Search Results (3,943)

Background/Objectives: Understanding the relationship between reactogenicity and immunogenicity after repeated BNT162b2 vaccination is critical for optimizing vaccination strategies. This study quantified their progressive dissociation across four vaccine doses. Methods: We conducted a prospective longitudinal cohort study among Croatian healthcare workers vaccinated with BNT162b2 from January 2021 to January 2024. Anti-SARS-CoV-2 IgG antibodies were measured at 16 timepoints using chemiluminescent immunoassay. Local (pain, erythema, swelling) and systemic (fever, fatigue, headache, myalgia, arthralgia, nausea) reactions were recorded for 7 days using FDA toxicity scale. Correlations were analyzed with Spearman’s method and Bonferroni correction. Fourth-dose responses were predicted by exponential modeling. Results: Of 631 participants, 524 completed primary immunization, 418 received a third dose (173 with complete data), and 56 received a fourth dose (22 with complete paired data). Local reactions declined from 82.4% after the first dose to 42.9% after the fourth (p < 0.001). Systemic reactions peaked at 44.8% after the second dose, then decreased to 26.0% after the third and 19.6% after the fourth. In contrast, median antibody levels rose from 9910 AU/mL after the primary series to 29,002 AU/mL after the third and 38,274 AU/mL after the fourth. Correlations between reactions and antibody titer progressively weakened: r = 0.37 (95% CI 0.29–0.44, p < 0.001) after the primary series, r = 0.08 (95% CI −0.07 to 0.23, p = 0.30) after the third, and r = 0.04 (95% CI −0.39 to 0.45, p = 0.86) after the fourth dose. Conclusions: Progressive dissociation between reactogenicity and immunogenicity was observed across four BNT162b2 doses. Booster doses maintain robust antibody responses despite reduced reactogenicity, reinforcing that minimal side effects are consistent with sustained humoral responses. Full article

In classical reliability engineering, failure is a probabilistic structural failure based on lifetime distributions of Weibull models. However, in the control-critical mechanical systems, it is possible that functional failure of the system happens before material failure occurs as a result of control power loss. This paper proposes a Controllability–Reliability Coupling (CRC) model, which redefines the concept of reliability as the stabilizability in the face of progressive degradation. The actuators’ deterioration is modeled using the time-varying input effectiveness factor $\alpha \left(t\right)$, and the actuator is said to be in failure when the minimum singular value of the finite-horizon controllability Gramian becomes less than a stabilizability threshold $\epsilon$. The performance of the simulation indicates that the functional failure is a precursor of structural failure in several degradation conditions. A baseline comparison shows that the CRC metric forecasts loss of controllability at $T_{CRC}=17.0$ s, but the classical Weibull reliability never attains the structural failure threshold even in the time horizon of 20 s. The system retains margins of Lyapunov stability and H infinity robustness are not lost, and it is still stable and attenuates disturbances even when control authority is lost. In practical degradation scenarios, the forecasted CRC failure times are 21.5 s (linear wear), 13.1 s (accelerated fatigue), 23.7 s (intermittent faults), and 24.4 s (shock damage), whereas maintenance recovery abated functional failure completely. In a case study of an industrial robotic joint, at 27.0 s, functional collapse occurred, and at the same time, structural reliability was still above the failure threshold. The findings support the hypothesis that structural survival and functional controllability are distinct concepts. The proposed CRC framework is an approach to control-conscious reliability measure, which can detect early failures and offer proactive maintenance advice in the context of a cyber–physical system. Full article

RV reducers are vital components in industrial robots and precision equipment, where the fatigue life of the crank arm and support bearings critically influences the overall system longevity. This study presents a comprehensive performance evaluation, with a specific focus on contact mechanics and wear analysis of these critical bearings. A theoretical mathematical model for force analysis is established based on static mechanics, which is further extended to incorporate wear depth prediction based on contact pressure and sliding velocity. To validate this model and investigate bearing behavior in detail, a high-fidelity parametric simulation model is developed using MASTA software. The simulation results, encompassing contact stress, shear stress, and wear patterns, demonstrate good correlation with the predictions from the theoretical mathematical model, effectively verifying its accuracy for performance and life assessment. The systematic analysis confirms that both the investigated tapered roller and needle roller bearings meet the design requirements. This integrated approach of theoretical modeling, which includes wear analysis, and software simulation provides a reliable methodology for assessing bearing performance and fatigue life, offering significant value for the design optimization and reliability enhancement of RV reducers. Full article

The corrosion of prestressed tendons in concrete structures remains a major durability concern, especially for post-tensioned members exposed to aggressive environments. High-strength aluminum alloy (AA) bars exhibit favorable characteristics such as corrosion resistance, low density, and high ductility and may therefore provide an alternative or supplementary prestressing material in durability-oriented structural design. In this study, a bonded post-tensioned T-shaped concrete beam with hybrid prestressing combining prestressed steel (PS) strands and 7075 AA bars was investigated. A refined finite element model was developed by considering the bond-slip relationship between the AA tendons and grout inside corrugated tubes. The flexural behavior of the beam was analyzed through a combination of finite element simulation and sectional theoretical analysis. In addition, a fatigue-life assessment framework was established based on vehicle fatigue loads and material fatigue constitutive models, and the fatigue performance of the proposed hybrid beams was compared with that of conventional prestressed concrete beams. The theoretical predictions agreed reasonably well with the numerical results. Results indicated that partial replacement of PS strands with corrosion-resistant AA bars could alter the governing fatigue failure mode and improve the fatigue durability of prestressed beams under corrosive conditions. These findings highlight the potential of hybrid AA–PS prestressing as a durability-oriented strategy for concrete beams in corrosive environments. Full article

Repeated impact loading can induce progressive fatigue delamination in composite laminates, in which both damage accumulation and strain-rate sensitivity of the interlaminar interface play important roles. In this work, an adopted rate-dependent fatigue cohesive formulation is extended to a three-dimensional framework for simulating interlaminar delamination in composite laminates subjected to repeated impact. The constitutive formulation incorporates separation-rate-dependent critical tractions and fracture toughness together with cumulative fatigue damage, enabling a unified description of dynamic rate effects and progressive interface degradation. A time-incremental algorithm is developed and implemented in ABAQUS 2020/Explicit through a user-defined cohesive element subroutine (VUEL). The cohesive formulation is further coupled with the Hashin intralaminar failure criterion to represent the interaction between interlaminar delamination and intralaminar damage. Numerical simulations are conducted for composite laminates with three structural configurations—conventional, drop-off, and wrapped drop-off—to systematically examine the influence of rate dependence on fatigue delamination under repeated impact. The results show that the developed framework captures the progressive evolution of delamination and impact response under repeated impact and indicate that the sensitivity to rate-dependent interlayer properties depends on both laminate configuration and impact velocity. The present study provides a feasible computational framework for the comparative simulation and assessment of fatigue delamination under repeated impact and offers numerical insight into the role of structural configuration and interfacial rate dependence in composite laminates. Full article

The rapid expansion of global commerce has escalated the complexity of money laundering schemes, making the detection of illicit transfers an urgent but highly challenging research problem. In operational anti-money laundering (AML) systems, the extreme rarity of illicit transactions often overwhelms compliance teams with false positives, leading to severe “alert fatigue.” To address this critical bottleneck, this paper introduces an enhanced, probability-driven risk-prioritization framework utilizing an XGBoost classifier integrated with Bayesian Optimization (BO-XGBoost). By optimizing directly for the Area Under the Precision–Recall Curve (PR-AUC), the model is specifically tailored to rank high-risk anomalies under severe class imbalance. We validate the proposed approach on a rigorously resampled transaction dataset simulating a realistic 5% laundering rate. The BO-XGBoost model demonstrates exceptional prioritization capability, achieving an ROC-AUC of 0.9686 and a PR-AUC of 0.7253. Most notably, it attains a near-perfect Precision@1%, meaning the top 1% of flagged transactions are 100% true illicit activities, entirely eliminating false positives at the highest priority tier. Comparative and SHAP-based interpretability analyses confirm that BO-XGBoost easily outperforms sequence-heavy deep learning baselines. Crucially, it matches computationally expensive stacking ensembles in peak predictive precision while significantly surpassing them in operational efficiency, indicating its immense promise for resource-optimized, real-world compliance screening. Full article

The paper investigates the applicability of time series models for processing data obtained from a customized crack-propagation sensor. Because the sensor records a variable and noise-affected waveform, the study focuses on models capable of forecasting signals composed of both trend and stochastic components. Adaptive, analytical, and autoregressive approaches were examined, with particular attention to their suitability for short, non-stationary sequences typical of fatigue-related measurements. Based on the statistical characteristics of the sensor output during crack growth, the ARIMA model was selected for further analysis and algorithm development. The forecasting performance of ARIMA was evaluated for different parameter configurations by comparing the range and variability of the base and predicted data. Initial tests using first-order parameters produced unsatisfactory results, with high variance observed in both raw and modeled signals. Therefore, model parameters were optimized using the aicbic function, and the analyses were repeated. For the selected datasets, variance reduction by 3–4 orders of magnitude was achieved, demonstrating a substantial improvement in prediction stability. The presented results confirm that the proposed methodology is effective for processing complex sensor signals and highlight the broader significance of applying statistically grounded time series models in structural health monitoring. The study introduces an innovative framework for evaluating fatigue-related sensor data and establishes a reliable baseline for future predictive methods. Full article

Wearable biosensors increasingly stream multi-channel physiological and behavioral data outside the laboratory, yet most deployments still end in dashboards or threshold alarms that leave interpretation open to the user. In high-stakes domains, such as military, emergency response, aviation, industry, and elite sport, the constraint is rarely data availability but the cognitive effort required to convert noisy signals into timely, actionable decisions. We argue for on-person cognitive co-pilots: systems that integrate multimodal sensing, compute probabilistic state estimates on devices, synthesize those states with task and environmental context using locally hosted large language models (LLMs), and deliver recommendations through attention-appropriate cues that preserve autonomy. Enabling conditions include mature wearable sensing, edge artificial intelligence (AI) accelerators, tiny machine learning (TinyML) pipelines, privacy-preserving learning, and open-weight LLMs capable of local deployment with retrieval and guardrails. However, critical research gaps remain across layers: sensor validity under real-world conditions, uncertainty calibration and fusion under distribution shift, verification of LLM-mediated reasoning, interaction design that avoids alarm fatigue and automation bias, and governance models that protect privacy and consent in constrained settings. We propose a layered technical framework and research agenda grounded in cognitive engineering and human–automation interaction. Our core claim is that local, uncertainty-aware reasoning is an architectural prerequisite for trustworthy, low-latency augmentation in isolated, confined, and extreme environments. Full article

This work presents a finite element investigation of the thermo-mechanical response of a SiC-based power module subjected to spatially non-uniform thermal gradients during active power cycling. The multilayer package, including die, solder, and encapsulant, was modeled by elastoplastic constitutive laws to capture stress and strain evolution in time and space. Two scenarios were considered: a time–space variability with fixed gradients (an initial non-uniform temperature distribution was uniformly varied in time) and a time–space variability with time-dependent gradients (an initial non-uniform temperature was non-uniformly varied in time). Results highlight critical stress concentrations at the SiC/solder interface, with plastic strains up to 5% in the solder. This study underlines the importance of transient gradient modeling for reliability assessment and fatigue life prediction of power modules. Full article

This study investigated the effects of mental fatigue induced by social media (SM) use and the Stroop task on decision-making, visual search strategies, and reaction time in elite collegiate handball players (n = 16). Using a randomized, counterbalanced cross-over design, both interventions successfully induced subjective mental fatigue, as confirmed by visual analog scale (VAS) ratings. Decision-making accuracy and reaction time improved following the Stroop task, likely due to compensatory mechanisms described in the regulatory-control model. In the SM condition, no significant impairments were observed in decision-making performance; however, visual reaction time was specifically delayed, while auditory reaction time remained unaffected, suggesting modality-specific effects of SM-induced fatigue. Visual search behaviors remained largely stable, with only marginal alterations observed in non-task-relevant areas following the Stroop task. These findings highlight the cognitive resilience and adaptive control mechanisms of elite athletes in maintaining and, in some cases, enhancing performance under mental fatigue. Future studies should integrate neurophysiological indices and manipulate motivational factors to further clarify these mechanisms across diverse athletic populations. Full article

The fatigue pitting model based on the minimum oil film thickness does not consider the influence of tooth surface roughness and residual stress, which limits the accuracy of predicting the fatigue pitting of the model. Micro pitting often initiates on the surface due to large external loads. Therefore, it is urgent to propose a new-micro pitting bearing capacity model based on gear surface integrity parameters. This paper studied a new fatigue pitting model considering surface integrity subjected to polishing processes. This model thoroughly analyzes the effects of teeth surface roughness dynamically on the oil film pressure and explores the complex mechanism of residual stress in the near-surface stress field of the gear teeth. The new model can more accurately simulate the micro-pitting bearing capacity under actual operating conditions by introducing teeth surface roughness and residual stress, and the prediction reliability of gear steel is greatly improved. This improved model provides a solid theoretical basis and technical support for optimizing gear transmission systems, accurate diagnosis of micro-pitting defects, and in-depth theoretical research in related fields. Full article

The present study investigates the thermo-mechanical behaviour and fatigue life associated with the shrink-fit process of shrink-fit tool holders. These holders are an indispensable component of high-precision and high-speed machining processes in modern manufacturing industries. Shrink-fit holders are subjected to elevated levels of stress as a consequence of repeated heating and cooling cycles, which can result in clamping fatigue over time. In this study, a three-dimensional finite element model (FEM) of a holder manufactured from H13 tool steel in accordance with BT40 standards was created using ANSYS software. The numerical analyses included transient thermal and structural analyses, consisting of a 4.5-s induction heating stage at 10 kW power, followed by a 1200-s cooling process. The analysis yielded results that were corroborated by the experimental data. It was established that, upon the conclusion of the heating process, the temperature in the conical region of the holder attained a range of approximately 388–417 °C. Furthermore, it was ascertained that a radial expansion of approximately 17.2–22 µm, which is required for the successful insertion of the cutting tool into the inner bore, was achieved. The fatigue life prediction, which constitutes the main focus of the study, applied the Soderberg criterion and evaluated two basic loading scenarios: the first tool assembly and repeated tool assembly cycles. The calculations yielded a life estimate of approximately 12,407 cycles for the first tool assembly cycle and approximately 19,400 cycles for the repeated tool assembly cycle. Accordingly, the repeated tool assembly condition exhibited a longer fatigue life than the first tool assembly condition. The enhanced longevity observed in the repeated tool assembly scenario is attributed to the stress cycle not fully reaching zero during this process, resulting in a lower stress amplitude. Full article

This study explores the use of spectral methods for fatigue life assessment, considering the effects of material plasticity. While these methods are widely used for high-cycle fatigue in the linear elastic regime, their application to low-cycle fatigue remains more complex due to nonlinear material behaviour. By incorporating models such as Neuber’s rule and the Ramberg-Osgood formulation, this work examines how spectral methods can be adapted to account for elastic-plastic effects. A comparison is made between fatigue life estimations obtained with spectral approaches and results from time-domain nonlinear simulations. The study provides insights into the applicability of strain-based spectral methods, contributing to a better understanding of their potential and limitations in fatigue assessment. Full article

Menopause is a natural biological transition marked by the cessation of regular menstrual cycles and is associated with significant endocrine, hormonal, and metabolic changes. Sleep disturbances are among the most common and distressing symptoms during this period, affecting approximately 40–60% of women in the menopausal transition and postmenopause. Vasomotor symptoms, including hot flushes and night sweats, often occur alongside fatigue, anxiety, and mood disturbances. These symptoms frequently coexist with sleep disorders such as insomnia, early morning awakenings, fragmented sleep, obstructive sleep apnea, restless legs syndrome, and circadian rhythm disruptions. Evidence from animal models, translational research, and clinical studies highlights the complex interaction between hormonal fluctuations, neuroendocrine dysregulation, metabolic changes, and circadian rhythm disruption. These factors contribute to altered sleep regulation, appetite control, and weight gain during the menopausal transition. This review summarizes current evidence on the mechanisms of underlying sleep disturbances in menopause, their clinical manifestations, diagnostic approaches, and available therapeutic strategies. Improving the management of sleep disorders during this stage may substantially enhance overall health and quality of life in menopausal women. We discuss presentation of different sleep disorders in menopause, their current management and future direction of research for development of precision-based algorithm of treatment considering the endocrine and hormonal profile of the women. Full article

This review examines fatigue damage in cement-based materials across the micro-, meso-, and macroscales, with emphasis on how damage initiates, transfers, and becomes structurally observable under cyclic loading. At the microscale, capillary pores, unhydrated cement particles, and the calcium–silicate–hydrate (C-S-H) phase govern local stress concentration, bond rupture, limited healing, and microcrack development. At the mesoscale, the interfacial transition zone (ITZ), cement paste, aggregates, and fiber reinforcement effects control crack initiation, deflection, bridging, and coalescence. At the macroscale, specimen size, boundary conditions, loading regime, and environmental exposure shape stiffness degradation, residual strain accumulation, crack growth, and fatigue life. Beyond summarizing existing studies, this review synthesizes a causal damage transfer interpretation that links microscale deterioration, mesoscale crack interaction, and macroscale response. Current gaps include the limited quantitative link between microstructure-informed models and three-dimensional experimental observations, the still-incomplete validation of multiscale predictive frameworks, and the insufficient treatment of coupled fatigue–environment effects. Addressing these gaps is essential for more reliable fatigue life prediction and for developing durable, resource-efficient concrete infrastructure. Full article