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Abstract The Janis–Newman–Winicour (JNW) spacetime can describe a naked singularity with a photon sphere that smoothly transforms into a Schwarzschild black hole. Our analysis reveals that photons, upon entering the photon sphere, converge to the singularity in a finite coordinate time. Furthermore, if the singularity is subjected to some regularization, these photons can traverse the regularized singularity. Subsequently, we investigate the gravitational lensing of distant sources and show that new images emerge within the critical curve formed by light rays escaping from the photon sphere. These newfound images offer a powerful tool for the detection and study of JNW naked singularities.

Abstract Background Recently, carbon quantum dots (CQDs) have been widely used in various fields, especially in the diagnosis and therapy of neurological disorders, due to their excellent prospects. However, the associated inevitable exposure of CQDs to the environment and the public could have serious severe consequences limiting their safe application and sustainable development. Results In this study, we found that intranasal treatment of 5 mg/kg BW (20 µL/nose of 0.5 mg/mL) CQDs affected the distribution of multiple metabolites and associated pathways in the brain of mice through the airflow-assisted desorption electrospray ionization mass spectrometry imaging (AFADESI-MSI) technique, which proved effective in discovery has proven to be significantly alerted and research into tissue-specific toxic biomarkers and molecular toxicity analysis. The neurotoxic biomarkers of CQDs identified by MSI analysis mainly contained aminos, lipids and lipid-like molecules which are involved in arginine and proline metabolism, biosynthesis of unsaturated fatty acids, and glutamine and glutamate metabolism, etc. as well as related metabolic enzymes. The levels or expressions of these metabolites and enzymes changed by CQDs in different brain regions would induce neuroinflammation, organelle damage, oxidative stress and multiple programmed cell deaths (PCDs), leading to neurodegeneration, such as Parkinson’s disease-like symptoms. This study enlightened risk assessments and interventions of QD-type or carbon-based nanoparticles on the nervous system based on toxic biomarkers regarding region-specific profiling of altered metabolic signatures. Conclusion These findings provide information to advance knowledge of neurotoxic effects of CQDs and guide their further safety evaluation.

Abstract Background Along with the wild applications of nitrogen-doped graphene quantum dots (N-GQDs) in the fields of biomedicine and neuroscience, their increasing exposure to the public and potential biosafety problem has gained more and more attention. Unfortunately, the understanding of adverse effects of N-GQDs in the central nervous system (CNS), considered as an important target of nanomaterials, is still limited. Results After we found that N-GQDs caused cell death, neuroinflammation and microglial activation in the hippocampus of mice through the ferroptosis pathway, microglia was used to assess the molecular mechanisms of N-GQDs inducing ferroptosis because it could be the primary target damaged by N-GQDs in the CNS. The microarray data suggested the participation of calcium signaling pathway in the ferroptosis induced by N-GQDs. In microglial BV2 cells, when the calcium content above the homeostatic level caused by N-GQDs was reversed, the number of cell death, ferroptosis alternations and excessive inflammatory cytokines release were all alleviated. Two calcium channels of L-type voltage-gated calcium channels (L-VGCCs) in plasma membrane and ryanodine receptor (RyR) in endoplasmic reticulum (ER) took part in N-GQDs inducing cytosolic calcium overload. L-VGCCs and RyR calcium channels were also involved in promoting the excess iron influx and triggering ER stress response, respectively, which both exert excessive ROS generation and result in the ferroptosis and inflammation in BV2 cells. Conclusion N-GQDs exposure caused ferroptosis and inflammatory responses in hippocampus of mice and cultured microglia through activating two calcium channels to disrupt intracellular calcium homeostasis. The findings not only posted an alert for biomedical applications of N-GQDs, but also highlighted an insight into mechanism researches of GQDs inducing multiple types of cell death in brain tumor therapy in the future. Graphical abstract

This paper proposes a parallel monitoring method for plant-wide processes by integrating mutual information and Bayesian inference into a global-local preserving projections (GLPP)-based multi-block framework. Unlike traditional multivariate statistic process monitoring (MSPM) methods, the proposed MI-PGLPP method transforms plant-wide monitoring into several sub-block monitoringtasks by fully taking advantage of a parallel distributed framework. First, the original datasets of the process are divided into a group of data blocks by quantifying the mutual information of process variables. The block indexes of new data are generated automatically. Second, each data block is modeled by the GLPP method. The variable information and local structure are well preserved during the whole projection. Third, Bayesian inference is introduced to generate final statistics of the process by the probability framework. To illustrate the algorithm performance, a detailed case study is performed on the Tennessee Eastman process. Compared with the principle component analysis and GLPP-based method, the proposed MI-PGLPP provides higher FDRs and superior performance for plant-wide process monitoring.

Abstract Background Graphene quantum dots (GQDs) provide a bright prospect in the biomedical application because they contain low-toxic compounds and promise imaging of deep tissues and tiny vascular structures. However, the biosafety of this novel QDs has not been thoroughly evaluated, especially in the central nervous system (CNS). The microarray analysis provides a hint that nitrogen-doped GQDs (N-GQDs) exposure could cause ferroptosis in microglia, which is a novel form of cell death dependent on iron overload and lipid peroxidation. Results The cytosolic iron overload, glutathione (GSH) depletion, excessive reactive oxygen species (ROS) production and lipid peroxidation (LPO) were observed in microglial BV2 cells treated with N-GQDs, which indicated that N-GQDs could damage the iron metabolism and redox balance in microglia. The pre-treatments of a specific ferroptosis inhibitor Ferrostatin-1 (Fer-1) and an iron chelater Deferoxamine mesylate (DFO) not only inhibited cell death, but also alleviated iron overload, LPO and alternations in ferroptosis biomarkers in microglia, which were caused by N-GQDs. When assessing the potential mechanisms of N-GQDs causing ferroptosis in microglia, we found that the iron content, ROS generation and LPO level in mitochondria of BV2 cells all enhanced after N-GQDs exposure. When the antioxidant ability of mitochondria was increased by the pre-treatment of a mitochondria targeted ROS scavenger MitoTEMPO, the ferroptotic biological changes were effectively reversed in BV2 cells treated with N-GQDs, which indicated that the N-GQDs-induced ferroptosis in microglia could be attributed to the mitochondrial oxidative stress. Additionally, amino functionalized GQDs (A-GQDs) elicited milder redox imbalance in mitochondria and resulted in less ferroptotic effects than N-GQDs in microglia, which suggested a slight protection of amino group functionalization in GQDs causing ferroptosis. Conclusion N-GQDs exposure caused ferroptosis in microglia via inducing mitochondrial oxidative stress, and the ferroptotic effects induced by A-GQDs were milder than N-GQDs when the exposure method is same. This study will not only provide new insights in the GQDs-induced cell damage performed in multiple types of cell death, but also in the influence of chemical modification on the toxicity of GQDs.

Cabled underwater information networks (CUINs) have evolved over the last decade to provide abundant power and broad bandwidth communication to enable marine science. To ensure reliable operation of the CUINs, the technology for high-impedance fault diagnosis and isolation with high reliability and accuracy is essential. In this paper, we review diagnosis and location methods as applied to a constant voltage ring and the tree topology network. A high-impedance fault diagnosis method based on the variation of the sampling voltage in the primary nodes (PNs) for a constant current remote power supply system is proposed. The methods for analyzing the fault voltage with using power monitoring and control system (PMACS) and communications monitoring, control system (CMACS), and hybrid detection with alternating current and direct current are used for research the high-impedance fault location based on the designed fault isolation circuit. In particular, a verification scheme for high-impedance fault location is designed for the CUINs based on the classical mesh topology. Furthermore, high-impedance faults of nodes and submarine cable sections in the trunk cable are simulated, and the variations of leakage voltage are analyzed. By researching the change of leakage voltage before and after the fault occurs, the feasibility and practicability of the diagnosis and location scheme are verified.

With the increasing use of silver nanoparticles (AgNPs) in biological materials, the cytotoxicity caused by these particles has attracted much attention. However, the molecular mechanism underlying AgNP cytotoxicity remains unclear. In this study, we aimed to systematically investigate the toxicity induced by AgNP exposure to the lung adenocarcinoma A549 cell line at the subcellular and signaling pathway levels and elucidate the related molecular mechanism. The survival rate of cells exposed to AgNPs at 0, 20, 40, 80, and 160 μg/mL for 24 or 48 h decreased in a dose- and time-dependent manner. AgNPs induced autophagy and mitophagy, determined by the transmission electron microscopy investigation and upregulation of LC3 II/I, p62, PINK1, and Parkin expression levels. AgNP treatment induced lysosomal injury, including the decline of lysosomal membrane integrity and increase in cathepsin B level. The decreased in mitochondrial membrane potential, along with upregulation of cytochrome c, caspases 9 and 3, and BAX/BCL2, further suggested that mitochondrial injury were involved in AgNP-induced apoptosis. In addition, mitochondrial injury may further lead to excessive production of reactive oxygen species and oxidative/ antioxidant imbalance. The results suggested that AgNPs could regulate autophagy via mitochondrial and lysosome injury in A549 cells. The information of the molecular mechanism will provide an experimental basis for the safe application of nanomaterials.

Along with the increasing application of graphene quantum dots (GQDs) in the fields of biomedicine and neuroscience, it is important to assess the probably adverse effects of GQDs in the central nervous system (CNS) but their underlying toxic mechanisms is still unclear. In this study, we evaluate the molecular mechanisms associated with circular RNAs (circRNAs) of nitrogen-doped GQDs (N-GQDs) and amino-functionalized GQDs (A-GQDs) damaging the cell viability and cellular structure in microglia by an integrative analysis of RNA microarray. The differentially expressed circRNA (DEcircRNAs)-miRNA- differentially expressed mRNA (DEmRNAs) regulatory networks were conducted in BV2 microglial cells treated with 25 µg/mL N-GQDs, 100 µg/mL N-GQDs and 100 µg/mL A-GQDs. Based on that, the protein-coding genes in each ceRNA network were collected to do bio-functional analysis to evaluate signaling pathways that were indirectly mediated by circRNAs. Some pathways that could play indispensable roles in the neurotoxicity of N-GQDs or both two kinds of GQDs were found. Low-dosed N-GQDs exposure mainly induced inflammatory action in microglia, while high-dosed N-GQDs and A-GQDs exposure both affect olfactory transduction and GABAergic synapse. Meanwhile, several classical signaling pathways, including mTOR, ErbB and MAPK, could make diverse contributions to the neurotoxicity of both two kinds of GQDs. These circRNAs could be toxic biomarkers or protective targets in neurotoxicity of GQDs. More importantly, they emphasized the necessity of comprehensive analysis of latent molecular mechanisms through epigenetics approaches in biosafety assessment of graphene-based nanomaterials.

The advent of cloud platform has promoted the complexity and scales of industries increasingly. Any deliberate or non-deliberate faults may cause enormous impact on system performance and server costs. Anomaly detection is a good way to identify anomalies and improve the dependability of the cloud platform. However, some of the anomaly detection methods are labeled data dependency, and some of them are sensitive to the dynamic runtime environment of the cloud platform. To address the problems, an adaptive and incremental clustering anomaly detection algorithm for virtual machines under the cloud platform runtime environment is proposed. Compared with the previous detection methods, the effect of the runtime environment factor is taken into account. Owning to the high level of dynamic cloud platform manages and the resources allocation of virtual machines, the environmental factors play an important role in the running performance of the virtual machines. In this paper, an improved adaptive and incremental clustering algorithm is introduced to perform the detection with the considerations of the cloud platform runtime environment. To demonstrate the effectiveness, two sets of experiments are performed. The experimental results indicate that the proposed anomaly detection method can greatly improve the detection accuracy rate even the cloud platform runtime environment changes.

Along with the rapidly increasing applications of nitrogen-doped graphene quantum dots (N-GQDs) in the field of biomedicine, the exposure of N-GQDs undoubtedly pose a risk to the health of human beings, especially in the nervous system. In view of the lack of data from in vivo studies, this study used the nematode Caenorhabditis elegans (C. elegans), which has become a valuable animal model in nanotoxicological studies due to its multiple advantages, to undertake a bio-safety assessment of N-GQDs in the nervous system with the assistance of a deep learning model. The findings suggested that accumulated N-GQDs in the nematodes’ bodies damaged their normal behavior in a dose- and time-dependent manner, and the impairments of the nervous system were obviously severe when the exposure dosages were above 100 μg/mL. When assessing the morphological changes of neurons caused by N-GQDs, a quantitative image-based analysis based on a deep neural network algorithm (YOLACT) was used because traditional image-based analysis is labor-intensive and limited to qualitative evaluation. The quantitative results indicated that N-GQDs damaged dopaminergic and glutamatergic neurons, which are involved in the neurotoxic effects of N-GQDs in the nematode C. elegans. This study not only suggests a fast and economic C. elegans model to undertake the risk assessment of nanomaterials in the nervous system, but also provides a valuable deep learning approach to quantitatively track subtle morphological changes of neurons at an unbiased level in a nanotoxicological study using C. elegans.

In view of the actual problems existing in life-cycle health monitoring and diagnosis of large complex equipment, the machine-learning algorithm is applied to data mining of the equipment operation big data, the expert knowledge base is established, the diagnosis rules related to the fault are obtained, the intelligent online monitoring and remote diagnosis of the equipment health condition are realised. The system uses uncertain fault prediction method and hybrid intelligent algorithm to discover the hierarchical association between operation feature big data and operation faults, the feature extraction of operation faults, and the intelligent diagnosis of operation faults. It effectively improved the sensitivity, robustness, and accuracy of monitoring and diagnosis. In the cloud service platform based on the Internet of things, the system realises the intelligent fault prediction and diagnosis, establishes a proactive maintenance system, improves the production efficiency, and ensures the production safety.

The resource management of virtualisation system is a core issue in the field of cloud computing. For achieving the goal of efficient manage system resource without performance loss in clouding environment, the authors propose an optimal load balancing controller to assign the resource to different virtual machines, which run on a single physical server. Here, the authors focus on dynamical web workloads that the system resource management problem can be formulated as a dynamical optimisation problem. In the face of dynamical and robust workloads, the authors present an alternative approach based on the constrained boundary linear quadratic control (cBLQC) method, the optimal resource allocation scheme is solved by minimising the quadratic cost function with constrains on the system input and output. Different with existing works that assume a particular distribution for system noise or ignore the noise directly, the authors loosen the restrictions on system noise, which simply assume that system noise belongs with some reasonable norm-bounded set. Experiments on the Xen-based platform with a set of workload patterns show that the efficiency of authors’ method in terms of control precision and stability.

With the applications of quantum dots (QDs) expanding, many studies have described the potential adverse effects of QDs, yet little attention has been paid to potential toxicity of QDs in the liver. The aim of this study was to investigate the effects of cadmium telluride (CdTe) QDs in mice and murine hepatoma cells alpha mouse liver 12 (AML 12). CdTe QDs administration significantly increased the level of lipid peroxides marker malondialdehyde (MDA) in the livers of treated mice. Furthermore, CdTe QDs caused cytotoxicity in AML 12 cells in a dose- and time-dependent manner, which was likely mediated through the generation of reactive oxygen species (ROS) and the induction of apoptosis. An increase in ROS generation with a concomitant increase in the gene expression of the tumor suppressor gene p53, the pro-apoptotic gene Bcl-2 and a decrease in the anti-apoptosis gene Bax, suggested that a mitochondria mediated pathway was involved in CdTe QDs’ induced apoptosis. Finally, we showed that NF-E2-related factor 2 (Nrf2) deficiency blocked induced oxidative stress to protect cells from injury induced by CdTe QDs. These findings provide insights into the regulatory mechanisms involved in the activation of Nrf2 signaling that confers protection against CdTe QDs-induced apoptosis in hepatocytes.

Self-Organizing Map (SOM) algorithm as an unsupervised learning method has been applied in anomaly detection due to its capabilities of self-organizing and automatic anomaly prediction. However, because of the algorithm is initialized in random, it takes a long time to train a detection model. Besides, the Cloud platforms with large scale virtual machines are prone to performance anomalies due to their high dynamic and resource sharing characters, which makes the algorithm present a low accuracy and a low scalability. To address these problems, an Improved Incremental Self-Organizing Map (IISOM) model is proposed for anomaly detection of virtual machines. In this model, a heuristic-based initialization algorithm and a Weighted Euclidean Distance (WED) algorithm are introduced into SOM to speed up the training process and improve model quality. Meanwhile, a neighborhood-based searching algorithm is presented to accelerate the detection time by taking into account the large scale and high dynamic features of virtual machines on cloud platform. To demonstrate the effectiveness, experiments on a common benchmark KDD Cup dataset and a real dataset have been performed. Results suggest that IISOM has advantages in accuracy and convergence velocity of anomaly detection for virtual machines on cloud platform.