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Iron overload is a common phenomenon in the elderly population. Many clinical studies have indicated an association between iron overload and the incidence and pathological progression of intervertebral disc degeneration (IVDD). However, the role and underlying mechanism by which iron participates in the progression of IVDD has not yet been reported. In the present study, we aimed to elucidate the connection between iron overload and IVDD, and explore the underlying mechanisms of disease. Firstly, a clinical epidemiology study was conducted and revealed that iron overload is an independent risk factor for human IVDD. To elucidate the role of iron overload in IVDD, an iron overload mouse model was established, and we observed that iron overload promoted IVDD and cartilage endplate degeneration in a dose dependent manner. Endplate chondrocytes were further isolated and treated with FAC to mimic iron overload in vitro. Excess iron significantly promoted mineralization of endplate chondrocytes in addition to their degeneration via oxidative stress. Moreover, a high dose of excess iron promoted chondrocytes ferroptosis. An iron chelator (DFO), an antioxidant (NAC) and a ferroptosis inhibitor (Fer-1) demonstrated effective inhibition of endplate chondrocyte degeneration induced by iron overload, and our in vivo studies further demonstrated that DFO, NAC and Fer-1 could rescue high dose iron-induced IVDD and cartilage endplate calcification. In conclusion, our results indicate that iron overload is strongly associated with the onset and development of IVDD via oxidative stress and ferroptosis. Inhibiting oxidative stress or ferroptosis could therefore be promising therapeutic strategies for IVDD induced by iron overload.

On account of the uneven level of ecological and environmental protection in the countries along the Belt and Road, infrastructure construction and investment cooperation around natural resource-intensive industries such as steel, energy, and nonferrous metals can easily induce serious ecological problems. It is necessary and practical to provide enterprises with timely and effective ecological circumstantial information of Belt and Road and pilot the establishment of the Belt and Road infrastructure construction activities with appropriate ecological risk prevention and control mechanisms. In this study, we researched the ecological risk level distribution of the countries along the Belt and Road by using ESA global land cover data and ecological risk index mode of land use. Kuwait, one of the most representative countries along the Belt and Road, was selected to analyze its spatial distribution of ecological risk by using ordinary Kriging interpolation. The results show several aspects. There existed high-ranking ecological risks in the following countries: Saudi Arabia in the Middle East, Turkmenistan in the Central Asian, Mongolia in East Asia and Pakistan in South Asia and so on; the medium ecological risks appear in the following countries: Ukraine in Eastern Europe, India, Bangladesh in South Asian, Kazakhstan and Kyrgyzstan in Central Asia the low ecological risk countries mainly include Russia, most Eastern European countries represented by Poland, and most Southeast Asian countries represented by Thailand. In the case, Kuwait's risk level is high in urban areas, medium in desert areas, and low along the Persian Gulf, showing a layered and striped distribution. Most areas in Kuwait are medium ecological risk zones with a relatively small area of high ecological risk and low ecological risk areas clustered in the eastern part of Kuwait. To effectively lower the ecological risk levels of countries along B&R, we suggest establishing B&R ecological risk prevention and control mechanisms while ecological conservation technologies are also worth noting.

Developing advanced electrode architectures through modifying active materials, conductive fillers, binders, and electrolytes as well as processing methods has drawn significant research interest. Due to the insufficient electrical conductivity of many active materials, adding conductive carbon fillers to composite electrodes provides the necessary electrical conductivity. The dimensionality effect among different conductive fillers has a significant impact on electrochemistry, which can be associated with morphological and chemical heterogeneities of electrodes. Here, synchrotron X-ray mosaic nanotomography and X-ray spectroscopy nanoimaging provided direct three-dimensional (3D) visualization and quantification capabilities to investigate the dimensionality effects of Super P (SP) and single-walled carbon nanotube (SWCNT) fillers on the capacity retention of LiNi1/3Mn1/3Co1/3O2 (NMC111). The results indicate that NMC/SWCNT electrodes, with a wrapping effect from the SWCNTs, exhibited more homogeneous particle size distributions, morphological changes, and chemical states than NMC/SP electrodes, without the wrapping effect. This work developed a framework of 3D quantification methods to study the capacity fading behavior associated with morphological and chemical heterogeneities and paved the way toward designing electrodes for high rate energy storage applications.

Inducing ferromagnetism into graphene is vital today because it has a wide range of applications such as spintronics devices and magnetic memory devices. In this paper, we will report a new method to synthesize ferromagnetic graphene by nitrogen doping. X-ray photoelectron spectroscopy (XPS) and Raman spectroscopy were utilized to testify the N-doped material and further discuss the N-doped process. The superconducting quantum interference device (SQUID) was put in and used to analyze the magnetic properties of the N-doped graphene sheets. It shows that the material exhibits ferromagnetism at both 3 K and 300 K and the ferromagnetic saturation moment is 0.412 emu/g and 0.051 emu/g respectively. The mechanism of the origin of the ferromagnetism in N-doped graphene sheets will also be discussed in this paper. It shows that, when the amount graphitic N reached the threshold, the origin of the ferromagnetism will change from defects induced by nitrogen atoms to the transition in energy band caused by graphitic N.

In this paper, a hydrothermal approach is utilized for the first time in integrating graphene oxide (GO), acetic acid (HAc) and nickel foam to prepare hydrogenated graphene (HG). There are two primary aims of this study: one is to ascertain the structure of the as-prepared HG, and the other one is to investigate the ferromagnetism of the HG. Under hydrothermal conditions, GO was reduced and hydrogenated by HAc, while the nickel foam served as a catalyst. This work provides a novel and facile route for the synthesis of hydrogenated graphene, which may lead to the application of hydrogenated graphene in spin electronic devices.

This paper considers the prespecified-time synchronization issue of switched coupled neural networks (SCNNs) under some smooth controllers. Different from the traditional finite-time synchronization (FTS), the synchronization time obtained in this paper is independent of control gains, initial values or network topology, which can be pre-set as to the task requirements. Moreover, unlike the existing nonsmooth or even discontinuous FTS control strategies, the new proposed control protocols are fully smooth, which abandon the common fractional power feedbacks or signum functions. Finally, two illustrative examples are provided to illustrate the effectiveness of the theoretical results.

In this paper, an efficient method for performing minimum weight optimization of composite laminates using artificial neural network (ANN) based surrogate models is proposed. By predicting the buckling loads of the laminates using ANN the use of time-consuming buckling evaluations during the iterative optimization process are avoided. Using for the first time lamination parameters, laminate thickness and other dimensional parameters as inputs for these ANN models significantly reduces the number of models required and therefore computational cost of considering laminates with many different numbers of layers and total thickness. Besides, as the stacking sequences are represented by lamination parameters, the number of inputs of the ANN models is also significantly reduced, avoiding the curse of dimensionality. Finite element analysis (FEA) is employed together with the Latin hypercube sampling (LHS) method to generate the database for the training and testing of the ANN models. The trained ANN models are then employed within a genetic algorithm (GA) to optimize the stacking sequences and structural dimensions to minimize the weight of the composite laminates. The advantages of using ANN in predicting buckling load is proved by comparison with other machine learning methods, and the effectiveness and efficiency of the proposed optimization method is demonstrated through the optimization of flat, blade-stiffened and hat-stiffened laminates.