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Gao, Y.; Wang, H.; Lu, X.; Xu, Y.; Zhang, Z., and Schmidt, A.R., 2018. Hydrologic impact of urbanization on catchment and river system downstream from Taihu Lake. In: Wang, D. and Guido-Aldana, P.A. (eds.), Select Proceedings from the 3rd International Conference on Water Resource and Environment (WRE2017). Journal of Coastal Research, Special Issue No. 84, pp. 82–88. Coconut Creek (Florida), ISSN 0749-0208.This study examined the hydrologic impacts from rapid urbanization of a river valley and associated floodplain. The catchment downstream of the Taihu Lake was selected as the study region. Remote sensing images and topographic data for the period of post-1990 were employed to estimate hydrologic indicators for analyzing evolutionary features of the river system. Time series methods were adopted to analyze the hydrological effects on the watershed and river network resulting from rapid urbanization over the past 20 years. The results demonstrated that: (1) the number, length, and drainage density of rivers decreased from 1991 to 2006. The overall number of rivers decreased by 36.3%, and the overall river length was reduced by 25.5%; (2) the relationship between river length, river number, and stream order showed a geometric series over time, indicating a self-similarity in the river network that persisted even as the network changed due to urbanization; (3) the river network fractal dimension and complexity showed a decreasing trend over time, which indicated a simplification of the river system. In 1991, 2001, and 2006, the river network fractal dimension values were 2.24, 2.12, and 1.9, respectively; (4) excluding the influence of meteorological factors, there were at different levels increases in regional and seasonal precipitation. In addition, the difference in precipitation between urban and suburban areas increased gradually. Water levels also rose when the influence of precipitation was excluded.

Pancreatic cancer remains one of the most malignant tumors with a poor prognosis. Despite advances in diagnosis and treatment, no reliable biomarkers are available for clinical practice. Circular RNAs (circRNAs) are a novel class of endogenous non‑coding RNA, which are abundant, stable and conserved, and serve crucial roles in disease, particularly in cancer. The purpose of the present study was to investigate the expression profile of circRNAs in 20 pancreatic cancer tissues and corresponding paracancerous tissues using arraystar human circRNA array analysis, high‑throughput circRNA microarray, bioinformatic analysis and reverse transcription‑quantitative polymerase chain reaction. It was revealed that the circRNAs expression profile was significantly different between pancreatic cancer tissue and paracancerous tissue, which indicates a potential role in pancreatic cancer. It was predicted that circRNAs may act as a micro RNA sponge to modulate gene expression in pancreatic cancer. Additionally, microarray expression analysis data was submitted to the Gene Expression Omnibus under accession no. GSE79634. The present study revealed that circRNAs expression was visibly diverse in pancreatic cancer compared with paracancerous tissue and provides more reliable biomarkers and new insights into the mechanisms of pancreatic cancer.

The planning of charging facilities (CFs) for electric vehicles (EVs) plays an important role for the extensive applications of EVs. Uncertainties existing in the development of future EV technology should be properly modeled to ensure the robustness of the planning scheme. The uncertainties concerned include EV development types, growth rate of load and traffic flow, and distributions of load and traffic flow in the smart grid. The existing single-stage planning model cannot fully evaluate the risks brought by all kinds of uncertainties. Given this background, the multistage CF planning problem considering uncertainties is studied in this work. First, several typical uncertainties in future smart grid with a high penetration of EVs are considered to generate development scenarios for multistage planning. Then, the well-established data envelopment analysis is utilized to evaluate the planning schemes while the novel EV expected energy not supplied cost is defined to measure the service ability of CFs. The final planning result obtained by the proposed framework will not only have good performance in the current stage but also exhibit robustness for all the considered scenarios in the future stage with respect to uncertainties. The application potential of the designed multistage planning framework is proved by an example with both the distribution network and traffic network included.

This paper aims to solve wind energy integrated electric power dispatch (EPD) problem from the perspectives of stochastic modelling and multiobjective optimization. At first, the competing objectives in modern electric power and energy system is analyzed and a new interval optimization model for each objective is proposed based on the uncertainties with respect to wind power. Then, a novel strength differential evolution (SDE) algorithm is developed to address the interval optimization model. The SDE adopts a population selection process based on chaotic sequence and Boltzmann distribution to balance the tradeoff between local exploitation and global exploration. Afterwards, a multiobjective EPD model is established and a novel generalized piecewise normal boundary intersection (GPNBI) method is mooted to transform multiobjective EPD into a series of single-objective sub-problems which can be effectively solved by SDE. In order to deal with the highly constrains in GPNBI, a new heuristic constraint handling strategy is proposed accordingly to accelerate the convergence speed. At last, a hyper-plane based decision-making strategy is originally developed to identify the best compromise solution in the obtained Pareto frontiers (PFs). The feasibility and effectiveness of the interval optimization model and GPNBI inspired multiobjective SDE (MOSDE) have been comprehensively evaluated on a modified IEEE 30-bus system and a 118-bus system. The statistical results confirm that the proposed interval optimization model can approximately quantify the potential uncertainty in each objective and also demonstrate that the proposed MOSDE exhibits better performance than the algorithms of the state. Therefore, it is convinced that the proposed optimization model and method have high potentials to address the practical implementation problems in electric power and energy systems with wind energy penetration.

Flood control with polders is prevalent in East China. Their impact on flood processes is critically important for flood control, but has not been well documented. The Qinhuai River Basin was selected as the study area. A Hydrologic Engineering Center – River Analysis System (HEC-RAS) hydraulic model was developed to simulate and predict storm flood processes and the associated impact of polders. The study shows that the HEC-RAS model is capable of simulating the impact of polders on flood processes in the Qinhuai River Basin. The polders increased the water level outside of the polders. The polders in upstream watersheds have a greater impact on the water level than polders close to basin outlets when individually distributed. The maximum water level at Dongshan section shows an increasing trend for different sized flood with the increasing number of polders in the basin, and a linear increasing trend associated with urbanization. The smaller the flood scale is, the greater the maximum water level changes.

The penetration of photovoltaic (PV) energy into modern electric power and energy systems has been gradually increased in recent years due to its benefits of being abundant, inexhaustible and clean. In order to reduce the negative impacts of PV energy on electric power and energy systems, advanced forecasting approach with high-accuracy is a pressing need. Aimed at this, a novel hybrid method for deterministic PV power forecasting based on wavelet transform (WT) and deep convolutional neural network (DCNN) is firstly proposed in this paper. WT is used to decompose the original signal into several frequency series. Each frequency has better outlines and behaviors. DCNN is employed to extract the nonlinear features and invariant structures exhibited in each frequency. Then, a probabilistic PV power forecasting model that combines the proposed deterministic method and spine quantile regression (QR) is originally developed to statistically evaluate the probabilistic information in PV power data. The proposed deterministic and probabilistic forecasting methods are applied to real PV data series collected from PV farms in Belgium. Numerical results presented in the case studies demonstrate that the proposed methods exhibit the ability of improving forecasting accuracies in terms of seasons and various prediction horizons, when compared to conventional forecasting models.

With the rapid growth of penetration of distributed generations and the strong interdependencies of multiple energy sources, the optimal operation of integrated energy systems (IESs) is becoming more important than ever before. However, current optimal operation strategies for IESs may be impractical because their potential negative impacts on utility grids are generally not considered. Therefore, in this study, an original multiobjective optimization model for IES operation is formulated to minimize the operational cost, primary energy consumption, and carbon dioxide emission of IESs and to optimally reduce the power loss and voltage magnitude deviation of the utility grid. Then, a novel Pareto optimization algorithm, called multiobjective strength firefly algorithm (MOSFA), is proposed to solve the multiobjective operation problem of IES. The strength firefly algorithm (SFA) uses a Boltzmann distribution and a chaotic-sequence-based population selection process to facilitate local exploitation and global exploration. In addition, a generalized piecewise normal boundary intersection (GPNBI) method is developed to transform the multiobjective operation problem into a series of highly constrained single-objective optimization sub-problems that can be effectively solved by the SFA. Finally, a hyper-plane-based decision making strategy is introduced to identify the best compromise solution for the obtained Pareto frontiers. The GPNBI-inspired MOSFA was comprehensively evaluated on a novel IES powered by natural gas, wind and photovoltaic generators. The standard IEEE 39-bus system is considered as the utility grid. The numerical results demonstrated that the proposed MOSFA exhibits competitive performance when compared to the algorithms of the state, which means that the optimized operation strategy provides a better trade-off between all objectives considered in this study.

Pancreatic cancer is characterized by neural alterations and aberrant expression of neural-specific factors. Semaphorins have been recognized as key contributors in axon guidance, the immune response and tumor progression. Recent studies have suggested the involvement of Semaphorin 3c (sema3c) in tumorigenesis and metastasis in numerous types of cancer, however, the clinical significance of sema3c and its role in the growth and metastasis of pancreatic ductal adenocarcinoma (PDAC) remain unclear. In this study, we found that aberrant sema3c expression was positively associated with a particular tumor stage and correlated with poor survival of PDAC patients. Knockdown of sema3c attenuated proliferation, migration, invasion and epithelial-mesenchymal transition (EMT) in a pancreatic cancer cell line and reduced PDAC cell tumorigenesis upon xenotransplantation into NOD/SCID mice. Overexpression of sema3c produced the opposite effects and promoted the extracellular signal-regulated kinase (ERK)1/2 signaling pathway. Overall, our findings demonstrate that aberrant expression of sema3c is correlated with poor prognosis of PDAC patients and promotes tumor growth and metastasis by activating ERK1/2 signaling pathway.

The critical bottleneck of electrocatalytic CO

Background: Due to the limitations of strategies for its early diagnosis and treatment, pancreatic cancer (PC) remains a substantial human health threat. We previously discovered a methylation-mediated lncRNA, LINC00261, which is downregulated in PC tissues. However, the underlying role of LINC00261 in PC remains largely unknown. Methods: Quantitative real-time PCR and in situ hybridization were performed to evaluate the expression levels of LINC00261 in PC, adjacent nontumor and normal pancreas tissues. The clinical significance of LINC00261 was assessed in multicenter PC samples. The functions of LINC00261 in PC were investigated by gain- and loss-of-function assays in vitro and in vivo. Potential downstream pathways and mechanisms were explored via RNA sequencing and bioinformatic analyses. RNA immunoprecipitation and chromatin immunoprecipitation assays were used to validate the underlying mechanisms. Pyrosequencing and targeted demethylation of the LINC00261 promoter were performed to explore the upstream epigenetic mechanisms and therapeutic potential. Results: LINC00261 was significantly downregulated in PC tissues, and its expression was positively associated with the prognosis of PC patients. Phenotypic studies indicated that LINC00261 overexpression significantly suppressed PC cell proliferation, migration and metastasis in vitro and in vivo. c-Myc was identified as a downstream target of LINC00261. LINC00261 repressed c-Myc transcription by physically interacting and binding with the bromo domain of p300/CBP, preventing the recruitment of p300/CBP to the promoter region of c-Myc and decreasing the H3K27Ac level. Moreover, the methylation level of the LINC00261 promoter was high in PC tissues and was correlated with poor prognosis. Targeted demethylation of the LINC00261 promoter inhibited PC progression both in vitro and in vivo. Conclusions: Our findings indicate that methylation-mediated LINC00261 suppresses PC progression by epigenetically repressing c-Myc expression. These findings expand the therapeutic potential of LINC00261, possibly providing evidence to support the development of epigenetic drugs or therapeutic strategies. This research adds further insights into the etiology of PC and indicates that LINC00261 may be a prognostic and therapeutic target in PC.

Pancreatic cancer (PC) is highly malignant and lacks an effective therapeutic schedule, hence that early diagnosis is of great importance to achieve a good prognosis. Oral bacteria have been proved to be associated with pancreatic cancer, but the specific mechanism has not been comprehensively illustrated. In our study, thirty-seven saliva samples in total were collected with ten from PC patients, seventeen from benign pancreatic disease (BPD) patients, and ten from healthy controls (HC). The oral bacterial community of HC, PC, and BPD groups was profiled by 16S rDNA high-throughput sequencing and bioinformatic methods. As shown by Simpson, Inverse Simpson, Shannon and Heip, oral microbiome diversity of HC, BPD and PC groups is in increasing order with the BPD and PC groups significantly higher than the HC group. Principal coordinate analysis (PCoA) suggested that grouping by PC, BPD and HC was statistically significant. The linear discriminant analysis effect size (LEfSe) identified high concentrations of Fusobacterium periodonticum and low concentrations of Neisseria mucosa as specific risk factors for PC. Furthermore, predicted functions showed changes such as RNA processing and modification as well as the pathway of NOD-like receptor signaling occurred in both PC and HC groups. Conclusively, our findings have confirmed the destruction of oral bacterial community balance among patients with PC and BPD and indicated the potential of Fusobacterium periodonticum and Neisseria mucosa as diagnostic biomarkers of PC.

Pancreatic cancer (PC) is a severe disease with the highest mortality rate among various cancers. It is urgent to find an effective and accurate way to predict the survival of PC patients. Gene set variation analysis (GSVA) was used to establish and validate a miRNA set-based pathway prognostic signature for PC (miPPSPC) and a mRNA set-based pathway prognostic signature for PC (mPPSPC) in independent datasets. An optimized miPPSPC was constructed by combining clinical parameters. The miPPSPC, optimized miPPSPC and mPPSPC were established and validated to predict the survival of PC patients and showed excellent predictive ability. Four metabolic pathways and one oxidative stress pathway were identified in the miPPSPC, whereas linoleic acid metabolism and the pentose phosphate pathway were identified in the mPPSPC. Key factors of the pentose phosphate pathway and linoleic acid metabolism, G6PD and CYP2C8/9/18/19, respectively, are related to the survival of PC patients according to our tissue microarray. Thus, the miPPSPC, optimized miPPSPC and mPPSPC can predict the survival of PC patients efficiently and precisely. The metabolic and oxidative stress pathways may participate in PC progression.

State estimation is critical for the normal operation of power system. However, due to the aging of electric infrastructures, the power grid become more vulnerable to cyber-attack. Therefore, in this paper, scenario-based two-stage sparse cyber-attack models for smart grid with complete and incomplete network information are proposed. In previous traditional state estimator, bad data detector (BDD) is used to prevent power systems from the smearing effect of the unsteady power system caused by the bad data. However, the researches have pointed out that BDD can’t recognize the abnormal state which launch by the hackers who know the topologies of the power system. Then, in order to detect false data effectively, we proposed a new detect mechanism based on extreme learning machine (ELM). In the proposed defense mechanism, we combine numbers of ELM whit certain rules, which objective is to improve the precision of the detection. Finally, the effectiveness and validation of the proposed method is verified on standard IEEE14-bus AIEEE57-bus system.

This paper proposes a temporary frequency supp-ort (TFS) scheme of a doubly fed induction generator (DFIG) that can improve the frequency nadir (FN) for wide-area power system (WAPS) with cyber uncertainties. In normal operation, DFIG runs in a de-loading state. Upon detecting a frequency event, the power reference is increased adaptively by the incremental power and this incremental power varies with the frequency deviation and frequency sample difference. The designed controller can compensate the influence of the delay by calculating the delay of the current time and selecting the gain coefficient according to delay value. The test results, which are based on the KUNDUR four-machine two-area system, demonstrate that the proposed scheme can improve the FN in a WAPS with cyber uncertainties.

In this paper, a solid-state transformer is studied, which adopts three-stage cascade control of input isolation output. The transformer can provide high-voltage and low-voltage DC bus, which can realize DC input and output of DC, and has strong compatibility. In this paper, the solid-state transformer is taken as the research object, focusing on the energy flow analysis of solid-state transformers and the coordinated control between solid-state transformers. The structure and control strategy of solid-state transformers are studied. The simulation model of solid state transformer is built by using Matlab/Simulink. The simulation results show that the coordinated control strategy system used in this paper has good dynamic performance, good anti-interference and fast response.

Pancreatic cancer is a highly malignant cancer and lacks effective therapeutic targets. Protein-tyrosine phosphatase 1B (PTP1B), a validated therapeutic target for diabetes and obesity, also plays a critical positive or negative role in tumorigenesis. However, the role of PTP1B in pancreatic cancer remains elusive. Here, we initially demonstrated that PTP1B was highly expressed in pancreatic tumors, and was positively correlated with distant metastasis and tumor staging, and indicated poor survival. Then, inhibition of PTP1B either by shRNA or by a specific small-molecule inhibitor significantly suppressed pancreatic cancer cell growth, migration and colony formation with cell cycle arrest in vitro and inhibited pancreatic cancer progression in vivo. Mechanism studies revealed that PTP1B targeted the PKM2/AMPK/mTOC1 signaling pathway to regulate cell growth. PTP1B inhibition directly increased PKM2 Tyr-105 phosphorylation to further result in significant activation of AMPK, which decreased mTOC1 activity and led to inhibition of p70S6K. Meanwhile, the decreased phosphorylation of PRAS40 caused by decreased PKM2 activity also helped to inhibit mTOC1. Collectively, these findings support the notion of PTP1B as an oncogene and a promising therapeutic target for PDAC.

In order to reduce the pressure caused by the precipitous depletion of natural resources and realize the sustainable development, renewable energy resource is playing a more and more significant role with an exponential growth speed in the smart grid, especially for wind energy source. Given the wind energy source generation and to maintain the security and reliability of the power grid operation, finding a forecasting method with higher accuracy is a pressing need. Facing with this challenge, a novel prediction model that combines Echo State Network with Wavelet Decomposition (ESN+WD) is presented in this paper. WD technique is aimed to decompose the historical wind power data into several wavelets with different frequencies. ESN is used to train the decomposed wavelets separately and predict the future output Wind Power. Two other kinds of models are investigated to compare with the presented approach. The performance is respectively assessed for 30mins and 60mins ahead power forecasting in deterministic prediction. Numerical results presented in simulations show that the proposed model has good reliability and high accuracy in comparison with other models selected for this study.

Treatment of pancreatic ductal adenocarcinoma (PDAC) remains a clinical challenge. There is an urgent need to develop novel strategies to enhance survival and improve patient prognosis. microRNAs (miRNAs) play critical roles as oncogenes or tumor suppressors in the regulation of cancer development and progression. In this study, we demonstrate that low expression of miR-15a is associated with poor prognosis of PDAC patients. miR-15a expression is reduced in PDAC while closely related miR-16 expression remains relatively unchanged. miR-15a suppresses several important targets such as Wee1, Chk1, Yap-1, and BMI-1 causing cell cycle arrest and inhibiting cell proliferation. Ectopic expression of miR-15a sensitizes PDAC cells to gemcitabine reducing the IC50over 6.5-fold. To investigate the therapeutic potential of miR-15a, we used a modified miR-15a (5-FU-miR-15a) with uracil (U) residues in the guide strand replaced with 5-fluorouracil (5-FU). We demonstrated enhanced inhibition of PDAC cell proliferation by 5-FU-miR-15a compared to native miR-15a.In vivowe showed the therapeutic power of 5-FU-miR-15a alone or in combination with gemcitabine with near complete elimination of PDAC lung metastatic tumor growth. These results support the future development of 5-FU-miR-15a as a novel therapeutic agent as well as a prognostic biomarker in the clinical management of PDAC.

The change of land use and land coverage (LULC) has a direct impact on the underlying surface of the drainage basin, hence alters the rainfall-runoff processes. This study described the procedure to use the CA-Markov model to predict the future distributed land use in the Qinhuai River basin in 2028 based on the historical evolution. The hydrological model HEC-HMS was then utilized to examine the impact of land use change on the hydrological responses of the drainage basin. The major findings were as follows: (1) from 2001 to 2010, major changes detected in land use were substantial conversion of paddy fields, forest land and dryland into urban land (increased by 56.81%), following the trend recognized by CA-Markov model, urban land and dry land were predicted to increase by 17.47% and 14.05% by 2028. (2) the projected land use in 2028 resulted in various degrees of increase in flood peak and volume according to the severity of the floods characterized by flood volumes, the smaller floods were predicted to result in more significant increase with 3.5% increase in flood peak and 2.9% increase in flood volume while these values decreased to 0.4% and 1.1% for a big scale flood; (3) greater increase in urbanization leads to greater change of the flood peak and volume change. For small scale floods, when the proportion of urban land use increases by 30% to 60% hypothetically, the relative increment in flood peaks increase from 4.7% to 8.1% with 4% to 6.6% increase in the flood volume; (4) on the sub-basin scale, the trend of change in urban land use and the flood peak and volume were consistent. The methods and conclusions may shed light on urban land development and management and design of flood control measures in a large river basin.

According to the development of smart grid, we propose a multi-dimensional protection mechanism based on PMU (phasor measurement unit) in this paper. In order to narrow down the variation range of state variables against false data injection attack, we propose a new dynamic network protection method, in which the local measurement with PMU used. Meanwhile we propose a detected strategy of interval state estimation, which could effectively identify dynamic network attacks and protect the smart grid. Finally, through numerical simulations on the standard IEEE14-node system, it is verified that the proposed model can alleviate the smearing effect from the perspective of attack, and improve the accuracy of the defense mechanism. In this paper, through the fusion of PMU data, the smart grid can be protected from the two dimensions included attack and defense, which effectively ensures the safe and stable operation of the smart grid.

With the development of energy internet, the hybrid energy systems are attracting increased attention. Optimum distributed energy sources placement is crucial to the operation of an energy internet since it determines how much power can generate for cold, heat and power demands. Cold and heat load models, PV model, cloudy day model, natural gas combined cold, heat and power system model are established to study the influences of solar radiations and temperature of outdoors. The running characteristics of the natural gas combined cold, heat and power system are studied by reference to C65. Three criteria are proposed to measure the technology and economic benefits of an energy grid comprising PV and natural gas combined cold, heat and power system. The PSO-Catastrophe hybrid algorithm is presented to find the optimal solution, including the optimum areas of PV and number of C65. There are four seasons in simulation: spring, summer, autumn and winter. The simulation results prove the effectiveness of the proposed method, and the influences of solar radiations on output in energy system are described in the end.

Due to the increasing hidden danger of power system, we may face more challenges and suffer more terrible effects when the state estimation is carried out. State estimation plays a central role in the monitoring and control of smart grid. So we proposed a new attack separation scheme to indicate the false data injection attack in cyber physical smart grid (CPSG), which consists of a bilinear stage state estimator, an anomaly detection method based on a worst-case analysis, and a bad data(BD) processor. We first recognize the false data using bilinear stage estimator. And then we develop an anomaly detection method that takes system uncertainties into account to estimate the possible deviations of states. Furthermore, we propose a BD processor to filter out the false data injected by the attackers, and reconstruct the real power system state. So we can eliminate the smearing affect, maintaining the steady states of the power system. Also, we decouple the power balancing formulation into P-Q forms to improve the computational efficiency. We then validate the efficiency of the proposed approach thought the IEEE 14-bus and 30-bus systems. Finally, the state reconstruction result after attack has been showed.

From the three processes of recycling, remanufacturing and remanufacturing product sales, this paper analyses the characteristics of the automobile engine cost. By using the theory of system dynamics method, it establishes system flow diagram of automobile engine recycling and remanufacturing cost under the methods of network recovery and 4S shop recovery, and constructs engine remanufacturing cost model. Taking Shandong heavy-duty automobile diesel engine remanufacturing as an example, the model is simulated and analyzed with Vensim PLE software, and the change trend of engine recovery cost and profit from 2015 to 2035 is analyzed and predicted. The sales revenue, cost and profit of remanufacturing under the two recovery methods are compared and analyzed. Furthermore, the simulation results show that the different selections of engine recovery strategies have significant differences on the economic impact of engine remanufacturing, and the economic analysis can provide a reference for the development decision of engine remanufacturing.

This paper proposes a new generalized attack separation scheme in energy internet to analyze how an attacker behaves. This scheme consists of a robust interval state estimation-based anomaly detection method and a coordinated bad data processor. We develop an anomaly detection method that takes system uncertainties, i.e., forecasting uncertainty and parameter perturbation, into account to approximate the largest possible deviations of state variables in smart grid, thus recognizing stealthy anomalies. In this method, we decouple the power balancing formulation into P-θ and Q-V sub-problems to improve the computational efficiency. Thereafter, a new coordinated bad data processor is proposed to automatically filter out the impact of malicious attacks on state estimation and to reconstruct the real states of a cyber physical smart grid. In this processor, three-stage state estimator is applied to rapidly assess the biased state from the current observations with the contamination of meters and communication channels. As a result, the implemented attack vector can be separated and isolated from the current observations online. Finally, the proposed scheme is validated via comprehensive tests on several IEEE systems. The obtained results show the strong robustness, high stability and promising performance for attack separation, indicating a high potential of the proposed scheme for practical implementations in deep-cyber penetrated smart grids.

The rapid development of distributed generators and demand response management programs are transforming the traditional consumers to emerging prosumers. While, it is difficult to manage these prosumers because different types of energy are locally generated and consumed with the autonomous operations. For this purpose, this paper proposes a multi-energy forecasting framework based on deep learning methodology to simultaneously predict the electrical, thermal and gas net load of integrated local energy systems. First, the inherent multi-energy load and generation features of heterogeneous prosumers are qualitatively analyzed, and a hierarchical clustering framework is formulated to classify these prosumers into various aggregations to facilitate the multi-energy forecasting model. Then, a deep belief network based forecasting method is developed to extract the hidden features in multi-energy time series, thereby achieving the net-load prediction of numerous prosumers. Finally, the proposed multi-energy net load forecasting methodology is extensively and comprehensively validated using the real data from household-scale prosumers. The comparative results demonstrate the superiority and high forecast accuracy of the proposed methodology, and confirm its capability to cope with the multi-prosumer prediction problem with multi-energy carriers.

The tight coupling of information and communication technology and traditional energy system has given birth to a cyber physical energy system (CPES). CPES indeed improves the economic operation and control efficiency of the energy system, but it also brings new cyber risk issues, threatening the secure operation of the energy system. Consequently, this paper proposes a new multivariate ensemble classification (MEC) method to detect intrusions in CPES, thereby enhancing the baseline cybersecurity of CPES. MEC simultaneously takes into account the detection accuracy, stability and computing efficiency. In MEC, extreme gradient boosting, light gradient boosting machine and extreme learning machine are separately designed as individual detectors for intrusion identification. Then, ensemble learning based decision-making is developed to strategically aggregate the results of all individual detectors. Finally, the effectiveness of the proposed MEC is validated on IEEE standard 14-, 57- and 118-bus systems. The obtained results demonstrate that the MEC method has an attractive potential in real applications.

Due to the economic and environmental benefits, wind power is becoming one of the more promising supplements for electric power generation. However, the uncertainty exhibited in wind power data is generally unacceptably large. Thus, the data should be accurately evaluated by operators to effectively mitigate the risks of wind power on power system operations. Recognizing this challenge, a novel deep learning based ensemble approach is proposed for probabilistic wind power forecasting. In this approach, an advanced point forecasting method is originally proposed based on wavelet transform and convolutional neural network. Wavelet transform is used to decompose the raw wind power data into different frequencies. The nonlinear features in each frequency that are used to improve the forecast accuracy are later effectively learned by the convolutional neural network. The uncertainties in wind power data, i.e., the model misspecification and data noise, are separately identified thereafter. Consequently, the probabilistic distribution of wind power data can be statistically formulated. The proposed ensemble approach has been extensively assessed using real wind farm data from China, and the results demonstrate that the uncertainties in wind power data can be better learned using the proposed approach and that a competitive performance is obtained.

// Wenhua Li 1, * , Jian Li 2, * , Yunchao Wang 2 , Keqian Zhang 1 , Ni Li 1 , Zhiqiang Tian 3 , Bing Ni 4, 3 , Huaizhi Wang 2 , Zhihua Ruan 1 1 Department of Oncology and Southwest Cancer Center, Southwest Hospital, Third Military Medical University, Chongqing 400038, PR China 2 Institute of Hepatopancreatobiliary Surgery, Southwest Hospital, Third Military Medical University, Chongqing 400038, PR China Institute of Hepatopancreatobiliary Surgery, Southwest Hospital, Third Military Medical University, Chongqing 400038, PR China 3 Institute of Immunology, PLA, Third Military Medical University, Chongqing 400038, PR China 4 Department of Pathophysiology and High Altitude Pathology, Third Military Medical University, Chongqing 400038, PR China * These authors contributed equally to this work Correspondence to: Bing Ni, email: nibingxi@126.com Huaizhi Wang, email: whuaizhi@gmail.com Zhihua Ruan, email: rzh1234@163.com Keywords: nasopharyngeal carcinoma, sphingosine kinase 1, therapeutic target, FTY720 Received: September 09, 2016 Accepted: October 25, 2016 Published: November 02, 2016 ABSTRACT Sphingosine kinase 1 (SPHK1) has been shown to be involved in the progression of various types of human cancers. We previously demonstrated that SPHK1 is overexpressed and associated with clinical stage, locoregional recurrence, distant metastasis and poor prognosis in nasopharyngeal carcinoma (NPC). However, the biological roles involving SPHK1 and its potential usefulness as a therapeutic target in NPC remain unknown. In this study, Blocking SPHK1 using siRNA or FTY720 (a SPHK1 inhibitor) significantly reduced proliferation and migration and increased cell cycle arrest and apoptosis in NPC cells. FTY720 also decreased SPHK1 activity, and overexpressing SPHK1 abrogated the FTY720-induced effects on cell viability. In addition, FTY720 sensitized NPC cells to radiotherapy by inhibiting SPHK1 activity in vitro and in vivo . Furthermore, high SPHK1 expression was associated with increased Ki-67 and p-Akt and decreased caspase-3 expression in human NPC specimens. These data suggest that SPHK1 might be a potential therapeutic target for NPC.

With the rapid growth of wind power penetration into modern power grids, wind speed forecasting (WSF) plays an increasingly significant role in the planning and operation of electric power and energy systems. However, the wind speed time series always exhibits nonlinear and non-stationary features, making it very difficult to be predicted accurately. Recognizing this challenge, a novel deep learning based approach is proposed for deterministic and probabilistic WSF. The approach is a hybrid of wavelet transform (WT), deep belief network (DBN) and spine quantile regression (QR). WT is employed to decompose raw wind speed data into different frequency series with better behaviors. The nonlinear features and invariant structures of each frequency are completely extracted by layer-wise pre-training based DBN. Then, the uncertainties in wind speed are statistically synthesized via the QR method. Case studies using real wind farm data from China and Australia have been presented. The comparative results demonstrate that the high-level nonlinear and non-stationary feature in the wind speed series can be learned better, and competitive performance can thus be obtained. It is therefore convinced that the proposed method has a high potential for practical applications in electric power and energy systems.

This study proposes a novel dynamic reliability network-equivalent approach (RNEA) for distribution generations (DGs) enhanced distribution system. This method includes a bottom-up process and a top-down process, which are developed based on equivalent principle, and the synthesis of both processes yields the reliability performance of any node in the system. In addition, a novel switch zone concept is defined and introduced into the bottom-up equivalent process as to save the computation cost. Besides, a hybrid restoration model is proposed to account for the practical restoration process in power grids, and solved using particle swarm optimisation. The most significant of the proposed approach over other equivalent methods such as section technique, RNEA, and zone-branch method is that it provides a more generalised element-by-element equivalent approach that can greatly simplify the network for reliability assessment irrespective of the network topology. The proposed methodology has been successfully tested on a real 362-node urban distribution system in China, with and without DGs considered.

The aberrant expression of microRNAs (miRNAs) has emerged as an important hallmark of cancer. However, the molecular mechanisms underlying the changes in miRNA expression remain unclear. In this study, we discovered a novel epigenetic mechanism of miR-506 regulation and investigated its functional significance in pancreatic cancer. Sequencing analysis revealed that the miR-506 promoter is highly methylated in pancreatic cancer tissues compared with non-cancerous tissues. Reduced miR-506 expression was significantly associated with clinical stage, pathologic tumor status, distant metastasis and decreased survival of pancreatic cancer patients. miR-506 inhibited cell proliferation, induced cell cycle arrest at the G1/S transition and enhanced apoptosis and chemosensitivity of pancreatic cancer cells. Furthermore, we identified sphingosine kinase 1 (SPHK1) as a novel target of miR-506, the expression of which inhibited the SPHK1/Akt/NF-κB signaling pathway, which is activated in pancreatic cancer. High SPHK1 expression was significantly associated with poor survival in a large cohort of pancreatic cancer specimens. Our data suggest that miR-506 acts as a tumor suppressor miRNA and is epigenetically silenced in pancreatic cancer. The newly identified miR-506/SPHK1 axis represents a novel therapeutic strategy for future pancreatic cancer treatment.

Short-term predictions of wind power and its ramp events play a critical role in economic operation and risk management of smart grid. This paper proposes a hybrid forecasting model based on semi-supervised generative adversarial network (GAN) to solve the short-term wind power outputs and ramp event forecasting problems. In the proposed model, the original time series of wind energy data can be decomposed into several sub-series characterized by intrinsic mode functions (IMFs) with different frequencies, and the semi-supervised regression with label learning is employed for data augmentation to extract non-linear and dynamic behaviors from each IMF. Then, the GAN generative model is used to obtain unlabeled virtual samples for capturing data distribution characteristics of wind power outputs, while the discriminative model is redesigned with a semi-supervised regression layer to perform the point prediction of wind power. These two GAN models form a min-max game so as to improve the sample generation quality and reduce forecasting errors. Moreover, a self-tuning forecasting strategy with multi-label classifier is proposed to facilitate the forecasting of wind power ramp events. Finally, the real data of a wind farm from Belgium is collected in the case study to demonstrate the superior performance of the proposed approach compared with other forecasting algorithms.

As the penetration of solar energy into electrical power and energy system expands in recent years over the world, accurate solar irradiance forecasting is becoming highly important. However, the existing solar irradiance forecasting methods based on soft-computing techniques are modeled as black-boxes, which are generally expressed by typical unreadable functions such as sigmoid. These functions are difficult to interpret the prediction results. Therefore, a new direct explainable neural network consisting of one input layer, two linear layers and one nonlinear layer, is innovatively proposed for solar irradiance forecasting. The proposed explainable neural network is basically a feed-forward neural network, using ridge function as the activation function to interpret the solar feature mapping. The training process of direct explainable neural network is designed based on back-propagation algorithm. It consists of data preprocessing, error-estimation pretraining and parameter fine-tuning. The main advantage of the proposed explainable neural network is that it can theoretically extract the nonlinear mapping features in solar irradiance, thereby providing a clear explanation of the relationship between the input and the output of the forecasting model. Solar irradiance samples from Lyon in France are used to simultaneously assess the forecasting accuracy and interpretability of the proposed explainable neural network. The experimental results demonstrate that direct explainable neural network not only exhibits a better prediction performance than traditional neural networks such as support vector regression, but also mathematically interprets how the input of the forecasting model affects the final prediction results, showing that the proposed explainable neural network has a high application potential in the real world.

This paper proposes a bilevel multi-house energy management (MHEM) framework to coordinate the residential demand response (DR) of heterogeneous households based on many-criteria optimality. In the upper level, the loss of life (LOL) cost of transformers is formulated into the DR cost model, and a stochastic scheduling is implemented to determine the optimum amount of transformer load deferment and curtailment. The lower level aims to optimally allocate the transformer load from the aggregator to individual households, and a many-criteria DR optimality model is proposed to maximize the multi-house benefits from DR while achieving coordination of the DR participation. Furthermore, a hypercube spatial transformation based classification and sorting scheme is developed to form an evolutionary many-objective (EMO) algorithm in order to solve the many-criteria decision making (MCDM) problem of coordinated DR with numerous participants. The performance of the proposed method was benchmarked and validated on different scaled neighborhood systems over a 24-h scheduling horizon, and comparative results demonstrated its superiority and optimality in solving many-house DR problems.

With the world-wide deployment of solar energy for a sustainable and renewable future, the stochastic and volatile nature of solar power pose significant challenges to the reliable, economic and secure operation of electrical energy systems. It is therefore imperative to improve the prediction accuracy of solar power to prepare for the unknown conditions in the future. So far, artificial intelligence (AI) algorithms such as machine learning and deep learning have been widely-reported with competitive prediction performance because they can reveal the invariant structure and nonlinear features in solar data. However, these reports have not been fully reviewed. Accordingly, this paper provides a taxonomy research of the existing solar power forecasting models based on AI algorithms. Taxonomy is a process of systematically dividing solar energy prediction methods, optimizers and prediction frameworks into several categories based on their differences and similarities. We also present the challenges and potential future research directions in solar power forecasting based on AI algorithms. This review can help scientists and engineers to theoretically analyze the characteristics of various solar prediction models, thereby helping them to select the most suitable model in any application scenario.

Pancreatic cancer (PC) is one of the most aggressive malignancies with high mortality due to a complex and latent pathogenesis leading to the severe lack of early diagnosis methods. To improve clinical diagnosis and enhance therapeutic outcome, we employed the newly developed precision-targeted metabolomics method to identify and validate metabolite biomarkers from the plasma samples of patients with pancreatic cancer that can sensitively and efficiently diagnose the onsite progression of the disease. Many differential metabolites have the capacity to markedly distinguish patients with pancreatic cancer (n = 60) from healthy controls (n = 60). To further enhance the specificity and selectivity of metabolite biomarkers, a dozen tumor tissues from PC patients and paired normal tissues were used to clinically validate the biomarker performance. We eventually verified five new metabolite biomarkers in plasma (creatine, inosine, beta-sitosterol, sphinganine and glycocholic acid), which can be used to readily diagnose pancreatic cancer in a clinical setting. Excitingly, we proposed a panel biomarker by integrating these five individual metabolites into one pattern, demonstrating much higher accuracy and specificity to precisely diagnose pancreatic cancer than conventional biomarkers (CA125, CA19-9, CA242 and CEA); moreover, this plasma panel biomarker used for PC diagnosis is also quite convenient to implement in clinical practice. Using the same metabolomics method, we characterized succinic acid and gluconic acid as having a great capability to monitor the progression and metastasis of pancreatic cancer at different stages. Taken together, this metabolomics method was used to identify and validate metabolite biomarkers that can precisely and sensitively diagnose the onsite progression and metastasis of pancreatic cancer in a clinical setting. Furthermore, such effort should leave clinicians with the correct time frame to facilitate early and efficient therapeutic interventions, which could largely improve the five-year survival rate of PC patients by significantly lowering clinical mortality.

Ubiquitin-conjugating enzyme 2C (UBE2C) is a core ubiquitin-conjugating enzyme in the ubiquitin-proteasome system that promotes cell cycle progression. Previous studies have indicated that UBE2C mediates tumorigenesis and progression in various cancers, but its role in pancreatic ductal adenocarcinoma (PDAC) remains unclear. This study elucidated the function of UBE2C in PDAC tumorigenesis and progression by determining UBE2C expression via real-time qPCR, western blotting and immunohistochemistry. The associations between UBE2C expression and clinicopathological characteristics and survival were assessed using a tissue microarray based on a multicentre PDAC cohort. We found that UBE2C was strongly expressed in PDAC patient tissues and was negatively associated with clinical stage, lymph node metastasis, perineural invasion and survival (all P < 0.05). Multivariate analysis revealed that high UBE2C expression is an independent risk factor for PDAC (P = 0.001). In the PDAC cell lines CFPAC-1 and Panc-1, silencing UBE2C suppressed cell proliferation by inducing G1/S arrest mediated by downregulation of cyclin D1. Furthermore, UBE2C knockdown decreased the migration of PDAC cells in vitro by downregulating epithelial-mesenchymal transition (EMT). RNA-seq analysis showed that upon silencing UBE2C in CFPAC-1 cells, cyclin D1 and vimentin were downregulated by approximately 3.5-fold and 2.6-fold, respectively, and the major enriched pathways were related to cell cycle progression. Experiments on tumour-bearing mice injected with CFPAC-1 cells indicated that UBE2C depletion significantly inhibits tumour growth in vivo. These results suggest that UBE2C is involved in the development and progression of PDAC by regulating cell proliferation and EMT. UBE2C is a novel potential therapeutic target for pancreatic cancer. DATABASE: Data are available in the GEO database under accession number GSE137172.

Programmed death-1 (PD-1), a key immune checkpoint molecule, has been developed as an oncotherapy target for various carcinomas. However, treatment with anti-PD-1 elicited only a minimal effect in pancreatic ductal adenocarcinoma (PDAC). Subsequent studies revealed the existence of a subset of PD-1

Accurate and reliable quantification of uncertainty in wind power forecasting is critical to the economic operation and real time control of the electric power and energy system. To this end, this paper proposes a novel direct method for probabilistic wind power forecasting based on spiking neural network. In this method, a new forecasting framework is firstly formulated to simultaneously calculate the coverage probability and sharpness with associated confidence levels. Then, group search optimizer is introduced and re-designed to optimize the parameters of the forecasting framework and directly generate the prediction intervals, so that the prediction reliability and stability are ensured. The main advantage of the proposed probabilistic forecasting method is that it does not involve any distribution assumption of the prediction errors required by most existing forecasting methods. The wind power datasets from real wind farms in Belgium and China are used in the case studies. Traditional back-propagation neural network (BPNN), support vector machine (SVM) and extreme learning machine (ELM) are selected as the benchmarking algorithms. The simulation results show that the average coverage of the proposed method is improved by 72.0%, 54.9% and 51.3% respectively, when compared to BPNN, SVM and ELM. The improvement rates of sharpness index are 43.1%, 28.1% and 21.0%, respectively. These statistical results show that the proposed method outperforms BPNN, SVM and ELM in terms of forecasting accuracy, demonstrating that this method has high practical applications in real power systems.

BACKGROUND: To identify key microRNAs (miRNAs) and their target mRNAs related to gemcitabine-resistant pancreatic cancer (PC) and investigate the association between gemcitabine-resistant-related miRNAs and mRNAs and immune infiltration. METHODS: Expression profiles of miRNAs and mRNAs were obtained from the Gene Expression Omnibus (GEO) database. The differentially expressed miRNAs and mRNAs (referred to as “DEmiRNAs” and “DEmRNAs”, respectively) were distinguished between gemcitabine-resistant PC cells and its parental cells. The DEmRNAs targeted by the DEmiRNAs were retrieved using miRDB, microT, and Targetscan. Furthermore, GO and KEGG pathway enrichment analysis and GSEA were performed. The Kaplan-Meier plotter was used to analyze the prognosis of key DEmiRNAs and DEmRNAs on PC patients. The relationship between the key DEmRNAs and tumor-infiltrating immune cells in PC was investigated using CIBERSORT method using the LM22 signature as reference. Key infiltrating immune cells were further analyzed for the associations with prognosis of TCGA PAAD patients. RESULTS: Four DEmiRNAs, including hsa-miR-3178, hsa-miR-485-3p, hsa-miR-574-5p, and hsa-miR-584-5p, were identified to target seven DEmRNAs, including MSI2, TEAD1, GNPDA1, RND3, PRKACB, TRIM68, and YKT6, individually, in gemcitabine-resistant PC cells versus parental cells. Gemcitabine-resistant PC cells were enriched in proteasome-related, immune-related, and memory CD4(+) T cell-related pathways, indicating a gemcitabine therapeutic effect on PC cells. All four DEmiRNAs and almost all DEmRNAs had an impact on the prognosis of PC patients. All seven DEmRNAs had remarkable effects on CD4(+) memory T cells, which were affected by the gemcitabine therapeutic effect. Effector memory CD4(+) T cells rather than central memory CD4(+) T cells predicted a good prognosis according to the TCGA PAAD dataset. CONCLUSIONS: Gemcitabine resistance can alter the fraction of memory CD4(+) T cells via hsa-miR-3178, hsa-miR-485-3p, hsa-miR-574-5p and hsa-miR-584-5p targeted MSI2, TEAD1, GNPDA1, RND3, PRKACB, TRIM68, and YKT6 network in PC.

Pancreatic ductal adenocarcinoma (PDAC) is a genetic disease and a leading cause of cancer-related mortality. However, the molecular mechanism underlying PDAC progression remains unclear. In this study, we first confirmed that ECM1 is significantly upregulated in PDAC tissues and that its high levels of expression are closely associated with an advanced histologic grade and a poor prognosis using The Cancer Genome Atlas (TCGA) dataset and the Gene Expression Omnibus (GEO) database. We then found that miR-23a-5p binds directly to the ECM1 3’-untranslated region (3’-UTR), thereby inhibiting ECM1 expression. Functional studies revealed that the induced expression of ECM1 promoted oncogenic abilities and reversed the suppressive effects induced by miR-23a-5p. Collectively, our findings indicate that ECM1 is a proto-oncogene and show that targeting the miR-23a-5p/ECM1 axis may represent a promising therapeutic strategy for PDAC.

The mathematical model of three-phase LCL inverter has coupling term in dq coordinate system. At the same time, the traditional proportional integrate (PI) controller can only realize the no steady-state error control of the sinusoidal AC current in dq coordinate system. This requires highly accurate phase angles and complex coordinate transformations. This paper proposes a control strategy which uses the Quasi-proportional resonant (QPR) controller in the αβ coordinate system. Using the capacitor current as the inner loop realizes active damping and inhibits the resonance peak of LCL filter. The grid-connected current external loop control sine AC current non-static tracking. Design capacitance current feedback coefficient, the proportional coefficient of QPR controller and resonance gain coefficient with frequency domain analysis and zero-pole assignment. The parameter design method and the effectiveness of the control strategy are proved in the simulation and experiment.

This paper presents an effective design method of the electromagnetic interference (EMI) based on single-phase SiC MOSFET inverter. To ensure the design is targeted and effective, the equivalent noise source impedance of common mode (CM) EMI and differential mode (DM) EMI has been measured and calculated at some specific frequencies according to proper model. Then the parameters of CM part and DM part can be respectively design based on the source impedance. The EMI attenuation ability of the filter designed by this method has been compared to a traditional one in simulation and experiments. The simulation and experiment results have verified the effectiveness of the proposed method.

Transformer is the heart of the power system, it converts the voltage levels. This paper presents the innovative solution of the Load management at distribution side or consumer side. An automated load sharing technique is proposed to share the load of one consumer side transformer to other slave transformer at the time of peak load or at the time of faulty condition. The proposed systems work on microcontroller and relays to share ones’ load. The real time monitoring of the system is also done using GSM (global system for mobile communication) technique for main control unit. The complete hardware system is implemented to validate the theoretical and real time practical results.

With the development of science and technology, wind speed prediction plays an important role in smart grid. In order to improve the efficiency of wind power and ensure the safety of the new smart grid, the accurate prediction of wind power is very important. Because the variation of wind speed is nonlinear, and wind speed is influenced by many factors, Scholars all over the world have proposed a variety of forecasting methods to achieve a relatively accurate prediction of wind speed.In this paper, a novel method of wind speed prediction based on long short-term memory network with peephole (peephole LSTM) and wavelet decomposition is proposed. Firstly, the wind speed data are processed by wavelet dynamic decomposition, using the peephole LSTM structure to analyze the data sequence, after that a new model of peephole network is established. Through the forward training and the back-propagation algorithm update the weights iteratively, as a result get the optimal parameters.Selecting one month’s data and simulating with MATLAB. High prediction accuracy is obtained by simulation. Predict the next 5 days through the first 25 days of training, and the MAPE is used to analyze prediction data. The feasibility of this method is proved by simulation results.

Power inverter is an important part of a grid-connected system, LCL filter is widely used in the grid-connected inverter due to its harmonic attenuation performance and system stability. Compared with the traditional power device IGBT, SiC MOSFET has shown obvious advantages in higher switching frequency as a result of high power density. This paper first analyzes the effect of passive damping method on the resonance peak; then a double closed-loop control strategy with the inner loop of capacitor current and the outer loop of grid current is introduced; finally, the experimental platforms of SiC MOSFET inverter are built. The experimental results show that the double closed-loop control based on the LCL filter three-phase grid-connected inverter significantly reduces the system harmonics, and confirms the feasibility of the control strategy parameters design scheme.

nowadays, developing electric vehicles (EVs) is an important measure to reduce the greenhouse effect of the traditional transportation system. While the number of EVs growing rapidly, how to charge EVs efficiently attracts researchers’ attention. Different from the traditional centralized charging power allocation, which is operated by a centralized control computer collecting all information and sending the commands to EVs, in this paper the decentralized power allocation strategy for EV charging network is proposed. Moreover, the centralized algorithm requires a high quality communication network, which cannot handle disturbances in communication like delays or packet losses. Therefore, the consensus algorithm is applied here to realize the efficiency and robust power allocation of the EV charging network in a decentralized fashion. The effectiveness of the proposed strategy has been fully investigated in three different cases.