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BACKGROUD: The sudden COVID-19 pandemic forced quick development of care pathways for patients with different needs. Trajectories of physical recovery in hospitalized patients for COVID-19 following different care pathways are unknown. We aimed to assess trajectories of physical recovery and levels of physical function reached within the different care pathways. Additionally, we assessed differences in physical function across care pathways at follow-up visits.METHODS: This multicenter prospective cohort study of adults who had been hospitalized for COVID-19 was performed in 10 centers, including 7 hospitals (1 academic and 6 regional hospitals) and 3 rehabilitation centers (1 medical rehabilitation center and 2 skilled nursing facilities), located in the Netherlands. Study visits were performed at 3, 6, and 12 months post-hospital discharge and included assessment of cardiorespiratory fitness (6 min walk test [6MWT], 1 min sit-to-stand test [1MSTST]), muscle strength (maximum handgrip strength [HGS]) and mobility (de Morton Mobility Index [DEMMI]).FINDINGS: We report findings for 582 patients who had been discharged from hospital between March 24, 2020 and June 17, 2021. Patients had a median age of 60·0 years, 68·9% (401/582) were male, 94·6% (561/582) had received oxygen therapy, and 35·2% (205/582) mechanical ventilation. We followed patients across four different rehabilitation settings: no rehabilitation (No-rehab, 19·6% [114/582]), community-based rehabilitation (Com-rehab, 54·1% [315/582]), medical rehabilitation (Med-rehab, 13·7% [80/582]), and rehabilitation in a skilled nursing facility (SNF-rehab, 12·5% [73/582]). Overall, outcomes in 6MWT (14·9 meters [95% CI 7·4 to 22·4]), 1MSTST (2·2 repetitions [1·5 to 2·8]), and HGS (3·5 kg [2·9 to 4·0]) improved significantly (pINTERPRETATION: Overall, physical function improved after hospitalization for COVID-19, with largest improvement within 6 months post-discharge. Patients with rehabilitation after hospital discharge improved in more than one component of physical function, whereas patients without rehabilitation improved solely in muscle strength. Patients who received rehabilitation, and particularly patients with Med- and SNF-rehab, had more severe impairment in physical function at 3 months, but reached equal levels at 12 months compared to patients without follow-up treatment. Our findings indicate the importance of rehabilitation.FUNDING: ZonMw, Rijndam Rehabilitation, Laurens (The Netherlands).

For those who experience sexual desire, sexuality and sexual expression are a vital part of their emotional well-being at every age. Prevailing discourses have created an ageist mentality against older people maintaining a sexual identity. The research states that older people who identify as sexual continue to desire, long for, and enjoy a variety of sexual activities, despite and sometimes because of increasing physical health concerns. In sex therapy, older clients that examine their dominant discourses and find that they desire a change that is more sex positive can become sexually empowered through a narrative approach. This article includes a review of the current literature and clinical practices which can enhance therapeutic outcomes for older couples and individuals who identify as sexual and desire sexual improvement. A case study example is included as well.

Recent advancements in satellite technology have resulted in the development of quickly-built, modular satellites that can be designed, built, tested, and launched within days of when their need is established. One consequence of the short time frame during which these satellites are prepared for launch is the increased possibility of having loose bolts within the assembly. Loose bolts are undesirable because they increase the risk of damage during launch and, therefore, a method for identifying the presence of loose bolts within the satellite structure is required. In this paper, the effectiveness of using impact modulation (IM) testing to detect loose bolts within a structure is investigated. Four structures with increasing geometric complexity are tested: a three-beam, two-bolt assembly; a four-beam, three-bolt assembly; a satellite panel; and a full satellite structure. IM results are quantified using an integration-based metric. The value of the metric is shown to increase as the torque on one or more of the bolts within each structure decreases. Results from all four test cases showed that torque loss of 50% or more with respect to the fully tightened, baseline torque level resulted in a change in the metric value of at least 20% from its baseline value.

Concussions and changes in neurophysiology due to sub-concussive blows are common in contact sports, motivating a need for improved safety systems. While there have been attempts to determine whether or not new helmet designs influence the incidence of concussion, the results to date have been inconclusive. Consequently, the goal of this study was to evaluate the ability of modern football helmets to mitigate linear impacts directed normal, as well as oblique, to the surface of the helmeted head using a system that quantifies both the input load and the resulting accelerations of a Hybrid III headform. The transfer function connecting the inputs and outputs of the system was developed using dimensional analysis to provide a means of comparing helmets across brands and sports. For translational accelerations, increased helmet mass and a secondary layer of padding helped mitigate the impacts. The attenuation of angular accelerations depended substantially on helmet model and impact location.

Design of helmets used in contact sports has been driven by the necessity of preventing severe head injuries. Manufacturing standards and pass or fail grading systems ensure protective headgear built to withstand large impacts, but design standards do no account for impacts resulting in subconcussive episodes and the effects of cumulative impacts on its user. Thus, it is important to explore new design parameters, such as the frequency-domain measures of transmissibility and mechanical impedance that are based on energy absorption from a range of impact loads. Within the experimentally determined frequency range of interest (FROI), transmissibilities above unity were found in the 0–40 Hz range with the magnitude characteristics varying considerably with impact location. A similar variability with location was observed for the mechanical impedance, which ranged from 9 N/m to 50 N/m. Additional research is required to further understand how changes in the components or materials of the components will affect the performance of helmets, and how they may be used to reduce both transmissibility and dynamic impedance.

This paper presents the use of the power harvesting ratio (PHR) approach for evaluating the power harvesting capabilities of an electromagnetic vibration energy harvester. This is done for different electrical loads and measured bridge vibration data displaying multiple frequency components. Bridge vibration data are collected and characterized. The modes of the bridge are determined using a model sledge hammer, and the response of the bridge to a single vehicle is measured. Analysis of the data reveals that several of the modes contribute toward a response with multiple non-negligible frequency components. Measured bridge time-series data are then replayed on an experimental setup with an electromagnetic vibration energy harvester. Six electrical loads are implemented on the experimental platform: four passive loads and two active loads. The PHR approach is used to predict the average power from each load. Experimentally measured average power is within 6% of the predicted average power. The PHR approach is also used to successfully predict harvester instability for the active load dictated by the maximum power transfer theorem and validated experimentally. This paper demonstrates the utility of the PHR approach in evaluating harvester stability and performance for multifrequency excitations and sophisticated electrical loads, including active loads.

The attention given to brain injury has grown in recent years as its effects have become better understood. A desire to investigate the causal agents of head trauma in athletes has led to the development and use of several devices that track head impacts. In order to determine which devices best measure these impacts, a Hybrid III headform was used to quantify the accuracy for translational and angular accelerations. Testing was performed by mounting each device into the helmet as instructed by its manufacturer, fitting the helmet on the headform, and impacting the helmet using an impulse hammer. The root mean square error for the peak translational acceleration varied with location. The worst root mean square error for a head-mounted device was 74.7% while the worst for a helmet-mounted device was 298%. Head-mounted devices consistently outperformed those mounted in helmets, suggesting that future sensor designs should avoid attachment to the helmet. Deployment to a high school football team affirmed differences between two of the device models, but strongly indicated that head-mounted systems require further development to account for variation between individuals, the relative motion of the skin, and helmet–sensor interactions. Future work needs to account for these issues, refine the algorithms used to estimate the translational and angular accelerations, and examine technologies that better locate the source of the impact.

The use of chopped fibers in the manufacturing of carbon fiber composite parts is becoming more popular in order to reduce production costs, especially in the automotive, wind, and gas storage industries. The orientation of the fibers in a chopped fiber part is important because the material properties of the part depend upon it. Phenomena such as shear alignment can result in undesired material properties, and therefore, a method for detecting the presence of undesired fiber orientations is needed. In this paper, a metric based on a part's curvature mode shapes is developed to identify the presence and location of fibers whose orientation is different from that of a desired alignment. A proof-of-concept experimental analysis shows the effectiveness of the metric at locating a region in a carbon fiber laminate plate that has been modified by rotating the fibers 90 deg. A finite element model is also developed to validate the experimental results and explore other modification scenarios. In each case, the metric is effective in identifying areas in which fiber alignment changed relative to a baseline model. In one case, a change as small as 3 deg was identified.

This paper investigates the feasibility of extending the application of impact modulation from crack detection to the assessment of bolted joints. To support this effort, both theoretical and experimental evidence of the effectiveness of impact modulation to detect changes in bolt torque are presented. First, a nonlinear, single degree of freedom model is used to verify the correlation between changes in the nonlinear coefficient that represents the nonlinear contact forces present at a bolted interface and the amplitude of the sidebands generated in impact modulation testing. Next, a finite element representation of a two-beam, one-bolt assembly is presented to study effects not captured by the single degree of freedom model. Results of impact modulation simulations using the finite element model revealed that the amplitude of the sidebands in the response spectra depends not only on the nonlinearity present at the bolted interface, but also on other test parameters including impact amplitude and location, probing force amplitude and frequency, and sensor location. These findings are corroborated with experiments in which impact modulation was performed on a two-beam, one-bolt assembly similar to the assembly used in the simulations. Finally, impact modulation testing was conducted on the two-beam, one-bolt assembly using different bolt torque levels to demonstrate the method’s sensitivity to changes in bolt torque. A modulation index based on the amplitudes of the sidebands in the response spectrum was developed to quantify the results of the impact modulation testing. Results showed that the magnitude of the modulation index increased as the bolt torque decreased. The rate of change in the modulation index was largest when the bolt torque was $$

Mitigating human errors is a priority in the designs of complex systems, especially through the use of body area networks. This paper describes early developments of a dynamic data driven platform to predict operator error and trigger appropriate intervention before the error happens. Using a two-stage process, data was collected using several sensors (e.g., electroencephalography, pupil dilation measures, and skin conductance) during an established protocol - the Stroop test. The experimental design began with a relaxation period, 40 questions (congruent, then incongruent) without a timer, a rest period followed by another two rounds of questions, but under increased time pressure. Measures such as workload and engagement showed responses consistent with what is expected for Stroop tests. Dynamic system analysis methods were then used to analyze the raw data using principal components analysis and the least squares complex exponential method. The results show that the algorithms have the potential to capture mental states in a mathematical fashion, thus enabling the possibility of prediction.

This article presents a new technique for identifying cracks in wind turbine blades undergoing operational loads using the Vibro-Acoustic Modulation technique. Vibro-Acoustic Modulation utilizes a low-frequency pumping excitation signal in conjunction with a high-frequency probing excitation signal to create the modulation that is used to identify cracks. Wind turbines provide the ideal conditions in which Vibro-Acoustic Modulation can be utilized because wind turbines experience large low-frequency structural vibrations during operation which can serve as the low-frequency pumping excitation signal. In this article, the theory for the vibro-acoustic technique is described, and the proposed crack detection technique is demonstrated with Vibro-Acoustic Modulation experiments performed on a small Whisper 100 wind turbine in operation. The experimental results are also compared with two other conventional vibro-acoustic techniques in order to validate the new technique. Finally, a computational study is demonstrated for choosing a proper probing signal with a finite element model of the cracked blade to maximize the sensitivity of the technique for detecting cracks.

The effectiveness of many structural health monitoring techniques depends on the placement of sensors and the location of input forces. Algorithms for determining optimal sensor and forcing locations typically require data, either simulated or measured, from the damaged structure. Embedded sensitivity functions provide an approach for determining the best available sensor location to detect damage with only data from the healthy structure. In this video and manuscript, the data acquisition procedure and best practices for determining the embedded sensitivity functions of a structure is presented. The frequency response functions used in the calculation of the embedded sensitivity functions are acquired using modal impact testing. Data is acquired and representative results are shown for a residential scale wind turbine blade. Strategies for evaluating the quality of the data being acquired are provided during the demonstration of the data acquisition process.

Subsurface damage in composite materials is difficult to detect using visual techniques, and other current inspection methods lack the ability to perform quick, wide-area inspections without the need for reference signatures or baseline measurements. This paper presents a method for detecting and locating subsurface damage in composite materials without historical reference measurements by considering the nonlinear behavior of the material in the vicinity of damage. Nonlinear behavior is identified by comparing frequency response functions measured at different input amplitudes. It will be shown that the nonlinear behavior of the material is most evident in the areas nearest to the damage. The proposed inspection method is demonstrated both analytically and experimentally. First, a finite element model of a sandwich beam is developed using Bernoulli–Euler beam elements to represent each layer of the beam and springs to represent the interface between the layers. A bilinear stiffness nonlinearity is simulated to represent disbond damage between the top and core layers of the beam. The simulated disbond damage is localized by identifying degrees of freedom which indicate significant nonlinear response through a comparison of frequency response functions measured at various input amplitudes. Next, the method is demonstrated experimentally by identifying disbond damage in a fiberglass sandwich panel. A three-dimensional scanning laser vibrometer is used to measure the forced frequency response of the panel in its damaged state as it is excited at two or more amplitudes of excitation by a piezoelectric actuator. Comparisons of the frequency response functions measured at different input amplitudes show that the subsurface damage introduces nonlinear behavior which resembles a bilinear stiffness nonlinearity, and the differences in the frequency response functions are largest in the vicinity of the damage location. In addition, it was found that improved localization of the damage is achieved by investigating the response at higher frequencies. This work has application as a nondestructive method for detecting and locating subsurface damage in composite materials and, by using a laser vibrometer for noncontact measurement, allows for quick, wide-area inspection of composite materials without the need for reference signatures or baseline measurements.

Structural health monitoring (SHM) techniques have traditionally been applied to mechanical, aerospace, and civil structures to identify loading and damage patterns. However, human operators in the loop play an important role in the operational performance of aircraft and other structural systems. The increased availability of sensors such as EEG, skin conductance, and eye-tracking systems are creating an opportunity to develop SHM techniques for assessing neuro-physiological factors that influence human decision-making. The parallels between the structural dynamic response of a system to an excitation source and the response of a human to the presentation of a scenario suggests that SHM algorithms can be used to interpret neurophysiological signals. As in traditional SHM, where the system’s dynamic response is measured to characterize the system’s state of health, the measured response of a human during decision-making can capture information about the human’s mental state, including levels of fatigue, engagement, workload, and other human factors. The ability to monitor the human’s mental state in real-time could also enable predictions of human susceptibility to poor decision-making and to trigger an appropriate intervention to prevent human errors. In this work, EEG, eye-tracking, and skin conductance data are acquired from multiple subjects while performing the Stroop test, a standard test designed to induce errors, under varying degrees of time pressure. Time pressure is induced by progressively reducing the time-to-answer allowed for each set of questions. The data is then analyzed using pattern recognition techniques including principal component analysis and the least squares complex exponential (LSCE) parameter estimation algorithm. Results from the principal component analysis identify the modes which dominate the response during decision-making. These modes are compared to the modes identified while the subject is at rest. Next, LSCE is applied to identify model parameters that can be used to perform a one-step-ahead prediction of the neurophysiological variables. The LSCE approach allows data from the different sensor types to be analyzed simultaneously. Results show that model error is reduced as the time pressure is increased. doi: 10.12783/SHM2015/145

In structural health monitoring, it is desirable to select sensor locations in order to minimize the number of sensors required for and the cost associated with an on-board monitoring system. When using a frequency response-based structural health monitoring technique, data measured at sensor locations which exhibit the greatest change in frequency response function (FRF) due to damage are expected to maximize the effectiveness of the chosen technique. In this work, an embedded sensitivity function is presented which identifies the sensor locations at which the maximum differences in FRFs due to damage at a known location will be observed. The formulation of the embedded sensitivity function is based on FRFs measured on a healthy structure in the frequency range in which the damage detection technique will be applied. The effectiveness of the embedded sensitivity functions in predicting the most effective sensor locations is demonstrated by applying a vibro-acoustic modulation (VAM) damage detection method to a residential-scale wind turbine blade. First, data from the healthy blade is acquired and the embedded sensitivity functions are calculated. Then, the blade is damaged and the VAM method is applied using several sensor locations. The data acquired using sensor locations identified by the embedded sensitivity functions as being most effective are shown to most clearly identify the damage on the blade.Copyright © 2014 by ASME

Many damage detection methods that are applied to composite structures rely on nonlinear features in the dynamic response of the structure to identify the presence of defects. Presently, there is not a complete understanding of the physical mechanisms that cause the nonlinear behavior of a damaged composite structure. Correlating specific types of damage mechanisms to the resulting nonlinear response characteristics they cause would allow the detection methods to classify the type of damage that is present in the structure. In this work, a drop tower was used to impact an aluminum honeycomb sandwich panel in order to induce core-crushing. The response of the damaged panel to sinusoidal excitations of various amplitudes at resonant, super-, and sub-harmonic frequencies was then measured. The amplitudes of these measured responses and the corresponding restoring force curves were then compared to a predictive model to identify the type of theoretical nonlinearity (i.e. quadratic or cubic stiffness, quadratic or cubic damping, etc.) that was present. The predictive model is based on a nonlinear, single degree-of-freedom system. Nonlinear features in the response of the system were identified for different types of stiffness and damping nonlinearities. The experimentally measured response was analyzed to see which of these features were present. Based on this analysis, the response of the panel of damage due to core-crushing indicated a quadratic spring-type stiffness.Copyright © 2014 by ASME

The purpose of this study was to determine how students aged 12 to 18 reacted to joining with a neighboring school district after their high school was destroyed by a tornado. Relocated students were compared to a control group of students randomly selected from the school district they were joining. Seven measures were used to determine levels of adjustment. Results indicate that relocated students had significantly lower grades and that their parents identified them as having various behavior difficulties as the results of relocation.

1. Fifty-seven percent of the hospitals with inpatient psychiatric units responded to the survey. 2. Out of these hospitals, 23% reported suspicions, 43% allegations, and 23% occurrences of staff-patient sexual interactions within the previous 2 years. 3. Neither the existence of staff educational programs, hospital size, nor ownership status were significant predictors of the number of events. 4. Patient abuse predictors must be identified.