Big data and partial least-squares prediction

Practical and comparative application of efficient data reduction - Multivariate curve resolution

The term 'Big Data' has recently attracted much attention in science. Working with big data sets can be both challenging and rewarding. The complexity and big data sets make the analysis difficult to deal with, and the increasing volume of data sets requires the development of new practical methods for their handling. In this contribution, we explored the efficient data reduction-multivariate curve resolution (EDR-MCR) strategy based on the convex hull theory for quantitative and qualitative analysis of large chemical data sets. For the quantitative example, the potential of the EDR-MCR method for selecting a representative calibration set was investigated, and the results were compared with the widely used Kennard-Stone (KS) algorithm. The EDR-MCR strategy strongly limits the number of calibration samples with a high potency of prediction performance. The priority of EDR-MCR over KS is its ability to find informative variables and eliminate redundant features. Moreover, the EDR-MCR strategy was also applied for the qualitative analysis of a large-scale metabolomic data set. The comparable analysis results of EDR-MCR with the region of interest (ROI) method confirmed the ability of this method for quantitative analysis of big mass spectrophotometer data sets.

Interpreting treatment effect size from single case experimental design data: a preliminary analysis of differential effects of treatments designed to increase or decrease behaviour

BACKGROUND

Estimates of treatment effect size from single case experimental design (SCED) data may be impacted by the direction for treatment effects (i.e. ascending or descending slope for the dependent variable). Estimating effect sizes for treatments designed to decrease behaviour are potentially more restricted because the intended direction for treatment is zero (i.e. an absolute basal). Conversely, effect sizes for interventions that increase behaviour are less restricted due to a relatively unconstrained ceiling from a pure measurement standpoint (i.e. no absolute ceiling). That is, treatments that increase behaviour have a broader range of possible effect size values as the ceiling is only limited by demand characteristics and the learners' skills and motivation to exhibit the behaviour.

METHOD

The current study represents a preliminary analysis of the mean and range of SCED effect sizes for treatments designed to either increase or decrease target behaviour. A within-case Cohen's d measure that was developed for SCED data was used to estimate treatment effect sizes.

RESULTS

Results indicated that the mean and range of effect size values for treatments that increased behaviour were significantly greater compared with treatments that decreased behaviour.

CONCLUSIONS

Results are discussed in terms of developing standards, or best practices, specific to interpreting effect size values and meeting quality control requirements for inclusion of the data set in future SCED meta-analytic studies estimating treatment effect size. Specifically, preliminary results suggest that benchmarks for low, medium and high SCED effect size values need to be developed separately for treatments that increase or decrease levels of the dependent variable.

Challenges and opportunities of the fourth revolution: a brief insight into the future of food

By 2050, the global population is projected to be in excess of nine billion people. This will result in an increased burden and stress on the food production systems, particularly in adjustments to several stages of the value chain that will require improvements and/or modifications in their effectiveness such as reducing waste, adapting to climate change, food security, and health. Disruptions such as digital agriculture, digital food, food agility, big data, have been utilized to characterize the changes in the way agro-food systems evolve and function, as well as in the approach they have been analyzed, measured, and monitored. It has been long recognized that the food industry is considered as a data driven enterprise. These characteristics are very important as the food industry becomes global and sustainable. The food industry is currently undergoing significant changes, and with this, challenges are occurring. These challenges are brought about from the food chains, climate changes, and the ability to be resilient in the production of food. Furthermore, health and cultural changes to food are occurring, where the diseases of obesity, diabetes, and aging in the population will continue to change the consumer's patterns and choices; whereby the consumer will be persuaded to choose and eat healthy and more nutritious foods. Indeed, the cultural awareness and social innovation to prevent food waste and therefore improve food security and sustainability will also prove to further complexities. This short review will briefly discuss some of the forefront issues in food value chains with a focus on using technology.