An optimized fractional order PID controller for suppressing vibration of AC motor

Fractional order Proportional-Integral-Derivative (PID) controller is composed of a number of integer order PID controllers. It is more accurate to control the complex system than the traditional integer order PID controller. The values of parameters of the fractional order PID controller play a decisive role for the control effect. Because the fractional order PID controller added two adjustable parameters than the traditional PID controller, it is very difficult to tune parameters. So the Back Propagation (BP) neural network is selected to optimize the parameters of the fractional order PID controller in order to obtain the high performance. Then the optimized fractional order PID controller and the traditional PID controller are used to control AC motor speed governing system. And the vibration spectrum and stator current spectrum under different rotating speeds are compared and analyzed in detail. The results show that the optimized fractional order PID controller has better vibration suppression performance than the traditional PID controller. The reason is that the optimized fractional order PID controller changed the stator current component, and further changed the frequency components and the amplitude of the vibration signal of the motor

Mobility-Aware Joint User Scheduling and Resource Allocation for Low Latency Federated Learning

As an efficient distributed machine learning approach, Federated learning (FL) can obtain a shared model by iterative local model training at the user side and global model aggregating at the central server side, thereby protecting privacy of users. Mobile users in FL systems typically communicate with base stations (BSs) via wireless channels, where training performance could be degraded due to unreliable access caused by user mobility. However, existing work only investigates a static scenario or random initialization of user locations, which fail to capture mobility in real-world networks. To tackle this issue, we propose a practical model for user mobility in FL across multiple BSs, and develop a user scheduling and resource allocation method to minimize the training delay with constrained communication resources. Specifically, we first formulate an optimization problem with user mobility that jointly considers user selection, BS assignment to users, and bandwidth allocation to minimize the latency in each communication round. This optimization problem turned out to be NP-hard and we proposed a delay-aware greedy search algorithm (DAGSA) to solve it. Simulation results show that the proposed algorithm achieves better performance than the state-of-the-art baselines and a certain level of user mobility could improve training performance

Energy-Efficient Wireless Federated Learning via Doubly Adaptive Quantization

Federated learning (FL) has been recognized as a viable distributed learning paradigm for training a machine learning model across distributed clients without uploading raw data. However, FL in wireless networks still faces two major challenges, i.e., large communication overhead and high energy consumption, which are exacerbated by client heterogeneity in dataset sizes and wireless channels. While model quantization is effective for energy reduction, existing works ignore adapting quantization to heterogeneous clients and FL convergence. To address these challenges, this paper develops an energy optimization problem of jointly designing quantization levels, scheduling clients, allocating channels, and controlling computation frequencies (QCCF) in wireless FL. Specifically, we derive an upper bound identifying the influence of client scheduling and quantization errors on FL convergence. Under the longterm convergence constraints and wireless constraints, the problem is established and transformed into an instantaneous problem with Lyapunov optimization. Solving Karush-Kuhn-Tucker conditions, our closed-form solution indicates that the doubly adaptive quantization level rises with the training process and correlates negatively with dataset sizes. Experiment results validate our theoretical results, showing that QCCF consumes less energy with faster convergence compared with state-of-the-art baselines

Ginkgolide K potentiates the protective effect of ketamine against intestinal ischemia/reperfusion injury by modulating NF-κB/ERK/JNK signaling pathway

Purpose: To investigate the effect of ginkgolide K and ketamine treatments, alone and in combination, on intestinal  ischemia/reperfusion injury (I/R)-induced injury in rats, as well as the mechanism involved. Methods: Rats were treated with ginkgolide K (GK, 15 mg/kg i.v) and ketamine (KTM, 100 mg/kg i.p.), either alone or in combination 30 min before the induction of intestinal I/R. The effects of GK and KTM were determined by assessing the levels of cytokines in serum, and parameters of oxidative stress and ROS production in the intestinal tissues of I/R rats. Moreover, intestinal mRNA expressions of JNK, ERK, p38 and NF-kB were determined by quantitative reverse transcription polymerase chain reaction (qRT-PCR). Results: GK and KTM treatments, alone and in combination, reduced cytokine levels in serum and oxidative stress parameters in intestinal tissues, when compared to I/R group of rats. Treatments with GK and KTM, alone and in combination, mitigated the altered mRNA expressions of JNK, ERK, p38 and NF-kB in intestinal tissues of I/R-injured rats. Conclusion: These results reveal that GK potentiates the protective effect of KTM100 on I/R-induced intestinal injury in rats by regulating the NF-kB/ERK/JNK signaling pathway. Therefore, GK and KTM may find use in the management of I/R Keywords: Ginkgolide K, Ketamine, Intestinal injury, Ischemia/Reperfusion, Inflammatio

On Dynamic Resource Allocation for Blockchain Assisted Federated Learning over Wireless Channels

Blockchain assisted federated learning (BFL) has been intensively studied as a promising technology to process data at the network edge in a distributed manner. In this paper, we focus on BFL over wireless environments with varying channels and energy harvesting at clients. We are interested in proposing dynamic resource allocation (i.e., transmit power, computation frequency for model training and block mining for each client) and client scheduling (DRACS) to maximize the long-term time average (LTA) training data size with an LTA energy consumption constraint. Specifically, we first define the Lyapunov drift by converting the LTA energy consumption to a queue stability constraint. Then, we construct a Lyapunov drift-plus-penalty ratio function to decouple the original stochastic problem into multiple deterministic optimizations along the time line. Our construction is capable of dealing with uneven durations of communication rounds. To make the one-shot deterministic optimization problem of combinatorial fractional form tractable, we next convert the fractional problem into a subtractive-form one by Dinkelbach method, which leads to the asymptotically optimal solution in an iterative way. In addition, the closed-form of the optimal resource allocation and client scheduling is obtained in each iteration with a low complexity. Furthermore, we conduct the performance analysis for the proposed algorithm, and discover that the LTA training data size and energy consumption obey an [$\mathcal{O}(1/V)$, $\mathcal{O}(\sqrt{V})$] trade-off. Our experimental results show that the proposed algorithm can provide both higher learning accuracy and faster convergence with limited time and energy consumption based on the MNIST and Fashion-MNIST datasets

Study on a novel fault diagnosis method based on information fusion method

For the low accuracy and calculation speed of traditional fault diagnosis methods, the chaos optimization algorithm (COA), quantum particle swarm optimization (QPSO) algorithm and support vector machine (SVM) are introduced into the fault diagnosis to propose a novel fault diagnosis (CQPSMFD) method in this paper. In the proposed CQPSMFD method, the COA is used to initialize the parameters of the QPSO algorithm in order to obtain the CQPSO algorithm with the better convergence speed. Then the CQPSO algorithm is used to optimize the parameters of the SVM model to construct a high-precision SVM model (CQPSM) with the higher accuracy and stronger generalization ability. Next, the CQPSMFD method based on CQPSM method is proposed for motor. Finally, the experiment data from Case Western bearing dataset and actual motor are selected to verify the CQPSMFD method. The results show that the CQPSO algorithm can obtain the optimal parameter combination and the CQPSMFD method can effectively improve the fault diagnosis accuracy and speed

Transcriptome and functional analysis revealed the intervention of brassinosteroid in regulation of cold induced early flowering in tobacco

Cold environmental conditions may often lead to the early flowering of plants, and the mechanism by cold-induced flowering remains poorly understood. Microscopy analysis in this study demonstrated that cold conditioning led to early flower bud differentiation in two tobacco strains and an Agilent Tobacco Gene Expression microarray was adapted for transcriptomic analysis on the stem tips of cold treated tobacco to gain insight into the molecular process underlying flowering in tobacco. The transcriptomic analysis showed that cold treatment of two flue-cured tobacco varieties (Xingyan 1 and YunYan 85) yielded 4176 and 5773 genes that were differentially expressed, respectively, with 2623 being commonly detected. Functional distribution revealed that the differentially expressed genes (DEGs) were mainly enriched in protein metabolism, RNA, stress, transport, and secondary metabolism. Genes involved in secondary metabolism, cell wall, and redox were nearly all up-regulated in response to the cold conditioning. Further analysis demonstrated that the central genes related to brassinosteroid biosynthetic pathway, circadian system, and flowering pathway were significantly enhanced in the cold treated tobacco. Phytochemical measurement and qRT-PCR revealed an increased accumulation of brassinolide and a decreased expression of the flowering locus c gene. Furthermore, we found that overexpression of NtBRI1 could induce early flowering in tobacco under normal condition. And low-temperature-induced early flowering in NtBRI1 overexpression plants were similar to that of normal condition. Consistently, low-temperature-induced early flowering is partially suppressed in NtBRI1 mutant. Together, the results suggest that cold could induce early flowering of tobacco by activating brassinosteroid signaling

Pharmacological targeting of STK19 inhibits oncogenic NRAS driven melanomagenesis

黑色素瘤是由黑色素细胞恶性转化产生的恶性程度极高的皮肤癌，含有NRAS激活突变的黑色素瘤约占20-30%，但至今还未有靶向NRAS的有效黑色素瘤治疗方案。针对这一难题，波士顿大学医学中心崔儒涛教授、厦门大学生命科学学院邓贤明教授、复旦大学附属肿瘤医院王鹏教授组成的联合研究团队利用激酶组siRNA文库筛选发现新颖的丝/苏氨酸激酶STK19是NRAS的上游激活子，进一步分子机制研究揭示STK19通过磷酸化NRAS的89位丝氨酸（S89）促进了NRAS介导的黑色素细胞恶性转化。该研究揭示了一种经由新颖激酶STK19调控NRAS突变黑色素瘤的分子机制，验证了STK19有望作为NRAS介导的黑色素瘤的有效靶标，为发展新的黑色素瘤靶向药物提供了先导化合物，同时也为发展其它素有“癌基因之王---RAS”驱动的相关肿瘤靶向药物发展提供了新思路。该论文由波士顿大学医学中心、厦门大学生命科学学院、复旦大学附属肿瘤医院等单位合作完成，共同第一作者厦门大学生命科学学院博士生张婷负责了该系列化合物的设计、合成与优化，崔儒涛教授、邓贤明教授和王鹏教授为共同通讯作者。【Abstract】Activating mutations in NRAS account for 20-30% of melanoma, but despite decades of research and in contrast to BRAF, no effective anti-NRAS therapies have been forthcoming. Here we identify a previously uncharacterized serine/threonine kinase STK19 as a novel NRAS activator. STK19 phosphorylates NRAS to enhance its binding to its downstream effectors and promotes oncogenic NRAS-mediated melanocyte malignant transformation. A recurrent D89N substitution in STK19 whose alterations were identified in 25% of human melanomas represents a gain-of-function mutation that interacts better with NRAS to enhance melanocyte transformation. STK19 D89N knockin leads to skin hyperpigmentation and promotes NRAS Q61R -driven melanomagenesis in vivo. Finally, we developed ZT-12-037-01 (1a) as a specific STK19-targeted inhibitor and showed that it effectively blocks oncogenic NRAS-driven melanocyte malignant transformation and melanoma growth in vitro and in vivo. Together, our findings provide a new and viable therapeutic strategy for melanomas harboring NRAS mutations.We thank Drs. Norman Sharpless and David Fisher for kindly providing the loxP/STOP/loxP NRAS Q61R knockin (LSL-NRAS Q61R ) mice. We thank Dr. Anurag Singh for kindly sharing cell lines. We also thank Drs. X. Shirley Liu, Tao Wang, Wantao Chen, Dali Liu, Chunxiao Xu, Jianming Zhang and Junrong Zou for discussion and assistance. This work was supported by grants from Boston University (to R.C.), the National Key R&D Program and the National Natural Science Foundation of China (No. 2017YFA0504504, 2016YFA0502001, 81422045, U1405223 and 81661138005 to X.D.), the Fundamental Research Funds for the Central Universities of China (No. 20720160064 to X.D.), and the Program of Introducing Talents of Discipline to Universities (111 Project, B12001).该研究得到了科技部重点研发计划、国家自然科学基金委和中央高校基本科研业务费等的资助