Search Results (55,324)

Guaranteed annuity options (GAOs) allow policyholders to convert accumulated funds into life annuities at maturity at a guaranteed minimum rate. Thus, insurers are exposed to both investment and longevity risks. Accurate valuation of these long-term, survival-contingent contracts is essential for solvency assessment and risk management. Many existing approaches assume independence between interest rate and mortality risks. This paper develops a computationally efficient pricing framework for GAOs that jointly models interest and mortality rates as correlated stochastic processes with regime-switching dynamics governed by a finite-state continuous-time Markov chain. Model parameters are estimated using U.S. interest rates and cohort mortality data via quasi-maximum likelihood estimation. A semi-analytic valuation formula is derived based on the joint distribution of the underlying processes. Numerical results show that incorporating correlation and regime-switching materially increases GAO prices relative to conventional one-state models. The proposed semi-analytic approach delivers substantial computational advantages over standard Monte Carlo simulations. Sensitivity analysis further identifies the parameters most relevant for long-horizon pricing and solvency considerations. This highlights the practical relevance of the framework for managing longevity-linked guarantees under economic and demographic uncertainty. Full article

The Controller Area Network (CAN) protocol lacks native authentication mechanisms, exposing modern vehicles to critical security threats. While deep learning-based intrusion detection systems show promise, existing solutions require computational resources far exceeding automotive-grade microcontroller constraints, hindering practical embedded deployment. This paper proposes LCSMC-Net, an ultra-lightweight neural architecture for resource-constrained CAN intrusion detection. The framework integrates three innovations: (1) Separable Multiscale Convolution Lite (SMC-Lite) blocks capturing multitemporal attack patterns with minimal parameters; (2) Lightweight Channel-Temporal Attention (LCTA) achieving linear $\mathcal{O}\left(N\right)$ complexity through adaptive pruning; and (3) 6-dimensional CAN-optimized features exploiting protocol-specific characteristics for aggressive compression. The framework employs Bayesian hyperparameter optimization and knowledge distillation for systematic model compression. Extensive experiments on CAN and CAN-FD datasets demonstrate that LCSMC-Net achieves 99.89% accuracy with only 9401 parameters and 2.84M FLOPs, outperforming existing solutions while meeting real-time constraints of automotive embedded systems, providing a viable edge AI deployment solution. Full article

Background/Objectives: Cells derived from direct chemical reprogramming into osteoblasts represent a promising source for bone regeneration, but the efficiency needs improvement. Here, we systematically evaluated whether the natural compound psoralen (Psr) could enhance this process and explored its therapeutic potential and mechanism of action. Methods: Mouse embryonic fibroblasts (MEFs) were treated with a cocktail of forskolin and phenamil (FP), supplemented with Psr. In vitro differentiation was assessed by alkaline phosphatase and Alizarin Red S staining, reverse transcription quantitative PCR, immunofluorescence and Western blot. The bone-regenerative potential of the derived chemically induced osteoblast-like cells (ciOBs) was evaluated in critical-sized calvarial defects, femoral cortical defects and a subcutaneous ectopic implantation model, using micro-computed tomography and histology. Mechanistic insights of Psr were gained by analyzing the adenylyl cyclase 9 (ADCY9)/cyclic adenosine monophosphate (cAMP)/protein kinase A (PKA)/cAMP response element-binding protein (CREB) axis using inhibitor SQ22536. Results: Psr acted synergistically with the FP cocktail to drive efficient osteogenic reprogramming of MEFs. At an optimal concentration of 25 μM, Psr enabled the most robust induction of early osteogenic markers and generation of mature, mineralizing ciOBs in vitro. In vivo, FP + Psr-induced ciOBs repaired critical-sized calvarial and femoral cortical defects and generated substantial, vascularized bone tissue in ectopic sites. Mechanistically, Psr co-treatment potently activated the ADCY9/cAMP/PKA/CREB pathway, and pharmacological inhibition of this pathway completely abolished the pro-osteogenic effects of Psr. Conclusions: Psr acts as a potent synergistic enhancer of direct chemical reprogramming, generating functional osteoblast-like cells with robust bone-regenerative capacity via activation of the ADCY9/cAMP/PKA/CREB pathway. Full article

Multi-sensor fusion provides richer information for bearing fault diagnosis. However, under variable working conditions, the coupling relationships among signals from different sensors exhibit significant non-stationarity and directionality, posing challenges for modeling and practical deployment. Existing methods often rely on fixed or symmetric graph structures or construct correlation relationships entirely based on data-driven approaches; this makes balancing physical consistency, robustness, and computational efficiency difficult. To address these issues, we propose a Physics-guided Adaptive Graph Transformer Network (AGTN) for multi-modal bearing fault diagnosis under variable working conditions. More specifically, we offer innovative improvements across three aspects. Firstly, we introduce domain knowledge priors into the graph structure learning process to adaptively construct sparse and asymmetric dynamic graph structures that capture physically meaningful directional dependencies among different sensor signals. Secondly, we combine a graph-aware transformer to jointly model the temporal features and structural correlations of multi-source signals. Finally, we further introduce a hierarchical subgraph training strategy that significantly reduces memory usage and training time while ensuring diagnostic performance. Experimental results on a self-built multi-condition bearing dataset show that AGTN achieves an average diagnostic accuracy of 99.42% under the same distribution conditions and demonstrates good generalization and robustness, e.g., variable speed and load and sensor failure. In particular, when using only 25% of the nodes for training, the model can still maintain a diagnostic accuracy of 97.9%, while reducing the peak memory usage to about 19% of that of full-graph training. The above results validate the effectiveness of the proposed method under complex industrial conditions, as well as its practical application potential in resource-constrained scenarios. Full article

Nonlinear energy harvesting systems based on multibody structures constitute a promising solution for autonomous devices powered by ambient vibrations. This paper presents the modeling and control of a nonlinear energy harvester employing a double pendulum configuration and BLDC motors operating as generators. The primary objective of the study was to develop a control strategy that enables the maximization of harvested power while simultaneously improving the energy conversion efficiency during the charging of the battery supplying the target system. The developed model incorporates the mechanical equations of motion of the double pendulum, an electrical model of the BLDC motors, and two independently controlled buck–boost converters, each connected to one joint of the pendulum. In addition, a perturb-and-observe (P&O) maximum power point tracking (MPPT) algorithm was implemented, which utilizes a portion of the computational resources of the target system’s microcontroller and allows for dynamic adjustment of the electrical loads seen by the generators. Simulation results obtained in the Simulink environment confirm that the application of independent power converters combined with local MPPT control leads to an increase in the total harvested power and ensures more stable battery charging under conditions of variable mechanical excitation. The obtained results demonstrate the effectiveness of the proposed approach and indicate its potential applicability in self-powered systems operating in environments characterized by irregular and stochastic vibrations. Full article

Background: The outbreak and spreading of antimicrobial resistance (AMR) in a very short time has made most of the old-fashioned antibiotics ineffective, and thus new therapeutic substances have to be developed. The traditional methods of antibiotics discovery are defined by long periods of time, high levels of expenditure, and high rates of failure, which contributes to the necessity of new approaches. Artificial intelligence (AI) has become a disruptive technology that can be used to accelerate and optimize various steps of antibiotic discovery, such as target detection and virtual screening, new molecular design, and early-stage testing. Methods: This review provides an in-depth discussion of the role of AI methodologies in the form of machine learning, deep learning, natural language processing, and generative models in the discovery of small-molecule antibiotics and antimicrobial peptides (AMPs). The major areas that are discussed include virtual screening, pharmacokinetics optimization, resistance mechanism prediction, and AMPs design, which is accompanied by relevant case studies, including the AI-based discovery of Abaucin. Results: The article highlights how AI can be used in a synergistic relationship with synthetic biology, nanotechnology, and multi-omics data as a core component in the next generation of antimicrobial approaches, such as personalized therapy and predictive stewardship. The existing issues, i.e., the lack of data, bias in algorithms, and the translational divide between research and clinical use, are addressed, as well as suggested measures of responsible, collaborative, and ethical AI use. Conclusions: The combination of computational innovation with experimentation validation, AI-driven antibiotic discovery paves the way for a potent and scalable approach in addressing the rising threat of AMR. Full article

In vertical mine hoisting systems, the rigid guide serves as a critical safety component whose failure may induce severe dynamic disturbances and potentially trigger cascading safety incidents. Existing data-driven diagnosis methods for rigid guides often lack robustness under variable operating conditions and require substantial labeled data. Yet in practical mine hoisting operations, variations in hoisting speed and lifting mass are inevitable, and acquiring sufficient fault samples is challenging due to safety constraints. To address these problems, this paper proposes a novel fault diagnosis framework that integrates a physics-informed feature-extraction pipeline with the zero-space observer theory. Vibration signals are processed to extract dimensionless and relative features, which are deliberately designed based on the dynamic mechanisms underlying different fault states. These features rely solely on the geometric characteristics of the waveform at the fault location, rendering them sensitive to fault types while remaining robust to variations in operating conditions. The feature set is subsequently optimized using the minimum redundancy maximum relevance (mRMR) algorithm to enhance computational efficiency, mitigate overfitting, and improve the generalization ability of the method. A set of zero-space observers is then constructed to perform efficient fault classification through geometric operations in the feature space, with each observer specifically sensitive to its corresponding health state while remaining insensitive to others. Experimental validation across multiple health states and operational variations demonstrates that the proposed method outperforms four widely used intelligent models in both classification accuracy and computational efficiency, showing strong suitability for real-world deployment in coal mining applications. Full article

The calibration of suspension geometry involves highly nonlinear kinematic relationships and leads to challenging optimization landscapes that are difficult to explore efficiently with classical local methods. Quadratic Unconstrained Binary Optimization (QUBO) provides a unified discrete formulation that enables the use of a wide range of metaheuristic solvers, but its practical behavior in realistic engineering-inspired problems remains insufficiently benchmarked. This paper presents a computational benchmarking study of classical, parallel, and hybrid metaheuristic solvers applied to a QUBO-formulated double wishbone suspension geometry problem. A symbolic multi-body kinematic model is constructed and discretized into a large-scale QUBO instance capturing camber and caster tracking objectives across multiple roll conditions. Using a fixed low-resolution binary encoding, we systematically evaluate solver performance in terms of objective value, runtime, and time-to-solution trade-offs. The benchmark includes standard simulated annealing and tabu search, parallel simulated annealing, population-based annealing, bandit-controlled hybrid heuristics, and continuous-relaxation-based ADMM methods with and without annealing refinement. Extensive experiments conducted on a Euro-HPC pre-exascale system demonstrate that parallel and hybrid solvers can achieve substantial reductions in wall-clock time—often exceeding two orders of magnitude—while attaining objective values comparable to classical simulated annealing. The results reveal clear trade-offs between solution quality and computational efficiency, and highlight how solver structure influences performance on large QUBO instances derived from symbolic engineering models. Rather than focusing on final design optimality, this study provides a reproducible reference benchmark and practical insights into solver behavior for QUBO-based engineering optimization problems. Full article

Off-axis reflective telescopes are prone to component misalignment due to external environmental factors and mechanical vibrations. This misalignment introduces low-order aberrations, which severely degrade imaging quality. Thus, active misalignment correction is crucial for maintaining the imaging performance of off-axis reflective telescopes. Current computer-aided alignment technologies for optical systems mostly rely on wavefront sensors to acquire aberrations at multiple fixed fields of view (FOVs) or even the full FOV. This significantly increases system complexity and hinders practical engineering applications. To address this issue, this study first conducts sensitivity analysis of misaligned degrees of freedom (DOFs) using a mode truncation algorithm based on singular value decomposition (SVD). A compensation strategy is proposed to avoid the aberration coupling effect. Furthermore, two novel misalignment aberration compensation methods for off-axis reflective telescopes are presented. These methods require only a single focal spot image and eliminate the need for aberration detection and iterative calculations. One method directly solves component misalignment errors using a convolutional neural network (CNN) based on the system’s point spread function (PSF). To further improve compensation performance, an improved method fusing spot images and Zernike coefficients is proposed. In practical misalignment correction, both methods input a single acquired focal spot image into a well-trained model to obtain the misalignment compensation amount. Simulation experiments demonstrate that the improved method, which uses Zernike polynomial coefficients as an intermediate feature bridge, effectively establishes the mapping relationship between spot images and misalignment amounts. It achieves higher solution accuracy and better aberration compensation effect compared to the direct CNN method. This verifies the necessity of extracting Zernike polynomial coefficient features from spot images. Comparative experiments with the traditional sensitivity matrix method show that the two proposed methods outperform the sensitivity matrix method in aberration compensation accuracy over a large misalignment range. Comprehensive simulation results confirm the feasibility and effectiveness of the proposed methods. They overcome the limitations of existing methods, such as complex structure, high cost, and low efficiency, to a certain extent. Full article

In large-scale medical data, the connection between hospital length of stay and medical expenses shows a complex and nonlinear relationship instead of a straightforward positive link. This study proposes a Cox–Log-Logistic–Copula joint modeling framework to describe the marginal distributions and latent dependence between the two variables. Specifically, a semi-parametric Cox proportional hazards model is used for hospitalization duration, while a Log-Logistic model handles medical costs. The two margins are flexibly coupled through a Copula function to capture dynamic variations in cost levels during different hospitalization stages. To address computational challenges in large datasets, this study also includes subsample correction and one-step adjustment algorithms, combined with parallel computing strategies, to enhance estimation efficiency and accuracy. Empirical results show that the length of hospital stays and medical costs are not always positively related. In some cases, higher medical expenses occur during shorter stays, suggesting possible over-treatment or uneven resource distribution. The proposed framework proves to have strong explanatory power in identifying nonlinear patterns in healthcare behavior and offers a new quantitative tool for optimizing medical resource allocation and controlling costs. Full article

Urban traffic noise poses escalating environmental challenges in rapidly urbanizing regions with high-density buildings, yet systematic investigations into its spatiotemporal characteristics remain relatively scarce. This study addresses this research gap via the synchronized on-site monitoring of traffic noise and traffic flow on a representative arterial road in Guangzhou, China. The analysis reveals that nighttime equivalent continuous A-weighted sound levels (L_Aeq) are 3.0–4.0 dB(A) higher than those during the congested daytime peak, a phenomenon primarily driven by higher vehicle speeds under nighttime free-flow traffic conditions. The spatial analysis uncovers complex three-dimensional noise propagation dynamics specific to urban street canyons. Vertical profiling demonstrates a counterintuitive pattern where noise levels do not attenuate with building height, and upper floors experience marginally higher noise exposure than the ground floor, which is attributed to the canyon effect, where multiple sound wave reflections offset the natural distance attenuation. A validated three-dimensional computational model was further employed to evaluate the efficacy of noise mitigation strategies, showing that an integrated intervention combining porous asphalt pavement and acoustic barriers achieves a maximum noise attenuation of 19.9 dB(A) at ground-level receptors. This significant reduction stems from a synergistic effect: porous asphalt reduces noise at the source on a global scale, while acoustic barriers provide localized shielding for the lower floors of adjacent buildings. This research concludes that effective traffic noise control in high-density urban areas requires three-dimensional, multi-faceted strategies addressing noise source characteristics, transmission pathways, and receptor vulnerabilities. Full article

Graph Neural Networks (GNNs) have become a central methodology for modelling biological systems where entities and their interactions form inherently non-Euclidean structures. From protein interaction networks and gene regulatory circuits to molecular graphs and multi-omics integration, the relational nature of biological data makes GNNs particularly well-suited for capturing complex dependencies that traditional deep learning methods fail to represent. Despite their rapid adoption, the effectiveness of GNNs in bioinformatics depends not only on model design but also on how biological graphs are constructed, parameterised and trained. In this review, we provide a structured framework for understanding and applying GNNs in bioinformatics, organised around three key dimensions: (1) graph construction and representation, including strategies for deriving biological networks from heterogeneous sources and selecting biologically meaningful node and edge features; (2) GNN architectures, covering spectral and spatial formulations, representative models such as Graph Convolutional Networks (GCNs), Graph Attention Networks (GATs), Graph Sample and AggregatE (GraphSAGE) and Graph Isomorphism Network (GIN), and recent advances including transformer-based and self-supervised paradigms; and (3) applications in biomedical domains, spanning disease–gene association prediction, drug discovery, protein structure and function analysis, multi-omics integration and biomedical knowledge graphs. We further examine training considerations, including optimisation techniques, regularisation strategies and challenges posed by data sparsity and noise in biological settings. By synthesising methodological foundations with domain-specific applications, this review clarifies how graph quality, architectural choice and training dynamics jointly influence model performance. We also highlight emerging challenges such as modelling temporal biological processes, improving interpretability, and enabling robust multimodal fusion that will shape the next generation of GNNs in computational biology. Full article

Coaches in professional football need to estimate how many minutes a player can tolerate in a match before relevant fatigue occurs. This study aimed to develop a framework to translate monitoring information into individualised, minute-based fatigue thresholds. Over four seasons in an elite club, external load (total distance, high-speed running, mechanical work) and heart rate were collected in training. Machine-learning-derived fitness and fatigue indices were computed and combined with 7- and 28-day load variables in a Random Forest regression model predicting match minutes. The trained model was then used to simulate four fatigue conditions by fixing the match-day fatigue index (z-FA_match = 0, −1, −2, −3). In an independent test season, the model showed a mean absolute error of 22.5 min and R² = 0.17 for playing time prediction, with z-FA_match as the most influential predictor. Simulated fatigue thresholds occurred in an ordered way (0 = 57.1, −1 = 64.9, −2 = 84.8, −3 = 84.4) and differed across season period, playing position, overall seasonal minutes, and return-to-play status. Integrating external load with fitness and fatigue indices via machine learning can provide individualised estimates of when players are likely to reach fatigue states, supporting decisions on selection, substitutions, and return-to-play management. Full article

To maximize competitive performance in motorsports, balancing high downforce for cornering with low drag for straight–line speed is essential. This paper presents the development and optimization of a sliding Drag Reduction System (DRS) integrated with a ducktail guide for a Student Formula racing car. To overcome the computational costs and time constraints of conventional CFD–based iterative design, a Graph Neural Network (GNN) surrogate model was developed to predict aerodynamic coefficients. Unlike traditional models, the GNN directly learns from the geometric graph structure of the multi–element wing, enabling near–instantaneous and highly accurate predictions. CFD results indicated that activating the DRS reduced drag from 82.68 N to 25.51 N, improving the lift–to–drag ratio from 1.67 to 2.67. The GNN surrogate model achieved an R² value exceeding 0.99, demonstrating exceptional predictive fidelity compared to high–resolution simulations. Physical track testing with a Formula SAE vehicle corroborated these findings, showing a 4.6% improvement in 50 m acceleration and a 5.8% increase in maximum speed. This research establishes that GNN–based surrogate models can significantly accelerate the design and optimization of complex variable aerodynamic systems, providing a robust framework for performance enhancement in racing applications. Full article

We assessed the vertical accuracy of six global digital elevation models—FABDEM (SRTM-enhanced), SRTM, ASTER GDEM, ALOS AW3D30, DeltaDTM and GEDTM—against a local photogrammetry-derived DEM as a benchmark in a flat coastal plain of the Colombian Caribbean. Using GNSS-RTK ground points and a high-accuracy reference DEM, we computed BIAS, RMSE, and MAE. Errors were analyzed by land cover class and along transverse profiles relative to the reference DEM. We also evaluated hydrologic suitability by comparing flow accumulation and drainage patterns derived from each model, treating the photogrammetry-derived model as the control and the global DEMs as treatments to gauge their ability to represent hydraulic/hydrologic behavior. DeltaDTM, GEDTM and FABDEM showed the best overall performance, with the lowest vertical error (particularly in non-urban areas with sparse vegetation) and the highest drainage agreement, along with their flood extent sensitivity to a 0.5 m water level rise, all of which were comparable to the benchmark. These results provide practical guidance for selecting and preprocessing topographic models for risk management and territorial planning in flat regions. Full article