Search Results (27)

Homeostasis, which supports and maintains brain function, results from the continuous regulation of thermodynamics within tissue: the balance of heat production, redox oscillations, and vascular convection regulates coherent energy flow within the organ. Neuroinflammation disturbs this balance, creating measurable entropy gradients that precede structural damage to its tissue components. This paper proposes that a thermodynamic unity can be devised that incorporates nanoscale physics, energetic neurophysiology, and systems neuroscience, and can be used to understand and treat neuroinflammatory processes. Using multifactorial modalities such as quantum thermometry, nanoscale calorimetry, and redox oscillometry we define how local entropy production (s_t), relaxation time (τ^R), and coherence lengths (λc) allow quantification of the progressive loss of energetic symmetry within neural tissues. It is these variables that provide the basis for the etiology of thermodynamic biomarkers which on a molecular-redox-to-network scale characterize the transitions governing the onset of the neuroinflammatory process as well as the recovery potential of the organism. The entropic probing of systems (PEP) further allows the translation of these parameters into dynamic patient-specific trajectories that model the behavior of individuals by predicting recurrent bouts of instability through the application of machine learning algorithms to the vectors of entropy flux. The parallel development of the nanothermodynamic intervention, which includes thermoplasmonic heat rebalancing, catalytic redox nanoreacting systems, and adaptive field-oscillation synchronicity, shows by example how the corrections that can be applied to the entropy balance of the cell and system as a whole offer a feasible form of restoration of energy coherence. Such closed loop therapy would not function by the suppression of inflammatory signaling, but rather by the re-establishment of reversible energy relations between mitochondrial, glial, and vascular territories. The combination of these factors allows for correction of neuroinflammation, which can now be viewed from a fresh perspective as a dynamic phase disorder that is diagnosable, predictable, and curable through the physics of coherence rather than the molecular suppression of inflammatory signaling. The significance of this set of ideas is considerable as it introduces a feasible and verifiable structure to what must ultimately become the basis of a new branch of science: predictive energetic medicine. It is anticipated that entropy, as a measurable and modifiable variable in therapeutic “inscription”, will be found to be one of the most significant parameters determining the neurorestoration potential in future medical science. Full article

Atmospheric sulfur (S) deposition, a key component of acid deposition, poses risks to ecosystems, human health, and sustainable development. In China, decades of coal-dominated energy use caused severe S pollution, but recent emission-control policies and energy restructuring have sought to reverse this trend. However, the effectiveness and regional differences in these measures remain insufficiently quantified. Here, we combined continuous observations from 43 monitoring sites (2013–2023), satellite-derived SO₂ vertical column density, and multi-source environmental datasets to construct a high-resolution record of wet S deposition. A random forest model, validated with R² = 0.52 and RMSE = 1.2 kg ha⁻¹ yr⁻¹, was used to estimate fluxes and spatial patterns, while ridge regression and SHAP analysis quantified the relative contributions of emissions, precipitation, and socioeconomic factors. This framework allows us to assess both the environmental and health-related sustainability implications of sulfur deposition. Results show a nationwide decline of more than 50% in wet S deposition during 2013–2023, with two-thirds of sites and 95% of grids showing significant decreases. Historical hotspots such as the North China Plain and Sichuan Basin improved markedly, while some southern provinces (e.g., Guizhou, Hunan, Jiangxi) still exhibited high deposition (>20 kg ha⁻¹ yr⁻¹). Over 90% of the reduction was attributable to emission declines, confirming the dominant effect of sustained policy-driven measures. This study extends sulfur deposition records to 2023, demonstrates the value of integrating ground monitoring with remote sensing and machine learning, and provides robust evidence that China’s emission reduction policies have delivered significant environmental and sustainability benefits. The findings offer insights for region-specific governance and for developing countries balancing economic growth with ecological protection. Full article

Urban riparian areas serve as vital blue-green infrastructure for climate adaptation, yet mechanisms governing energy exchange remain underexplored. This study aims to quantify the spatiotemporal patterns of sensible heat flux (H) and latent heat flux (LE) across riparian plant communities on daily and annual scales, and to disentangle the interactive effects of vegetation structure and water bodies on these fluxes. Using year-long field monitoring (September 2020–August 2021) across seven riparian plant communities along the Danshui River in Shanghai, environmental parameters were collected at multiple distances from the river. Interpretable machine learning models (Random Forest with SHAP analysis) were employed to identify key drivers. Results reveal significant diurnal and seasonal dynamics: LE amplitude exceeded H in summer but reversed in winter, with spatial gradients in H and LE strongly influenced by proximity to water bodies in grasslands and broadleaf forests but weakened in conifers. Meteorological factors such as photosynthetically active radiation and sunshine duration dominated daily-scale fluxes, while vegetation structures such as canopy height and leaf area index (LAI) contributed >50% to annual-scale variability. These findings underscore vegetation’s role in modulating energy partitioning, providing a theoretical basis for optimizing riparian plant configurations to enhance microclimate regulation in urban riparian. Full article

The flux-reversal permanent magnet machine (FRPMM) exhibits superior energy conversion efficiency, enhanced fault resilience, and structural simplicity. It demonstrates excellent adaptability across variable speed conditions. Nevertheless, high torque ripple remains a primary constraint on the application prospects of FRPMMs. A novel FRPMM with concave stator poles and auxiliary teeth is proposed to address this issue. Sensitivity analysis is carried out, and the dominant parameters affecting the electromechanical characteristics of the machine are determined to be the rotor tooth radian, the permanent magnet tooth radian, and the auxiliary tooth width. Furthermore, the response surface method (RSM) is utilized in conjunction with a multi-objective genetic algorithm (MOGA) to optimize the parameters. Finally, a comparative analysis of electromagnetic characteristics between the proposed FRPMM and a conventional FRPMM is conducted. The results show that the proposed FRPMM can reduce torque ripple, improve output torque, and reduce the amount of permanent magnet. Full article

The sensorless control of doubly fed induction machine (DFIM) rotor magnetic flux based on a model reference adaptive system (MRAS) is proposed to improve the reliability of a large-scale variable-speed pumped storage (VSPS) system and reduce operation and maintenance costs. The existing sensorless control of doubly fed induction machines (DFIMs) is mostly focused on generator operation, making it difficult to apply to the VSPS system. The proposed strategy realizes the reversible operations of the VSPS through the design of an adaptive law under variable operating conditions, eliminating mechanical sensors, and possessing the characteristics of simple implementation and accurate identification. The mathematical model of the DFIM in a VSPS system is constructed, and an MRAS vector control strategy based on stator voltage orientation is established. The rotor angle and speed under reversible operating conditions are effectively identified by dynamically adjusting the angle error between the rotor flux reference model and the adaptive model to approach zero. Subsequently, comparative analysis with the closed-loop direct detection method verifies the advantages of the proposed strategy. The proposed control method can accurately identify rotor position and speed in the pumping and power generation conditions of the VSPS system and it demonstrates robust adaptability. Full article

Owing to the distinct advantages of stator–permanent magnet (PM) motors over other PM machines, their prominence in high-power-density applications is surging dramatically, capturing growing interest across diverse applications. This article proposes an innovative design procedure for two primary stator–PM motor types, flux switching and biased flux, yielding 30 novel motor designs. The procedure involves splitting teeth, incorporating a flux reversal effect, and embedding flux barriers into the conventional structure. The analytical reasons behind the novel motors’ architecture are mathematically expressed and verified using finite element analysis (FEA). Through an effective optimisation based on a multi-objective genetic algorithm, various feasible stator/rotor pole combinations are explored, with over 36,000 samples evaluated using FEA coupled with the algorithm. The electromagnetic characteristics of promising motors are analysed, revealing that adding the flux reversal effect and flux barriers, which reduce PM volume while decreasing leakage flux and enhancing air gap flux, improves torque production by up to 68%. Beyond torque enhancement, other electromagnetic parameters, including torque ripple, core loss, and the power factor, are also improved. The proposed motors enhance the PM torque density significantly by about 115% compared to conventional motors and reduce the motor costs. A generalised decision-making process and thermal analysis are applied to the top-performing motors. Additionally, the prototyping measures and considerations are thoroughly discussed. Finally, a comprehensive conclusion is reached. Full article

To address the inherent nonlinearity and strong coupling among rotor displacement, speed, and flux linkage in the composite cage rotor bearingless induction motor (CCR-BIM), an inverse system decoupling control strategy based on a support vector machine (SVM) optimized by the improved simulated annealing-genetic algorithm (ISA-GA) is proposed. First, based on the structure and working principle of CCR-BIM, the mathematical model of CCR-BIM is derived, and its reversibility is rigorously analyzed. Subsequently, an SVM regression equation is established, and the SVM kernel function parameters are optimized using the ISA-GA to train a high-precision inverse system decoupling control model. Finally, the inverse system is cascaded with the original system to construct a pseudo-linear system model, achieving linearization and decoupling control of CCR-BIM. To verify the effectiveness and practicability of the proposed decoupling control strategy, the proposed control method is compared with the traditional inverse system decoupling control strategy through simulation and experimentation. Both simulation and experimental results demonstrate that the proposed decoupling control strategy can effectively achieve decoupling control of rotor displacement, rotational speed, and flux linkage in CCR-BIM. Full article

Enhancing the dynamic response of Flux-Switching Permanent Magnet Synchronous Machines (FSPMSMs) is crucial for high-performance applications such as electric vehicles, renewable energy systems, and industrial automation. Conventional Proportional Integral (PI) controllers within model predictive current control (MPCC) frameworks often struggle to meet the demands of rapid transient response and precise speed tracking, particularly under dynamic operating conditions. To address these challenges, this paper presents a hybrid control strategy that integrates Sliding Mode Control (SMC) into the speed loop of MPCC, aiming to significantly improve the dynamic response and control robustness of FSPMSMs. The feasibility and effectiveness of the proposed approach are validated through high-fidelity real-time simulations using OPAL-RT Technologies’ OP5707XG simulator. Two control schemes are compared: MPCC with a PI controller in the speed loop (MPCC-PI) and MPCC with SMC in the speed loop (MPCC-SMC). Testing was conducted under various operating scenarios, including starting tests, load variations, speed ramping, and speed reversals. The results demonstrate that the MPCC-SMC strategy achieves superior dynamic performance, faster settling times, smoother transitions, and enhanced steady-state precision compared to the MPCC-PI scheme. The comparative results confirm that the MPCC-SMC method outperforms conventional MPCC strategies, making it a compelling solution for advanced motor drive applications requiring enhanced dynamic control. Full article

This paper proposes a high torque density dual-stator flux-reversal-machine with multiple poles Halbach excitation (MPHE-DSFRM), which uses two pole pairs’ numbers (PPNs) of PM excitation on one outer stator tooth, and one PPN of PM excitation on one inner stator tooth. The introduction of different PPNs of PM excitation on the outer and the inner stators can optimize magnetic circuit and airgap flux density. A Halbach array is formed by inserting three pieces of circumferentially magnetized PMs into four pieces of radially magnetized permanent magnets (PMs) on the outer stator, which aims to further enhance torque density, and reduce torque ripple. Based on the flux modulation effect, the analytical modeling of the proposed MPHE-DSFRM is established, together with the evolution process, and the working principle is presented. Then, the key design parameters of MPHE-DSFRM are optimized to achieve high torque density and low torque ripple for high torque quality. Three representative DSFRMs and a conventional FRM are designed and analyzed, and they share the same design key parameters, including PM usage, outer radius of the outer stator, and active airgap length. The electromagnetic performances, including airgap flux density, back electromotive force (back-EMF), and torque characteristics, are analyzed and compared by finite element analysis (FEA). The calculated results show that the proposed MPHE-DSFRM can provide high torque density and high PM utilization. Full article

This paper presents the efficiency improvement of a pole-changing vernier machine (PCVM) by considering the residual magnetic flux density ($B_r$) of low coercivity force (LCF) permanent magnets (PMs). The PCVM operates in two modes: vernier machine (VM) mode and permanent magnet synchronous machine (PMSM) mode, achieved through pole-changing. Pole-changing involves reversing the magnetic flux direction of LCF PM to alter the number of rotor pole pairs. By changing the number of rotor pole pairs, the PCVM operates as a VM mode at low speeds, providing high torque, and as a PMSM mode at high speeds, offering high efficiency. To achieve this, a combination of high coercivity force (HCF) PM and LCF PM is utilized in a single structure. The magnetic flux direction in the LCF PM is determined by $B_r$, and the highest efficiency is achieved when $B_r$ reaches its maximum value |$B_{rm}$|. This paper focuses on improving efficiency by obtaining $B_{rm}$ in VM mode and −$B_{rm}$ in PMSM mode through the design process. Additionally, finite element analysis (FEA) is employed to compare the performance of the improved model, which considers $B_r$, with that of the conventional model, designed without considering $B_r$. The improved model achieves higher $B_r$ values in each mode compared to the conventional model, resulting in increased torque density. Consequently, this leads to improved efficiency. Full article

Irreversible demagnetization of permanent magnets (PMs) in PM synchronous motors (PMSMs) degrades the performance and efficiency of a machine and its drive system. There are numerous fault diagnosis methods for detecting demagnetization under steady-state conditions. However, only a few works could be found on fault diagnosis under dynamic conditions, whereas the dynamic operation of a motor is a very common scenario, e.g., electric vehicles. The voltage and current signal-based traditional fault detection method is not only affected by the structure of the motor, but it also becomes complicated to extract signals involving fault characteristics. Hence, this paper proposes a search coil-based online method for detecting demagnetization faults in PMSMs under dynamic conditions, which are not affected by the motor structure. To gather the flux of the stator tooth, flexible Printed circuit board (FPCB) search coils are positioned at the stator slot. The search coil is made up of two branches that are one pole apart and arranged in reverse sequence. In this installation option, the output signal in the fault state cannot be eliminated, and the output signal in the health state is zero. This paper defines only that characteristic value related to the position angle of the rotor. Further, the aim was to simultaneously eliminate the influence of elements like the search coil installation error and the inherent dispersion of the permanent magnet on the detection results. To characterize the fault degree, the measurement differential between the health state and the fault state is further integrated according to a predetermined angle range. Last but not least, speed-independent detection of individual permanent magnet demagnetization faults is possible using rotor position and stator tooth flux. A six-phase PMSM was used in experiments to show the efficiency of the suggested approach. The findings of the experiment demonstrate that the suggested strategy may precisely ascertain when a defect will occur. Full article

Hypothermic oxygenated machine perfusion (HOPE) can enhance organ preservation and protect mitochondria from hypoxia-ischemic injury; however, an understanding of the underlying HOPE mechanism that protects mitochondria is somewhat lacking. We hypothesized that mitophagy may play an important role in HOPE mitochondria protection. Experimental rat liver grafts were exposed to 30 min of in situ warm ischemia. Then, grafts were procured, followed by cold storage for 3 or 4 h to mimic the conventional preservation and transportation time in donation after circulatory death (DCD) in clinical contexts. Next, the grafts underwent hypothermic machine perfusion (HMP) or HOPE for 1 h through portal vein only perfusion. The HOPE-treated group showed a better preservation capacity compared with cold storage and HMP, preventing hepatocyte damage, nuclear injury, and cell death. HOPE can increase mitophagy marker expression, promote mitophagy flux via the PINK1/Parkin pathway to maintain mitochondrial function, and reduce oxygen free radical generation, while the inhibition of autophagy by 3-methyladenine and chloroquine could reverse the protective effect. HOPE-treated DCD liver also demonstrated more changes in the expression of genes responsible for bile metabolism, mitochondrial dynamics, cell survival, and oxidative stress. Overall, HOPE attenuates hypoxia-ischemic injury in DCD liver by promoting mitophagy flux to maintain mitochondrial function and protect hepatocytes. Mitophagy could pave the way for a protective approach against hypoxia-ischemic injury in DCD liver. Full article

In recent years, high permeability membranes (HPMs) have attracted wide attention in seawater reverse osmosis (SWRO) desalination. However, the limitation of hydrodynamics and mass transfer characteristics for conventional spiral wound modules defeats the advantage of HPMs. Feed spacer design is one of the effective ways to improve module performance by enhancing permeation flux and mitigating membrane fouling. Herein, we propose a multiscale modeling framework that integrates a three-dimensional multi-physics model with a permeable wall and an impermeable wall, respectively, at a sub-millimeter scale and a system-level model at a meter scale. Using the proposed solution framework, a thorough quantitative analysis at different scales is conducted and it indicates that the average errors of the friction coefficient and the Sherwood number using the impermeable wall model are less than 2% and 9%, respectively, for commercial SWRO membrane (water permeability 1 L m⁻² h⁻¹ bar⁻¹) and HPMs (3 L m⁻² h⁻¹ bar⁻¹, 5 L m⁻² h⁻¹ bar⁻¹ and 10 L m⁻² h⁻¹ bar⁻¹) systems, compared to the predictions using the permeable wall model. Using both the permeable and impermeable wall models, the system-level simulations, e.g., specific energy consumption, average permeation flux, and the maximum concentration polarization factor at the system inlet are basically the same (error < 2%), while the impermeable wall model has a significant advantage in computational efficiency. The multiscale framework coupling the impermeable wall model can be used to guide the efficient and accurate optimal spacer design and system design for HPMs using, e.g., a machine learning approach. Full article

The traditional flux reversal permanent magnet (FRPM) machine has high torque ripple due to the double salient-pole structure, and the effective air-gap length is increased by the permanent magnet structure of the stator tooth surface, which affects the size of the air-gap magnetomotive force (MMF). This paper proposes an axial modular flux-reversal permanent magnet (AM-FRPM) machine with attractive torque capabilities. Based on air-gap magnetic field modulation theory, a method to achieve optimal air-gap harmonic torque contributions was developed. Then, the principle for high-torque-density generation in the AM-FRPM machine under an alternating magnetization topology was investigated using the PM magnetic field modulation and armature reaction magnetic field modulation. In addition, the cogging torque suppression mechanism, which guides the selection of stator-slot and rotor-pole combinations, was investigated. In addition, a comprehensive comparison of the electromagnetic characteristics of two AM-FRPM machines and a traditional FRPM machine was conducted. Then, the advantages and disadvantages of the three machines were analyzed. Finally, prototypes were manufactured and tested to verify the correctness of the theoretical analysis. Full article

Increasing industrial development puts forward high requirements for the performances of stator permanent magnet (PM) machines, such as torque density and efficiency. The paper proposes a new dual stator PM machine based on field modulation theory (DSPMM), which employs the intermediate rotor participating in the electromechanical energy conversion of the internal and external machine. The proposed machine has the advantages of high torque density and high efficiency and solves the problem of insufficient space utilization of a single stator machine. The evolution process and working principle of the proposed DSPMM are studied. The flux-switching-type PM (FSPM) and the flux-reversal-type PM (FRPM) are employed in the proposed DSPMM, which forms four representative machines. For a fair comparison, the proposed machines employ identical key parameters, i.e., PM volume, the outer radius of the outer stator, and active airgap length. Based on finite element analysis (FEA), the electromagnetic performances of the four representative DSPMM under no-load and rated load, and different copper consumption conditions are analyzed and compared. The calculated results show that the proposed DSPMM with inner FSPM stator and outer FRPM stator can provide high output torque, low torque ripple, high power factor, and high efficiency. Full article