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Spinal cord injury is a serious disease of the central nervous system, but there is no effective treatment. And zinc is an essential nutrient for human body and participates in many physiological processes, such as immune response, homeostasis, oxidative stress, cell cycle progression, DNA replication, DNA damage repair, apoptosis, and aging. This article mainly summarizes that zinc could predict the prognosis and treat the spinal cord injury. Especially, zinc could help to inhibit inflammation, regulate autophagy, and reduce oxidative stress. However, excessive zinc will damage neurons.

The converter steelmaking process smelts hot metal to liquid steel and occupies an important position in industry. The composition of liquid steel at the endpoint is an essential quality index, including the concentrations of multiple elements, such as carbon, silicon, and manganese. Accurately predicting endpoint composition is the basis of production optimization. Hence, a multichannel diffusion graph convolutional network (MCDGCN) is presented in this article. Unlike conventional models, the developed MCDGCN describes the converter steelmaking process as a graph to exploit the correlations among element concentrations for an accurate endpoint composition prediction. We also develop a unique $K$ -hop diffusion method to extract the globally consistent information over the graph for predicting each element. The proposed method addresses the composition prediction task for a realistic converter steelmaking process. To the best of our knowledge, this is the first time that up to 15 elements of liquid steel are covered and predicted to present a comprehensive process model. Compared with six benchmark models, MCDGCN presents state-of-the-art results, i.e., an average $R^{2}$ of 0.8475 and an average MAE of 0.0189, which shows that the correlation mining of graph deep learning can indeed improve the prediction performance for endpoint composition.

As an ensemble algorithm, network boosting enjoys a powerful classification ability but suffers from the tedious and time-consuming training process. To tackle the problem, in this paper, a broad network gradient boosting system (BNGBS) is developed by integrating gradient boosting machine with broad networks, in which the classification loss caused by a base broad network is learned and eliminated by followed networks in a cascade manner. The proposed system is constructed as an additive model and can be easily optimized by a greedy strategy instead of the tedious back-propagation algorithm, resulting in a more efficient learning process. Meanwhile, triple incremental learning capabilities including the increment of feature nodes, increment of input samples, and increment of target classes are designed. The proposed system can be efficiently updated and expanded based on the current status instead of being entirely retrained when the demands for more feature nodes, input samples, and target classes are proposed. The node-increment ability allows to add more feature nodes into the built system if the current structures are not effective for learning. The sample-increment ability is developed to allow the model to keep learning from the coming batch data. The class-increment ability is used to tackle the issue that the coming batch data may contain unseen categories. In comparison with existing popular machine learning methods, comprehensive results based on eight benchmark datasets illustrate the effectiveness of the proposed broad network gradient boosting system for the classification task.

Background: Little is known about the pathophysiological diversity of myocardial injury in type 2 diabetes mellitus (T2DM), but analyzing these differences is important for the accurate diagnosis and precise treatment of diabetic cardiomyopathy. This study aimed to elucidate the key cardiac pathophysiological differences in myocardial injury between obese and non-obese T2DM from mice to humans. Methods: Obese and non-obese T2DM mouse models were successfully constructed and observed until systolic dysfunction occurred. Changes in cardiac structure, function, energy metabolism and oxidative stress were assessed by biochemical and pathological tests, echocardiography, free fatty acids (FFAs) uptake fluorescence imaging, transmission electron microscopy, etc. Key molecule changes were screened and verified by RNA sequencing, quantitative real-time polymerase chain reaction and western blotting. Further, 28 human heart samples of healthy population and T2DM patients were collected to observe the cardiac remodeling, energy metabolism and oxidative stress adaptations as measured by pathological and immunohistochemistry tests. Results: Obese T2DM mice exhibited more severe cardiac structure remodeling and earlier systolic dysfunction than non-obese mice. Moreover, obese T2DM mice exhibited severe and persistent myocardial lipotoxicity, mainly manifested by increased FFAs uptake, accumulation of lipid droplets and glycogen, accompanied by continuous activation of the peroxisome proliferator activated receptor alpha (PPARα) pathway and phosphorylated glycogen synthase kinase 3 beta (p-GSK-3β), and sustained inhibition of glucose transport protein 4 (GLUT4) and adipose triglyceride lipase (ATGL), whereas non-obese mice showed no myocardial lipotoxicity characteristics at systolic dysfunction stage, accompanied by the restored PPARα pathway and GLUT4, sustained inhibition of p-GSK-3β and activation of ATGL. Additionally, both obese and non-obese T2DM mice showed significant accumulation of reactive oxygen species (ROS) when systolic dysfunction occurred, but the NF-E2-related factor 2 (Nrf2) pathway was significantly activated in obese mice, while was significantly inhibited in non-obese mice. Furthermore, the key differences found in animals were reliably verified in human samples. Conclusion: Myocardial injury in obese and non-obese T2DM may represent two different types of complications. Obese T2DM individuals, compared to non-obese individuals, are more prone to develop cardiac systolic dysfunction due to severe and persistent myocardial lipotoxicity. Additionally, anti-oxidative dysfunction may be a key factor leading to myocardial injury in non-obese T2DM.

Myocardial infarction (MI) leads to cardiomyocyte death resulting in cardiac dysfunction. However, the molecular mechanism is still poorly understood. Cardiac fibroblasts can be marked by CD90, a N-glycosylated glycophosphatidylinositol anchored conserved cell surface protein, and the CD90 positive cardiac fibroblasts can release exosome against ischemic damage. Reduce apoptosis can promote the recovery of myocardial function after ischemic heart. Interestingly, exosome from CD90 positive cardiac fibroblasts can decrease the level of apoptosis after MI in vivo. However, the ability of CD90 positive cardiac fibroblasts exosome to reduce apoptosis, and the potential effects on the recovery of cardiomyocytes in heart after MI remain to be clarified. In this study, we found that CD90 positive cardiac fibroblasts exosome treated induced myocardial function recovery in a mouse MI model. Indeed, CD90 positive cardiac fibroblasts exosome increased phosphatidylinositol 3-kinase (PI3K) and kinase B (AKT) after MI. In addition, CD90 positive cardiac fibroblasts exosome decreased TUNEL in cardiomyocytes after MI. Furthermore, PI3K was inhibited by PI3K inhibitor LY294002, while hypoxia-stimulated primary cardiomyocytes were treated with CD90 positive cardiac fibroblasts exosome. Collectively, our results suggest that CD90 positive cardiac fibroblasts exosome can improve the recovery of cardiac function by decreasing apoptosis after MI in mice, likely via the PI3K/AKT signaling pathway.

In this paper, we design a vehicle target tracking system based on roadside cameras. This system contains vehicles and devices on roadside including computation units. The motion of vehicles is captured by cameras, and then is sent to computation units, in which the YOLO-V3 algorithm is used to identify the target vehicles. In particular, the Kalman filter is utilized to estimate the position and velocity of vehicles we are interested in. Moreover, we design a PID control law to drive the target vehicles to the designed sites. Experimental results show that the system we design can achieve vehicle target tracking by using roadside cameras.

To support future 6G communication at high data rate of terabit-per-second (Tb/s), high gain and low-profile terahertz (THz) antennas operating above 1 THz are required. The current horn, lens and microfabricated cavity-backed THz antennas are either bulky or low gain. Antenna with a Fabry-Perot cavity is a promising candidate for to achieve high gain and low-profile in THz. However, conventional technologies such as metal milling, printed circuit board fabrication, or 3D printing have limitations of low precision, large size and rough surface. They are not able to fabricate high performance spherical Fabry-Perot cavity structures at micrometer scale. To overcome these limitations, imprint technology in silicon (Si) was developed to fabricate THz antenna with a Fabry-Perot cavity. The initial results show that high gain and low-profile THz antenna at 1 THz was achieved by using imprint technology in Si. To the best of our knowledge, this is the first time a THz antenna with a Fabry-Perot cavity was successfully developed to work up to 1 THz.

Model-based reinforcement learning (MBRL) has been proposed as a promising alternative solution to tackle the high sampling cost challenge in the canonical RL, by leveraging a system dynamics model to generate synthetic data for policy training purpose. The MBRL framework, nevertheless, is inherently limited by the convoluted process of jointly optimizing control policy, learning system dynamics, and sampling data from two sources controlled by complicated hyperparameters. As such, the training process involves overwhelmingly manual tuning and is prohibitively costly. In this research, we propose a "reinforcement on reinforcement" (RoR) architecture to decompose the convoluted tasks into two decoupled layers of RL. The inner layer is the canonical MBRL training process which is formulated as a Markov decision process, called training process environment (TPE). The outer layer serves as an RL agent, called intelligent trainer, to learn an optimal hyperparameter configuration for the inner TPE. This decomposition approach provides much-needed flexibility to implement different trainer designs, referred to "train the trainer." In our research, we propose and optimize two alternative trainer designs: 1) an unihead trainer and 2) a multihead trainer. Our proposed RoR framework is evaluated for five tasks in the OpenAI gym. Compared with three other baseline methods, our proposed intelligent trainer methods have a competitive performance in autotuning capability, with up to 56% expected sampling cost saving without knowing the best parameter configurations in advance. The proposed trainer framework can be easily extended to tasks that require costly hyperparameter tuning. Energy Market Authority (EMA) Info-communications Media Development Authority (IMDA) National Research Foundation (NRF) This work was supported in part by the Energy Program, Nation Research Foundation, Prime Minister’s Office, Singapore, administrated by the Energy Market Authority of Singapore, under Award NRF2017EWT-EP003-023, in part by the Green Data Centre Research administrated by the Info-communications Media Development Authority, under Award NRF2015ENC-GDCR01001-003, and in part by the Behavioral Studies in the Energy, Water, Waste and Transportation Sector under Award BSEWWT2017_2_06.

Different with traditional aboveground data centres, underground data centres have attracted increasing attention during recent years due to its inherent geographical advantages such as zero solar heat gain, low ambient temperature, natural geothermal cooling, and solid rock surrounded structures. The energy reduction in air-conditioning and mechanical ventilation (ACMV) system of the underground data centre is even more significant for the tropical regions where air is hot and humid all the year round. However, little study has been conducted to systematically analyse the relationship between the air-conditioning effectiveness and the corresponding energy efficiency. In this work, both energy simulations and computational fluid dynamics (CFD) simulations are conducted to the underground air-conditioning data centres in the tropics. The former one studies the energy consumption and energy efficiency of the whole ACMV system for a data centre. Meanwhile, the latter one studies the corresponding detailed airflow pattern within the data centre room. Optimizations in air management of the ACMV system are also proposed based on the energy and CFD simulation results. Published version

The psychological state of coal miners may have negative changes because of the long-term underground working environment and complex production process and will cause unsafe behaviour. Resilience can enhance the adaptability of individuals to high-risk and high-pressure environments and predict unsafe behaviour. However, currently little research has been done to develop resilience measuring tools for coal miners. Therefore, this paper revised and verified the Connor-Davidson Resilience Scale (CD-RISC) among coal miners in China and developed an individual resilience scale available for the coal industry. Five hundred and twenty subject coal miners have finished the initial test, and the exploratory factor analysis and reliability analysis were used to revise the CD-RISC. Another survey on 639 samples was then conducted to assess a confirmatory factor analysis and test-retest reliability of the resilience scale. The resulting scale has two dimensions, tenacity and strength, including a collection of 6 items. This study assists in further exploring the structural factor of individual resilience level for China coal industry, and provides a new research idea for occupational health, and contributes to the safety management and sustainable development of the coal enterprises.

This paper revisits the problem of estimating the domain of attraction and the nonlinear L2 gain of a saturated system with an algebraic loop. A max quadratic Lyapunov function, the maximum function of a group of quadratic Lyapunov functions, is proposed for use in the stability and performance analysis for a saturated system. The max quadratic Lyapunov function involves a partitioning of the state space. Exploiting the special properties of regions of the state space, we propose in this paper an enhanced max quadratic Lyapunov function, which results from adding a term that characterizes the regions of the state space to the max quadratic Lyapunov function. The matrix associated with the enhanced max quadratic Lyapunov function is not required to be positive definite, and thus a set of less conservative stability and performance conditions are established, from which a larger estimate of the domain of attraction and a tighter estimate of the nonlinear L2 gain can be obtained. Simulation results indicate the effectiveness and superiority of the proposed method.

Converter transformer is the key equipment of high voltage direct current transmission system. The solid suspending particles originating from the process of installation and operation of converter transformer have significant influence on the insulation performance of transformer oil, especially in presence of DC component in applied voltage. Under high electric field, the particles easily lead to partial discharge and breakdown of insulating oil. This paper investigated copper particle effect on the breakdown voltage of transformer oil at combined AC and DC voltage. A simulation model with single copper particle was established to interpret the particle effect on the breakdown strength of insulating oil. The experimental and simulation results showed that the particles distort the electric field. The breakdown voltage of insulating oil contaminated with copper particle decreases with the increase of particle number, and the breakdown voltage and the logarithm of particle number approximately satisfy the linear relationship. With the increase of the DC component in applied voltage, the breakdown voltage of contaminated insulating oil decreases. The simulation results show that the particle collides with the electrode more frequently with more DC component contained in the applied voltage, which will trigger more discharge and decrease the breakdown voltage of insulating oil.

Background Endothelial cell injury, induced by dyslipidemia, is the initiation of atherosclerosis, resulting in an imbalance in endothelial fatty acid ( FA ) transport. Pigment epithelial‐derived factor ( PEDF ) is an important regulator in lipid metabolism. We hypothesized that PEDF is involved in endothelium‐mediated FA uptake under hyperlipidemic conditions. Methods and Results Circulating PEDF levels were higher in patients with atherosclerotic cardiovascular disease than in normal individuals. However, decreasing trends of serum PEDF levels were confirmed in both wild‐type and apolipoprotein E–deficient mice fed a long‐term high‐fat diet. Apolipoprotein E–deficient/PEDF‐deficient mice were generated by crossing PEDF‐deficient mice with apolipoprotein E–deficient mice, and then mice were fed with 24, 36, or 48 weeks of high‐fat diet. Greater increases in body fat and plasma lipids were displayed in PEDF ‐deficient mice. In addition, PEDF deficiency in mice accelerated atherosclerosis, as evidenced by increased atherosclerotic plaques, pronounced vascular dysfunction, and increased lipid accumulation in peripheral tissues, whereas injection of adeno‐associated virus encoding PEDF exerted opposite effects. Mechanistically, PEDF inhibited the vascular endothelial growth factor B paracrine signaling by reducing secretion of protein vascular endothelial growth factor B in peripheral tissue cells and decreasing expression of its downstream targets in endothelial cells, including its receptors (namely, vascular endothelial growth factor receptor‐1 and neuropilin‐1), and FA transport proteins 3 and 4, to suppress endothelial FA uptake, whereas PEDF deletion in mice activated the vascular endothelial growth factor B signaling pathway, thus causing markedly increased lipid accumulation. Conclusions Decreasing expression of PEDF aggravates atherosclerosis by significantly impaired vascular function and enhanced endothelial FA uptake, thus exacerbating ectopic lipid deposition in peripheral tissues.

Most coal mine accidents are attributed to the unsafe behavior of miners. Adjusting the safety attitude and thus improving miners&rsquo, safety behavior is important for accident prevention. However, the relationship between safety attitude and safety behavior in the coal mining industry has not been explored. The coal miners&rsquo, safety attitude scale and safety behavior scale were used to analyze the impact of safety attitude on safety behavior and investigate the correlation between four dimensions of safety attitude and two kinds of safety behavior. The impact of demographic characteristics including age, length of service, and education level on safety attitude and safety behavior was also measured. A survey of miners at four coal mines in China resulted in 593 valid responses. The result indicates that safety attitude is not only positively related to safety behavior but also positively related to safety participation and safety compliance. From the four dimensions of safety attitude, the team safety climate directly affects safety participation and safety compliance. Management safety commitment, job stress, and fatalism are not significantly related to safety participation and safety compliance. The results show that age and length of service were slightly related to safety attitude, and the education level was not significantly related to safety attitude. Age, length of service and education level had no impact on safety behavior. The contribution of this study to the current literature is that the safety attitude of coal miners can positively affect safety behavior and can be improved by fostering a good team safety climate. Practical implications emphasize safety training and safety education, especially for young miners. Managers should reinforce safety commitment, provide adequate safety equipment, timely communicate with miners, and encourage miners to actively communicate with colleagues to improve safety behavior and prevent accident in the coal mining industry.

In this paper, we propose a vehicle following control algorithm based on object detection for intelligent multi-vehicle systems. We use the SSD-MobileNet as the object detection model, and collect more than 3,000 pictures of the target signs as the training dataset. By detecting the target signs from the camera on-board, we obtain the relative position between the front and follower vehicles. Based on the relative position, the following control of the intelligent vehicle is carried out. In particular, we use the discrete incremental PID control method for the longitudinal control of the vehicle, and apply the pure pursuit algorithm to calculate the vehicle steering angles. Experimental results show the effectiveness of the control algorithms.

With the development of the new energy vehicle industry, the problem of energy consumption has gradually attracted attention. The existing researches on energy consumption of new energy vehicles are mainly based on clustering methods. However, clustering does not construct a direct relationship between energy consumption and other factors. This paper will explore the relationship between driving behavior and energy consumption. We use the driving data of electric vehicles to construct a set of statistical indicators describing driving behavior, and then divide the kinematic segments and calculate the statistical features in each segment. The features in one segment form a sample and thus we obtain a large number of samples. These samples are used to train a BP neural network. By using the BP neural network, the results on fitting the relationship between driving behavior and electricity consumption per kilometer are derived, that is, the mean square error is 0.014, and R2 score is 0.91. The analysis of the network parameters further indicates that there exist some features that are more important than others. This implies that the driving behavior related to these features may improve the vehicle’s energy consumption level.

The geological prospecting industry has developed rapidly in China over the past few years. It has made outstanding contributions to the discovery of new mineral resources, new energy sources, and the excavation and utilization of resources. However, geological prospecting projects do not have effective safety management measures at present. Moreover, the geological prospecting project has its own traits and features that differ from other industries, leading to the fact that safety management measures in other industries cannot be used in geological prospecting projects. Therefore, development of an effective safety management measuring tool is urgent and necessary. In recent years, safety climate has drawn great attention from scholars, and research results have been successfully applied in construction, coal mining and other industries. Based on the extensive literature review on safety climate as well as its organizational structure and employees&rsquo, individual behavior characteristics, this paper first extracted the factor structure of the safety climate and then developed a safety climate scale for geological prospecting projects. This paper used the methods of exploratory factor analysis and reliability analysis to ensure the developed safety climate scale was valid and reliable. The safety climate scale developed has four dimensions, i.e., project leader&rsquo, s safety commitment, safety institutions, risk response, and employee&rsquo, s safety attitude, containing a total of 17 measurable items. This study contributes to the current literature by exploring the factor structure of the safety climate for geological prospecting projects, and further provides a scientific basis for improvements in the geological prospecting industry. Meanwhile, the findings not only provide technical support for investigating and analyzing the safety management levels of the geological prospecting industry, but also contribute to the benchmarking standards among different enterprises and projects.

Background: Pigment epithelial-derived factor (PEDF) is protectively involved in atherosclerosis induced by hyperlipidemia, but expression characteristics of PEDF and the role of PEDF in the later stage of progressive atherosclerosis remain unclear. Methods: Circulating PEDF were analyzed in patients with atherosclerotic cardiovascular disease (ASCVD) and in murine models of wildtype (WT) mice and ApoE-/- mice under long-term high-fat diet (HFD). PEDF-/-/ ApoE-/- mice and matching PEDF+/+/ ApoE-/- mice fed with HFD for 24, 36 and 48 weeks, combined with injections of adeno-associated virus encoding PEDF (AAV-PEDF) in ApoE-/-/PEDF-/- mice were used to investigate the effects of PEDF knockdown on vascular function and structure. Experiments in vitro and in vivo were applied to identify PEDF regulate vascular endothelial growth factor (VEGFB) signaling pathway mediated endothelial fatty acid (FA) uptake. Findings: Circulating PEDF was increased in ASCVD patients compared to normal individuals, while decreasing trends were confirmed in murine models of WT mice and ApoE-/- mice on long-term HFD. ApoE-/-/PEDF-/- mice placed on HFD were fatter and had accelerated atherosclerotic formation, as evidenced by higher serum lipid levels, increased atherosclerotic plaques, pronounced vascular dysfunction and increased lipid accumulation in peripheral tissues, whereas injections of AAV-PEDF in ApoE-/-/PEDF-/- mice significantly improved. Mechanistically, PEDF deletion activated VEGFB paracrine signaling by upregulating expressions of VEGFB in peripheral tissues and its downstream targets on endothelial cells, including its receptors, namely VEGFR1 and neuropilin-1, and fatty acid transport protein 3 and 4 to enhance FA uptake. Interpretation: Decreasing expression of PEDF may decompensate to aggravate atherosclerosis. Funding: National Nature Science Foundation of China, Science and Technology Project of Guangzhou. Declaration of Interest: The authors declare no conflict of interest. Ethical Approval: The study was approved by the ethical committees of Sun Yat-sen Memorial Hospital and Sun Yat-sen University.

Spinal cord injury (SCI) is a severe neurological trauma that involves complex pathological processes. Inflammatory response and oxidative stress are prevalent during the second injury and can influence the functional recovery of SCI. Specially, Apolipoprotein E (APOE) induces neuronal repair and nerve regeneration, and the deficiency of Apoe impairs spinal cord-blood-barrier and reduces functional recovery after SCI. However, the mechanism by which Apoe mediates signaling pathways of inflammatory response and oxidative stress in SCI remains largely elusive. This study was designed to investigate the signaling pathways that regulate Apoe deficiency-dependent inflammatory response and oxidative stress in the acute stage of SCI. In the present study, Apoe-/- mice retarded functional recovery and had a larger lesion size when compared to wild-type mice after SCI. Moreover, deficiency of Apoe induced an exaggerated inflammatory response by increasing expression of interleukin-6 (IL-6) and interleukin-1β (IL-1β), and increased oxidative stress by reducing expression of Nrf2 and HO-1. Furthermore, lack of Apoe promoted neuronal apoptosis and decreased neuronal numbers in the anterior horn of the spinal cord after SCI. Mechanistically, we found that the absence of Apoe increased inflammation and oxidative stress through activation of NF-κB after SCI. In contrast, an inhibitor of nuclear factor-κB (NF-κB; Pyrrolidine dithiocarbamate) alleviates these changes. Collectively, these results indicate that a critical role for activation of NF-κB in regulating Apoe-deficiency dependent inflammation and oxidative stress is detrimental to recovery after SCI.

Whole body vibration (WBV) has a marked impact on lipid metabolism and the endocrine system, which is related to the progression of atherosclerosis (AS). To investigate the effects of WBV, we measured the atherosclerotic plaque area of apolipoprotein E-knockout (ApoE−/−) AS mice, which were trained by WBV (15 Hz, 30 min) for 12 weeks. Simultaneously, serum levels of lipids, insulin-like growth factor 1 (IGF-1), insulin-like growth factor 1 receptor (IGF-1R), interleukin 6 (IL-6), and the mRNA and protein levels of the same in the aorta were compared between the control and WBV groups. The results indicated that WBV significantly reduced the atherosclerotic plaque area with lower very low-density lipoprotein (VLDL) and oxidized low-density lipoprotein (ox-LDL) in the blood. Moreover, the levels of IGF-1 in serum and expression of IL-6, IGF-1R, and p-IGF-1R protein in the mice aorta decreased significantly in the WBV group. In addition, we found that serum IGF-1 in mice increased to the highest concentration in 30 min after WBV for 10, 30, 60, and 120 minutes. These results suggested that appropriate WBV may delay the progression of AS, which was associated with acutely elevated serum IGF-1 and lower levels of IGF-1 and IL-6 in the aorta for long-term treatment.

In this paper, we propose to detect the running applications in a server by classifying the observed power consumption series for the purpose of data center energy consumption monitoring and analysis. Time series classification problem has been extensively studied with various distance measurements developed; also recently the deep learning-based sequence models have been proved to be promising. In this paper, we propose a novel distance measurement and build a time series classification algorithm hybridizing nearest neighbor and long short term memory (LSTM) neural network. More specifically, first we propose a new distance measurement termed as local time warping (LTW), which utilizes a user-specified index set for local warping, and is designed to be noncommutative and nondynamic programming. Second, we hybridize the 1-nearest neighbor (1NN)-LTW and LSTM together. In particular, we combine the prediction probability vector of 1NN-LTW and LSTM to determine the label of the test cases. Finally, using the power consumption data from a real data center, we show that the proposed LTW can improve the classification accuracy of dynamic time warping (DTW) from about 84% to 90%. Our experimental results prove that the proposed LTW is competitive on our data set compared with existed DTW variants and its noncommutative feature is indeed beneficial. We also test a linear version of LTW and find out that it can perform similar to state-of-the-art DTW-based method while it runs as fast as the linear runtime lower bound methods like LB_Keogh for our problem. With the hybrid algorithm, for the power series classification task we achieve an accuracy up to about 93%. Our research can inspire more studies on time series distance measurement and the hybrid of the deep learning models with other traditional models. Accepted version

Data center (DC) plays an important role to support services, such as e-commerce and cloud computing. The resulting energy consumption from this growing market has drawn significant attention, and noticeably almost half of the energy cost is used to cool the DC to a particular temperature. It is thus an critical operational challenge to curb the cooling energy cost without sacrificing the thermal safety of a DC. The existing solutions typically follow a two-step approach, in which the system is first modeled based on expert knowledge and, thus, the operational actions are determined with heuristics and/or best practices. These approaches are often hard to generalize and might result in suboptimal performances due to intrinsic model errors for large-scale systems. In this paper, we propose optimizing the DC cooling control via the emerging deep reinforcement learning (DRL) framework. Compared to the existing approaches, our solution lends itself an end-to-end cooling control algorithm (CCA) via an off-policy offline version of the deep deterministic policy gradient (DDPG) algorithm, in which an evaluation network is trained to predict the DC energy cost along with resulting cooling effects, and a policy network is trained to gauge optimized control settings. Moreover, we introduce a de-underestimation (DUE) validation mechanism for the critic network to reduce the potential underestimation of the risk caused by neural approximation. Our proposed algorithm is evaluated on an EnergyPlus simulation platform and on a real data trace collected from the National Super Computing Centre (NSCC) of Singapore. The resulting numerical results show that the proposed CCA can achieve up to 11% cooling cost reduction on the simulation platform compared with a manually configured baseline control algorithm. In the trace-based study of conservative nature, the proposed algorithm can achieve about 15% cooling energy savings on the NSCC data trace. Our pioneering approach can shed new light on the application of DRL to optimize and automate DC operations and management, potentially revolutionizing digital infrastructure management with intelligence. date of current version April 15, 2020. This work was supported in part by the Green Data Centre Research Project, administrated by the Singapore Infocomm and Media Development Authority. This paper was recommended by Associate Editor Y. Zhang

The metallic particles originating from the process of installation and operation of converter transformers can affect the insulating performance of transformer oil when subjected to electric field containing DC component and may cause discharge accidents. Therefore, the influence of particles on the breakdown strength of insulating oil at DC voltage and the mechanism behind the phenomena has gained much attention and needs to be further investigated. The breakdown voltage test of insulating oil contaminated with iron particles of different sizes under AC and DC electric field was carried out according to ASTM D877 and the influence of iron particles on breakdown voltage was obtained. With the change of iron particle concentration and particle size, the change regulation of breakdown voltage of insulating oil under AC and DC electric fields was obtained. In the meanwhile, this paper analyzed the electric field distribution of insulating oil containing different particle sizes of iron particles under AC and DC electric fields with COMSOL simulation software. The causes of the experimental phenomena are analyzed with the simulation results. The simulation results show that the maximum field strength and non-uniformity factor inside insulating oil will increase with the increase of particle concentration. Besides, the experimental results show that the small impurity bridge is more likely to form with the change of electric field, especially under the DC electric field, thus increasing the probability of breakdown.

Hierarchical Identity-Based Encryption (HIBE) is a generalization of Identity-Based Encryption (IBE) that mirrors an organizational hierarchy, however, the residual key escrow problem has still remained. This paper introduces a new scheme, based on the security notion of anonymous ciphertext indistinguishability against key generation center (ACI-KGC) security proposed by Chow, to remove the inherent key escrow problem. The scheme employs multiple Identity Certification Authorities (ICAs) which can ensure that the Private Key Generators (PKGs) distribute private key without knowing the list of user’s identity information, so as to prevent malicious PKGs from decrypting message on behalf of user or maliciously make user’s private key public. Security analysis shows that the scheme can solve the key escrow with keeping the high-efficiency and security of HIBE system. In contrast to Chen’s T-HIBE and Chow’s scheme, to generating the user’s private key, our scheme does not require the multiple KPAs or the complex interactive protocol which take too much extra computation costs. And comparing with GS-HIBE, our scheme focuses on solving the key escrow problem with outstanding performance and security of HIBE system.

Web services serve as the cornerstone of the Internet for rendering webpages. The initial rendering latency of webpages, which depends on a subset of critical objects required by the webpage, is a key metric for web services. In this work, we propose to identify this set of critical objects systematically with the goal of caching them at a higher priority to reduce the initial rendering time. We first conduct a measurement study on a mainstream content delivery network provider, the results of which suggest that not all currently cached objects are critical and that only a small portion of the critical objects are cached. Thus, we model the critical-object aware caching scheme as a constrained optimization problem. Using the stochastic optimization framework, we decompose the problem into a set of one-shot optimization problems, which are proved to be NP-hard. We then develop two greedy algorithms with different computational complexity but the same performance bound. Finally, we integrate the resulting approximation algorithms into an online algorithm. Through trace-based simulations, we verify that our proposed algorithm can reduce service latency and network traffic by ensuring a higher cache hit ratio. MOE (Min. of Education, S’pore)

Modelling and analyzing power consumption for data centres can diagnose potential energy-hungry components and applications, and facilitate in-time control, benefiting the energy efficiency of data centers. However, solutions to this problem, including static power models and canonical prediction models, either aim to build a static relationship between power consumption and hardware/application configurations without considering the dynamic fluctuation of power; or simply treat it as time series, ignoring the inherit power data characteristics. To tackle these issues, in this paper, we present a systematic power prediction framework based on extensive power dynamic profiling and deep learning models. In particular, we first analyse different power series samples to illustrate their noise patterns; accordingly we propose a power data de-noising method, which lowers noise interference to the modelling. With the pretreated data, we propose two deep learning based prediction models, including a fine-grained model and a coarse-grained model, which are suitable for different time scales. In the fine-grained prediction model, a recursive autoencoder (AE) is employed for short-duration prediction; in the coarse-grained model, an AE is used to encode massive fine-grained historical data as a further data pretreatment for long-duration prediction. Experimental results show that our proposed models outperform canonical prediction methods with higher accuracy, up to 79% error reduction for certain cases.

The insulating performance of transformer oil is vulnerable to metallic particles, especially oil used in HVDC converter transformers. The electric field distribution in converter transformer insulating oil is different from that in traditional AC transformer insulating oil, so extra attention should be paid to the influence of metallic particles on its breakdown voltage. This paper investigated the influence of metallic particles on the breakdown voltage of insulating oil at AC and DC voltage. The breakdown voltages of insulating oll contaminated with iron particles and copper particles of different sizes and particle numbers were measured at AC and DC voltage, respectively. The Weibull distribution was adopted to characterize the breakdown voltage data and the relationship between the breakdown voltage and particle numbers was obtained. The effect of particle diameter was also investigated. The experiment results showed that the breakdown voltage of the insulating oil decreases with the increase of the particle number. More importantly, the breakdown voltage at DC voltage is smaller than that at AC voltage and the insulating oil contaminated with iron particles of bigger median diameter has a smaller breakdown voltage.

The oil-paper insulation of converter transformer withstands a long time electrical aging by combined AC-DC voltage and thermal aging during operation. The aging failure mode of converter transformers insulation exhibits a great difference with that of AC transformers. In this paper, a set of accelerated thermal aging experiments at 130 °C were designed, oil-paper specimens were aged for 5, 10, 20 and 35 days, respectively. The unaged oil-paper specimens were also prepared for comparison. The breakdown voltage and failure time of oilpaper specimens of different aging times at AC and combined AC-DC voltage were measured, respectively. The relationship between the failure time and aging time is obtained by using Weibull analysis method. The experiment results show that the breakdown voltage of oil-paper specimens all decrease not much with the increase of thermal aging time. The oil-paper specimens have a longer failure time at combined AC-DC voltage compared with that at AC voltage.

Converter transformer is one of the most important equipment in ultra-high voltage direct current transmission system, periodic preventive maintenance strategy is widely in its maintenance. Excessive maintenance often arises in the attempt to always ensure the reliable operation of converter transformers. To solve this problem, this study proposes a maintenance decision optimization method for converter transformers that is based on life-cycle cost(LCC). This approach introduces the concepts of LCC and discount rate, obtains an LCC model of the converter transformer and presents the value conversion method; by improving the maintenance equipment failure rate and the calculation method of economic losses, this method improves the accuracy of failure loss cost in the LCC model; by comparing the different maintenance modes of the converter transformer LCC, the reasonable maintenance strategy is optimized, thereby achieving the objectives of reliability and economy. A decision-making example using a ±800kV converter transformer shows that the proposed method can calculate the LCC of converter transformers, yield the optimized maintenance strategy and provide technical support for condition-based maintenance of the converter transformers.

In the network security situation assessment based on hidden Markov model, the establish of state transition matrix is the key to the accuracy of the impact assessment. The state transition matrix is often given based on experience. However, it often ignores the current status of the network. In this paper,based on the game process between the security incidents and protect measures,we improve the efficiency of the state transition matrix by considering the defense efficiency. Comparative experiments show the probability of the network state generated by improved algorithm is more reasonable in network security situation assessment.

A fully nonlinear, three-dimensional nonhydrostatic model driven by four principal tidal constituents (M2, S2, K1, and O1) is used to investigate the spatial-temporal characteristics and energetics of internal tides in Luzon Strait (LS). The model results show that, during spring (neap) tides, about 64 (47) GW (1 GW=109 W) of barotropic tidal energy is consumed in LS, of which 59.0% (50.5%) is converted to baroclinic tides. About 22 (11) GW of the derived baroclinic energy flux subsequently passes from LS, among which 50.9% (54.3%) flows westward into the South China Sea (SCS) and 45.0% (39.7%) eastward into the Pacific Ocean, and the remaining 16 (13) GW is lost locally owing to dissipation and convection. It is revealed that generation areas of internal tides vary with the spring and neap tide, indicating different source areas for internal solitary waves in the northern SCS. The region around the Batan Islands is the most important generation region of internal tides during both spring and neap tides. In addition, the baroclinic tidal energy has pronounced seasonal variability. Both the total energy transferred from barotropic tides to baroclinic tides and the baroclinic energy flux flowing out of LS are the highest in summer and lowest in winter.

Sequence correlation method has limits in unknown attacks identification and requires pre-defining the causal relationship between attack behavior. To solve this problem, an alert correlation algorithm, denoted as TPrefixSpan, based on the sequence pattern mining is proposed in this paper, based on PrefixSpan algorithm, TPrefixSpan algorithm introduces time interval that can thoroughly narrow, the search space, then time cost on repeated dataset scan in the sequence pattern mining is greatly saved, the efficiency of the PrefixSpan algorithm is ensured. Compared with PrefixSpan algorithm, TPrefixSpan algorithm generates much precise attacks identification. In order to visualize the correlation rules better, a sequence diagram generation algorithm orf attack behavior is put forward.

A relationship between transmitting data rate of communication channel and the state expanding rate of continuous linear system is derived to guarantee that the system is asymptotically stabilizable through networked control.

Safety attitude is of vital importance to accident prevention, and the high accident rate in the coal mining industry makes it urgent to undertake research on coal miners’ safety attitude. However, the current literature still lacks a valid and reliable safety attitude measurement scale for coal miners, which stands as a barrier against their safety attitude improvement. In this study, a scale is developed that can be used to measure coal miners’ safety attitude. The preliminary scale was based on an extended literature review. Empirical data were then collected from 725 coal miners using the preliminary scale. Both exploratory and confirmatory factor analyses were undertaken to validate and improve the scale. The final scale, which consists of 17 items, contains four dimensions: management safety commitment, team safety climate, fatalism and work pressure. Results show that this safety attitude scale can effectively measure the safety attitude of coal miners, showing high psychological measurement validity. This paper contributes to the occupational safety research by developing the factor structure and indicator system of coal miners’ safety attitude, thus providing more profound interpretation of this crucial construct in the safety research domain. The measurement scale serves as an important tool for safety attitude benchmarking among different coal mining enterprises and, thus, can boost the overall safety improvement of the whole industry. These findings can facilitate improvement of both theories and practices related to occupational safety attitude.