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Glycans are known to be involved in many biological processes, while little is known about the expression of N-glycans during vertebrate development. We now report the first quantitative studies of both the expression of N-linked glycans at six early development stages and the expression of N-glycosylated peptides at two early development stages in Xenopus laevis, the African clawed frog. N-Glycans were labeled with isobaric tandem mass tags, pooled, separated by capillary electrophoresis, and characterized using tandem mass spectrometry. We quantified 110 N-glycan compositions that spanned four orders of magnitude in abundance. Capillary electrophoresis was particularly useful in identifying charged glycans; over 40% of the observed glycan compositions were sialylated. The glycan expression was relatively constant until the gastrula-neurula transition (developmental stage 13), followed by massive reprogramming. An increase in oligomannosidic and a decrease in the paucimannosidic and phosphorylated oligomannosidic glycans were observed at the late tailbud stage (developmental stage 41). Two notable and opposing regulation events were detected for sialylated glycans. LacdiNAc and Lewis antigen features distinguished down-regulated sialylation from up-regulated species. The level of Lewis antigen decreased at later stages, which was validated by Aleuria aurantia lectin (AAL) and Ulex europaeus lectin (UEA-I) blots. We also used HPLC coupled with tandem mass spectrometry to identify 611 N-glycosylation sites on 350 N-glycoproteins at the early stage developmental stage 1 (fertilized egg), and 1682 N-glycosylation sites on 1023 N-glycoproteins at stage 41 (late tailbud stage). Over two thirds of the N-glycoproteins identified in the late tailbud stage are associated with neuron projection morphogenesis, suggesting a vital role of the N-glycome in neuronal development.

We report a miniaturized Filter Aided Sample Preparation method (micro-FASP) for low-loss preparation of submicrogram proteomic samples. The method employs a filter with ~0.1 mm(2) surface area, reduces the total volume of reagents to < 10 μL, and requires only two sample transfer steps. The method was used to generate 25,883 unique peptides and 3,069 protein groups from 1,000 MCF-7 cells (~100 ng protein content), and 13,367 peptides and 1,895 protein groups were identified from 100 MCF-7 cells (~10 ng protein content). Single blastomeres from Xenopus laevis embryos at the 50-cell stage (~200 ng yolk free protein/blastomere) generated 20,943 unique peptides and 2,597 protein groups; the proteomic profile clearly differentiated left and right blastomeres and provides strong support for models in which this asymmetry is established early in the embryo. The parallel processing of 12 samples demonstrates reproducible label free quantitation of 1-μg protein homogenates.

Renewable energy plays an increasingly significant role in the power generation process. Wind energy, in particular, has brilliant prospects, especially high-power wind energy. The three-level neutral-point-clamped (3L-NPC) converter for power transformed into the grid has proven to be a promising and effective configuration. For this topology, finite control set model predictive control (FCS-MPC) is an effective control option. However, the performance of FCS-MPC, like most model based control schemes, will deteriorate when system parameter (in particular, the inductance of grid side filters) varies. In this work, we propose a robust FCS-MPC scheme with revised predictions. This proposed robust FCS-MPC outperforms the classic scheme at both normal and parameter mismatched conditions, which has been validated experimentally for such systems with a fully FPGA based real-time hardware. Besides, the proposed method enlarges the range of the stability of the control method. The stability of the proposed scheme is derived theoretically and proven to be better in comparison with classic FCS-MPC.

In this paper, applying a full state-variable Finite Control Set Model Predictive Control (FCS-MPC) with improved droop controller for parallel operated power inverters in islanded microgrids is described. Virtual impedance and robust droop control are conducted as the outer loop to generate the appropriate reference. Then the FCS-MPC allows the power inverter to track the voltage and current reference with improved performance, considering the dynamic response, the plug-and-play capability, and the amicable realization which are challenging tasks for conventional method. The proposed control scheme has a more compact structure and simplifies the design procedure. Experimental measurements in different operation modes verified its effectiveness. Consequently, FCS-MPC manifests great potential to be a promising alternative for power inverter control of AC microgrids.

Objectives: In this study, we used skin-derived precursors derived Schwann cells (SKP-SCs) as a treatment to alleviate the erectile dysfunction resulting from bilateral cavernous nerve injury (BCNI) in rat models. And the effect was evaluated 2 weeks after treatment. Materials and Methods: SKP-SCs and primary Schwann cells(SCs) were extracted and incubated from 4-week-old male Sprague Dawley(SD) rats. The cells were used to co-culture with PC12 cells for measurement of neurites growth. Conditional medium of them was used for chemotaxis of M0 macrophages and ELISA assays. 24 male SD rats (10-week old) were randomly divided into 4 groups (n=6). One group was sham group and the other 3 groups underwent BCNI. The groups undergoing BCNI were treated with PBS, SKP-SCs, primary SCs respectively. We evaluated the therapeutic effects on BCNI rats by measuring the erectile function, histologic analysis and western blotting of penis and MPG tissues. Results: Treatment with SKP-SCs significantly restored the erectile function, accelerated the recovery of endothelial and smooth muscle tissues in the penis and promoted the nerve repair process. Expression of p-smad2 and p-smad3 decreased after treatment, indicating fibrosis in the corpus cavernosum was significantly blocked. Conclusion: Implantation of SKP-SCs is an effective and promising therapy for erectile dysfunction induced by BCNI. Funding Statement: Support of research grants from National Natural Science Foundation of China (No. 81971378) and Science and Technology Program of Guangzhou (No. 201903010027). Declaration of Interests: We claim that no support or financial relationships have been received from any organization that may have an interest in the submitted work, and there are no other relationships or activities that could appear to have influenced the submitted work. Ethics Approval Statement: The present study has been reviewed and permitted by the Institutional Animal Care and Use Subcommittee of the Third Affiliated Hospital of Sun Yat-sen University.

The smart-grid has requirements of flexible automation, efficiency, reliability, resiliency and scalability. These are necessitated by the increasing penetration of power-electronics converters that interface distributed renewable energy systems which energize the fast-evolving electric power network. Microgrids (MGs) have been identified as modular grids with the potential to effectively satisfy these characteristics when enhanced with advanced control capabilities. Model predictive control (MPC) facilitates the multivariable control of power electronic systems while accommodating physical constraints without the necessity for a cascaded structure. These features result in fast control dynamic response and good performance for systems involving non-linearities. This paper is a survey of the recent advances in MPC-based converters in MGs. Schemes for the primary control of MG parameters are presented. We also present opportunities for the MPC converter control of autonomous MGs (power quality and inertia enhancement), and transportation electrification. Finally, we demonstrate MPC’s capabilities through hardware-in-the-loop (HiL) results for a proposed adaptive MPC scheme for grid-forming converters.

Model predictive control (MPC) facilitates the multi-variable control of power electronic systems while accommodating physical constraints without the necessity for a cascaded structure. These features result in fast control dynamic response and good performance when system involving non-linearities. In this second part of this paper, we present a review of the recent advances in the application of MPC to the secondary and tertiary control of microgrids (MGs). We also present comprehensive surveys of MPC techniques to the energy management and power flow control of autonomous and networked MGs. The application opportunities for MPC to both distributed secondary control and networked microgrids are also discussed.

A direct-drive permanent magnet synchronous generator (PMSG) with a three-level neutral-point-clamped back-to-back power converter is an attractive configuration for high-power wind energy conversion systems. For such a topology, finite-control-set model-predictive control (FCS-MPC) has emerged as a promising alternative. However, due to its fully model-based concept, variation of system parameters (in particular, the stator and grid filter inductance and rotor permanent-flux linkage) will (seriously) affect the system control performances when using the classical FCS-MPC. In this work, a robust FCS-MPC method with revised predictions is proposed and validated for such a system. With the proposed solution, not only the system robustness against parameter variations is improved, but also the control variable ripples are evidently reduced. The proposed method has been implemented with a fully field-programmable-gate-array-based real-time hardware. Its performance improvements in comparison with the conventional solutions are validated with experimental data.

Bottom-up proteomics characterizes proteins by analysis of peptides generated through proteolysis. Bottom-up analysis of a complex proteome inevitably generates tens of thousands of peptides, and the analysis of these peptides is a serious challenge. Capillary zone electrophoresis (CZE) generates separations that are orthogonal to reversed phase liquid chromatography, which has led to consideration of CZE as an alternative separation technology in proteomic analysis. The steady improvement in mass spectrometer (MS) technology coupled with improvements in capillary coatings and the development of robust CZE-MS interfaces have contributed to the rapid advancement of CZE's identification performance in bottom-up proteomics analysis. In this review, we focus on recent advances of CZE-MS based bottom-up proteomics, including optimization of CZE and MS conditions, and the application of CZE-MS in phosphoproteomics, glycoproteomics, clinical diagnosis, host cell protein analysis, ultrasensitive proteomics, and quantitative proteomics. Finally, we outline future opportunities and challenges in this field.

We show that capillary-zone electrophoresis–electrospray ionization–tandem mass spectrometry (CZE-ESI-MS/MS) generates very large numbers of peptide and protein identifications (IDs) by combining four technologies: a separation capillary coated to generate very low electroosmosis, an electrokinetically pumped sheath-flow nanoelectrospray interface to produce high-sensitivity ionization, an Orbitrap Fusion Lumos Tribrid platform to provide high-speed analysis, and an advanced-peak-determination (APD) algorithm to take advantage of the mass spectrometer’s data-acquisition speed. The use of the APD algorithm resulted in 2 times more identifications than the standard peak algorithm. We also investigated the effect of the isolation window, injection time, and loading amount. Optimization of these parameters produced over 27 000 peptide identifications and nearly 4400 protein-group identifications from 220 ng of K562-cell digest in a single 120 min run, which is 2.7 times more IDs produced by CZE-ESI-MS/MS than...

Direct power control (DPC) strategy has attracted wide attention due to its advantages of simple structure, quick response, strong robustness, and elimination of current regulation loops/PWM blocks. Unfortunately, under unbalanced grid voltage, the conventional DPC (CDPC) scheme with the conventional definitions of active and reactive power cannot work well. In order to solve this problem, a new definition of the active power instead of the conventional one is proposed, discussed and used in this paper. As a result, good performance of the system is achieved and neither complicated calculation of a power compensation term nor positive/negative sequence extraction of grid voltages/currents are required. Then, a switching table based DPC strategy is designed based on the new definition of active power and conventional definition of reactive power. The corresponding switching table is suitable to achieve constant active power, constant reactive power and sinusoidal grid currents with very low total harmonic distortions (THDs). Simulation results are presented to confirm the theoretical study and the effectiveness of the proposed DPC with the new definition of active power (DPC-NP). The performance of the proposed DPC-NP is compared with that of the CDPC and that of the DPC with a new definition of reactive power (DPC-NQ).

Growth differentiation factor 15 (GDF15) is a divergent member of the transforming growth factor-β (TGF-β) superfamily that has been associated with colorectal cancers (CRC). However, the role of GDF15 in the progression of CRC remains unknown. We demonstrated that GDF15 expression was higher in fresh CRC tissues than in adjacent normal tissues. Moreover, we found that GDF15 overexpression significantly facilitated cell viability, cell invasion and migration (p

In this paper, an extended Kalman filter (EKF) is employed to estimate stator currents, rotor speed, rotor position, and mechanical torque of a direct-driven surface-mounted permanent-magnet synchronous generator (PMSG) based variable-speed wind turbine systems controlled by a robust predictive deadbeat (DB) algorithm. Therefore, the flux-oriented control of the PMSG is realized without mechanical sensors (i.e., no position encoders or speed transducers). The estimated stator currents are fed back to the prediction model in order to compensate the one-step delay caused by the digital controller and to reduce the total harmonic distortion of the stator currents. Thus, the torque ripples are also reduced. Furthermore, a simple disturbance observer is presented, which estimates the perturbations caused by parameter variations of the PMSG or by any unmodeled dynamics in order to enhance the robustness of the proposed DB algorithm. The proposed scheme is experimentally validated using a 14.5-kW PMSG and a dSPACE DS1007 real-time platform. Its performance is compared with that of the conventional DB control strategy for all operation conditions and under parameter variations of the PMSG.

In this paper, a general optimum control for power converters and drives is proposed. The proposed optimum control will select an optimum voltage vector from the whole hexagonal plane, leading to the best control performance fulfilling a predefined performance index. With the proposed concept, two subsolutions, i.e., both continuous and discrete solutions, are derived and unified in the frame of optimum control . The continuous solution utilizes the averaged continuous-time model of the system and is capable of dealing with multiple system constraints, showing good performance with less calculation efforts, while the discrete solution takes the finite set of the power converter switching vectors into consideration and the state transition of the system can be predicted with a chosen vector. Both methods require less calculation efforts compared with the well-known finite-control-set model predictive control method, which makes it very suitable for practical realizations. Finally, as a case of study, the proposed concept is tested at a current-controlled 3-kW surface-mounted permanent magnet synchronous motor drive under different scenarios. The experimental results validate the effectiveness of both solutions.

Reversed-phase chromatographic separation of glycopeptides tends to be dominated by the peptide composition. In contrast, capillary zone electrophoresis separation of glycopeptides is particularly sensitive to the sialic acid composition of the glycan. In this paper, we combine the two techniques to achieve superior N-glycopeptide analysis. Glycopeptides were first isolated from a tryptic digest using hydrophilic interaction liquid chromatography (HILIC) solid-phase extraction. The glycopeptides were separated using reversed-phase ultra high-performance liquid chromatography (UHPLC) to generate four fractions corresponding to different peptide backbones. Capillary zone electrophoresis-electrospray ionization-tandem mass spectrometry (CZE-ESI-MS/MS) was used to analyze the fractions. We applied this method for the analysis of alpha-1-acid glycoprotein (AGP). A total of 268 site-specific N-glycopeptides were detected, representing eight different glycosylation sites from two isomers of AGP. Glycans included tetra-sialic acids with multi N-acetyllactosamine (LacNAc) repeats and unusual pentasialylated terminal sialic acids. Reversed-phase UHPLC coupled with CZE generated ∼35% more N-glycopeptides than direct reversed-phase UHPLC-ESI-MS/MS analysis and ∼70% more N-glycopeptides than direct CZE-ESI-MS/MS analysis. This approach is a promising tool for global, site-specific glycosylation analysis of highly heterogeneous glycoproteins with mass-limited samples.

Finite-control-set-based predictive torque control (PTC) method has received more and more attention in recent years due to its fast torque response. However, it also has two drawbacks that could be improved. First, the torque reference in the cost function of the existing PTC method is generated by the proportional–integral speed controller, so torque reference's generation rate is not fast and its accuracy is low especially when the load torque is given suddenly and inertia value is varying. In addition, the variable prediction of the traditional PTC method depends on the system model, which also has the problem of parameter uncertainties. This paper investigates a disturbance observer (DOB)-based PTC approach for induction motor systems subject to load torque disturbances, parameter uncertainties, and time delays. Not only does the speed loop adopt a DOB-based feed-forward compensation method for improving the system disturbance rejection ability and robustness, but the flux, current, and torque predictions are also improved by using this technique. The simulation and experimental results verified the effectiveness of the proposed method.

Full power scale back-to-back power converter PMSG wind turbine system, with direct-drive configuration, is an attractive solution, particularly for off-shore wind energy applications. For such systems, (nonlinear) direct control, which requires neither a modulation process nor cascaded linear controllers, but will operate the system at very high control dynamics, is a very promising control class. In this work, we reviewed and experimentally assessed the classical (C-), the duty-optimal (DO-), the ripple-reduced (RR-) and the multi-vector direct model predictive torque control (MV-DMPTC) solutions to deal with the generator side control of grid-tied full power scale back-to-back power converter PMSG wind turbine systems. Their theoretical background, realizations and control performances are presented and discussed. The realizations and experimental assessments of all the discussed control approaches are carried out with a fully FPGA based realtime controller, at a lab-constructed test-bench. The resource usage and implementation complexity are provided. Comprehensive evaluation results are given at the end.

A surface-confined aqueous reversible addition-fragmentation chain transfer (SCARAFT) polymerization method was developed to coat capillaries for use in capillary zone electrophoresis (CZE). SCARAFT polymerization primarily takes place on the inner surface of the capillary instead of in solution, which greatly improves the homogeneity of the coating. Capillaries treated with this coating produced an electroosmotic mobility of 2.8 ± 0.2 × 10−6 cm2·V−1·s−1 (N=3), which is roughly an order of magnitude lower than that of commercial linear polyacrylamide (LPA) coated capillaries. Coated capillaries were evaluated for bottom-up proteomic analysis using CZE. The very low electroosmotic mobility results in a 200-minute separation and improved single-shot analysis. An average of 977 protein groups and 5,605 unique peptides were identified from 50 ng of an E. coli digest and 2,158 protein groups and 10,005 peptides were identified from 25 ng of a HeLa digest using single-shot analysis with a SCARAFT-acrylamide capillary coupled to a Q Exactive HF mass spectrometer. The coating is stable. A single capillary was used for over 200 hours (8.4 days) of continuous operation. RSD in migration time was between 2 and 3% for selected ion electropherograms (SIEs) generated for six ions; median theoretical plate counts ranged from 240,000 to 600,000 for these SIEs. Various types of coatings could be prepared by simply changing the functional vinyl monomers in the polymerization mixture. Positively charged coatings using direct attachment and formation of a block-copolymer were prepared and demonstrated for the separation of mixtures of intact proteins.

Sensorless predictive current control (PCC) for an induction machine is proposed in this study. In the controller, the errors between the current references and the predicted values are evaluated; therefore, it guarantees good current behaviours which are essential to the low-voltage drives. By designing proper adaptive scheme and gain matrix, a stable sensorless PCC system is achieved. Furthermore, the sensitives of parameters such as stator resistance and magnetic inductance are discussed with both experimental results and theoretic analysis. The sensorless system is carried out experimentally in a very wide-speed range both with and without loads.

We demonstrate an electrokinetically pumped sheath flow nanospray interface for capillary electrophoresis coupled to negative mode electrospray mass spectrometry. In this interface, application of an electric field generates electro-osmotic flow at the interior of a glass emitter that is pulled to a 10–20 micrometer inner diameter orifice. Electro-osmotic flow pumps liquid around the distal tip of the separation capillary, ensheathing analyte into the electrospray electrolyte. In negative ion mode, negative potential applied to an untreated glass emitter drives sheath flow away from the emitter orifice, decreasing the stability and efficiency of the spray. In this manuscript, we treat a portion of the interior of the electrospray emitter with 3-aminopropyltrimethoxysilane, which grafts primary amines to the interior. The amines take on a positive charge, which reverses electro-osmosis and generates stable sheath flow to the emitter orifice under negative potential. Negative mode operation is demonstrated by analyzing a metabolite extract from stage 1 Xenopus laevis embryos. Production of the treated emitters was quite reproducible. We evaluated the performance of three emitters using a set of amino acids; the relative standard deviation in peak intensity was 7% for the most intense component.

Saliency detection is an essential pre-processing step for intensifying image objects in many computer vision fields. Other than most bottom-up methods, in this paper, we propose a novel saliency model based on high-level image edges and low-level feature contrast. Edges are inherent features for an image, and it can locate the object via a salient selection. With this theory, an accurate object contour is generated after two salient discriminations for edge-contiguous superpixel couples. After position is confirmed, foreground and background of the image can be divided by the contour. With an ingrowth model, we then obtain a foreground (or background) seeds referencing spatial adjacent relation. According to the seed set, a homologous saliency map is computed via a seed-based saliency approach, which is proposed on the basis of affinity matrix. Compared with some pre-existing algorithms, low-level information is utilized purposefully by salient edges in the presented method, which makes the extracted fore regions more precise.

A linear polyacrylamide (LPA) coating and a sulfonate-silica hybrid strong cation exchange (SCX) monolith were prepared sequentially in a single fused silica capillary. The SCX-LPA capillary was used for sample preconcentration using solid-phase extraction (SPE) followed by capillary zone electrophoresis (CZE) separation and mass spectrometry detection. Samples were prepared in 1 M acetic acid, loaded by pressure onto the SCX SPE monolith, and eluted using 200 mM ammonium bicarbonate (pH 8). The background electrolyte for CZE was 1 M acetic acid. This combination of elution buffer and background electrolyte results in formation of a dynamic pH junction, which further improves the preconcentration performance. All experiments were performed using a CZE autosampler and an electrokinetically pumped nanospray interface. By loading 50 ng of Xenopus laevis eggs protein digest, 330 protein groups and 872 peptides were identified. The system was also applied to the analysis of 11 μL of a 10−4 mg⋅mL−1 (1.1 ng) bovine serum albumin tryptic digest; 12 peptides were identified and the protein coverage was 25%. Finally, by loading 5.5 μL of a 10−3 mg⋅mL−1 (5.5 ng) E. coli digest, 145 protein groups and 365 peptides were identified.

Fault diagnosis and fault-tolerant control are significant for the reliability of an MMC system, which usually has a large number of switching devices and high potential of operation failure. This paper proposes a simple fault diagnosis method for sub-module open-circuit faults and a new fault-tolerant control method. The proposed methods combine a novel sequential indirect MPC structure with sorting algorithm. Different from the well-known solutions, we detect and locate open-circuit faults in any sub-module efficiently with the maximum voltage output of the capacitor voltage sorting result, which is based on the physical characteristics of circuits when capacitor voltage rises. Thereafter, a new fault-tolerant control solution based on MPC is implemented through modifying circuit structure and control algorithm. The proposed methods are easy to implement and add little computational complexity to the control algorithm. Finally, the MATLAB/Simulink verification results confirm the effectiveness of the proposed solutions.

This work proposes a new model predictive control for three-phase four-wire voltage source converters operated at standalone mode. The proposed solution uses single cost function to deal with output filter voltage and current to improve output voltage quality, meanwhile, resonant energy oscillation between inductance and capacitance is effectively suppressed. Enhanced performance in both steady state and transient has been demonstrated by simulation data under nonlinear and unbalanced load condition.

Wind power penetration of the modern grid is seeing an aggressive increase in recent years. This leads to the concerns of the general stability of the grid. To ensure the stability of both the grid and the wind power generation systems, grid code requirements must be respected properly. To fulfill part of these requirements, wind turbines shall be capable of riding through short term grid dip faults without disconnection to support the grid recovery, i.e., Low Voltage Ride Through (LVRT) capability. This work summarizes the available LVRT solutions and presents a LVRT control method combined within the direct model predictive control frame dealing with back-to-back power converter PMSG wind turbine systems. Both the energy buffering capability via the turbine inertia and energy dissipation via an active craw-bar circuit are fully explored. Moreover, an efficient fully FPGA based Hardware-in-the-Loop (HiL) solution is employed to verify the effectiveness of the proposed solution in real-time, which makes the controller verification with great flexibility and at low cost.

We propose a compressive sensing based model predictive control scheme of three level neutral point clamped inverters with permanent magnet synchronous machines. Therefore an inverse model of the system is proposed. The inverse model is then used to set up an optimization problem that yields the optimal switching state. Contrary to the two level inverter the geometric structure of the space vectors for the three level inverters is not optimal for compressive sensing. For this reason we propose a modified space vector selection scheme to overcome this problem that furthermore takes the neutral point voltage into account. The framework is verified and evaluated with numerical simulations.

Sliding mode observer(SMO) is an effective extended back-EMF (EEMF)-based sensorless strategy for Interior permanent magnet synchronous motor drives in middle- or high-speed region. When the switching frequency is low, the rich low harmonics limit the use of this method. Especially, when the carrier ratio(CR) is less than or equal to 21, the carrier-based synchronous modulation is used to guarantee the symmetry of the output waveform. In order to further reduce interference harmonics and improve the performance of SMO in synchronous modulation region, an optimized asymmetric space vector modulation (OASVM) method is proposed, in the switching cycles that voltage reference vector is located at 17th and 19th vectors, the switching frequency is doubled. Compared with conventional ASVM, OASVM can significantly reduce the signal overlaps. As a result, in the process of position estimation with a phase-lock loop (PLL), the estimation error is reduced. The results are also verified on a 3.7kW IPMSM platform.

Dichotomy solution-based model predictive control (DS-MPC) for permanent magnet linear synchronous motors (PMLSM) is proposed in this paper. The conventional finite control set model predictive control (FCS-MPC) can be combined with a pulse width modulation (PWM) modulator due to an effective cost function optimization algorithm which is from the idea of dichotomy. In the algorithm, all the voltage vectors in the constrained vector plane are dynamically selected and calculated through iteration. The proposed DS-MPC for PMLSM not only keeps the same dynamic transient performance as FCS-MPC but also greatly decreases the thrust force ripple in steady state. Meanwhile, in order to enhance the robustness of PMLSM drive systems, DS-MPC is combined with a sensorless control algorithm based on model reference adaptive system (MRAS). The proper designed MRAS can easily obtain the positon and speed information based on DS-MPC. Simulation results illustrate that DS-MPC together with MRAS has an attractive performance for PMLSM drives.

The dependence of capillary zone electrophoresis (CZE) separations on the charge state of the analyte is useful for the analysis of many post-translational modifications in proteins. In this work, we coupled CZE to an Orbitrap Fusion Lumos Tribrid platform with an advanced peak determination algorithm for phosphoproteomics analysis. A linear-polyacrylamide-coated capillary with very low electroosmotic flow was used for the separation. The optimal injection volume was between 100 and 150 nL of a solution of phosphopeptides in 30 mM ammonium bicarbonate (pH 8.2) buffer, which produces a dynamic pH junction sample injection. Larger injection volumes resulted in serious peak broadening and decreased numbers of phosphopeptide identifications. The optimized system identified 4405 phosphopeptides from 220 ng of enriched phosphopeptides from mouse brain, which represents the state-of-the-art result for single-shot CZE-ESI-MS/MS-based phosphoproteome analysis. We found that the migration time for phosphopeptides is much longer than that for non-phosphopeptides and increased along with the number of phosphorylation sites on the peptides, as expected for the additional negative charges associated with the phosphate groups. We also investigated the phosphorylation site motifs; a number of motifs appeared in the CZE-ESI-MS/MS data but not in LC-ESI-MS/MS data, which suggested the complementary performance of the techniques. The data are available via ProteomeXchange with identifier PXD012888.

Modular multilevel converters (MMCs) have become an attractive topology for medium-voltage motor drive applications due to their modular construction and voltage scalability. The main drawback of using MMC to drive motors is the large voltage variation of submodule (SM) capacitors when the motor is running at a low speed with a large load torque condition. Operating in the quasi two-level mode of MMCs is an effective solution for such a problem. The arm current commutation control is the most important part for the operation of MMCs in the quasi two-level mode. This paper proposes a new current commutation method. The general idea is to use the delay control method to realize the arm current commutation. Therefore, the key point is to decide when to delay the operation of a submodule at each step during the commutation process. In this paper, the model predictive control (MPC) method with extrapolation technique has been used to determine whether to delay the SM at the present moment. Therefore, the waveforms of the two arms in the same phase may not be completely complementary throughout the whole commutation period. Due to such non-complementary PWMs, the currents between two arms can be exchanged.

In this work, we proposed an encoderless finite control set predictive flux control (FCS-PFC) method for an induction motor (IM) fed by a three-level neutral-point clamped (3L-NPC) power converter. The FSC-PFC employs stator flux vector as control objective to eliminate the weighting factor(s) between torque and flux commonly used in the predictive torque control (PTC) approach. An improved sliding-mode observer (SMO) is applied to achieve encoder-less control, where the feedback speed signal is not necessary, which is novel add-on to the state-of-the-art solutions. Simulation and experimental results verify the effectiveness of the proposed encoderless PFC method.

This work closely investigates the generalized relationship among the four popular control methods using Finite-Control-Set Model Predictive Control (FCS-MPC)concept for voltage source power converter fed PMSM drivers. The as-investigated control methods include: direct model predictive torque control (DMPTC)in $dq$ frame, direct model predictive current control (DMPCC)in $dq$ frame, direct model predictive current control (DMPCC)in $\alpha\beta$ frame and direct model predictive flux control (DMPFC)in $\alpha\beta$ frame. Our conclusion is that, for nonsaliency based PMSM, DMPTC, DMPCC (both in $\alpha\beta$ and $dq$ frames), DMPFC are theoretically equivalent in achieving the control targets. However, realization effort and computational demands, i.e., the amount calculation required, are different.

We propose a novel control approach for two level inverter drives that is based on a reformulation of the model predictive current control framework. Using inverse modeling we define a sparse optimization problem to achieve the control goal. In order to reduce the computational effort the optimization problem is relaxed to an l 1 -minimization. A structured iterative hard thresholding algorithm is proposed to solve the l 1 -minimization. Furthermore, an outlook to multistep model predictive control is given. The results are then verified by means of simulations.

In this paper, we propose a predictive control algorithm for back-to-back converters that is robust to low DC-link capacitance. To achieve this goal, an additional control objective, i.e., the charging of the DC-link capacitor within each sampling period, is included in the cost function to penalize the DC-link voltage ripple. In this way, a global optimized switching state is obtained and the good performances are illustrated with small DC-link capacitance in both transients and steady state. Simulation results confirm the effectiveness of the proposed method for both, high and low DC-link capacitance.

Finite control set predictive torque control (FCSPTC) is well-known for its fast response and good flexibility. However, drawbacks such as speed dependence and tedious tuning efforts for weighting factors lead to system failure and implementation complexity. This work proposes a revised FCSPTC method for 3L-NPC converter-fed induction motor (IM) using a sliding mode observer (SMO) to estimate the speed errors caused by the rotor shaft encoder. The novelty lies in two aspects: I. The revised SMO frees the controller from the speed signal dependence, eliminating potential accumulated errors from the speed feedback channels. II. The cost function is realized with a sequential structure, avoiding tedious tuning efforts for weighting factors design. Theoretical analysis and simulation results reveal that: the proposed control scheme performs well without weighting factors and achieves encoderless torque control at both transient and steady-state operation phases.

This paper presents a sliding-mode (SM) model reference adaptive system (MRAS) observer for predictive torque control (PTC) in the induction machine drives. The PTC scheme is one of the most promising control strategy for electrical drives. However, its performance is constrained due to the accuracy of parameter and speed estimation. The proposed SM MRAS is employed to estimate rotor speed for PTC. The dominant feature of the methodology is that the adjustable model is modified by a sliding manifold in accordance with the error signal. Compared with conventional MRAS, the proposed method is more robust and easily tuned. To enhance robustness of the PTC scheme to the parameter variation, the extended rotor resistance compensation is employed in the estimator. In this paper, the experimental results confirm that the proposed method achieves fast dynamic performance and strong robustness.

Based on the finite control set predictive current control, a parallel control strategy is proposed in this paper. According to the characteristic of current control in electric drive system, a new predictive current control algorithm, which designs two parallel cost functions to evaluate all possible voltage vectors and uses an adaptive selection strategy to select the optimal one, is formulated. In particular, this algorithm eliminates the monotonous weighting factor tuning work and achieves better control performance simultaneously. The effectiveness of proposed algorithm has been confirmed by experimental results in induction machine system.

We present a novel model predictive control algorithm for three level neutral point clamped converter back-to-back PMSG wind turbine systems. In order to reduce the computational complexity we utilize a sequential approach. The wind turbine as well as the grid outputs are optimized within the outer loop. In order to reduce the computational effort a modified sphere decoder is applied. The best K solution candidates of the outer loop are then passed on to the inner loop. The DC-link balance is optimized in the inner loop. Since the number of solution candidates is comparably small for the inner loop, an exhaustive search is applied. The framework is verified by means of numerical simulations.

Model predictive control (MPC) is a flexible and multivariable control technique with better dynamic performance than linear control. However, MPC is sensitive to parametric mismatches that reduce its control capabilities. In this paper, we present a new method of improving the robustness of MPC to filter parameter variations/mismatches by easily implementable parameter estimation. Furthermore, we extend the proposed technique for wider operating conditions by novel neuro-fuzzy estimation. The results, which are demonstrated by both simulations and real-time hardware-in-the-loop tests, show a steady-state parameter estimation accuracy of 95%, and at least 20% improvement in total harmonic distortion (THD) than conventional non-adaptive MPC under parameter mismatches up to 50% of the nominal values.

In this study, multilayer graphene metamaterials comprising graphene blocks and graphene ribbon are proposed to realize dynamic plasmon-induced transparence (PIT). By changing the position between the graphene blocks, PIT phenomenon will occur in different terahertz bands. Furthermore, PIT with a transparent window width of 1 THz has been realized. In addition, the PIT shows redshifts or blueshifts or disappears altogether upon changing the Fermi level of graphene, and hence a frequency selector from 3.91 to 7.84 THz and an electro-optical switch can be realized. Surprisingly, the group index of this structure can be increased to 469. Compared with the complex and fixed structure of previous studies, our proposed structure is simple and can be dynamically adjusted according to demands, which makes it a valuable platform for ideas to inspire the design of novel electro-optic devices.

A silicon-graphene-silica periodic graphene metamaterial, comprising of a graphene-strip, graphene-block, and graphene-ribbon, is proposed to realize dynamic triple plasmon-induced transparency (PIT). Coupled mode theory is employed to explain this phenomenon, and the results are in close agreement with the finite-difference time-domain simulation. It is interesting to note that the triple-PIT can also be realized by using silver instead of graphene and silicon layer. When the Fermi level of the graphene is 0.75 eV, the difference of the triple-PIT based on the two structure types is easily analyzed because the resonant frequencies are effectively coincident. As a result, the structure of graphene exhibits better absorption characteristics compared with that of silver, however, it decreases with increasing Fermi levels within graphene. Thus, the higher Fermi level of graphene is not suitable for the absorbers. Furthermore, the anomalous property of graphene triple-PIT that is attributed to the interaction way of the dark mode differing from the bright mode is analyzed compared with the silver triple-PIT, and it can be used to compensate for the defects of silver PIT. This study is significant for the absorber for which the graphene is utilized. The insights gained from the comparison between metal and graphene are crucial to the design of the metamaterials for which graphene and metal are used.

A mono-layer metamaterial comprising four graphene-strips and one graphene-square-ring is proposed herein to realize triple plasmon-induced transparency (PIT). Theoretical results based on the coupled mode theory (CMT) are in agreement with the simulation results obtained using the finite-difference time-domain (FDTD). An optical switch is investigated based on the characteristics of graphene dynamic modulation, with modulation degrees of the amplitude of 90.1%, 80.1%, 94.5%, and 84.7% corresponding to 1.905 THz, 2.455 THz, 3.131 THz, and 4.923 THz, respectively. Moreover, the proposed metamaterial is insensitive to the change in the angle of polarized light, for which the triple-PIT is equivalent in the cases of both x- and y-polarized light. The optical switch based on the proposed structure is effective not only for the linearly polarized light in different directions but also for left circularly polarized and right circularly polarized light. As such, this work provides insight into the design of optoelectronic devices based on the polarization characteristics of the incident light field on the optical switch and PIT.

Twisted bilayer graphene strips are proposed to excite dual-plasmon-induced-transparency (DPIT). Coupled mode theory is employed to fit the transmission spectra of finite-difference time-domain numerical simulations. When the upper- and lower-layers graphene are perpendicular to each other, an electric-optic switch can be realized only by changing the Fermi level of graphene, and the modulation degree of amplitude reaches 55%. Interestingly, DPIT can be degenerated to single PIT and gradually disappeared by changing the angle between the upper- and lower-layers graphene, thus multimode filters can be designed. Therefore, this work provides new ideas for designing multifunctional electro-optic devices.

Model predictive control (MPC) is an attractive technique for power electronics and drives. It requires no extra modulators and considers the non-linear nature of the power converter, so remarkable control performance can be achieved. However, since the conventional MPC scheme uses only one switching state during the whole control interval, ripples of the control variables are more evident than that of the classical modulator based techniques. This study proposed a ripple-reduced model predictive direct power control (RR-MPDPC) scheme for the grid-tied active front end using a fully field programmable gate array-based platform. The novelty of the proposed RR-MPDPC is that through extended switching vectors and time-optimised control, more freedoms and precise tracking possibility are realised in the predictive controller. The proposed control scheme is compared with the conventional MPDPC and the recently reported duty optimal MPDPC scheme (DutyOpt-MPDPC) through experimental data. Experimental results confirm that better performance is achieved using the proposed RR-MPDPC scheme.

Single cell analysis is required to understand cellular heterogeneity in biological systems. We propose that single cells (blastomeres) isolated from early stage invertebrate, amphibian, or fish embryos are ideal model systems for the development of technologies for single cell analysis. For these embryos, although cell cleavage is not exactly symmetric, the content per blastomere decreases roughly by half with each cell division, creating a geometric progression in cellular content. This progression forms a ladder of single-cell targets for the development of successively higher sensitivity instruments. In this manuscript, we performed bottom-up proteomics on single blastomeres isolated by microdissection from 2-, 4-, 8-, 16-, 32-, and 50-cell Xenopus laevis (African clawed frog) embryos. Over 1 400 protein groups were identified in single-run reversed-phase liquid chromatography-electrospray ionization-tandem mass spectrometry from single balstomeres isolated from a 16-cell embryo. When the mass of yolk-free proteins in single blastomeres decreased from ∼0.8 μg (16-cell embryo) to ∼0.2 μg (50-cell embryo), the number of protein group identifications declined from 1 466 to 644. Around 800 protein groups were quantified across four blastomeres isolated from a 16-cell embryo. By comparing the protein expression among different blastomeres, we observed that the blastomere-to-blastomere heterogeneity in 8-, 16-, 32-, and 50-cell embryos increases with development stage, presumably due to cellular differentiation. These results suggest that comprehensive quantitative proteomics on single blastomeres isolated from these early stage embryos can provide valuable insights into cellular differentiation and organ development.