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As the worldwide energy crisis worsens, renewable energy sources have undeniably opened new horizons for our society. It will offer an efficient solution to this severe energy crisis, environmental issues and fulfilling human energy needs. Therefore, six main criteria, social, economic, quality of energy, political, technical, environmental, and sixteen sub-criteria have been employed, and an assessment model is developed for prioritizing the most pertinent renewable energy sources for electricity generation in developing countries in which four major resources, solar energy, wind energy, hydropower, and biomass energy, are considered and their electricity generation potential. Furthermore, the integrated method with the analytical hierarchy process and VIekriterijumsko KOmpromisno Rangiranje are acquired for a comprehensive assessment. The results demonstrate that the economic criterion had the highest weight of (0.353) compared to the technical criterion, which took place second with a weight of (0.244) followed by environmental (0.205) and quality of energy with a weight (0.098), respectively. The political and social criteria have the lowest weights of (0.057) and (0.043). From the review, renewable resources seem to have sufficient potential to develop a sustainable electricity system. Likewise, the model priorities these resources, revealing that hydropower is the best renewable energy source option, followed by wind energy. Biomass energy and solar energy ranked third and fourth. The assessment performed in this study can help decision-makers in Pakistan to frame long-term energy policy directing for sustainability.

In this article, we propose an economic storage sharing framework for prosumers and energy storage providers (ESPs) to promote renewable energy utilization cooperatively. The optimal shared capacities of ESPs and the energy sharing profiles of prosumers are first derived via minimizing social energy costs. Then the storage sharing profits of ESPs and the energy sharing payments of prosumers are successively determined by the asymmetric bargaining-based benefits sharing model. Specifically, the prosumer group bargains with the ESPs with the nominal required capacity and the shared capacities as their bargaining power to share the storage sharing benefits. Then prosumers bargain with each other to share the energy sharing benefits with their bargaining power quantified by a nonlinear energy sharing mapping method. Therefore, the benefits sharing model based on the contributions of prosumers and ESPs is fair enough for the participants. Numerical simulation tests verify the efficiency of the proposed framework.

This article investigates the energy cooperation between photovoltaic prosumers and community energy storage (CES) to improve community energy efficiency. The optimal energy sharing profiles between prosumers and CES are firstly derived by solving the energy optimization problem minimizing social energy cost. To guarantee the incentives for prosumers and CES to participate in the energy cooperation, a Nash bargaining based benefits sharing model is presented to determine the energy sharing payments. Two implementation modes, i.e., Data-Centric and Prosumers-to-CES mode, are developed to protect privacy for both prosumers and CES. Cheating behaviors in benefits sharing, the critical yet challenging matter, are analyzed; a cheating equilibrium based solution is proposed and achieved by a relaxation algorithm for a stable cooperation. In addition, the alternating direction method of multipliers with adaptive parameter selection is introduced to solve the formulated problems in a distributed way. Numerical simulation tests show the efficiency of the novel models.

This article presents an efficient peer-to-peer energy-sharing framework for numerous community prosumers to reduce energy costs and to promote renewable energy utilization. Specifically, for day-ahead and real-time energy management of prosumers, an intercommunity energy-sharing strategy and an intracommunity energy-sharing strategy are proposed, respectively. In the former strategy, prosumers can share energy with any community peers, and community aggregators represent their own prosumers to coordinate energy sharing. A two-phase model is designed. In the first phase, the optimal energy-sharing profiles of prosumers are derived to minimize the global energy costs, and in the second phase, equilibrium-based energy-sharing prices are induced considering the individual interests of prosumers. In the latter strategy, prosumers share energy only with its community peers for time saving to handle real-time uncertainties collaboratively to reduce real-time costs. The framework efficiency is verified by the simulation cases on a typical distribution network.

With the rapid development of energy buildings, advanced energy management is urgently demanded for a green society. In this paper, focusing on the coordinated energy management for a building community, we present a new and fair peer-to-peer energy sharing framework to realize an economic and sustainable building community. Specifically, in the building-centric peer-to-peer mode, buildings directly share their energy supplies/demands and offer the related payments within the community under the constraints of community energy and payment balance. We propose a non-cooperative energy sharing game for the selfish buildings, and we further show that a generalized Nash equilibrium of the game is independent of the energy sharing payments. Consequently, we firstly derive the energy sharing profiles by seeking the equilibrium. Since the buildings’ energy sharing payments are mutually coupled and influenced, we propose a cost reduction ratio distribution model to determine the payments to ensure the fairness in the sense that buildings can get as large cost reductions and similar cost reduction ratios as possible. Simulation results show that all buildings can reduce their energy costs and have smoother and smaller net demand profiles on the main grid, thus making the proposed schemes and algorithms promising in real applications.

Household photovoltaic (HPV) prosumers and the community photovoltaic (CPV) system have been growing rapidly with the development of the sustainable technology. The uncertainties of these distributed renewable energy resources bring a significant challenge to the design of power-market mechanism and the energy dispatch of the power system for promoting energy efficiency. It is essential to develop a novel efficient energy management strategy for addressing this challenge from the perspective of community prosumers. Accordingly, a risk aversion energy sharing model based on a devised local energy market is presented. A stochastic game is established to minimize prosumers’ energy costs and the weighted conditional value-at-risk of energy sharing loss of uncertain CPV through optimal energy sharing profiles. The household loads and HPV outputs are considered as stochastic parameters in the game model. Moreover, a sample weighted average approximation (SWAA) method is proposed for a better estimation of the stochastic game while the SWAA equilibrium is obtained by a relaxation method-based algorithm with theoretical proof. In addition, the blockchain technology is introduced as a distributed and secure way to facilitate the energy sharing model. The case studies show the efficiency of the proposed energy sharing model and the algorithm.

川西木里混杂带位于扬子板块西缘，向西与甘孜-理塘弧前混杂带相接，位于一个大地构造上十分重要的部位。我们详细野外地质调查揭示，木里混杂带由不同类型的大洋板片地层组成，其中海山岩石组合保存相对完整，枕状熔岩与上覆碳酸盐岩帽接触关系以及海山斜坡滑塌堆积组合完整清晰。海山枕状玄武岩具斑状结构，主要斑晶矿物相为单斜辉石，少量基性斜长石，基质为微晶斜长石和辉石。原生熔体包裹体主要寄存于单斜辉石斑晶中，形状不规则，大小20~50μm。熔体包裹体内部组成和结构简单清晰，主要为不透明玻璃质，有的熔体包裹体含圆形-椭圆形收缩气泡，个别熔体包裹体壁可见子矿物结晶析出。对单斜辉石斑晶及其中熔体包裹体的地球化学分析结果揭示，木里混杂带中保留了OIB和E-MORB两类海山玄武岩，其原始岩浆源区为石榴子石二辉橄榄岩低程度熔融。其中，OIB型海山玄武岩（样品HS5）是地幔柱轴部岩浆活动的产物，而E-MORB型海山玄武岩（样品HS2）是某种程度的热点（地幔柱）与洋中脊相互作用的产物。磷灰石U-Pb测年结果和古生物化石证据表明，木里混杂带中的海山形成于石炭纪末-二叠纪初（302±11Ma）。这表明甘孜-理塘（松潘-甘孜）古特提斯分支洋在石炭纪末或更早时期就已经发育。在该分支洋盆内发育地幔柱以及地幔柱（热点）与洋中脊的相互作用。

Efficient building energy management is essential for energy saving and green society. This paper investigates sustainable energy management for an energy building cluster with distributed transaction. The building cluster consists of several types of energy buildings, e.g., office, industrial, and commercial buildings. We first formulate utility functions for the buildings of consuming energy based on the characteristics of their controllable loads. Then a two-stage energy sharing strategy is presented. In the first stage, the total social energy cost is minimized through finding the optimal energy sharing profiles in a distributed way. In the second stage, the clearing for mutual energy sharing is modeled as a non-cooperative game, and the existence of the equilibrium of the game is illustrated and a relaxation-based algorithm is introduced to search for the equilibrium. Moreover, a real-time model for each building to overcome real-time uncertainties, such as renewable energy generation and base loads is provided. The simulation results show that the proposed energy sharing strategy is economically beneficial for the energy buildings, computationally efficient, and is promising to facilitate a sustainable regional building cluster.

Background: The sulfonylurea receptor 1–transient receptor potential melastatin 4 (SUR1–TRPM4) channel is a target key mediator of brain edema. Sulfonylureas (SFUs) are blockers of the SUR1–TRPM4 channel. We made two assessments for the pretreatment of SFUs: (1) whether it associates with lower perihematomal edema (PHE) and (2) whether it associates with improved clinical outcomes in diabetic patients who have acute basal ganglia hemorrhage.Methods: This retrospective case-control study was conducted in diabetic adults receiving regular SFUs before the onset of intracerebral hemorrhage (ICH). All of the patients received the clinical diagnosis of spontaneous basal ganglia hemorrhage. The diagnosis was confirmed by a CT scan within 7 days after hemorrhage. For each case, we selected two matched controls with basal ganglia hemorrhage based on admission time (≤5 years) and age differences (≤5 years), with the same gender and similar hematoma volume. The primary outcome was PHE volume, and the secondary outcomes were relative PHE (rPHE), functional independence according to modified Rankin Scale score and Barthel Index at discharge, and death rate in the hospital.Results: A total of 27 patients (nine cases and 18 matched controls), admitted between January 1, 2009 and October 31, 2018, were included in our study. There was no significant association between SFU patients and non-SFU patients on PHE volumes [15.4 (7.4–50.2 ml) vs. 8.0 (3.1–22.1) ml, p = 0.100]. Compared to non-SFU patients, the SFU patients had significantly lower rPHE [0.8 (0.7–1.3) vs. 1.5 (1.2–1.9), p = 0.006]. After we adjusted the confounding factors, we found that sulfonylureas can significantly reduce both PHE volume (regression coefficient: −13.607, 95% CI: −26.185 to −1.029, p = 0.035) and rPHE (regression coefficient: −0.566, 95% CI: −0.971 to −0.161, p = 0.009). However, we found no significant improvement in clinical outcomes at discharge, in the event of pretreatment of SFUs before the onset of ICH, even after we adjusted the confounding factors.Conclusion: For diabetic patients with acute basal ganglia hemorrhage, pretreatment of sulfonylureas may associate with lower PHE and relative PHE on admission. No significant effect was found on the clinical outcomes when the patients were discharged. Future studies are needed to assess the potential clinical benefits using sulfonylureas for ICH patients.

This paper studies coordination of a team of nonholonomic mobile robots with smart actuators for defending against invasive threat to a planar convex area. The threat refers to a kind of harmful substance such as chemical pollutant appearing outside and moving towards the area. The invasion of threat can be modeled by a 2D unsteady reaction-diffusion process. To reflect the adverse effect of threat on the area, a so-called risk intensity field is introduced. The value of risk intensity is equal to the concentration of threat measured by a static mesh sensor network. Based on this risk intensity field, a coordination control scenario using Voronoi tessellation is formulated. In order to minimize the actuator performance loss and reduce the total average risk intensity simultaneously, a generalized centroidal Voronoi tessellation (CVT) algorithm including optimal motion control and risk mitigation control is designed. The proposed algorithm is gradient-based and guides mobile robots to track their optimal trajectories asymptotically. Meanwhile, two conditions of choosing control gains are derived to keep the total average risk intensity below a safety level. Several simulation examples with different cases of threat invasion are provided and the advantage of proposed algorithm over traditional control method is presented.

The paper proposes a two-stage energy sharing framework for a new prosumer microgrid with renewable energy generation, multiple storage units, and load shifting. In the first stage, a robust bilevel energy sharing model is formulated to provide a robust energy sharing schedule for prosumers and retailer overcoming the impact of the uncertainties of market prices and renewable energy. Through proper linearization techniques, the bilevel optimization problem is transformed into a single-level mixed integer linear programming problem that is practically solvable. In the second stage, an online optimization model is formulated for each prosumer to continually optimize its energy schedule at each hour according to the latest system state, and the proposed punishment mechanism is embedded for prosumers adjusting their previous energy sharing schedules. The simulation cases show the benefits of the energy sharing management framework.

Respiratory syncytial virus (RSV) infection is the main cause of lower respiratory tract infection in children. However, there is no effective treatment for RSV infection. Here, we aimed to identify potential biomarkers to aid in the treatment of RSV infection. Children in the acute and convalescence phases of RSV infection were recruited and proteomic analysis was performed to identify differentially expressed proteins (DEPs). Subsequently, promising candidate proteins were determined by functional enrichment and protein-protein interaction network analysis, and underwent further validation by western blot both in clinical and mouse model samples. Among the 79 DEPs identified in RSV patient samples, 4 proteins (BPGM, TPI1, PRDX2, and CFL1) were confirmed to be significantly upregulated during RSV infection. Functional analysis showed that BPGM and TPI1 were mainly involved in glycolysis, indicating an association between RSV infection and the glycolysis metabolic pathway. Our findings provide insights into the proteomic profile during RSV infection and indicated that BPGM, TPI1, PRDX2, and CFL1 may be potential therapeutic biomarkers or targets for the treatment of RSV infection.

The complexity of hard-to-treat diseases such as ischemic stroke strongly undermines the therapeutic potential of available treatment options. Therefore, current developments have gently shifted from a focus on monotherapy to combined or multiple therapies. Both dexmedetomidine and Netrin-1 have anti-neuronal apoptosis effects, but the mechanism is still unclear. The study aimed to estimate the efficacy of dexmedetomidine and Netrin-1 combination therapy against ERS-induced apoptosis after cerebral ischemia injury in vivo and in vitro, and whether the mechanism is related to the ERK5/MEF2A pathway. Adult male Sprague-Dawley rats were subjected to middle cerebral artery occlusion (MCAO) in vivo, 90 min ischemia and 24 h reperfusion. The hippocampus slices used to establish oxygen-glucose deprivation (OGD) injury model in vitro. Neterin-1 and Dexmedetomidine were pretreated and post-treated, respectively, before and after the model establishment. MEF2A knockdown was performed by microinjection of AAV9-MEF2A RNAi vector. Orthodromic population spike (OPS) at the end of reoxygenation were recorded. Neurobehavioral tests, TTC staining, Nissl staining, TUNEL staining were performed to assess the effect of the drugs. The expression of CHOP, GRP78, MEF2A, ERK5, and p-ERK5 were investigated by Western blot and immunofluorescence staining. Neurological deficit score, infarct volume, the expression of GRP78, CHOP, and neural apoptotic rate of MCAO group increased markedly. Combination of dexmedetomidine and Netrin-1 resulted in lower infarct volumes and fewer neurological impairments, higher OPS recovery rate, and less damaged and apoptotic cells after cerebral ischemia injury. Furthermore, expression levels of GRP78 and CHOP decreased in the combination therapy group, and it was more effective than the single drug group. Meanwhile, Combination of dexmedetomidine and Netrin-1 increased MEF2A expression and promoted ERK5 phosphorylation. However, the protective effect of dexmedetomidine combined with Netrin-1 in improving neurological function was significantly eliminated by pre-knockdown MEF2A. The neuroprotective effects of dexmedetomidine combined with Netrin on cerebral ischemia-reperfusion injury and hippocampal hypoxia injury in terms of ERS. The synergistic effect of combination therapy is related to the activation of ERK5/MEF2A signaling pathway.

Atherosclerotic plaque is the primary cause of cardiovascular disorders and remains a therapeutic hurdle for the early intervention of atherosclerosis. Traditional clinical strategies are often limited by surgery‐related complications or unsatisfactory effects of long‐term drug administration. Inspired by the plaque‐binding ability of platelets, a biomimic photodynamic therapeutic system is designed to mitigate the progression of atherosclerotic plaques. This system is composed of photosensitizer‐loaded upconversion nanoparticle cores entrapped in the platelet membrane. The platelet membrane coating facilitates specific targeting of the therapeutic system to macrophage‐derived foam cells, the hallmark, and main component of early stage atherosclerotic plaques, which is firmly confirmed by in vivo fluorescent and single‐photon emission computed tomography/computed tomography (SPECT/CT) radionuclide imaging. Importantly, in vivo phototherapy guided by SPECT/CT imaging alleviates plaque progression. Further immunofluorescence analysis reveals foam cell apoptosis and ameliorated inflammation. This biomimic system, which combines plaque‐binding with radionuclide imaging guidance, is a novel, noninvasive, and potent strategy to mitigate the progression of atherosclerotic plaque., Inspired by the early stage atherosclerotic plaque‐binding ability of platelets, a biomimic photodynamic therapeutic system is designed to accurately localize and noninvasively mitigate the progression of atherosclerotic plaques. This biomimic system synergizes plaque‐binding and radionuclide imaging guidance, providing a novel, noninvasive, and potent strategy for mitigating the progression of atherosclerotic plaque.

In this paper, an E-band MIMO radar with 1 transmit and 4 receive channels is designed. The signal bandwidth is 2GHz at 77GHz, the max power of transmitted signal which is Frequency-modulated continuous-wave (FMCW) is 13dBm. This radar consists of two cascade parts: RF frond-end and digital signal process block. The RF front-end part includes antenna array, millimeter wave transceiver chips, and the digital signal process part includes FPGA, DSP and power supply circuits. It could be used in foreign object detection (FOD), landing assistance of helicopter and security checking.

Although many types of simulated radionuclides have been widely used as a substitute for actual nuclear waste in the studies of nuclear waste solidification, the understanding of the applicability and validity of simulated radionuclides is still insufficient. In particular, the selection and use of simulated radionuclides, which can play a decisive role in the accuracy of the experimental results, still lack unified or integrated references. This paper provides a critical review on the selection, experimental methods, and applicability of the most commonly studied simulated radionuclides, followed by a careful discussion and recommendation of simulated radionuclides suitable for different solidified bodies. The main factors (e.g., temperature, pH, and atmosphere) affecting the choice of simulated radionuclides were analyzed in detail. This work helps to integrate the selection and use of simulated radionuclides, and it will be beneficial for improving the effectiveness of nuclide solidification research.

Patients undergoing maintenance hemodialysis (MHD) are subject to a higher-than-usual prevalence of depressive disorders. However, the lack of consensus regarding the best assessment method remains an important problem. Thus, there is a clear need for more effective screening tools and an easily administered, disease-specific self-report measure of depression in MHD patients. After we developed and administered an initial depression inventory for MHD patients (I-DI-MHD), we created the DI-MHD and administered the DI-MHD and the Beck Depression Inventory (BDI) to 354 patients from four hospitals. Reliability, construct validity and receiver operator characteristic curves were assessed. The 17-item DI-MHD instrument displayed good internal consistency (Cronbach's alpha =0.893), provided excellent convergent validity, and correlated with the BDI scale (kappa =0.785, P

Due to the ill-posed phenomenon of the classical physical model, single image dehazing based on the model has been a challenging vision task. In recent years, applying machine learning techniques to estimate a critical parameter transmission has proven to be an effective solution to this issue. Accordingly, the robustness and accuracy of learning-based transmission estimation model is extremely important, since it does impact on the final dehazing effects. The state-of-the-art dehazing algorithms by this means generally use haze-relevant features as the single input to their transmission estimation models. However, the used haze-relevant features sometimes are not sufficient and reliable in holding real intrinsic information related to haze due to their two shortcomings and ultimately bring about their less effectiveness for some dehazing cases. Based on related efforts on representation learning and deep convolutional neural networks, in this paper, we seek to achieve the robustness and accuracy of transmission estimation model for bolstering the effectiveness of single image dehazing. Specifically, we propose a hybrid model combining unsupervised and supervised learning in a considerably deep neural networks architecture, termed DeeptransMap, in order to achieve accurate transmission map from a single image. Experimental results demonstrate that our work performs favorably against several state-of-the-art dehazing methods with the same estimated goal and keeps efficient in terms of the computational complexity of transmission estimation.

Realizing multi-energy optimization management is very important for the microgrids (MGs) with different forms of multi-energy. This paper proposes a community-level energy system (CES) which consists of community-level energy hub (CEH) and the users with electric and thermal demand. Firstly, the optimization model of users is designed, where the users can adjust the shiftable load and thermal demand spontaneously according to the internal electricity price and heat price. Secondly, the CEH achieves heat and electricity coupled energy management considering the gas distribution coefficient, and gets optimal strategies according to the best response of users. Then the Stackelberg game model is applied, where the CEH acts as the leader and the users act as the followers. Finally, the case study shows the effectiveness of the proposed model.

In this paper, the feature weight coefficient of neural network is proposed based on the results of neural network feature selection. Firstly, this paper introduces the neural network feature selection method based on Group Least absolute shrinkage and selection operator (Lasso) penalty, smooth approximation technique and the feature weight coefficient. Then, a two-stage algorithm for back propagation (BP) neural network feature selection based on Group Lasso penalty term is proposed. And then, the paper compares and analyzes the influence of smooth approximation technique on BP neural network feature selection. Finally, the reliability of the algorithm and the validity of the feature weight coefficient are verified by comparing the results of four datasets.

Demand response is a promising technology to cope with the ever-increasing peak energy consumption in smart grid. For the smart home energy management system, it may choose to charge the battery when the electricity price is low, and discharge the battery to supply the home load when the electricity price is high. Under this circumstance, the temporally and spatially coupled constraints coexist in the energy demand, battery charging and discharging energy, and energy supply. The coupled constraints make the demand response problem more challenging than the existing ones and thus the existing distributed algorithms cannot be employed to find the optimal solution of the problem. In this paper, we focus on the real-time two-way communications between utility company and multiple residential users, and each user is regarded as a smart home energy management system. A distributed real-time algorithm is proposed to find the optimal energy management scheduling scheme for each user and utility company to maximize the social welfare. To overcome the obstacle brought by the temporally and spatially coupled constraints, dual decomposition technique is employed and the primal problem is decoupled into several independent subproblems which can be solved in a distributed way by each user and utility company without revealing or exchanging their private information. Simulation results are provided to demonstrate that the proposed distributed algorithm can bring potential benefits to the society.

This paper investigates a model of hybrid impulsive and switching stochastic systems (HISSS) with time-delay. Several stability criteria, including the globally asymptotical and exponential stability criteria in the pth moment and mean square, are established by using Ito formula, a generalized switching Lyapunov functions and some relating techniques. Especially, a more general Lyapunov-Krasivskii functional is constructed, and it is observed that the mean square asymptotic and exponential stability conditions of HISSS are delay-dependent. A numerical example shows that the obtained stability results can reduce the conservativeness.

Dempster-Shafer evidence theory (DS theory) is widely used in brain magnetic resonance imaging (MRI) segmentation, due to its efficient combination of the evidence from different sources. In this paper, an improved MRI segmentation method, which is based on fuzzy c-means (FCM) and DS theory, is proposed. Firstly, the average fusion method is used to reduce the uncertainty and the conflict information in the pictures. Then, the neighborhood information and the different influences of spatial location of neighborhood pixels are taken into consideration to handle the spatial information. Finally, the segmentation and the sensor data fusion are achieved by using the DS theory. The simulated images and the MRI images illustrate that our proposed method is more effective in image segmentation.

The effects of serum hepatitis B e antigen (HBeAg) on the prognosis of hepatocellular carcinoma (HCC) patients after hepatectomy remain controversial. Our aim was to explore the prognostic significance of serum HBeAg on the prognosis of patients with HCC using a propensity matching model. Between January 2009 and March 2015, 953 patients with HCC who underwent hepatectomy in West China Hospital were analysed. Propensity matching analysis was applied, and survival analysis was performed using the Kaplan-Meier method. Risk factors were identified by the Cox proportional hazards model. All patients with HCC were classified into an HBeAg(-) group (n = 775, 81.3%) or an HBeAg(+) group (n = 178, 18.7%). Patients with positive serum HBeAg had poorer recurrence-free survival and overall survival before and after propensity matching. Similar results were found in patients within the Milan criteria. For patients beyond the Milan criteria, the HBeAg(+) group had poor overall survival before and after propensity matching. In term of recurrence-free survival, there was no statistically significant impact after propensity matching (P = .055), although there was a trend for HBeAg(+) patient to have reduced recurrence-free survival. Positive serum HBeAg, positive HBV-DNA load, largest tumour size, multiple tumours, microvascular invasion and a high serum level of preoperative alpha-fetoprotein were risk factors for recurrence. Our propensity model confirmed that positive serum HBeAg had a negative impact on the recurrence and long-term survival irrespective of tumour stages. HBeAg seroconversion might be beneficial for reducing the rate of recurrence.

Shaping residential electricity consumption behaviors can achieve the effect of load shifting to emit less carbon. Electricity consumption pattern is a good way to describe electricity consumption behavior, and is basically determined by household characteristics (HCs). Effective mining of important factors in HCs can help to shape residential electricity consumption behaviors. This paper proposes a new mining framework for discovering the association between HCs and residential electricity consumption patterns. At first, HCs are classified into six categories by the theory of planned behavior based factor classification. Then, typical electricity consumption patterns (TECPs) of each individual resident in different periods divided by seasons and weekday are extracted by a proposed piecewise symbolic spatial clustering with noise algorithm which combines symbolic aggregate approximation and density-based spatial clustering of applications with noise algorithm. Afterward, TECPs are clustered by hierarchical clustering method. To determine the clustering number, six clustering validity indexes are applied to evaluate the clustering results. Finally, a feature-selection-based multifactor analysis method is designed to evaluate 95 factors in HCs impacts on TECPs. Results of the case study using 3117 records including smart meter data and survey data show that most factors of attitudes toward energy category and socio-demographic category are effective factors for predicting TECPs, while the factors in appliance and heating category are non-significant. The proposed mining framework and the findings can provide guidance of shaping grid-friendly electricity consumption behavior.

Based on engineering information and geographic information data, through the integrated application of 3D modeling technology and digital collaborative design technology, the 3D visual design and information integration of power transmission and transformation projects are completed, 3D design can realize complex underground pipeline layout, cable optimization layout, collision inspection, visual display, etc. It is helpful to reduce the soft and hard collision of the project, ensure the design safety and quality, optimize the work quantity and guide the construction. In this paper, through the application of 3D design technology in the design of 220kV substation in Miluo Western, the main aspects and benefits of application are summarized, and the existing problems are analyzed for reference in other engineering applications.

This paper investigates the output formation-containment problem of the coupled heterogeneous linear systems under intermittent communication. The systems considered in this paper are more general in the sense that each system, whether a leader or a follower, has different dimension and different dynamic. Besides, each system only communicates with its neighbors intermittently. Based on the intermittent information, both the state-feedback and the output-feedback distributed control protocols are designed and a criterion is derived to calculate the lower bound of the communication ratio. Furthermore, a heuristic algorithm based on the Fireworks Algorithm is developed to obtain an optimized communication ratio, which greatly reduces the communication burden. Finally, numerical examples are provided to demonstrate the effectiveness of the theoretical results.

In a pursuit-evasion game, the pursuers usually can capture the evaders successfully when the practical application environment is similar to the one that the pursuers was trained on. However, when there are some pursuers broken down or some new pursuers joining, which will result in that the number of agents in practice is different from the number of agents that was trained on. In other words, the environment has changed. In multi-agent deep reinforcement leaning algorithm, which means that the input and output dimension of network has changed, the trained pursuers may can not capture the evaders in the real-world application. To solve this problem, we proposed a multi-agent reinforcement learning framework so that when the number of pursuers has changed, the pursuers can also capture the evaders. Based on deep deterministic policy gradient (DDPG) framework and Bi directional recurrent neural network (Bi-RNN), we proposed the scalable deep reinforcement learning method for pursuit-evasion game, and apply it into multi-agent pursuit-evasion game in 2D-Dynamic environment. In this game, the speed of evaders is higher than the pursuers, but the number of evaders is less than the pursuers. Our experimental results show that this algorithm can increase the scalability and stability of multi-agent pursuit-evasion game.

While renewable energy resources have been well-established and researched for single-user houses and buildings, there still lacks renewable energy installations for apartment users. To evaluate the energy benefits of installing renewable energy resources for apartment users, we investigate the energy sharing management of apartment renewable resources among multiple users in this paper. The energy sharing problem is modeled as a bi-level optimization problem where the energy provider holds the upper level deciding the prices for its energy supplies and the users hold the lower level deciding their energy demands from energy provider in response to the prices over the time horizon. The initial bi-level optimization, known as NP-hard, is then transformed into a practical solvable mixed integer linear programming problem through proper mathematical techniques. Simulation cases are carried out to verify the energy efficiency of the proposed energy management model.

Objective This study aims to discuss the efficacy and safety of the application of thrombus aspiration catheters during emergency PCI operations for acute ST-elevation myocardial infarction (STEMI) patients with high thrombus load. Methods A total of 204 patients diagnosed with acute STEMI and high thrombus load in the Sixth Affiliated Hospital of Guangxi Medical University from July 1, 2016 to June 30, 2017 were selected for the present study. These patients were randomly divided into two groups: thrombus catheter aspiration group (group A, n = 101), and balloon dilatation group (group B, n = 103). The blood flow of the culprit coronary artery in the thrombolysis in myocardial infarction (TIMI) immediately after the emergency PCI operation in these two groups of patients was recorded. Then, an echocardiogram was performed to determine the left ventricular end-diastolic diameter (LVEDD) and left ventricular ejection fraction (LVEF) after the operation, and data on major adverse cardiovascular events (MACE) during the 30 days of postoperative follow-up were collected. Results The comparative difference between these two groups of patients in terms of hypertension, smoking, diabetes, usage rate of GPIIb/IIIa receptor antagonist, time from hospitalization to balloon dilatation (D2B) and other basic clinical data was not statistically significant (P > 0.05). The postoperative TIMI flow grade of these two groups of patients improved, and the comparative difference between the data obtained from these two groups was statistically significant (P P > 0.05). There was a difference in the occurrence rate of MACE in these two groups of patients during the 30 days of postoperative follow-up, but the comparative difference between these two groups was not statistically significant (P = 0.335). Conclusion The application of thrombus aspiration catheter during the emergency PCI operation of STEMI patients with high thrombus load can better improve the myocardial reperfusion. There is no basis for increasing the stroke occurrence risk. However, it obviously fails to improve the recent prognosis and more studies need to explore its effect on myocardial remodeling and major adverse cardiovascular events.

When the swept angle of the wing varies, the parameters such as the aspect ratio of the forward-swept wing aircraft and the relative speed of the cross-section airfoil relative to the incoming flow will change, which will play a great influence on the aerodynamic characteristics of the wing. Based on this purpose, this paper explores the impact of the oblique angle of the basic airfoil on the aerodynamic performances of the forward-swept wing. Firstly, the wing models of the upright and oblique basic airfoil were established, meanwhile the one-equation Spalart-Allmaras turbulence model and CFD technique were used for numerical simulation calculation. The change law of the aerodynamic characteristics of the wing with the fundamental airfoil upright and tilted are compared and analyzed when the swept angle varies.

To establish the restoring force model of a kind of prefabricated beam-column joints with ring-grooved bolts, the dimensionless skeleton curves and the stiffness degradation law of the joint are obtained by the regression analysis. Therein, the skeleton curves are a trilinear model divided into elastic, reinforcing and degradation stages. The calculated skeleton curves and restoring force model are in good agreement with the experimental skeleton and hysteretic curves. The result shows the restoring model can express the stiffness degradation characteristics of the joint well, as well as provide a theoretical basis for the nonlinear dynamic analysis of structures.

Quantum artificial intelligence exploits the interplay between artificial intelligence and quantum physics: on the one hand, a plethora of tools and ideas from artificial intelligence can be adopted to tackle intricate quantum problems; on the other hand, quantum computing could also bring unprecedented opportunities to enhance, speed up, or innovate artificial intelligence. Yet, quantum learning systems, similar to classical ones, may also suffer adversarial attacks: adding a tiny carefully-crafted perturbation to the legitimate input data would cause the systems to make incorrect predictions at a notably high confidence level. In this paper, we introduce the basic concepts and ideas of classical and quantum adversarial learning, as well as some recent advances along this line. First, we introduce the basics of both classical and quantum adversarial learning. Through concrete examples, involving classifications of phases of two-dimensional Ising model and three-dimensional chiral topological insulators, we reveal the vulnerability of classical machine learning phases of matter. In addition, we demonstrate the vulnerability of quantum classifiers with the example of classifying hand-written digit images. We theoretically elucidate the celebrated no free lunch theorem from the classical and quantum perspectives, and discuss the universality properties of adversarial attacks in quantum classifiers. Finally, we discuss the possible defense strategies. The study of adversarial learning in quantum artificial intelligence uncovers notable potential risks for quantum intelligence systems, which would have far-reaching consequences for the future interactions between the two areas.

Energy building techniques have gained significant attention for the potential to achieve local energy sustainability. Different from the research on individual energy management for zero energy buildings requiring sufficiently large renewable energy systems due to the mismatch of energy loads and generations, this paper investigates the coordinated energy management for a community of energy buildings with peer-to-peer energy sharing to promote community energy efficiency. Specifically, the energy buildings have controllable heating, ventilation and air conditioning (HVAC) units and renewable energy installations. In the coordinated energy management, in a peer-to-peer mode, the buildings determine their energy sharing profiles and related payments within the community while the sharing energy balance and payment balance are required to be satisfied. Since the buildings are self-interested, the problem with peer-to-peer energy sharing is modeled as a noncooperative game with global constraints. Then the existence of the game equilibrium is illustrated, and a distributed algorithm is proposed to seek the game equilibrium. A 6-building case and a 30-building case are carried out to verify the energy efficiency, economic benefits, and the scalability of the proposed peer-to-peer energy sharing management model.

Malignant melanoma is extremely rare neoplasm arising from uncontrolled growth of melanocytes, accounting for 0.5% of all oral malignancies. It has a great tendency to metastasize and locally invade tissues more readily than any other malignant tumor of the oral cavity. It present as the rare oral malignant condition, asymptomatic and poor prognosis. The necessity of a highly specialized treatment is factors that should be seriously considered by the involved health care provider. Herein, we report a rare and interesting case of oral malignant melanoma of the maxillary anterior gingiva, which was clinically and histo-pathologically diagnosed with a brief review of literature, has been discussed.

The electrical structure of the power distribution system in data center is generally a tree-like structure, with fewer branches closer to the power supply side and more branches closer to the load side. In this article, the electrical topology structure diagram is established, and the structure of power distribution network between 10kV input line and power distribution units is discussed. In the electrical structure of power system, 10kV distribution cabinets and 400V distribution cabinets are connected with electrical equipment such as electrical cable, transformer or enclosed busbar. By analysing the equivalent impedance model of equipment above, the theoretical impedance values are obtained.

With the innovation of steel structural joints, requirements for the reasonable design methods tend to be intensified to guarantee the effectiveness of joints’ working performance. To provide a certain reference for the design of assembled joints, related checking calculation methods and detailing requirements of a new type of fully assembled beam-column joints are given based on experimental phenomenon and according to related standards.

In real life, most of the infrastructure networks closely related to the national economy and people's livelihood do not exist independently, but are interconnected with or dependent on each other, so the multilayer network model is proposed to study the independent complex systems and infrastructures. When the nodes in the multilayer network suffer initial failure or attack, the cascade occurs due to the interaction between the “intra-layer” and “inter-layer”, and the failure can propagate in the network layer and across the layers iteratively, so that the scale of the failures is enlarged gradually. As a result, many multilayer networks are more fragile than single networks. The cascading failure of multilayer network usually brings very serious catastrophes to our society. So, conducting the research on preventing the multilayer network from cascading failure and recovering is of great significance. As far as the prevention of cascading failure is concerned, what are mainly included are the strategies such as the fault detection, the protection of important nodes, the optimization of the coupling method of networks, and the backup of nodes. As for the recovery of multi-layer network, included mainly are the strategies such as common boundary node recovery, the idle connected link recovery, the link addition, the priority recovery of important nodes, the topology perturbation, and the repairing of localized attack and adaptive link.

Galvanization is widely applied to bolts as an anti-corrosion method in bolted connection of steel structures in power transmission engineer. After being galvanized by hot-dipping, the torque coefficient of bolts will be changed. To investigate this change, the torque coefficients of ordinary bolts in different grades and specifications were measured before and after galvanization. Results indicate that mean values and standard deviations of torque coefficients of the galvanized bolts increase to a certain extent, but the torque coefficients and the standard deviations tend to decline as the improvement of bolt specification, and the stress of ungalvanized ones is larger than that of hot-dip galvanized bolts in the same grade and specification.

Service composition is a research hotspot with practical value. With the development of Web service, many Web services with the same functional attributes emerge. However, service composition optimization is still a big challenge since the complex and unstable composition environment. To solve this problem, we propose an adaptive service composition based on deep reinforcement learning, where recurrent neural network (RNN) is utilized for predicting the objective function, improving its expression and generalization ability, and effectively solving the shortcomings of traditional reinforcement learning in the face of large-scale or continuous state space problems. We leverage heuristic behavior selection strategy to divide the state set into hidden state and fully visible state. Effective simulation of hidden state space and fully visible state of the evaluation function can further improve the accuracy and efficiency of the combined results. We conduct comprehensive experiment and experimental results have shown the effectiveness of our method.

In order to simplify a complex structure of goaf in a coal mine, a new flow region of porous media is presented and established. It consists of eight spheres, one smaller sphere and their empty cavities in cube with double diameters of spheres. Being equal to porosity of the flow region, the minimum flow unit is constructed with this region cutting. Applying Navier-Stocks equations, it is deduced that a one-dimension flow model of this unit is based by one assumption of section-averaged velocity. Numerical calculations have also been conducted using MATLAB, and the viscosity coefficient influenced on pressure gradient, and obtained the quantitative relationship between viscosity coefficient and pressure gradient in four flow stages are discussed. By using the method of weighted length ratio, the influence of the viscosity coefficient on the pressure gradient of the minimum flow unit can be obtained. The flow models of porous media would provide a theoretical reference for the prevention of coal spontaneous combustion in goaf.

In this paper, we present a novel regression-based metric learning (RML) algorithm for solving the regression problems. Most of the metric learning (ML) algorithms can be used to solve the classification problems effectively, but none of them can be used to solve the regression problems. The main reason lies in the increasing difficulty brought by the establishment of the sample constraints for the regression problems. In order to overcome this obstacle, we establish the sample constraints in the objective space of the sample, instead of the feature space. Like most of the ML algorithms, the RML can also be used to reduce data dimensionality. It projects each sample point into a low-rank space through learning a proper Mahalanobis matrix. In addition, the RML can be applied to the arbitrary regression model. Simulation results based on various UCI datasets are provided to demonstrate that the proposed RML approach can obtain better performance.

Objective: The objective of this study was to evaluate the usefulness of T2 high signal intensity (T2-HSI) and decreased anteroposterior diameter (APD), diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI) in evaluating postoperative cervical cord function. Methods: The study included 57 postoperative cervical spondylotic myelopathy patients. Clinical evaluation and functional recovery assessments were performed using the modified Japanese Orthopaedic Association (mJOA) score and recovery rate. The presence of T2-HSI and decreased APD was recorded for exploring the relevance. Spearman correlation was applied to investigate the relationships between DTI and NODDI metrics and mJOA score. Multiple comparisons of T2 signal intensity, APD and diffusion metrics were evaluated by using multiple linear regression. Results: Only the recovery rate was significantly different between T2-HSI and non-T2-HSI (nT2-HSI) patients (χ2 = 4.466, p = 0.045). Significant differences were not observed between cervical cords with and without decreased APD. Diffusion metrics, including fractional anisotropy (p = 0.0005), mean diffusivity (p = 0.0008), radial diffusivity (p = 0.0003) and intracellular volume fraction (p = 0.001), were significantly correlated with mJOA score. The ability of T2 signal intensity (p = 0.421) and APD (p = 0.420) to evaluate the postoperative function was inferior to that of fractional anisotropy (p = 0.002), mean diffusivity (p = 0.001), radial diffusivity (p = 0.001) and intracellular volume fraction (p = 0.004). Conclusion: Conventional magnetic resonance imaging signs could be considered as a reference to make an approximate assessment, whereas DTI and NODDI could be better quantitative tools for evaluating the postoperative function and may help in interpreting residual symptoms. The translational potential of this article: DTI and NODDI could provide reliable postoperative evaluation and analysis for cervical spondylotic myelopathy patients. Keywords: Anteroposterior diameter, Cervical spondylotic myelopathy, Diffusion tensor imaging, Neurite orientation dispersion and density imaging, T2 high signal intensity