Search Results (196)

Data acquired from a processing system using industrial-scale electrical resistance tomography (ERT) could provide valuable information on the operational performance of hydrocyclones. Tomography images of hydrocyclones, in general, are used to analyze operational parameters, but their analysis may not be fast enough to capture transient changes or provide clear phase boundaries between the object of interest and the medium. In such cases, one of the alternative approaches is to utilize least-squares modeling of the raw data to interpret transient changes, which is relatively faster and more efficient. In hindsight, this method may not be able to identify the location of the object of interest. In this paper, a new data analysis approach to estimate transient changes in the disturbance and a simplified conductivity matrix to estimate the location of the disturbance are considered. The conductivities measured across a cross-section were used to calculate the size of the disturbance. The disturbance’s position with respect to the cross-section was estimated using a simplified reconstruction of the conductivity matrix. In both cases, the same conductivity matrix was used. Several fundamental ERT experiments with different disturbance sizes were carried out to establish a suitable algorithm that could identify the disturbance. The analysis method presented in this paper can provide a basis to further explore an additional approach to analyze the performance of the hydrocyclone. The estimated radius of the disturbance was overlaid on an actual cross-section to infer the position with respect to the cross-section of the system. An attempt was also made to develop an empirical relationship that can estimate the effective size of the disturbance. The paper also discusses some implementation and practical challenges that need to be addressed for us to gain confidence in the proposed analysis method. Full article

Screw compressors are critical equipment in oil and gas production and transportation, where efficiency losses caused by rotor geometry, inlet pressure pulsations, and harsh climatic conditions can accumulate into substantial annual energy penalties and reliability degradation. This study provides a quantitative assessment of these coupled effects within a unified multiphysics framework that combines time-accurate transient CFD simulations based on a fixed Cartesian immersed-boundary formulation with a climate-calibrated offline physics-based digital twin—functioning as a digital shadow with one-way data flow from archival SCADA records—a reduced-order seasonal model with no real-time updating, calibrated against a full calendar year of SCADA records and validated against a held-out cold-season dataset (October–December 2022, T_amb = −15 to +8 °C); summer-period predictions rely on calibrated extrapolation beyond the validation window—an integration not previously demonstrated for oil-flooded screw compressors. Two rotor profile configurations (Type A and Type B) were analyzed to quantify geometry-driven differences in static pressure distribution, leakage tendency, and pulsation sensitivity. Transient suction conditions were modeled using harmonic and quasi-random inlet pressure disturbances to evaluate pressure amplification, phase lag, leakage intensification, and efficiency degradation. Seasonal performance was assessed by integrating temperature-dependent gas properties, oil viscosity behavior, and external heat transfer into an annual climatic load framework. The results show that inlet oscillations are amplified inside the chambers (pressure amplification factor П_p ≈ 1.95; П_p up to 2.3 under quasi-random excitation), reducing mass flow and volumetric efficiency by 8–10% and decreasing polytropic efficiency from 0.78 to 0.69–0.71, while increasing leakage by up to 27% and raising peak contact pressures to 167–171 MPa. Seasonal variability (+30 to −30 °C) increased suction density by 38% but raised drive power by ~9% due to viscosity-driven mechanical losses, producing an energy penalty up to 10.8% and an estimated annual additional consumption of approximately 186 MWh per compressor, decomposed as: cold-season contribution ~113 MWh (±10 MWh, directly field-validated against October–December 2022 SCADA data) and summer-season contribution ~51 MWh (calibrated extrapolation; additional uncertainty unquantified and not included in the ±10 MWh bound). The full annual figure of 186 MWh should be interpreted as a model-based estimate rather than a fully validated result. These findings demonstrate that rotor design optimization and mitigation of nonstationary suction effects, coupled with climate-aware offline physics-based digital shadow operation, represent high-priority levers for improving efficiency and reducing energy penalties in field conditions; reliability implications require further validation against summer-season field measurements. Full article

Accurate and physically consistent state prediction is essential for shipboard power systems (SPS) operating under dynamic conditions. However, purely data-driven models often exhibit degraded robustness and physically inconsistent outputs when exposed to transient disturbances or limited data coverage. To address these limitations, this paper proposes a physics-constrained hybrid prediction model that integrates a convolutional neural network–bidirectional long short-term memory (CNN–BiLSTM) architecture with wide residual connections (WRC) and a physics-constrained loss (PCL). The proposed modeling approach combines real operational measurement data with high-resolution simulation data to enhance data diversity and improve generalization capability. The CNN–BiLSTM structure captures nonlinear temporal dependencies, while the WRC preserves critical low-level transient electrical features during deep temporal modeling. In addition, multiple physical constraints, including power balance, voltage conversion relationships, and battery state-of-charge (SOC) dynamics, are incorporated into the training process to enforce physically consistent predictions. The model is validated using charging and discharging experiments on a laboratory-scale SPS under both steady-state and transient conditions. Comparative results demonstrate that the proposed approach achieves higher prediction accuracy, improved dynamic stability, and faster recovery following disturbances compared with conventional data-driven models. These results indicate that physics-constrained deep learning provides an effective and interpretable modeling framework for SPS state prediction, supporting digital twin-oriented monitoring and real-time prediction applications. Full article

Background/Objectives: C1q/TNF-related protein 3 (CTRP3), progranulin (PGRN), and chemerin are adipokines that participate in systemic inflammation. This study systematically examined adipokine levels in cystic fibrosis patients of different ages to evaluate their role in inflammatory, metabolic, and hepatic processes. Thirty-seven pediatric and thirty-three adult CF patients were enrolled to assess the potential of these adipokines as biomarkers. Methods: Anthropometric and physiological data, pulmonary function (forced expiratory volume, FEV1; vital capacity, VC), and liver fibrosis score FIB-4 were assessed. Liver stiffness was measured by transient elastography. Serum samples from 40 healthy adult volunteers served as the control group. Serum concentrations of chemerin, CTRP3, and PGRN were quantified by enzyme-linked immunosorbent assay (ELISA). Results: Compared with healthy controls, adults with CF had markedly lower circulating CTRP3 levels, whereas PGRN concentrations were significantly higher. Among CF patients, both CTRP3 and PGRN were higher in the pediatric group than in adults, while chemerin did not vary with age. The presence of cystic fibrosis-related liver disease (CFLD) did not significantly alter adipokine levels relative to CF patients without liver disease. Receiver operator characteristic (ROC) analysis showed that circulating PGRN could reliably differentiate CF patients from controls; none of the three adipokines predicted the presence of CFLD. CTRP3 and PGRN were inversely correlated with age, BMI, and pulmonary function. Conclusions: Overall, our data support systemic PGRN as a potential biomarker for CF and indicate an age-dependent regulation of circulating CTRP3 and PGRN. Both proteins were inversely associated with BMI, inflammatory markers, liver fibrosis, and pulmonary capacity. Full article

Accurate prediction of distribution network operating states is essential for implementing proactive fault warning systems. However, with the high penetration of distributed energy resources, measurement data exhibit strong nonlinearity and multi-scale temporal characteristics, posing significant challenges to existing prediction methods. Current mainstream approaches face a critical dilemma: traditional recurrent neural network (RNN) models (e.g., LSTM) suffer from vanishing gradients and memory bottlenecks in long-sequence forecasting, making it difficult to capture long-term evolutionary trends. In contrast, while standard Transformer models excel at global modeling, their smoothing effect renders them insensitive to subtle transient abrupt changes such as voltage sags, and they incur high computational complexity. To address the dual challenges of “difficulty in capturing transient abrupt changes” and “inability to simultaneously handle long-term trends,” this paper proposes a fault precursor trend prediction model that integrates Extended Long Short-Term Memory (XLSTM) with Informer, termed XLSTM-Informer. To tackle the challenge of extracting transient features, an XLSTM-based local encoder is constructed. By replacing the conventional Sigmoid activation with an improved exponential gating mechanism, the model achieves significantly enhanced sensitivity to instantaneous fluctuations in voltage and current. Additionally, a matrix memory structure is introduced to effectively mitigate information forgetting issues during long-sequence training. To overcome the challenge of modeling long-term dependencies, Informer is employed as the global decoder. Leveraging its ProbSparse sparse self-attention mechanism, the model substantially reduces computational complexity while accurately capturing long-range temporal dependencies. Experimental results on a real-world distribution network dataset demonstrate that the proposed model achieves substantially lower Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE) compared to standalone CNN, LSTM, and other baseline models, as well as conventional LSTM–Informer hybrid approaches. Particularly under extreme operating conditions—such as sustained high summer loads and winter heating peak loads—the model successfully overcomes the trade-off limitations of traditional methods, enabling simultaneous and accurate prediction of both local precursors and global trends. This provides a reliable technical foundation for proactive warning systems in distribution networks. Full article

The expansion of the invasive species Pedicularis kansuensis threatens the ecological integrity of alpine wetlands, particularly in the Bayinbuluke, northwestern China. However, operational monitoring remains challenging. Conventional indices often lack specificity in heterogeneous alpine backgrounds, while deep learning models are typically too data-intensive to support consistent, multi-year mapping. To develop a rapid, reliable, and operational method for monitoring this invader, we proposed a novel, species-specific spectral index, the Pedicularis kansuensis Index (PKI), using the blue, green, and red-edge bands of high-resolution (3 m) PlanetScope imagery. The PKI constructs a robust target signal by integrating distinct spectral features derived from in situ hyperspectral measurement with a grayscale morphological opening (GrMO) refinement to suppress background noise. A comprehensive validation against seven established benchmarks indices (e.g., NDVI, RI, and ARI) demonstrated the superior performance of PKI across the central alpine wetlands of Bayinbuluke (2841 km²). It achieved the highest separability with an M-statistic of 1.36. Furthermore, the index attained an overall accuracy of 93.52% (95% CI: 92.3–94.7%), and an F1-score of 93.28% (95% CI: 92.0–94.5%), effectively minimizing confusion with co-occurring native vegetation and background. Applying this framework to a five-year time series (2021–2025) revealed a distinct cycle of outbreaks and relaxation. Specifically, the invaded area increased to 2168 ha in 2022, then decreased to 160 ha in 2025. Spatial analysis further identified stable invasion hotspots of 161.6 ha, highlighting key targets for long-term containment. Meanwhile, 94.4% of the invaded area was transient, lasting only one year (4824.7 ha). These results confirm that the PKI is a physically interpretable, accurate, and computationally efficient tool for monitoring invasive species in heterogeneous alpine environments. It facilitates timely and targeted ecosystem management. Full article

Background & Aims: The accurate, noninvasive assessment of hepatic steatosis is essential in living liver donor evaluation, where disease prevalence is low, and donor safety is paramount. This study evaluated commonly used noninvasive diagnostic tools for detecting hepatic steatosis in a real-world donor screening setting. Methods: We analyzed 108 living liver donor candidates (18–53 years) with complete MRI, CT, transient elastography (FibroScan^®), and biochemical data obtained during routine donor evaluation. Hepatic steatosis was defined as an MRI-proton density fat fraction (PDFF) ≥5%, which served as the noninvasive reference standard. Diagnostic performance metrics, receiver operating characteristic (ROC) analyses, and correlations with serum fibrosis indices (FIB-4 and APRI) were assessed. Results: MRI-PDFF identified hepatic steatosis in 21 donors (19.4%). Controlled attenuation parameter (CAP), measured by transient elastography, demonstrated high sensitivity (90.5%) and negative predictive value (97.1%), supporting its role as a rule-out screening tool. CT showed excellent specificity (97.7%) but lower sensitivity (61.9%), consistent with a confirmatory role when MRI is unavailable. Serum fibrosis indices were generally low and did not correlate strongly with imaging-based steatosis. Conclusions: In the low-prevalence setting of living liver donor evaluation, CAP-based transient elastography provides effective noninvasive screening for hepatic steatosis, while MRI-PDFF serves as a confirmatory reference when indicated. These findings support a stepwise, clinically practical diagnostic approach that prioritizes donor safety and workflow efficiency. Full article

The roof aquifer of the Jurassic coal seam is the primary source of water inrush in the Nalinhe Mining Area. It poses a severe threat to safe mining operations. Accurate prediction of its water richness is crucial for production safety. This study focuses on the Nalinhe No. 2 Coal Mine. Seven key controlling factors were selected as evaluation indicators, including aquifer thickness, burial depth, core recovery rate, the thickness ratio of brittle to plastic rock, fault scale density, fault fractal dimension, and the density of fault endpoints and intersections. A hybrid weighting strategy was applied in this study. This strategy integrates the Analytic Hierarchy Process (AHP) and the Entropy Weight Method (EWM) to assign scientific weights to the evaluation indices. A water richness evaluation model was subsequently developed based on matter-element extension theory. The model calculates the comprehensive correlation degree for each grid node and determines the corresponding water richness level. Zoning results were validated with unit inflow data from pumping test boreholes, mine inflow observations, and ground transient electromagnetic survey findings. The predicted water richness zones closely matched the measured hydrogeological data. These results demonstrate the scientific rigor and reliability of the matter-element extension model. The proposed model provides a novel approach for assessing water richness in coal seam roof aquifers. Full article

This paper presents a signal-processing methodology for assessing thermal stress and fatigue damage in IGBT modules. This study utilizes junction temperature data from operational frequency converters at a belt conveyor station rather than conventional approaches. These in situ measurements ensure that thermal profiles accurately reflect actual loading conditions. A reliability framework based on mission profiles assesses the contribution of each operational regime. We examine transient overloads, steady-state operation, and periods of low load specifically. We apply Miner’s rule and rainflow counts to the analyzed temperature profiles. This enables the assessment of accumulated damage in each operational segment. The primary finding indicates that a minimal duration of operational time constitutes the majority of total lifetime utilization. This disproportionate impact is attributable to transient overloads. This study quantitatively evaluates this phenomenon using Rainflow analysis to disaggregate mission profiles. The proposed framework enhances the precision of reliability engineering. It provides a valuable foundation for enhancing maintenance planning and control strategies in practical scenarios. Full article

Small-scale concentrated solar power (CSP) systems coupled with micro gas turbines (MGTs) offer a promising solution for decentralised and sustainable power generation. However, CSP–MGT systems are subject to pronounced transient behaviour during start-up and operation due to fluctuating solar irradiance, making accurate transient modelling essential. This work introduces a fully coupled transient electro-thermo-mechanical model of a CSP-driven micro gas turbine, explicitly linking thermal transients and heat soakage effects to electrical performance during start-up. Unlike existing models, the proposed approach captures the interaction between turbomachinery thermal inertia, shaft dynamics, and detailed electrical machine and power converter losses under real-world transient operating conditions. The model integrates thermodynamic, mechanical, electrical, and control subsystems within a unified framework using a lumped-volume formulation suitable for real-time-capable simulations. To improve prediction accuracy at low rotational speeds, a dedicated interpolation strategy for turbomachinery performance maps is implemented. The model is validated at both component and system levels using experimental data from a 6 kWe CSP–MGT test facility. The results show good agreement with measurements, with maximum deviations of approximately 8% in receiver outlet temperature and less than 6% in air mass flow rate. The findings demonstrate that accounting for heat soakage is critical for a realistic prediction of thermal and electrical transients, as neglecting thermal inertia leads to an underestimation of the start-up electrical energy consumption by up to 140%, highlighting the dominant role of thermal mass effects in small-scale micro gas turbines compared to larger systems. The proposed model provides a robust tool for analysing start-up behaviour and supports improved control and operational strategy development for CSP–MGT systems under variable solar conditions. Full article

This study quantifies the effect of air and fuel filter restriction on fuel consumption, regulated pollutants (CO and HC), and CO₂ greenhouse gas emissions under real driving conditions in a hilly high-altitude environment. Four filter configurations were evaluated: clean air filter–clean fuel filter (CAF–CFF, reference), dirty air filter–clean fuel filter (DAF–CFF), clean air filter–dirty fuel filter (CAF–DFF), and dirty air filter–dirty fuel filter (DAF–DFF). Each test was repeated three times over the same RDE route in Quito (≈2100–2900 m). Fuel consumption was estimated from ECU-based signals, and CO₂ emission factors and regulated pollutant (CO and HC) emission factors were computed from measured exhaust concentrations and distance normalization. Results were analyzed by RDE section (urban, rural, motorway) and expressed as percent changes relative to the reference configuration to directly isolate filter restriction effects. Relative to CAF–CFF, DAF–CFF produced the largest increase in average fuel consumption (+7.2%) and the largest urban CO₂ penalty (+22.7%), indicating a strong efficiency sensitivity to intake restriction under transient operation. CAF–DFF increased average fuel consumption by 6% and produced the strongest motorway penalties for CO (+77.3%) and HC (+44.4%), suggesting that fuel delivery restriction has a stronger influence on incomplete oxidation products under sustained higher load. The combined restriction (DAF–DFF) showed non-additive responses depending on the operating regime. Random Forest models were trained to estimate CO₂, CO, and HC, achieving R² values of 0.8571, 0.8229, and 0.7690, respectively, while multiple linear regression achieved an R² of 0.852 for fuel consumption. The proposed approach supports data-driven monitoring of filter restriction effects under real driving operation, while acknowledging that fuel consumption and CO₂ are obtained through different measurement and conversion paths and may not yield identical percent changes. Full article

Background: Anxiety symptoms among medical students often emerge at the intersection of sustained academic pressure, anticipatory uncertainty and early professional socialisation, complicating their distinction from transient stress responses. Instruments employed in this context are therefore expected to operate consistently across subgroups while preserving conceptual clarity under non-clinical conditions. The Generalized Anxiety Disorder scale (GAD-7), widely adopted as a brief screening measure, has shown variable factorial behaviour across populations, particularly when applied to student cohorts. Materials and methods: Using confirmatory factor analysis with robust weighted least squares estimation, the latent structure of a culturally adapted Spanish version of the GAD-7 was examined in a sample of medical students enrolled across all academic years at a public university. Model performance was evaluated through multiple fit indices suited for ordinal data, alongside estimates of convergent validity based on average variance extracted and reliability assessed via both Cronbach’s α and McDonald’s ω. Measurement invariance across sex was explored through a sequence of increasingly constrained multi-group models. Results: The unidimensional configuration originally proposed for the scale remained statistically coherent, despite minor tensions between absolute and incremental fit indicators commonly reported in comparable university-based samples. Convergent validity estimates suggested that the latent construct accounted for a substantial proportion of item variance, while reliability coefficients fell within the upper range observed internationally. Invariance testing supported comparability at the configurational and scalar levels, although full metric equivalence was less stable. Conclusions: Rather than resolving ongoing debates regarding the internal structure of the GAD-7, these findings situate its psychometric behaviour within the specific demands of medical education, where anxiety-related symptoms may fluctuate between normative adaptation and clinically relevant distress. This positioning invites further examination of how screening instruments perform when anxiety is shaped as much by institutional context as by individual psychopathology. Full article

The increasing integration of electric vehicles (EVs) and renewable energy sources has accelerated the adoption of DC microgrids, where maintaining voltage stability and effective power sharing remains a critical challenge. Hybrid energy storage systems (HESS), combining batteries and supercapacitors, are commonly employed to address dynamic power variations. However, conventional proportional–integral (PI)-based control strategies for HESS can exhibit performance limitations under nonlinear and varying operating conditions. To overcome this drawback, this paper presents an adaptive neuro-fuzzy inference system (ANFIS)-based control strategy for HESS located in a DC microgrid, with comparative evaluation against both conventional PI and traditional Fuzzy Logic controller (FLC) schemes. The proposed approach is evaluated using a detailed MATLAB/Simulink R2024a model of a DC microgrid including EVs. Simulation results show that, under normal operating conditions, the ANFIS-based control demonstrates improved transient response, reduced voltage fluctuations, and effective coordination between the battery and supercapacitor during renewable power variations, compared to PI and FLC-controlled systems. In addition to nominal performance assessment, this work investigates the vulnerability of the ANFIS controller to cyber-attacks. Two representative attack scenarios, false data injection (FDI) and denial-of-service (DoS), are applied to critical measurement and control signals of HESS. Simulation results reveal that, although the DC-bus voltage regulation is largely maintained during attack intervals, cyber manipulation significantly disrupts the intended HESS power-sharing behavior. Full article

In this study, software-based, measurement-driven automation and control architecture is developed for magnetic refrigeration systems. The proposed structure integrates real-time measurement data obtained from magnetic, hydraulic, and thermal sub-processes within a single decision layer. Control actions are generated based on cycle-level performance feedback. Instead of directly regulating absolute performance values, the control logic relies on performance trends between successive cycles as the primary decision variable. The method is experimentally implemented on a reciprocating magnetic refrigerator prototype. The system is first operated with fixed parameters, after which cycle-level adaptation is activated using measurement-based decisions. Experimental results show that adaptive control drives the system toward a stable and high-performance regime following a short transient phase. The average coefficient of performance (COP) increases from approximately 0.21 under manual operation to about 1.20 in adaptive operation, while cycle-to-cycle fluctuations are significantly reduced. The results indicate that operation based on fixed timing and preset parameters is insufficient for magnetic refrigeration systems. In contrast, software-based control using cyclic feedback shifts the system to a more stable and efficient regime. The proposed architecture provides a high-level control framework with low hardware dependency and can be adapted to different magnetic refrigeration configurations. Full article

A vibration energy harvester (VEH) based on the principle of variable magnetic reluctance has been developed to enable wireless and maintenance-free power supply for condition monitoring sensors in vibrating machinery. Conventional battery or wired solutions are often impractical due to limited lifetime and high installation costs, motivating the use of vibration-based energy harvesting. The proposed VEH converts mechanical vibrations into electrical energy through the relative motion of a movable ferromagnetic core within a magnetic circuit. Unlike conventional VEH designs, where the magnet is the moving element, this concept utilizes a movable ferromagnetic core in combination with a stationary pole piece for voltage induction. This configuration enables a compact and easily adjustable proof mass, as neither the coil nor the magnet needs to be moved. The VEH is designed to operate effectively under excitation frequencies between $16 \mathrm{H}\mathrm{z}$ and $50 \mathrm{H}\mathrm{z}$ and acceleration levels from $9.81 {\displaystyle \frac{\mathrm{m}}{{\mathrm{s}}^2}}$ (equivalent to $1 \mathrm{g}$) up to $98.1 {\displaystyle \frac{\mathrm{m}}{{\mathrm{s}}^2}}$ (equivalent to $10 \mathrm{g}$). To ensure a reliable power supply, the VEH must deliver a minimum electrical output of $0.1 \mathrm{m}\mathrm{W}$ at the lowest excitation ($1 \mathrm{g}$) while maintaining structural integrity. Additionally, the maximum permissible displacement amplitude of the movable core is limited to $1.15 \mathrm{m}\mathrm{m}$ to avoid mechanical damage and ensure durability over long-term operation. Coupled magnetic-transient and mechanical finite element method (FEM) simulations were conducted to analyze the system’s dynamic behavior and electrical power output across varying excitation frequencies and accelerations. A laboratory prototype was developed and tested under controlled vibration conditions to validate the simulation results. The experimental measurements confirm that the VEH achieves an electrical output of $0.166 \mathrm{m}\mathrm{W}$ at $9.81 {\displaystyle \frac{\mathrm{m}}{{\mathrm{s}}^2}}$ and $16 \mathrm{H}\mathrm{z}$, while maintaining the maximum allowable displacement amplitude of $1.15 \mathrm{m}\mathrm{m}$, even at $98.1 {\displaystyle \frac{\mathrm{m}}{{\mathrm{s}}^2}}$ ($10 \mathrm{g}$) and $50 \mathrm{H}\mathrm{z}$. The strong agreement between simulation and experimental data demonstrates the reliability of the coupled FEM approach. Overall, the proposed VEH design meets the defined performance targets and provides a robust solution for powering wireless sensor systems under a wide range of vibration conditions. Full article