Search Results (923)

Measure-While-Drilling (MWD) data provide real-time insight into subsurface conditions and drilling performance, yet their complexity and operational noise often hinder reliable modeling. This study demonstrates the role of Exploratory Data Analysis (EDA) in developing robust machine learning (ML) models for lithology classification and penetration rate (PR) prediction in mining operations. A structured EDA workflow—comprising data integrity assessment, feature distribution analysis, correlation mapping, and depth-wise parameter profiling—was implemented to identify redundant attributes, isolate non-productive intervals, and enhance dataset consistency. Through EDA-informed normalization and feature selection, data consistency and model performance were significantly improved. Machine learning algorithms, including Decision Tree, Random Forest, and Multi-Layer Perceptron, were trained on the refined dataset. The Random Forest Classifier achieved 98.45% accuracy in lithology prediction, while the Random Forest Regressor produced the most accurate PR estimation (R² = 0.83, RMSE = 0.52). These results highlight EDA as a critical foundation for constructing physics-informed, data-driven models that enhance predictive reliability and operational efficiency in mining environments. Full article

This paper is devoted to the introduction and systematic study of $(P,Q)$-normal operators in the context of semi-Hilbertian spaces, where P and Q are non-constant complex polynomials in one variable. This class generalizes the well-known notion of polynomially normal operators and offers a natural setting to study their structural properties in spaces endowed with a semi-inner product induced by a positive operator. We establish fundamental properties of $(P,Q)$-normal operators, including conditions for commutativity with respect to the $\mathbf{A}$-adjoint and relations to other classes of $\mathbf{A}$-operators. Several examples are provided to illustrate the theory and demonstrate how $(P,Q)$-normality extends classical concepts in operator theory. Full article

Background: Coronary slow flow (CSF) is a microvascular disorder characterized by delayed perfusion despite the absence of significant epicardial stenosis. Although its exact pathophysiology remains unclear, endothelial dysfunction, oxidative stress, and atherogenic dyslipidemia have been implicated. Traditional lipid parameters may not fully capture the atherogenic burden, whereas atherogenic indices such as the atherogenic index of plasma (AIP), atherogenic coefficient (AC), and Castelli risk indices (CRI-I and CRI-II) may provide better predictive value. This study aimed to investigate the association between atherogenic indices and CSF in a large real-world angiographic cohort. Methods: This retrospective study included 25,486 patients who underwent coronary angiography between September 2020 and June 2024. A total of 464 patients with CSF (diagnosed by TIMI frame count criteria) and 408 controls with normal coronary flow (NCF) were identified. Atherogenic indices, including AIP, AC, CRI-I, CRI-II, and non-HDL cholesterol (non-HDL-C), were calculated. Multivariate logistic regression analysis identified independent predictors of CSF, while receiver operating characteristic (ROC) curve analysis was used to assess the diagnostic performance of each lipid-related parameter. Results: Patients with CSF had significantly higher AIP, AC, non-HDL-C, and CRI indices and lower HDL-C levels compared to controls (all, p < 0.05). Multivariate analysis identified AIP (OR: 1.73, 95% CI: 1.18–2.44, p = 0.004), age (OR: 1.02, 95% CI: 1.01–1.06, p = 0.014) and smoking (OR: 2.22, 95% CI: 1.36–2.84, p = 0.003) as independent predictors of CSF. ROC analysis showed modest but statistically significant discriminatory capacity for AIP (cut-off: 0.50; AUC: 0.629; 95% CI: 0.591–0.667; p < 0.001). AIP also demonstrated a weak yet significant correlation with mean TIMI frame count (rho = 0.245, p < 0.001), suggesting a potential link to microvascular dysfunction. Conclusions: Among the evaluated atherogenic indices, only AIP demonstrated an independent association with CSF. Despite modest discriminative performance that does not support standalone clinical prediction, AIP may reflect an underlying metabolic phenotype associated with CSF and serve as a complementary marker alongside traditional risk assessment. These findings should be interpreted as hypothesis-generating and warrant prospective validation. Full article

Acoustic detection methods offer a non-destructive alternative to manual inspection for identifying insect infestations in stored products, but their performance is compromised by ambient noise in operational environments. This study presents an enhanced detection algorithm for the Acoustic Stored Product Insect Detection System (A-SPIDS) that enables reliable single-insect detection in the presence of strong external noise. The platform’s physical noise isolation achieved an average attenuation of 45 $\mathrm{d}$$\mathrm{B}$ above 2000 $\mathrm{Hz}$. Spectral analysis revealed that insect signals dominate over ambient noise, generating insect-like impulses in the high-frequency band, enabling optimization of the Normalized Signal Pulse Amplitude (NSPA) detection metric to the 1565 $\mathrm{Hz}$ to 6000 $\mathrm{Hz}$ frequency band, resulting in 99.4% detection accuracy at 80 $\mathrm{d}$$\mathrm{B}$A ambient noise levels. The external microphone was leveraged to identify and remove noise-generated impulses from internal piezoelectric sensor recordings, achieving 100% detection with zero false alarms across the recorded dataset featuring species Callosobruchus maculatus, Tribolium confusum, and Tenebrio molitor in oatmeal, rice, wheat, and corn products at noise levels exceeding 100 $\mathrm{d}$$\mathrm{B}$A. Full article

Load forecasting has become a key tool, especially for distribution system operators, to ensure optimal grid management and control. In recent years, attention has shifted toward probabilistic load forecasting (PLF), as it can model forecast uncertainty. Because electricity demand is strongly influenced by time-dependent factors such as seasonal patterns and daily habits, non-parametric PLF methods are particularly suitable because they make no assumptions about the distribution of variables. This study focuses on quantile regression (QR), a widely studied non-parametric PLF technique that models forecast uncertainty by only assuming a linear dependency among variables. It is applied every hour to forecast the daily consumption of three large public buildings—an elderly healthcare center, a biomedical research facility, and a polyclinic—with different demand variability profiles. Forecasts are carried out using real-world consumption data and evaluated considering both univariate and multivariate approaches. The performance of both QR approaches is rigorously evaluated against that of two persistence-based methods through standard evaluation metrics. For the univariate case, two aggregation levels are considered: single buildings and aggregation of buildings. The results confirm the effectiveness of both uQR and mQR, which consistently outperform persistence-based benchmarks. In terms of the pinball loss (PL) function, the QR approaches exhibit values ranging from 1% to 1.8% across all case studies. Both approaches demonstrate reliable and sharp prediction intervals (PIs); for example, for the PI(10–90) using the uQR, the PI coverage probability (PICP) ranges from 0.78 to 0.89 and the PI normalized average width (PINAW) from 0.09 to 0.26. Overall, uQR achieves lower PL, whereas mQR yields slightly better PICP and PINAW results for the building characterized by an irregular and unpredictable consumption profile. Full article

Early detection of fire-related risks in lithium-ion batteries (LIBs) remains a critical challenge, as conventional protection mechanisms typically activate only after irreversible degradation or macroscopic failure occurs. In this study, an innovative sensor-based diagnostic framework is proposed for proactive fire prediction in LIBs by simultaneously monitoring low-frequency and high-frequency electrical signatures generated during battery charge–discharge processes. An electromagnetic (EM) antenna sensor and a high-frequency current transformer (HFCT) sensor were employed to capture complementary voltage- and current-based transient signals associated with internal degradation phenomena. Cell-level experiments were conducted under various C-rates and temperature conditions, including high-stress environments, while module-level validation was performed on a 4-series, 1-parallel (4S1P) configuration at a 2C-rate under ambient temperature. Time–frequency characteristics of the measured signals were systematically evaluated using MATLAB-based continuous wavelet transform (CWT) and discrete wavelet transform (DWT) techniques. The results reveal that degradation-induced transient events exhibit non-stationary, impulsive voltage and current signatures with distinct frequency-band localization, which intensify with increasing C-rate, elevated temperature, and aging progression. At the module level, although signal amplitudes were partially attenuated due to current redistribution, characteristic wavelet energy patterns and time–frequency concentrations remained clearly distinguishable, demonstrating the scalability of the proposed approach. The combined EM antenna–HFCT sensing strategy, together with multi-resolution wavelet analysis, enables effective phenomenological differentiation between normal operational noise and incipient internal fault signatures well before conventional thermal or capacity-based indicators become evident. These findings demonstrate feasibility of the proposed method for early-stage fault diagnosis and highlight its potential applicability to advanced battery management systems for proactive fire prevention in large-scale energy storage and electric vehicle applications. Unlike conventional voltage-, temperature-, or gas-based diagnostics, the proposed approach enables the detection of incipient degradation phenomena at the microsecond scale by exploiting complementary low- and high-frequency electrical signatures. This study provides experimental evidence that wavelet-based EM and HFCT sensing can identify MISC-related precursors significantly earlier than conventional battery management indicators. Full article

Wastewater-based epidemiology (WBE) allows for early surveillance of viral pathogens, including SARS-CoV-2. Simplified low-cost approaches are needed to deploy WBE surveillance in resource-limited small-island settings, where high sensitivity must be maintained. In this study, we optimized key upstream steps in an electronegative membrane virus adsorption–elution (VIRADEL) workflow, including sample acidification, composite sampling duration, and RT-qPCR inhibition mitigation. Wastewater influent was sampled at a pump station in Grenada using 12 h and 24 h time-weighted composite samples, concentrated using electronegative membrane VIRADEL with and without sample acidification (pH 3.5), and used Phi 6 (enveloped virus) and MS2 (non-enveloped virus) bacteriophages as process controls and PMMoV as a fecal-derived normalization target. Targets for SARS-CoV-2 N1 and a non-enveloped virus surrogate were measured by RT-qPCR. Quantitative wastewater data were compared to reported clinical cases in the community. Sample acidification significantly increased recovery of the enveloped process control, Phi 6 (p < 0.01) indicating improved efficiency in capturing enveloped viral targets during filtration. Twelve-hour composite samples had a false-negative percentage of 88%, while 24 h samples had only 6% false negatives and were able to mirror clinical case trends. Wastewater viral signals were detected 3–5 days prior to an increase in clinical cases. Hydraulic travel time within the contributing sewer network was not directly measured; therefore, the reported 3–5 day lead time reflects the combined effect of shedding dynamics, sampling integration, and sewer transport. This optimized workflow was deployed for nine months showing sustained analytical performance and operational feasibility. Full article

To address the practical engineering challenges of limited fault samples for high-voltage circuit breaker spring operating mechanisms and the inability of single features to fully reflect equipment status, this paper proposes a small-sample fault diagnosis method based on multi-source feature fusion and Stacking ensemble learning. First, a multi-source sensing system containing MEMS (Micro-Electro-Mechanical System) pressure and travel, coil, and motor current was constructed to achieve comprehensive monitoring of the mechanical and electrical states of a 220 kV circuit breaker; in particular, the introduction of non-invasive MEMS sensors effectively solves the difficulty of capturing static spring fatigue characteristics inherent in traditional methods. Second, a high-dimensional feature space was constructed using Savitzky–Golay filtering and physical feature extraction techniques. To address the characteristics of small-sample data distribution, a two-layer Stacking ensemble learning model based on 5-fold cross-validation was designed. This model utilizes the SVM (Support Vector Machine), RF (Random Forest), and KNN (K-Nearest Neighbors) as base classifiers and Logistic Regression as the meta-learner, achieving an adaptive fusion of the advantages of heterogeneous algorithms. True-type experimental results show that the average diagnostic accuracy of this method under normal conditions and four typical fault conditions reaches 96.1%, which is superior to single base models (the RF was 94.2%). Feature importance analysis further confirms that closing and opening pressures are the most critical features for distinguishing mechanical faults. This study provides effective theoretical basis and technical support for condition-based maintenance of high-voltage circuit breakers under small-sample 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

A flexible visible/near-infrared (Vis/NIR) sensing system (FVNS) was developed for the non-destructive assessment of almond composition. Almonds from four distinct varieties were measured under non-contact conditions, and the acquired spectra were preprocessed using Savitzky–Golay (S–G) smoothing and standard normal variate (SNV). Based on the spectral data captured by the FVNS, random forest (RF) regression models were established to quantify protein and fat contents. The optimized RF models achieved prediction coefficients of determination (R²_p) of 0.91 for protein and 0.86 for fat, with corresponding residual predictive deviation (RPD) values of 3.32 and 2.67, respectively. These results demonstrate that the FVNS possesses reliable quantitative capability and can accurately capture compositional variations in almonds while maintaining low cost, portability, and real-time wireless operation. Full article

To address the vulnerability of traditional linear frequency-modulated continuous wave (FMCW) fuze to jamming due to fixed modulation parameters, this paper proposes a novel fuze waveform design scheme using chaotic code-based frequency and phase composite modulation along with a Normalized Rate-Invariant Ranging algorithm (NRIR). Leveraging the ergodicity and initial value sensitivity of the Logistic chaotic map, a dual-dimensional composite modulation system is constructed. In the frequency domain, the frequency modulation slope undergoes periodic binary variation according to chaotic states to break the signal periodicity. In the phase domain, phase encoding is implemented based on chaotic binary sequences to further improve waveform entropy and complexity, effectively destabilizing the parameter stability required for coherent jamming. To resolve the distance–Doppler coupling challenges and spectral dispersion issues caused by variable-slope modulation, the NRIR algorithm is developed. By introducing a resampling transformation operator, the non-stationary rate-varying beat frequency signal is mapped to a normalized “constant-slope” space, enabling coherent accumulation and ranging of targets. Using the ambiguity function as an analytical tool, theoretical analyses, simulation experiments, and test results demonstrate that this design scheme exhibits excellent performance in suppressing DRFM jamming and sweep-frequency jamming, providing theoretical support and technical approaches for fuze anti-jamming design. Full article

Disaggregating end-use electricity consumption from aggregate meter data remains a fundamental challenge in non-intrusive load monitoring, particularly in smart buildings where heating, ventilation, and air-conditioning systems dominate demand and direct sub-metering is often unavailable. Contextual variables such as weather and calendar information provide valuable explanatory signals, but in low-frequency settings, these drivers are typically insufficient to fully characterise building operation. As a result, attribution strategies that implicitly assume complete explainability can lead to unstable driver contributions and reduced physical interpretability when building behaviour is non-stationary or partially unobserved. This paper introduces MD-ADD, a multi-driver automatic dependency disaggregation framework designed for low-frequency smart meter data in commercial and public buildings. The framework supports joint attribution of multiple contextual drivers. It explicitly represents unexplained energy as a meaningful component of the decomposition. It combines robust baseline estimation, leakage-resistant out-of-fold contextual modelling, conservative driver attribution without hard mass-balance constraints, and uncertainty quantification using block bootstrap resampling. A consistency mechanism is included to restrict driver attributions to temporal scales compatible with their expected physical influence. The framework is evaluated on the ADRENALIN Load Disaggregation Challenge dataset, which contains multi-resolution electricity and weather data from commercial and public buildings, using normalized mean absolute error alongside stability and residual-structure diagnostics. Rather than optimising solely for pointwise accuracy, the proposed formulation emphasises robustness, interpretability, and diagnostic transparency, making it suitable for decision-support and analytical workflows under realistic low-frequency monitoring conditions. Full article

Background: Intravenous fluids modify acid–base balance by changing plasma strong ion difference (SID_PL) and total non-volatile weak acids. Experimental data suggest that pre-infusion plasma bicarbonate (HCO₃⁻) may further modulate these effects. We tested this hypothesis in a large cohort of postoperative ICU patients receiving intravenous fluids. Methods: We retrospectively analyzed all-consecutive post-operative ICU admissions over a 21-month period who received fluid therapy. Fluid inputs/outputs, plasma electrolytes, and arterial blood gases were collected from admission to 9:00 A.M. of postoperative day one. Average SID of infused fluids (SID_INF) was calculated, and SID_PL and standard base excess variations (ΔSBE) were assessed. Patients were stratified by SID_INF tertiles (low, <41.0 mEq/L; medium, 41.2–54.6 mEq/L; high, ≥55.0 mEq/L), median pre-infusion HCO₃⁻ (24.3 [22.4–26.3] mmol/L), and tertiles of SID_INF-HCO₃⁻ difference. Results: Among 650 admissions, 641 were included (83% elective surgery). Pre-infusion acid–base was, as average, within normal ranges. Total infused volume averaged 2327 ± 1111 mL. Across SID_INF tertiles, ΔSBE increased from 1.2 ± 3.4 to 3.0 ± 3.0 and 3.4 ± 3.0 mmol/L (p < 0.001), paralleled by ΔSID_PL rise (0.6 ± 2.3, 1.3 ± 2.4 and 1.4 ± 2.3 mEq/L, respectively; p = 0.004). For any given SID_INF, patients with lower pre-infusion HCO₃⁻ showed greater ΔSBE and ΔSID_PL (p < 0.001). When analyzed by tertiles of SID_INF-HCO₃⁻ difference, ΔSBE rose from 1.0 ± 3.2 to 2.7 ± 2.9 and 4.0 ± 3.0 mmol/L (p < 0.001), with amplified effects at higher infused volume (>2500 mL). Conclusions: In postoperative ICU patients, fluid-induced acid–base changes are largely driven by SID_INF-HCO₃⁻ difference, supporting individualized fluid selection based on baseline HCO₃⁻. Full article

A significant amount of frictional heat is generated during the braking process of mine-used monorail cranes under heavy-load and low-speed creeping (or reciprocating speed regulation) conditions, causing thermal softening and performance degradation of the brake pads. Thus, investigating the tribological evolution mechanism is necessary to ensure reliable braking in deep underground environments. In this paper, full-scale tribological testing technology is applied to the brake system, and the friction and wear characteristics of copper-based powder metallurgy (P/M) brake pads under complex hygrothermal environments are studied. A physical experimental model coupling normal load, sliding speed, and humidity is established using a custom-designed open-structure reciprocating tester, revealing the “load weakening effect” under dry conditions and the “dual regulation mechanism” of mixed lubrication and cooling flushing under high humidity. Then, a surrogate prediction model of friction coefficient and wear rate, with respect to the operating parameters, is constructed based on Central Composite Design (CCD) and Response Surface Methodology (RSM). The reliability of the model under non-linear working conditions is estimated based on Analysis of Variance (ANOVA) and blind tests. The results indicate that the model possesses high prediction accuracy (relative error < 5%), and the feasibility of utilizing the high-humidity environment to enhance wear resistance and stability is verified. Full article

Decarbonization has emerged as a crucial objective in the optimization of port collection and distribution networks. To investigate the synergistic effects of carbon trading mechanisms and the implementation of electric autonomous container trucks (EACTs), this study develops a multi-objective bi-level programming model that simultaneously minimizes transportation cost, carbon trading cost, and transportation time. The model is solved using the Non-dominated Sorting Genetic Algorithm II (NSGA-II), generating a Pareto-optimal solution set, from which the optimal solution is selected using a normalized ideal point method. Simulation-based case studies validate the feasibility and practical applicability of the proposed model. The results show that the optimized network significantly outperforms the traditional road-dominant mode. Under the baseline carbon price of 70 CNY/ton, the optimal deployment rate of EACTs reaches 25.03% and 33.87%. Sensitivity analysis reveals a distinct non-linear threshold effect: increasing the carbon price to 90 CNY/ton drives the EACT adoption rate to 32.76% and 45.38%, resulting in a 6.98% reduction in carbon emissions and a 12.75% decrease in total operational costs compared to the baseline scenario. Additionally, strict carbon quotas (e.g., 3000 tons) are found to further compel a modal shift, peaking EACT usage at 35.08% and 46.71%. These quantitative findings offer actionable insights for optimizing multimodal transport structures and refining carbon trading policies. Full article