Search Results (41)

This paper proposes a data-driven model-free robust predictive control strategy for parallel three-level NPC inverters based on finite control set model predictive control (FCS-MPC), focusing on the zero-sequence circulating current (ZSCC) problem under parameter mismatch conditions. A set of virtual voltage vectors with zero average common-mode voltage (CMV) is introduced to effectively suppress ZSCC without adding additional constraints to the cost function. Meanwhile, an Integral Sliding Mode Observer (ISMO) is integrated into the predictive control framework to enhance robustness and enable reliable control using only input–output data. Unlike existing studies that primarily consider ZSCC suppression under an ideal system, this work specifically addresses the practical scenario in which system parameters deviate from their nominal values. Even when ZSCC suppression strategies are employed, parameter mismatch can still lead to noticeable circulating currents, motivating the need for a more robust solution. Simulation and experimental results validate that the proposed approach achieves excellent current tracking, neutral-point voltage balance, and effective ZSCC suppression under parameter variations, demonstrating strong robustness and feasibility for practical applications. Full article

This paper proposes a super-twisting sliding mode observer-based model-free current controller (ST-MFCC) for permanent-magnet synchronous motor (PMSM). First, the mathematical model of the PMSM is established, and the model dependence of the deadbeat predictive current controller which serves as the foundation for the proposed ST-MFCC is analyzed, along with the stability impact of parameter variations on deadbeat predictive current control. Subsequently, the ST-MFCC is designed based on an ultralocal model and the super-twisting algorithm, eliminating dependence on the current model. Additionally, an adaptive method for tuning the key coefficients of the ultralocal model is introduced, enabling controller parameters to be rapidly optimized when deviations from actual system parameters occur. This approach reduces dependency on inductance parameters and aims to achieve high-performance PMSM current control with deadbeat characteristics. Finally, the effectiveness of the ST-MFCC is verified on a 400 W experimental platform. Full article

Background/Objectives: Omalizumab is a monoclonal anti-IgE antibody approved for the treatment of chronic urticaria. There are no established or validated prognostic markers currently available to identify likely responders. The aim of this study was to retrospectively analyze a cohort of chronic urticaria patients treated with omalizumab, in order to determine the clinical and laboratory characteristics associated with complete response to therapy. Methods: Medical records of chronic urticaria patients receiving omalizumab were reviewed. The following parameters were collected: age, sex, disease duration, Urticaria Activity Score over 7 days (UAS7), time to response, total serum IgE levels, presence or absence of atopy, neutrophil-to-lymphocyte ratio, eosinophil and basophil counts, presence or absence of autoimmune conditions, and treatment duration. Complete response was classified as dependent on continued drug administration or drug-free (sustained remission after discontinuation). Adverse events were also recorded. Results: Omalizumab was well tolerated by all patients, with no serious adverse events reported. Complete response was achieved in 81.3% of patients; partial and no responses were observed in 8.3% and 10.1%, respectively. The majority of responders (~79.5%) maintained complete control of hives with low-dose omalizumab; subsequently, most of these patients eventually achieved sustained, drug-free remission. Total serum IgE levels appeared to predict complete response, with 164.7 IU/mL identified as the cut-off value potentially distinguishing responders from nonresponders. Conclusions: Omalizumab is a safe and effective treatment for chronic urticaria. Total serum IgE levels may help identify complete responders. Long-term low-dose regimens could be considered to reduce the economic burden on healthcare systems, although this is currently an off-label approach. Full article

To address the issues of excessive current ripple and poor dynamic response in conventional angle position control (APC) for high-speed switched reluctance generator (SRG), this paper proposes an online parameter identification-based model-free predictive control (MFPC) strategy. First, the system dynamics are represented as an ultra-local model (ULM), enabling the design of an extended state observer (ESO) for two-step current prediction to compensate for control delays. Second, an improved Recursive Least Squares (RLS) algorithm with covariance resetting and error clearance is implemented to accurately identify dynamic inductance online, thereby enhancing the prediction accuracy of the ESO. Third, a bus current estimation-based adaptive feedforward compensation (AFC) technique is introduced to accelerate DC-bus voltage regulation and system dynamic response. Finally, simulations conducted on a 250 kW SRG platform demonstrate that the proposed method achieves superior dynamic performance and significantly reduced current ripple compared to conventional APC method. Full article

The enhancement of drug delivery into the lung surfactant is facilitated by research on the interaction between drugs and the lung surfactant. Drug designers must have a thorough theoretical understanding of a drug before performing clinical tests to reduce the experimental cost. The current study uses a coarse-grained molecular dynamics (MD) approach with the MARTINI force field to parameterize the corticosteroid drug mometasone furoate, which is used to treat lung inflammation. Here, we investigate the accurate parametrization of drug molecules and validate the parameters with the help of umbrella sampling simulations. A collection of thermodynamic parameters was studied during the parametrization procedure. The Gibbs free energy gradient was used to calculate the partition coefficient value of mometasone furoate, which was approximately 10.49 based on our umbrella sampling simulation. The value was then matched with the experimental and predicted the partition coefficient of the drug, showing good agreement. The drug molecule was then delivered into the lung surfactant monolayer membrane at the alveolar air–water interface, resulting a concentration-dependent drop in surface tension while controlling the underlying continual compression–expansion of alveoli that maintains the exhalation–inhalation respiratory cycle. The dynamical properties of the monolayer demonstrate that the drug’s capacity to diffuse into the monolayer is considerably diminished in larger clusters, and this effect is intensified when there are more drug molecules present in the monolayer. The monolayer microstructure analysis shows that the drug concentration controls monolayer morphology. The results of this investigation may be helpful for corticosteroid drug delivery into the lung alveoli, which can be applied to comprehend how the drug interacts with lung surfactant monolayers or bilayers. Full article

Model predictive control (MPC) is significantly affected by parameter mismatch in inverter applications, whereas model-free predictive control (MFPC) avoids parameter dependence through the ultra-local model (ULM). However, the traditional MFPC based on the algebraic method needs to store historical data for multiple cycles, which results in a sluggish dynamic response. To address the above problems, this paper proposes a model-free predictive control method based on the ultra-local model and an adaptive super-twisting sliding mode observer (ASTSMO). Firstly, the effect of parameter mismatch on the current prediction error of conventional MPC is analyzed through theoretical analysis, and a first-order ultra-local model of the inverter is established to enhance robustness against parameter variations. Secondly, a super-twisting sliding mode observer with adaptive gain is designed to estimate the unknown dynamic terms in the ultra-local model in real time. Finally, the superiority of the proposed method is verified through comparative validation against conventional MPC and the algebraic-based MFPC. Simulation results demonstrate that the proposed method can significantly enhance robustness against parameter variations and shorten the settling time during dynamic transients. Full article

Traditional model predictive current control (MPCC) is heavily dependent on the accuracy of motor parameters and incurs high computational costs. To address these challenges, this paper proposes an enhanced model-free predictive current control (MFPCC) strategy based on ultra-local models (ULMs). Initially, a Kalman filter (KF) is used to estimate the current gain, while an adaptive sliding mode observer (SMO) is employed to estimate current disturbances. Subsequently, an equivalent transformation of the cost function is carried out in the αβ domain, and the voltage vector combinations are reduced to a single one via sector distribution. Hence, the proposed MFPCC is independent of motor parameters and capable of reducing computational complexity. Simulation and experimental results demonstrate that the proposed MFPCC method significantly improves computational efficiency and the robustness of current prediction, enabling precise current tracking even in the presence of motor parameter mismatches. Full article

Background/Objectives: There is emerging evidence that task-specific pain-related psychological measures may better predict movement behaviour in chronic low back pain (CLBP) than general pain-related psychological measures. Currently, little is known regarding the prediction of movement duration and movement velocity. Methods: Baseline data from a previously published randomized controlled trial were used (clinicaltrials.gov NCT02773160). Fifty-five patients with CLBP and 54 pain-free persons performed a lifting task while kinematic measurements were obtained to calculate movement velocity of the L1 vertebra, S1 vertebra, and the lumbar spine, as well as the time to complete the lifting task. Scores on the Photograph Daily Activities Series-Short Electronic Version (PHODA-SeV), Tampa Scale for Kinesiophobia (TSK), and its Activity Avoidance and Somatic Focus subscales were used as general pain-related psychological measures. The score on a picture of the PHODA-SeV, showing a person lifting an object with a bent back (PHODA-Lift), was used as task-specific measure of perceived harmfulness. Results: The task-specific measure best predicted movement duration and movement velocity of L1 and the lumbar spine, and explained 35%, 19%, and 25% of the respective movement parameters. Although general perceived harmfulness predicted S1 velocity and movement duration, it only explained 6% and 8% of the respective movement parameters. General measures of pain-related fear were not predictive for any of the movement parameters. It took patients with CLBP significantly longer to complete the lifting task when compared to the pain-free participants (ES = 1.01, p < 0.0001), and patients with CLBP also moved significantly slower at L1 (ES = 0.85, p < 0.0001) and the lumbar spine (ES = 1.01, p < 0.0001). These between-groups differences were larger for CLBP subgroups with higher scores on the PHODA-Lift, and to some extent for subgroups with higher total scores on the PHODA-SeV. Conclusions: Task-specific perceived harmfulness best predicts movement velocity. General pain-related fear measures (i.e., TSK and its subscales) do not predict these movement parameters. Full article

Modular multilevel converters (MMCs) have gained widespread adoption in high-voltage direct current (HVDC) transmission due to their high voltage levels, low harmonic content, and high scalability. However, conventional control methods such as finite control set model predictive control (FCS-MPC) suffer from a heavy computational burden and sensitivity to system parameter variations, limiting the performance of MMCs. This paper proposes a data-driven approach based on model-free adaptive control with an event-triggered mechanism that demonstrates superior robustness against parameter mismatches and enhanced dynamic performance in response to sudden output changes. Moreover, the introduction of the event-triggered mechanism effectively reduces redundant operations, decreasing the computational burden and switching losses. Finally, the proposed strategy is validated through a MATLAB/Simulink simulation model. Full article

Climate warming affects the growth and development of pests, resulting in changes in their geographical distribution, which increases the difficulty in terms of prevention and control. The fruit fly, Neoceratitis asiatica (Becker), is a predominant frugivorous pest that causes serious yield loss in the goji berry, Lycium barbarum L. In recent years, with the expansion of cultivation area, the damage induced by the pest has become increasingly severe, significantly impeding the production of the goji berry. In this study, the potential suitable habitats of N. asiatica under current and future climate scenarios were simulated and predicted using the optimal MaxEnt model, based on the screening distribution records and environmental factors. The changes in the pest distribution under climate change were determined using ArcGIS. The results showed that the best combination of parameters for MaxEnt were feature combination (FC) = LQPT and regularization multiplier (RM) = 1. The dominant environmental factors influencing pest distribution were mean temperature of driest quarter, mean temperature of coldest quarter and precipitation of coldest quarter. Under different climate conditions, the suitable habitats of the pest primarily ranged between 27°–47° N and 73°–115° E. Under current climate conditions, the area of moderately and highly suitable habitats was 42.18 × 10⁴ km², and mainly distributed in Inner Mongolia (13.68 × 10⁴ km²), Gansu (9.40 × 10⁴ km²), Ningxia (5.07 × 10⁴ km²), Qinghai (4.10 × 10⁴ km²), and Xinjiang (3.97 × 10⁴ km²) Provinces. Under future climate scenarios, the suitable area was projected to be lower than the current ones, except SSP245–2050s and SSP370–2070s, and the centroids of suitable habitats were mainly shifted to the northeast, except SSP370–2050s and SSP585–2070s. Our results provide valuable guidance for the monitoring and management of N. asiatica, as well as the selection of pest-free goji berry cultivation sites. Full article

The stabilization of asphalt pavement bases with granular soil and aggregates emulsified with asphalt is a widely used technique in road construction and maintenance. It aims to improve the mechanical properties and durability of the lower pavement layers. Currently, there is no consensus on the most suitable method for designing emulsified granular aggregates with reclaimed asphalt pavement (RAP), as it is very complex. Therefore, the methodology is generally based on compliance with one or more volumetric or mechanical parameters established in the highway regulations for conventional asphalt mixtures, which does not guarantee the optimization and characterization of the recycled mixture in the base course. In this study, granular mixtures were developed, including five with emulsion and one emulsion-free as a control mix. Granular RAP mixes were designed in this study, including five with emulsion and one emulsion-free as a control mix. The five mixes ranged from 1% to 5% emulsion and were characterized by multi-stage triaxial tests with repeated load resilient modulus (RM) and permanent deformation (PD) to evaluate their mechanical behavior. The results showed that the mixes had RM values between 350 and 500 MPa, consistent with literature values. However, they showed similar levels of accumulated deformation to the control mix without RAP emulsion. The sample with 1 % RAP emulsion exhibited a satisfactory RM value and better performance in PD than the control mix (5 mm) and showed accumulated PD values of up to 4 mm. In contrast, the other samples exhibited deformations of up to 6 mm. In this study, the multi-stagge triaxial RM and PD tests were found to be an effective predictive method for characterizing the behavior of RAP materials in base courses, regardless of the types of admixtures contained. Multi-stage resilient modulus and PD tests can be considered as a predictive method for the behavior of milled material in base courses. They were able to provide initial data for interpreting the behavior of ETB mixtures. Full article

This article proposes an improved model-free deadbeat predictive current control (MFCC) method for permanent magnet synchronous motors (PMSMs) based on the ultralocal model and H∞ norm. Firstly, the traditional deadbeat predictive current control (DPCC) method is introduced and a theoretical analysis is conducted on its sensitivity to parameters. Building upon this, the limitations of model dependence and the limited robustness of the deadbeat predictive current control method based on the extended state observer (ESO-DPCC) are theoretically analyzed. Furthermore, an improved MFCC method based on the ultralocal model is proposed, and the influence of the observer on MFCC is theoretically analyzed. This study combined the proposed method with the H∞ norm, and the optimal coefficients of the observer were tuned to enhance the robustness and dynamic performance of the current loop. Finally, the proposed algorithms were validated on a 400 W PMSM platform. Full article

Vector control of an induction machine (IM) is typically performed by using cascade control structures with conventional linear proportional–integral (PI) controllers, the inner loop being designed for current control and the outer loop for rotor flux and speed control. In this paper, starting with the dq model of the IM, advanced control algorithms are proposed for the two control loops of the cascade structure. For the current inner loop, after the decoupling of the two dq currents, predictive control algorithms are employed to independently control the currents, considering the constraints imposed by the electrical signal physics limitations. Since the outer loop has a nonlinear affine multivariable plant model, a homotopy-based variant of feedback linearization is used to obtain a nonsingular decoupling matrix of the feedback transformation even when the rotor flux is zero at the start-up of the motor. During the continuous variation in the homotopy parameter, the plant model is variable and, for this reason, model-free algorithms are used to control the flux and speed of the IM due to their capabilities to manage complex dynamics from data without requiring knowledge of the plant model. The performances of the proposed cascade control strategy with advanced algorithms in the two loops were tested by simulation and compared with those obtained with conventional PI controllers, resulting in better dynamic behavior for predictive and model-free control. Full article

Model predictive control is highly dependent on accurate models and the parameters of electric motor drives. Multiphase permanent magnet synchronous motors (PMSMs) contain nonlinear parameters and mutual cross-coupling dynamics, resulting in challenges in modeling and parameter acquisition. To lessen the parameter dependence of current predictions, a model-free predictive current control (MFPCC) strategy based on an ultra-local model and motor outputs is proposed for five-phase PMSM drives. The ultra-local model is constructed according to the differential equation of current. The inherent relation between the parameters in the predictive current model and the ultra-local model is analyzed in detail. The unknowns of the ultra-local model are estimated using the motor current and voltage at different time instants without requiring motor parameters or observers. Moreover, space vector modulation technology is employed to minimize the voltage tracking error. Finally, simulations and experiments are conducted to verify the effectiveness of the MFPCC with space vector modulation. The results confirm that the proposed method can effectively eliminate the impact of motor parameters and improve steady-state performance. Moreover, this control strategy demonstrates good robustness against load variations. Full article

Dengue virus (DENV) is a major mosquito-borne human pathogen in tropical countries; however, there are currently no targeted antiviral treatments for DENV infection. Compounds 27 and 29 have been reported to be allosteric inhibitors of DENV RdRp with potent inhibitory effects. In this study, the structures of compounds 27 and 29 were optimized using computer-aided drug design (CADD) approaches. Nine novel compounds were synthesized based on rational considerations, including molecular docking scores, free energy of binding to receptor proteins, predicted Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) parameters, structural diversity, and feasibility of synthesis. Subsequently, the anti-DENV activity was assessed. In the cytopathic effect (CPE) assay conducted on BHK-21 cells using the DENV2 NGC strain, both SW-b and SW-d demonstrated comparable or superior activity against DENV2, with IC₅₀ values of 3.58 ± 0.29 μM and 23.94 ± 1.00 μM, respectively, compared to that of compound 27 (IC₅₀ = 19.67 ± 1.12 μM). Importantly, both SW-b and SW-d exhibited low cytotoxicity, with CC₅₀ values of 24.65 μmol and 133.70 μmol, respectively, resulting in selectivity indices of 6.89 and 5.58, respectively. Furthermore, when compared to the positive control compound 3′-dATP (IC₅₀ = 30.09 ± 8.26 μM), SW-b and SW-d displayed superior inhibitory activity in an enzyme inhibitory assay, with IC₅₀ values of 11.54 ± 1.30 μM and 13.54 ± 0.32 μM, respectively. Molecular dynamics (MD) simulations elucidated the mode of action of SW-b and SW-d, highlighting their ability to enhance π–π packing interactions between benzene rings and residue W795 in the S1 fragment, compared to compounds 27 and 29. Although the transacylsulphonamide fragment reduced the interaction between T794 and NH, it augmented the interaction between R729 and T794. In summary, our study underscores the potential of SW-b and SW-d as allosteric inhibitors targeting the DENV NS5 RdRp domain. However, further in vivo studies are warranted to assess their pharmacology and toxicity profiles. Full article