Search Results (1,725)

Charcot-Marie-Tooth (CMT) disease type 2A is a rare heritable disorder caused by pathogenic variants of mitofusin (MFN) 2 that suppress mitochondrial fusion and motility in peripheral nerves, culminating in denervation myoatrophy. The rarity of this condition and the limited choice of animal models preclude pre-clinical evaluation of many tests that could be translated to human trials. Here, we introduced the CMT2A pathogenic variant MFN2 T105M into the rat genome for phenotype characterization and evaluation of disease response to a third-generation mitofusin activator, 8015-P2. CMT2A rats exhibited peripheral motor and sensory neuron dysfunction. Functional, histological, neuroelectrophysiological and magnetic resonance imaging testing readily distinguished between wild-type (WT) and mutant rats via axonopathy and myoatrophy. Compound 8015-P2 reversed CMT2A-linked neuromuscular degeneration in a dose- and time-dependent manner; at 10 mg/kg/d, normalization occurred at 4 weeks. The minimal effective 8015-P2 dose was 2 mg/kg/day. Rapidity of phenotype reversal and primary muscle abnormalities are consistent with extra-neuronal effects of the causal MFN2 DNA variant. These data demonstrate unprecedented utility of the Mfn2 T105M rat as a model of CMT2A, expand the menu of clinically applicable tests that may have use in future human trials, and establish a strong foundation for exploration of extra-neuronal consequences of pathogenic mitofusin variants in non-mouse models. Full article

Parameter variation caused by manufacturing tolerances and thermal drift makes PMSM fault-severity estimation difficult, because motor-level offsets and fault effects are coupled in the d–q model. This paper proposes a channel-selective BO-Fusion-PINN for parameter-generalized fault diagnosis. A healthy reference window is first used to estimate motor-parameter deviations through an integral least-squares observer, avoiding neural extrapolation of these offsets. A diagnostic window is then processed by a physics-informed LSTM branch and a data-driven LSTM branch, and Bayesian optimization assigns separate fusion weights to stator-resistance degradation and permanent-magnet flux weakening. Experiments over parameter out-of-distribution buckets and non-ideal simulation settings show that the fused estimator consistently improves on either branch alone. The method is especially effective in the flux channel and remains competitive with high-capacity data baselines while preserving physical interpretability. The primary scientific contribution is an identifiability-guided fusion rule that assigns physics and data trust to each fault channel according to its statistical observability rather than through a single global weight; in practical terms, this yields a compact and interpretable estimator that transfers across the parameter-tolerance band of a machine class and can, in principle, support controlled end-of-line screening and scheduled diagnostic assessment under a matched-window acquisition protocol. Full article

This work presents a numerical investigation of a direct oil cooling system for Axial Flux Permanent Magnet (AFPM) machines. Building upon the authors’ previous study, which established the fundamental fluid dynamic mechanisms governing oil jet impingement on curved coil surfaces, the present research extends the analysis by performing a systematic parametric optimization of nozzle diameter and axial position. A validated CFD model, benchmarked against experimental data from the literature, is employed to quantify the influence of jet momentum, stagnation pressure, and flow attachment on the resulting thermal performance. Nine configurations are simulated at constant coolant mass flow rate, revealing that the nozzle diameter is the dominant parameter: smaller diameters generate higher jet velocities, stronger stagnation regions, and larger jet-induced forces, leading to significantly enhanced heat transfer coefficients and Nusselt numbers. Nozzle height plays a secondary yet relevant role, as higher positions promote a more coherent jet core and improve impingement quality. Among the nine simulated cases, the configuration with D = 3 mm and L = 14 mm achieves the lowest hotspot temperature and the most efficient energetic behavior within the simulated set, with only a modest increase in pumping power. The results confirm that direct oil impingement is highly sensitive to jet momentum and angle of attack and demonstrate that optimized nozzle design can substantially improve the thermal management of high power density AFPM machines. This extended analysis provides quantitative references for nozzle sizing and placement within the simulated operating conditions with enhanced cooling efficiency. Full article

Wake-induced magnetic fields provide a potential non-acoustic signature for underwater-vehicle sensing, but their weak amplitudes can be masked by ocean-wave magnetic backgrounds. This study evaluates their engineering observability under representative wind–wave conditions. The wake-induced field at fixed observation points was calculated from CFD-derived wake velocities of an engineering-scale fully appended SUBOFF model using discrete Biot–Savart summation. The ocean-wave background was computed using a JONSWAP spectrum and linear wave theory, and a peak-to-background-rms SNR was used as the observability indicator. Results show that speed and diving depth strongly control the target signal. At the baseline point, increasing speed from 10 to 40 kn raised $B_{wake,max}$ from 0.0406 to 1.65 nT and SNR from −1.01 to 31.2 dB under W2. Increasing diving depth from $2D$ to $4D$ reduced $B_{wake,max}$ from 0.129 to 0.0204 nT and SNR from 9.03 to −6.99 dB. Wind speed dominated the wave background: at $U_{10}=10$ m/s, $B_{wave,rms}$ reached 0.542 nT and the Case 2 SNR decreased to −12.5 dB. Sensor placement affected both signal and background; deeper underwater sensors improved observability, whereas aerial observations suffered from weak wake-signal amplitudes. Wake-field observability is therefore jointly governed by wake source strength, ocean-wave magnetic background, and observation geometry. Full article

In this study, novel exact solutions of the Duffing equation with their phase portraits are proposed and reasoned. It is shown that phase trajectories are initially elliptical and become distorted in the unstable area with the growth of the variable parameter in the damped case. The instability criteria of the identified solutions have been determined together with the Fourier series transformation up to the first and high harmonics in the sense of the physical interpretation. An explicit form for the $Ax=dx/dt={(x+M)}^2\cdot N$ non-linear differential operator corresponding to the considered functions has been derived, and its main functional spectrum has been evaluated. Non-isothermal creep tests of different materials were completely described using the Duffing equation via noted solutions up to the fracture as processes with a personal deformation response. We successfully examined the relationship between the thermal and magnetic properties of the ferromagnetic amorphous alloy under its non-linear deformation, using the critical exponents equal to α₁ = 2 and α₂ = 1. With high linear correlation coefficients (0.9 and above) between our model and experiments (within ±0.01 mm of residual error), the behavior of organic and metallic systems is well predicted under the same thermomechanical testing conditions on the mesoscale. Full article

Organic phosphorus (OP) transformation in perennial orchard soils is strongly regulated by planting duration. However, the pathways of soil OP turnover in the orchards with different planting years remain largely unknown. This study was conducted in Penglai District, Shandong Province, China, characterized by a warm-temperate continental monsoon climate and brown sandy loam soil. Soil samples were collected from the 0–20 cm layer of vineyards established for 0.5, 4, 16, and 22 years (Y0.5, Y4, Y16, and Y22, respectively). Soil P pools, OP forms determined by solution ³¹P nuclear magnetic resonance spectroscopy, acid and alkaline phosphomonoesterase (ACP and ALP), phosphodiesterase (PDE) activities, and the diversity and composition of phoC- and phoD-harboring bacterial communities were analyzed. Differences among vineyard ages were assessed using one-way ANOVA, while principal coordinate analysis, canonical correlation analysis, and structural equation modeling were employed to evaluate microbial community variation and the relationships among soil properties, functional bacterial communities, phosphatase activities, and OP forms. We observed that compared with that in Y0.5, total P content in Y4 was significantly lower, whereas available P (AP) content in Y16, and OP content in both Y16 and Y22, were significantly higher. The contents of orthophosphate, myo-inositol hexakisphosphate (myo-IHP), scyllo-IHP, choline phosphate and corrected monoester (cMonoester) in Y16 and Y22 were significantly higher than that in Y0.5. Soil ACP activity in Y16 was significantly higher than that in Y4, but ALP and PDE activities in Y16 were significantly lower than those in other planting years. The phoC- and phoD-harboring community composition in Y16 was significantly altered, with phoC- and phoD-harboring community α-diversity remarkably increased and decreased, respectively. The phoC- and phoD-harboring community composition was driven by soil pH, AP and cMonoester, with phoD-harboring community composition predicted by the soil C:P ratio. Soil OP transformation is mainly regulated by the activity of PDE and ACP, and the diversity and composition of phoC-harboring communities, in orchards with different planting years. Full article

The model predictive current control (MPCC) of an interior permanent magnet synchronous machine (IPMSM) requires an accurate motor parameter model to predict future currents and achieve high control performance. However, the inductance parameters of an IPMSM are easily affected by factors such as magnetic field saturation, leading to large current prediction errors, high current ripple, and poor stability. Therefore, an MPCC strategy for an IPMSM based on parameter adaptive feedback correction is proposed. First, based on the mathematical model of the IPMSM in the synchronous rotary coordinate, the cross-coupling relationship between the dq-axis inductance deviations and the current prediction error is derived to form an explicit prediction error model. Then, the influence of the d-axis and q-axis inductance parameter deviations of the IPMSM on the current prediction error is discussed in detail. Next, based on the established mathematical model of the prediction error, the recursive least squares scheme is adopted to identify the d-axis and q-axis deviations of the inductance parameters online. Finally, unlike conventional open-loop RLS correction, a PI-based closed-loop correction loop is designed that feeds the prediction error back to adjust the inductance deviations, thereby forcing the prediction error toward zero while inherently compensating for inverter dead-time effects. Simulations and experiments were conducted, and the results show that the proposed scheme greatly improves the accuracy of current prediction and inductance parameter estimation, and enhances robustness against parameter mismatch and dead-time disturbances. The key novelty lies in the PI-feedback-driven RLS closed-loop structure that simultaneously achieves error elimination and dead-time compensation. Full article

Geogenic processes and human activities are both major causes of soil pollution. Soils can get toxic transition metals from the materials they are formed from, but most pollution comes from industrial and farming activities. The presence of these transition metals in soil can be shown through changes in chemical, biochemical, and microbial properties, as well as how plants react. This research aims to remove transition metals like chromium (Cr), manganese (Mn), iron (Fe), zinc (Zn), tungsten (W), and cadmium (Cd) from soil using a boron nitride (BN) nanocage. The electromagnetic and thermodynamic properties of these metals when trapped in BN were studied using materials modeling. The metals are captured through chemisorption. The research looked at how Cr, Mn, Fe, Zn, W, and Cd are trapped by BN to detect soil metal cations. BN was designed in the presence of these transition metals. The covalent characteristics of these complexes show similar energy levels and a view of the partial density of states between the p states of boron and nitrogen in BN and the d states of Cr, Mn, Fe, Zn, W, and Cd in B(X)N complexes. Also, nuclear magnetic resonance (NMR) analysis showed clear peaks around Cr, Mn, Fe, Zn, W, and Cd when they were trapped in BN during atomic detection and removal from soil, although there were some variations in chemical shielding for isotropic and anisotropic tensors. Based on these results, the ability of BN (as an atom sensor) to adsorb toxic metals, metalloids, and nonmetals is ordered as: Cd > Zn > Fe > Cr > Mn ≈ W. This article suggests that elements absorbed by BN could be used to develop and improve the optoelectronic properties of BN, helping to create photoelectric devices for soil cleaning. Full article

Schizophrenia is a heterogeneous neurodevelopmental disorder in which genetic liability, developmental exposures, illness stage, treatment, and metabolic or inflammatory comorbidity may shape redox biology. This narrative review evaluates the human and mechanistic evidence for impaired redox adaptation as a convergence mechanism linking glutathione (GSH) regulation, N-methyl-D-aspartate receptor hypofunction, parvalbumin-interneuron and perineuronal-net vulnerability, mitochondrial–glial dysfunction, and neuroimmune signalling. The findings do not support a uniform oxidative abnormality across all patients or compartments: the peripheral biomarkers are heterogeneous, the group-level brain GSH magnetic resonance spectroscopy findings are largely null, and treatment-related changes vary by marker and illness phase. Beyond the established GSH–NMDA–redox–immune models, we integrate iron–lipid redox regulation as a conditional, hypothesis-generating extension and apply a deliberately conservative evidence hierarchy. Human studies more often report a lower peripheral iron and reduced or redistributed brain iron than a uniform iron excess; the plasma signals for predominantly intracellular proteins remain analytically unvalidated, and ferroptotic neuronal death has not been demonstrated. Longitudinal, sex-aware, multi-compartment, and challenge-based studies are needed to define meaningful redox subgroups; no redox- or ferroptosis-related biomarker is currently validated to guide treatment selection. Full article

Background: Multiple sclerosis (MS) is a chronic autoimmune demyelinating disease of the central nervous system, affecting more than 2.8 million individuals worldwide. Automated segmentation of white matter lesions on fluid-attenuated inversion recovery (FLAIR) magnetic resonance images (MRI) is essential for reproducible diagnosis and treatment monitoring, yet remains challenging due to extreme class imbalance, high lesion load variability, and poor contrast at lesion boundaries. Method: We propose a 2D U-Net with a ResNet50 encoder that exploits ImageNet-pretrained representations through a novel pseudo-RGB input strategy: three consecutive FLAIR slices centred on the target slice are stacked channel-wise to form a three-channel input, recovering inter-slice spatial context while enabling direct reuse of pretrained convolutional weights without modality-specific pretraining. A two-phase transfer-learning protocol first optimises only the decoder with the encoder frozen, then fine-tunes the upper encoder blocks at a reduced learning rate. Test-time augmentation (TTA) averaging over horizontal-flip and vertical-flip transformations further improves prediction robustness. Results: Evaluation on the held-out test set of the MSLesSeg2024 benchmark (12 patients, approximately 1650 axial slices) shows that the proposed model achieves a Dice similarity coefficient (DSC) of 0.714 (95% confidence interval (CI): 0.6845–0.7194), intersection-over-union (IoU) of 0.6571 (95% CI: 0.6272–0.6632), and area under the receiver operating characteristic (ROC) curve (AUC) of 0.9628 (95% CI: 0.9422–0.9793). Critically, the model records the lowest false-negative pixel count per slice (FNV = 33.6 px/slice) across all ablation conditions, indicating superior sensitivity to lesion tissue; this is a property of direct clinical relevance for MS monitoring, where missed lesions carry the greatest diagnostic risk. A patient-matched comparison against a 3D nnU-Net baseline shows statistically comparable DSC (0.714 vs. 0.726; paired Wilcoxon $p=0.68$–$0.79$, not significant) alongside a substantially higher pixel-level AUC for the proposed model (0.963 vs. 0.773) and a true volumetric Hausdorff distance gap smaller than an earlier estimate (12.5 mm vs. 10.7 mm), showing an honest mixed-strengths result rather than an outright improvement. A supplementary ablation further shows that replicating a single FLAIR slice across all three channels significantly outperforms the pseudo-RGB adjacent-slice encoding ($p<0.001$), indicating that the anticipated inter-slice-context benefit did not materialise here (see Discussion section). Cross-dataset evaluation on the independent MSSEG 2016 benchmark confirms generalisability: zero-shot transfer achieves DSC = 0.6562, recovering to DSC = 0.7046 after brief fine-tuning (30 epochs), within 1.5 percentage points of in-domain performance. Conclusions: The present study reveals that an optimized, lightweight 2D pipeline can rival the segmentation overlap of context-aware 3D baselines on specific datasets, doing so with a significantly reduced computational footprint. Given its strong pixel-wise discrimination, this methodology offers an effective and practical tool for routine automated MS lesion assessment in clinical settings. Full article

Permanent magnet synchronous motors (PMSMs) are widely used in new energy vehicles, electric drive systems, and industrial servo applications. Excessive permanent magnet temperature may lead to magnetic performance degradation or even irreversible demagnetization; therefore, accurate estimation of permanent magnet temperature is of considerable importance. However, existing data-driven methods generally rely heavily on high-frequency measurements, and their prediction accuracy tends to deteriorate under low-frequency sampling conditions. Moreover, purely data-driven models lack explicit physical constraints, which limits their interpretability and generalization capability. To address these issues, this study proposes a physics-informed long short-term memory model for permanent magnet temperature prediction. A physics-based loss function is formulated using the PMSM d–q-axis voltage balance equations, while the d- and q-axis inductances are treated as trainable parameters during network optimization. This design enables the temperature prediction task and the electromagnetic constraints to be optimized jointly. Multi-operating-condition experiments are conducted using a publicly available electric motor temperature dataset, and the proposed model is compared with CNN, GRU, MLP-PINN and TNN models. In addition, experiments involving different downsampling ratios, errors in the high-temperature region, parameter sensitivity, physical parameter identification, and input-feature effects are performed to comprehensively evaluate the proposed model. The results show that the PINN-LSTM model achieves the best overall prediction performance, with an MAE of 1.6048 °C, an RMSE of 2.1890 °C, and an R² of 0.9861, outperforming all comparison models. The model also maintains high prediction accuracy in the high-temperature region, with an MAE of 1.363 °C and an RMSE of 1.896 °C. Furthermore, the parameters learned by the model can effectively reconstruct the variation trends of the d- and q-axis voltages under the test operating conditions. Sensitivity analysis of the temperature coefficients further demonstrates that the model is robust to deviations in key physical parameters. These results indicate that the proposed method can achieve accurate and robust permanent magnet temperature prediction under low-frequency sampling conditions, providing an effective solution for motor thermal-state monitoring and health management. Full article

Accurate magnetic-field characterization is essential for evaluating wireless power transfer (WPT) systems. Conventional near-field measurements require setup-specific probe calibration, increasing experimental complexity. This paper proposes a calibration-free framework based on a minimal small-loop magnetic-field probe model derived from Faraday’s law, where the voltage-to-magnetic-field conversion coefficient is analytically determined from probe geometry and operating frequency. Its validity is examined by comparing an ideal analytical model, a spatially aware Bessel-function-based model, and full-wave simulations. Full-wave simulations indicate that the loop circumference should remain within approximately $0.1\lambda$ for the modeled probe, while experimental validation at 13.56 MHz supports the method in the tested WPT configuration. Combined with three-axis measurement and augmented-reality (AR) visualization, the method enables practical three-dimensional (3D) magnetic-field mapping. Full article

Left atrium segmentation from magnetic resonance imaging (MRI) is essential for ablation planning in atrial fibrillation; however, clinical MRI quality is often degraded by noise, artifacts, and incomplete spatial coverage, making traditional recurrent neural networks (RNNs) vulnerable to such distortions. We developed a hybrid architecture inspired by cortico–cerebellar interactions to enhance segmentation stability without compromising mean accuracy. We utilized the open ATRIA dataset (100 patients, isotropic 3D MRI scans with manual left atrium annotations). The model comprises a convolutional encoder, a cortical RNN, and a cerebellar predictive module trained to predict future encoder features across multiple temporal horizons, generating a corrective feedback signal for the RNN. Experiments were conducted on unperturbed and degraded datasets with performance evaluated using the Dice coefficient. On unperturbed data, the cerebellar model achieved a mean best Dice of 0.835 ± 0.032 vs. 0.832 ± 0.027 for the baseline. Under degraded conditions, it showed significantly higher Dice (0.815 ± 0.019 vs. 0.801 ± 0.021; p = 0.014) and Surface Dice (p = 0.040), with a directionally lower between-run variance, though this difference in variance was not formally tested given the limited number of runs. nnU-Net achieved higher absolute accuracy but required three orders of magnitude more inference time and an order of magnitude more parameters. The cerebellar module improved boundary accuracy and reproducibility relative to the non-predictive baseline at a fraction of nnU-Net’s computational cost, offering a lightweight alternative for settings where deploying a full 3D self-configuring model is impractical. Full article

A fully coupled three-dimensional (3D) thermo-magneto-elastic layer-wise formulation is developed for the analysis of multilayered flat and curved panels used in aerospace and aeronautical applications. The model relies on a system of coupled second-order differential equations along the thickness coordinate z, formulated in a mixed orthogonal curvilinear reference system. The governing equations combine the three-dimensional equilibrium equations with the magnetic induction divergence equation and the heat conduction equation, providing a unified multifield framework for thermo-magneto-elastic analyses. Through a suitable definition of the curvature parameters, the same formulation can be directly applied to plates, cylinders, cylindrical panels, and shells with constant radii of curvature. The governing equations are analytically solved by adopting harmonic expansions in the in-plane directions together with the exponential matrix method along the thickness coordinate. The harmonic representation naturally satisfies simply-supported boundary conditions along the panel edges. The multilayered structure is modeled according to a layer-wise strategy, where the continuity of the selected mechanical, magnetic, and thermal variables is enforced across the interfaces between adjacent layers. Different loading boundary conditions can be assigned at the external surfaces by prescribing pressure loads, magnetic potential, transverse magnetic induction, and over-temperature. The numerical investigation is divided into two stages. First, the accuracy of the proposed formulation is verified through comparisons with thermo-magneto-elastic solutions available in the literature. Then, a comprehensive set of new benchmark results is presented by considering different geometries, thickness ratios, and loading boundary conditions. Both tabulated values and through-the-thickness distributions are reported for the most significant field variables. These benchmark results provide useful reference data for the assessment and validation of future two-dimensional and three-dimensional analytical and numerical formulations devoted to coupled thermo-magneto-elastic problems. Full article

Parkinson’s disease (PD) is characterised by the pathological aggregation of α-synuclein (α-syn) into Lewy body inclusions, yet no disease-modifying therapy exists. To address this, we developed an integrated computational pipeline combining quantitative structure–activity relationship (QSAR) modelling, structure-based virtual screening, molecular dynamics (MD) simulation, and molecular mechanics Poisson–Boltzmann surface area (MM-PBSA) binding free energy calculations to repurpose FDA-approved drugs as α-syn fibril inhibitors. Two complementary QSAR model families were trained on 501 α-syn binding affinity records from BindingDB: Morgan extended-connectivity fingerprint (ECFP4) classifiers and a frozen ChemBERTa-77M-MLM transformer encoder, each using Random Forest and Logistic Regression. The applicability domain (AD) was assessed using Morgan–Tanimoto similarity (Tc ≥ 0.40) and calibrated ChemBERTa cosine distance (θ ≤ 0.367). A three-stage funnel applying central nervous system (CNS) permeability filters, a consensus QSAR probability threshold (≥0.80), and AD gating reduced 2241 FDA-approved drugs to 205 candidates for AutoDock Vina 1.2.6 docking against two sites on the cryo-electron microscopy (cryo-EM) α-syn fibril structure, PDB 6SSX: the inter-protofilament cleft (Site 1) and the non-amyloid-beta component (NAC) groove (Site 2). The Morgan fingerprint models achieved an area under the receiver operating characteristic curve (AUROC) of up to 0.940 and a balanced accuracy of 0.810; the ChemBERTa models achieved an AUROC of 0.785 and a balanced accuracy of 0.728. Notably, ChemBERTa AD covered 76.8% of the FDA drugs versus only 5.5% for Morgan–Tanimoto, enabling broad-spectrum screening. The top docking candidates were Olaparib (−7.91 kcal/mol), Paliperidone (−7.75 kcal/mol), Niraparib (−7.18 kcal/mol), Dordaviprone (−7.06 kcal/mol), and Parecoxib (−6.89 kcal/mol). The MD simulations over 200 ns across three independent replicates confirmed stable NAC groove binding, and replicate-averaged MM-PBSA calculations yielded ΔG = −20.6 ± 1.9 kcal/mol for Olaparib at Site 2, −17.1 ± 0.9 kcal/mol for Risperidone, and −16.9 ± 0.8 kcal/mol for Paliperidone, reported as the mean ± standard error of the mean (SEM) across replicates. Olaparib additionally formed five hydrogen bonds in the representative pose, while MD trajectories maintained approximately 2–5 hydrogen bonds, together with a halogen bond within the NAC groove, the largest contact count of any screened compound. These findings identify Olaparib as a novel high-affinity repurposing lead, while Paliperidone and Risperidone are reported as chemically informative secondary NAC–groove binders rather than proposed antiparkinsonian therapeutics, given that their dopamine D2-antagonist pharmacology is clinically associated with drug-induced parkinsonism. All of the candidates warrant experimental validation via thioflavin-T fluorescence or nuclear magnetic resonance (NMR) spectroscopy. Full article