Search Results (675)

Noise-induced spectral fluctuations can substantially degrade gas concentration retrieval in tunable diode laser absorption spectroscopy (TDLAS), particularly when the absorption features are weak. A coordinate-attention-enhanced convolutional neural network and Transformer (CA-CNN-Transformer) is developed for direct retrieval of N₂O concentration from noisy mid-infrared absorption spectra. The model combines convolutional feature extraction, channel-wise feature recalibration using the CA module, and Transformer-based modeling of spectral dependencies, allowing for direct extraction of concentration-related features from the measured spectra without an explicit denoising step. After transfer learning using experimentally acquired spectra, the model achieved an R² of 0.99998 across the concentration range of 100–1000 ppb. In one-hour continuous measurements at approximately 400 ppb, the standard deviation of the retrieved concentration decreased from 2.1 ppb for a conventional CNN to 1.0 ppb for the proposed model. The minimum Allan deviation reached 150 ppt at an averaging time of 180 s. SHapley Additive exPlanations (SHAP) analysis identified the spectral region associated with the dominant N₂O absorption feature and showed that the model uses information from multiple spectral regions rather than depending on individual spectral points. The proposed CA-CNN-Transformer thus provides accurate and stable N₂O concentration retrieval from noisy TDLAS spectra while offering an interpretable basis for understanding the spectral information used by the model. Full article

Poly(bisphenol A carbonate) (BPA-PC) is an engineering thermoplastic whose recycling is hindered by its Code-7 classification and environmental BPA leaching. Here, we report water-soluble molecularly imprinted nanozymes for the selective depolymerization of BPA-PC under mild alkaline conditions. Synthesized within cross-linked surfactant micelles, the optimized nanozyme combines shape-complementary BPA-matched pockets with surface basicity, achieving a 72-fold rate enhancement over porcine pancreatic lipase. Fluorescence titrations with a soluble dansyl-labeled BPA-PC dimer confirm strong substrate recognition (K_a ~10⁶ M⁻¹). Gel permeation chromatography reveals uniform reductions of 52–55% across M_n, M_w, and M_z, indicative of a random endo-chain cleavage mechanism. On macroscopic BPA-PC films, the nanozyme induces pronounced cracking and surface erosion, whereas polyethylene terephthalate (PET) control films remain undamaged. Furthermore, the aqueous nanozyme retains ~75% of its initial catalytic activity across five consecutive degradation cycles following simple phase extraction. This study demonstrates that tailored, shape-complementary nanozymes provide a generalizable, selective biocatalytic alternative for the circular chemical recycling of recalcitrant engineering plastics. Full article

Epidermal barrier damage triggers sensitive skin disorders. Synthetic skin repair agents frequently incur irritation, whereas medicinal and edible herbal ingredients offer milder alternatives. As a traditional Chinese medicinal herb, Atractylodes macrocephala Koidz remains inadequately characterized regarding its epidermal barrier repair efficacy. This work systematically characterized the barrier restoring capacity of A. macrocephala extract (AKE) via keratinocyte injured models, biochemical assays, spectroscopic interaction analysis, molecular docking and a tape stripping-induced mouse model of skin barrier impairment. AKE promoted keratinocyte migration, alleviated sodium lauryl sulfate triggered oxidative injury and enhanced ceramide synthesis. Moreover, AKE inhibited hyaluronidase, collagenase and elastase activities. Fluorescence quenching spectroscopy and thermodynamic analysis demonstrated spontaneous binding between AKE constituents and these skin degrading enzymes. At 50 μg/mL, AKE downregulated expression of pro-inflammatory cytokines interleukin (IL)-6 and IL-8 by suppressing the NF-κB inflammatory cascade, and restored occludin and keratin levels (p < 0.01 vs. the model group). It also exerted anti-Microsporum gypseum activity, with a minimum inhibitory concentration (MIC) of 92.36 ± 1.03 μg/mL, which alleviated inflammation and pruritus associated with dermatomycosis. In barrier-damaged mice, topical application of AKE recovered 81.2% of epidermal integrity (p < 0.001 vs. the model group) and normalized skin biomarkers. UHPLC-ESI-HRMS/MS combined with molecular docking identified atractylenolide I-II as potential bioactive compounds. In summary, AKE repairs damaged epidermis through multi-target modulation, indicating promising potential as a natural functional candidate for skin barrier repair. Full article

Delayed healing of diabetic wounds arises from a self-sustaining pathological microenvironment featuring persistent inflammation, excessive matrix degradation, impaired angiogenesis, and insufficient collagen deposition, and current single-process interventions remain unsatisfactory. Red yeast rice, produced by fermentation of rice with Monascus spp., is a traditional fermented product with documented anti-inflammatory and antioxidant activities; however, its direct extract is dominated by macromolecular and bound-state constituents with low release efficiency and poor skin permeation, which restricts its topical application. In this study, an enzymatic hydrolysate of red yeast rice (RYR-EH) was prepared by composite enzymatic hydrolysis, and the compositional changes induced by hydrolysis were characterized by widely targeted UPLC-MS/MS metabolomics. A total of 810 metabolites were detected, among which 199 showed differential accumulation after hydrolysis (179 upregulated and 20 downregulated; FC ≥ 2 or ≤0.5, VIP ≥ 1, and p < 0.05); the differential metabolites were significantly enriched (FDR < 0.05) in purine metabolism, nucleotide metabolism, ABC transporters, and the cGMP-PKG signaling pathway, a pattern consistent with hydrolysis-driven release of free amino acids, small peptides, and other low-molecular-weight constituents. In type 2 diabetic mice induced by a high-fat diet combined with low-dose streptozotocin, topical RYR-EH (2% and 5%) significantly accelerated the closure of full-thickness excisional wounds, with the 5% formulation showing more advanced closure than the 2% formulation and an effect approaching that of recombinant human epidermal growth factor (rhEGF). At the day-14 endpoint, RYR-EH treatment was associated with enhanced re-epithelialization, lower TNF-α, IL-6, and IL-1β levels in both wound tissue and serum, reduced MMP-2 and MMP-9 immunoreactivity, reduced Ki67-positive cellularity in the late-stage wound bed, increased Collagen I/III deposition, and increased VEGF/CD31 immunoreactivity. These results demonstrate that RYR-EH exerts multi-process reparative effects on diabetic wounds, simultaneously attenuating inflammation, restraining excessive matrix degradation, promoting angiogenesis, and restoring collagen deposition, and support RYR-EH as an effective multi-process topical agent for diabetic wound healing. Full article

Laser point cloud change detection is susceptible to spatial–structural uncertainties induced by residual registration errors, uneven point cloud density, viewpoint shift and object occlusion, which frequently produce numerous false alarms of spurious changes. To address this issue, this paper proposes an ROI-level 3D point cloud change detection method based on keypoint similarity. Potential change points are first filtered according to point-to-point distances, and Regions of Interest (ROIs) are generated via RBNN clustering. Global point cloud comparison is converted into local comparison among ROI units. Keypoints within ROIs are extracted for surface reconstruction, and effective overlapping regions between paired ROIs are obtained. Regional similarity is constructed by fusing surface normal consistency and dynamic-evolution features of complex networks for change discrimination. Experiments on simulated mine-tunnel data and field-measured underground-garage data demonstrate that, under unseen background disturbances including residual registration error, occlusion and uneven point cloud density, the proposed method achieves the highest F1-score of 0.9004 for the simulated tunnel scenario compared with competing methods, and delivers effective false-alarm suppression without degrading detection recall for real-change regions. Full article

Contemporary mine restoration faces challenges in achieving self-sustaining ecosystems. Accelerating pedogenesis within iron tailings offers a promising strategy by fostering organic carbon (OC) stabilization and soil structure development via organo-mineral complex formation. This study elucidated the microscale mechanisms governing exogenous OC fixation at mineral interfaces in iron tailings during simulated pedogenesis. Controlled microcosm experiments employed iron tailings of sandy and loam textures amended with farmyard manure (F), Mollisols (M), or their combination (FM), alongside original iron tailings as a control. Over 160 days, we tracked OC fractionation, Fe/Al oxide dynamics and mineralogical evolution. Key findings revealed that FM treatment induced the most significant OC accumulation, increasing total OC by 163% (loam) and 108% (sand), with MOC exhibiting the largest relative surge (455% loam, 217% sand). This coincided with pronounced increases in reactive Fe/Al oxides, particularly oxalate-extractable Fe (Fe_ox; +230% loam, +212% sand) and pyrophosphate-extractable Al (Al_p; +122% loam, +54% sand). Mineralogical characterization (XRD) demonstrated distinct phase transformations in amended tailings (e.g., formation of anorthite, grunerite, ankerite), indicative of accelerated weathering. XPS analysis confirmed the presence of surface Fe³⁺/Fe²⁺ species and suggested Al-O-C bonding. These reactive secondary phases, characterized by high specific surface area, facilitated robust OC sequestration primarily through MOC formation. The synergistic input of farmyard manure and Mollisols proved most effective in driving these processes, particularly in loam-textured tailings. Our results provide novel mechanistic insights into Fe/Al-OC association dynamics during tailings pedogenesis. This understanding underpins strategies for enhancing in situ soil formation in iron tailings, ultimately promoting sustainable ecological restoration of mine-degraded landscapes. Full article

This paper presents, analyzes, and experimentally validates a bootstrapped inverter implemented via low-voltage enhancement-mode HEMTs offered by a 200-V E-mode GaN-on-SOI monolithic platform. Unlike conventional resistor–transistor logic (RTL), the proposed topology exploits intrinsic device capacitances to achieve a transient current-source-like pull-up behavior without dedicated bootstrap capacitors. A detailed analytical model is developed to describe the dynamic operation and to identify the conditions under which the low-to-high transition overcomes the RC limitation of RTL inverters. Measurements demonstrate a more than 30% reduction in rise time, higher static gain, and an overall improvement in total noise margins under comparable static current conditions. The temperature dependence of bootstrap efficiency from −40 °C to 150 °C is also investigated. By combining experimental measurements with model-based parameter extraction, we show that the high-temperature performance crossover is attributed primarily to leakage-induced overdrive reduction. In contrast, threshold-voltage drift acts in the opposite direction and mobility degradation affects both topologies similarly. Full article

Accurate steel alloy classification is a core task in industrial quality control, yet laser-induced breakdown spectroscopy (LIBS) data suffers from spectral noise, matrix effects, and multi-element peak overlap. Femtosecond laser-ablation spark-induced breakdown spectroscopy (fs-LA-SIBS) enables high-sensitivity detection, but plasma fluctuations degrade the performance of conventional chemometric methods and standalone deep learning models. To address these issues, we propose HybridTFNet, a hybrid fuzzy-Transformer-convolutional neural network (CNN) framework for uncertainty-aware spectral learning and multi-scale feature fusion. A fuzzy logic layer with Gaussian membership functions quantifies spectral uncertainty and suppresses noise-dominated regions. A Transformer encoder captures long-range wavelength dependencies and global inter-element coupling, while a one-dimensional CNN extracts fine-grained local features such as characteristic peaks and wavelength shifts. Adaptive fusion of global and local representations constructs a highly discriminative feature space for classification. Experiments on nine certified steel alloy fs-LA-SIBS datasets show that HybridTFNet achieves 100% classification accuracy across four independent test sets, significantly outperforming traditional machine learning (PLS, KNN, RF) and deep learning models (CNN, LSTM, Transformer). Multi-test-set evaluation on the current dataset indicates stable classification performance; we note that all results are from a single representative run, and variability across repeated runs is not quantified in this work. With low complexity and low inference latency, the proposed framework is suitable for near-real-time industrial deployment, providing an effective solution for intelligent spectral information processing. Full article

To improve the structural adaptability and biological suitability of cochlear implant electrode carriers, this study systematically develops and evaluates novel 3D-printed, swellable hydrogel–silicone composites. A vinylphosphonic acid–acrylamide hydrogel powder (VPA-AAm) was pre-washed, milled, and sieved, incorporated into a silicone matrix, and 3D-printed before rapid thermal curing, followed by microstructural, swelling, and in vitro WST-1 cytotoxicity analyses. Pre-washing minimized particle agglomeration and facilitated high-resolution printing. Incorporating the hydrogel induced complete macroscopic opacity at 25 wt.% and localized microporosity during rapid thermal curing. The non-percolating morphology influenced swelling and mass transport through the composite, providing a mechanistic link between water uptake and the observed cell-viability response. While a 35 wt.% loading was associated with matrix degradation and cell viability below the 70% threshold, the 30 wt.% composition achieved the highest water absorption of approximately 400–460%, consistent with a favorable balance between osmotic swelling and matrix elasticity. This swelling behavior was associated with controlled release characteristics and non-cytotoxic extract responses at intermediate hydrogel loading. Consequently, the 30 wt.% composite represents a promising material combination for implantable medical devices, balancing swelling performance, structural stability, and favorable in vitro cell viability under the investigated extraction conditions. Full article

The chemical identification of indigoid dyes in archeological textiles remains a significant analytical challenge due to the diversity of indigoid components, their susceptibility to post-deposition degradation, and the risk of further transformation during sample preparation. Seventeen samples of early medieval textiles from the ancient city of Deraheib (Sudan) were studied. Tryptanthrin—an indoloquinazoline alkaloid previously documented only in model solutions—was identified in archeological textiles for the first time, providing a new qualitative marker of indigo-type dyeing that may persist when the original dye has undergone substantial degradation. Two variants of the methanolic hydrochloric acid treatment step—ultrasonication without heating versus boiling in a water bath at 100 °C—and the replacement of dimethylformamide with acetonitrile in the third extraction stage were systematically compared. Full article

The rapid expansion of photovoltaic (PV) installations necessitates advanced monitoring solutions to ensure optimal performance, early fault detection, and long-term reliability. This study presents a hybrid methodology for real-time PV module monitoring and fault diagnosis, integrating I–V curve analysis, IEC 60891 standardization, and Teaching–Learning-Based Optimization (TLBO) to estimate parameters of the Single Diode Model (SDM). An I-V Swinger-based test bench equipped with Arduino data acquisition and environmental sensors was developed to measure I–V characteristics under various operating conditions (OPC), including low irradiance, elevated temperature, and partial shading. The IEC 60891 procedure enabled OPC-to-STC conversion, facilitating accurate performance evaluation and degradation rate estimation. Concurrently, the TLBO algorithm efficiently extracted SDM parameters (I_L, I_o, R_s, R_sh, a), achieving a mean absolute percentage error (MAPE) below 5% and an R² of 0.99, confirming the method’s robustness. Experimental results on an 11-year-old LX-80M module revealed moderate degradation (<13%) and demonstrated the system’s ability to capture shading-induced power losses and fill factor collapse, distinguishing between permanent aging and transient faults. A comparative analysis with the recent literature confirms that this integrated, low-cost, and high-accuracy approach bridges the gap between academic PV diagnostics and industrial monitoring needs. The proposed framework provides a foundation on which IoT-enabled predictive maintenance and digital-twin integration could subsequently be built; neither extension was implemented or validated in the present study. Full article

Hydraulic supports serve as crucial equipment in a fully mechanized coal mining face, and the performance degradation assessment is a core topic for the prevention of roof disasters. Existing support degradation assessment is empirical, and transferable approaches from other machinery suffer from inadequate representation of fault-induced degradation behaviors and overlook multi-parameter correlations. This paper proposes a novel predictive performance degradation assessment approach using multivariate statistical fusion. An attention-enhanced TCN-BiLSTM model is developed to predict the future evolution of multiple operating parameters of a hydraulic support. Multi-domain features are then extracted and selected to characterize the support behavior under various fault-induced degradation conditions. Two statistics are constructed to quantify the deviation from the healthy state by evaluating both amplitude deviations and correlation changes in multiple features. A novel overall degradation indicator DI is then proposed, and the baselines at different degradation levels are determined by evaluating the probability density function using adaptive kernel density estimation. Multiple fault-induced degradation experiments are carried out on a support test rig at different severity levels. Results show that the proposed indicator achieves a degradation assessment accuracy above 85% for all fault types and above 95% for most fault types. Full article

Tree-induced single-phase high-impedance faults (THIFs) threaten the reliability of distribution networks because their fault currents typically fall below the operating thresholds of conventional overcurrent relays. Accurately identifying tree species based on fault records is crucial for implementing differentiated vegetation management; however, the limited size of sample sets obtained from field experiments leads to severe overfitting in deep learning models. This paper proposes a framework that integrates physics-guided and data-driven approaches. First, a branch utilizing manually designed physical features extracts time-domain and frequency-domain metrics, while a MiniRocket branch captures multi-resolution temporal patterns. A bootstrap-based stability selection procedure is employed to retain discriminative features, and a rigorous “leave-one-file-out” cross-validation (LOFO-CV) scheme is used to train a strongly regularized Ridge classifier. Additionally, ablation studies are conducted to compare the feature information content at sampling rates of 100 kHz and 10 kHz under the experimental conditions. Finally, a carbonization degradation index (CDI) threshold is proposed. Experiments involving 37 fault records across five tree species demonstrate that the Hybrid + Ridge method achieves a file-level F1 score of 97.3%, achieving a file-level F1 of 97.3%, higher than standalone handcrafted features and MiniRocket. Full article

Waste seed melon processing by-products are rich in bioactive polysaccharides. Nevertheless, their high moisture content and perishability restrict the high-value utilization of these polysaccharides. In this study, four pretreatment drying methods, namely sun drying (SD), 55 °C hot-air drying (HAD), weak microwave-assisted hot-air drying (WMHAD), and vacuum freeze drying (VFD), were adopted to investigate the effects of drying methods on the quality characteristics of dried seed melon, as well as the structural characteristics (micromorphology, monosaccharide composition, molecular weight distribution, and functional groups), biological activities (antioxidant capacity, α-glucosidase inhibitory activity, and pancreatic lipase inhibitory activity), and gastrointestinal stability of seed melon polysaccharides (SMPs). The structure–activity relationship of SMPs was further elucidated. The results showed that none of the four drying methods disrupted the glycosidic backbone of polysaccharides, only altering the molar ratios of monosaccharides. VFD preserved the loose structure of pulp; VFD-SMP possessed the highest weight-average molecular weight and galacturonic acid content and exhibited optimal α-glucosidase inhibitory activity and gastrointestinal stability. HAD induced polysaccharide degradation, and HAD-SMP displayed the strongest antioxidant capacity. WMHAD-SMP exerted prominent pancreatic lipase inhibitory activity. The SD-treated samples exhibited the lowest polysaccharide extraction yield as well as the poorest overall biological activities. This work may provide a preliminary theoretical basis and data reference for the targeted preparation of functional polysaccharides from seed melon processing by-products. Full article

Background: Ultraviolet (UV) radiation, urban pollution and depletion of cutaneous antioxidant reserves are interconnected drivers of photoaging, matrix metalloproteinase (MMP) activation, mitochondrial dysfunction and dermal extracellular matrix degradation. Plant polyphenols can counteract these processes, but topical efficacy is limited by chemical instability, polarity-driven partitioning and a hostile stratum corneum delivery interface. Here we report a green nanostructured lipid carrier co-loading a phenolic-rich jabuticaba (Plinia cauliflora) peel extract and caffeine (NLC-JC), engineered with Amazonian lipids (Astrocaryum murumuru butter and Orbignya phalerata oil) and stabilized by a Natural Deep Eutectic Solvent (NaDES)-like microenvironment. Methods: UPLC profiling was used to determine the polyphenolic composition of the extract. Physicochemical characterization included hydrodynamic diameter and PDI, ζ-potential, lipid matrix organization (XRD) and morphology (TEM), as well as colloidal stability over 30 days. Cytotoxicity was assessed in SH-4 melanocytes (ISO 10993-5) and in zebrafish (Danio rerio) embryos (96-h IC₅₀, GHS acute hazard threshold). Functional antioxidant and photoprotective activity was evaluated by DPPH radical scavenging, and by attenuation of UVC- and H₂O₂-induced anion superoxide generation, alongside assessment of cellular and nuclear morphology under combined oxidative stress. Results: UPLC profiling revealed a polyphenolic fingerprint dominated by ellagic acid (476.8 ± 19.7 µg g⁻¹), quercetin-3-rhamnoside (90.9 ± 4.6 µg g⁻¹), quercetin (83.7 ± 1.4 µg g⁻¹), cyanidin-3-glucoside (73.0 ± 1.2 µg g⁻¹) and protocatechuic acid (68.2 ± 2.2 µg g⁻¹), bioactives with documented anti-MMP, NF-κB-suppressing and ROS-scavenging activities. Optimized NLC-JC showed a hydrodynamic diameter of 56.1 nm (PDI = 0.131), negative ζ-potential, amorphous lipid matrix (XRD) and spherical morphology (TEM, ≈150–200 nm electron-dense particles), with 30-day colloidal stability. NLC-JC exhibited low cytotoxicity in SH-4 melanocytes (IC₅₀ = 2304 μg mL⁻¹), preserving ≥70% viability across the cosmetically relevant range (ISO 10993-5), and a 96-h LC₅₀ of 2013 μg mL⁻¹ in zebrafish (Danio rerio) embryos, three orders of magnitude above the GHS acute hazard threshold (100 mg L⁻¹). Functionally, NLC-JC produced ≈50% DPPH radical scavenging at concentrations comparable to ascorbic acid, attenuated UVC- and H₂O₂-induced anion superoxide generation and preserved cellular and nuclear morphology under combined oxidative stress. Conclusion: These data position NLC-JC as a safe-by-design, multifunctional dermocosmetic platform combining agro-industrial valorization with mechanism-anchored photoprotection. Full article