Automated Data Acquisition System Using a Neural Network for Prediction Response in a Mode-Locked Fiber Laser

In this paper, we proposed a system to integrate optical and electronic instrumentation devices to predict a mode-locking fiber laser response, using a remote data acquisition with processing through an artificial neural network (ANN). The system is made up of an optical spectrum analyzer (OSA), oscilloscope (OSC), polarimeter (PAX), and the data acquisition automation through transmission control protocol/internet protocol (TCP/IP). A graphic user interface (GUI) was developed for automated data acquisition with the purpose to study the operational characteristics and stability at the passively mode-locked fiber laser (figure-eight laser, F8L) output. Moreover, the evolution of the polarization state and the behavior of the pulses are analyzed when polarization is changed by proper control plate adjustments. The data is processed using deep learning techniques, which provide the characteristics of the pulse at the output. Therefore, the parameter classification-identification is in accordance with the input polarization tilt used for the laser optimization.

Mobbing-like response to secondary predator cues is not a form of teaching in meerkats

Across many taxa, individuals learn how to detect, recognise and respond to predators via social learning. Learning to recognise and interpret predator cues is essential in the accurate assessment of risk. Cues can come directly from a predator’s presence (visual, acoustic) or from secondary predator cues (SPCs, such as hair/feathers, urine or faeces) left in the environment. Animals show various responses to encountering SPCs, which are thought to act in reducing risk to the individual. Meerkats, Suricata suricatta, show a response to SPCs not described in any other species: they display a mobbing-like behaviour. The function of this behaviour is unclear as unlike mobbing, the response it so closely resembles, it cannot serve to drive predators away. We used experiments to investigate whether adults may use this mobbing-like response to teach naïve young how to recognise and respond to predators. Meerkats are known to teach pups hunting skills, but there is as yet no evidence that any species other than humans teaches across multiple contexts. We used experimental presentations of SPCs to test whether wild adult meerkats respond more intensely to SPCs in the presence of naïve pups, as would be expected if the behaviour serves to promote learning. Contrary to this prediction, response intensity was lower when pups were present than when they were absent, and declined as the number of pups in the group increased, possibly due to costs associated with foraging with dependent young. Response intensity instead increased with increasing group size, number of group members interacting with the cue, and varied with predator cue type. These results suggest that the mobbing-like response to SPCs is not a form of teaching in meerkats. Instead, this behaviour may function to increase the recruitment of others to investigate the SPC. Exposing group members to SPCs may better inform them of the nature of the threat, facilitating more effective defensive group responses.

Statistical method of steepest improvement of response

A new statistical method for response steepest improvement is proposed. This method is based on an initial experiment performed on two-level factorial design and first-order statistical linear model with coded numerical factors and response variables. The factors for the runs of response steepest improvement are estimated from the data of initial experiment and determination of the conditional extremum. Confidence intervals are determined for those factors. The first-order polynomial response function fitted to the data of the initial experiment makes it possible to predict the response of the runs for response steepest improvement. The linear model of the response prediction, as well as the results of the estimation of the parameters of the linear model for the initial experiment and factors for the experiments of the steepest improvement of the response, are used when finding prediction response intervals in these experiments. Kknowledge of the prediction response intervals in the runs of steepest improvement of the response makes it possible to detect the results beyond their limits and to find the limiting values of the factors for which further runs of response steepest improvement become ineffective and a new initial experiment must be carried out.

Optimization of Formula Film based on Amylopectin Cassava Starch and Carrageenan as a Raw Materials of Capsule Shell

Capsules are very important in the packaging of pharmaceutical preparations. Commercial capsule shell is generally made of gelatin from cows and pigs. Alternatives to gelatin from non-animal raw materials can be obtained from polysaccharides like starch and carrageenan. The purpose of this study was to obtain the optimum formula between amylopectin and carrageenan as a raw material subtitute for gelatin capsule shell. Program Design Expert 7.0.0 (trial version) with Response Surface Methodology (RSM) Central Composite Design was used to optimize formula with three variable factors and three response variables. Based on the analysis by determining the adjusted range, program recommends 29 optimization solution with desirability value 1. Formula 6 and 28 was selected for validation with factors 1,01% of amylopectin, 1.01% of carrageenan, 2.17% of glycerin (formula 6) and 3.00% of amylopectin, 2.00% of carrageenan, 2.90% of glycerin (formula 28). Prediction response value was 12.94% of moisture content, 6.35% of ash content (formula 6) and 12.99% of moisture content, 8.67% of ash content (formula 28). Validation result value was 21.45% of moisture content, 7.58% of ash content, 6.12 minutes of solubility in water (formula 6) and 17.67% of moisture content, 7.78% of ash content, 9.30 minutes of solubility in water.