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The length of the standing long jump (SLJ) is widely recognized as an indicator of developmental motor competence or sports conditional performance. This work aims at defining a methodology to allow athletes/coaches to easily measure it using the inertial measurement units embedded on a smartphone. A sample group of 114 trained young participants was recruited and asked to perform the instrumented SLJ task. A set of features was identified based on biomechanical knowledge, then Lasso regression allowed the identification of a subset of predictors of the SLJ length that was used as input of different optimized machine learning architectures. Results obtained from the use of the proposed configuration allow an estimate of the SLJ length with a Gaussian Process Regression model with a RMSE of 0.122 m in the test phase, Kendall's τ < 0.1. The proposed models give homoscedastic results, meaning that the error of the models does not depend on the estimated quantity. This study proved the feasibility of using low-cost smartphone sensors to provide an automatic and objective estimate of SLJ performance in ecological settings.

RESUMEN

Global soft fruit supply chains rely on trustworthy descriptions of product quality. However, crucial criteria such as sweetness and firmness cannot be accurately established without destroying the fruit. Since traditional alternatives are subjective assessments by human experts, it is desirable to obtain quality estimations in a consistent and non-destructive manner. The majority of research on fruit quality measurements analyzed fruits in the lab with uniform data collection. However, it is laborious and expensive to scale up to the level of the whole yield. The "harvest-first, analysis-second" method also comes too late to decide to adjust harvesting schedules. In this research, we validated our hypothesis of using in-field data acquirable via commodity hardware to obtain acceptable accuracies. The primary instance that the research concerns is the sugariness of strawberries, described by the juice's total soluble solid (TSS) content (unit: °Brix or Brix). We benchmarked the accuracy of strawberry Brix prediction using convolutional neural networks (CNN), variational autoencoders (VAE), principal component analysis (PCA), kernelized ridge regression (KRR), support vector regression (SVR), and multilayer perceptron (MLP), based on fusions of image data, environmental records, and plant load information, etc. Our results suggest that: (i) models trained by environment and plant load data can perform reliable prediction of aggregated Brix values, with the lowest RMSE at 0.59; (ii) using image data can further supplement the Brix predictions of individual fruits from (i), from 1.27 to as low up to 1.10, but they by themselves are not sufficiently reliable.

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The growth of illicit drugs is a serious social problem, putting great pressure on law enforcement officers to screen numerous suspicious samples at crime scenes. Although commercial colorimetric kits are available, they are limited to common illicit drugs. With increasing numbers of new psychoactive substances in the market, accurate and rapid screening assays are highly demanded. Carbon dots (C-dots) are photoluminescent (PL) carbon nanomaterials with unique properties of excellent stability against salt and photo-irradiation, low blinking, and biocompatibility. They can be prepared easily through various routes from numerous precursors. This Focus provides examples of C-dots based PL assays for screening illicit drugs. The drugs induce PL changes of C-dots mostly through electron transfer and energy transfer. Liquid- and solid-phase PL assays of C-dots can be applied for in-field screening, with advantages of rapidity, low cost, selectivity, and minimum color interference, showing their great commercial potential.

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RESUMEN

Foot-and-mouth disease (FMD) is a highly contagious viral disease of cloven-hooved animals. Global outbreaks have highlighted the significant economic, trade, psychosocial and animal welfare impacts that can arise from the detection of disease in previously 'FMD-free' countries. Rapid and early diagnosis provides significant advantages in disease control and minimization of deleterious consequences. We describe the process of further development and validation of a reverse-transcription loop-mediated isothermal amplification foot-and-mouth disease virus (RT-LAMP-FMDV) test, using a published LAMP primer set, for use in the field. An internal positive control (IPC) was designed and introduced for use with the assay to mitigate any intrinsic interference from the unextracted field samples and avoid false negatives. Further modifications were included to improve the speed and operability of the test, for use by non-laboratory trained staff operating under field conditions, with shelf-stable reaction kits which require a minimum of liquid handling skills. Comparison of the assay performance with an established laboratory-based real-time reverse transcriptase PCR (rRT-PCR) test targeting the 3D region of FMD virus (Tetracore Inc) was investigated. LAMP has the potential to complement current laboratory diagnostics, such as rRT-PCR, as a preliminary tool in the investigation of FMD. We describe a strategic approach to validation of the test for use in the field using extracted RNA samples of various serotypes from Thailand and then finally unextracted field samples collected from FMD-suspected animals (primarily oral lesion swabs) from Bhutan and Australia. The statistical approach to validation was performed by Frequentist and Bayesian latent class methods, which both confirmed this new RT-LAMP-FMDV test as fit-for-purpose as a herd diagnostic tool with diagnostic specificity >99% and sensitivity 79% (95% Bayesian credible interval: 65, 90%) on unextracted field samples (oral swabs).

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