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In hierarchically controlled microgrids, economic dispatch and corresponding real-time control action are inherently performed at different timescales. Owing to the uncertainty in renewables, running economic dispatch in advance may not adequately guarantee the anticipated outcomes. In this paper, distributed optimization algorithms are developed to manage economic dispatch and frequency regulation in microgrids concurrently, leaving the inter-layer coordination across timescales unnecessary. We first formulate a distributed economic dispatch problem that minimizes the overall opportunity cost yielded by renewable energy sources participating in frequency regulation. Then, by considering the dynamical characteristics of inverter-based microgrids, we reconstruct the optimization problem from the perspective of feedback control design utilizing local frequency feedback to perceive and compensate supply-demand imbalance in an online fashion, despite resistance losses. As a result, the proposed algorithms can settle the optimal dispatch and restore the frequency to its nominal value. Under reasonable operational assumptions, the closed-loop stability and asymptotic convergence to the optimal solution are rigorously established. Simulation results demonstrate the effectiveness and tractability of the proposed algorithms.

Despite the potential role of microgrids is well recognized in supporting the integration of renewable energy sources into the future power system, the impacts of intermittence renewable generation on the microgrid frequency stability is still being explored, and the related challenges remain to be addressed. In this paper, we focus on an islanded inverter-interfaced microgrid and present a consensus-based gradient algorithm for optimal frequency restoration via distributed energy management, preserving the standard hierarchical control architecture but merging the interdependent layers. The network-preserving model is applied to prevent loss of network topology and transient characteristics. Furthermore, not only distributed generators but also demand-side customers are considered to be actively participating in the proposed architecture. Convergence analysis implies that the closed-loop system is asymptotically stable, and its equilibrium will converge to the optimal solution of the associated energy management problem, which is consistent with the observation from the simulation study, including intermittent generation and load perturbation, carried out on a 6-bus test microgrid system. Therefore, the effectiveness of the proposed algorithm is verified.

Although the potential role of energy storage to support integration of Renewable Energy Sources (RES) and help meet the challenging decarbonization and energy targets, is well recognized, there is still little understanding of impacts and synergies of Thermal Energy Storage (TES) and Electric Energy Storage (EES) systems as underpinning viable solution for bringing flexibility and support the grid by providing demand-side services. In this study, a robust hierarchical Model Predictive Control (MPC) approach for the energy management of commercial buildings with multi-energy systems, in particular, TES and EES systems, is proposed. The proposed control framework integrates cost-saving, demand response, environmental aspects. The proposed control architecture is hierarchical to better deal with the complexity of the energy management problem. At the higher level, optimal trajectories are scheduled by taking into account the fluctuation of the electricity tariffs and longer prediction horizons, while, at the lower level, the regulator is responsible for a robust reference tracking, which takes the uncertainty into account. A data-driven approach for modeling the building’s thermal dynamics and the storage systems are adopted. Numerical results carried on a university building verify economic benefits, promising control performance, and robustness of the proposed strategy.

Osteosarcoma becomes the second leading cause of cancer death in the younger population. Current outcomes of chemotherapy on osteosarcoma were unsatisfactory to date, demanding development of effective therapies. Tea is a commonly used beverage beneficial to human health. As a major component of tea, theabrownin has been reported to possess anti‐cancer activity. To evaluate its anti‐osteosarcoma effect, we established a xenograft model of zebrafish and employed U2OS cells for in vivo and in vitro assays. The animal data showed that TB significantly inhibited the tumour growth with stronger effect than that of chemotherapy. The cellular data confirmed that TB‐triggered DNA damage and induced apoptosis of U2OS cells by regulation of Mki67, PARP, caspase 3 and H2AX, and Western blot assay showed an activation of p53 signalling pathway. When P53 was knocked down by siRNA, the subsequent downstream signalling was blocked, indicating a p53‐dependent mechanism of TB on U2OS cells (p53 wt). Using osteosarcoma cell lines with p53 mutations (HOS, SAOS‐2 and MG63), we found that TB exerted stronger inhibitory effect on U2OS cells than that on p53‐mut cell lines, but it also exerted obvious effect on SAOS‐2 cells (p53 null), suggesting an activation of p53‐independent pathway in the p53‐null cells. Interestingly, theabrownin was found to have no toxicity on normal tissue in vivo and could even increase the viability of p53‐wt normal cells. In sum, theabrownin could trigger DNA damage and induce apoptosis on U2OS cells via a p53‐dependent mechanism, being a promising candidate for osteosarcoma therapy.

Power generation is an important energy conversion process. A prominent feature of wind power plant compared with traditional power plants is that the equipment utilization has great intermittency and uncertainty. Fortunately, with the popularity of converter-based wind turbines, doubly-fed induction generator wind power plants are able to not only employ wind turbines in converting wind power to electrical power, but also use the converter capacity to produce reactive power. Traditionally, P Q curves considering current constraints are used for active and reactive power distribution, and therein the fixed maximum slip in power control is conservative for the utility of converters. To fully extract the power generation capacity, a novel power control realizing adaptive variation of maximum slip is proposed for each wind turbine to dynamically expand the reactive power capacity in the high active power region. In the meanwhile, the dispatch scheme of the wind power plant based on the conventional P Q curve has to be upgraded accordingly. Given that the unfixed maximum slip makes it impossible to obtain the P Q curve with explicit expression, an online learning method based on neural network is adopted and further developed in a distributed architecture to be consistent with natural distributed characteristics of the large-scale wind power plant. With the help of communication among agents through consensus protocol, the algorithm convergence can be guaranteed even under distinct information fragments. Case studies demonstrate that the proposed scheme is able to achieve up to 30% expansion of reactive power capacity and 16.3% voltage sag alleviation under certain conditions.

// Xin Chen 1, * , Yufei Fu 2, * , Hongfei Xu 1, * , Peng Teng 1 , Qiong Xie 1 , Yiran Zhang 1 , Caochong Yan 1 , Yiqiao Xu 4 , Chunqi Li 4 , Jianying Zhou 3 , Yiming Ni 1 and Weidong Li 1 1 Department of Thoracic and Cardiovascular Surgery, The First Affiliated Hospital of Zhejiang University, Hangzhou, PR China 2 Zhejiang Key Laboratory of Gastro-Intestinal Pathophysiology, Zhejiang Hospital of Traditional Chinese Medicine, First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, PR China 3 Department of Respiratory Disease, The First Affiliated Hospital, Zhejiang University, Hangzhou, PR China 4 Hunter Biotechnology, Inc., Hangzhou, PR China * These authors contributed equally to this work Correspondence to: Weidong Li, email: dr_liweidong@163.com Yiming Ni, email: Ni_yiming@hotmail.com Jianying Zhou, email: drzjy@163.com Keywords: SOX5; EMT; lung adenocarcinoma; prognosis Received: January 31, 2017 Accepted: October 11, 2017 Published: November 06, 2017 ABSTRACT Lung cancer is the leading cause of cancer-related death worldwide. Epithelial-mesenchymal transition (EMT) promotes lung cancer progression and metastasis, especially in lung adenocarcinoma. Sex determining region Y-box protein 5 (SOX5) is known to stimulate the progression of various cancers. Here, we used immunohistochemical analysis to reveal that SOX5 levels were increased in 90 lung adenocarcinoma patients. The high SOX5 expression in lung adenocarcinoma and non-tumor counterparts correlated with the patients’ poor prognosis. Inhibiting SOX5 expression attenuated metastasis and progression in lung cancer cells, while over-expressing SOX5 accelerated lung adenocarcinoma progression and metastasis via EMT. An in vivo zebrafish xenograft cancer model also showed SOX5 knockdown was followed by reduced lung cancer cell proliferation and metastasis. Our results indicate SOX5 promotes lung adenocarcinoma tumorigenicity and can be a novel diagnosis and prognosis marker of the disease.

Blended courses that mix in-person instruction with online platforms are increasingly popular in secondary education. These tools record a rich amount of data on students' study habits and social interactions. Prior research has shown that these metrics are correlated with students' performance in face to face classes. However, predictive models for blended courses are still limited and have not yet succeeded at early prediction or cross-class predictions even for repeated offerings of the same course. In this work, we use data from two offerings of two different undergraduate courses to train and evaluate predictive models on student performance based upon persistent student characteristics including study habits and social interactions. We analyze the performance of these models on the same offering, on different offerings of the same course, and across courses to see how well they generalize. We also evaluate the models on different segments of the courses to determine how early reliable predictions can be made. This work tells us in part how much data is required to make robust predictions and how cross-class data may be used, or not, to boost model performance. The results of this study will help us better understand how similar the study habits, social activities, and the teamwork styles are across semesters for students in each performance category. These trained models also provide an avenue to improve our existing support platforms to better support struggling students early in the semester with the goal of providing timely intervention.

Massive Data analysis is becoming increasingly prominent in a variety of application fields ranging from scientific studies to business researches. In this paper, we demonstrate the necessity and possibility of using MapReduce [1] module on Hadoop System [2]. Furthermore, we conducted MapReduce module to implement Clustering Algorithms [3] on our Hadoop System [4] and improved the efficiency of the Clustering Algorithms sharply. We showed how to design parallel clustering algorithms based on Hadoop System. Experiments by different size of data demonstrate that our purposed clustering algorithms have good performance on speed-up, scale-up and size-up. So, it is suitable for big data mining and analysis.