Search Results (621)

The use of biodiesel blends in heavy-duty diesel engines changes the relationship between engine operating conditions, fuel properties, and exhaust emissions, which may limit the reliability of data-driven emission models trained under a single fuel condition. This study investigates the cross-fuel transferability of virtual emission sensors for a heavy-duty diesel engine operating on B7 and B20 fuel blends. The analysis was carried out for three target signals: nitrogen oxides concentration, hydrocarbon concentration, and dry carbon dioxide concentration, using data from the World Harmonized Transient Cycle (WHTC) and World Harmonized Stationary Cycle (WHSC) tests. A structured modelling workflow was developed, including signal time alignment, construction of baseline, dynamic, and memory-based features, feature selection, and separate evaluation scenarios: within-domain, cross-cycle, and cross-fuel transfer. Three tree-based regression algorithms were compared: Random Forest (RF), Histogram-Based Gradient Boosting (HGB), and Extreme Gradient Boosting (XGBoost). XGBoost achieved the best predictive performance in the source domain and was selected as the reference model. The results showed that a change in cycle characteristics led to a significant decrease in predictive performance, whereas the transition from B7/WHTC to B20/WHTC resulted in a clearly smaller drop in the evaluation metrics. The relationship between engine operating signals and emission response remained partially transferable across fuels. The highest stability was observed for carbon dioxide, intermediate stability for nitrogen oxides, and the lowest stability for hydrocarbons. The findings support the development of robust data-driven virtual sensing methods for emission monitoring and calibration of heavy-duty diesel engines operating with biodiesel blends. Full article

The reliable discrimination between magnetizing inrush currents and internal faults is essential for effective power transformer protection and has a direct impact on the security and stability of modern power systems. Although the second-harmonic restraint method has been widely adopted in transformer differential protection, its dependability can be affected by several operating conditions, including asymmetric energization, current transformer saturation, and the use of modern low-loss cores with reduced harmonic content. This paper presents a comprehensive and critical review of advanced techniques for distinguishing inrush currents from internal faults. The reviewed methods are classified into five main methodological categories: harmonic-based methods, time-domain approaches, signal-processing techniques, artificial intelligence-based schemes, and hybrid strategies. For each category, the fundamental operating principles, key advantages, and inherent limitations are discussed. A comparative assessment is also provided to highlight the trade-offs among detection accuracy, operating speed, robustness under adverse conditions, and practical implementation feasibility. The review shows a clear shift toward intelligent and data-driven protection schemes that combine effective feature extraction or deep learning with fast decision-making mechanisms. However, several challenges remain, particularly in relation to cross-site generalization, guaranteed response time, and hardware implementation constraints. Finally, the paper outlines a future research agenda for adaptive and computationally efficient transformer protection, emphasizing the need for benchmark datasets that include field cases, reproducible evaluation protocols, and the co-design of protection algorithms with embedded hardware platforms. Full article

Introduction: Rutin is a heterocyclic flavonol glycoside found in plants like apples, citrus fruits and buckwheat, with demonstrated anti-inflammatory properties. However, the molecular mechanisms underlying rutin’s direct effects on microglia, the main immune effector cells in the central nervous system, are not fully understood. The SHH/GLI-1 pathway is a neuronal repair pathway that modulates microglial activity and cell proliferation. Objective: For better compression of the rutin anti-inflammatory effects, this work evaluated the action of rutin on SHH/GLI-1 regulation. Methodology: For this, primary cultures of microglia from postnatal P0–2 days Wistar rats were stimulated with LPS (1 µg/mL) and/or treated with rutin (0.5–1 µM). Microglia morphology was characterized by immunofluorescence for Iba1. Gene expression of cytokines, inflammasome, glial-derived neurotrophic factors (GDNFs), and Sonic Hedgehog and family zinc finger-1 (SHH/GLI) were evaluated by real-time qPCR. Result: The results demonstrated that rutin inhibited the LPS-induced inflammatory response in microglia regulating negatively TNF-alpha, IL-6, and NLR family pyrin domain-containing 3 (NLRP3) mRNA expression. In addition, rutin increased GDNF and SHH-GLI-1 mRNA expression. Furthermore, conditioned medium from rutin-treated microglia showed a protective effect on PC-12 cells against LPS-induced cytotoxicity, reducing cell death as measured by the propidium iodide test and preserving cell morphology. Conclusions: This is the first evidence of the effect of rutin in SHH-GLI-1 signaling, contributing to the understanding of its pharmacological mechanisms and potentially revealing new molecular targets for treatment of neuroinflammatory diseases. Full article

Complex human activities consist of sequential, simple limb movements, acting as impulse responses from the motor system. In short-term human activity recognition (ST-HAR), the inherently brief observation window results in non-stationary signals and “information starvation,” breaking the time-translational symmetry of kinetic signals. Moreover, traditional Variational Mode Decomposition (VMD) and Hilbert Transform (HT) suffer from suboptimal decomposition levels (K) and spectral asymmetry. This paper proposes an improved VMD-HT framework to enhance feature extraction from short-term Inertial Measurement Unit (IMU) signals. First, an instantaneous-frequency-driven adaptive VMD method is developed to mitigate mode mixing by automatically determining the optimal K. Second, an information-enhanced instantaneous energy density (IEIE) feature is introduced. By fusing kinetic energy from both positive and negative frequency domains, this feature restores the spectral symmetry of the energy representation, precisely quantifying fine motion variations and compensating for information loss caused by the limited temporal span. Experimental results on PAMAP2, WARD, and a self-collected dataset, NOITOM, demonstrate the method’s effectiveness. With a 0.5 s window, the proposed model achieves outstanding recognition accuracies of 93.60%, 96.41%, and 97.22%, respectively, outperforming state-of-the-art approaches in capturing transient short-term information. Full article

Background/Objectives: Clinical application of Doxorubicin (Doxo) is limited by cardiotoxicity, a process strongly associated with an interplay between oxidative stress and inflammatory signaling, particularly Nuclear Factor kappa-light-chain-enhancer of activated B cells (NF-κB) activation and Nucleotide oligomerization domain-like receptor family, pyrin domain containing 3 (NLRP3) inflammasome engagement. Identifying strategies capable of mitigating these interconnected pathways is of critical importance in cardio-oncology. Simvastatin (SIM) is a promising option since it modulates oxidative stress, inflammation, and cell death through its pleiotropic effects, so this study aimed to evaluate whether SIM attenuates Doxo-induced inflammatory responses. Methods: Human Cardiomyocyte (HCM) cells were pre-treated with SIM (10 µM) for 4 h and then co-exposed to SIM and Doxo (1 µM) for 20 h. Cytofluorimetric analysis was used to evaluate inducible nitric oxide synthase (iNOS), Connexin 43 (Cx43), and Cx43 phosphorylated at Serine 368 (p^S368Cx43) levels. Real-time qPCR was performed to evaluate iNOS gene expression, while Nitric oxide (NO) release was evaluated by spectrophotometric analysis. Interleukin (IL)-1β, IL-18, IL-6, tumor necrosis factor alpha (TNF-α) production, and NLRP3 levels were evaluated by means of ELISA assay. Expression levels of inhibitor of nuclear factor kappa B alpha (IκB-α), Caspase-1, and Gasdermin D (GSDMD) were evaluated by Western Blot analysis. Nuclear translocation of NF-κB was evaluated by immunofluorescence assay. Results: In our experimental model, SIM significantly (p < 0.01) reduced Doxo-induced nitrite release, as well as iNOS gene expression (p < 0.05) and protein levels (p < 0.01). SIM also markedly attenuated Doxo-induced NF-κB signaling, pro-inflammatory cytokines production (TNF-α and IL-6, p < 0.01), and inflammosome-related responses (cleaved caspase-1, IL-1β, N-terminal domain of GSDMD), and NLRP3 expression p < 0.05). Additionally, SIM significantly attenuated the overexpression of Cx43 and its phosphorylated form (p^S368Cx43), which are responsible for impairing intercellular communication and electrical coupling in cardiomyocytes and contribute to arrhythmias and conduction abnormalities characteristic of acute Doxo-induced cardiotoxicity. Conclusions: Overall, these findings demonstrate that SIM exerts a multifaceted cardioprotective effect against Doxo-induced injury, thereby targeting interconnected inflammatory and pro-arrhythmic pathways implicated in Doxo cardiotoxicity. Full article

People spend the majority of their lives within environments shaped by multiple interacting exposures, including thermal conditions, acoustic noise, lighting, and air quality, yet remain largely unaware of how these settings affect their comfort. Existing comfort research treats domains in isolation under controlled laboratory conditions, leaving real-world multi-domain effects on personal comfort underexplored. This paper proposes a unified Comfort Framework that fuses three practical data layers: macro-environmental conditions retrieved via location-based APIs, kinematic and micro-environmental context captured through smartphone sensors, and physiological responses recorded by a chest-worn ECG sensor. Binary comfort states are labelled in real time using a minimal-disruption lap-button protocol on a consumer smartwatch. We validate the pipeline through a single-subject pilot of 18 free-living sessions. Random Forest classification across 10 valid leave-one-session-out folds achieved an F1 macro of 0.456 ± 0.151, indicating that consumer wearable comfort prediction in unconstrained free-living conditions is more challenging than controlled chamber studies suggest. Descriptive statistics showed dataset-level differences between comfort states in wrist skin temperature (31.9 vs. 33.3 °C), heart rate (70.7 vs. 77.1 bpm), and RMSSD (42.1 vs. 34.3 ms), with overlap between classes consistent with the modest classification performance. SHAP analysis identified acoustic features, HRV features, and wrist temperature as the strongest comfort signals. The framework is architecturally designed to address all four IEQ domains, though this pilot empirically validated only thermal and acoustic signals. Full article

Ground-based scatterometers are widely used for quantitative microwave backscattering measurements in soil moisture retrieval, vegetation monitoring, and satellite scatterometer validation. However, low-cost software-defined radio (SDR) transceivers provide limited instantaneous bandwidth, making it difficult to transmit and process signals with bandwidths on the order of hundreds of MHz for fine range resolution, especially for systems requiring real-time onboard processing. To address this problem, this paper presents a vehicular, fully polarimetric, SDR-based scatterometer that achieves an equivalent wideband response by sequentially transmitting adjacent narrow subbands and coherently synthesizing them onboard. To enable real-time operation on a resource-limited field-programmable gate array/system-on-chip (FPGA/SoC) platform, we adopt a frequency-domain synthesis-pulse-compression pipeline that avoids interpolation and eliminates repeated matched filtering across subbands. A slot-based online phase calibration is performed within the settling window after each fast lock to estimate and compensate random local oscillator (LO) phase offsets, preserving coherent stitching. In addition, pulse repetition within each subband and coherent accumulation are integrated to improve the signal-to-noise ratio (SNR) under real-time throughput constraints. A Zynq-based implementation demonstrates deterministic onboard range-profile output, with a minimum processing latency of about 1.57 ms per frame. Loopback and outdoor experiments validate the equivalent 200 MHz bandwidth (five 40 MHz subbands), achieving approximately 0.75 m resolution and yielding sidelobe metrics consistent with the designed windowing, including a peak sidelobe ratio (PSLR) of −27.43 dB and an integrated sidelobe ratio (ISLR) of −12.38 dB. Field scans over farmland further show consistent ${\mathsf{\sigma }}_0$ trends across incidence angle and azimuth, indicating reliable onboard quantitative backscattering measurement. These results demonstrate that the proposed method provides a feasible solution for deterministic real-time equivalent wideband scatterometry on a low-cost SDR platform. Full article

Based on monthly meteorological observations from 57 stations in Jiangsu Province during 1961–2022, the Standardized Precipitation Evapotranspiration Index (SPEI) was calculated to characterize regional dry–wet variability. Empirical Orthogonal Function (EOF) analysis was applied to extract the dominant spatially coherent dry–wet modes, and cross-wavelet analysis was further employed to examine, in the time–frequency domain, the mode-specific responses to multiscale climate drivers, including the El Niño–Southern Oscillation (ENSO), Sunspot Number (SSN), Arctic Oscillation (AO), and Pacific Decadal Oscillation (PDO). The results show that dry–wet variability in Jiangsu Province is primarily organized by a regionally coherent mode (EOF1, explaining 56.3% of the total variance) and a north–south dipole mode (EOF2, explaining 17.8%), with the zero-value line of EOF2 closely aligned with the Huaihe River–Subei Irrigation Canal climatic transition zone. The temporal coefficient of EOF1 (PC1) exhibits a significant regime shift around 2013, followed by a pronounced wetting trend across the entire region. This change may reflect recent hydroclimatic adjustments in the study area, although the present study does not attempt a formal attribution of the respective thermal and precipitation contributions. In contrast, the temporal coefficient of EOF2 (PC2) undergoes an abrupt change around 1980, indicating a transition of the spatial dry–wet pattern from “southern drought–northern flood” to “southern flood–northern drought,” broadly consistent with an interdecadal climatic transition. Cross-wavelet analysis further reveals that PC1 is closely associated with ENSO at interannual timescales, with a lag of approximately 4–6 months, while its long-term variability shows time–frequency coherence with SSN. PC2 also exhibits time–frequency coherence with SSN at longer timescales, with an apparent phase transition around the 1980s; however, this low-frequency signal should be interpreted cautiously because the underlying physical mechanism remains uncertain. Overall, this study shows that dry–wet variability in Jiangsu Province is organized by two leading spatial modes with distinct temporal evolution and scale-dependent climate linkages. These findings provide new evidence for understanding hydroclimatic variability in monsoon transition zones and offer a basis for spatially differentiated drought–flood risk assessment. Full article

Reliable detection of internal defects in pressure vessel structures remains essential for structural safety and condition-based maintenance. This study presents a low-complexity structural health monitoring framework based on fiber Bragg grating (FBG) sensing and multiresolution wavelet analysis for void detection in curved pressure vessel structures under guided wave excitation. Guided waves are introduced using piezoelectric actuators, while the FBG sensors capture the resulting strain-induced wavelength variations. Due to the limited bandwidth of the optical interrogator, the recorded signals represent the strain envelope response associated with guided wave interaction rather than the resolved ultrasonic carrier waveform. To characterize defect-induced changes, the acquired signals are analyzed using continuous wavelet transform (CWT) for time–frequency interpretation, and discrete wavelet transform (DWT) and wavelet packet transform (WPT) for energy-based multiresolution feature extraction. Experimental results show that void defects lead to consistent redistribution of wavelet-domain energy and increased non-stationarity in the measured strain responses. These trends are further supported by finite-element simulations, which reproduce similar energy redistribution patterns between intact and damaged cases. The proposed framework provides a physically interpretable and computationally efficient approach for defect detection using low-bandwidth FBG sensing, without reliance on high-speed acquisition or data-intensive learning models. The results demonstrate the feasibility of using energy-based multiresolution analysis of FBG strain signals for practical and scalable structural health monitoring of pressure vessel systems. Full article

High-frequency (HF) skywave propagation exploits ionospheric reflection for beyond-line-of-sight transmission, making time-difference-of-arrival (TDOA)-based geolocation a primary technique for localizing non-cooperative HF emitters. However, reliable TDOA estimation remains challenging due to time-varying ionospheric conditions, wideband multipath dispersion, and low signal-to-noise ratio (SNR). This paper proposes an integrated framework coupling realistic channel synthesis, deep learning-based TDOA estimation, and convex optimization-based localization. Three contributions are made. First, an improved wideband ionospheric channel model is constructed by integrating the International Reference Ionosphere (IRI) with region-specific calibration and a stochastic perturbation module, yielding time-varying multipath responses for physics-consistent waveform generation. Second, a convolutional neural network (CNN)-based TDOA estimator is designed to jointly exploit time-domain complex-baseband in-phase/quadrature (I/Q) waveforms, multi-weight generalized cross-correlation (GCC) feature maps, and channel-state information (CSI) within a unified regression network, achieving robust delay estimation under severe noise and multipath conditions. Third, the geolocation problem is formulated as a bias-regularized constrained least-squares problem with unknown ionospheric excess-delay surrogates, and a semidefinite programming (SDP) relaxation is derived to yield a tractable solution without prescribing a fixed virtual reflection height. Simulations show that the proposed estimator consistently outperforms competing algorithms across a wide SNR range and narrows the gap to the Cramér–Rao lower bound (CRLB) at high SNR. On field-recorded signals, the estimator reduces the mean absolute TDOA deviation by 51% relative to GCC with phase transform (GCC-PHAT), and the end-to-end pipeline achieves a mean geolocation error of 19.67 km across 100 field segments, outperforming all compared baselines. Full article

To improve the low sensitivity of electromagnetic acoustic testing (EMAT) to micro-debonding defects in metal–rubber bonded structures, this study proposes a detection framework combining a magnetic-field-enhanced focusing EMAT with entropy-weighted multi-feature fusion imaging. First, a Halbach-type focusing magnet was designed and evaluated through finite element simulations, showing a substantial enhancement of the effective bias magnetic field in the working region. Then, three complementary echo features, namely amplitude (AMP), time-domain integral (TDI), and power spectral density (PSD), were extracted from the acquired resonance signals and integrated using an adaptive entropy-weighted fusion strategy. Comparative and ablation analyses were further conducted to distinguish the respective contributions of probe enhancement and feature fusion, and to compare entropy-weighted fusion with single-feature imaging and equal-weight fusion. The results indicate that the focused probe mainly improves the defect-response strength at the hardware level, whereas feature fusion mainly improves image contrast, background suppression, and segmentation consistency at the image level. Among the compared methods and under the present experimental conditions, entropy-weighted fusion provides the best overall imaging performance. Under the present experimental conditions, the proposed framework enables reliable detection of 5 mm debonding defects in aluminum-alloy–rubber bonded specimens and 10 mm debonding defects in titanium-alloy–rubber bonded specimens. These results suggest that the combined use of magnetic-field focusing and adaptive multi-feature fusion is a promising approach for the detection and quantitative characterization of micro-debonding defects in metal–rubber bonded structures. Full article

Non-destructive characterization of electromagnetic (EM) wave propagation properties in drill cores is gaining prominence as a foundation for reliable geophysical inversion, improved rock-physics modeling, and increasingly data-driven mineral exploration workflows. Lab-based rock characterization requires benchmarks that link the density, elastic, electrical, magnetic, and EM properties of studied cores to lithology and mineralization, enabling more accurate interpretation of geophysical data. This study develops a robust high-frequency EM (HFEM) wave velocity measurement technique and incorporates it within a standardized non-destructive framework validated across multiple mineral systems in Newfoundland and Labrador, Canada. The developed method derives EM velocities from two-way travel time through drill cores positioned above a metallic reflector, supported by finite-difference time-domain simulations to optimize antenna frequency and test geometry. A repeatable signal-processing workflow was implemented to enhance reflection picking. Results reveal systematic EM velocity contrasts among host rocks and oxide or sulfide-bearing systems, with oxide-rich and massive sulfide intervals exhibiting higher density, elevated conductivity and susceptibility with strong EM attenuation. The integrated dataset shows that conductivity and magnetic susceptibility significantly influence EM velocity response and detectability limits. The proposed multi-parameter benchmark enables enhanced discrimination of lithological and mineralization controls in mineral exploration workflows and supports more accurate time–depth conversion in HFEM geophysical and ground-penetrating radar (GPR) methods. Full article

Accurately and promptly acquiring the load history characteristics of impact events on composite aircraft structures is crucial for identifying impact-induced damage and developing high-fidelity digital twin models. To address this need, we propose a method for reconstructing the impact load history on composite structures, leveraging Generalized Cross-Validation (GCV) and a Fiber Bragg Grating (FBG) pattern. An equivalent expansion technique based on discretized time-domain sparse strain sampling is developed to mitigate the local distortion of impact response signals, a common issue arising from the low sampling rates of quasi-distributed FBG. By incorporating Tikhonov regularization, the ill-posed nature of the impact frequency response matrix is effectively managed. Furthermore, an adaptive optimization method based on the GCV criterion is introduced to overcome the limitations of manually selecting regularization parameters and the associated constraints on noise suppression. The results show that the proposed GCV-based reconstruction method achieves an average peak relative error of 11.4% and an average root mean square error of 0.36 N for the reconstructed impact load, demonstrating that the proposed method synergistically enhances both the reconstruction of the overall impact load waveform profile and the precise characterization of transient details, even with low-rate sampling. This provides robust technical support for health monitoring and condition-based maintenance of composite structures. Full article

A novel broadband ultrasonic transducer design based on a non-uniform-thickness double-layer piezoelectric structure and a variable-thickness matching layer is proposed to overcome the limitations of conventional thickness-mode piezoelectric ultrasonic transducers, such as weak even-order harmonic responses and restricted bandwidth. The implementation of a non-uniform-thickness double-layer piezoelectric structure enables the simultaneous excitation and reception of ultrasonic signals containing both fundamental and second-harmonic frequencies. Furthermore, through the integration of variable-thickness matching layers with a backing material of non-uniform acoustic impedance, the dual resonant frequency responses are effectively merged into a broad bandwidth. The broadband transducer prototype is manufactured and characterized through electrical input impedance, time-domain pulse-echo signals, and corresponding frequency spectrum. Experimental results indicate a center frequency of 411.5 kHz, with dual resonant peaks observed near 298.6 kHz and 585.6 kHz, achieving a −6 dB relative bandwidth of 116%. The findings demonstrate that the self-developed broadband transducer is capable of effectively generating and receiving broadband signals containing both fundamental and second-harmonic components, thereby offering a new design strategy for broadband piezoelectric transducers. Full article

Business-to-consumer (B2C) e-commerce firms operate in fast-changing digital markets, where timely interpretation of external signals may strengthen organisational agility. This study examines how four dimensions of competitive intelligence—market, technological, social, and competitor intelligence—relate to organisational agility in Croatian B2C e-commerce firms. The study adopted a pragmatic explanatory sequential mixed-methods design. Quantitative data were collected through an online survey, and 208 valid responses were analysed using reliability testing, construct-validity assessment, correlation analysis, and multiple regression. Qualitative follow-up evidence was used to support the interpretation of the quantitative results. The findings show that the effects of competitive intelligence dimensions on organisational agility are not uniform. In the final validated model, social intelligence emerged as the only significant positive predictor of organisational agility, while market intelligence, technological intelligence, and competitor intelligence did not show statistically significant effects. The study therefore suggests that, in this context, systematic attention to customer conversations, online feedback, and socially visible market signals may play a more decisive role in supporting agile organisational responses than other intelligence domains. The study contributes to the competitive intelligence and agility literature by showing that intelligence dimensions should be examined separately rather than treated as a single undifferentiated capability in digital commerce settings. Full article