Search Results (971)

Water and nitrogen supply are key factors limiting the establishment of alpine plant seedlings and the efficiency of ecological restoration on the Tibetan Plateau. As an endemic shrub to Tibet, the morphological and physiological response mechanisms of Piptanthus nepalensis (Hook.) D. Don to coupled water and nitrogen stress remain poorly understood. This study employed a pot experiment with a completely randomized two-factor design, incorporating five water gradients (0–100% field capacity, FC) and five nitrogen levels (0–4 g·plant⁻¹ urea). The aim was to elucidate the regulatory mechanisms of water/nitrogen coupling on Piptanthus nepalensis growth, physiology, and morphogenesis. The results indicated the following: (1) A significant water/nitrogen coupling effect was observed, with optimal water/nitrogen combinations producing pronounced synergistic effects. Principal component analysis (PCA) revealed that the first two axes cumulatively explained 99.32% of the morphological variation. The W3N3 treatment (40–60% FC water + 2 g·plant⁻¹ nitrogen) exhibited optimal growth traits and maximum leaf elongation, establishing the optimal water and fertilizer management threshold for this species. (2) Confronted with two starkly contrasting stresses—drought (W4, W5) and waterlogging (W1)—plants adopted convergent “conservative” morphological adaptation strategies (significantly reduced leaf length and width) to lower metabolic expenditure. (3) Photosynthetic physiological analysis revealed that under extreme water deficiency (W5) or waterlogging (W1) stress, intercellular CO₂ concentration (Ci) paradoxically increased, indicating a shift in photosynthetic suppression mechanisms from stomatal limitation to non-stomatal limitation (metabolic injury). (4) The Mantel Test confirmed that photosynthetic physiological traits significantly drove morphological trait variation (p < 0.001), establishing a close feedback loop between “physiological function and morphological structure”. Conclusions: Moderate water deficit (40–60% FC) combined with moderate nitrogen fertilization (2 g·plant⁻¹) effectively alleviates non-stomatal limitation and releases morphological constraints, thereby promoting rapid growth in Piptanthus nepalensis. This study reveals the phenotypic plasticity and convergent adaptation mechanisms of Piptanthus nepalensis under water/nitrogen co-stress, providing precise water and fertilizer management guidelines for vegetation restoration in degraded ecosystems of Tibet. Full article

Motor position sensors are critical parts for traction motors control in electrified automotive powertrains. As motors are becoming more compact due to the advance of technology the packaging space for motor position sensors is becoming increasingly restricted. This study presents a two-layer (2L) printed circuit board (PCB) routing strategy for inductive motor position sensors with limited area. A prototype was fabricated and tested on a test bench using a comprehensive design of experiments that contains 625 combinations of X- and Y-offsets, tilt angle, and airgap at various levels (±0.5 mm in X/Y, ±0.5° tilt, 1.9–3.1 mm airgap). Across the tolerance box, the accuracy under all test cases remained within ±1 electrical degree. The accuracy analysis through Fourier series on a circle shows that the DC offset and magnitude mismatches of the 3 Rx signals are the dominant error contributors due to the routing modification. An Extreme Gradient Boosting (XGBoost) model was trained and validated with R² = 0.9951. A comparison with a Multiple Linear Regression baseline (R² = 0.0565) demonstrates that installation-induced accuracy degradation is inherently non-linear. The SHapley Additive exPlanations (SHAP) and interaction intensity analysis identified tilt and Y-offset as dominant error drivers, revealing a strong coupled influence (interaction intensity = 0.9581). The model revealed a mild Y-axis asymmetry introduced by routing modifications. This integrated workflow provides a general, quantitative framework for optimizing and analyzing inductive sensor layouts and establishing installation tolerances. Full article

Fiber-reinforced polymer (FRP) composites are increasingly used in civil, marine, offshore, and energy infrastructure, where components routinely experience temperatures above ambient conditions. While the design of these components is largely driven by fiber-dominated characteristics, the deterioration of shear properties can lead to premature weakening and even failure. Thus, the performance and reliability of these systems depend intrinsically on the response of interlaminar shear characteristics, in-plane shear characteristics, and flexure-based shear characteristics to thermal loads ranging from uniform and monotonically increasing to cyclic and spike exposures. This paper presents a critical review of current knowledge of shear response in the presence of thermal exposure, with emphasis on temperature regimes that are below T_g in the vicinity of T_g and approaching T_d. Results show that thermal exposures cause matrix softening and microcracking, interphase degradation, and thermally induced residual stress redistribution that significantly reduces shear-based performance. Cyclic and short-duration spike/flash exposures result in accelerated damage through thermal fatigue; steep thermal gradients, including through the thickness; and localized interfacial failure loading to the onset of delamination or interlayer separation. Aspects such as layup/ply orientation, fiber volume fraction, degree of cure, and the availability and permeation of oxygen through the thickness can have significant effects. The review identifies key contradictions and ambiguities, pinpoints and prioritizes areas of critically needed research, and emphasizes the need for the development of true mechanistic models capable of predicting changes in shear performance characteristics over a range of thermal loading regimes. Full article

In this study, the gradient pressure enrichment method was first used to screen out an environmental bacterium with the degradation ability of typical petroleum hydrocarbons such as phenanthrene and n-hexadecane, identified as Pseudomonas and named TB-1, from soil samples collected from 9 crude oil-contaminated sites; then, enhanced degradation of mixed organic pollutants, including petroleum and chlorinated hydrocarbons which are commonly coexistent, was achieved by a dual-bacteria system, with the addition of a laboratory storage strain Pseudomonas BL5. The degradation rate of phenanthrene and n-hexadecane by the dual-bacteria system was lower compared with the single bacterium Pseudomonas TB-1 under the tested conditions: phenanthrene degradation decreased from 44.2% to 23.1%, and n-hexadecane degradation decreased from 77.9% to 54.7% at a pollutant concentration of 100 mg/L after 7 days of cultivation. In contrast, the degradation ability of the dual-bacteria system against the mixed pollutants composed of petroleum and chlorinated hydrocarbons was good, with a degradation rate of 82.2% for phenanthrene, 89.2% for n-hexadecane, 73.1% for p-chlorobenzene, and 95.7% for dichloroethane with each concentration of 100 mg/L after 7 days. These results indicate that, although the dual-bacteria system does not enhance degradation under single-hydrocarbon conditions, its performance under chemically complex co-contamination suggests a potential cooperative or complementary interaction between the two strains. Such interactions are proposed here as a working hypothesis rather than a confirmed mechanism. Overall, the defined dual-Pseudomonas system shows promising potential for the treatment of environments co-contaminated with petroleum and chlorinated hydrocarbons. Full article

Lithium-ion batteries are widely applied in transportation, communication, and other fields. Nevertheless, during prolonged cycling operation, internal electrochemical reactions inevitably lead to the degradation of the state-of-health (SOH). To ensure the reliability and safety of lithium-ion batteries, accurate SOH estimation is of critical importance. Nevertheless, under practical operating conditions, obtaining fully recorded charge–discharge data is often impractical. Motivated by the practical charging behaviors of lithium-ion batteries, this paper proposes a practical SOH estimation method based on incremental capacity analysis, dynamic time warping (DTW), and gradient-boosting regression trees (GBRTs). Three health indicators—interval incremental capacity features, local capacity–voltage curve similarity, and segmented voltage curve similarity—are extracted. The proposed method requires only 0.13 V and 0.07 V voltage windows on the Oxford and CALCE datasets. The effectiveness of the proposed model is verified across both public datasets and laboratory test data. Experimental results demonstrate RMSE values of approximately 2.5% and 2.0%, respectively. Compared with mainstream SOH estimation algorithms, the proposed approach delivers comparable accuracy while achieving training time reductions of up to 57.6% and 91.9% relative to GPR and SVM, making it suitable for real-time battery management systems. Full article

The operational reliability of Insulated Gate Bipolar Transistor (IGBT) modules in demanding transportation applications, such as traction systems, is critically challenged by solder layer and bond wire failures under cyclic thermal stress. To address this, this paper proposes a novel health monitoring framework that innovatively synergizes micro-scale spatial thermal analysis with microsecond electrical dynamics inversion. The method requires only non-invasive temperature measurements on the module baseplate and utilizes standard electrical signals (load current, duty cycle, switching frequency, DC-link voltage) readily available from the converter’s controller, enabling simultaneous diagnosis without dedicated voltage or high-bandwidth current sensors. First, a non-invasive assessment of solder layer fatigue is achieved by correlating the normalized thermal gradient ($\nabla T_{\mathrm{P}}$) on the baseplate with the underlying thermal impedance ($Z_{\mathrm{JC}}$). Second, for bond wire aging, a cost-effective inversion algorithm estimates the on-state voltage ($V_{\mathrm{ce},\mathrm{on}}$) by calculating the total power loss from temperature, isolating the conduction loss ($P_{\mathrm{cond}}$) with the aid of a Foster-model-based junction temperature ($T_{\mathrm{J}}$) estimate, and finally computing $V_{\mathrm{ce},\mathrm{on}}$ at a unique current inflection point ($I_{\mathrm{C},\inf }$) to nullify $T_{\mathrm{J}}$ dependency. Third, the health states from both failure modes are fused for comprehensive condition evaluation. Experimental validation confirms the method’s accuracy in tracking both degradation modes. This work provides a practical and economical solution for online IGBT condition monitoring, enhancing the predictive maintenance and operational safety of transportation electrification systems. Full article

Carbon sequestration and oxygen release (CSOR) are core regulating functions of terrestrial ecosystems. However, regional assessments often fail to (i) separate scale-driven high supply from per-area efficiency, (ii) detect structural instability and degradation risk from long-term trajectories, and (iii) provide evidence that is comparable across units for management prioritization. Using Minnesota, USA, we integrated satellite-derived net primary productivity (NPP; 1998–2021) with a Quantity–Intensity–Structure (Q–I–S) framework to quantify CSOR, detect trends and change points (Mann–Kendall and Pettitt tests), map spatial clustering and degradation risk (Exploratory Spatial Data Analysis, ESDA), and attribute natural and human drivers (principal component regression and GeoDetector). CSOR increased overall from 1998 to 2021, with a marked shift around 2013 from a slight, variable decline to sustained recovery. Spatially, CSOR showed a persistent north–south gradient, with higher and improving services in northern Minnesota and lower, more degraded services in the south; persistent degradation was concentrated in a central high-risk belt. The Q–I–S framework also revealed inconsistencies between total supply and condition, identifying high-supply yet degrading areas and low-supply areas with recovery potential that are not evident from the totals alone. Climate variables primarily controlled CSOR quantity and structure, whereas human factors more strongly influenced intensity; the interactions of the two further shaped observed patterns. These results provide an interpretable and transferable basis for diagnosing degradation and prioritizing restoration under long-term environmental change. Full article

A biomimetic cartilage scaffold featuring a continuous hydroxyapatite (HA) concentration gradient and a spatially curved architecture was developed using a dual-channel mixing extrusion-based 3D printing approach. By dynamically regulating the feeding rates of two bioinks during printing, a continuous HA gradient decreasing from the bottom to the top of the scaffold was precisely achieved, mimicking the compositional transition from the calcified to the non-calcified cartilage region in native articular cartilage. The integration of gradient material deposition with synchronized multi-axis motion enabled accurate fabrication of curved geometries with high structural fidelity. The printed scaffolds exhibited stable swelling and degradation behavior and showed improved compressive performance compared with step-gradient counterparts. Rheological analysis confirmed that the bioinks possessed suitable shear-thinning and recovery properties, ensuring printability and shape stability during extrusion. In vitro evaluations demonstrated good cytocompatibility, supporting bone marrow mesenchymal stem cell (BMSC) adhesion and proliferation. Chondrogenic assessment based on scaffold extracts indicated that the incorporation of HA and its gradient distribution did not inhibit cartilage-related extracellular matrix synthesis, confirming the biosafety of the composite hydrogel system. Overall, this study presents a controllable and versatile fabrication strategy for constructing curved, compositionally graded cartilage scaffolds, providing a promising platform for the development of biomimetic cartilage tissue engineering constructs. Full article

Reversed-phase high-performance liquid chromatography (RP-HPLC) remains one of the most widely applied analytical techniques in the development and quality control testing of finished pharmaceutical products. The combination of gradient chromatographic methods with diode-array detection (DAD) enhances selectivity, ensuring accuracy and reliability when testing drugs with diverse chemical properties in a single dosage form (i.e., fixed-dose combination (FDC) products). In this study, an RP-DAD-HPLC method was developed for the quantitative analysis of clofazimine (CFZ) and pyrazinamide (PZA) for inclusion in an FDC topical drug delivery system. Chromatographic separation was achieved using a C18 column (4.6 mm × 150 mm, 5 µm particle size) with gradient elution at 1 mL/min, employing 0.1% aqueous formic acid and acetonitrile (mobile phases). PZA and CFZ were detected at 254 nm and 284 nm, respectively. The method was validated in accordance with ICH Q2 guidelines, assessing specificity (considering interference from solvents, product matrix, and degradation products), linearity (7.8–500.0 µg/mL, r² = 0.9999), system repeatability (%RSD ≤ 2.7%), and intermediate precision (25–500 µg/mL, %RSD ≤ 0.85%). Method robustness was evaluated using a three-level Box–Behnken design (BBD) with response surface methodology (RSM) to assess the effects of variations in detection wavelength, mobile phase flow rate, and column temperature. Full article

Additive manufacturing has been adopted in several industries including the medical field to develop new personalised medical implants including tissue engineering scaffolds. Custom patient-specific scaffolds can be additively manufactured to speed up the wound healing process. The aim of this study was to design, fabricate, and evaluate a range of materials and scaffold architectures for 3D-printed wound dressings intended for soft tissue applications, such as skin repair. Multiple biocompatible polymers, including polylactic acid (PLA), polyvinyl alcohol (PVA), butenediol vinyl alcohol copolymer (BVOH), and polycaprolactone (PCL), were fabricated using a material extrusion additive manufacturing technique. Eight scaffolds, five with circular designs (knee meniscus angled (KMA), knee meniscus stacked (KMS), circle dense centre (CDC), circle dense edge (CDE), and circle no gradient (CNG)), and three square scaffolds (square dense centre (SDC), square dense edge (SDE), and square no gradient (SNG), with varying pore widths and gradient distributions) were designed using an open-source custom toolpath generator to enable precise control over scaffold architecture. An in vitro degradation study in phosphate-buffered saline demonstrated that PLA exhibited the greatest material stability, indicating minimal degradation under the tested conditions. In comparison, PVA showed improved performance relative to BVOH, as it was capable of absorbing a greater volume of exudate fluid and remained structurally intact for a longer duration, requiring up to 60 min to fully dissolve. Tensile testing of PLA scaffolds further revealed that designs with increased porosity towards the centre exhibited superior mechanical performance. The strongest scaffold design exhibited a Young’s modulus of 1060.67 ± 16.22 MPa and withstood a maximum tensile stress of 21.89 ± 0.81 MPa before fracture, while maintaining a porosity of approximately 52.37%. This demonstrates a favourable balance between mechanical strength and porosity that mimics key properties of engineered tissues such as the meniscus. Overall, these findings highlight the potential of 3D-printed, patient-specific scaffolds to enhance the effectiveness and customisation of tissue engineering treatments, such as meniscus repair, offering a promising approach for next-generation regenerative applications. Full article

Brain magnetic resonance imaging (MRI) is highly susceptible to motion artifacts that degrade fine structural details and undermine quantitative analysis. Conventional U-Net-based deep learning approaches for motion artifact reduction typically operate only in the image domain and are often trained on data with simplified motion patterns, thereby limiting physical plausibility and generalization. We propose Sim-DDNet, a simulation-data-based dual-domain network that combines k-space-based motion simulation with a joint image-k-space reconstruction architecture. Motion-corrupted data were generated from T2-weighted Alzheimer’s Disease Neuroimaging Initiative brain MR scans using a k-space replacement scheme with three to five random rotational and translational events per volume, yielding 69,283 paired samples (49,852/6969/12,462 for training/validation/testing). Sim-DDNet integrates a real-valued U-Net-like image branch and a complex-valued k-space branch using cross attention, FiLM-based feature modulation, soft data consistency, and composite loss comprising L1, structural similarity index measure (SSIM), perceptual, and k-space-weighted terms. On the independent test set, Sim-DDNet achieved a peak signal-to-noise ratio of 31.05 dB, SSIM of 0.85, and gradient magnitude similarity deviation of 0.077, consistently outperforming U-Net and U-Net++ across all three metrics while producing less blurring, fewer residual ghost/streak artifacts, and reduced hallucination of non-existent structures. These results indicate that dual-domain, data-consistency-aware learning, which explicitly exploits k-space information, is a promising approach for physically plausible motion artifact correction in brain MRI. Full article

Underwater images often suffer from luminance attenuation, structural degradation, and color distortion due to light absorption and scattering in water. The variations in illumination and color distribution across different water bodies further increase the uncertainty of these degradations, making traditional enhancement methods that rely on fixed parameters, such as underwater dark channel prior (UDCP) and histogram equalization (HE), unstable in such scenarios. To address these challenges, this paper proposes a multi-operator underwater image enhancement framework with adaptive parameter optimization. To achieve luminance compensation, structural detail enhancement, and color restoration, a collaborative enhancement pipeline was constructed using contrast-limited adaptive histogram equalization (CLAHE) with highlight protection, texture-gated and threshold-constrained unsharp masking (USM), and mild saturation compensation. Building upon this pipeline, an adaptive multi-operator parameter optimization strategy was developed, where a unified scoring function jointly considers feature gains, geometric consistency of feature matches, image quality metrics, and latency constraints to dynamically adjust the CLAHE clip limit, USM gain, and Gaussian scale under varying water conditions. Subjective visual comparisons and quantitative experiments were conducted on several public underwater datasets. Compared with conventional enhancement methods, the proposed approach achieved superior structural clarity and natural color appearance on the EUVP and UIEB datasets, and obtained higher quality metrics on the RUIE dataset (Average Gradient (AG) = 0.5922, Underwater Image Quality Measure (UIQM) = 2.095). On the UVE38K dataset, the proposed adaptive optimization method improved the oriented FAST and rotated BRIEF (ORB) feature counts by 12.5%, inlier matches by 9.3%, and UIQM by 3.9% over the fixed-parameter baseline, while the adjacent-frame matching visualization and stability metrics such as inlier ratio further verified the geometric consistency and temporal stability of the enhanced features. Full article

Glass-insulated terminals (GITs) are widely used in high-reliability microelectronic systems, where glass fall-offs in the sealing region may seriously degrade the reliability of the microelectronic component and further degrade the device reliability. Automatic inspection of such defects is challenging due to strong light reflection, irregular defect appearances, and limited defective samples. To address these issues, a coarse-to-fine machine-learning framework is proposed for glass fall-off detection in GIT images. By exploiting the circular-ring geometric prior of GITs, an adaptive sector partition scheme is introduced to divide the region of interest into sectors. Four categories of sector features, including color statistics, gray-level variations, reflective properties, and gradient distributions, are designed for coarse classification using a gradient boosting decision tree (GBDT). Furthermore, a sector neighbor (SN) feature vector is constructed from adjacent sectors to enhance fine classification. Experiments on real industrial GIT images show that the proposed method outperforms several representative inspection approaches, achieving an average IoU of 96.85%, an F1-score of 0.984, a pixel-level false alarm rate of 0.55%, and a pixel-level missed alarm rate of 35.62% at a practical inspection speed of 32.18 s per image. Full article

Aiming to address insufficient feature extraction, vanishing gradients, and low prediction accuracy in tool wear prediction, this paper proposes a hybrid deep neural network based on a Convolutional Neural Network (CNN), Residual Network (ResNet) residual connections, the Convolutional Block Attention Module (CBAM), and a Bidirectional Gated Recurrent Unit (BiGRU). First, a 34-dimensional multi-domain feature set covering the time domain, frequency domain, and time–frequency domain is constructed, and multi-sensor signals are standardized using z-score normalization. A CNN–BiGRU backbone is then established, where ResNet-style residual connections are introduced to alleviate training degradation and mitigate vanishing-gradient issues in deep networks. Meanwhile, CBAM is integrated into the feature extraction module to adaptively reweight informative features in both channel and spatial dimensions. In addition, a BiGRU layer is embedded for temporal modeling to capture bidirectional dependencies throughout the wear evolution process. Finally, a fully connected layer is used as a regressor to map high-dimensional representations to tool wear values. Experiments on the PHM2010 dataset demonstrate that the proposed hybrid architecture is more stable and achieves better predictive performance than several mainstream deep learning baselines. Systematic ablation studies further quantify the contribution of each component: compared with the baseline CNN model, the mean absolute error (MAE) is reduced by 47.5%, the root mean square error (RMSE) is reduced by 68.5%, and the coefficient of determination (R²) increases by 14.5%, enabling accurate tool wear prediction. Full article

Welding repair of cracks in FSX-414 cobalt-based alloy, used in high-temperature components, poses significant challenges due to the presence of surface oxide films within the cracks. By comparing the formation of oxide films on the external surface and inside the narrow crevice of FSX-414 alloys preserved at 900 °C for up to 1000 h, we found that the oxide film growth rate on the external surface was slightly larger than that inside the narrow crevice, and the latter slowed down after 672 h. Additionally, the oxide films on both surfaces were mainly composed of O and Cr elements, providing excellent protection to the underlying metal and resulting in minimal internal oxidation. A compositional transition region formed between the oxide film and the base metal. The width of the transition region decreased with heating duration and was narrower in the external surface sample, leading to a steeper composition gradient between the oxide film and the inner metal. With prolonged exposure, increasing numbers of “pores” rich in W and O appeared near the oxide films, creating channels that connect the oxide layer with the internal metal and accelerate material degradation. “Pores” extended deeper into the metal within the narrow crevice compared to those on the surface. Prior to welding repair, channels composed of W and O near the oxide films must be cleaned along with the oxide layer itself, and the removal of oxide from narrow cracks poses greater difficulty. Full article