The risk of blue light exposure to human health has attracted increased research attention. Blue light, with relatively high energy, can cause irreversible photochemical damage to eye tissue. Excessive exposure of the eye to blue light tends to cause a series of alterations, such as oxidative stress, mitochondrial apoptosis, inflammatory apoptosis, mitochondrial apoptosis and DNA damage, resulting in the development of dry eye disease, glaucoma, and keratitis. Accordingly, physical protection, chemical and pharmaceutical protective measures, gene therapy, and other methods are widely used in the clinical treatment of blue light hazard. We reviewed the studies on possible blue light-induced signaling pathways and mechanisms in the eye and summarized the therapeutic approaches to addressing blue light hazard.

The temperature of molten iron at the taphole of blast furnace (BF) is an important parameter that reflects molten iron quality and BF conditions. It is not easy to measure the temperature of molten iron at the taphole in real time. To achieve continuous and accurate detection of molten iron temperature (MIT) at taphole, this paper proposes a temperature measurement and compensation method of molten iron based on infrared computer vision. First, an infrared computer vision system is designed and installed to capture the infrared thermal images of molten iron flow at the taphole. Then, the molten iron flow area is determined by using image processing. Afterward, the temperature information of slag region is obtained to calculate the MIT based on threshold segmentation. Furthermore, considering the measurement error caused by dust, the texture features influenced by dust are extracted based on the defined temperature-level co-occurrence matrix and the neighboring temperature-level-dependence matrix, and a compensation model is established to compensate the measurement error based on ensemble neural network and support vector regression. Finally, considering that the MIT and the slag temperature are approximately the same at the taphole, the MIT at the taphole is acquired according to the slag temperature. Industrial experiments and applications demonstrate that the proposed method can measure the MIT at the taphole continuously and accurately and provide reliable MIT data for operators to control BF.

Myopia has become one of the most critical health problems in the world with the increasing time spent indoors and increasing close work. Pathological myopia may have multiple complications, such as myopic macular degeneration, retinal detachment, cataracts, open-angle glaucoma, and severe cases that can cause blindness. Mounting evidence suggests that the cause of myopia can be attributed to the complex interaction of environmental exposure and genetic susceptibility. An increasing number of researchers have focused on the genetic pathogenesis of myopia in recent years. Scleral remodeling and excessive axial elongating induced retina thinning and even retinal detachment are myopia's most important pathological manifestations. The related signaling pathways are indispensable in myopia occurrence and development, such as dopamine, nitric oxide, TGF-β, HIF-1α, etc. We review the current major and recent progress of biomedicine on myopia-related signaling pathways and mechanisms.

Froth color can be referred to as a direct and instant indicator to the key flotation production index, for example, concentrate grade. However, it is intractable to measure the froth color robustly due to the adverse interference of time-varying and uncontrollable multisource illuminations in the flotation process monitoring. In this article, we proposed an illumination-invariant froth color measuring method by solving a structure-preserved image-to-image color translation task via an introduced Wasserstein distance-based structure-preserving CycleGAN, called WDSPCGAN. WDSPCGAN is comprised of two generative adversarial networks (GANs), which have their own discriminators but share two generators, using an improved U-net-like full convolution network to conduct the spatial structure-preserved color translation. By an adversarial game training of the two GANs, WDSPCGAN can map the color domain of froth images under any illumination to that of the referencing illumination, while maintaining the structure and texture invariance. The proposed method is validated on two public benchmark color constancy datasets and applied to an industrial bauxite flotation process. The experimental results show that WDSPCGAN can achieve illumination-invariant color features of froth images under various unknown lighting conditions while keeping their structures and textures unchanged. In addition, WDSPCGAN can be updated online to ensure its adaptability to any operational conditions. Hence, it has the potential for being popularized to the online monitoring of the flotation concentrate grade.

Data-driven process monitoring methods have attracted many attentions and gained wide applications. However, the real industrial process data are much more complex which is characterized by multimode, high dimensional, corrupted, and less labeled data. In order to eliminate these unfavourable factors simultaneously, a semisupervised robust projective and discriminative dictionary learning method is proposed. First, a semisupervised strategy is introduced to label unsupervised training data. Then, by utilizing low-rank and sparse features of raw data and outliers, a robust decomposition method is used to obtain clean data. After that, a simultaneously projective and discriminative model is proposed to extracting the feature of the low-rank clean data. Finally, the projection matrix and global dictionary, as well as the threshold are obtained through iterative dictionary learning. This hybrid framework provides a robust model for process monitoring and mode identification, and its efficiency is demonstrated by both synthetic examples and real industrial process cases.

An adaptive generalized predictive control (GPC) scheme for industrial process is designed based on a neuron adaptive splitting and merging radial basis function neural network (NASM-RBFNN) in this paper. The NASM-RBFNN is developed to identify the dynamic behaviors of industrial process. In order to provide an accurate prediction model for the GPC, a neuron adaptive splitting and merging strategy and a weighted parameters adaptive correction approach are proposed. The neuron adaptive splitting and merging strategy can automatically add or delete the hidden neurons on-line, and the weighted parameters adaptive correction approach can update the weights based on the error of neural network. The proposed approaches can enable the NASM-RBFNN to be adapted to the time-varying production condition and stochastic disturbance. The stability analysis and convergence of neuron adaptive splitting and merging strategy and weighted parameters adaptive correction approach are given. Finally, the NASM-RBFNN-based GPC (NASM-RBFNN-GPC) is applied to control the iron removal process in the largest zinc hydrometallurgy plant in China. The industrial experiments demonstrate that the NASM-RBFNN- GPC has a satisfactory tracking and control performance.