Dramatically increased accessibility and decreased cost-per-person impacts are needed for scaling IPM in Africa.

While traditional scaling for integrated pest management (IPM) in Africa requires the movement of expert trainers from village to village, these efforts are often costly, time-inefficient, hampered by distance, and became impossible under COVID-19's movement restrictions (despite tremendously increased public need for IPM-scaling knowledge). One solution to this dilemma is IPM-scaling, usable by a diversity of development actors expending limited or few resources, to deliver critical information to large numbers of people with systems-approach information and communication technologies. This paper describes one such systems-approach scaling platform, Scientific Animations Without Borders, which effectively elicited end-user solution-adoption and decreased unit costs over increasing scales in three African countries during COVID-19. How to scale game-changing IPM insights 'off the shelf' and 'into people's hands in the field' is also discussed.

Selecting Sensitive Parameter Subsets in Dynamical Models With Application to Biomechanical System Identification.

Estimating many parameters of biomechanical systems with limited data may achieve good fit but may also increase 95% confidence intervals in parameter estimates. This results in poor identifiability in the estimation problem. Therefore, we propose a novel method to select sensitive biomechanical model parameters that should be estimated, while fixing the remaining parameters to values obtained from preliminary estimation. Our method relies on identifying the parameters to which the measurement output is most sensitive. The proposed method is based on the Fisher information matrix (FIM). It was compared against the nonlinear least absolute shrinkage and selection operator (LASSO) method to guide modelers on the pros and cons of our FIM method. We present an application identifying a biomechanical parametric model of a head position-tracking task for ten human subjects. Using measured data, our method (1) reduced model complexity by only requiring five out of twelve parameters to be estimated, (2) significantly reduced parameter 95% confidence intervals by up to 89% of the original confidence interval, (3) maintained goodness of fit measured by variance accounted for (VAF) at 82%, (4) reduced computation time, where our FIM method was 164 times faster than the LASSO method, and (5) selected similar sensitive parameters to the LASSO method, where three out of five selected sensitive parameters were shared by FIM and LASSO methods.

Time-Domain Optimal Experimental Design in Human Seated Postural Control Testing.

We are developing a series of systems science-based clinical tools that will assist in modeling, diagnosing, and quantifying postural control deficits in human subjects. In line with this goal, we have designed and constructed a seated balance device and associated experimental task for identification of the human seated postural control system. In this work, we present a quadratic programming (QP) technique for optimizing a time-domain experimental input signal for this device. The goal of this optimization is to maximize the information present in the experiment, and therefore its ability to produce accurate estimates of several desired seated postural control parameters. To achieve this, we formulate the problem as a nonconvex QP and attempt to locally maximize a measure (T-optimality condition) of the experiment's Fisher information matrix (FIM) under several constraints. These constraints include limits on the input amplitude, physiological output magnitude, subject control amplitude, and input signal autocorrelation. Because the autocorrelation constraint takes the form of a quadratic constraint (QC), we replace it with a conservative linear relaxation about a nominal point, which is iteratively updated during the course of optimization. We show that this iterative descent algorithm generates a convergent suboptimal solution that guarantees monotonic nonincreasing of the cost function value while satisfying all constraints during iterations. Finally, we present successful experimental results using an optimized input sequence.

Comparison of trunk stiffness provided by different design characteristics of lumbosacral orthoses.

BACKGROUND: Lumbosacral orthoses (LSOs) are class I medical devices that are used in conservative and postoperative management of low back pain. The effectiveness of LSOs depends on their design aimed at enhancing trunk stiffness. Therefore, the purpose of this study was to compare two lumbar supports: extensible (made of neoprene and lycra) and non-extensible (made of polyester and nylon). METHODS: Trunk stiffness and damping was estimated from trunk displacement data in response to a quick force release in trunk flexion, extension, and lateral bending. Fourteen male and 6 female subjects performed five trials at each experimental condition: (1) No LSO, (2) extensible LSO, (3) non-extensible LSO, (4) non-extensible LSO with a small rigid front panel, and (5) non-extensible LSO with a large rigid front panel. Testing order was randomized and the LSOs were cinched to a pressure of 70 mmHg (9.4 kPa) measured between posterior aspect of the iliac crest and the orthosis. FINDINGS: The non-extensible LSO reduced trunk displacement by 14% and increased trunk stiffness by 14% (P<0.001). The extensible LSO did not result in any significant change in trunk displacement or stiffness. The addition of rigid front panels to the non-extensible LSO did not improve its effectiveness. The trunk damping did not differ between the LSO conditions. INTERPRETATION: A non-extensible LSO is more effective in augmenting trunk stiffness and limiting trunk motion following a perturbation than an extensible LSO. The rigid front panels do not provide any additional trunk stiffness most likely due to incongruence created between the body and a brace.