Search Results (8,799)

Chunmee tea quality is critical to its grading, and accurate identification is essential for quality evaluation and market valuation. However, traditional machine learning relies on manual feature extraction and causes spectral information loss, while conventional one-dimensional convolutional neural networks (1D-CNNs) are restricted by fixed kernels and narrow receptive fields, making multi-scale feature capture difficult. In this study, an improved DeepSpectra model integrated with the Inception module and residual connections was proposed for end-to-end automatic grading of Chunmee tea. A total of 360 samples across six grades (60 samples per grade) were collected using an Antaris II near-infrared spectrometer and preprocessed by multiplicative scatter correction (MSC). The proposed model was compared with other models. Results showed that under a 7:1:2 train–validation–test split, the proposed DeepSpectra achieved an average test accuracy of 96.39 ± 1.63% across ten random sample divisions, significantly outperforming the other models (p < 0.05). The model also exhibited excellent stability in five-fold cross-validation and superior generalization in small-sample scenarios, and a lightweight structure with low inference latency of 2.2 ms, which is suitable for real-time industrial applications. This work provides a reliable, efficient, and end-to-end method for grading Chunmee tea and offers a promising strategy for intelligent and rapid quality control of green tea. Full article

As new energy vehicles and energy storage systems become more common, safety accidents caused by lithium-ion batteries overheating have become more of a concern. Early detection based on distinctive gases (such as H₂ and CO) can give an earlier warning than typical monitoring methods like temperature, voltage, or impedance. Nonetheless, attaining high-precision identification in intricate mixed-gas settings continues to be difficult because of the considerable cross-sensitivity of metal oxide semiconductor (MOS) gas sensors. This research presents an ISA-LSTM-TCN multi-task learning model utilizing an enhanced spatial attention mechanism for the swift identification and concentration forecasting of distinctive gases during lithium-ion battery thermal runaway. The model improves key feature extraction and anti-noise performance by combining the long-term temporal modeling ability of the Long Short-Term Memory (LSTM) network with the multi-scale feature extraction ability of the Temporal Convolutional Network (TCN). It also adds an Improved Spatial Attention (ISA) module with a residual multiplication structure. Moreover, in a multi-task learning framework, joint optimization of gas categorization and concentration regression is facilitated using a hard parameter-sharing method. Tests using a built MOS sensor array dataset show that the model is 99.23% accurate at classifying gases and that the $R^2$ values for predicting H₂ and CO concentrations are 0.9510 and 0.8400, respectively. Tests on public datasets and in different noisy environments show that the model is even better at generalizing and is more robust. The results show that the suggested method allows for quick, accurate detection of thermal runaway gases. This makes it an effective and smart way to monitor battery safety warning systems. Full article

Automatic detection of sensitive fields in structured data is a critical prerequisite for privacy compliance and data governance. However, existing approaches face severe cross-domain generalization challenges. Hand-crafted pattern rules often fail under highly heterogeneous naming conventions, while single statistical models tend to overfit and degrade sharply under distribution shifts between training and deployment domains. These limitations stem from the weak semantic signals and distributional heterogeneity of structured data, which make it difficult to simultaneously capture explicit rules and latent, variant-sensitive attributes. To address these challenges, we propose a detection framework based on multi-view complementary features and a Hierarchical Gated Residual Network (HGRN). The framework first constructs a full-spectrum feature system that integrates explicit rules and implicit statistical fingerprints (e.g., entropy and character texture) to fill the semantic gap. It then introduces a decision mechanism combining robust priors with dynamic residual calibration: a random forest provides a stable probabilistic anchor, which is further nonlinearly corrected by a learnable gating-and-expert network. This design explicitly resolves the cognitive conflict between rule-dominated regions and complex distributional regions. Experiments on multiple real-world datasets—including DeSSI, CMS Open Payments and Home Credit—show that the proposed method achieves a Macro-F1 of 0.9408 on DeSSI and exhibits strong in-domain performance. Under strict frozen-model cross-domain transfer, HGRN mitigates the catastrophic collapse observed in pure neural baselines and maintains moderate detection capability, offering interpretable trust allocation between rule-based priors and data-driven correction in both financial and healthcare scenarios. Full article

With the increasing integration of high-penetration renewable energy and emerging loads, power system operation simulation faces two major challenges, namely strong uncertainty and significant heterogeneity in the output characteristics of multiple generator types. Traditional mathematical programming methods struggle to effectively handle uncertainty while meeting real-time computational requirements. Existing deep learning approaches fail to decouple the heterogeneous output characteristics of different generator types, which limits their ability to achieve coordinated operation. To address these issues, this paper proposes a task-decoupled and multi-task synergistic LLM-MoE method for power system operation simulation. First, a feature encoder based on Residual-Gated Linear Units is constructed to perform deep filtering and efficient representation of multi-source heterogeneous data. Second, a pre-trained large language model is employed as a temporal feature extractor to enhance temporal modeling capability and cross-scenario generalization. Finally, a customized gating-controlled mixture-of-experts decoder is developed. It dynamically coordinates task-specific and shared experts, which enables unified modeling of task decoupling, cross-task information sharing, and system physical constraints. Simulation results based on a provincial-level power grid in China demonstrate that the proposed method achieves high-accuracy and high-efficiency operation simulation while ensuring physical consistency. Full article

In this paper, we introduce a new extension of the Topp–Leone-G family, called the generalized Marshall–Olkin Topp–Leone-G (GMOTL-G) family of distributions. The proposed family is obtained by combining the generalized Marshall–Olkin and Topp–Leone-G generators, leading to a more flexible class of models for lifetime data. We study several of its mathematical and statistical properties and focus in particular on the generalized Marshall–Olkin Topp–Leone exponential (GMOTL-E) distribution as an important special case. For this model, we derive and discuss a number of useful characteristics, including the moment generating function, moments, order statistics, residual and reversed residual life functions, mean deviations, asymptotic behavior, and stochastic ordering. We also develop maximum likelihood estimation for the model parameters under both complete and right-censored samples. In addition, we construct a goodness-of-fit test for the proposed model under independent right censoring using a chi-square type approach. The performance of the estimation and testing procedures is investigated through simulation, and the results show good behavior of the estimators and satisfactory agreement between empirical and theoretical significance levels. Finally, two real data applications, one with complete data and one with right-censored data, are presented to illustrate the flexibility and practical usefulness of the proposed model. These results show that the new family provides an effective tool for modeling lifetime data and for assessing model adequacy in the presence of right censoring. Full article

This study aimed to investigate the regulatory effect and cryoprotective mechanism of melatonin (MT) on the physiological functions of Lactobacillus plantarum FQR during freezing and freeze-drying. Results indicated that the addition of 5 mg/mL MT as a cryoprotectant maximized the freeze-drying survival rate to 32.04 ± 2.14%. MT effectively alleviated low-temperature and freeze-drying stress by reducing extracellular alkaline phosphatase activity, enhancing intracellular lactate dehydrogenase activity, and decreasing extracellular β-galactosidase activity without significant differences. Higher survival rates in defining medium further suggested that MT reduced damage to cell wall and membrane structures during lyophilisation, decreased membrane permeability, and preserved cellular physiological functions. In addition, MT supported cellular energy metabolism and protein synthesis, enhanced transmembrane potential to facilitate ATP transport, and helped maintain intracellular and extracellular pH balance. The prepared freeze-drying protectant containing 69.80 mg/mL exopolysaccharides (EPS) and 4.25 mg/mL MT showed better protective effects than the control group. MT also increased bound water content, lowered the freezing point of the solution, and inhibited ice crystal formation. Transcriptomic analysis revealed that amino acid biosynthesis, amino acid metabolism, and ABC transport systems were the primary pathways affected by MT treatment. These findings demonstrate that MT improves freeze-drying tolerance by maintaining membrane integrity, regulating cellular metabolism, and enhancing oxidative stress resistance. Given its natural biosynthetic origin, generally recognized as safe (GRAS) status, and absence of residual solvents or allergenic proteins, MT can be safely considered for incorporation into food and nutraceutical products. This study underscores the practical relevance of MT as a functional component in compound cryoprotectants, providing a feasible strategy to enhance the viability, stability, and industrial applicability of Lactobacillus plantarum during freeze-drying and storage. Full article

Background: In zoster-associated pain (ZAP), earlier epidural steroid injection (ESI) has been associated with better outcomes, but optimal timing remains unclear, and prior studies have largely relied on pain reduction alone. Methods: In this multicenter retrospective cohort, 215 patients with ZAP who completed a three-session ESI course were classified into early (<30 days) and delayed (≥30–≤180 days) groups. The primary endpoint was complete remission at 12 weeks (≥50% visual analog scale [VAS] reduction, VAS ≤ 2, and sensory normalization); successful response (≥50% VAS reduction) served as the secondary endpoint. An ordered three-category framework and an exploratory generalized Youden index threshold analysis were applied. Results: Complete remission occurred in 82.1% versus 39.0% and successful response in 91.7% versus 67.8%. Each additional day of delay was associated with lower odds of complete remission (adjusted odds ratio [aOR], 0.957; p < 0.001) and higher odds of a worse outcome category (aOR, 1.030; p < 0.001). Exploratory candidate boundaries were 22 and 42 days. Conclusions: Earlier ESI initiation was associated with a higher likelihood of complete remission incorporating pain reduction, low residual pain intensity, and sensory normalization. These findings highlight the clinical relevance of treatment timing and recovery assessment beyond pain reduction alone in ZAP. Full article

Background: Persistent lymph node metastases after neoadjuvant radiochemotherapy (RCT) for locally advanced rectal cancer indicate poor response to treatment. This study evaluated the long-term prognosis of patients with residual nodal disease following neoadjuvant RCT and total mesorectal excision (TME) in comparison with patients who underwent upfront TME without adjuvant radiotherapy. Methods: Consecutive patients with rectal adenocarcinoma and histopathologically confirmed lymph node metastases after TME were identified from the prospectively maintained database of the colorectal unit at Dresden-Friedrichstadt General Hospital. Patients with distant metastases, in-hospital mortality, or postoperative radiotherapy were excluded. The two groups were comprehensively compared regarding patient-, tumor-, and treatment-related characteristics. Cumulative local recurrence, time to recurrence, cancer-specific survival, and overall survival were analyzed using the Kaplan–Meier method. Results: Between 1996 and 2021, 155 eligible patients were identified, including 101 patients in the RCT group and 54 in the upfront surgery group. Baseline characteristics were largely comparable, except for a higher median age (70.5 vs. 64 years, p < 0.001) and a higher proportion of lymphovascular invasion (36.0% vs. 15.2%, p = 0.004) in the upfront surgery group. Ten-year local recurrence rates were similar between groups (21.0% [95% CI: 10.4–31.6] vs. 20.8% [95% CI: 8.5–33.1], p = 0.609). No significant differences were observed in time to recurrence or cancer-specific survival. Overall survival was lower in the upfront surgery group, most likely reflecting the substantially higher age of these patients. Conclusions: Despite more intensive treatment, patients with a persistent ypN-positive category have outcomes no better than untreated patients with node-positive disease after TME, indicating a biologically high-risk subgroup. Non-response is therefore a sign of a negative selection. These patients may lose the opportunity for optimal local tumor control during prolonged neoadjuvant treatment, underscoring the urgent need for reliable predictive markers to identify non-responders and guide individualized treatment strategies. Full article

With the rapid development of the edible fungus industry, the environmental pressure and resource waste caused by the massive generation of fungal residue have become increasingly prominent. Meanwhile, heavy metal wastewater pollution and the growing demand for clean energy pose dual challenges to sustainable development. This study focuses on Auricularia cornea var. Li. fungal residue, exploring the establishment of a multi-level resource utilization pathway integrating “porous carbon material preparation—heavy metal adsorption—photocatalytic hydrogen evolution.” Firstly, the Auricularia cornea var. Li. residue-based porous carbon material was examined by combining hydrothermal carbonization, activation and slow pyrolysis. In optimal conditions, the porous carbon obtained yielded a surface area of 675.56 m²/g and formed a composite pore structure consisting of micropores with coexisting micropore and mesopore. Secondly, we performed batch adsorption experiments to study the effects of solution pH, adsorbent dosage and contact time and the adsorption behavior via fitting adsorbing kinetic models. Under optimal conditions, Cd(II) removal efficiency reached 92.36% and an equilibrium adsorption capacity of 92.47 mg/g. We used Cd(II) adsorbed porous carbon as a cadmium source and converted into a CdS photocatalyst using a hydrothermal sulfidation process. The CdS prepared using sodium sulfide as a sulfur source gave an average hydrogen evolution rate of 668.01 μmol·g⁻¹·h⁻¹ and showed higher photocatalytic performance for water splitting to produce hydrogen. Full article

Surrogate models for nuclear reactor calculations typically provide point predictions without quantifying uncertainty, limiting their use in risk-informed applications. While several studies have applied machine learning to reactor physics, systematic evaluation of prediction interval calibration against Monte Carlo statistical uncertainty remains underexplored. This study develops a Gaussian Process regression (GPR) surrogate model that provides both accurate predictions and calibrated uncertainty estimates for the infinite multiplication factor ($k_{\infty }$) of a pressurized water reactor pin-cell. A dataset of 400 OpenMC Monte Carlo simulations was generated using Latin Hypercube Sampling across boron concentration (0–2000 ppm), fuel temperature (600–1200 K), and moderator temperature (500–600 K). The GPR model achieves $R^2=0.9971$ with prediction errors below the Monte Carlo statistical uncertainty ($\mathrm{MAE}/{\sigma }_{\mathrm{MC}}=0.75$), indicating that model accuracy is limited only by inherent training data noise. The key contribution is demonstrating that GPR prediction intervals are well-calibrated, achieving 92.5% coverage for 95% confidence bounds (bootstrap 95% CI: [87.5%, 97.5%], containing the nominal level; binomial test p = 0.297), with mean prediction uncertainty closely matching the Monte Carlo statistical uncertainty (${\sigma }_{\mathrm{GPR}}=0.00192$ vs. ${\sigma }_{\mathrm{MC}}=0.00200$). This near-perfect match suggests the surrogate has captured essentially all deterministic variation, with residual uncertainty attributable to Monte Carlo noise alone. Variance-based sensitivity analysis confirms boron concentration accounts for 99% of output variance. The surrogate preserves physically meaningful reactivity coefficients (Doppler: $-2.1$ pcm/K; boron worth: $-6.1$ pcm/ppm) while providing ${10}^5$-fold computational speedup. The framework is restricted to fresh fuel with fixed enrichment; extension to burnup-dependent scenarios is left for future work. Full article

Impinging injectors are extensively utilized in liquid rocket engines, characterized by a large number of paired inclined injection orifices. The diameter and axis alignment deviation of these orifices directly influence propellant flow distribution, atomization and mixing behavior, and engine operational stability. To address the challenges associated with micro-sized orifices, inclined axes, large quantities, spatial intersection, and the low detection efficiency of conventional approaches, this paper proposes a dual-line laser 3D point cloud reconstruction-based method for measuring the diameter and impact alignment deviation of injector orifices. A dual-line laser measurement system is established to capture surface point clouds on both sides of the orifice inlets. Through system calibration and point cloud registration, the 3D point cloud data of the injector orifices within a unified coordinate system are reconstructed. Cross-sectional mapping, boundary extraction, and geometric fitting techniques are applied to determine the diameter and axis parameters of the orifices, and the spatial alignment deviation of paired orifices is subsequently calculated. To validate the feasibility of the proposed method, experimental investigation is conducted on test specimens with both 8 pairs of Φ2 mm through-holes and Φ0.5 mm micro-orifices. For the Φ2 mm specimen, the diameter measurement results are compared with industrial computed tomography (CT) data, while the alignment deviation results are verified using a combination of pin gauges and coordinate measuring machine (CMM) measurements. For the Φ0.5 mm micro-orifices, both diameter and alignment deviation results are verified using a 3D coaxial line confocal sensor. After system calibration, the fitting residuals of three Φ8 mm standard spheres are all maintained within 0.08 mm. The diameter measurement results of 8 selected Φ2 mm orifices show good overall agreement with industrial CT data: the maximum absolute deviation is 22 μm, the average absolute deviation is 15 μm, the maximum relative error is 1.09%, and the average relative error is 0.74%. The diameter and alignment deviation results of Φ0.5 mm micro-orifices show good consistency with the 3D coaxial line confocal sensor: the maximum absolute deviation is 13 μm for diameter and 0.047° for alignment deviation, with maximum relative errors of 2.41% and 0.058%, respectively. The alignment deviation results of 8 pairs of Φ2 mm orifices indicate that the proposed dual-line laser method is generally consistent with the combined pin gauge and CMM approach: the maximum absolute deviation is 0.170°, the average absolute deviation is 0.125%, the maximum relative error is 0.284%, and the average relative error is 0.125%. The results demonstrate that the proposed method enables non-contact and high-efficiency measurement of the diameter and alignment angle of injector orifices in impinging injectors for both conventional Φ2 mm orifices and micro Φ0.5 mm orifices, with high measurement accuracy and promising engineering application potential, thereby providing a new technical approach for the geometric parameter inspection of multi-scale micro-injection orifices. Full article

Background: Achilles tendon re-rupture following operative repair remains a challenging complication, and biomechanical evidence guiding revision strategies is limited. The mechanical behavior of commonly used revision constructs has not been well characterized. The objective of this exploratory study was to provide a descriptive biomechanical characterization of commonly used Achilles tendon revision constructs, focusing on viscoelastic behavior, load-to-failure properties, and failure mechanisms under standardized loading conditions. Although limited by the absence of construct replication, this study provides hypothesis-generating biomechanical insight into the failure mechanisms of revision constructs, which may inform future comparative studies and surgical strategy selection. Methods: Four fresh-frozen human cadaveric lower limbs underwent standardized Achilles tendon transection with segmental excision to simulate revision conditions. Five revision techniques were evaluated: tensioned cross-lock Bunnell, Krakow, posterior tibial tendon (PTT) augmentation with Bunnell repair, double Kessler with circumferential running suture, and V–Y advancement combined with three simple sutures and double Kessler. All repairs were performed using No. 2 high-strength suturing by a single surgeon. Constructs underwent stress relaxation testing under a constant 100 N load followed by uniaxial load-to-failure testing. Mechanical parameters and failure modes were recorded. Results: All constructs demonstrated time-dependent stress relaxation. The tensioned cross-lock Bunnell repair retained the highest residual force during sustained loading. The PTT-augmented construct exhibited the highest load to failure among the constructs tested and failed at the tendon substance, whereas non-augmented repairs failed predominantly at the suture–tendon interface. The V–Y advancement construct failed at relatively low applied loads under the applied testing protocol. Conclusions: Achilles tendon revision constructs demonstrate distinct biomechanical behaviors. Augmented constructs exhibited higher resistance to tensile loading in this experimental setting and shifted failure away from the repair site, while non-augmented repairs were limited by suture–tendon interface strength. Given that each construct was tested only once and that one specimen was used sequentially for two repairs, the findings should be interpreted strictly as descriptive and hypothesis-generating, without any basis for comparative or inferential conclusions. Full article

Accurate segmentation of seismic salt bodies is of great significance for oil and gas exploration. Although existing deep learning methods have achieved remarkable progress within a single survey, segmentation performance degrades significantly when deployed to target surveys that exhibit systematic differences in geological settings and salt morphology, owing to a distributional shift in feature space. To address the cross-domain generalization problem, this paper first designs a CNN-Transformer dual-branch fusion network, DBF-CTSaltNet, as the backbone, which enhances the collaborative modeling of local boundaries and global morphology through the synergy of a Morphology-Adaptive Residual Unit, a Structured Global Guidance Unit, and a Bidirectional Cross-aware Fusion Unit. Building upon this, an unsupervised domain adaptation framework, UDA-DBF-CTSaltNet, is proposed, which independently constructs class prototypes in the respective feature spaces of the two branches and simultaneously drives cross-domain alignment at both local-boundary and global-morphology semantic granularities via dual-granularity prototype contrast. Experiments demonstrate that DBF-CTSaltNet outperforms mainstream models on the TGS source domain, and UDA-DBF-CTSaltNet significantly improves cross-domain segmentation performance on both the F3 and SEAM target domains. Full article

Background: Lentigo maligna (LM) represents melanoma in situ and predominantly affects elderly individuals, typically arising on chronically sun-exposed skin of the head and neck. Although LM is characterized by slow horizontal growth and generally favourable prognosis, progression to invasive lentigo maligna melanoma may occur, making timely and effective treatment essential. Surgical excision remains the standard of care; however, advanced age, comorbidities, lesion size, and cosmetic or functional considerations may limit surgical feasibility. Case presentation: We report the case of a 93-year-old woman with no prior history of skin cancer who presented with a gradually enlarging pigmented lesion on the forehead. Clinical examination revealed an irregularly pigmented macule measuring 25 × 27 mm. Multiple mapping biopsies confirmed melanoma in situ of the lentigo maligna type, with adnexal extension and no evidence of dermal invasion. Given the patient’s advanced age and lesion location, a non-surgical approach was selected. Topical imiquimod 5% cream was applied five times per week for 12 weeks to the visible lesion and to a 20 mm margin around it. The patient was monitored closely throughout the treatment. Local inflammatory reactions were mild to moderate, consisting mainly of erythema, crusting, and superficial erosion, without systemic adverse effects. At treatment completion, marked clinical improvement with near-complete resolution of pigmentation was observed. Follow-up dermoscopic evaluation demonstrated only minimal residual granular pigmentation. Post-treatment mapping biopsies confirmed complete histological clearance of atypical melanocytic cells. Conclusions: This case illustrates that topical imiquimod may serve as a safe and effective alternative to surgery in carefully selected elderly patients with lentigo maligna. Close clinical follow-up and histological confirmation of clearance are essential to ensure treatment success and durable outcomes. Full article

Calcium carbide slag is a highly alkaline solid waste generated during acetylene production, but its long-term accumulation causes land occupation and persistent environmental risks such as soil alkalinization and water pollution. To support circular economy and carbon emission reduction goals, in this study, we develop an integrated physical decontamination–mineralization process combining calcination, magnetic separation, sedimentation, and CO₂ mineralization. After calcination, magnetic separation, and 8 h of gravity sedimentation, the removal efficiency of Si reaches about 67% (residual Si content reduces to 0.43%), while those of Fe and Al are 75.4% and 74.2%, respectively. The purified calcium-rich slurry is then used for CO₂ mineralization. Under a solid-to-liquid ratio of 10% and a CO₂ flow rate of 0.4 L/min, CO₂ is fixed as carbonate solids, yielding calcite-type CaCO₃ with 97.88% ± 0.35% purity. This process is centered on physical separation and uses no acids, alkalis, or ammonium salts, avoiding secondary pollution while achieving waste valorization and permanent CO₂ sequestration. In this study, we provide a scalable, low-impact pathway for alkaline solid waste valorization and carbon emission reduction, contributing to sustainable consumption and production (SDG 12) and climate action (SDG 13). Full article