Powertrain modelling for engine stop-start dynamics and control of micro/mild hybrid construction machines

Engine stop-start control is considered as the key technology for micro/mild hybridisation of vehicles and machines. To utilize this concept, especially for construction machines, the engine is desired to be started in such a way that the operator discomfort can be minimized. To address this issue, this paper aims to develop a simple powertrain modelling approach for engine stop-start dynamic analysis and an advanced engine start control scheme newly applicable for micro/mild hybrid construction machines. First, a powertrain model of a generic construction machine is mathematically developed in a general form which allows to investigate the transient responses of the system during the engine cranking process. Second, a simple parameterisation procedure with a minimum set of data required to characterise the dynamic model is presented. Third, a model- based adaptive controller is designed for the starter to crank the engine quickly and smoothly without the need of fuel injection while the critical problems of machine noise, vibration and harshness can be eliminated. Finally, the advantages and effectiveness of the proposed modelling and control approaches have been validated through numerical simulations. The results imply that with the limited data set for training, the developed model works better than a high fidelity model built in AMESim while the adaptive controller can guarantee the desired cranking performance

Challenges of micro/mild hybridisation for construction machinery and applicability in UK

In recent years, micro/mild hybridisation (MMH) is known as a feasible solution for powertrain development with high fuel efficiency, less energy use and emission and, especially, low cost and simple installation. This paper focuses on the challenges of MMH for construction machines and then, pays attention to its applicability to UK construction machinery. First, hybrid electric configurations are briefly reviewed; and technological challenges towards MMH in construction sector are clearly stated. Second, the current development of construction machinery in UK is analysed to point out the potential for MMH implementation. Thousands of machines manufactured in UK have been sampled for the further study. Third, a methodology for big data capturing, compression and mining is provided for a capable of managing and analysing effectively performances of various construction machine types. By using this method, 96% of data memory can be reduced to store the huge machine data without lacking the necessary information. Forth, an advanced decision tool is built using a fuzzy cognitive map based on the big data mining and knowledge from experts to enables users to define a target machine for MMH utilization. The numerical study with this tool on the sampled machines has been done and finally realized that one class of heavy excavators is the most suitable to apply MMH technology

Ligand-based virtual screening using binary kernel discrimination

This paper discusses the use of a machine-learning technique called binary kernel discrimination (BKD) for virtual screening in drug- and pesticide-discovery programmes. BKD is compared with several other ligand-based tools for virtual screening in databases of 2D structures represented by fragment bit-strings, and is shown to provide an effective, and reasonably efficient, way of prioritising compounds for biological screening

Cosmological foundations revisited with Pantheon+

We reanalyse the Pantheon+ supernova catalogue to compare a cosmology with non-FLRW evolution, the "timescape cosmology", with the standard $\Lambda$CDM cosmology. To this end, we consider the Pantheon+ supernova catalogue, which is the largest available Type Ia supernova dataset for a geometric comparison between the two models. We construct a covariance matrix to be as independent of cosmology as possible, including independence from the FLRW geometry and peculiar velocity with respect to FLRW average evolution. Within this framework, which goes far beyond most other definitions of "model independence", we introduce new statistics to refine Type Ia supernova (SneIa) light-curve analysis. In addition to conventional galaxy correlation functions used to define the scale of statistical homogeneity we introduce empirical statistics which enables a refined analysis of the distribution biases of SneIa light-curve parameters $\beta c$ and $\alpha x_1$. For lower redshifts, the Bayesian analysis highlights important features attributable to the increased number of low-redshift supernovae, the artefacts of model-dependent light-curve fitting and the cosmic structure through which we observe supernovae. This indicates the need for cosmology-independent data reduction to conduct a stronger investigation of the emergence of statistical homogeneity and to compare alternative cosmologies in light of recent challenges to the standard model. "Dark energy" is generally invoked as a place-holder for "new physics". Our from-first-principles reanalysis of the Pantheon+ catalogue supports future deeper studies of the interplay of matter and nonlinear spacetime geometry, in a data-driven setting. For the first time in 25 years, we find evidence that the Pantheon+ catalogue already contains such a wealth of data that with further reanalysis, a genuine "paradigm shift" may soon emerge. [Abridged]Comment: 23 pages, 14 figures, 3 table

Noninvasive fractal biomarker of clock neurotransmitter disturbance in humans with dementia

Human motor activity has a robust, intrinsic fractal structure with similar patterns from minutes to hours. The fractal activity patterns appear to be physiologically important because the patterns persist under different environmental conditions but are significantly altered/reduced with aging and Alzheimer's disease (AD). Here, we report that dementia patients, known to have disrupted circadian rhythmicity, also have disrupted fractal activity patterns and that the disruption is more pronounced in patients with more amyloid plaques (a marker of AD severity). Moreover, the degree of fractal activity disruption is strongly associated with vasopressinergic and neurotensinergic neurons (two major circadian neurotransmitters) in postmortem suprachiasmatic nucleus (SCN), and can better predict changes of the two neurotransmitters than traditional circadian measures. These findings suggest that the SCN impacts human activity regulation at multiple time scales and that disrupted fractal activity may serve as a non-invasive biomarker of SCN neurodegeneration in dementia

Powertrain modelling and engine start control of construction machines

This paper aims to develop an engine start control approach for a micro/mild hybrid machine for a capable of cranking the engine without injection. First, the powertrain is physically modelled using a co-simulation platform. Second, experiment data of the traditional machine is acquired to optimize the model. Third, a model-based adaptive controller is designed for the starter to crank the engine quickly and smoothly to minimize the operator discomfort. The effectiveness of the proposed approach is validated through numerical simulations with the established model

Comparison of equations to predict the metabolisable energy content as applied to the vertical strata and plant parts of forage sorghum (Sorghum bicolor)

Context: Nutritive values, particularly energy content of tropical forages, need to be accurately assessed so that rations can be more precisely formulated. Aims: The research aimed to collate and compare equations used to predict metabolisable energy content in forage sorghum (Sorghum bicolor (L.) Moench) to ascertain the effect of vertical strata on metabolisable energy content to assist in producing silage of defined quality. Methods: Twenty-four predictive metabolisable energy equations derived from international feeding standards were compared using forage sorghum samples grown under fertiliser and growth stage treatments. Samples were separated into leaf, stem and seed heads (where present) over four vertical strata. Key results: Equations based on digestibility with crude protein were robust in the prediction of metabolisable energy and had application to routine laboratory use. Conclusions: The current study suggests that predictions based on digestibility and crude protein content are best placed for metabolisable energy application. Such equations should be originally based on measured metabolisable energy content to establish a regression so as to be used for predictive purposes, and satisfy the biological requirement of in vivo and the laboratory measurement relationship with acceptable statistical error. Chemical composition relationships predicted different metabolisable energy contents. Implications: Improved accuracy of the prediction of metabolisable energy content in tropical forages will provide better application of production models and more accurate decisions in ration formulation