study proposes operational recommendations tailored to different hydrological years, such as timely adjustments to the southern branch's water inflow and optimizing flow distribution ratios. This research provides a scientific basis for the implementation and dispatch of comprehensive remediation projects and offers insights into water resource management in similar regions.

Fractional-order iterative learning control for fractional-order systems with initialization non-repeatability.

The effect of initialization non-repeatability on iterative learning control performance for fractional-order systems has not been sufficiently investigated. It is a hidden deficiency that leads directly to the breaking of perfect tracking conditions in both theoretical analysis and real-world applications. Therefore, under the framework of general fractional-order nonlinear systems, this paper proposes an open-close loop Dα-type iterative learning control scheme based on system preconditioning and strictly derives two convergence conditions. By applying the preconditioning optimization strategy based on the short-memory principle, the tracking error due to initialization nonrepetition can converge to any desired range. Compared with the existing results, the proposed iteration scheme fully considers the complexity of the initialization and initial conditions of fractional-order systems, and provides several practical preconditioning methods to improve the tracking efficiency. Two numerical examples are presented to validate the above conclusions.

Machine learning phase modulation of liquid crystal devices for three-dimensional display.

A machine learning phase modulation scheme based on convolutional neural networks (CNN) and recurrent neural network (RNN) is proposed to carry out the regression task of liquid crystal (LC) device electric field prediction for the 2D/3D switchable display. The hybrid neural network is built and trained based on the illuminance distribution under three-dimensional (3D) display. Compared with manual phase modulation, the modulation method using a hybrid neural network can achieve higher optical efficiency and lower crosstalk in the 3D display. The validity of the proposed method is confirmed through simulations and optical experiments.

Data-driven adaptive compensation control for a class of nonlinear discrete-time system with bounded disturbances.

This paper considers the compensation control problem for a class of nonlinear discrete-time systems subject to bounded disturbances. With the help of the dynamic linearization technique (DLT), an equivalent data model to the unknown disturbed controlled plant is first established. Based on the data model, two data-driven controllers are designed through novel disturbance-related compact-form and partial-form DLT, which are equivalent to the unknown ideal compensation controller in theory. Adaptive gains designed for the proposed controllers are time-varying and are adaptively updated by directly utilizing the I/O data without involving any model information of the controlled plant, making both controllers purely data-driven adaptive disturbance compensation controllers. Further, in practice, unmeasurable disturbances are commonly encountered due to expensive measuring instruments, unreliable performance or large lags. Therefore, both proposed control laws provide solutions for measurable disturbance (MD) and unmeasurable disturbance (UD) in a unified framework, where the time-varying adaptive gains fuse more system dynamics when disturbance is completely unknown except for some boundedness. The stability of the proposed controllers are strictly guaranteed, and their effectiveness and applicability are verified by a numerical simulation and a distillation column.

Temperature Self-Adaptive and Color-Adjustable Smart Window Based on Templated Cholesteric Liquid Crystals.

Cholesteric liquid crystals (CLCs) exhibit selective reflection due to their self-assembled helical superstructures. Reconfigurable templates can achieve integration functions via inducing processes of molecular assemblies. Here we demonstrate temperature self-adaptive and color-adjustable smart windows using CLCs, which are fabricated via the templating method and exhibit simultaneous reflections in the visible and infrared spectra. Reflection bands formed by the refilled CLC materials can be adjusted reversibly both upon thermal and electrical actuation. In CLC with adjustable reflection in the infrared, the central wavelength of the infrared reflection band can be adjusted from 950 nm to 1305 nm via temperature, and from 1150 nm to 950 nm via electric field. A temperature variation of 10.3 °C within 55 s was induced by the single-layer templated CLC cell, and a comfortable temperature range could be effectively maintained by the CLC cell in a varied environment. In CLC with dynamic color in the visible spectrum, color shifts from 530 nm to 650 nm tuned by temperature and from 530 nm to 440 nm adjusted by electric field were obtained. Temperature-responsive reflection in the infrared spectrum contributes to automatic thermal management, and electric-field-induced band shift in the visible spectrum enables active dynamic color adjustment. The presented templated CLC smart windows show considerable potential in energy conservation and biological clock regulation fields.

Optical Filters Based on Cholesteric, Blue and Sphere Mesophases.

An optical filter is one of the indispensable devices in massive and high-speed communication, optical signal processing, and display. Twist-structure liquid crystals, cholesteric liquid crystals, blue-phase liquid crystals, and sphere-phase liquid crystals show potential application in optical filters originating from the periodic nanostructures. Wavelength and bandwidth tuning can be controlled via temperature, electric fields, light, angle, spatial control, and templating technology. In this review, we discuss the recent developments of twist-structure liquid crystal filters.

[Remediation Potential of Three Dwarf Bamboos on Farmland Soils Contaminated with Mixed Heavy Metals].

A two-year field experiment was carried out in a Cu-Zn-Cd-Pb-contaminated field with Sasa auricoma, Indocalamus tessellatus, and Shibataea chinensis Nakai to investigate their growth and accumulation characteristics. Based on changes in heavy metal content in the soil, we compared their phytoremediation potentials for the combined pollution of heavy metals. After two years of planting, S. auricoma showed the best tolerance to soil contaminated by heavy metals, with the number of plants being 63.8 times that at the time of planting. For each bamboo type, heavy metals were most concentrated in the root, with the best enrichment ability of Cd and worst ability of Pb. The bioconcentration factor of Cd in the roots was far greater than 1 for all three species, with 17.68 for S. auricoma and 14.63 for I. tessellatus. The accumulation of heavy metals was mainly concentrated in the roots and rhizomes, and the accumulations of Cu, Zn, and Cd in S. auricoma were 157.14, 363.3, and 7.18 g·hm-2, respectively, which were higher than those of I. tessellatus and S. chinensis Nakai. Compared with that before planting, the content of heavy metals in the non-rhizosphere soil decreased after two years of planting, among which Cd content decreased the most, from 39.6%-40.4%. In addition, the contents of Cu, Zn, and Cd in rhizosphere soil of S. auricoma and S. chinensis Nakai were significantly lower than those in non-rhizosphere soil (P<0.05). The results showed that the three species had strong accumulation ability of Cd and could be further studied as Cd-repaired plants. The growth condition and content and accumulation of heavy metals in S. auricoma were excellent among the dwarf bamboos, which showed the highest phytoremediation potential.

Zero-liquid discharge technologies for desulfurization wastewater: A review.

Zero-Liquid Discharge (ZLD) has received a lot of attention due to water scarcity and pollution. This article reviews the treatment of desulfurization wastewater in pretreatment, concentration and solidification with the trend of zero-liquid discharge in China. We summarize the advantages, disadvantages, performance, benefits and other characteristics of different ZLD technologies. Membrane-based technology is an effective means of recycling wastewater in ZLD systems. Therefore, we focus on the application of bipolar membrane electrodialysis (BMED) in desulfurization wastewater and high-salt wastewater treatment, discussing its limitations and solutions. In addition, several Chinese ZLD cases and economic analysis are introduced. It is believed that ZLD will become a new trend in desulfurization wastewater treatment in the future. Therefore, exploring new materials and technologies with low cost and high efficiency is the focus of future work.

Templated Twist Structure Liquid Crystals and Photonic Applications.

Twist structure liquid crystals (TSLCs) have attracted increasing attention in photonic applications due to their distinct properties: Bragg reflection, scattering, and optical rotation. However, there exist some issues due to the defects of TSLCs: weak thermal stability, narrow bandwidth, and complicated fabrication. In this review, we introduce the templating technique which includes device structure, templating process, and photonic properties of templated TSLCs to improve the issues. Furthermore, a variety of photonic applications including lasing, optical filters and gratings based on TSLCs with polymer templates are presented. Additionally, other applications of TSLCs are briefly introduced. Finally, the remaining challenges and future perspectives of templated TSLCs are proposed.

A Fusion Transformer for Multivariable Time Series Forecasting: The Mooney Viscosity Prediction Case.

Multivariable time series forecasting is an important topic of machine learning, and it frequently involves a complex mix of inputs, including static covariates and exogenous time series input. A targeted investigation of this input data is critical for improving prediction performance. In this paper, we propose the fusion transformer (FusFormer), a transformer-based model for forecasting time series data, whose framework fuses various computation modules for time series input and static covariates. To be more precise, the model calculation consists of two parallel stages. First, it employs a temporal encoder-decoder framework for extracting dynamic temporal features from time series data input, which analyzes and integrates the relative position information of sequence elements into the attention mechanism. Simultaneously, the static covariates are fed to the static enrichment module, which is inspired by gated linear units, to suppress irrelevant information and control the extent of nonlinear processing. Finally, the prediction results are calculated by fusing the outputs of the above two stages. Using Mooney viscosity forecasting as a case study, we demonstrate considerable forecasting performance improvements over existing methodologies and verify the effectiveness of each component of FusFormer via ablation analysis, and an interpretability use case is conducted to visualize temporal patterns of time series. The experimental results prove that FusFormer can achieve accurate Mooney viscosity prediction and improve the efficiency of the tire production process.

An Enhanced Full-Form Model-Free Adaptive Controller for SISO Discrete-Time Nonlinear Systems.

This study focuses on the full-form model-free adaptive controller (FFMFAC) for SISO discrete-time nonlinear systems, and proposes enhanced FFMFAC. The proposed technique design incorporates long short-term memory neural networks (LSTMs) and fuzzy neural networks (FNNs). To be more precise, LSTMs are utilized to adjust vital parameters of the FFMFAC online. Additionally, due to the high nonlinear approximation capabilities of FNNs, pseudo gradient (PG) values of the controller are estimated online. EFFMFAC is characterized by utilizing the measured I/O data for the online training of all introduced neural networks and does not involve offline training and specific models of the controlled system. Finally, the rationality and superiority are verified by two simulations and a supporting ablation analysis. Five individual performance indices are given, and the experimental findings show that EFFMFAC outperforms all other methods. Especially compared with the FFMFAC, EFFMFAC reduces the RMSE by 21.69% and 11.21%, respectively, proving it to be applicable for SISO discrete-time nonlinear systems.

One-Step In Situ Patternable Reduction of a Ag-rGO Hybrid Using Temporally Shaped Femtosecond Pulses.

In recent years, metallic nanoparticle (NP)-two-dimensional material hybrids have been widely used for photocatalysis and photoreduction. Here, we introduce a femtosecond laser reduction approach that relies on the repetitive ablation of recast layers by usi-ng temporally shaped pulses to achieve the fast fabrication of metallic NP-two-dimensional material hybrids. We selectively deposited silver-reduced graphene oxide (Ag-rGO) hybrids on different substrates under various fabrication conditions. The deposition of the hybrids was attributed to the redistribution of the cooling ejected plume after multiple radiation pulses and the exchange of carriers with ejected plume ions containing activated species such as small carbon clusters and H2O. The proposed one-step in situ fabrication method is a competitive fabrication process that eliminates the additive separation process and exhibits morphological controllability. The Ag-rGO hybrids demonstrate considerable potential for chemomolecular and biomolecular detection because the surface-enhanced Raman scattering signal of the enhancement factor reached 4.04 × 108.

Dynamic NOx emission prediction based on composite models adapt to different operating conditions of coal-fired utility boilers.

An accurate NOx concentration prediction model plays an important role in low NOx emission control in power stations. Predicting NOx in advance is of great significance in satisfying stringent environmental policies. This study aims to accurately predict the NOx emission concentration at the outlet of boilers on different operating conditions to support the DeNOx procedure. Through mutual information analysis, suitable features are selected to build models. Long short-term memory (LSTM) models are utilized to predict NOx concentration at the boiler's outlet from selected input features and exhibit power in fitting multivariable coupling, nonlinear, and large time-delay systems. Moreover, a composite LSTM model composed of models on different operating conditions, like steady-state and transient-state condition, is prosed. Results of one whole day of typical operating data show that the accuracy of the NOx concentration and fluctuation trend prediction based on this composite model is superior to that using a single LSTM model and other non-time-sequence models. The root mean square error (RMSE) and R2 of the composite LSTM model are 3.53 mg/m3 and 0.89, respectively, which are better than those of a single LSTM (i.e., 5.50 mg/m3 and 0.78, respectively).

High-Reflective Templated Cholesteric Liquid Crystal Filters.

Cholesteric liquid crystals (CLCs) have been widely applied in optical filters due to Bragg reflection caused by their helical structure. However, the reflectivity of CLC filters is relatively low, commonly less than 50%, as the filters can only reflect light polarized circularly either left- or right-handedly. Therefore, a high-reflective CLC filter with a single-layer template was proposed which may reflect both right- and left-handed polarized light. The CLC filters of the red, green, blue color were fabricated by the templating technology, which show good wavelength consistency. Additionally, a multi-phase liquid crystal filter with high reflectance was demonstrated by the single-layer templating technology. The templated CLC or multi-phase liquid crystal filters show great potential applications in the optical community, reflective display, and lasing.

Unsupervised Learning with Generative Adversarial Network for Automatic Tire Defect Detection from X-ray Images.

Automatic defect detection of tire has become an essential issue in the tire industry. However, it is challenging to inspect the inner structure of tire by surface detection. Therefore, an X-ray image sensor is used for tire defect inspection. At present, detection of defective tires is inefficient because tire factories commonly conduct detection by manually checking X-ray images. With the development of deep learning, supervised learning has been introduced to replace human resources. However, in actual industrial scenes, defective samples are rare in comparison to defect-free samples. The quantity of defective samples is insufficient for supervised models to extract features and identify nonconforming products from qualified ones. To address these problems, we propose an unsupervised approach, using no labeled defect samples for training. Moreover, we introduce an augmented reconstruction method and a self-supervised training strategy. The approach is based on the idea of reconstruction. In the training phase, only defect-free samples are used for training the model and updating memory items in the memory module, so the reproduced images in the test phase are bound to resemble defect-free images. The reconstruction residual is utilized to detect defects. The introduction of self-supervised training strategy further strengthens the reconstruction residual to improve detection performance. The proposed method is experimentally proved to be effective. The Area Under Curve (AUC) on a tire X-ray dataset reaches 0.873, so the proposed method is promising for application.

A Tension/Pressure Integrated Resistive Sensor Comprising of a PDMS-LC-MWCNT Composite.

A flexible strain sensor which integrates both pressure sensing and tension sensing functions is demonstrated with an active layer comprising of polydimethy-lsiloxane (PDMS) elastomer, liquid crystal (LC), and multi-walled carbon nanotubes (MWCNTs). The introduction of LC improves the agglomeration of MWCNTs in PDMS and decreases Young's modulus of flexible resistive sensors. The tension/pressure integrated resistive sensor not only shows a broad tensile sensing range of 140% strain but also shows a good sensitivity of the gauge factor, 40, with tensile force. Besides, the tension/pressure integrated resistive sensor also shows good linearity and sensitivity under pressure. The resistance of the pressure sensor increases as the applied pressure increases because of the decrease in the cross-sectional area of the path. The sensor also shows good hydrophobic properties which may help it to work under complex environment. The tension/pressure integrated sensor shows great promising applications in electronic skins and wearable devices.

Multi-Pitch Liquid Crystal Filters with Single Layer Polymer Template.

Multi-reflective peak and bandwidth scalable liquid crystal (LC) filters were investigated. By refilling a cholesteric LC (CLC) whose chiral pitch is different to the target template into a blue phase LC (BPLC) template, a multi-reflective peak single layer LC filter can be fabricated. With multiple templating and refilling processes, the number of reflective peaks can be further increased. Moreover, by refilling the CLCs of designed chiral pitch into a CLC template sequentially, a bandwidth scalable single layer CLC filter can be fabricated. The LC filters show great potential applications in optical communication, display, and LC lasing.

[Seasonal Characteristics and Source Analysis of Water-soluble Inorganic Ions in PM2.5 in Suqian City].

To study the seasonal pollution characteristics and sources of water-soluble inorganic ions in atmospheric PM2.5 in Suqian City, 171 samples were collected at three monitoring points, which were in the water vapor channel, from May 2017 to January 2018. The mass concentrations of PM2.5 and nine water-soluble inorganic ions were analyzed. The results showed that the annual average concentration of water-soluble inorganic ions in PM2.5 in Suqian City was (44.08±34.61) µg ·m-3, accounting for 41.8% of PM2.5. The concentrations of these species were in the order of ρ(NO3-) > ρ(SO42-) > ρ(NH4+) > ρ(ρl-) > ρ(Na+) > ρ(Ca2+) > ρ(K+) > ρ(F-) > ρ(Mg2+); NO3-, SO42-, and NH4+ accounted for 75.6% of the total water-soluble ions. The annual average ratio of ρ(NO3-) to ρ(SO42-) was 1.53±0.88, indicating that mobile sources contributed more to PM2.5 pollution. Based on the correlation analysis of NH4+ and SO42-, NO3- may exist in the form of (NH4)2 SO4, NH4HSO4, or NH4NO3. According to the principal component analysis, secondary transformation, industrial pollution, biomass burning, and dust were the major sources of water-soluble inorganic ions. PM2.5concentrations were positively related to relative humidity in winter. Water vapor transmission is more likely to promote PM2.5 accumulation in winter.

High SNR fingerprinting structure for LC displays.

An optical fingerprinting solution with a switchable backlight is demonstrated for liquid crystal displays (LCDs). In fingerprinting mode, the collimated infrared light is applied to improve the signal-noise ratio (SNR) between the valley and ridge of the fingerprint. Compared with the conventional backlight, the proposed structure can effectively improve the SNR. Furthermore, the fingerprinting mode and the display mode may work at the same time without interference because of the different wavelengths and light paths. It shows the great potential application of LCD-based optical fingerprints.