Search Results (909)

To prolong the service life of asphalt pavement and reduce its maintenance cost, a fiber Bragg grating (FBG) sensor encapsulated in carboxylated carbon nanotube (CNT-COOH)-modified gel material suitable for strain monitoring of asphalt pavement was developed. Through tensile and bending tests, the effects of carboxylated carbon nanotubes on the mechanical properties of gel materials under different dosages were evaluated and the optimal dosage of carbon nanotubes was determined. Infrared spectrometer and scanning electron microscopy were used to compare and analyze the infrared spectra and microstructure of carbon nanotubes before and after carboxyl functionalization and modified gel materials. The results show that the incorporation of CNTs-COOH increased the tensile strength, elongation at break, and tensile modulus of the gel material by 36.2%, 47%, and 17.2%, respectively, and increased the flexural strength, flexural modulus, and flexural strain by 89.7%, 7.5%, and 63.8%, respectively. Through infrared spectrum analysis, it was determined that carboxyl (COOH) and hydroxyl (OH) were successfully introduced on the surface of carbon nanotubes. By analyzing the microstructure, it can be seen that the carboxyl functionalization of CNTs improved the agglomeration of carbon nanotubes. The tensile section of the modified gel material is rougher than that of the pure epoxy resin, showing obvious plastic deformation, and the toughness is improved. According to the data from the calibration experiment, the strain and temperature sensitivity coefficients of the packaged sensor are 1.9864 pm/μm and 0.0383 nm/°C, respectively, which are 1.63 times and 3.61 times higher than those of the bare fiber grating. The results of an applicability study show that the internal structure strain of asphalt rutting specimen changed linearly with the external static load, and the fitting sensitivity is 0.0286 με/N. Combined with ANSYS finite element analysis, it is verified that the simulation analysis results are close to the measured data, which verifies the effectiveness and monitoring accuracy of the sensor. The dynamic load test results reflect the internal strain change trend of asphalt mixture under external rutting load, confirming that the encapsulated FBG sensor is suitable for the long-term monitoring of asphalt pavement strain. Full article

Agentic AI systems, possessing capabilities for autonomous planning and action, show great potential across diverse domains. However, their practical deployment is hindered by challenges in aligning their behavior with varied human values, complex safety requirements, and specific compliance needs. Existing alignment methodologies often falter when faced with the complex task of providing personalized context without inducing confabulation or operational inefficiencies. This paper introduces a novel solution: a ‘superego’ agent, designed as a personalized oversight mechanism for agentic AI. This system dynamically steers AI planning by referencing user-selected ‘Creed Constitutions’—encapsulating diverse rule sets—with adjustable adherence levels to fit non-negotiable values. A real-time compliance enforcer validates plans against these constitutions and a universal ethical floor before execution. We present a functional system, including a demonstration interface with a prototypical constitution-sharing portal, and successful integration with third-party models via the Model Context Protocol (MCP). Comprehensive benchmark evaluations (HarmBench, AgentHarm) demonstrate that our Superego agent dramatically reduces harmful outputs—achieving up to a 98.3% harm score reduction and near-perfect refusal rates (e.g., 100% with Claude Sonnet 4 on AgentHarm’s harmful set) for leading LLMs like Gemini 2.5 Flash and GPT-4o. This approach substantially simplifies personalized AI alignment, rendering agentic systems more reliably attuned to individual and cultural contexts, while also enabling substantial safety improvements. Full article

Image super-resolution (SR) is essential for enhancing image quality in critical applications, such as medical imaging and satellite remote sensing. However, existing methods were often limited in their ability to effectively process and integrate multi-scales information from fine textures to global structures. To address these limitations, this paper proposes DSCNN, a dynamic snake convolution neural network for enhanced image super-resolution. DSCNN optimizes feature extraction and network architecture to enhance both performance and efficiency: To improve feature extraction, the core innovation is a feature extraction and enhancement module with dynamic snake convolution that dynamically adjusts the convolution kernel’s shape and position to better fit the image’s geometric structures, significantly improving feature extraction. To optimize the network’s structure, DSCNN employs an enhanced residual network framework. This framework utilizes parallel convolutional layers and a global feature fusion mechanism to further strengthen feature extraction capability and gradient flow efficiency. Additionally, the network incorporates a SwishReLU-based activation function and a multi-scale convolutional concatenation structure. This multi-scale design effectively captures both local details and global image structure, enhancing SR reconstruction. In summary, the proposed DSCNN outperforms existing methods in both objective metrics and visual perception (e.g., our method achieved optimal PSNR and SSIM results on the Set5 $\times 4$ dataset). Full article

Addressing the limitations of existing path planning algorithms for mobile robots in complex environments, such as poor adaptability, low convergence efficiency, and poor path quality, this study establishes a clear connection between mobile robots and real-world challenges such as unknown environments, dynamic obstacle avoidance, and smooth motion through innovative strategies. A novel multi-strategy fusion white shark optimization algorithm is proposed, focusing on actual scenario requirements, to provide optimal solutions for mobile robot path planning. First, the Chaotic Elite Pool strategy is employed to generate an elite population, enhancing population diversity and improving the quality of initial solutions, thereby boosting the algorithm’s global search capability. Second, adaptive weights are introduced, and the traditional simulated annealing algorithm is improved to obtain the Rapid Annealing Method. The improved simulated annealing algorithm is then combined with the White Shark algorithm to avoid getting stuck in local optima and accelerate convergence speed. Finally, third-order Bézier curves are used to smooth the path. Path length and path smoothness are used as fitness evaluation metrics, and an evaluation function is established in conjunction with a non-complete model that reflects actual motion to assess the effectiveness of path planning. Simulation results show that on the simple 20 × 20 grid map, the fusion of the Fused Multi-strategy White Shark Optimisation algorithm (FMWSO) outperforms WSO, D*, A*, and GWO by 8.43%, 7.37%, 2.08%, and 2.65%, respectively, in terms of path length. On the more complex 40 × 40 grid map, it improved by 6.48%, 26.76%, 0.95%, and 2.05%, respectively. The number of turning points was the lowest in both maps, and the path smoothness was lower. The algorithm’s runtime is optimal on the 20 × 20 map, outperforming other algorithms by 40.11%, 25.93%, 31.16%, and 9.51%, respectively. On the 40 × 40 map, it is on par with A*, and outperforms WSO, D*, and GWO by 14.01%, 157.38%, and 3.48%, respectively. The path planning performance is significantly better than other algorithms. Full article

In complex dynamic environments, robotic arms face multiple challenges such as real-time environmental changes, high-dimensional state spaces, and strong uncertainties. Trajectory planning tasks based on deep reinforcement learning (DRL) suffer from difficulties in acquiring human expert strategies, low experience utilization (leading to slow convergence), and unreasonable reward function design. To address these issues, this paper designs a neural network-based expert-guided triple experience replay mechanism (NETM) and proposes an improved reward function adapted to dynamic environments. This replay mechanism integrates imitation learning’s fast data fitting with DRL’s self-optimization to expand limited expert demonstrations and algorithm-generated successes into optimized expert experiences. Experimental results show the expanded expert experience accelerates convergence: in dynamic scenarios, NETM boosts accuracy by over 30% and safe rate by 2.28% compared to baseline algorithms. Full article

Rolling bearings serve as the most widely utilized general components in drive systems for rotating machinery, and they are susceptible to regular malfunctions. To address the performance degradation encountered by current convolutional neural network-based rolling-bearing-fault diagnosis methods due to significant noise interference and variable working conditions in industrial settings, we propose a rolling-bearing-fault diagnosis method based on dual multi-scale mechanism applicable to noisy-variable operating conditions. The suggested approach begins with the implementation of Variational Mode Decomposition (VMD) on the initial vibration signal. This is succeeded by a denoising process that utilizes the goodness-of-fit test based on the Anderson–Darling (AD) distance for enhanced accuracy. This approach targets the intrinsic mode functions (IMFs), which capture information across multiple scales, to obtain the most precise denoised signal possible. Subsequently, we introduce the Dynamic Weighted Multi-Scale Feature Convolutional Neural Network (DWMFCNN) model, which integrates two structures: multi-scale feature extraction and dynamic weighting of these features. Ultimately, the signal that has been denoised is utilized as input for the DWMFCNN model to recognize different kinds of rolling-bearing faults. Results from the experiments show that the suggested approach shows an improved denoising performance and a greater adaptability to changing working conditions. Full article

The standard genetic code (SGC) plays a fundamental role in encoding biological information, but its evolutionary origins remain unresolved and widely debated. Thus, we used a methodology based on the evolutionary algorithm to investigate the emergence of stable coding systems. The simulation began with a population of varied primitive genetic codes that ambiguously encoded only a limited set of amino acids (labels). These codes underwent mutation, modeled by dynamic reassignment of labels to codons, gradual incorporation of new amino acids, and information exchange between themselves. Then, the best codes were selected using a specific fitness function F that measured the accuracy of reading genetic information and coding potential. The evolution converged towards stable and unambiguous coding systems with a higher coding capacity facilitating the production of more diversified proteins. A crucial factor in this process was the exchange of encoded information among evolving codes, which significantly accelerated the emergence of genetic systems capable of encoding 21 labels. The findings shed light on key factors that may have influenced the development of the current genetic code structure. Full article

Metaheuristics are powerful optimization techniques that are well-suited for addressing complex combinatorial problems across diverse scientific and industrial domains. However, their application to computationally expensive problems remains challenging due to the high cost and significant number of fitness evaluations required during the search process. Surrogate modeling has recently emerged as an effective solution to reduce these computational demands by approximating the true, time-intensive fitness function. While surrogate-assisted metaheuristics have gained attention in recent years, their application to complex scheduling problems such as the Permutation Flowshop Scheduling Problem (PFSP) under learning, deterioration, and maintenance effects remains largely unexplored. To the best of our knowledge, this study is the first to investigate the integration of surrogate modeling within the artificial bee colony (ABC) framework specifically tailored to this problem context. We develop and evaluate two distinct strategies for integrating surrogate modeling into the optimization process, leveraging the ABC algorithm. The first strategy uses a Kriging model to dynamically guide the selection of the most effective search operator at each stage of the employed bee phase. The second strategy introduces three variants, each incorporating a Q-learning-based operator in the selection mechanism and a different evolution control mechanism, where the Kriging model is employed to approximate the fitness of generated offspring. Through extensive computational experiments and performance analysis, using Taillard’s well-known standard benchmarks, we assess solution quality, convergence, and the number of exact fitness evaluations, demonstrating that these approaches achieve competitive results. Full article

The estimation of runout distances for long return period avalanches is vital in zoning schemes for mountainous countries. There are two broad methods to estimate snow avalanche runout distance. One involves the use of a physical model to calculate speeds along the incline, with runout distance determined when the speed drops to zero. The second method, which is used here, is based on empirical or statistical models from databases of extreme runout for a given mountain range or area. The second method has been used for more than 40 years with diverse datasets collected from North America and Europe. The primary reason for choosing the method used here is that it is independent of physical models such as avalanche dynamics, which allows comparisons between methods. In this paper, data from diverse datasets are analyzed to explain the relation between them to give an overall view of the meaning of the data. Runout is formulated from nine different datasets and 738 values of extreme runout, mostly with average return periods of about 100 years. Each dataset was initially fit to 65 probability density functions (pdf) using five goodness-of-fit tests. Detailed discussion and analysis are presented for a set of extreme value distributions (Gumbel, Frechet, Weibull). Two distributions had exemplary results in terms of goodness of fit: the generalized logistic (GLO) and the generalized extreme value (GEV) distributions. Considerations included both the goodness-of-fit and the heaviness of the tail, of which the latter is important in engineering decisions. The results showed that, generally, the GLO has a heavier tail. Our paper is the first to compare median extreme runout distances, the first to compare exceedance probability of extreme runout, and the first to analyze many probability distributions for a diverse set of datasets rigorously using five goodness-of-fit tests. Previous papers contained analysis mostly for the Gumbel distribution using only one goodness-of-fit test. Given that climate change is in effect, consideration of stationarity of the distributions is considered. Based on studies of climate change and avalanches, thus far, it has been suggested that stationarity should be a reasonable assumption for the extreme avalanche data considered. Full article

This research integrates stochastic optimization techniques with robust modeling and probabilistic modeling approaches to enhance photovoltaic cell manufacturing processes and product reliability. The study employed an adapted genetic algorithm to tackle uncertainties in the manufacturing process, resulting in improved operational efficiency. It consistently achieved optimal fitness, with values remaining at 1.0 over 100 generations. The model displayed a dynamic convergence rate, demonstrating its ability to adjust performance in response to process fluctuations. The system preserved resource efficiency by utilizing approximately 2600 units per generation, while minimizing machine downtime to 0.03%. Product reliability reached an average level of 0.98, with a maximum value of 1.02, indicating enhanced consistency. The manufacturing process achieved better optimization through a significant reduction in defect rates, which fell to 0.04. The objective function value fluctuated between 0.86 and 0.96, illustrating how the model effectively managed conflicting variables. Sensitivity analysis revealed that changes in sigma material and lambda failure had a minimal effect on average reliability, which stayed above 0.99, while average defect rates remained below 0.05. This research exemplifies how stochastic, robust, and probabilistic optimization methods can collaborate to enhance manufacturing system quality assurance and product reliability under uncertain conditions. Full article

The escalating sophistication of cyber threats underscores the critical need for intelligent and adaptive intrusion detection systems (IDSs) to identify known and novel attack vectors in real time. Feature selection is a key enabler of performance in machine learning-based IDSs, as it reduces the input dimensionality, enhances the detection accuracy, and lowers the computational latency. This paper introduces a novel optimization framework called Quantum Epigenetic Algorithm (QEA), which synergistically combines quantum-inspired probabilistic representation with biologically motivated epigenetic gene regulation to perform efficient and adaptive feature selection. The algorithm balances global exploration and local exploitation by leveraging quantum superposition for diverse candidate generation while dynamically adjusting gene expression through an epigenetic activation mechanism. A multi-objective fitness function guides the search process by optimizing the detection accuracy, false positive rate, inference latency, and model compactness. The QEA was evaluated across four benchmark datasets—UNSW-NB15, CIC-IDS2017, CSE-CIC-IDS2018, and TON_IoT—and consistently outperformed baseline methods, including Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Quantum Genetic Algorithm (QGA). Notably, QEA achieved the highest classification accuracy (up to 97.12%), the lowest false positive rates (as low as 1.68%), and selected significantly fewer features (e.g., 18 on TON_IoT) while maintaining near real-time latency. These results demonstrate the robustness, efficiency, and scalability of QEA for real-time intrusion detection in dynamic and resource-constrained cybersecurity environments. Full article

Rolling element bearings serve as critical transmission components in industrial automation systems, yet their fault signatures are susceptible to interference from strong background noise, complex operating conditions, and nonlinear impact characteristics. Addressing the limitations of conventional methods in adaptive parameter optimization and weak feature enhancement, this paper proposes an innovative diagnostic framework integrating Improved Goose optimized Variational Mode Decomposition (IGOOSE-VMD), RobustICA, and CYCBD. First, to mitigate modal aliasing issues caused by empirical parameter dependency in VMD, we fuse a refraction-guided reverse learning mechanism with a dynamic mutation strategy to develop the IGOOSE. By employing an energy-feature-driven fitness function, this approach achieves synergistic optimization of the mode number and penalty factor. Subsequently, a multi-channel observation model is constructed based on optimal component selection. Noise interference is suppressed through the robust separation capabilities of RobustICA, while CYCBD introduces cyclostationarity-based prior constraints to formulate a blind deconvolution operator with periodic impact enhancement properties. This significantly improves the temporal sparsity of fault-induced impact components. Experimental results demonstrate that, compared to traditional time–frequency analysis techniques (e.g., EMD, EEMD, LMD, ITD) and deconvolution methods (including MCKD, MED, OMEDA), the proposed approach exhibits superior noise immunity and higher fault feature extraction accuracy under high background noise conditions. Full article

Resilience refers to an enduring and yet fluid characteristic that enhances children’s adaptation. It is a dynamic developmental process that is highly promoted by individuals’ internal characteristics, such as self-efficacy and locus of control. The present study examined whether self-efficacy mediates the relationship between locus of control and resilience among Greek primary school students. Participants were 690 students aged 9–12 years who were enrolled at primary schools in Greece in Grades 4, 5, and 6. Participants completed a questionnaire including measures assessing resilience, locus of control, and self-efficacy. Structural equation modeling using AMOS 26.0 was used to analyze the data. The results indicated that locus of control and self-efficacy function as significant predictors for all dimensions of resilience, while demographic characteristics such as gender and grade only predict some dimensions of resilience. The hypothesized model was a good fit to the data, and self-efficacy partially mediates the relationship between locus of control and resilience. Psychologists, instructors, and practitioners can develop and apply intervention programs in order to strengthen children’s resilience by enhancing their self-efficacy and helping them adopt an internal locus of control. Full article

Background/Objectives: The glymphatic system plays a crucial role in clearing brain metabolic waste, and its dysfunction has been correlated to various neurological disorders. The Diffusion Tensor Imaging Analysis Along the Perivascular Space (DTI-ALPS) index has been proposed as a non-invasive marker of glymphatic function by measuring diffusivity along perivascular spaces; however, its sensitivity to sleep-related changes in glymphatic activity has not yet been validated. This study aimed to evaluate the feasibility of using the DTI-ALPS index as a quantitative marker of dynamic glymphatic activity during sleep. Methods: Diffusion tensor imaging (DTI) data were obtained from 12 healthy male participants (age = 24.44 ± 2.5 years; Pittsburgh Sleep Quality Index (PSQI) < 5), once while awake and 16 times during sleep, following 24 h sleep deprivation and administration of 10 mg zolpidem. Simultaneous MR-compatible electroencephalography was used to determine whether the subject was asleep or awake. DTI preprocessing included eddy current correction and tensor fitting. The DTI-ALPS index was calculated from nine regions of interest in projection and association areas aligned to standard space. The final analysis included nine participants (age = 24.56 ± 2.74 years; PSQI < 5) who maintained a continuous sleep state for 1 h without awakening. Results: Among nine ROI pairs, three showed significant increases in the DTI-ALPS index during sleep compared to wakefulness (Friedman test; p = 0.027, 0.029, 0.034). These ROIs showed changes at 14, 19, and 25 min after sleep induction, with FDR-corrected p-values of 0.024, 0.018, and 0.018, respectively. Conclusions: This study demonstrated a statistically significant increase in the DTI-ALPS index within 30 min after sleep induction through time-series DTI analysis during wakefulness and sleep, supporting its potential as a biomarker reflecting glymphatic activity. Full article

Minimizing energy consumption remains a critical challenge in wireless sensor networks (WSNs) because of their reliance on nonrechargeable batteries. Clustering-based hierarchical communication has been widely adopted to address this issue by improving residual energy and balancing the network load. In this architecture, cluster heads (CHs) are responsible for data collection, aggregation, and forwarding, making their optimal selection essential for prolonging network lifetime. The effectiveness of CH selection is highly dependent on the choice of metaheuristic optimization method and the design of the fitness function. Although numerous studies have applied metaheuristic algorithms with suitably designed fitness functions to tackle the CH selection problem, many existing approaches fail to fully capture both the spatial distribution of nodes and dynamic energy conditions. To address these limitations, we propose the binary secretary bird optimization clustering (BSBOC) method. BSBOC introduces a binary variant of the secretary bird optimization algorithm (SBOA) to handle the discrete nature of CH selection. Additionally, it defines a novel multiobjective fitness function that, for the first time, considers the Voronoi diagram of CHs as an optimization objective, besides other well-known objectives. BSBOC was thoroughly assessed via comprehensive simulation experiments, benchmarked against two advanced methods (MOBGWO and WAOA), under both homogeneous and heterogeneous network models across two deployment scenarios. Findings from these simulations demonstrated that BSBOC notably decreased energy usage and prolonged network lifetime, highlighting its effectiveness as a reliable method for energy-aware clustering in WSNs. Full article