State-of-the-art controllers for active back exosuits rely on body kinematics and state machines. These controllers do not continuously target the lumbosacral compression forces or adapt to unknown external loads. The use of additional contact or load detection could make such controllers more adaptive; however, it can be impractical for daily use. Here, we developed a novel neuro-mechanical model-based controller (NMBC) that uses a personalized electromyography (EMG)-driven musculoskeletal (MSK) model to estimate lumbosacral joint loading. NMBC provided adaptive, subject- and load-specific assistive forces proportional to estimates of the active part of biological joint moments through a soft back support exosuit. Without a priori information, the maximum assistive forces of the cable were modulated across weights. Simultaneously, we applied a non-adaptive, kinematic-dependent, trunk inclination-based controller (TIBC). Both NMBC and TIBC reduced the mean and peak biomechanical metrics, although not all reductions were significant. TIBC did not modulate assistance across weights. NMBC showed larger reductions of mean than peak values, significant reductions during the erect stance and the cumulative compressive loads by 21% over multiple cycles in a cohort of 10 participants. Overall, NMBC targeted mean lumbosacral compressive forces during lifting without a priori information of the load being carried. This may facilitate the adoption of non-hindering wearable robotics in real-life scenarios. As NMBC is informed by an EMG-driven MSK model, it is possible to tune the timing of NMBC-generated torque commands to the exosuit (delaying or anticipating commands with respect to biological torques) to target further reduction of peak or mean compressive forces and muscle fatigue.

Low-density polymer foams pre-ionized by a well-controlled nanosecond pulse are excellent plasma targets to trigger direct laser acceleration (DLA) of electrons by sub-picosecond relativistic laser pulses. In this work, the influence of the nanosecond pulse on the DLA process is investigated. The density profile of plasma generated after irradiating foam with a nanosecond pulse was simulated with a two-dimensional hydrodynamic code, which takes into account the high aspect ratio of interaction and the microstructure of polymer foams. The obtained plasma density profile was used as input to the three-dimensional particle-in-cell code to simulate energy, angular distributions and charge carried by the directional fraction of DLA electrons. The modelling shows good agreement with the experiment and in general a weak dependence of the electron spectra on the plasma profiles, which contain a density up-ramp and a region of near-critical electron density. This explains the high DLA stability in pre-ionized foams, which is important for applications.

Regression is a fundamental prediction task common in data-centric engineering applications that involves learning mappings between continuous variables. In many engineering applications (e.g., structural health monitoring), feature-label pairs used to learn such mappings are of limited availability, which hinders the effectiveness of traditional supervised machine learning approaches. This paper proposes a methodology for overcoming the issue of data scarcity by combining active learning (AL) for regression with hierarchical Bayesian modeling. AL is an approach for preferentially acquiring feature-label pairs in a resource-efficient manner. In particular, the current work adopts a risk-informed approach that leverages contextual information associated with regression-based engineering decision-making tasks (e.g., inspection and maintenance). Hierarchical Bayesian modeling allow multiple related regression tasks to be learned over a population, capturing local and global effects. The information sharing facilitated by this modeling approach means that information acquired for one engineering system can improve predictive performance across the population. The proposed methodology is demonstrated using an experimental case study. Specifically, multiple regressions are performed over a population of machining tools, where the quantity of interest is the surface roughness of the workpieces. An inspection and maintenance decision process is defined using these regression tasks, which is in turn used to construct the active-learning algorithm. The novel methodology proposed is benchmarked against an uninformed approach to label acquisition and independent modeling of the regression tasks. It is shown that the proposed approach has superior performance in terms of expected cost—maintaining predictive performance while reducing the number of inspections required.

Novel methods of data collection and analysis can enhance traditional risk management practices that rely on expert engineering judgment and established safety records, specifically when key conditions are met: Analysis is linked to the decisions it is intended to support, standards and competencies remain up to date, and assurance and verification activities are performed. This article elaborates on these conditions. The reason engineers are required to perform calculations is to support decision-making. Since humans are famously weak natural statisticians, rather than ask stakeholders to implicitly assimilate data, and arrive at a decision, we can instead rely on subject matter experts to explicitly define risk management decision problems. The results of engineering calculation can then also communicate which interventions (if any) are considered to be risk-optimal. It is also proposed that the next generation of engineering standards should learn from the success of open source software development in community building. Interacting with open datasets and code can promote engagement, identification (and resolution) of errors, training and ultimately competence. Finally, the profession’s tradition of independent verification should also be applied to the complex models that will increasingly contribute to the safety of the built environment. Model assurance will be required to keep pace with model development to identify suitable use cases as adequately safe. These are considered to be increasingly important components in ensuring that methods of data-centric engineering can be safely and appropriately adopted in industry.

Safe and efficient flight operations depend on effective air traffic management and the decision-making skills of air traffic control officers (ATCOs). However, managing air traffic in terminal control areas (TMAs), especially in approach control units, is challenging due to the complexity of the airspace. This is particularly evident in metroplex airspaces like the Istanbul TMA, which features multiple civil and military airports, diverse approach systems, and heavy traffic volumes, all contributing to an exceptionally complex operational environment. This study examines how experienced ATCOs perceive airspace complexity, focusing on approach control units within TMAs. Using Istanbul TMA as a case study, the research combines qualitative and quantitative methods to analyse the factors contributing to complexity. In the first phase, the Content Validity Method (CVM) is used to identify and confirm the key factors influencing airspace complexity. In the second phase, the Best-Worst Method (BWM) is applied to measure the importance of these factors. The study involves two groups of ATCOs: 40 in the first group and 20 in the second. The results reveal that ‘conflicts’ are the most critical factor affecting airspace complexity, highlighting the importance of conflict resolution in air traffic control. Other significant factors include rules and procedures, airspace design and traffic density. This study provides clear insights into the challenges of managing TMA, especially in metroplex airspace. Identifying and analysing the key factors offers valuable guidance for improving air traffic management and supporting ATCOs in making better decisions.

Accurate characterization of high-power laser parameters, especially the near-field and far-field distributions, is crucial for inertial confinement fusion experiments. In this paper, we propose a method for computationally reconstructing the complex amplitude of high-power laser beams using modified coherent modulation imaging. This method has the advantage of being able to simultaneously calculate both the near-field (intensity and wavefront/phase) and far-field (focal-spot) distributions using the reconstructed complex amplitude. More importantly, the focal-spot distributions at different focal planes can also be calculated. To verify the feasibility, the complex amplitude optical field of the high-power pulsed laser was measured after static aberrations calibration. Experimental results also indicate that the near-field wavefront resolution of this method is higher than that of the Hartmann measurement. In addition, the far-field focal spot exhibits a higher dynamic range (176 dB) than that of traditional direct imaging (62 dB).

Research in lower limb wearable robotic control has largely focused on reducing the metabolic cost of walking or compensating for a portion of the biological joint torque, for example, by applying support proportional to estimated biological joint torques. However, due to different musculotendon unit (MTU) contractile speed properties, less attention has been given to the development of wearable robotic controllers that can steer MTU dynamics directly. Therefore, closed-loop control of MTU dynamics needs to be robust across fiber phenotypes, that is ranging from slow type I to fast type IIx in humans. The ability to perform closed-loop control the in-vivo dynamics of MTUs could lead to a new class of wearable robots that can provide precise support to targeted MTUs for preventing onset of injury or providing precision rehabilitation to selected damaged tissues. In this paper, we introduce a novel closed-loop control framework that utilizes nonlinear model predictive control to keep the peak Achilles tendon force within predetermined boundaries during diverse range of cyclic force production simulations in the human ankle plantarflexors. This control framework employs a computationally efficient model comprising a modified Hill-type MTU contraction dynamics component and a model of the ankle joint with parallel actuation. Results indicate that the closed-form muscle-actuation model’s computational time is in the order of microseconds and is robust to different muscle contraction velocity properties. Furthermore, the controller achieves tendon force control within a time frame below $ 18\mathrm{ms} $, aligning with the physiological electromechanical delay of the MTU and facilitating its potential for future real-world applications.

We present a higher-order spectral element method (SEM) for analyzing eccentric anisotropic multilayered waveguides. The formulation uses conformal transformation optics to map the original eccentric waveguide to an equivalent concentric one. This transformation is extended to handle anisotropic non-reciprocal media characterized by non-symmetric and non-Hermitian tensors. In the transformed concentric domain, higher-order two-dimensional basis functions associated with the zeros of the completed Lobatto polynomial are used to expand the fields. To simulate radially unbounded problems, we employ a complex-stretched perfectly matched layer to mimic open space. The method is validated with radially bounded two-layer and three-layer waveguides and radially unbounded three-layer waveguides. Comparisons with the finite element method (FEM) demonstrate that our SEM approach requires significantly fewer degrees of freedom than FEM.

The betatron radiation source features a micrometer-scale source size, a femtosecond-scale pulse duration, milliradian-level divergence angles and a broad spectrum exceeding tens of keV. It is conducive to the high-contrast imaging of minute structures and for investigating interdisciplinary ultrafast processes. In this study, we present a betatron X-ray source derived from a high-charge, high-energy electron beam through a laser wakefield accelerator driven by the 1 PW/0.1 Hz laser system at the Shanghai Superintense Ultrafast Laser Facility (SULF). The critical energy of the betatron X-ray source is 22 ± 5 keV. The maximum X-ray flux reaches up to 4 × 109 photons for each shot in the spectral range of 5–30 keV. Correspondingly, the experiment demonstrates a peak brightness of 1.0 × 1023 photons·s−1·mm−2·mrad−2·0.1%BW−1, comparable to those demonstrated by third-generation synchrotron light sources. In addition, the imaging capability of the betatron X-ray source is validated. This study lays the foundation for future imaging applications.

Traditional wavefront control in high-energy, high-intensity laser systems usually lacks real-time capability, failing to address dynamic aberrations. This limits experimental accuracy due to shot-to-shot fluctuations and necessitates long cool-down phases to mitigate thermal effects, particularly as higher repetition rates become essential, for example, in inertial fusion research. This paper details the development and implementation of a real-time capable adaptive optics system at the Apollon laser facility. Inspired by astronomical adaptive optics, the system uses a fiber-coupled 905 nm laser diode as a pilot beam that allows for spectral separation, bypassing the constraints of pulsed lasers. A graphics processing unit-based controller, built on the open-source Compute And Control for Adaptive Optics framework, manages a loop comprising a bimorph deformable mirror and a high-speed Shack–Hartmann sensor. Initial tests showed excellent stability and effective aberration correction. However, integration into the Apollon laser revealed critical challenges unique to the laser environment that must be resolved to ensure safe operation with amplified shots.