A fast multi-step prediction and rolling optimization excitation control method for multi-machine power system

Predstavlja se metoda brzog reguliranja predviđanja uzbude za elektro-energetski sustav s više strojeva. Ta metoda predviđanja u nekoliko koraka ostvaruje se dinamičkim modelom sustava. Neka ograničenja neujednačenosti stanja, ulaza i izlaza razmatraju se u optimizaciji valjanja. U svrhu uštede vremena optimizacije otvorene petlje prediktivnog upravljanja modela primijenjuje se Gramian metoda balansirane redukcije i poboljšani algoritam optimizacije. Za provjeru učinkovitosti tog pristupa koristi se elektro-energetski sustav s 50 strojeva. U usporedbi sa simuliranim rezultatima uz različite regulatore, ovom se metodom može znatno reducirati vrijeme računanja. Naponi terminala generatora zadržani su u zadanim točkama. Poboljšana je stabilnost elektroenergetskog sustava.A fast excitation predictive control method for multi-machine power system is presented. The multi-step prediction technique is realized via system dynamic model. Some inequality constraints on states, inputs and outputs are considered in rolling optimization. The Gramian balanced reduction technique and the improved optimization algorithm are used in order to save the time of open-loop optimization in model predictive control. A 50-machine power system is used to verify the effectiveness of this approach. Compared with simulated results under different controllers, this method can greatly reduce the calculating-time. The voltages of generator terminals are maintained within the set points. The stability of power system is improved

Caffeic Acid Phenethyl Ester (CAPE) mediated decrease in metastasis of colon cancer cells: an in vitro and in vivo study

Background: Caffeic acid phenethyl ester (CAPE) is a phytochemically active component obtained from honeybee hive propolis. CAPE has been reported to show antimitogenic, anticancer, and other beneficial medicinal properties. Many of its activities have been reported to be mediated by inhibiting levels of matrix metalloproteinase, that is, MMP-2 and MMP-9. We hypothesize the effect of CAPE on the metastasis of colon cancer cells in both in vitro and in vivo.Methods: Cell migration, motility, invasion were evaluated also expression of protein and matrix metalloproteinases (MMPs) such as MMP-2 and MMP-9 were measured in SW-480 cancer cells in vitro. The cells were exposed to Phorbol 12-myristate 13-acetate (PMA) and were treated with various concentration of CAPE.Results: The treatment of CAPE caused significant decrease (P<0.05) in both cell motility and invasion. The treatment of CAPE inhibited activity of MMP-2 and MMP-9 and their protein with increasing dose in SW-480 cancerous cells. Antimetastatic activity was evaluated in vivo in BALB/c mice by injecting them with CT-26 mouse colon cancer cells via tail vein and were treated with CAPE (20 mg/kg) orally for 21 days. The CAPE treatment significantly (P<0.05) reduced count of pulmonary nodules. The mice showed decreased plasma MMP-2 and MMP-9 activity after 21 days treatment with CAPE.Conclusion: The study suggested beneficial role of CAPE in preventing invasion of colon cancer and metastasis via MMP- 2 and MMP-9 mediated pathway.Keywords: CAPE, colon cancer, SW-480, CT-26, anti-metastati

In-Vessel Co-Composting of Food Waste Employing Enriched Bacterial Consortium

Svrha je ovoga rada bila pripremiti odgovarajuću smjesu za kompostiranje s pomoću kulture bakterija i 2 % vapna za učinkovitu obradu otpada od hrane u posudi zapremnine 60 litara. U pokusu, koji je trajao 42 dana, otpaci su od hrane prvo pomiješani s piljevinom i 2 % (suhe tvari) vapna, zatim je u jedan reaktor dodana obogaćena kultura bakterija, dok se u drugom reaktoru nalazio kontrolni uzorak. Rezultati pokazuju da se inokuliranjem smjese za kompostiranje bakterijskom kulturom može uspješno riješiti problem zasićenosti uljem te poboljšati mineralizacija. Osim toga, parametri kao što su: emisija ugljičnog dioksida od (0,81±0,2) g/(kg·dan), indeks klijanja od (105±3) %, maseni udjel ekstraktibilnog amonijaka od 305,78 mg/kg, omjer ugljika i dušika od 16,18, pH=7,6 i električna vodljivost od 3,12 mS/cm potvrđuju zrelost komposta, koji je zadovoljio standarde kompostiranja. U kontrolnom je uzorku opaženo kašnjenje termofilne faze, pa kompost nije sazrio ni nakon 42 dana. Stoga je zaključeno da su dobro pripremljena smjesa za kompostiranje i bakterijska kultura s odgovarajućim svojstvima za razgradnju ulja nužni za uspješan sustav kompostiranja otpada od hrane.The aim of the present study is to develop a good initial composting mix using a bacterial consortium and 2 % lime for effective co-composting of food waste in a 60-litre in-vessel composter. In the experiment that lasted for 42 days, the food waste was first mixed with sawdust and 2 % lime (by dry mass), then one of the reactors was inoculated with an enriched bacterial consortium, while the other served as control. The results show that inoculation of the enriched natural bacterial consortium effectively overcame the oil-laden co-composting mass in the composter and increased the rate of mineralization. In addition, CO2 evolution rate of (0.81±0.2) g/(kg·day), seed germination index of (105±3) %, extractable ammonium mass fraction of 305.78 mg/kg, C/N ratio of 16.18, pH=7.6 and electrical conductivity of 3.12 mS/cm clearly indicate that the compost was well matured and met the composting standard requirements. In contrast, control treatment exhibited a delayed thermophilic phase and did not mature after 42 days, as evidenced by the maturity parameters. Therefore, a good composting mix and potential bacterial inoculum to degrade the oil are essential for food waste co-composting systems

PP1A-Mediated Dephosphorylation Positively Regulates YAP2 Activity

Background: The Hippo/MST1 signaling pathway plays an important role in the regulation of cell proliferation and apoptosis. As a major downstream target of the Hippo/MST1 pathway, YAP2 (Yes-associated protein 2) functions as a transcriptional cofactor that has been implicated in many biological processes, including organ size control and cancer development. MST1/Lats kinase inhibits YAP2’s nuclear accumulation and transcriptional activity through inducing the phosphorylation at serine 127 and the sequential association with 14-3-3 proteins. However, the dephosphorylation of YAP2 is not fully appreciated. Methodology/Principal Findings: In the present study, we demonstrate that PP1A (catalytic subunit of protein phosphatase-1) interacts with and dephosphorylates YAP2 in vitro and in vivo, and PP1A-mediated dephosphorylation induces the nuclear accumulation and transcriptional activation of YAP2. Inhibition of PP1 by okadiac acid (OA) increases the phosphorylation at serine 127 and cytoplasmic translocation of YAP2 proteins, thereby mitigating its transcription activity. PP1A expression enhances YAP2’s pro-survival capability and YAP2 knockdown sensitizes ovarian cancer cells to cisplatin treatment. Conclusions/Significance: Our findings define a novel molecular mechanism that YAP2 is positively regulated by PP1mediate

CDK5-dependent BAG3 degradation modulates synaptic protein turnover

阿尔茨海默病（AD）是严重威胁人类健康的重大神经系统疾病，AD的发生发展与衰老密切相关，目前临床治疗方法十分有限。因此迫切需要从AD致病早期入手，发现和鉴定导致AD神经功能紊乱的机制和靶点，为AD的早期防治提供基础。张杰教授及其团队从高通量磷酸化蛋白质组学入手，系统研究了CDK5在神经细胞中的磷酸化底物，鉴定出了在蛋白质量控制中发挥重要功能的BAG3蛋白是CDK5的全新底物。课题组从磷酸化蛋白质组学入手，发现和阐明了细胞周期蛋白激酶5（CDK5）通过调控BAG3在维持突触蛋白水平调控中的作用机制，及其在阿尔茨海默病（AD）发生发展中的机理。 该研究是多个团队历时8年合作完成的，香港中文大学的周熙文教授、美国匹兹堡大学的Karl Herrup教授、美国Sanford-Burnham研究所的许华曦教授、美国梅奥医学中心的卜国军教授，厦门大学医学院的文磊教授、张云武教授、赵颖俊教授、薛茂强教授，军事医学科学院的袁增强教授等都参与了该工作。 厦门大学医学院2012级博士生周杰超等为文章的第一作者，张杰教授为通讯作者。Background Synaptic protein dyshomeostasis and functional loss is an early invariant feature of Alzheimer’s disease (AD), yet the unifying etiological pathway remains largely unknown. Knowing that cyclin-dependent kinase 5 (CDK5) plays critical roles in synaptic formation and degeneration, its phosphorylation targets were re-examined in search for candidates with direct global impacts on synaptic protein dynamics, and the associated regulatory network was also analyzed. Methods Quantitative phospho-proteomics and bioinformatics analyses were performed to identify top-ranked candidates. A series of biochemical assays were used to investigate the associated regulatory signaling networks. Histological, electrochemical and behavioral assays were performed in conditional knockout, shRNA-mediated knockdown and AD-related mice models to evaluate its relevance to synaptic homeostasis and functions. Results Among candidates with known implications in synaptic modulations, BCL2-associated athanogene-3 (BAG3) ranked the highest. CDK5-mediated phosphorylation on Ser297/Ser291 (Mouse/Human) destabilized BAG3. Loss of BAG3 unleashed the selective protein degradative function of the HSP70 machinery. In neurons, this resulted in enhanced degradation of a number of glutamatergic synaptic proteins. Conditional neuronal knockout of Bag3 in vivo led to impairment of learning and memory functions. In human AD and related-mouse models, aberrant CDK5-mediated loss of BAG3 yielded similar effects on synaptic homeostasis. Detrimental effects of BAG3 loss on learning and memory functions were confirmed in these mice, and such were reversed by ectopic BAG3 re-expression. Conclusions Our results highlight that neuronal CDK5-BAG3-HSP70 signaling axis plays a critical role in modulating synaptic homeostasis. Dysregulation of the signaling pathway directly contributes to synaptic dysfunction and AD pathogenesis.This work was supported by the National Science Foundation in China (Grant: 31571055, 81522016, 81271421 to J.Z.; 81801337 to L.L; 81774377 and 81373999 to L.W.); Fundamental Research Funds for the Central Universities of China-Xiamen University (Grant: 20720150062, 20720180049 and 20720160075 to J.Z.); Fundamental Research Funds for Fujian Province University Leading Talents (Grant JAT170003 to L.L); Hong Kong Research Grants Council (HKUST12/CRF/13G, GRF660813, GRF16101315, AoE/M-05/12 to K.H.; GRF16103317, GRF16100718 and GRF16100219 to H.-M,C.); Offices of Provost, VPRG and Dean of Science, HKUST (VPRGO12SC02 to K.H.); Chinese University of Hong Kong (CUHK) Improvement on Competitiveness in Hiring New Faculty Funding Scheme (Ref. 133), CUHK Faculty Startup Fund and Alzheimer’s Association Research Fellowship (AARF-17-531566) to H.-M, C. 该研究受到了国家自然科学基金、厦门大学校长基金、福建省卫生教育联合攻关基金等的资助

Prediction and Analysis of the Relationship between Energy Mix Structure and Electric Vehicles Holdings Based on Carbon Emission Reduction Constraint: A Case in the Beijing-Tianjin-Hebei Region, China

In response to air pollution problems caused by carbon emissions, electric vehicles are widely promoted in China. Since thermal power generation is the main form of power generation, the large-scale development of electric vehicles is equivalent to replacing oil with coal, which will accordingly result in carbon emissions increasing if the scale of electric vehicles exceeds a certain limit. A relationship model between regional energy mix structure and electric vehicles holdings under the constraint of carbon emission reduction is established to perform a quantitative analysis of the limitation mechanism. In order to measure the scale of the future electric vehicle market under the constraint of carbon emissions reduction, a method called Extreme Learning Machine optimized by Improved Particle Swarm Optimization (IPSO-ELM) with higher precision than Extreme Learning Machine (ELM) is proposed to predict the power structure and the trend of electric vehicle development in the Beijing-Tianjin-Hebei region from 2019–2030. The calculation results show that the maximum number of electric vehicles must not exceed 19,340,000 and 26,867,171 based on emissions reduction aims and also the predicted energy mix structure in the Beijing-Tianjin-Hebei region in 2020 and 2030. At this time, the ratio of electric vehicles to traditional car ownership is 75.6% and 78.3%. The proportion of clean energy generation should reach 0.314 and 0.323 to match a complete replacement of traditional fuel vehicles for electric vehicles. A substantial increase in clean energy generation is needed so that the large-scale promotion of electric vehicles can still achieve the goal of carbon reduction. Therefore, this article will be helpful for policy-making on electric vehicle development scale and energy mix structure in the Beijing-Tianjin-Hebei region

An Improved Internal Model Principle Based Multivariable Nonlinear Control Method with Multiclass Nonharmonic Disturbances and Its Application to Speed Control of a Motor Drive System

We study the global disturbance rejection problem for a class of general multivariable nonlinear systems with multiclass nonharmonic disturbances. The paper first introduces the importance and state of the art for disturbance rejection problem and describes the control problem in the form of mathematical expressions. It stresses the multiclass disturbances produced by the exosystem satisfying certain characteristic conditions. Then, the nonlinear internal models are designed in accordance with different characteristics of multiclass external disturbances. On the basis of introduction of the control law for disturbance-free system, a multivariable state feedback controller is devised in terms of the designed internal model equations and corresponding assumptions. A Lyapunov function is constructed to theoretically prove the global uniform boundness of all signals for the multivariable closed-loop system. Finally, the presented method is applied to implement the speed control and reject the multiclass nonharmonic disturbances for a two-input motor drive system. The simulation results testify correctness and effectiveness of the presented algorithm

Comparative Study on KNN and SVM Based Weather Classification Models for Day Ahead Short Term Solar PV Power Forecasting

Accurate solar photovoltaic (PV) power forecasting is an essential tool for mitigating the negative effects caused by the uncertainty of PV output power in systems with high penetration levels of solar PV generation. Weather classification based modeling is an effective way to increase the accuracy of day-ahead short-term (DAST) solar PV power forecasting because PV output power is strongly dependent on the specific weather conditions in a given time period. However, the accuracy of daily weather classification relies on both the applied classifiers and the training data. This paper aims to reveal how these two factors impact the classification performance and to delineate the relation between classification accuracy and sample dataset scale. Two commonly used classification methods, K-nearest neighbors (KNN) and support vector machines (SVM) are applied to classify the daily local weather types for DAST solar PV power forecasting using the operation data from a grid-connected PV plant in Hohhot, Inner Mongolia, China. We assessed the performance of SVM and KNN approaches, and then investigated the influences of sample scale, the number of categories, and the data distribution in different categories on the daily weather classification results. The simulation results illustrate that SVM performs well with small sample scale, while KNN is more sensitive to the length of the training dataset and can achieve higher accuracy than SVM with sufficient samples

Hybrid Biogeography Based Optimization for Constrained Numerical and Engineering Optimization

Biogeography based optimization (BBO) is a new competitive population-based algorithm inspired by biogeography. It simulates the migration of species in nature to share information. A new hybrid BBO (HBBO) is presented in the paper for constrained optimization. By combining differential evolution (DE) mutation operator with simulated binary crosser (SBX) of genetic algorithms (GAs) reasonably, a new mutation operator is proposed to generate promising solution instead of the random mutation in basic BBO. In addition, DE mutation is still integrated to update one half of population to further lead the evolution towards the global optimum and the chaotic search is introduced to improve the diversity of population. HBBO is tested on twelve benchmark functions and four engineering optimization problems. Experimental results demonstrate that HBBO is effective and efficient for constrained optimization and in contrast with other state-of-the-art evolutionary algorithms (EAs), the performance of HBBO is better, or at least comparable in terms of the quality of the final solutions and computational cost. Furthermore, the influence of the maximum mutation rate is also investigated