Models to predict injury, physical fitness failure and attrition in recruit training: a retrospective cohort study

Background:Attrition rate in new army recruits is higher than in incumbent troops.In the current study,we identified the risk factors for attrition due to injuries and physical fitness failure in recruit training.A variety of predictive models were attempted.Methods:This retrospective cohort included 19,769 Army soldiers of the Australian Defence Force receiving recruit training during a period from 2006 to 2011.Among them,7692 reserve soldiers received a 28-day training course,and the remaining 12,077 full-time soldiers received an 80-day training course.Retrieved data included anthropometric measures,course-specific variables,injury,and physical fitness failure.Multivariate regression was used to develop a variety of models to predict the rate of attrition due to injuries and physical fitness failure.The area under the receiver operating characteristic curve was used to compare the performance of the models.Results:In the overall analysis that included both the 28-day and 80-day courses,the incidence of injury of any type was 27.8%.The 80-day course had a higher rate of injury if calculated per course(34.3%vs.17.6%in the 28-day course),but lower number of injuries per person-year(1.56 vs.2.29).Fitness test failure rate was significantly higher in the 28-day course(30.0%vs.12.1%).The overall attrition rate was 5.2%and 5.0%in the 28-day and 80-day courses,respectively.Stress fracture was common in the 80-day course(n=44)and rare in the 28-day course(n=1).The areas under the receiver operating characteristic curves for the course-specific predictive models were relatively low(ranging from 0.51 to 0.69),consistent with"failed"to"poor"predictive accuracy.The course-combined models performed somewhat better than the course-specific models,with two models having AUC of 0.70 and 0.78,which are considered"fair"predictive accuracy.Conclusion:Attrition rate was similar between 28-day and 80-day courses.In comparison to the 80-day full course,the 28-day course had a lower rate of injury but a higher number of injuries per person-year and of fitness test failure.These findings suggest fitness level at the commencement of training is a critically important factor to consider when designing the course curriculum,particularly short courses.

Cluster Analysis for IR and NIR Spectroscopy: Current Practices to Future Perspectives

Supervised machine learning techniques have become well established in the study of spectroscopy data.However,the unsupervised learning technique of cluster analysis hasn’t reached the same level maturity in chemometric analysis.This paper surveys recent studies which apply cluster analysis to NIR and IR spectroscopy data.In addition,we summarize the current practices in cluster analysis of spectroscopy and contrast these with cluster analysis literature from the machine learning and pattern recognition domain.This includes practices in data pre-processing,feature extraction,clustering distance metrics,clustering algorithms and validation techniques.Special consideration is given to the specific characteristics of IR and NIR spectroscopy data which typically includes high dimensionality and relatively low sample size.The findings highlighted a lack of quantitative analysis and evaluation in current practices for cluster analysis of IR and NIR spectroscopy data.With this in mind,we propose an analysis model or workflow with techniques specifically suited for cluster analysis of IR and NIR spectroscopy data along with a pragmatic application strategy.

Effect of corrosion inhibiting compounds on the corrosion behaviour of pure magnesium and the magnesium alloys EV31A, WE43B and ZE41A

This paper studied corrosion of pure Mg and the Mg alloys EV31A,WE43B,ZE41A,coated with commercial corrosion inhibiting compounds(CICs)(LPS 3,LPS2,AMLGuard,Ardrox 3961)immersed in 3.5 wt%(0.6M)Na Cl solution saturated with Mg(OH)_(2).All four CICs reduced corrosion rates.LPS 3 resulted in zero corrosion rates and 100%inhibition in most cases.LPS 2 and AMLGuard had comparable inhibition efficiencies,whilst Ardrox 3961 had the lowest inhibition efficiency.Reduction in corrosion rates was tentatively attributed to barrier films formed by chemical adsorption for LPS 3 and AMLGuard,and by physical adsorption for LPS2 and Ardrox 3961.

A simplified approach to modelling blasts in computational fluid dynamics (CFD)

This paper presents a time-efficient numerical approach to modelling high explosive(HE)blastwave propagation using Computational Fluid Dynamics(CFD).One of the main issues of using conventional CFD modelling in high explosive simulations is the ability to accurately define the initial blastwave properties that arise from the ignition and consequent explosion.Specialised codes often employ Jones-Wilkins-Lee(JWL)or similar equation of state(EOS)to simulate blasts.However,most available CFD codes are limited in terms of EOS modelling.They are restrictive to the Ideal Gas Law(IGL)for compressible flows,which is generally unsuitable for blast simulations.To this end,this paper presents a numerical approach to simulate blastwave propagation for any generic CFD code using the IGL EOS.A new method known as the Input Cavity Method(ICM)is defined where input conditions of the high explosives are given in the form of pressure,velocity and temperature time-history curves.These time history curves are input at a certain distance from the centre of the charge.It is shown that the ICM numerical method can accurately predict over-pressure and impulse time history at measured locations for the incident,reflective and complex multiple reflection scenarios with high numerical accuracy compared to experimental measurements.The ICM is compared to the Pressure Bubble Method(PBM),a common approach to replicating initial conditions for a high explosive in Finite Volume modelling.It is shown that the ICM outperforms the PBM on multiple fronts,such as peak values and overall overpressure curve shape.Finally,the paper also presents the importance of choosing an appropriate solver between the Pressure Based Solver(PBS)and Density-Based Solver(DBS)and provides the advantages and disadvantages of either choice.In general,it is shown that the PBS can resolve and capture the interactions of blastwaves to a higher degree of resolution than the DBS.This is achieved at a much higher computational cost,showing that the DBS is much preferred for quick turnarounds.

Feature Selection for Cluster Analysis in Spectroscopy

Cluster analysis in spectroscopy presents some unique challenges due to the specific data characteristics in spectroscopy,namely,high dimensionality and small sample size.In order to improve cluster analysis outcomes,feature selection can be used to remove redundant or irrelevant features and reduce the dimensionality.However,for cluster analysis,this must be done in an unsupervised manner without the benefit of data labels.This paper presents a novel feature selection approach for cluster analysis,utilizing clusterability metrics to remove features that least contribute to a dataset’s tendency to cluster.Two versions are presented and evaluated:The Hopkins clusterability filter which utilizes the Hopkins test for spatial randomness and the Dip clusterability filter which utilizes the Dip test for unimodality.These new techniques,along with a range of existing filter and wrapper feature selection techniques were evaluated on eleven real-world spectroscopy datasets using internal and external clustering indices.Our newly proposed Hopkins clusterability filter performed the best of the six filter techniques evaluated.However,it was observed that results varied greatly for different techniques depending on the specifics of the dataset and the number of features selected,with significant instability observed for most techniques at low numbers of features.It was identified that the genetic algorithm wrapper technique avoided this instability,performed consistently across all datasets and resulted in better results on average than utilizing the all the features in the spectra.

A bayesian optimisation methodology for the inverse derivation of viscoplasticity model constants in high strain-rate simulations

We present an inverse methodology for deriving viscoplasticity constitutive model parameters for use in explicit finite element simulations of dynamic processes using functional experiments, i.e., those which provide value beyond that of constitutive model development. The developed methodology utilises Bayesian optimisation to minimise the error between experimental measurements and numerical simulations performed in LS-DYNA. We demonstrate the optimisation methodology using high hardness armour steels across three types of experiments that induce a wide range of loading conditions: ballistic penetration, rod-on-anvil, and near-field blast deformation. By utilising such a broad range of conditions for the optimisation, the resulting constitutive model parameters are generalised, i.e., applicable across the range of loading conditions encompassed the by those experiments(e.g., stress states, plastic strain magnitudes, strain rates, etc.). Model constants identified using this methodology are demonstrated to provide a generalisable model with superior predictive accuracy than those derived from conventional mechanical characterisation experiments or optimised from a single experimental condition.

A machine learning method to quantitatively predict alpha phase morphology in additively manufactured Ti-6Al-4V

Quantitatively defining the relationship between laser powder bed fusion(LPBF)process parameters and the resultant microstructures for LPBF fabricated alloys is one of main research challenges.To date,achieving the desired microstructures and mechanical properties for LPBF alloys is generally done by time-consuming and costly trial-and-error experiments that are guided by human experience.Here,we develop an approach whereby an image-driven conditional generative adversarial network(cGAN)machine learning model is used to reconstruct and quantitatively predict the key microstructural features(e.g.,the morphology of martensite and the size of primary and secondary martensite)for LPBF fabricated Ti-6Al-4V.The results demonstrate that the developed image-driven machine learning model can effectively and efficiently reconstruct micrographs of the microstructures within the training dataset and predict the microstructural features beyond the training dataset fabricated by different LPBF parameters(i.e.,laser power and laser scan speed).This study opens an opportunity to establish and quantify the relationship between processing parameters and microstructure in LPBF Ti-6Al-4V using a GAN machine learning-based model,which can be readily extended to other metal alloy systems,thus offering great potential in applications related to process optimisation,material design,and microstructure control in the additive manufacturing field.

Recent progress in additive manufacturing of bulk MAX phase components:A review

The MAX phases are a group of layered ternary,quaternary,or quinary compounds with characteristics of both metals and ceramics.Over recent decades,the synthesis of bulk MAX phase parts for wider engineering applications has gained increasing attention in aerospace,nuclear,and defence industries.The recent adoption of additive manufacturing(AM)technologies in MAX phase fabrication is a step forward in this field.This work overviews the recent progress in additive manufacturing(AM)of bulk MAX phases along with the achieved geometric features,microstructures,and properties after briefing the conventional powder sintering methods of fabricating MAX phase components.Critical challenges associated with these innovative AM-based methods,including,poor AM processability,low MAX phase purity,and insufficient geometric accuracy of the final parts,are also discussed.Accordingly,outlooks for the immediate future in this area are discussed based on the optimization of present fabrication routes and the potential of other AM technologies.

Proximal detecting invertebrate pests on crops using a deep residual convolutional neural network trained by virtual images

Detecting invertebrate pests on crops at early stages is essential for pest management.Traditionally,traps were used to sample pests and then human experts undertook classification and counting to estimate the levels of infestation,which is subjective,error-prone and labour intensive.Recently,semi-automatic pest detection is possible by using computer vision technologies to classify and count pest samples in laboratories or insect traps,however,the decision made by the laboratory-based or trap-based approaches are still too late for more optimised pest management decisions.Today,precision agriculture needs detection of pests on crops so that real-time actions can be taken or optimised decision can be made based on accurate information of time and location pest occurs.In this study,we used computer vision and machine learning technologies to detect invertebrates on crops in the field.We first evaluated the performances of the state-of-art convolutional neural networks(CNNs)and proposed a standard training pipeline.Facing the challenge of rapidly developing comprehensive training data,we used a novel method to generate a virtual database which was successfully used to train a deep residual CNNwith an accuracy of 97.8%in detecting four species of pests in farming environments.The proposed method can be applied to a robotic system for proximal detection of invertebrate pests on crops in real-time.

A machine learning enabled hybrid optimization framework for efficient coarse-graining of a model polymer

This work presents a framework governing the development of an efficient,accurate,and transferable coarse-grained(CG)model of a polyether material.The framework combines bottom-up and top-down approaches of coarse-grained model parameters by integrating machine learning(ML)with optimization algorithms.In the bottom-up approach,bonded interactions of the CG model are optimized using deep neural networks(DNN),where atomistic bonded distributions are matched.In the top-down approach,optimization of nonbonded parameters is accomplished by reproducing the temperature-dependent experimental density.We demonstrate that developed framework addresses the thermodynamic consistency and transferability issues associated with the classical coarse-graining approaches.The efficiency and transferability of the CG model is demonstrated through accurate predictions of chain statistics,the limiting behavior of the glass transition temperature,diffusion,and stress relaxation,where none were included in the parametrization process.The accuracy of the predicted properties are evaluated in context of molecular theories and available experimental data.