Search Results (10,534)

This paper presents a probabilistic, multi-layered framework designed to forecast the longevity and security of cryptographic systems under the dual pressures of classical and quantum computational threats. The model integrates thermodynamic decay analogies, stochastic transitions via Hidden Markov Models, and an adapted financial option pricing method to quantify cryptographic degradation, strategic risk, and transition readiness. This framework can guide standardization roadmaps, cipher retirement, or quantum-migration planning, guiding proactive, instead of reactive, crypto agility. Furthermore, it provides a quantitative methodology to complement the current opinions expressed in surveys, as well as a qualitative approach to cryptographic security risk projections. Full article

Background/Objectives: Contemporary food systems face dual imperatives of ensuring nutritional adequacy while minimizing environmental resource consumption, yet conventional dietary assessment methodologies inadequately integrate these competing objectives. This simulation-based proof-of-concept study developed an artificial intelligence-driven computational framework synthesizing nutritional evaluation, environmental footprint quantification, and economic accessibility assessment. Methods: The analytical architecture integrated random forest classification, dimensionality reduction, and scenario-based optimization across a simulated population cohort of 1500 individuals. Food composition data encompassed 55 representative foods across eight categories linked with greenhouse gas emissions, water use, and price parameters. Four dietary patterns (Mediterranean, Western, Plant-based, Mixed) were characterized across nutrient adequacy, greenhouse gas emissions, water consumption, and economic cost. Results: Random forest classification achieved 39.1% accuracy, with cost, greenhouse gas emissions, and water consumption emerging as the most discriminating features. Dietary patterns exhibited convergent macronutrient profiles (protein 108.8–112.8 g per day, 4% variation) despite categorical distinctions, while calcium inadequacy pervaded all patterns (867–927.5 mg per day, 7–13% below requirements). Environmental footprints demonstrated limited differentiation (greenhouse gas 3.73–3.96 kg CO₂e per day, 6% range). Bootstrap resampling (n = 1000) confirmed narrow confidence intervals, with NHANES validation revealing substantial energy intake deviations (38–58% above observed means) attributable to adequacy-prioritized design rather than observed consumption patterns. Scenario modeling identified seasonally flexible dietary configurations maintaining micronutrient and protein adequacy while reducing water use to 87% of baseline at modest cost increases. Conclusions: This framework establishes a validated computational infrastructure for integrated dietary assessment benchmarked against sustainability thresholds and epidemiological reference data, demonstrating the feasibility of AI-driven evaluation of dietary patterns across nutritional, environmental, and economic dimensions. Full article

Connect-4, a solved two-player perfect-information game, offers a compact benchmark for artificial intelligence research due to its strategic depth and structural regularities, including board symmetries. This review presents a taxonomy-driven synthesis of Connect-4 AI research, encompassing game-theoretical foundations, classical search algorithms, reinforcement learning methods, explainable AI, and formal verification approaches. Analysis of search-, learning-, and hybrid-based methods reveals three dominant patterns: (i) classical search techniques prioritize determinism and efficiency but face scalability limits; (ii) reinforcement learning and neural approaches improve adaptability at the cost of interpretability and computational resources; and (iii) explainable and formally verified frameworks enhance transparency and reliability while imposing additional performance constraints. Recent advances in Connect-4 AI are driven less by raw performance gains than by strategic integration of efficiency, adaptability, interpretability, and robustness. Structuring the literature through a multidimensional taxonomy clarifies conceptual relationships, highlights underexplored research intersections, and points to emerging trends, including hybrid search–learning systems and explainable game intelligence. Overall, Connect-4 serves as a concise experimental domain for investigating fundamental challenges in game-playing AI, system design, and human–AI interaction. Full article

Surgical robots are increasingly utilized in medicine for their reliability and convenience. An accurate kinematic model is essential for precise robot control and enhanced surgical safety. However, the high nonlinearity and computational complexity of kinematics pose significant challenges to traditional numerical methods. This study designs a surgical robotic arm and establishes the motion mapping relationship between the joint space and the end-effector workspace. Subsequently, a hybrid kinematic estimation model based on deep pyramid convolutional neural network (DPCNN) is proposed, which integrates data sampling and an attention mechanism to improve computational accuracy. The Latin hypercube sampling technique is used to improve the uniformity of dataset sampling, and the triplet-fusion self-attention mechanism (TFSAM) is employed for multi-scale feature information. Experimental results show that the TFSAM-DPCNN model achieves coefficient of determination (R²) values exceeding 0.99 across all testing scenarios. Compared with other models, the proposed model reduced the root mean square error (RMSE) by up to 81.34%, exhibiting superior performance. Furthermore, the developed 3D simulation platform validates the effectiveness of the proposed model. This study offers a robust solution for multi-degree-of-freedom robot modeling, with potential applications across a range of robotic motion control systems. Full article

Contemporary artificial intelligence policies systematically externalize environmental costs. Despite divergent governance models, the European Union, the United States, and China converge on the same outcome: none impose binding restrictions on the energy intensity, carbon footprint, or infrastructural expansion of AI systems. This article demonstrates that sustainability is treated as an externality, rather than as a mandatory regulatory constraint, in all major jurisdictions. Focusing on energy consumption, computational infrastructure, and carbon budgets, the analysis shows that current AI policy choices generate predictable patterns of environmental omission and cost externalization. Policy measures aimed at strengthening rights protection and technological autonomy—such as tightening compliance requirements, developing large-scale models, and duplicating infrastructure—are adopted without corresponding limits on energy use or emissions, generating growing tensions with planetary constraints. This article makes three contributions to the literature on AI governance and sustainability. First, it conceptualizes sustainability as a binding material constraint, rather than as a normative objective or efficiency-based goal. Second, through a comparative policy analysis, it shows that despite divergent regulatory styles, the European Union, the United States, and China converge in the absence of enforceable environmental limits applicable to AI systems. Third, it identifies the policy mechanisms—compliance-driven computational expansion, infrastructure duplication, and scale-oriented incentives—that systematically generate environmental externalization across jurisdictions. The article concludes that effective AI policy requires recognizing sustainability as a hard material limit, translated into binding environmental restrictions that condition regulatory design, infrastructure planning, and the permissible scale of computational systems. Full article

Automated visual inspection of safety-critical metal assemblies such as automotive door lock strikes remains challenging due to their complex three-dimensional geometry, highly reflective surfaces, and scarcity of defect samples. While 3D sensing technologies are often constrained by cost and speed, traditional 2D optical methods struggle with severe imaging artifacts and poor generalization under few-shot conditions. This work constructs a complete system integrating defect imaging, generation, and detection. It proposes an integrated framework through the co-design of an image acquisition system and deep generative models to holistically enhance defect perception capability. First, we develop an imaging system using dome illumination and a small-aperture lens to acquire high-quality images of non-planar metal surfaces. Subsequently, we introduce a dual-stage generation strategy: stage one employs an improved FastGAN with Dynamic Multi-Granularity Fusion Skip-Layer Excitation (DMGF-SLE) and perceptual loss to efficiently generate high-quality local defect patches; stage two utilizes Poisson image editing and an optimized loss function to seamlessly fuse defect patches into specified locations of normal images. This strategy avoids modeling the complete complex background, concentrating computational resources on creating realistic defects. Experiments on a dedicated dataset demonstrate that our method can efficiently generate realistic defect samples under few-shot conditions, achieving 11–24% improvement in Fréchet Inception Distance (FID) scores over baseline models. The generated synthetic data significantly enhances downstream detection performance, increasing YOLOv8’s mAP@50:95 from 50.4% to 60.5%. Beyond proposing individual technical improvements, this research provides a complete, synergistic, and deployable system solution—from physical imaging to algorithmic generation—delivering a computationally efficient and practically viable technical pathway for defect detection in highly reflective, non-planar metal components. Full article

The advent of bilayer silicon carbide as a critical two-dimensional material has opened up a range of potential applications in various fields. The field of nanoelectronics and nanomechanical systems is distinguished by its exceptional mechanical robustness, yet the combined effects of environmental and structural factors on its mechanical integrity remain poorly understood. Molecular dynamics simulations are used in this study to systematically examine the tensile response of bilayer SiC across a range of strain rates, temperatures, vacancy concentrations, and pre-existing crack lengths. Results indicate that mechanical properties converge at a system size of 18,144 atoms, ensuring computational efficiency. Increasing strain rate enhances strength and toughness by suppressing atomic relaxation, while elevated temperature induces thermal softening, reducing failure strain and strength by up to 50% at 900 K. Vacancy defects drastically degrade performance, with 3% concentration causing over 70% toughness loss, and crack propagation follows Griffith-type brittle fracture, where the zigzag direction exhibits superior resistance compared to the armchair orientation. These findings highlight the sensitivity of bilayer SiC to defects and environmental conditions, providing critical insights for designing reliable SiC-based nanodevices. Full article

This paper reviews constitutive models used to predict concrete spalling under elevated temperatures, with emphasis on fire exposure and concrete linings in deep geological repositories for spent fuel and nuclear waste. The review synthesizes (1) how material composition (ordinary Portland cement concrete, geopolymer concrete, and fiber-reinforced systems using polypropylene and steel fibers) affects spalling resistance; (2) how coupled environmental and mechanical actions (temperature, moisture, stress state, chloride ingress, and radiation) drive damage initiation and spalling; and (3) how constituent-scale characteristics (microstructure, porosity, permeability, elastic modulus, and water content) govern thermal–hydro–mechanical–chemical (THMC) transport and damage evolution. We compare major constitutive modeling frameworks, including plasticity–damage models (e.g., concrete damage plasticity), statistical damage approaches, and fully coupled THM/THMC formulations, and highlight how key parameters (e.g., water-to-binder ratio, temperature-driven pore-pressure gradients, and crack evolution laws) control predicted spalling onset, depth, and timing. Several overarching challenges emerge: lack of standardized experimental protocols for spalling tests and assessments, which limits cross-study benchmarking; continued debate on whether spalling is dominated by pore pressure, thermo-mechanical stress, or their interaction; limited integration of multiscale and constituent-level material characteristics; and high data and computational demands associated with advanced multi-physics models. The paper concludes with targeted research directions to improve model calibration, validation, and performance-based design of concrete systems for high-temperature and repository applications. Full article

As intelligent vehicle technologies evolve, reliable driver monitoring systems have become increasingly critical for ensuring the safety of human drivers and operational reliability. This paper proposes a novel visual computing framework for Driver Fatigue Monitoring Systems (DFMSs) based on hierarchical decoupling and scenario element analysis, specifically designed for intelligent transportation environments. By treating the monitoring system as an engineering-level embodied perception–decision system deployed within the vehicle, rather than a purely disembodied vision module, the framework decouples low-level algorithmic perception from application-layer decision logic, enabling a more granular evaluation of visual computing performance in real-world scenarios. We leverage Python 3.9-driven automated test case generation to simulate diverse environmental variables, improving testing efficiency by 50% over traditional manual methods. The system utilizes deep learning-based visual computing to achieve high-fidelity monitoring of eye closure (PERCLOS, EAR), yawning (MAR), and head pose dynamics, enabling real-time assessment of the driver’s state within the embodied system loop. Comparative benchmarking reveals that our framework significantly outperforms existing models in visual understanding accuracy, achieving perfect confidence scores (1.000) for eye closure and smoking behavior detection, while drastically reducing false positives in mobile phone usage detection (misidentification rate: 0.016 vs. 0.805). These results demonstrate that an embodied approach to visual perception enhances the robustness and reliability of driver monitoring systems deployed in real vehicles, providing a scalable pathway for the development of next-generation intelligent transportation safety standards. Full article

Networked systems operating under uncertainty require decision making frameworks capable of balancing nominal efficiency and robustness against correlated risks. In this work, we study a distributionally robust weighted dominating set problem as a system-level model for robust network design, where node selection decisions are affected by uncertainty in costs and their correlation structure. We formulate the problem as a bi-objective optimization model that simultaneously minimizes the expected price and a risk measure derived from mean–covariance ambiguity. Rather than proposing new optimization algorithms, we conduct a systematic, methodological, and computational analysis of classical multiobjective solution approaches within this nonconvex and combinatorial setting. In particular, we compare weighted-sum, lexicographic, and $\epsilon$-constraint methods, highlighting their ability to reveal different structural properties of the Pareto Frontier. Our numerical results demonstrate that the methods that use scalarization allow us to obtain only partial insights for networked systems where robustness is inherent. However, the $\epsilon$-constraint method is highly efficient in recovering the full set of Pareto-optimal solutions. Once obtained, the Pareto Frontier exposes non-supported solutions and disruptive changes in its form. Notice that the latter is directly related to different configurations of dominating sets which are induced by the uncertainties. Consequently, these observations allow us to select from different subsets of relevant operating conditions for robust network designs that are significantly different for a decision maker. Full article

Modulated twelve-voltage-vector model predictive current control (MPCC), which applies two or three voltage vectors per control period, exhibits superior steady-state performance compared to modulated six-active-voltage-vector MPCC and conventional MPCC. However, implementing modulated twelve-voltage-vector MPCC requires accurate knowledge of the permanent magnet synchronous motor drive’s inductance and permanent magnet (PM) flux linkage parameters for selecting suboptimal and optimal voltage vectors, as well as computing the duty cycles of optimal vectors. Consequently, its control performance is more sensitive to model parameter inaccuracies. To mitigate parameter sensitivity, a robust modulated twelve-voltage-vector MPCC algorithm based on a model reference adaptive system (MRAS) is proposed. The MRAS-based observer estimates the inductance and PM flux linkage parameters in real time, enhancing model accuracy. The observer is designed with a stability analysis framework, where the proportional and integral gains of the MRAS are theoretically derived to ensure precise parameter estimation. The effectiveness of the proposed algorithm is validated through simulation results, demonstrating satisfactory control performance even under parameter mismatches. Specifically, the torque ripple is reduced from 1.1 A to 0.6 A, corresponding to a reduction of 45.5%. Similarly, the stator flux ripple decreases from 1.75 A to 1 A (42.9% reduction), while the total harmonic distortion (THD) is reduced from 8.39% to 5.48%, representing a 34.7% improvement. Full article

As a non-contact identification technology, RFID (Radio Frequency Identification) is widely used in various Internet of Things applications. However, RFID systems are highly vulnerable to diverse attacks due to the openness of communication links between readers and tags, leading to serious security and privacy concerns. Numerous RFID authentication protocols have been designed that employ hash functions and symmetric cryptography to secure communications. Despite these efforts, such schemes generally exhibit limitations in key management flexibility and scalability, which significantly restricts their applicability in large-scale RFID deployments. Confronted with this challenge, public key cryptography offers an effective solution. Taking into account factors such as parameter size, computational complexity, and resistance to quantum attacks, the NTRU algorithm emerges as one of the most promising choices. Since the NTRU signature algorithm is highly complex and requires large parameters, there are currently only a few NTRU encryption-based RFID authentication protocols available, all of which exhibit significant security flaws—such as supporting only one-way authentication, failing to address public key distribution, and so on. Moreover, performance evaluations of the algorithm in these contexts are often incomplete. This paper proposes a mutual authentication protocol for RFID based on the NTRU encryption algorithm to address security and privacy issues. The security of the protocol is analyzed using the BAN-logic tools and some non-formalized methods, and it is further validated through simulation with the AVISPA tool. With the parameter set (N, p, q) = (443, 3, 2048), the NTRU algorithm can provide 128 bits of post-quantum security strength. This configuration not only demonstrates greater foresight at the theoretical security level but also offers significant advantages in practical energy consumption and computation time when compared to traditional algorithms such as ECC, making it a highly competitive candidate in the field of post-quantum cryptography. Full article

The widespread distribution of digital videos in social networks, streaming services, and surveillance systems has increased the risk of manipulation, unauthorized redistribution, and adversarial tampering. This paper presents a lightweight deep learning framework for robust and imperceptible video watermarking designed specifically for cybersecurity environments. Unlike heavy architectures that rely on multi-scale feature extractors or complex adversarial networks, our model introduces a compact encoder–decoder pipeline optimized for real-time watermark embedding and recovery under adversarial attacks. The proposed system leverages spatial attention and temporal redundancy to ensure robustness against distortions such as compression, additive noise, and adversarial perturbations generated via Fast Gradient Sign Method (FGSM) or recompression attacks from generative models. Experimental simulations using a reduced Kinetics-600 subset demonstrate promising results, achieving an average PSNR of 38.9 dB, SSIM of 0.967, and Bit Error Rate (BER) below 3% even under FGSM attacks. These results suggest that the proposed lightweight framework achieves a favorable trade-off between resilience, imperceptibility, and computational efficiency, making it suitable for deployment in video forensics, authentication, and secure content distribution systems. Full article

This study introduces a novel single-step hybrid block method with three intra-step points that attains fifth-order accuracy, offering an accurate and computationally economical tool for solving first-order differential equations. The method is specifically designed to handle first-order differential equations with efficiency and precision while employing a constant step size throughout the computation. To further enhance accuracy, interpolation techniques are incorporated to approximate function values at specific positions, addressing the fundamental properties of the method and verifying its mathematical soundness. These analyses confirm that the scheme satisfies the essential requirements of stability, consistency, and convergence, ensuring reliability in practical applications. In addition, the method demonstrates strong adaptability, making it suitable for a broad spectrum of problem settings that involve both stiff and non-stiff systems. Numerical experiments are carried out, and the results consistently demonstrate that the proposed method is robust and effective under various test cases. The outcomes further reveal that it frequently outperforms several existing numerical approaches in terms of both accuracy and computational efficiency. Full article

Broadband piezoelectric energy harvesters (PEHs) are attractive for powering self-sustained sensing nodes in industrial monitoring, structural health monitoring, and distributed IoT systems, where ambient vibration spectra are often uncertain, drifting, and broadband. However, tuning multiple resonant peaks in PEH arrays usually relies on time-consuming finite element (FE) parameter sweeps or iterative optimizations, which becomes a practical bottleneck when rapid, site-specific customization is required. This study presents a data-driven inverse-design framework for a five-beam PEH array based on a tandem neural network (TNN). A forward multilayer perceptron (MLP) surrogate is first trained using 10,000 COMSOL-generated samples to predict the array’s characteristic frequencies from the design variables (end masses $M_1$–$M_5$ and tilt angle $\alpha$), achieving >98% prediction accuracy with a prediction time <1 s, thereby enabling efficient replacement of repeated FE evaluations during design. The trained MLP is then coupled with an inverse-design network to form the TNN, which maps target characteristic-frequency sets directly to physically feasible parameters through the learned surrogate. Multiple representative target frequency sets are demonstrated, and the TNN-generated designs are independently verified by COMSOL frequency–response simulations. The resulting arrays achieve broadband operation, with bandwidths exceeding 10 Hz. By shifting most computational cost to offline dataset generation and training, the proposed spectrum-to-parameter pathway enables near-instant parameter design and reduces reliance on exhaustive FE tuning, supporting rapid, application-specific deployment of broadband PEH arrays. Full article