Search Results (5,171)

Timely detection of anomalies in nuclear power plants (NPPs) is essential for operational safety, especially under conditions where process signals deviate gradually or abruptly from nominal patterns. Traditional detection methods often struggle to adapt under transient conditions or in the absence of well-labeled fault data. To address this challenge, we propose KD-ADWIN, an adaptive concept drift-detection framework designed for unsupervised anomaly detection in dynamic industrial environments. The method integrates three core components: a Kalman-based prediction module to extract smoothed signal trends, a multi-channel detection strategy combining statistical and derivative-based drift indicators, and an adaptive thresholding mechanism that tunes detection sensitivity based on local signal variability. Evaluations on a synthetic dataset show that KD-ADWIN accurately detects both abrupt and gradual drifts, outperforming classical baselines. Further validation using full-scope simulation data from a modular high-temperature gas-cooled reactor (MHTGR) demonstrates its effectiveness in identifying concept drifts under realistic actuator and sensor fault conditions. Full article

Optical wireless sensor networks (OWSNs) offer promising capabilities for high-speed, energy-efficient communication, particularly in mission-critical environments such as industrial automation, healthcare monitoring, and smart buildings. However, dynamic spectrum management and fault tolerance remain key challenges in ensuring reliable and timely data transmission. This paper proposes an adaptive spectrum management framework (ASMF) that addresses these challenges through a mathematically grounded and implementation-driven approach. The ASMF formulates the spectrum allocation problem as a constrained Markov decision process and leverages a dual-layer optimization strategy combining Lyapunov drift-plus-penalty for queue stability with deep reinforcement learning for adaptive long-term decision making. Additionally, ASMF integrates a hybrid fault-tolerant mechanism using LSTM-based link failure prediction and lightweight recovery logic, achieving up to 83% prediction accuracy. Experimental evaluations using real-world datasets from industrial, healthcare, and smart infrastructure scenarios demonstrate that ASMF reduces critical traffic latency by 37%, improves reliability by 42% under fault conditions, and enhances energy efficiency by 22.6% compared with state-of-the-art methods. The system also maintains a 99.94% packet delivery ratio for critical traffic and achieves 69.7% faster recovery after link failures. These results confirm the effectiveness of ASMF as a robust and scalable solution for adaptive spectrum management in dynamic, fault-prone OWSN environments. Full article

This study presents a high-performance external strengthening strategy for reinforced concrete (RC) beam–column joints, integrating near-surface mounted (NSM) Carbon Fiber Reinforced Polymer (C-FRP) ropes with externally bonded C-FRP sheets. The X-shaped ropes, anchored diagonally on both principal joint faces and complemented by vertical ropes at column corners, provide enhanced core confinement and shear reinforcement. C-FRP sheets applied to the beam’s plastic hinge region further increase flexural strength and delay localized failure. Three full-scale, shear-deficient RC joints were subjected to cyclic lateral loading. The unstrengthened specimen (JB0V) exhibited rapid stiffness deterioration, premature joint shear cracking, and unstable hysteretic behavior. In contrast, the specimen strengthened solely with X-shaped C-FRP ropes (JB0VF2X2c) displayed a markedly slower rate of stiffness degradation, delayed crack development, and improved energy dissipation stability. The fully retrofitted specimen (JB0VF2X2c + C-FRP) demonstrated the most pronounced gains, with peak load capacity increased by 65%, equivalent viscous damping enhanced by 55%, and joint shear deformations reduced by more than 40%. Even at 4% drift, it retained over 90% of its peak strength, while localizing damage away from the joint core—a performance unattainable by the unstrengthened configuration. These results clearly establish that the combined C-FRP rope–sheet system transforms the seismic response of deficient RC joints, offering a lightweight, non-invasive, and rapidly deployable retrofit solution. By simultaneously boosting shear resistance, ductility, and energy dissipation while controlling damage localization, the technique provides a robust pathway to extend service life and significantly enhance post-earthquake functionality in critical structural connections. Full article

Influenza is a serious and global health issue, and it is a major cause of morbidity, fatality, and economic loss every year. Seasonal vaccines exist but are not very effective due to strain mismatches, delays in production, and antigenic drift. This comprehensive overview discusses the current situation of influenza vaccination, including the numerous types of vaccines—inactivated, live attenuated, and recombinant vaccines—and their effectiveness, efficacy, and associated challenges. It highlights the effects of the COVID-19 pandemic on the trends of influenza vaccination and the level to which innovation should be practiced. In the future universal influenza vaccines will be developed that target conserved viral antigens to provide long-term protection to people. In the meantime, novel vaccine delivery platforms, such as mRNA technology, virus-like particle (VLP), and nanoparticle-based systems, and less cumbersome and invasive administration routes, as well as immune responses are also under development to increase access and production capacity. Collectively, these innovations have the potential to not only reduce the global influenza epidemic but also to change the way influenza is prevented and prepare the world for a pandemic. Full article

In late August and mid-November 2024, and late February and mid-May 2025, four surveys were conducted in the Kalasuke Reservoir section of the Irtysh River, resulting in the collection of 296 samples of P. fluviatilis. Sampling tools included drift gillnets with a mesh size of 5 cm and an outer mesh size of 10 cm, bottom cages with a mesh size of 1 cm, and fishing rods (4.5 m and 5.4 m). The age structure and growth characteristics of P. fluviatilis in the reservoir were analyzed. Results showed that the body length of the sampled fish ranged from 100.53 to 305.30 mm, with the dominant length group being 100.53–150.00 mm, accounting for 90.09% of the total. Body mass ranged from 24.20 to 490.20 g, with the dominant mass group below 66.5 g, accounting for 89.86%. The age composition of the population consisted of age classes 1–5, with ages 1–2 years old being dominant, accounting for 96.2% of the total samples. Among these, 1-year-old individuals were the most abundant, accounting for 78.3%, while older fish were relatively scarce. The relationship between body length (L_t) and body mass (W_t) was modeled as W_t = 4.298 × 10⁻⁵ L_t^2.85 (R² = 0.998, n = 296). The von Bertalanffy growth equations were L_t = 652.866 [1 − e^−0.108(^t+0.778)] and W_t = 4990.21 [1 − e^−0.108(^t+0.778)]^2.85, with a growth coefficient K = 0.108. The inflection point of growth was determined to be 1.9 years by fitting growth rate and acceleration equations. The b < 3 indicates allometric growth, where body length increases faster than body mass, suggesting that P. fluviatilis prioritizes elongating its body to enhance swimming ability and expand its range, while accumulating muscle and fat at a slower pace. Principal component analysis (PCA) revealed that the cumulative contribution rate of the first three principal components was 55.45%, reflecting the morphological characteristics of the species. The accuracy of discriminant analysis for sex determination based on external morphology was 67.20%, indicating limited reliability in gender identification using only morphological traits. Full article

The application of precast assembled pier systems in high-seismicity regions is often constrained by their seismic performance limitations. To validate the optimization effect of GFRP confinement on the hysteretic performance of bridge piers, this study first conducted axial compression tests on 54 glass fiber-reinforced polymer (GFRP)-confined concrete cylindrical specimens. The investigation focused on the effects of fiber layers (6 and 10), orientation angles ($\pm 45°$, $\pm 60°$, $\pm 80°$), slenderness ratios (2 and 4), and compression section configurations (fully loaded vs. core concrete loading only) on confinement efficacy. The experimental results demonstrate that specimens with $\pm 60°$ fiber angles achieved an optimal balance between strength and ductility, exhibiting an average strength enhancement of 298.0% and a maximum axial strain of 2.7% compared to unconfined concrete. Subsequently, two GFRP tube-confined concrete bridge piers with varying fiber layers (PRCG1: 6 layers; PRCG2: 10 layers) and one unconfined reference pier (PRC) were designed and fabricated. All specimens employed grout-filled sleeves to connect caps and piers. Pseudo-static tests revealed that GFRP confinement effectively mitigated damage in plastic hinge zones and enhanced seismic performance. Compared to the PRC, PRCG1 and PRCG2 exhibited increases in ultimate displacement by 19.50% and 28.57%, in ductility coefficients by 18.56% and 27.84%, and in cumulative hysteretic energy dissipation by 13.90% and 26.43%, respectively. At the 5% drift ratio, their load capacities increased by 26.74% and 23.25%, stiffnesses improved by 28.91% and 25.51%, and residual displacements decreased by 20.89% and 11.17%. The accuracy and applicability of the GFRP tube-confined bridge pier model, developed based on the Lam–Teng model, were validated through numerical simulations using the OpenSees fiber element approach. Full article

Aerospace engines and hypersonic vehicles, among other high-temperature components, often operate in environments characterized by temperatures exceeding 1000 °C and high-speed airflow impacts, resulting in severe thermal erosion conditions. Coaxial thermocouples (CTs), with their unique self-eroding characteristic, are particularly well suited for use in such extreme environments. However, fabricating high-temperature electrical insulation layers for coaxial thermocouples remains challenging. Inspired by the self-healing mechanism of pine trees, we designed a composite electrical insulation layer with a similar self-healing function. This composite layer exhibits excellent high-temperature insulation properties (insulation resistance of 14.5 kΩ at 1200 °C). Applied as the insulation layer in K-type coaxial thermocouples via dip-coating, the thermocouples were tested for temperature and heat flux. Temperature tests showed an accuracy of 1.72% in the range of 200–1200 °C, a drift rate better than 0.474%/h at 1200 °C, and hysteresis better than 0.246%. The temperature response time was 1.08 ms. Heat flux tests demonstrated a measurable range of 0–41.32 MW/m² with an accuracy better than 6.511% and a heat flux response time of 7.6 ms. In simulated extreme environments, the K-type coaxial thermocouple withstood 70 s of 900 °C flame impact and 50 cycles of high-power laser thermal shock. Full article

Fault diagnosis for embedded sensors in asphalt pavement faces significant challenges, including the scarcity of real-world fault data and the difficulty in identifying compound faults, which severely compromises the reliability of monitoring data. To address these issues, this study proposes an intelligent diagnostic framework that integrates a Discrete Wavelet Transform (DWT) with a staged, attention-based Long Short-Term Memory (LSTM) network. First, various fault modes were systematically defined, including short-term (i.e., bias, gain, and detachment), long-term (i.e., drift), and their compound forms. A fine-grained fault injection and labeling strategy was then developed to generate a comprehensive dataset. Second, a novel diagnostic model was designed based on a “Decomposition-Focus-Fusion” architecture. In this architecture, the DWT is employed to extract multi-scale features, and independent sub-models—a Bidirectional LSTM (Bi-LSTM) and a stacked LSTM—are subsequently utilized to specialize in learning short-term and long-term fault characteristics, respectively. Finally, an attention network intelligently weights and fuses the outputs from these sub-models to achieve precise classification of eight distinct sensor operational states. Validated through rigorous 5-fold cross-validation, experimental results demonstrate that the proposed framework achieves a mean diagnostic accuracy of 98.89% (±0.0040) on the comprehensive test set, significantly outperforming baseline models such as SVM, KNN, and a unified LSTM. A comprehensive ablation study confirmed that each component of the “Decomposition-Focus-Fusion” architecture—DWT features, staged training, and the attention mechanism—makes an indispensable contribution to the model’s superior performance. The model successfully distinguishes between “drift” and “normal” states—which severely confuse the baseline models—and accurately identifies various complex compound faults. Furthermore, simulated online diagnostic tests confirmed the framework’s rapid response capability to dynamic faults and its computational efficiency, meeting the demands of real-time monitoring. This study offers a precise and robust solution for the fault diagnosis of embedded sensors in asphalt pavement. Full article

This study presents a comprehensive statistical analysis of ionospheric midnight collapse events over Arecibo, based on incoherent scatter radar (ISR) observations collected between 1971 and 2019. A total of 224 nights with valid measurements were examined to characterize the timing, intensity, and seasonal variation of these collapse events. The results showed that midnight collapses occurred on 94.6% of the nights, with the highest occurrence rate observed during spring and winter. The first collapse typically began between 22:00 and 00:00 LT, lasted for 1–4 h, initiated at altitudes between 350 and 400 km, and involved a vertical collapse of 50–100 km. A second collapse was identified on 18.8% of nights, occurring predominantly between 01:00 and 02:00 LT, with a notably higher frequency during winter. Compared to the first collapse, the second collapse tended to originate at lower altitudes and exhibited faster collapse rates. Seasonal patterns in the vertical ion drift ($V_z$) were also identified, with winter events characterized by a persistently downward $V_z$ throughout the night. Further decomposition of $V_z$ into field-aligned ($V_{ap}$) and perpendicular ($V_{pn}$) components indicated that $V_{ap}$ played a dominant role in modulating $V_z$, particularly on nights with double collapses. Analysis of meridional wind variations revealed that nighttime changes in $V_{ap}$ were largely controlled by meridional wind, suggesting a strong coupling between thermospheric wind dynamics and field-aligned ion motion. These findings suggest that variations in $V_z$, primarily driven by meridional-wind-controlled changes in $V_{ap}$, are a key driver of ionospheric midnight collapse events at Arecibo. Full article

The electrical properties of contacts to p-type nitride semiconductor devices, based on gallium nitride, were simulated by ab initio and drift-diffusion calculations. The electrical properties of the contact are shown to be dominated by the electron-transfer process from the metal to GaN, which is related to the Fermi-level difference, as determined by both ab initio and model calculations. The results indicate a high potential barrier for holes, leading to the non-Ohmic character of the contact. The electrical nature of the Ni–Au contact formed by annealing in an oxygen atmosphere was elucidated. The influence of doping on the potential profile of p-type GaN was calculated using the drift-diffusion model. The energy-barrier height and width for hole transport were determined. Based on these results, a new type of contact is proposed. The contact is created by employing multiple-layer implantation of deep acceptors. The implementation of such a design promises to attain superior characteristics (resistance) compared with other contacts used in bipolar nitride semiconductor devices. The development of such contacts will remove one of the main obstacles in the development of highly efficient nitride optoelectronic devices, both LEDs and LDs: energy loss and excessive heat production close to the multiple-quantum-well system. Full article

As large language model (LLM) tutors evolve from scripted helpers into adaptive educational partners, their capacity for self-regulation, ethical decision-making, and internal monitoring will become increasingly critical. This paper introduces the Needs-Driven Consciousness Framework (NDCF) as a novel, integrative architecture that combines Dennett’s multiple drafts model, Damasio’s somatic marker hypothesis, and Tulving’s tripartite memory system into a unified motivational design for synthetic consciousness. The NDCF defines three core regulators, specifically Survive (system stability and safety), Thrive (autonomy, competence, relatedness), and Excel (creativity, ethical reasoning, long-term purpose). In addition, there is a proposed supervisory Protect layer that detects value drift and overrides unsafe behaviours. The core regulators compute internal need satisfaction states and urgency gradients, feeding into a softmax-based control system for context-sensitive action selection. The framework proposes measurable internal signals (e.g., utility gradients, conflict intensity Ω), behavioural signatures (e.g., metacognitive prompts, pedagogical shifts), and three falsifiable predictions for educational AI testbeds. By embedding these layered needs directly into AI governance, the NDCF offers (i) a psychologically and biologically grounded model of emergent machine consciousness, (ii) a practical approach to building empathetic, self-regulating AI tutors, and (iii) a testable platform for comparing competing consciousness theories through implementation. Ultimately, the NDCF provides a path toward the development of AI tutors that are capable of transparent reasoning, dynamic adaptation, and meaningful human-like relationships, while maintaining safety, ethical coherence, and long-term alignment with human well-being. Full article

In complex urban wireless environments, unmanned aerial vehicle–mobile edge computing (UAV-MEC) systems face challenges like link blockage and single-antenna eavesdropping threats. The traditional single reconfigurable intelligent surface (RIS), limited in collaboration, struggles to address these issues. This paper proposes a double-RIS cooperative UAV-MEC optimization scheme, leveraging their joint reflection to build multi-dimensional signal paths, boosting legitimate link gains while suppressing eavesdropping channels. It considers double-RIS phase shifts, ground user (GU) transmission power, UAV trajectories, resource allocation, and receiving beamforming, aiming to maximize secure energy efficiency (EE) while ensuring long-term stability of GU and UAV task queues. Given random task arrivals and high-dimensional variable coupling, a dynamic model integrating queue stability and secure transmission constraints is built using Lyapunov optimization, transforming long-term stochastic optimization into slot-by-slot deterministic decisions via the drift-plus-penalty method. To handle high-dimensional continuous spaces, an end-to-end proximal policy optimization (PPO) framework is designed for online learning of multi-dimensional resource allocation and direct acquisition of joint optimization strategies. Simulation results show that compared with benchmark schemes (e.g., single RIS, non-cooperative double RIS) and reinforcement learning algorithms (e.g., advantage actor–critic (A2C), deep deterministic policy gradient (DDPG), deep Q-network (DQN)), the proposed scheme achieves significant improvements in secure EE and queue stability, with faster convergence and better optimization effects, fully verifying its superiority and robustness in complex scenarios. Full article

Investors face the challenge of how to incorporate economic and financial forecasts into their investment strategy, especially in times of financial crisis. To model this situation, we consider a financial market consisting of a risk-free asset with a constant interest rate as well as a risky asset whose drift and volatility is influenced by a stochastic process indicating the probability of potential market downturns. We use a dynamic portfolio optimization approach in continuous time to maximize the expected utility of terminal wealth and solve the corresponding HJB equations for the general class of HARA utility functions. The resulting optimal strategy can be obtained in closed form. It corresponds to a CPPI strategy with a stochastic multiplier that depends on the information from the crisis indicator. In addition to the theoretical results, a performance analysis of the derived strategy is implemented. The specified model is fitted using historic market data and the performance is compared to the optimal portfolio strategy obtained in a Black–Scholes framework without crisis information. The new strategy clearly dominates the BS-based CPPI strategy with respect to the Sharpe Ratio and Adjusted Sharpe Ratio. Full article

Background/Objectives: Overnutrition increases comorbidities such as gestational diabetes during pregnancy that can have detrimental consequences for both parent and progeny. We previously reported that high-fat (HF) diet and late-gestation diabetes (DM) incite mitochondrial dysfunction, oxidative stress, and cardiometabolic disease in first generation (F1) rat offspring, partially through epigenomic and transcriptomic programming. Primordial germ cells, which become the second generation (F2), are also exposed, which could incite generational risk. This study aimed to determine whether the F2 transcriptome already has genomic variation at the preimplantation embryo stage, and whether variations normalize, persist or compound in the third generation (F3). Methods: F0 female rats were fed a control or HF diet, then DM was induced in HF-fed dams on gestational day (GD)14, exposing F1 offspring and F2 primordial germ cells to hyperlipidemia, hyperglycemia and fetal hyperinsulinemia during the last third of pregnancy. F1 pups were reared by healthy dams and bred to produce F2 embryos (F2e) and F2 pups. F2 offspring were bred to produce F3 embryos (F3e). Embryos were assessed by a novel grading method, live cell imaging, and single-cell RNA sequencing. Results: Embryo grades were not different, but HF+DM F2e had more cells while F3e had fewer cells and overall fewer embryos. HF+DM F2e had similar mitochondria quantity but a downregulation of genes involved in lipid metabolism and more oxidative stress, consistent with mitochondrial dysfunction. They also had an upregulation of chromatin-remodeling genes. The predicted developmental effect is accelerated embryo aging and epigenetic drift. In contrast, HF+DM F3e had an adaptive stress response leading to increased mitochondria quantity and an upregulation of genes involved in mitochondrial respiration, metabolism, and genomic repair that led to a predicted developmental effect of delayed embryo maturation. Conclusions: Although pathways vary, both generations have metabolically linked differentially expressed genes that influence cell fate and developmental pathways. In conclusion, HF+DM pregnancy can program the early embryonic transcriptome for three generations, despite an intergenerational healthy diet. Full article

Federated learning (FL) has emerged as a powerful framework for decentralized model training, preserving data privacy by keeping datasets localized on distributed devices. However, data heterogeneity, characterized by significant variations in size, statistical distribution, and composition across client datasets, presents a persistent challenge that impairs model performance, compromises generalization, and delays convergence. To address these issues, we propose FedDPA, a novel framework that utilizes dynamic prototypical alignment. FedDPA operates in three stages. First, it computes class-specific prototypes for each client to capture local data distributions, integrating them into an adaptive regularization mechanism. Next, a hierarchical aggregation strategy clusters and combines prototypes from similar clients, which reduces communication overhead and stabilizes model updates. Finally, a contrastive alignment process refines the global model by enforcing intra-class compactness and inter-class separation in the feature space. These mechanisms work in concert to mitigate client drift and enhance global model performance. We conducted extensive evaluations on standard classification benchmarks—EMNIST, FEMNIST, CIFAR-10, CIFAR-100, and Tiny-ImageNet 200—under various non-identically and independently distributed (non-IID) scenarios. The results demonstrate the superiority of FedDPA over state-of-the-art methods, including FedAvg, FedNH, and FedROD. Our findings highlight FedDPA’s enhanced effectiveness, stability, and adaptability, establishing it as a scalable and efficient solution to the critical problem of data heterogeneity in federated learning. Full article