Search Results (2,867)

Luminescent cellulose-based fibers are promising materials for anti-counterfeiting applications because they can provide covert and spectrally distinguishable optical signatures compatible with paper- and textile-based authentication systems. In this study, Lyocell fibers modified with selected inorganic and organic luminescent compounds were subjected to accelerated xenon-lamp aging in order to evaluate their functional durability under simulated environmental exposure. The effects of aging on the mechanical properties and luminescent behavior of the fibers were investigated. The results showed that accelerated aging led to a reduction in tensile strength and elongation at break for all fiber variants, although the extent of these changes depended on the type of modifier. Spectroscopic analysis indicated that, despite changes in emission intensity, the characteristic luminescent responses of the modified fibers remained detectable after aging. These findings suggest that luminescent Lyocell fibers can retain their practical identification potential under the applied test conditions and may be considered promising candidates for use as covert security elements. The observed stability is attributed to the immobilization of luminophores within the cellulose matrix and the intrinsic photostability of the applied luminescent systems. At the same time, the study highlights the need for further investigations into the structural and photophysical stability of such systems under long-term environmental exposure. Full article

With the development of geological data analysis and big data technology, intelligent mineral prospectivity mapping (MPM) has become a key direction in the integration of geoscience and artificial intelligence, showing promising applications in the identification and evaluation of strategic mineral resources such as gold. To address the limitations of conventional methods—including insufficient training samples, complex model structures, and weak capability in recognizing anomalous zones—this study proposes an improved convolutional neural network (CNN) approach for mineral prediction. A lightweight, modular CNN structure with repeatable stacking is designed to reduce computational cost while enhancing model robustness and generalization. In addition, a dynamic learning rate scheduling strategy is adopted to optimize the training process, significantly improving convergence speed and training stability. Furthermore, high-probability prediction samples and low-probability background samples are combined to form a new training dataset for regional prospectivity evaluation, yielding a high area under the curve (AUC) score. The method is applied and validated in the Xiong’ershan region, and the predicted high-potential zones account for 30% of the study area and contain 81.4% of the known gold deposits. These results demonstrate the method’s effectiveness in mineral information extraction and blind-area targeting, offering a new approach for mineral prospectivity mapping. Full article

The global demand for Salvia miltiorrhiza Bunge in the botanical medicine market is steadily increasing. However, its production has long relied on asexual root propagation, making it highly susceptible to germplasm degradation. Transitioning to seed reproduction offers the advantage of genetic renewal, yet it is constrained by unstable seed vigor and slow seedling growth. In the present study, comprehensive physiological and microbiome analyses of S. miltiorrhiza seeds from 14 regions across 7 provinces in China were conducted to elucidate the association between the seed microbiome and vigor, and to identify plant growth-promoting (PGP) strains. The results demonstrated: (1) Seed physical traits and germination characteristics varied significantly across geographic origins. Seed vigor, exhibiting the highest coefficient of variation, served as a key parameter reflecting germination quality. (2) High-vigor seeds harbored distinct microbial communities characterized by higher diversity indices, greater network complexity, and the significant enrichment of potentially beneficial bacteria (e.g., Pseudomonas). (3) Through correlation-directed screening of isolated pure cultures, Pseudomonas mendocina P-6 and Enterobacter ludwigii BM-12 were identified as exhibiting robust, multi-trait PGP capacity. In planta validation showed that these two strains significantly promoted the growth of 1-month-old S. miltiorrhiza seedlings, increasing total fresh weight by 33.9–71.3%. This study reveals the microecological drivers of seed vigor and provides candidate strains for inoculant development, thereby supporting the sustainable, seed-based propagation of S. miltiorrhiza. Full article

Mangrove represent vital coastal ecosystems that contribute to shoreline stabilization, ecological balance, and environmental management. Nevertheless, the precise delineation of mangrove regions using remote sensing data is often impeded by spectral similarities with intertidal mudflats and aquatic features, alongside the irregular spatial patterns and intricate margins of mangrove stands. This research utilizes high-resolution Gaofen-6 (GF-6) satellite observations as the foundational data to develop Triplet Axial High-Resolution Network (TAHRNet), a semantic segmentation architecture derived from the High-Resolution Network with Object-Contextual Representations (HRNet-OCR) framework for mangrove identification. The model integrates a Triplet Attention module to facilitate cross-dimensional feature dependencies and an improved Multi-Head Sequential Axial Attention mechanism to capture long-range spatial context while maintaining structural consistency. Based on evaluations using the test dataset, TAHRNet yielded a Mean Intersection over Union (MIoU) of 92.01% and a Overall Accuracy of 96.38%. Relative to U-Net and SegFormer, the proposed approach showed MIoU improvements of 5.25% and 1.88%, with corresponding Accuracy gains of 2.68% and 0.94%. Further application to coastal mapping in Zhanjiang produced results that align with manual visual interpretation. These findings suggest that TAHRNet is a viable tool for mangrove extraction and can provide technical support for coastal monitoring and ecological analysis. Full article

This paper presents the results of tests on concrete modified with waste powder from the production of epoxy powder coating, planned using design of experiment’s (DOE) experimental design methods. The scope of the investigation included detailed identification of the waste itself (TG/DTA, FTIR, SEM + EDS, laser diffraction), as well as evaluation of selected properties of concretes containing this waste, including compressive strength, density, and durability parameters such as frost resistance and chemical resistance. The scope of the experiment was defined by varying modifier content in the range of 4 to 11% of the cement mass and a water-cement ratio between 0.44 and 0.56. The concrete mixes obtained were characterized by good workability, fluidity, and consistency stability over time, despite the use of the modifier as an additional component in the concrete mix. No adverse effect of the waste used on the durability of the concrete was observed. Concretes modified with waste from the production of epoxy powder coating achieved a frost resistance class of F150 and showed good resistance to chemically aggressive environments (sulfates and chlorides). No products indicating adverse reactions between waste powder and reagents were found. The use of the DOE approach made it possible to determine, in the form of functional relationships, the influence of the modifier content depending on the water-cement ratio (w/c) of the concrete on its compressive strength and density. In general, a decrease in the compressive strength of concrete containing a waste powder modifier was observed, ranging from approximately 11% to 26% compared to unmodified concrete. However, the trend of decreasing compressive strength was reduced as the water-cement ratio of concrete decreased. At a water-cement ratio (w/c) of 0.443, no further decrease in compressive strength was observed. Concrete with 11% waste powder and a w/c ratio of 0.443 achieved 4.7% higher compressive strength than unmodified concrete with the same water-cement ratio. A beneficial interaction was found between a carboxylate-based plasticizer and the waste powder from the production of epoxy powder coatings. The proposed method of using waste as a concrete component is promising and may contribute to reducing the problem of waste management, as well as greenhouse gas emissions. Full article

Bayesian network-based causal structure learning provides an effective framework for uncovering causal relationships among continuous variables. However, many existing methods for continuous data still rely on strong parametric distribution assumptions, which may introduce information loss and reduce Bayesian network modeling accuracy. Kernel density estimation (KDE), a non-parametric statistical method that is more flexible in density estimation form, offers a versatile framework for conducting conditional independence (CI) tests. This approach enables the estimation of mutual information and conditional mutual information, thereby facilitating the identification of underlying structural relationships. Nevertheless, the high computational cost of KDE-based CI testing restricts its practical application in continuous-variable causal learning. To address this issue, this study introduces a radial symmetric kernel-based acceleration scheme within a Fast Fourier Transform (FFT) framework to improve the efficiency of density estimation. On this basis, an enhanced Bayesian network structure learning method is developed for continuous variables, enabling more efficient estimation of mutual information and conditional mutual information while improving the computational efficiency and empirical stability of variable dependency discovery. With proper bandwidth and grid resolution, the proposed MMHC-FFTKDE framework achieves a reduction in computational runtime and improves efficiency compared to MMHC-KDE in the ablation setting, while maintaining competitive F1-scores and SHD for causal structure discovery. Full article

Joint modulation format identification (MFI) and optical signal-to-noise ratio (OSNR) monitoring constitutes one of the most critical functions integrated in digital coherent receivers, ensuring high flexibility and stability in elastic optical networks (EONs). Since signal amplitude information captures inherent characteristics associated with modulation formats and fluctuations induced by OSNR variations, a simple and effective optical performance monitoring (OPM) scheme based on an amplitude-analytic complex plane is proposed. By employing a multi-task learning algorithm incorporating the multi-order gated aggregation (MOGA) module, the proposed scheme enables simultaneous MFI and OSNR monitoring for polarization division multiplexed (PDM)-QPSK/-16QAM/-32QAM/-64QAM/-128QAM signals. The performance of the proposed scheme is numerically verified in 28 GBaud coherent optical communication systems of various configurations. Numerical simulation results show that 100% identification accuracy is obtainable for all five modulation formats, even at OSNR values lower than the corresponding theoretical 20% forward error correction (FEC) limit. Meanwhile, the mean absolute error (MAE) of OSNR monitoring for QPSK, 16QAM, 32QAM, 64QAM, and 128QAM are 0.16 dB, 0.15 dB, 0.17 dB, 0.28 dB, and 0.33 dB, respectively. Furthermore, simulation results show that the proposed scheme is robust to residual chromatic dispersion (CD) and the nonlinear effects with strong generalization capability. These results suggest that the proposed scheme is promising for applications in next-generation EONs. Full article

Objective: Deep Brain Stimulation (DBS) has been consolidated as a valid therapeutic option for advanced Parkinson’s disease (PD). The identification of specific targets can be achieved through different methods, including conventional direct and indirect methods. The aim of our multicentric study is to provide a comparison between these traditional methods and artificial intelligence (AI) in the ascertainment of the ideal targets. Materials and Methods: A total of eight patients, who received bilateral STN (subthalamic nucleus) DBS implantation between 2022 and 2023 were analyzed. Target coordinates were calculated based on the Schaltenbrand and Wahren atlases and the AI using the RebrAIn system during the planning phase; intraoperatively, the targets were either confirmed or adjusted according to microelectrode recordings (MERs). The differences in the three Cartesian axes of stereotactic coordinates (X, Y, and Z) according to these methods were evaluated and compared through non-parametric ANOVA Friedman test. Results: The results revealed significant agreement in the lateral–lateral coordinates (X, X′, X″), indicating stability in target determination along this axis across the methods. However, more substantial discrepancies were observed in the antero-posterior and cranio-caudal coordinates, suggesting lower consistency between the examined methodologies. Conclusions: Our preliminary study results suggest that, despite the challenges posed by interindividual anatomical variability and the limitations of imaging techniques, artificial intelligence has shown comparable values on the lateral–lateral X coordinates. The accuracy of predictive targeting using machine learning models needs to be validated by further studies, but the preliminary results appear to indicate a potential promising role for artificial intelligence in integrating the preoperative workflow. Full article

Rhododendron lapponicum (L.) Wahlenb., prized for its vibrant and diverse floral displays, holds significant ornamental and ecological value. However, advances in its molecular breeding have been constrained by the absence of reliable tools for accurate gene expression analysis. A fundamental requirement for such studies is the identification of stable reference genes for qRT-PCR. To date, no systematically validated reference genes exist for normalizing gene expression across R. lapponicum cultivars with diverse flower colors, representing a major technical obstacle to elucidating the molecular mechanisms of color formation. This study aimed to fill this gap by systematically identifying and validating optimal reference genes for petal tissues in six distinct R. lapponicum cultivars. We assessed the expression stability of 11 candidate genes using four independent algorithms and integrated the results via RefFinder. Our comprehensive analysis across multiple algorithms consistently identified RlaEF1-α and RlaACT as the most stably expressed reference genes. Their reliability was robustly validated by normalizing the expression of RlaMYB113, a key anthocyanin regulator; the normalized expression levels showed an extremely significant difference between rose-red and white cultivars (p < 0.001) and produced a coherent, phenotype-correlated profile, in contrast to the distorted patterns obtained with unstable references. This study establishes RlaEF1-α and RlaACT as a precise dual-gene internal control for qRT-PCR. By providing a validated normalization framework, our work enables accurate quantification of color-related genes and directly supports molecular breeding efforts aimed at the targeted development and selection of novel R. lapponicum cultivars with desirable and stable flower colors. Full article

The torque ripples of robotic permanent magnet synchronous motors (PMSMs) degrade motion smoothness and positioning accuracy of the system, while inevitable load transients in robotic tasks further complicate torque ripple attenuation. To address this issue, this paper develops an event-triggered torque ripple attenuation method that explicitly distinguishes torque ripple from dynamic load transients. First, a sliding-mode torque observer is constructed to obtain real-time torque information, whose stability is rigorously analyzed using a Lyapunov function. Second, frequency-selective torque ripple extraction schemes are proposed to accurately isolate steady-state high-frequency torque ripple from the estimated torque signal. In particular, two specially designed filtering structures are developed and compared, one of which is selected to preserve ripple-related frequency content during test, ensuring robust and accurate ripple identification under varying operating conditions in robotics. Third, a torque-ripple-regulation-based compensation strategy is used within a vector-controlled PMSM drive, in which the extracted torque ripple is processed by a dedicated ripple regulator to generate voltage compensation signals. This strategy achieves effective steady-state torque ripple attenuation with low implementation complexity, while avoiding performance degradation during dynamic load transients. Finally, experimental results are provided to validate the effectiveness of the proposed methods. Full article

Accurate and trustworthy prediction of lithium-ion battery aging remains challenging due to multi-mechanistic degradation, cell-to-cell variability, and distribution shift between laboratory calibration and deployment. Fractional-order models have been proposed to capture long-memory effects in electrochemical systems; however, it remains unclear when such memory is empirically identifiable and beneficial within the common prognostics abstraction of state-of-health (SOH) versus cycle index. This work develops a fully reproducible computational pipeline for mechanistic battery aging based on a Caputo fractional differential equation (FDE) and evaluates its cross-cell generalization on open NASA cycling data. Parameters are identified using bounded robust nonlinear least squares and validated under a strict transfer protocol: calibration on cells B0005/B0006 and evaluation on held-out cells B0007/B0018 without refitting. The fractional model is benchmarked against a classical ODE surrogate, an ECM-inspired resistance-proxy baseline, and one-step-ahead machine-learning predictors. Uncertainty quantification is performed via parameter bootstrap and subsequently calibrated using conformal correction to target nominal coverage under transfer. Results show that the fractional order tends to collapse toward the integer-order limit (α → 1) in this dataset, indicating limited evidence of additional long-memory at the SOH-versus-cycle level under the considered protocol, while robust identification remains essential for stability. Calibrated prediction intervals achieve near-nominal coverage on held-out cells, highlighting the importance of UQ calibration under cell-to-cell shift. The proposed scripts and environment specifications enable direct replication and facilitate future extensions to stress-aware fractional models and hybrid physics–ML approaches. Full article

Background/Objectives: The optimal management of anterior cruciate ligament (ACL) rupture remains debated, especially regarding long-term outcomes after early ACL reconstruction (ACLR) versus rehabilitation-first with optional delayed ACLR. The interpretation of randomized evidence is complicated by frequent treatment crossover. This review synthesized evidence from randomized controlled trial (RCT) cohorts comparing surgical versus rehabilitation-first management strategies across available follow-up durations. Methods: A structured review based on a systematic literature search and narrative synthesis was conducted, with study identification and reporting guided by PRISMA 2020. MEDLINE (via PubMed) and Google Scholar were searched in February 2026 for English-language human RCTs (2000–2026) comparing early ACLR plus rehabilitation with rehabilitation-first management allowing delayed ACLR for persistent instability. A linked-report PubMed search using the KANON trial registration number (ISRCTN84752559) was additionally performed to identify cohort-derived follow-up publications. Reports were grouped by underlying RCT cohort. Data were extracted on crossover, follow-up, and clinical outcomes. Risk of bias for primary RCT reports was assessed with Cochrane RoB 2. Results: Twenty-seven reports representing three RCT cohorts (KANON, COMPARE, ACL SNNAP) were included; six index reports were prioritized for synthesis. In acute ACL rupture (KANON, COMPARE), early ACLR did not show a consistent long-term superiority in patient-reported outcomes versus rehabilitation-first with optional delayed ACLR, although COMPARE reported a statistically significant 2-year subjective functional difference favoring early ACLR; early ACLR more consistently improved mechanical stability and reduced instability episodes. Crossover from rehabilitation to delayed ACLR was common. In non-acute ACL injury with persistent symptomatic instability (ACL SNNAP), surgery-first improved 18-month patient-reported outcomes. Meniscal procedure rates and osteoarthritis-related outcomes did not consistently favor early ACLR. Conclusions: In acute ACL rupture, rehabilitation-first with timely access to delayed ACLR appears to provide long-term patient-reported outcomes comparable to an early ACLR strategy in many patients, while early ACLR more consistently improves knee stability. In non-acute symptomatic ACL deficiency, a surgery-first strategy appears more effective in the mid-term. These randomized trials should be interpreted as comparisons of management strategies rather than of “pure” operative versus nonoperative treatment approaches. Full article

Background: Obesity represents a major public health challenge worldwide and contributes substantially to the burden of type 2 diabetes and hypertension. While body mass index (BMI) is widely used in clinical practice, indices reflecting central adiposity may provide additional prognostic value. This study aimed to assess the prevalence of general and central obesity in an adult population across different age groups from Stara Zagora, Bulgaria, and to examine their associations with cardiometabolic outcomes and lifestyle factors. Methods: A quasi-representative cross-sectional study was conducted among 3512 adults (mean age 53.7 ± 14.9 years). Anthropometric indices, including BMI, waist circumference, waist-to-hip ratio, and waist-to-height ratio were measured. Cardiometabolic outcomes included diabetes, hypertension, and their combined presence. Multicollinearity was assessed using the Variance Inflation Factor (VIF), and the discriminatory ability of indices was evaluated using Receiver Operating Characteristic (ROC) analysis and DeLong’s test. Results: The prevalence of overweight/obesity (BMI ≥25) was 68.4%, while central obesity (WHtR ≥0.5) affected 66.9% of participants. BMI demonstrated the highest discriminatory ability in this dataset for hypertension (AUC = 0.852) and diabetes (AUC = 0.796), significantly outperforming WC and WHR (p < 0.05). However, 24.4% of individuals with normal BMI exhibited high-risk central adiposity. Significant sex-specific differences were observed: short sleep duration (<6 h) was a strong predictor of obesity in women (aOR = 2.98), whereas smoking showed stronger associations in men. Age-stratified analyses revealed that while BMI stabilizes in the oldest age group (75–89 years), WHtR continues to increase, reflecting age-related redistribution of visceral fat. A strong protective effect of physical activity was observed, supported by quasi-complete separation in active subgroups. Conclusions: General and central obesity represent a substantial health burden in this urban population. While BMI remains a robust screening tool, the integration of WHtR enhances the identification of “hidden” cardiometabolic risk particularly in older adults and individuals with normal BMI. Given the quasi-representative nature of the sample, these findings are primarily generalizable to similar urban populations and may inform targeted regional public health strategies. Full article

Surface subsidence induced by underground coal mining is a primary driver of ecological degradation. The traditional Remote Sensing Ecological Index (RSEI), however, struggles to capture surface deformation constraints and vegetation response lags. To address this, we developed a Mining Deformation–Ecology Coupling Index (MDECI). This index integrates Interferometric Synthetic Aperture Radar (InSAR)-monitored surface stability with multi-spectral indicators via Principal Component Analysis (PCA). We applied this method to the Datong Coalfield, China, using 231 Sentinel-1A SAR scenes and 8 Landsat images (2017–2024) to validate the effectiveness of the index. Meanwhile, we systematically analyzed non-linear response mechanisms, the Ecological Turning Point (ETP), and spatial clustering characteristics. The results demonstrate the following: (1) InSAR and MDECI effectively identified patterns of surface subsidence and ecological decline. Subsidence centers expanded to a maximum of −2085 mm, causing the mean MDECI in these areas to drop to 0.185 (<−1800 mm). This represents a 57.4% decrease relative to the regional average (0.434). (2) MDECI outperformed traditional models with a stable Average Correlation Coefficient (ACC) (0.63–0.75) and high cross-correlation coefficients with RSEI (0.906) and the Mine-specific Eco-environment Index (MSEEI) (0.931). During the 2018 drought, MDECI maintained a robust ACC of 0.628 while RSEI dropped to 0.482. (3) Multi-scale analysis revealed a unimodal MDECI response with an ETP at −100 mm. Initial ‘micro-disturbance gain’ (0.371 to 0.471) is followed by a progressive decline to a minimum of 0.185 under severe deformation. (4) Local Indicators of Spatial Association (LISA) spatial clustering characterized the distribution patterns of ecological damage and localised high-maintenance areas. High–Low damaged areas accounted for 5.09%, while High–High high-maintenance areas reached 9.00%. The scale of High–High areas was approximately 1.77 times that of the damaged areas. The MDECI addresses the deficiencies of traditional indices in high-disturbance areas and isolates the impact of mining on the ecology, providing a quantitative basis for risk identification and differentiated restoration. Full article

To address the actuator fault problem faced by underactuated surface vessels (USVs), this study develops an active fault-tolerant control scheme based on finite-time output feedback. First, a finite-time neural terminal homogeneous state observer with a portional-integral structure is established. High-precision pose reconstruction enables finite-time synchronous reconstruction of unmeasured states. This allows unknown nonlinearities to be explicitly expressed online and incorporated into the compensation channel, significantly reducing the sensitivity of modeling errors to control performance. A neural damping mechanism is used to structurally reconstruct uncertain dynamics and loss-of-effectiveness (LOE) fault factors within the system, thereby constructing an online approximator to achieve real-time identification and compensation of composite uncertainties. This integrates the unknown nonlinearities and fault effects of the original system into an online-updatable estimation channel. Adopting a backstepping-based design methodology, a finite-time hybrid event-triggered control (ETC) architecture is further constructed. By introducing an event-triggered update mechanism at the control layer, the real-time continuous control signal is transformed into a discrete update. Based on Lyapunov stability theory, a comprehensive analysis is carried out to verify the stability of the proposed control scheme. Numerical simulations are finally carried out to validate the effectiveness of the scheme. Simulation results show that the tracking error is reduced by about $93\%$ and $60\%$ compared to the comparison scheme. Full article