Search Results (4,463)

Objective evaluation of pilot fatigue is crucial for enhancing aviation safety. Although electroencephalography (EEG) is regarded as an effective tool for recognizing pilot fatigue, the direct application of deep learning models to raw EEG signals faces significant challenges due to issues such as massive data volume, excessively long training time, and model overfitting. Moreover, existing feature-based methods often suffer from data redundancy due to the lack of effective feature and channel selections, which compromises the model’s recognition efficiency and accuracy. To address these issues, this paper proposes a framework, named ASFT-Transformer, for fast and accurate detection of pilot fatigue. This framework first extracts time-domain and frequency-domain features from the four EEG frequency bands. Subsequently, it introduces a feature and channel selection strategy based on one-way analysis of variance and support vector machine (ANOVA-SVM) to identify the most fatigue-relevant features and pivotal EEG channels. Finally, the FT-Transformer (Feature Tokenizer + Transformer) model is employed for classification based on the selected features, transforming the fatigue recognition problem into a tabular data classification task. EEG data is collected from 32 pilots before and after actual simulator training to validate the proposed method. The results show that ASFT-Transformer achieved average accuracies of 97.24% and 87.72% based on cross-clip data partitioning and cross-subject data partitioning, which were significantly superior to several mainstream machine learning and deep learning models. Under the two types of cross-validation, the proposed feature and channel selection strategy not only improved the average accuracy by 2.45% and 8.07%, respectively, but also drastically reduced the average training time from above 1 h to under 10 min. This study offers civil aviation authorities and airline operators a tool to manage pilot fatigue objectively and effectively, thereby contributing to flight safety. Full article

Introduction: Despite global improvements in immunization, major gaps persist. By 2024, an estimated 14.3 million infants, predominantly in low- and middle-income countries (LMICs), remained zero-dose (ZD), never having received even the first DTP vaccine. In 2022, 33 million children missed their measles vaccination (22 million missed the first dose, 11 million missed the second dose), highlighting entrenched structural, behavioral, and systemic barriers that continue to exclude marginalized populations. Addressing these inequities requires innovative, context-adapted approaches that strengthen primary health care (PHC) and extend services to the hardest-to-reach populations. Objectives: This study aims to document and synthesize implementation research (IR) projects on immunization programs in LMICs, identifying key enablers and effective strategies that reduce inequities, improve outcomes, and support efforts to reach ZD children. Methods: We conducted a retrospective multiple-case study of 36 IR projects across 13 LMICs, embedded within an evidence review framework and complemented by policy analysis. Data were drawn from systematic document reviews and validation discussions with project leads. A total of 326 strategies were extracted, coded using a structured codebook, and mapped to the WHO–UNICEF PHC Levers for Action. Descriptive analysis synthesized patterns across service delivery and policy outcomes, including coverage gains, improved microplanning, community engagement, and system integration. Results: Of the 326 immunization strategies identified, most (76.1%) aligned with operational PHC levers, particularly monitoring and evaluation (19.3%), workforce development (18.7%), and models of care (12%). Digital technologies (11.7%) were increasingly deployed for real-time tracking and oversight. Core strategic levers comprised 23.9% of strategies, with community engagement (8.9%) and governance frameworks (7.7%) emerging as critical enablers, though sustainable financing (4%) and private-sector engagement (0.9%) were rarely addressed. While the majority of projects focused on routine immunization (n = 32), only a few directly targeted ZD children (n = 3). Interventions yielded improvements in both service delivery and policy outcomes. Improvements in microplanning and data systems (23.5%) reflected the increased uptake of digital dashboards, GIS-enabled tools, and electronic registries. Community engagement (16.2%) emphasized the influence of local leaders and volunteers in building trust, while health system strengthening (15.7%) invested in cold chain, supervision, and workforce capacity. Coverage gains (10.6%) were achieved through delivery innovations, though sustainable financing remained a critical problem (3.4%). Conclusions: Reaching ZD children requires equity-driven strategies that combine digital innovations, community engagement, and resilient system planning. Sustained progress depends on strengthening governance, financing, and research. Embedding IR in immunization programs generates actionable evidence, supports context-specific strategies, and reduces equity gaps, offering practical insights that complement health system research and advance the Immunization Agenda 2030. Full article

To address the problem of transient disturbance arising during the grid-following (GFL) and grid-forming (GFM) mode switching of energy storage converters, this paper proposes a dual-mode seamless switching control strategy. First, we conduct an in-depth analysis of the mechanism behind switching transients, identifying that sudden changes in current commands and angle-control misalignment are the key factors triggering oscillations in system power and voltage frequency. To overcome this, we design a virtual synchronous generator (VSG) control angle-tracking technique based on the construction of triangular functions, which effectively eliminates the influence of periodic phase-angle jumps on tracking accuracy and achieves precise pre-synchronization of the microgrid phase in GFM mode. Additionally, we employ a current-command seamless switching technique involving real-time latching and synchronization of the inner-loop current references between the two modes, ensuring continuity of control commands at the switching instant. The simulation and hardware-in-the-loop (HIL) experimental results show that the proposed strategy does not require retuning of the parameters after switching, greatly suppresses voltage and frequency fluctuations during mode transition, and achieves smooth, rapid, seamless switching between the GFL and GFM modes of the energy storage converter, thereby improving the stability of microgrid operation. Full article

Fractional heat conduction models extend classical formulations by incorporating fractional differential operators that capture multiscale relaxation effects. In this work, we introduce an electrical analogy that represents the action of these operators via generalized longitudinal impedance and admittance elements, thereby clarifying their physical role in energy transfer: fractional derivatives account for the redistribution of heat accumulation and dissipation within micro-scale heterogeneous structures. This analogy unifies different classes of fractional models—diffusive, wave-like, and mixed—as well as distinct fractional operator types, including the Caputo and Atangana–Baleanu forms. It also provides a general computational methodology for solving heat conduction problems through the concept of thermal impedance, defined as the ratio of surface temperature variations (relative to ambient equilibrium) to the applied heat flux. The approach is illustrated for a semi-infinite sample, where different models and operators are shown to generate characteristic spectral patterns in thermal impedance. By linking these spectral signatures of microstructural relaxation to experimentally measurable quantities, the framework not only establishes a unified theoretical foundation but also offers a practical computational tool for identifying relaxation mechanisms through impedance analysis in microscale thermal transport. Full article

Port operations involving ship unloading have traditionally posed significant complexity and have proven difficult to solve optimally using exact methods. This study investigates the long continuous unloading of ships carrying liquid products, where transportation is carried out using full truckload deliveries. For the first time, this work integrates the problem of liquid-based ship unloading with full truckload vehicle routing and truck driver scheduling. The primary objective is to minimize the total transportation costs during the continuous unloading process, while satisfying extra constraints such as driver rest–break–drive regulations, time windows, a heterogeneous fleet structure, and port-specific constraints such as maintaining a minimum number of backup vehicles at the port during unloading. To address this complex problem, a route-based insertion heuristic is employed as an initial step in a column generation framework designed for exact optimization. The approach incorporates a nested label setting algorithm for column generation, enhanced with acceleration techniques involving multi-search strategies, and refined selection methods. Performance analysis, based on artificial datasets closely resembling real-world scenarios and consisting of 112 instances, demonstrates that optimality gaps below 1% can be achieved within computational times considered reasonable in the context of the existing literature, while the total number of customer nodes and the minimum number of required vehicles at the port are at most 100 and 5, respectively. Full article

Industrial scheduling plays a central role in Industry 4.0, where efficiency, robustness, and adaptability are essential for competitiveness. This review surveys recent advances in reinforcement learning, digital twins, and hybrid artificial intelligence (AI)–operations research (OR) approaches, which are increasingly used to address the complexity of flexible job-shop and distributed scheduling problems. We focus on how these methods compare in terms of scalability, robustness under uncertainty, and integration with industrial IT systems. To move beyond an enumerative survey, the paper introduces a structured analysis in three domains: comparative strengths and limitations of different approaches, ready-made tools and integration capabilities, and representative industrial case studies. These cases, drawn from recent literature, quantify improvements such as reductions in makespan, tardiness, and cycle time variability, or increases in throughput and schedule stability. The review also discusses critical challenges, including data scarcity, computational cost, interoperability with Enterprise Resource Planning (ERP)/Manufacturing Execution System (MES) platforms, and the need for explainable and human-in-the-loop frameworks. By synthesizing methodological advances with industrial impact, the paper highlights both the potential and the limitations of current approaches and outlines key directions for future research in resilient, data-driven production scheduling. Full article

This review paper analyzes the recent applications of computational mechanics methods in combination with machine learning (ML) and neural network (NN) techniques, as found in the literature published between 2015 and 2024. We present how ML and NNs are enhancing traditional computational methods, such as the finite element method, enabling the solution of complex problems in material modeling, surrogate modeling, inverse analysis, and uncertainty quantification. We categorize current research by considering the specific computational mechanics tasks and the employed ML/NN architectures. Furthermore, we discuss the current challenges, development opportunities, and future directions of this dynamically evolving interdisciplinary field, highlighting the potential of data-driven approaches to transform the modeling and simulation of mechanical systems. The review has been updated to include pivotal publications from 2025, reflecting the rapid evolution of the field in multiscale modeling, data-driven mechanics, and physics-informed/operator learning. Accordingly, the timespan is now 2015–2025, with a focused inclusion of high-impact contributions from 2024 to 2025. Full article

In this paper, we present rigorous asymptotic componentwise perturbation bounds for regular Hermitian indefinite matrix eigendecompositions, obtained via the method of splitting operators. The asymptotic bounds are derived from exact nonlinear expressions for the perturbations and allow each entry of every matrix eigenvector to be bounded in the case of distinct eigenvalues. In contrast to the perturbation analysis of the Schur form of a nonsymmetric matrix, the bounds obtained here do not rely on the Kronecker product, which significantly reduces both memory requirements and computational cost. This enables efficient sensitivity analysis of high-order problems. The eigenvector perturbation bounds are further applied to estimate the angles between perturbed and unperturbed one-dimensional invariant subspaces spanned by the corresponding eigenvectors. To reduce conservatism in the case of high-order problems, we propose the use of probabilistic perturbation bounds based on the Markov inequality. The analysis is illustrated by two numerical experiments of order 5000. Full article

Unmanned Aerial Vehicles (UAVs), commonly known as drones, have become widely used in many fields, ranging from agriculture to military operations, due to recent advances in technology and decreases in costs. Quadrotors are particularly important UAVs, but their complex, coupled dynamics and sensitivity to outside disturbances make them challenging to control. This paper introduces a new control method for quadrotors called Backstepping Sliding Mode Control (BSMC), which combines the strengths of two established techniques: Backstepping Control (BC) and Sliding Mode Control (SMC). Its primary goal is to improve trajectory tracking while also reducing chattering, a common problem with SMC that causes rapid, high-frequency oscillations. The BSMC method achieves this by integrating the SMC switching gain directly into the BC through a process of differential iteration. Herein, a Lyapunov stability analysis confirms the system’s asymptotic stability; a genetic algorithm is used to optimize controller parameters; and the proposed control strategy is evaluated under diverse payload conditions and dynamic wind disturbances. The simulation results demonstrated its capability to handle payload variations ranging from 0.5 kg to 18 kg in normal environments, and up to 12 kg during gusty wind scenarios. Furthermore, the BSMC effectively minimized chattering and achieved a superior performance in tracking accuracy and robustness compared to the traditional SMC and BC. Full article

The adoption of artificial intelligence has risen, yet research on its impact on innovation processes between actual businesses remains sparse. This research fills the present gap by investigating ten workers from a tech startup who utilize artificial intelligence tools in operational and creative activities. The paper analyzes business-related AI functionality through a qualitative analysis of ten tech start-up employees. The examination reveals that AI produces significant enhancements in problem resolution by executing mundane actions while analyzing large datasets to deliver data-driven suggestions to users. The interview respondents mentioned that AI’s role in diminishing supply chains is 15%, while allowing AI to manage customer service without employee engagement in 80% of interactions. The implementation costs, along with data dependency and occasional contextual blindness in AI systems, represented some of the problems in this system. Analysis demonstrated that AI tools enable the development of innovative concepts and challenge established viewpoints, prompting participants to create a gamified loyalty system and dynamic content planning. Participants in the study emphasized the need for human involvement to refine AI-based insights, recognizing how human imagination complements AI capabilities effectively. The work enhances academic discussions about AI-related problem-solving and creativity while offering specific business-related recommendations for implementation. The recommendations begin with establishing initial experimental programs, while providing support for employee’s skills development, and fostering strong alliances between technical AI personnel and professional subject matter experts. Research topics focused on AI application fields and the anticipated impacts on company decision-making, as well as the ethical ramifications, need further exploration. This research confirms the revolutionary potential of artificial intelligence systems for problem-solving methods, but requires proper execution, along with human supervision, to fully realize their advantages. Full article

High-overload testing equipment is a key platform for evaluating mechanical performance under extreme conditions, requiring specialized functional design to meet stringent operational demands. The current design process faces numerous challenges, including overreliance on designers’ experience, incomplete requirement analysis, and insufficient automation, resulting in suboptimal solutions. To address these issues, in this study, we propose an integrated method based on axiomatic design (AD) and the Theory of Inventive Problem Solving (TRIZ). This method systematically decomposes technical specifications, maps requirements to a structural framework, and resolves design conflicts using inventive principles. The method employs a comprehensive evaluation framework combining the analytic hierarchy process (AHP) and quality function deployment (QFD) to quantitatively assess candidate designs. It facilitates the development of efficient, standardized high-load testing equipment solutions, enhancing design reliability and innovation capabilities. Full article

To improve the control accuracy and address the parameter disturbance issues of joint-driven permanent magnet synchronous motors in intelligent manufacturing, this paper proposes an improved deadbeat predictive current predictive control (DPCC) scheme that eliminates dead zones. This scheme establishes a multi-parameter identification model based on the error equation of the d-q axis predicted current, which improves the problem of not being able to identify all parameters caused by insufficient input signals. It also implements decoupling compensation for the coupling between the d-q axis inductance, stator resistance, and magnetic flux linkage. To meet the anticipated control objectives and account for external disturbances, a low-complexity specified performance tracking controller (LCSPC) based on output target error signals has been designed. This mitigates output delay issues arising from nonlinear components during motor operation. Finally, simulation analysis and experimental testing demonstrate that the proposed control scheme achieves high identification accuracy with minimal delay, thus meeting the transient control performance requirements for motors in intelligent manufacturing processes. Full article

Objective: To evaluate patient-assessed quality of life (QoL) following operative treatment of acetabular fractures and pelvic ring injuries and identify differences in their assessment of QoL depending on sex and age group. Methods: The study included 75 patients, 41 who had suffered acetabular fractures and 34 pelvic ring fractures, who had been treated operatively over a 6-year period (2017–2022). Post-operative HRQOL scores were evaluated using the World Health Organization Quality of Life BREF scale (WHQOL-BREF) and RAND Short Form 36 (SF-36). Separate analyses were completed for men and women, and we compared the data between five age groups. Results: The male patients scored slightly higher in all domains than the women in the acetabular fracture group. In the ring fracture group women reported higher scores in the psychological (72.67 vs. 69.44) and social domains (81.67 vs. 77.08). The men (80%) reported more significant overall satisfaction with their health in the acetabular group compared with the women (p = 0.0306). In the SF36 analysis, for both acetabular and ring fracture groups the lowest average QoL was recorded in the physical health composite summary (PHC) with a score of 41.34 ± 9.49 and 41.21 ± 9.19, respectively. Men scored higher for all eight scales in the ring fracture and all except general health in the acetabular fracture group (p = 0.0166). For the mental health composite (MHC), men had a better mean score in both fracture groups with significant differences for between both genders for the acetabular group (p = 0.0352). For age group analysis of the SF36, in the acetabular group, the youngest age group (<40 years) reported a significant decline in their performance due to role limitations due to physical health (RP) and emotional problems (RE) compared with the oldest group (>70 years) (p = 0.0306 and p = 0.0069, respectively), similarly to the PHC (p = 0.0279). Additionally, for the overall mental health summary of the acetabular group for the five age groups, there were significant differences between the youngest age group and the oldest age group (p = 0.0372). In contrast, for the ring fracture group, the oldest age group (>70 years) scored the worst in all four scales of the physical health composite and the <40 years patients had the highest score for the physical functioning (PF) scale, with statistical significance when compared with the oldest group (85 (17.53), p = 0.01501). Additionally, the lowest mean score for the PHC was recorded in >70 years and the highest in the 61–70 years age group, with a statistical significance (p = 0.0367). Conclusions: Patients that sustain a pelvic fracture and are treated operatively are at a higher risk of deterioration in quality of life. Using both functional assessments, male patients emphasized improved quality in more spheres that was evaluated than women. Full article

In order to solve the problem encountered by traditional potato–stem separation devices, that is, they cannot meet the requirements when installed in small-scale harvesters, a new type of vertical differential roller potato–stem separation device was developed. The device features a compact structure and simultaneously possesses both separating and conveying functions. Through the analysis of the separation force between potato and stem, the structure and parameters of the separation device were determined. The simulation and the field test of the potato–stem separation process were carried out with the vertical differential roller speed, the vertical differential roller gap width and the conveyor chain speed as the influencing factors. The simulation test analysed the influence law of different working parameters on the performance of potato–stem separation. The field test revealed the order of the effects of various factors on the impurity rate and skin-breaking rate, concluding that the optimal combination of operational parameters was a vertical differential roller rotational speed of 6 s⁻¹, a vertical differential roller gap width of 7 mm, and a conveyor chain speed of 1.4 m·s⁻¹. This experiment fills the research gap in the study of potato–stem separation devices suitable for small-scale potato harvesters and promotes the development of compact potato harvesters. Full article

This paper presents a flutter analysis of the UHBR Open Fan Testcase ECL5 for an off-design point at part speed and focuses on the second eigenmode, which has a strong torsional character near the blade tip. Recent studies by Pagès et al., using a time-linearized solver, showed strong negative damping for an operating point at 80% speed close to the maximal pressure ratio. This was identified as a phenomenon of convective resonance; for a certain nodal diameter and frequency, the blade vibration is in resonance with convective disturbances that are linearly unstable. In this work, a nonlinear frequency domain method (harmonic balance) is applied to the problem of aerodynamic damping prediction for this off-design operating point. It is shown that, to obtain plausible results, it is necessary to treat the turbulence model as unsteady. The impact of spurious reflections due to numerical boundary conditions is estimated for this case. While strong negative damping is not predicted by the analysis presented here, we observe particularly high sensitivity of the aerodynamic response with respect to turbulence model formulation and the frequency for certain nodal diameters. The combination of nodal diameter and frequency of maximal sensitivities are interpreted as points near resonance. We recover from these near-resonance points convective speeds and compare them to studies of the onset of nonsynchronous vibrations of the ECL5 fan at part-speed conditions. Full article