Search Results (123)

Parkinson’s disease (PD) is characterized by motor symptoms, with key diagnostic features, such as rigidity, traditionally assessed through subjective clinical scales. This study proposes a novel hybrid framework integrating model-driven biomechanical features (damping ratio, decay rate) and data-driven statistical features (maximum detail coefficient) from wearable sensor data during a modified pendulum test to quantify shoulder girdle rigidity objectively. Using weak supervision, these features were unified to generate robust labels from limited data, achieving a 10% improvement in PD/healthy control classification accuracy (0.71 vs. 0.64) over data-driven methods and matching model-driven performance (0.70). The damping ratio and decay rate, aligning with Wartenberg pendulum test metrics like relaxation index, revealed velocity-dependent aspects of rigidity, challenging its clinical characterization as velocity-independent. Outputs correlated strongly with UPDRS rigidity scores (r = 0.78, p < 0.001), validating their clinical utility as novel biomechanical biomarkers. This framework enhances interpretability and scalability, enabling remote, objective rigidity assessment for early diagnosis and telemedicine, advancing PD management through innovative sensor-based neurotechnology. Full article

To address the insulation degradation caused by moisture intrusion due to damage to the outer sheath of power cables, this study systematically analyzed the charge transport characteristics of XLPE cables at different water immersion stages using polarization/depolarization current (PDC) measurements. An evaluation method for assessing water immersion levels was proposed based on conductivity, charge density, and charge mobility. Experiments were conducted on commercial 10 kV XLPE cable samples subjected to accelerated water immersion for durations ranging from 0 to 30 days. PDC data were collected via a custom-built three-electrode measurement platform. The results indicated that with increasing immersion time, the decay rate of polarization/depolarization currents slowed, the steady-state current amplitude rose significantly, and the DC conductivity increased from 1.86 × 10⁻¹⁷ S/m to 2.70 × 10⁻¹⁵ S/m—a nearly two-order-of-magnitude increase. The Pearson correlation coefficient between charge mobility and immersion time reached 0.96, indicating a strong positive correlation. Additional tests on XLPE insulation slices showed a rapid rise in conductivity during early immersion, a decrease in breakdown voltage from 93.64 kV to 66.70 kV, and enhanced space charge accumulation under prolonged immersion and higher electric fields. The proposed dual-parameter criterion (conductivity and charge mobility) effectively distinguishes between early and advanced stages of cable water immersion, offering a practical approach for non-destructive assessment of insulation conditions and early detection of moisture intrusion, with significant potential for application in predictive maintenance and insulation diagnostics. Full article

The gas desorption behavior of coal is a key basis for guiding gas parameter determination, optimizing gas extraction, and preventing gas-related disasters. Coal in mine working faces typically exhibits a mixed particle size distribution. However, research on the gas desorption behavior of mixed-size coal samples and comparative studies with single-sized samples remains insufficient. This study employed a self-developed experimental system for the multi-field coupled seepage desorption of gas-bearing coal to conduct comparative experiments on gas desorption behavior between single-sized and mixed-size coal samples. Systematic analysis revealed significant differences in their desorption and diffusion patterns: smaller particle sizes and higher proportions of small particles correlate with greater total gas desorption amounts and higher desorption rates. The desorption process exhibits distinct stages: the initial desorption amount is primarily influenced by the particle size, while the later stage is affected by the proportion of coal samples with different particle sizes. The desorption intensity for both single-sized and mixed-size samples decays exponentially over time, with the decay rate weakening as the proportion of small particles decreases. The gas diffusion coefficient decays over time during desorption, eventually approaching zero, and increases as the proportion of small particles rises. Conversely, the gas desorption attenuation coefficient increases with a higher proportion of fine particles. Based on the desorption laws of coal samples with single and mixed particle sizes, this study can be applied to coalbed gas content measurements, emission prediction, and extraction design, thereby providing a theoretical foundation and technical support for coal mine operations. Full article

Cavitation has been widely explored to enhance physical and chemical processes across various applications. This study aimed to model the key characteristics of a cavitation jet, induced by a triangular-orifice nozzle, using both experimental and numerical methods. Optical instrumentation, a pressure transducer and the Reynolds-Averaged Navier–Stokes (RANS) equations were employed. Optical instrumentation and high-speed photography detected the two-phase flow generated by water vaporization, revealing a mean decay pattern. Irradiance fluctuations and photographic evidence provided results about the light transmission dynamics through cavitating jets. Pressure fluctuations exhibited similar growth and decay, supporting optical instrumentation as a viable method for assessing cavitation intensity. Experimental data showed a strong relationship between irradiance and flow rate (R² = 0.998). This enabled the correlation of the standard deviation of instantaneous pressure measurements and normalized flow rate (R² = 0.977). Furthermore, vapor volume fraction and normalized flow rate reached a correlation coefficient of 0.999. On the simulation side, the SSG-RSM turbulence mode showed better agreement with experimental data, with relative deviations ranging from 2.1% to 6.6%. The numerical results suggest that vapor jet length is related to vapor fraction through a power law, enabling the development of new equations. These results demonstrated that anisotropic turbulence modeling is essential to reproduce experimental observations compared to mean flow properties. Based on the agreement between the numerical model and the experimental data for mean flow quantities, a formulation is proposed to estimate the jet length originating from the nozzle, offering a predictive approach for cavitating jet behavior. Full article

This study investigates the evolution laws and prediction models of anti-skid and wear-resistant performance for asphalt pavements during the operation period. Using a combination of indoor accelerated wear tests and field detection, mixed specimens are prepared with SBS modified asphalt, limestone, and basalt aggregates. Through accelerated wear tests of different durations, the structural depth and friction coefficient are measured. Combined with the field data from the G56 K2319 section of the Hangrui Expressway, the decay laws of anti-skid performance are analyzed, and prediction models are established. The results show that the anti-skid performance of basalt mixtures is superior to that of limestone. The deepened structure technology significantly enhances the performance of basalt but has a negative impact on the pendulum value of limestone. The influence degrees of wear duration, aggregate type, and deepened structure state on structural depth and pendulum value vary. The initial structural depth of basalt mixtures (0.85 mm) is 11.8% higher than that of limestone (0.76 mm). The longitudinal pendulum value of basalt (44) is 10% higher than that of limestone (40), while the transverse pendulum value of limestone (50) is 4.2% higher than that of basalt (48). After 21 h of wear, the structural depth of basalt (0.68 mm) is 4.6% higher than that of limestone (0.65 mm), with a decay rate 23.6% lower. The pendulum value of basalt remains above 50, while limestone’s longitudinal pendulum value drops to 36 (10% lower than its initial value), even below the unmodified state. The influence order for structural depth is deepened structure state > wear duration > aggregate type, and for lateral pendulum value, it is wear duration > deepened structure state > aggregate type. There is a significant linear relationship between structural depth/pendulum value and wear duration, and the prediction models are reliable. The indoor accelerated wear of 44.5 h is equivalent to the field operation wear of 3 years. The research findings provide a theoretical basis for the evaluation of anti-skid performance, maintenance decision-making, and material optimization of asphalt pavements. Full article

In regions characterized by complex terrain and diverse pollution sources, high-precision air pollution prediction remains challenging due to nonlinear spatiotemporal coupling and the difficulty of modeling local pollutant agglomeration. To address these issues, this study proposes a CNN–LSTM–Transformer multimodal prediction framework integrated with Bayesian Optimization. First, the Local Moran’s Index (LMI) is introduced as a spatial perception feature and concatenated with pollutant concentration sequences before being input into the CNN module. This design enhances the model’s ability to identify local pollutant clustering and spatial heterogeneity. Second, the LSTM architecture adopts a dual-channel structure: the main channel employs bidirectional LSTM to extract temporal dependencies, while the auxiliary channel uses unidirectional LSTM to capture evolutionary trends. A Transformer with a multi-head attention mechanism is then introduced to perform global modeling. Bayesian Optimization is employed to automatically adjust key hyperparameters, thereby improving the model’s stability and convergence efficiency. Empirical results based on atmospheric pollution monitoring data from Sichuan Province during 2021–2024 demonstrate that the proposed model outperforms various mainstream methods in predicting six pollutants in Chengdu. For instance, the MAE for PM_2.5 decreased by 14.9–22.1%, while the coefficient of determination (R²) remained stable between 87% and 89%. The accuracy decay rate across four-day forecasts was controlled within 12.4%. Furthermore, in PM_2.5 generalization prediction tasks across four other cities—Yibin, Zigong, Nanchong, and Mianyang—the model exhibited superior stability and robustness, achieving an average R² of 87.4%. These findings highlight the model’s long-term stability and regional generalization capability, offering reliable technical support for air pollution prediction and control strategies in Sichuan Province and potentially beyond. Full article

To overcome the limitations of single-objective optimization in permanent magnet synchronous motor (PMSM) performance enhancement, this study proposes an adaptive moving least squares (AMLS) for a 12-pole/36-slot built-in PMSM. Through comprehensive exploration of the design space, a systematic approach is established for holistic motor performance improvement. The Gaussian weight function is modified to improve the model’s fitting accuracy, and the decay rate of the control weight is optimized. The optimal adaptive meta-model for the built-in PMSM is selected based on the coefficient of determination. Subsequently, sensitivity analysis is conducted to identify the parameters that most significantly influence key performance indicators, including torque ripple, stator core loss, electromagnetic force amplitude, and average output torque. These parameters are then chosen as the optimal design variables. A multi-objective optimization framework, built upon the optimal adaptive meta-model, is developed to address the multi-objective optimization problem. The results demonstrate increased output torque, along with reductions in stator core loss, torque ripple, and radial electromagnetic force, thereby significantly improving the overall performance of the motor. Full article

The chiral nonlinear Schrödinger equation (CNLSE) serves as a simplified model for characterizing edge states in the fractional quantum Hall effect. In this paper, we leverage the generalization and parameter inversion capabilities of physics-informed neural networks (PINNs) to investigate both forward and inverse problems of 1D and 2D CNLSEs. Specifically, a hybrid optimization strategy incorporating exponential learning rate decay is proposed to reconstruct data-driven solutions, including bright soliton for the 1D case and bright, dark soliton as well as periodic solutions for the 2D case. Moreover, we conduct a comprehensive discussion on varying parameter configurations derived from the equations and their corresponding solutions to evaluate the adaptability of the PINNs framework. The effects of residual points, network architectures, and weight settings are additionally examined. For the inverse problems, the coefficients of 1D and 2D CNLSEs are successfully identified using soliton solution data, and several factors that can impact the robustness of the proposed model, such as noise interference, time range, and observation moment are explored as well. Numerical experiments highlight the remarkable efficacy of PINNs in solution reconstruction and coefficient identification while revealing that observational noise exerts a more pronounced influence on accuracy compared to boundary perturbations. Our research offers new insights into simulating dynamics and discovering parameters of nonlinear chiral systems with deep learning. Full article

Ultraviolet (UV) radiation can be used to inactivate microorganisms, with upper-room UV germicidal irradiation (UR-UVGI) representing a promising approach. This study investigated the inactivation of the airborne surrogate virus Phi6 by a UR-UVGI system based on light-emitting diodes (LEDs) in a realistic test setup. Two test scenarios were used, one with continuous Phi6 release, simulating a source located in the room and leading to a dynamic equilibrium, and the second simulating a situation in which the source has left the room and an exponential decay is evaluated. The “Incremental Evaluation Model” was adapted and used to evaluate the dynamic equilibrium measurement. At a position in the breathing direction 5 m away from the Phi6 source, the loss coefficient (air exchange rate) was 25 h⁻¹ in the first scenario and 30 h⁻¹ in the second. These results show that UR-UVGI systems can effectively inactivate microorganisms. However, at 1 m distance from the Phi6 source perpendicular to the breathing direction, only minimal inactivation was observed due to short-circuit airflow. At this position, the loss coefficient was <2 h⁻¹ in the first scenario and 17 h⁻¹ in the second scenario, indicating that short-circuit airflows can only be detected by dynamic equilibrium measurements. Full article

The attenuation of bolt preload is a critical factor leading to bolt fatigue failure, whereas the study of the nonlinear attenuation behavior of preload and its mechanism during installation is an inevitable challenge in engineering practice. The attenuation of the preload of a bolt is mainly related to the stiffness of the bolt body as well as the stiffness of the connected parts. This study aimed to develop an experimental system to analyze the nonlinear attenuation behavior of preload during bolt tightening. First, a simulation system replicating the bolt installation process was constructed in a laboratory setting, incorporating blade and pitch bearing specimens identical to those used in a 10 MW wind turbine, restoring the stiffness coupling characteristics of the “composite-metal bearing” heterogeneous interface at the blade root through a 1:1 full-scale simulation system for the first time. Second, ultrasonic preload measurement equipment was employed to monitor preload variations during the bolt tightening process. Finally, the instantaneous preload decay rate of the wind turbine blade-root bolts and the over-draw coefficient were quantified. Experiments have shown that the preload decay rate of commonly used M36 leaf root bolts is 11–16%. If a more precise value is required, each bolt needs to be calibrated. These findings provide valuable insights for optimizing bolt installation procedures, enabling precise preload control to mitigate fatigue failures caused by abnormal preload attenuation. Full article

Background/Objectives: This research presents a novel analytical method for breast tumor characterization and tissue classification by leveraging intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) combined with hyperspectral imaging techniques and deep learning. Traditionally, dynamic contrast-enhanced MRI (DCE-MRI) is employed for breast tumor diagnosis, but it involves gadolinium-based contrast agents, which carry potential health risks. IVIM imaging extends conventional diffusion-weighted imaging (DWI) by explicitly separating the signal decay into components representing true molecular diffusion ($D$) and microcirculation of capillary blood (pseudo-diffusion or $D*$). This separation allows for a more comprehensive, non-invasive assessment of tissue characteristics without the need for contrast agents, thereby offering a safer alternative for breast cancer diagnosis. The primary purpose of this study was to evaluate different methods for breast tumor characterization using IVIM-DWI data treated as hyperspectral image stacks. Dice similarity coefficients and Jaccard indices were specifically used to evaluate the spatial segmentation accuracy of tumor boundaries, confirmed by experienced physicians on dynamic contrast-enhanced MRI (DCE-MRI), emphasizing detailed tumor characterization rather than binary diagnosis of cancer. Methods: The data source for this study consisted of breast MRI scans obtained from 22 patients diagnosed with mass-type breast cancer, resulting in 22 distinct mass tumor cases analyzed. MR images were acquired using a 3T MRI system (Discovery MR750 3.0 Tesla, GE Healthcare, Chicago, IL, USA) with axial IVIM sequences and a bipolar pulsed gradient spin echo sequence. Multiple b-values ranging from 0 to 2500 s/mm² were utilized, specifically thirteen original b-values (0, 15, 30, 45, 60, 100, 200, 400, 600, 1000, 1500, 2000, and 2500 s/mm²), with the last four b-value images replicated once for a total of 17 bands used in the analysis. The methodology involved several steps: acquisition of multi-b-value IVIM-DWI images, image pre-processing, including correction for motion and intensity inhomogeneity, treating the multi-b-value data as hyperspectral image stacks, applying hyperspectral techniques like band expansion, and evaluating three tumor detection methods: kernel-based constrained energy minimization (KCEM), iterative KCEM (I-KCEM), and deep neural networks (DNNs). The comparisons were assessed by evaluating the similarity of the detection results from each method to ground truth tumor areas, which were manually drawn on DCE-MRI images and confirmed by experienced physicians. Similarity was quantitatively measured using the Dice similarity coefficient and the Jaccard index. Additionally, the performance of the detectors was evaluated using 3D-ROC analysis and its derived criteria (AUC_OD, AUC_TD, AUC_BS, AUC_TD−BS, AUC_ODP, AUC_SNPR). Results: The findings objectively demonstrated that the DNN method achieved superior performance in breast tumor detection compared to KCEM and I-KCEM. Specifically, the DNN yielded a Dice similarity coefficient of 86.56% and a Jaccard index of 76.30%, whereas KCEM achieved 78.49% (Dice) and 64.60% (Jaccard), and I-KCEM achieved 78.55% (Dice) and 61.37% (Jaccard). Evaluation using 3D-ROC analysis also indicated that the DNN was the best detector based on metrics like target detection rate and overall effectiveness. The DNN model further exhibited the capability to identify tumor heterogeneity, differentiating high- and low-cellularity regions. Quantitative parameters, including apparent diffusion coefficient ($ADC$), pure diffusion coefficient ($D$), pseudo-diffusion coefficient ($D*$), and perfusion fraction ($PF$), were calculated and analyzed, providing insights into the diffusion characteristics of different breast tissues. Analysis of signal intensity decay curves generated from these parameters further illustrated distinct diffusion patterns and confirmed that high cellularity tumor regions showed greater water molecule confinement compared to low cellularity regions. Conclusions: This study highlights the potential of combining IVIM-DWI, hyperspectral imaging techniques, and deep learning as a robust, safe, and effective non-invasive diagnostic tool for breast cancer, offering a valuable alternative to contrast-enhanced methods by providing detailed information about tissue microstructure and heterogeneity without the need for contrast agents. Full article

La-doped Li_1.2Ni_0.13Mn_0.54Co_0.13O₂ cathode materials were successfully synthesized by the sol-gel method. The structure, morphology, element valence states, cyclic voltammetry, and cyclic properties were characterized to investigate the properties of the synthesized materials. The as-prepared La-doped Li_1.2Ni_0.13Mn_0.54Co_0.13O₂ materials exhibit well the crystalline hexagonal layered structures with lamellar-like particles featuring a rough surface. The optimal sample, designated as LLRMO-2 with 1/100 La³⁺ doping, delivers an impressive discharge capacity of 271.2 mAh g⁻¹ with a capacity retention of 87.8% after 100 cycles at the current density of 100 mA g⁻¹ compared with that of 203.5 mAh g⁻¹ with only 110.6 mAh g⁻¹ after 100 cycles for the pristine sample. Furthermore, the LLRMO-2 cathode exhibits a superior rate capability compared to the pristine sample and shows excellent cyclic performances with the capacity retention of 48.1% after 400 cycles. The voltage decay per cycle is only 1.60 mV, which is less than 3.70 mV of the pristine one. The enhanced capacity, rate capability, and cyclic performance observed in the La-doped Li-rich layered cathode can be attributed to the improved structural stability as well as the higher diffusion coefficient of lithium ions. These results suggest that the strategy of introducing La³⁺ into the transition metal slabs is an efficient approach for boosting electrochemical performances of Li-rich Mn-based cathode materials via enhancing structural stability. Full article

A cable accessory is a critical component in constructing high-voltage direct current (HVDC) power grids, and it is typically composed of multiple materials. Due to the discontinuity of the insulation medium, it is prone to failure. This study focuses on a two-layered composite insulation medium simplified from HVDC cable accessories, and its surface potential decay (SPD) characteristics are related to the space charge transport characteristics. Previous studies on surface charge migration have been limited and primarily focused on single-layered insulation materials. However, the actual insulation structure is mostly composite. Therefore, it is of great practical significance to explore the surface charge migration characteristics of two-layered structures. This study presents a bipolar charge transport model after pre-depositing surface charges to investigate the surface charge migration characteristics of an ethylene–propylene–diene monomer (EPDM)/polyethylene (PE) two-layered polymer film. The effects of charge injection and trap related to nano-doping, local defects, and thermal aging on the surface potential decay (SPD) and space charge distribution in EPDM/PE were analyzed. The results show that the increase in the electron injection barrier slows surface charge dissipation and inhibits charge accumulation at the interface. An increase in the trapping coefficient leads to a higher surface potential in the stable state and a greater space charge density. During the early depolarization stage, the SPD rate is weakly dependent on the trap depth, with charge migration primarily governed by the external electric field. Full article

Sodium hypochlorite is a commonly used disinfectant in reclaimed water, and the decay law of its free chlorine directly affects the disinfection effect and the safety of reclaimed water. Currently, most of the decay studies have been carried out on the temperature, pH value, and concentration of organic matter in water, without fully considering the differences between fresh and corroded pipeline materials and their effects. This study aims to compare the influences of fresh and corroded carbon steels on the decay law of sodium hypochlorite through dynamic reaction devices and static flask experiments, based on simulations using uncorroded and pre-corroded carbon steel hanging plates. The effects of Fe⁰ and corrosion products on sodium hypochlorite decay are investigated to provide data support for disinfection strategies in reclaimed water distribution networks. By integrating DPD spectrophotometry, ATP detection, XRD analysis, and corrosion weight loss analysis, the microbial control efficacy and corrosion of sodium hypochlorite under the effects of fresh and corroded carbon steels are compared. The differences in decay kinetics are quantified using the restricted first-order decay model, and the dominant mechanisms of sodium hypochlorite consumption that cause differences in the effectiveness of the action are explored. The influences of fresh and corroded carbon steels on the decay are evaluated. Additionally, the percentages of consumption are also analyzed. The results show that in order to effectively kill microorganisms while reducing corrosion, it is recommended to add sodium hypochlorite under simulated conditions for fresh and corroded carbon steels to achieve free chlorine concentrations of 5 mg/L and 9 mg/L in the water, respectively. The effective control time of sodium chlorate on microorganisms in the bulk of the water under the fresh carbon steel conditions can be maintained for up to 48 h. However, under the corroded carbon steel conditions, the activity of microorganisms in the bulk of the water is relatively high, with an effective action time of only 8 h. The decay coefficient of sodium chlorate under the corroded carbon steel conditions is 2.61~6.94 times that of the fresh carbon steel. The additional average consumption of sodium hypochlorite under the corroded carbon steel conditions is 13.91~26.57% compared to the fresh carbon steel. Both Fe⁰ and corrosion products accelerate the decay of sodium hypochlorite in the initial stage, with an average consumption increase rate of 18.9% for Fe⁰ and 17.4% for corrosion products. The bulk decay coefficient is 0.073 h⁻¹, and the wall decay coefficient represented by Fe⁰ is 0.204 h⁻¹, which is higher than the wall decay coefficient represented by corrosion products, which is 0.077 h⁻¹. Full article

The kinetics of Br-atom reactions with C₂H₄S and ClNO were studied as a function of temperature at a total pressure of 2 Torr of helium using a discharge–flow system combined with mass spectrometry: Br + C₂H₄S → SBr + C₂H₄ (1) and Br + ClNO →BrCl + NO (2). The rate constant of reaction (1) was determined at T = 340–920 K by absolute measurements under pseudo-first-order conditions, either by monitoring the kinetics of Br-atom or C₂H₄S consumption in excess of C₂H₄S or of Br atoms, respectively, and by using a relative rate method: k₁ = (6.6 ± 0.7) × 10⁻¹¹ exp(−(2946 ± 60)/T) cm³molecule⁻¹s⁻¹ (where the uncertainties represent the precision at the 2σ level, the estimated total uncertainty on k₁ being 15% at all temperatures). The rate coefficient of reaction (2), determined either from the kinetics of the formation of the reaction product, BrCl, or from the decays of Br-atoms in an excess of ClNO, showed non-Arrhenius behavior, being practically independent of temperature below 400 K and increasing significantly at temperatures above 500 K. The measured rate constant is well reproduced by a sum of two exponential functions: k₂ = 1.2 × 10⁻¹¹ exp(−19/T) + 8.0 × 10⁻¹¹ exp(−1734/T) cm³ molecule⁻¹ s⁻¹ (with an estimated overall temperature-independent uncertainty of 15%) at T = 225–960 K. Full article