Search Results (128,707)

This work investigates the physicochemical characteristics of waste tire pyrolysis char (WTP-char) produced at 500 °C in an industrial-scale auger reactor. The study uniquely combines material profiling with environmental safety assessment, specifically targeting organic contaminants and polymer stabilizers, evaluating WTP-char’s potential for circular economy applications. The samples underwent comprehensive analysis, including GC-MS, TGA, SEM-EDS, TXRF, and BET surface area measurements. The results revealed a high volatile matter content (13 wt.%), attributed to the thermal inertia typical of industrial-scale units. The organic fraction was dominated by n-alkanes (48.3%) and a significant concentration (6.97%) of the stabilizer Tris(2,4-di-tert-butylphenyl) phosphate (bDtBPP), posing potential environmental risks due to its cytotoxicity. Polycyclic aromatic hydrocarbon (PAH) analysis showed a prevalence of high-molecular-weight (HMW) compounds (79.7%), indicating high chemical stability. Although the specific surface area was low (28.9 m²/g), suggesting the need for activation, the material exhibits potential as a low-cost semi-reinforcing filler or solid fuel. By moving beyond laboratory-scale experiments to real industrial production, this study establishes a practical framework for evaluating both the performance and environmental safety of waste tire pyrolysis char. Full article

Extracellular vesicles (EVs), as key mediators of intercellular communication, play multifaceted roles in the pathogenesis, treatment, drug resistance, and monitoring of B-cell non-Hodgkin lymphomas (B-NHLs), including diffuse large B-cell lymphoma (DLBCL), Burkitt lymphoma (BL), follicular lymphoma (FL), and mantle cell lymphoma (MCL). EVs derived from lymphoma cells or tumor microenvironment cells carry diverse cargoes such as proteins, microRNAs (miRNAs), and viral oncoproteins, which regulate tumor progression by modulating signaling pathways related to cell proliferation, invasion, apoptosis, autophagy, and immune suppression. In terms of treatment, accumulating evidence suggests that EVs may be associated with the efficacy of classical regimens such as R-CHOP, and they also hold potential as therapeutic targets and drug delivery vehicles for B-NHL. They contribute to drug resistance by altering the expression of key molecules or reshaping the tumor niche. Additionally, EV-derived biomarkers enable non-invasive diagnosis and monitoring of treatment response and prognosis. This review summarizes the latest research progress on the roles of EVs in major B-NHL subtypes, aiming to provide new insights for the development of innovative diagnostic and therapeutic strategies for B-NHL. Full article

Vibration and noise generated by rail transit systems pose significant constraints on their environmental sustainability. Although extensive research has been conducted by scholars on vibration and noise in rail transit, quantitative studies specifically investigating the influence of train speed on the vibration and noise of elevated rail transit are scarce. Therefore, this study selected a typical elevated section of Wuhan Metro Line 21 and systematically performed field tests to measure the vibration and noise induced by trains passing at speeds of 20, 40, 60 and 80 km·h⁻¹. Based on the test results, the vibration characteristics of the rails, track slab, and bridge structure, as well as the radiation characteristics of wheel–rail noise and bridge structure-borne noise under different speeds, were investigated. The study further explored the impact of train speed variation on the vibration and noise of the elevated rail transit system. The results indicate that the vibration acceleration levels of both the outer and inner rails increase significantly with train speed. Each time the speed doubles, the vibration level rises by approximately 11.5 dB for the outer rail and 10.0 dB for the inner rail. The vibration of the track slab and bridge structure is notably lower than that of the rails. Each time the speed doubles, the vibration acceleration level at various measurement points increases by an average of about 8.5–9.0 dB. Wheel–rail noise is primarily concentrated in the frequency bands around 630 Hz and 3150 Hz. Each time the speed doubles, the trackside noise level increases by an average of approximately 7.2–7.6 dB(A). Noise measured under the bridge shows a distinct peak around 100 Hz, which aligns with the vibration frequency of the bottom slab. Due to the shielding effect of shrubs, noise in the 63–100 Hz frequency band is attenuated at measurement points above ground level. Each time the speed doubles, bridge structure-borne noise increases by about 4.5–5.0 dB(A), representing a lower growth rate compared to wheel–rail noise. The findings of this research are expected to contribute to vibration and noise reduction strategies and support the sustainable development of rail transit systems. Full article

Distinguishing Active from Inactive Tuberculosis (TB) on Chest X-rays presents a clinical challenge due to overlapping radiological signs. This study introduces Vision Mamba CGSM, a deep learning framework integrating a State Space Model (SSM) backbone with a Context-Guided Slot Mixing (CGSM) module. The SSM captures global anatomical context, while the CGSM module isolates subtle pathological features by applying localized spatial attention. We validated the model using a hierarchical diagnostic scheme covering Normal, Pneumonia, Active TB, and Inactive TB. Experimental evaluations demonstrate an accuracy of 92.96% and a Youden Index of 79.55% on the independent test set. In the specific binary classification of Active vs. Inactive TB, the model recorded a specificity of 97.04%, outperforming standard baseline architectures including ResNet152 and ViT-B. Additional validations on external datasets confirm the consistent generalization of the proposed feature extraction mechanism. Full article

Against the global carbon neutrality target, predictive maintenance (PdM) of automotive engines represents a core technical strategy to advance the sustainable development of the automotive industry. Conventional single-modal diagnostic approaches for engine abnormal sound defects suffer from low accuracy and weak anti-interference capability. Existing multi-modal fusion methods fail to deeply mine the physical coupling between cross-modal features and often entail excessive model complexity, hindering deployment on resource-constrained on-board edge devices. To resolve these limitations, this study proposes a Physical Prior-Embedded Cross-Modal Attention (PPE-CMA) mechanism for lightweight multi-modal fusion diagnosis of engine abnormal sound defects. First, wavelet packet decomposition (WPD) and mel-frequency cepstral coefficients (MFCC) are integrated to extract time-frequency features from engine audio signals, while a channel-pruned ResNet18 is employed to extract spatial features from engine thermal imaging and vibration visualization images. Second, the PPE-CMA module is designed to adaptively assign attention weights to audio and image features by exploiting the physical coupling between engine fault acoustic and visual characteristics, enabling efficient cross-modal feature fusion with redundant information suppression. A rigorous theoretical derivation is provided to link cosine similarity with the physical correlation of engine fault acoustic-visual features, justifying the attention weight constraint (β = 1 − α) from the perspective of fault feature physical coupling. Third, an improved lightweight XGBoost classifier is constructed for fault classification, and a hybrid data augmentation strategy customized for engine multi-modal data is proposed to address the small-sample challenge in industrial applications. Ablation experiments on ResNet18 pruning ratios verify the optimal trade-off between diagnostic performance and computational efficiency, while feature distribution analysis validates the authenticity and effectiveness of the hybrid augmentation strategy. Experimental results on a self-constructed multi-modal dataset show that the proposed method achieves 98.7% diagnostic accuracy and a 98.2% F1-score, retaining 96.5% accuracy under 90 dB high-level environmental noise, with an end-to-end inference speed of 0.8 ms per sample (including preprocessing, feature extraction, and classification). Cross-engine and cross-domain validation on a 2.0T diesel engine small-sample dataset and the open-source SEMFault-2024 dataset yield average accuracies of 94.8% and 95.2%, respectively, demonstrating strong generalization. This method effectively enhances the accuracy and robustness of engine abnormal sound defect diagnosis, offering a lightweight technical solution for on-board real-time fault diagnosis and in-plant online quality inspection. By reducing engine fault-induced energy loss and spare parts waste, it further promotes energy conservation and emission reduction in the automotive industry. Quantified experimental data on fuel efficiency improvement and carbon emission reduction are provided to substantiate the ecological benefits of the proposed framework. Full article

Bacillus subtilis is aerobic or facultatively anaerobic. After entering the gastrointestinal tract, its spores germinate and colonize the gut, inhibiting the growth of harmful aerobic bacteria (Escherichia coli, Streptococcus, Staphylococcus aureus). However, it remains unclear whether B. subtilis can inhibit Clostridium perfringens type A infection. In this study, B. subtilis PB6 was added to the diets of pregnant sows infected with Clostridium perfringens type A, which significantly improved the reproductive performance and reduced the incidence of bloat in sows and diarrhea in neonatal piglets. The treatment significantly increased the abundance of intestinal probiotics (B. subtilis, Lactobacillus, Limosilactobacillus reuteri, Lactobacillus johnsonii, Muribaculaceae, Lactobacillus amylovorus, and Lactobacillus reuteri) in sows and decreased the relative abundance of Clostridium perfringens type A after feeding B. subtilis administration. These probiotics can repair the intestinal tissue and improve intestinal histomorphology, and enhance the expression of MUC2 and sIgA in sows, thereby further strengthening the mucosal immune function. B. subtilis can also reduce the levels of inflammatory factors (CRP, IL-1β, and IFN-γ) and attenuate the inflammatory response in sows and neonatal piglets. Taken together, our results suggest that dietary supplementation with B. subtilis PB6 could reduce bloat in sows and diarrhea in piglets while improving intestinal barrier function and microbial balance in sows. Full article

In this work, a flexible and sustainable radar-absorbing material (RAM) based on porous carbon derived from raw Kraft black liquor was developed. The porous carbon filler was synthesized through a simple, eco-friendly one-pot polymerization route, thereby avoiding lignin extraction, purification, and chemical activation steps. Macroporosity was introduced by using poly(methyl methacrylate) microspheres as a hard template, yielding a lightweight carbon material with a foam-like morphology, low density, and high porosity. The carbon filler was incorporated into a silicone rubber matrix at different loadings (5–25 wt.%) to produce flexible composites. The structural, morphological, and textural properties of porous carbon were investigated by SEM, EDX, Raman spectroscopy, nitrogen adsorption, and mercury porosimetry. The electromagnetic properties of composites were measured in the X-band (8.2–12.4 GHz) using a vector network analyzer. The mechanical behavior was evaluated through Young’s modulus. The results show that increasing filler content enhances dielectric losses and attenuation capability. Among all composites, the sample containing 20 wt.% of porous carbon exhibited the best electromagnetic performance, achieving a reflection loss of −42.3 dB at 10.97 GHz with a thickness of 2.43 mm, corresponding to an absorption efficiency of 99.99%. This performance is attributed to a favorable combination of impedance matching and quarter-wavelength cancellation effects. The developed sustainable, lightweight, and flexible composites demonstrate potential as low-cost RAM for aerospace and electromagnetic interference mitigation applications. Full article

The access and benefit sharing (ABS) concept was originally intended to promote the conservation and sustainable use of biodiversity and the fair and equitable sharing of benefits from the use of biological resources and traditional knowledge for research and development (R and D). Many anecdotal studies suggest that global regulatory models are failing to deliver expected outcomes, with increasing calls to rethink ABS governance. Through the first nationwide survey of Australian scientists, this article aims to fill a significant gap in empirical research about biological resource user perspectives on the effectiveness of ABS regulation. The survey results include insights into: (a) the nature and diversity of bioscience activities; (b) bioscience researcher engagement with benefit sharing and ABS procedures; (c) perspectives of effectiveness (impacts and efficiency); and (d) ideas for regulatory reform. The study finds that participants support benefit sharing goals but generally perceive current approaches to ABS to be ineffective. Highlighting a thriving benefit sharing culture in R and D but low levels of engagement with ABS processes, the study reveals insights into motivations for benefit sharing and indicators of effectiveness for regulatory regimes. The findings offer lessons for countries that are developing ABS measures to achieve conservation, sustainable use, and equity outcomes. Full article

The pore pressure coefficient B, defined as the change in pore pressure per unit increment of confining pressure under undrained conditions, is a fundamental parameter in soil mechanics. It characterizes the coupling between soil skeleton deformation and pore water pressure and plays a critical role in establishing the effective stress framework for frozen soils. Existing studies mainly focus on unfrozen soils, while the temperature sensitivity and stress-path dependence of B in frozen soils undergoing phase transition remain insufficiently understood. To address this gap, this study conducts temperature-controlled triaxial tests and constant strain-rate loading tests to investigate the evolution of B in frozen sandy silt over a temperature range of −11 °C to −2 °C under different stress histories. The results show that: (1) post-loading B-values at −5 °C to −8 °C are significantly higher than those at −2 °C and −10 °C, by 6.5% and 8.2%, respectively; (2) within the framework of Gassmann’s equation, a theoretical model incorporating the soil freezing characteristic curve and the coupled effects of ice–water phase transition and soil skeleton deformation is developed to explain the temperature-dependent behavior of unfrozen water and B; and (3) a predictive model incorporating a temperature correction factor is proposed, which accurately captures the variation trend of B in frozen sandy silt. This study elucidates the evolution mechanism of the pore pressure coefficient under multi-field coupling conditions and provides a theoretical basis for frost heave assessment and constitutive modeling in cold-region engineering. Full article

Hypoxia, a hallmark of hepatocellular carcinoma (HCC), regulates metabolic reprogramming, tumor progression, and therapy resistance. Although hypoxia-induced glycolytic changes are recognized, it remains unclear how intrinsic tumor aggressiveness influences the magnitude and plasticity of metabolic and transcriptional responses to oxygen deprivation. In this study, we investigated the effects of chronic hypoxia (1% O₂ for 48 h) in spheroids generated from two immortalized (HepG2, Hep3B) and two patient-derived HCC cell lines with distinct aggressiveness (HLC19, HLC21). The metabolic activity, energetic status, proliferation, and expression of hypoxia- and metabolism-related genes were assessed, with oxygen levels monitored to validate experimental conditions. It has resulted that immortalized HCC spheroids displayed similar metabolic and transcriptional responses to hypoxia, with enhanced glycolytic activity but limited phenotypic plasticity. Primary HCC spheroids exhibited aggressiveness-dependent differences. Aggressive HLC19 cells showed a pre-established glycolytic phenotype, stable ATP levels, sustained proliferation, and minimal transcriptional remodeling under hypoxia. Less aggressive HLC21 cells relied on the delayed glycolytic activation and induction of hypoxia-responsive genes to maintain viability. Clustering analyses indicated that metabolic strategies, rather than absolute activity, aligned with tumor aggressiveness. These findings suggest that intrinsic tumor aggressiveness shapes hypoxia-driven metabolic programs in HCC and supports the relevance of patient-derived 3D models for studying metabolic adaptation. Full article

This paper presents an action potential detector (APD) based on a high-order non-linear energy operator (HONEO). The APD consists of a HONEO, a positive threshold generator, a negative threshold generator, and an XOR. The APD is capable of detecting the half-width of an action potential since it can determine both the positive peak and the negative peak of the action potential by means of the HONEO and two threshold generators. In addition, the signal-to-noise ratio (SNR) of the APD can also be improved due to the two adaptive threshold generators. The circuit is designed in a standard 0.18 $\mathsf{\mu }$m CMOS process with a 1.8 V supply voltage. Pre-layout simulations are performed under typical conditions (TT process corner, 1.8 V supply, 27 ${}^{\circ }$C). The results show that the output amplitudes of the HONEO remain almost constant ($\pm 100$ mV) when the amplitude of the source signal varies from −10 mV to 30 mV at 1 kHz. Across temperature variations from $-20$${}^{\circ }$C to 80 ${}^{\circ }$C, the output amplitude remains within $\pm 12\%$ of the nominal value, demonstrating acceptable stability for the target implantable application. Compared to the conventional NEO, the APD achieves 14–$20\mathrm{dB}$ SNR improvement, a detection accuracy of $97\%$. The power consumption of the APD is approximately $62\mathsf{\mu }\mathrm{W}$. Full article

To develop high-performance iridium phosphorescent complexes, we designed and synthesized a series of iridium phosphorescent complexes (G-1, G-2, B-1, B-2, R-1, R-2) using 3-hydroxy-2-methyl-4-pyrone (maltol, short for mal) and 3-hydroxy-2-ethyl-4-pyrone (ethyl maltol, short for emal) as auxiliary ligands, in combination with 2-phenylpyridine (ppy), 2-(2,4-difluorophenyl)pyridine (dfppy), and 1-phenylisoquinoline (piq) as cyclometalating ligands. We systematically investigated their crystal structures, photophysical behavior, electrochemical properties, and electroluminescent performance. The results revealed that the combination of a pyranone auxiliary ligand with the highly conjugated piq ligand leads to the formation of R-1 and R-2, which possess high molecular symmetry and display favorable photophysical performance. These complexes exhibit solution-phase phosphorescence quantum yields of 64% and 55%, and electroluminescent devices incorporating them reach a maximum external quantum efficiency of 13.4%, with brightness exceeding 13,000 cd/m² and minimal efficiency roll-off. In contrast, complexes incorporating pyridine-based cyclometalating ligands (ppy, dfppy)—G-1, G-2, B-1, and B-2—display weak emission in solution but show enhanced solid-state emission through π–π stacking, with a maximum quantum yield of 25.8%. Density functional theory calculations and electrochemical analysis indicate that the presence of both the pyranone auxiliary ligand and the piq ligand results in optimized frontier orbital energy alignment, enhanced metal-to-ligand charge transfer, and reduced non-radiative transitions, thereby improving emission efficiency. This study provides a theoretical framework and molecular design strategy for the application of pyranone auxiliary ligands in high-performance iridium phosphorescent materials. Full article

Utility-scale photovoltaic (PV) plants rely on supervisory control and data acquisition (SCADA) streams for performance verification, yet high-frequency measurements are routinely affected by sensor dropouts, intermittency, and operating-state transitions that bias regression-based capacity estimates. This study evaluates a reproducible SCADA processing workflow for capacity-style reporting and a complementary soiling–clean temperature prediction model using data from a documented October 2022 test window (5 s SCADA aggregated to 1 min). The following three filtering approaches are compared: (i) naïve thresholds (Baseline A), (ii) deterministic stability screening using ramp-rate and rolling-variability constraints (Baseline B), and (iii) an optional residual-based outlier trimming step (Method C). Capacity is estimated via a multivariate regression evaluated on a fixed-size reporting-condition subset (RC197) with day-coverage constraints. All methods achieved high fit quality on RC197 ($R^2\approx 0.99$), with Baseline B improving error and uncertainty over Baseline A (RMSE 2.05 vs. 2.18 MW; $U_{95}$ 0.97% vs. 1.03%) while preserving day coverage; Method C yielded the lowest in-sample RMSE (1.89 MW) but reduced day coverage. For temperature prediction, a baseline-plus-residual learning formulation substantially improved leave-one-day-out performance, reducing MAE/RMSE from 2.99/3.76 °C to 1.43/1.80 °C and increasing $R^2$ from 0.60 to 0.91. The results highlight trade-offs between fit tightness and representativeness in capacity-style filtering and demonstrate residual learning is an effective approach for SCADA-based thermal characterization. Full article

Although high-frequency electromagnetic methods, such as Radio Magnetotellurics (RMT) and Controlled-Source Radio Magnetotellurics (CSRMT), are highly effective for shallow-to-medium depth exploration, deploying traditional transmitter–receiver setups remains labor-intensive and significantly slows down large-scale surveys. To overcome these logistical bottlenecks, we developed a mobile Ultra-Audio Frequency Electromagnetic (UAEM) measurement system. While the hardware is designed with dual-mode capabilities supporting conventional controlled-source operations, this paper specifically focuses on its application in a Signals of Opportunity (SOOP) mode. By utilizing pre-existing, stable anthropogenic signals, including Amplitude Modulation (AM) broadcasts and naval very low frequency communications, the system effectively functions as a broadband RMT receiver. Technical evaluations demonstrate that the instrument operates across a 1 Hz to 1000 kHz bandwidth with a high sampling rate of 2.5 MHz. Furthermore, it achieves a dynamic range of 143 dB and maintains an apparent resistivity measurement accuracy of better than 3%. Thanks to its modular, vehicle-towed design, the UAEM system enables continuous, on-the-move data acquisition wherever ambient field sources are available. This approach eliminates the need for dedicated transmitter deployment, fundamentally reducing exploration costs and boosting overall survey efficiency. Full article

Background: The decoding of motor imagery electroencephalography (MI-EEG) is constrained by core issues including low signal-to-noise ratio (SNR) and cross-session as well as cross-subject domain shift, which seriously impedes the practical deployment of brain–computer interfaces (BCIs). Methods: To address these challenges, this paper proposes a novel end-to-end MI-EEG decoding method named BARN-DA. Two innovative modules, Band-Aware Channel Attention (BACA) and Multi-Scale Kernel Perception (MSKP), are designed: one enhances discriminative channel features by modeling channel information fused with frequency band feature representation, and the other captures complex data correlations via multi-scale parallel convolutions to improve the discriminability of the network’s feature extraction. Subsequently, the features are mapped onto the Riemannian manifold. For the source and target domain features residing on this manifold, a Riemannian Maximum Mean Discrepancy (R-MMD) loss is designed based on the log-Euclidean metric. This approach enables the effective embedding of Symmetric Positive Definite (SPD) matrices into the Reproducing Kernel Hilbert Space (RKHS), thereby reducing cross-domain discrepancies. Results: Experimental results on four public datasets demonstrate that the BARN-DA method achieves average cross-session classification accuracies of 84.65% ± 8.97% (BCIC IV 2a), 89.19% ± 7.69% (BCIC IV 2b), and 61.76% ± 12.68% (SHU), as well as average cross-subject classification accuracies of 65.49% ± 11.64% (BCIC IV 2a), 78.78% ± 8.44% (BCIC IV 2b), and 78.14% ± 14.41% (BCIC III 4a). Compared with state-of-the-art methods, BARN-DA obtains higher classification accuracy and stronger cross-session and cross-subject generalization ability. Conclusions: These results confirm that BARN-DA effectively alleviates low SNR and domain shift problems in MI-EEG decoding, providing an efficient technical solution for practical BCI systems. Full article