Search Results (17,988)

Powder bed fusion–laser beam (PBF-LB) of nickel–titanium (NiTi) has attracted increasing interest in aerospace, biomedical, and energy applications owing to its shape memory and superelastic properties, combined with the capability to fabricate complex geometries. However, the strong sensitivity of NiTi to thermal history and process variability makes predictive modeling and process parameter selection challenging. In this work, a physics-guided decision-support framework is developed for melt pool prediction and stability assessment during the PBF-LB processing of NiTi. A high-fidelity thermal finite element model incorporating CALPHAD-derived, temperature-dependent material properties was calibrated using a subset of experimental measurements and independently validated against additional experimental melt pool data. The calibrated model demonstrated good agreement with experiments, yielding mean absolute percentage errors of 4.22% and 5.63% for melt pool width and depth, respectively, on the validation dataset. A multi-output random forest surrogate trained on the validated simulation dataset enabled rapid prediction of melt pool geometric features, achieving test-set $R^2$ values exceeding 0.95, together with low MAE and RMSE values, while five-fold cross-validation confirmed robust predictive performance. The proposed framework integrates surrogate predictions with physics-based melt pool stability criteria to rapidly identify physically feasible processing conditions, thereby providing a computationally efficient foundation for future supervisory process control strategies. Full article

Polyphenolic antioxidants are incorporated into pharmaceutical, dermopharmaceutical, and cosmetic products because of their capacity to modulate oxidative stress, inflammation, microbial imbalance, skin aging, wound repair, and tumor-related processes. However, formulation is constrained by chemical instability, limited bioavailability, insufficient skin permeation, and degradation during processing or storage. This review integrates the chemical characteristics, natural sources, extraction approaches, antioxidant mechanisms, and evaluation of curcumin and chlorogenic acid, and critically examines their delivery through liposomes, niosomes, ethosomes, and transethosomes. Curcumin is lipophilic and poorly water-soluble, whereas chlorogenic acid is hydrophilic but permeability-limited. Their antioxidant activity is discussed through hydrogen atom transfer, single-electron transfer, interruption of lipid peroxidation, metal chelation, and localization within lipid interfaces, together with chemical, biomimetic, and cellular assessment methods. Vesicular carriers can improve encapsulation, stability, release control, skin interaction, biological performance, and incorporation into semisolid dosage forms. However, these benefits are accompanied by formulation-dependent trade-offs involving manufacturing complexity and cost, long-term stability and reproducibility, excipient-related skin tolerability, scale-up, and an application scope that depends on the intended dermal-delivery endpoint. Therefore, efficacy depends on the interplay among antioxidant properties, vesicle architecture, excipient selection, and processing conditions. Curcumin-loaded vesicles are better documented than chlorogenic-acid-loaded systems, particularly for deformable carriers. Future progress requires quality-by-design strategies, standardized characterization, predictive skin models, long-term stability and safety studies, and scalable manufacturing. Overall, antioxidant-loaded vesicles represent multifunctional platforms for developing stable and effective pharmaceutical and cosmetic products. Full article

Augmented reality (AR) enhances the real world scene by overlaying virtual information onto it. Vision-based 3D tracking and registration is the key technology for ensuring the fusion of virtual and real content in monocular AR systems. Existing mainstream visual tracking and registration methods are susceptible to illumination variations, motion blur, target occlusion, and dynamic background interference in complex scenarios. They also suffer from low computational efficiency, cumulative pose errors, and insufficient stability, making them difficult to deploy on low power edge devices such as embedded systems and mobile terminals. To address these issues, this paper proposes a lightweight monocular AR 3D tracking and registration method that integrates ORB-FREAK features, mismatching outlier filtering, background weighted mean shift, and template-based relocalization. The method does not rely on depth sensors or neural network inference, enabling efficient and accurate lightweight pose estimation. Specifically, we first combine the ORB (Oriented FAST and Rotated BRIEF) descriptor with the FREAK (Fast Retina Keypoint) algorithm for feature detection and initial matching. Hamming distance is used for coarse filtering of mismatched point pairs, and an ascending sort combined with an iterative sequential sampling strategy is applied to solve the optimal homography matrix, significantly improving the accuracy and efficiency of matrix estimation. Then, distance constraints among feature points are imposed on the target registration region to optimize the selection, and camera pose is computed based on the matching between 2D feature points and their corresponding 3D spatial coordinates, eliminating the error accumulation problem of conventional algorithms. Real-time feature matching is further used to correct the optical flow tracking sequence and camera pose, ensuring the continuity of the AR tracking process. Finally, a background weighted mean shift algorithm is introduced to narrow the feature detection range and suppress background interference, complemented by a template-matching relocalization module and a dynamic model update strategy, which effectively enhance the robustness of continuous tracking and registration under complex conditions. Experimental results demonstrate that, in extreme scenarios such as low light conditions, high speed motion, and occlusion, the proposed method achieves AR 3D tracking and registration success rates of 86.7%, 82.3%, and 78.5%, respectively. It exhibits superior performance in pose estimation accuracy and anti-interference capability in complex environments, with significantly reduced computational overhead. Moreover, it can achieve robust and continuous AR 3D tracking and registration on low power edge devices, effectively adapting to demanding AR application scenarios and providing reliable technical support for lightweight AR applications. Full article

This study proposes a flexible three-parameter distribution to capture complex patterns in continuous positive data. By combining the generalized log-logistic odd generator with an error function distribution, our model successfully accommodates various density shapes, including strong skewness and bimodality. Using Monte Carlo simulations and maximum likelihood estimation, we validate our regression model’s estimators and confirm that larger sample sizes yield high precision, consistency, and inferential stability. Using data from hospitalized depression patients, we demonstrate the model’s practical application. This new distribution fits the data better than competing models, supported by lower statistical metrics and likelihood ratio tests. Furthermore, the variables’ “simplicity” and “fatalism” significantly influence observed depression levels. Full article

Stabilizing food prices is essential for ensuring food security in an aging society. Using panel data from 30 Chinese provinces spanning 2005 to 2022, this study employs a nonlinear panel model and a Spatial Durbin Model (SDM) to analyze the impact of rural population aging on food prices and its spatial spillover effects. This study derives the following findings based on empirical research: (1) Rural population aging exhibits a significant inverted U-shaped relationship with food prices, with an inflection point at approximately 19.03%. Before reaching this point, rural population aging helps facilitate food prices. When the inflection point is passed, rural population aging adversely impacts food prices. This effect is significant in western regions but not in eastern and central regions. (2) Farmland transfer and agricultural technological progress significantly influence this relationship, causing the curve to reverse into a U-shaped pattern, which implies a gradual future increase in food prices. (3) Local rural population aging has a significant U-shaped spillover effect on food prices in neighboring provinces. These findings indicate that China’s rural population aging presents a complex dynamic for food price fluctuations. To address the current changes in the population and capital structure and ensure food security, the government will need to formulate forward-looking policies, further improve socialized agricultural services, and systematically optimize food production models. Full article

We investigate the origin of long-term spectroscopic and photometric variability in the blue supergiant $\rho$ Leo, with particular emphasis on distinguishing between intrinsic pulsations and variability induced by a possible companion. Our analysis is based on an 11.5-year spectroscopic time series obtained at Tartu Observatory, complemented by high-cadence, high-resolution spectroscopy from the Hertzsprung SONG telescope and space-based photometry from TESS. We studied line-profile variability using normalised moments of the He i $\lambda$6678, He i $\lambda$5875, and Si iii $\lambda$4552 lines. Periodic signals were identified using the generalised Lomb–Scargle periodogram with iterative pre-whitening, and their temporal stability was examined with the weighted wavelet Z-transform. We detect a persistent periodic signal at $P=16.46$ d in the first and third moments, present throughout the full observing interval, with a radial-velocity amplitude of 3.9 km/s. This signal is also present in the SONG data and is visible in multiple spectral lines, indicating a global origin. Photometric observations reveal a dominant variability timescale near ≈33 d, approximately twice the spectroscopic period. The stable 16.46-day period, present throughout the entire observing interval, most likely results from non-radial pulsations of the supergiant. A binary origin of the signal is not excluded, but distinguishing between these scenarios is complicated by the supergiant’s complex variability pattern. Full article

Accurate computational prediction of antiviral peptides (AVPs) can accelerate peptide screening and reduce experimental costs. However, existing deep learning-based methods still suffer from severe class imbalance, over-reliance on handcrafted features and limited interpretability. Here, we propose PMAVP, a multi-task learning framework that integrates the ProtT5 pre-trained protein language model with a Mamba-inspired module for AVP identification and functional activity prediction. We use ProtT5 to extract deep semantic representations from peptide sequences and a Mamba module to capture long-range dependencies at a lower computational complexity. We introduce Focal Loss to mitigate class imbalance and leverage transfer learning to enhance performance on functional activity prediction. Experimental results demonstrate that our model achieves superior performance in terms of prediction accuracy, stability, and computational efficiency. Furthermore, DeepSHAP-based interpretability analysis reveals that the first 40 amino acid residues contribute substantially to AVP prediction. Full article

The Structural Maintenance of Chromosomes 5/6 (SMC5/6) complex is a key regulator of genome stability that participates in the recognition, stabilization, and processing of DNA intermediates generated during DNA replication and homologous recombination. Recent advances in structural and biochemical studies have revealed the architecture of the SMC5/6 complex and elucidated the molecular mechanisms by which ATPase activity, DNA-binding modules, and SUMO-and ubiquitin-mediated regulatory pathways cooperate to maintain genome integrity. Extensive evidence has further demonstrated that SMC5/6 functions as a broad-spectrum antiviral restriction factor by recognizing and silencing episomal viral genomes, whereas diverse viruses have evolved strategies to evade or counteract its activity. In addition, dysfunction of the SMC5/6 complex has been associated with cancer, developmental disorders, and genome instability syndromes. This review summarizes recent advances in the structure, molecular mechanisms, antiviral functions, and disease relevance of the SMC5/6 complex, providing a basis for a deeper understanding of its diverse biological functions and potential applications. Full article

Neurodegenerative diseases remain a major therapeutic challenge, with oxidative stress playing a central role in central and peripheral immune system dysfunction that leads to neuronal loss. Natural products from extreme environments represent an underexplored source of neuroprotective agents. In this study, viridicatin, a quinoline-derived alkaloid, was isolated from the Antarctic fungus Penicillium sp. collected from sediments taken from Deception Island, and its structure was unambiguously confirmed by 1D/2D-NMR spectroscopy and single-crystal X-ray diffraction. Viridicatin (100 µM) significantly attenuated H₂O₂-induced cytotoxicity in HMC-3 human microglial cells, preserving cell viability and mitochondrial membrane potential. Viridicatin modulated the Nrf2 antioxidant signaling pathway, accompanied by increased expression of the downstream antioxidant enzymes HO-1 and NQO1. Moreover, molecular docking revealed preferential binding to the KEAP1 Kelch domain (−8.0 kcal/mol), suggesting indirect Nrf2 pathway modulation. A 100 ns molecular dynamics simulation with MM-GBSA analysis supported the stability of the viridicatin–KEAP1 complex. Notably, viridicatin rescued peripheral immune cells, i.e., peripheral blood mononuclear cells (PBMCs) obtained from older adults with mild cognitive impairment from H₂O₂-induced cell death, bridging the gap between in vitro mechanistic evidence and clinically relevant human cellular models. This is the first report of neuroprotective activity for viridicatin, positioning this Antarctic-derived alkaloid as a compelling candidate for further preclinical development against age-related neurodegeneration. Full article

Confined enzyme catalysis is a promising approach for pharmaceutical synthesis because it can combine high selectivity, catalytic efficiency and mild reaction conditions. This review examines recent advances in multiscale enzyme confinement for pharmaceutical applications, with emphasis on atomic-scale active-site regulation, molecular-scale immobilization and transport control and multi-enzyme cascade organization. We discuss how confined microenvironments modulate enzyme electronic states, conformational dynamics, substrate accessibility, local reaction conditions and intermediate transfer, thereby influencing catalytic activity, stability and stereoselectivity. Representative applications across major enzyme classes are discussed, with a focus on the synthesis of chiral drug intermediates and complex pharmaceutical molecules. The review also considers process-level sustainability, including process mass intensity (PMI) and the E-factor, together with limitations related to mass transfer, support preparation, long-term stability, scale-up and support material burdens. Overall, integrating multiscale confinement with spatially organized cascade catalysis offers a promising route toward more efficient and potentially more sustainable pharmaceutical manufacturing, although its net environmental benefit must be established through system-level assessment. Full article

Object detection under adverse weather conditions remains a challenging problem for autonomous driving systems due to image degradation caused by rain, snow, haze, and complex illumination. Although RT-DETR achieves a good balance between detection accuracy and efficiency, its performance is limited in adverse weather scenarios due to weather-induced feature distortion and unreliable query selection. In this paper, we propose WFG-RT-DETR, a robust real-time object detection framework for adverse weather conditions. A Weather-Focused Guidance (WFG) module is introduced to enhance feature representation by combining frequency-domain noise suppression with spatial structural compensation. Furthermore, a Weather-Aware Query Selection (WAQS) module is proposed to improve query initialization by incorporating weather-aware noise estimation, reducing false-positive proposals caused by environmental interference. An Exponential Moving Average (EMA) strategy is also employed to stabilize model training. Extensive experiments on the DAWN and WEDGE datasets demonstrate the effectiveness of the proposed method. Compared with RT-DETR, WFG-RT-DETR improves the mAP@50 on the DAWN dataset from 64.62% to 68.89% while maintaining real-time inference capability. Cross-dataset evaluations on the WEDGE dataset further verify its robustness and generalization under diverse adverse weather conditions. The proposed framework provides an effective solution for reliable object detection in intelligent transportation and autonomous driving applications. Full article

Leprosy is characterized by complex biological and cellular interactions, driven primarily by the interplay between Th${}_1$ and Th${}_2$ cells. In this study, we develop a deterministic mathematical model to investigate the interactions among healthy and infected Schwann cells, M. leprae bacteria, and Th${}_1$–Th${}_2$ immune responses. The positivity and boundedness of the proposed five-dimensional system are established, and the disease persistence condition is characterized in terms of the basic reproduction number (${\mathcal{R}}_0$). The existence conditions for the endemic equilibrium are derived, while the global asymptotic stability of the endemic state is established through the construction of an appropriate Lyapunov function. To evaluate the robustness of the proposed model, parameter sensitivity with respect to ${\mathcal{R}}_0$ is investigated using Latin Hypercube Sampling (LHS) and Partial Rank Correlation Coefficient sensitivity analysis. Furthermore, Monte Carlo uncertainty analysis (UA) is incorporated to account for the inherent uncertainty in the system, whereas a Sobol-based global sensitivity analysis quantifies the contribution of individual model parameters to the overall uncertainty. To further validate the dynamical behavior of the proposed system numerically, Lyapunov exponents are computed using the Benettin (renormalization) algorithm. The impact of combined multidrug therapy (MDT) and IL-10 inhibitor therapy is subsequently investigated within an optimal control framework, and the corresponding optimal treatment strategies are derived using Pontryagin’s maximum principle. Numerical simulations demonstrate that suppressing the Th${}_2$-mediated weakening of the host immune response through IL-10 inhibitor therapy provides superior long-term control of leprosy. The proposed treatment strategy therefore identifies IL-10 inhibitor therapy as a promising adjunct immunomodulatory intervention alongside MDT for enhancing protective cellular immunity against M. leprae in a cost-effective manner. Full article

We develop a design-conditional limit theory for kernel estimators of conditional U-functionals based on locally stationary functional random fields observed at irregular random locations and under incomplete response observation. The covariates take values in a separable Hilbert space, the responses are allowed to take values in a general Polish space, and the target is indexed by a class of symmetric kernels of a fixed order. Functional localization is induced by single-index semi-metrics, while spatial localization is performed on the rescaled observation domain. Missing responses are incorporated through a complete-case construction under a Missing At Random condition and a uniform-positivity assumption. The resulting estimator is a ratio of spatially weighted U-statistics with random tuplewise observation indicators. The asymptotic analysis must account simultaneously for four sources of complexity: dependence within the spatial field, nonstationarity across an expanding domain, concentration in an infinite-dimensional covariate space, and the random thinning generated by missing responses. Conditioning on the sampling locations removes the randomness of the spatial design weights but does not eliminate dependence among the observations. We therefore derive a design-conditional projection decomposition adapted to the triangular-array structure of the model. The leading component is represented by a spatially dependent complete-case empirical process, whereas the higher-order canonical terms are controlled uniformly over the response kernels, functional-target points, single-index directions, and rescaled spatial locations. The proofs combine stationary tangent-field approximations for locally stationary random fields, large-block–small-block decompositions, coupling arguments under spatial absolute regularity, small-ball probability estimates, and entropy bounds for the joint indexing class. These arguments yield a uniform stochastic expansion in which the empirical fluctuation, the spatial–functional smoothing bias, and the local-stationarity approximation error appear as distinct contributions. In particular, the local-stationarity remainder has no counterpart in the strictly stationary theory and quantifies the cost of replacing the observed nonstationary field with its stationary tangent approximation. Under the MAR and positivity conditions, complete-case sampling reduces the effective local information and modifies the covariance structure, but it does not change the formal order of the uniform-convergence rate. Under strengthened moment, mixing, entropy, and negligibility conditions, we establish weak convergence of the normalized conditional U-process in the corresponding supremum-norm function space to a tight centered Gaussian process. The limiting covariance is determined by the complete-case first-order projection and consequently retains the effect of the observation propensity and the spatial dependence structure. We also introduce a complete-case leave-tuple-out spatial prediction criterion for bandwidth selection and prove oracle optimality over admissible bandwidth families. The general theory applies to conditional rank association, discrimination probabilities, set-indexed conditional distribution functionals, and related pairwise statistical-learning criteria. Simulation experiments and applications to spatial environmental and epidemiological data illustrate the finite-sample implications of the theory and the stabilizing role of single-index localization. Viewed through the lens of data-driven science, the framework addresses a fundamental asymmetry between the information carried by irregular, locally heterogeneous functional covariates and the selectively observed response tuples. By combining design conditioning, complete-case normalization, tangent-field localization, and single-index dimension reduction, the proposed approach resolves this inferential asymmetry at the level of the model by matching estimation and uncertainty quantification to the information actually available locally, without imposing artificial stationarity or complete-data symmetry. Full article

State-level health resource systems need precise and timely prediction models, but they are plagued by the ongoing problem of deteriorating model performance because of constantly shifting data distributions (data drift). Predicting uncommon but important events like sickle cell crisis is a classification task where this problem is most noticeable. This paper presents the Evolving AI-Driven Ensemble Learning Framework, which blends novelty detection using the F1-score with sophisticated ensemble approaches (stacking XGBoost, Deep Neural Network, and Random Forest with a meta-learner). Complex, high-dimensional health data are handled using sophisticated feature engineering techniques, such as automated feature selection via evolutionary algorithms and meta-learning (MAML). We empirically assessed a reactive retraining technique that was improved by ensemble stacking and simulated real-time data drift. After five retraining cycles, the improved ensemble and feature engineering showed significant performance improvements over the initial model, achieving substantial improvements: F1-score improved from 0.1250 to 0.9734 (an absolute increase of 0.8484, representing a 678.7% relative improvement), recall from 0.0714 to 0.9767 (an absolute increase of 0.9053), and precision from 0.5000 to 0.9702 (an absolute increase of 0.4702). The framework maintained high specificity (0.9700) and demonstrated outstanding discriminative performance with an AUC-ROC of 0.9909 (an 8.5% improvement). The model’s strong predictive capacity was confirmed by improvements in the Matthews Correlation Coefficient from 0.1000 to 0.9467 (846.7% improvement) and Cohen’s Kappa from 0.0800 to 0.9467 (1083.3% improvement). Model transparency in pipeline development is now made possible by a fixed runtime issued in the SHAP explainability layer. The efficiency of the framework is empirically validated by this study, showing that automated feature engineering and optimized ensemble learning greatly increase model stability and preserve remarkable accuracy for minority classes in complicated data contexts. Full article

Background and Objectives: COPD is a heterogeneous disease in which conventional clinical classifications may not fully capture the complexity of patient profiles. This exploratory study aimed to examine whether multidimensional COPD phenotypes could be identified using unsupervised cluster analysis integrating clinical, functional, radiological and laboratory features. Materials and Methods: We enrolled 161 patients with confirmed COPD evaluated between January 2020 and January 2024. Demographic, clinical, functional, radiological and laboratory findings were collected. Mixed-type data were analyzed using Gower distance and Partitioning Around Medoids (PAM) clustering. The optimal solution was selected by average silhouette width; stability was assessed by 1000 bootstrap resamples and sensitivity analyses. Results: The two-cluster solution had the highest silhouette width (0.184), although separation was modest. Cluster 1 (n = 78) was characterized by greater symptom and exacerbation burden, worse lung function, greater static hyperinflation, shorter 6-min walking distance and more frequent emphysema than cluster 2 (n = 83). Bootstrap resampling indicated internal stability, although concordance with the primary partition varied across sensitivity analyses. After correction for multiple post hoc comparisons, only LAMA/LABA/ICS use differed between clusters, whereas demographic characteristics, comorbidity burden and blood eosinophil levels were comparable. Conclusions: These exploratory findings suggest multidimensional assessment may complement conventional classifications, but external and longitudinal validation is needed before clinical implementation. Full article