A meta-parameter tuning model to improve the genetic algorithms design of labeling diversity mappers

Evolutionary algorithms (EAs) have recently been applied to Uncoded Space-Time Labeling Diversity (USTLD) systems to produce labeling diversity mappers. However, the most challenging task is choosing the best parameter setting for the EA to create a more ‘optimal’ mapper design. This paper proposes a ‘meta-Genetic Algorithm (GA)’ used to tune hyperparameters for the Labeling Diversity EA. The algorithm is examined on 16, 32 and 64QAM; 32 and 64PSK; 16, 32 and 64APSK and 16APSK constellations that do not show diagonal symmetry. Furthermore, the meta-GA settings and original GA settings are compared in terms of the number of generations taken to converge to a solution. For QAM constellations, the output using the meta-GA settings matched but did not improve with the original settings. However, the number of generations needed to converge to a solution took 120 times less than the number of generations using the original settings. In the 64PSK constellation, a diversity gain of [Formula: see text][Formula: see text]dB was observed while improving on the actual fitness value from 0.0575 to 0.0661. Similarly, with 32APSK constellation, an improvement in fitness value from 0.1457 to 0.1748 was made while showing diversity gains of [Formula: see text][Formula: see text]dB. 64APSK constellation fitness value improved from 0.0708 to 0.0957, and a [Formula: see text][Formula: see text]dB gain was observed. The most significant improvement was made by the asymmetric 16APSK constellation, with gains of [Formula: see text][Formula: see text]dB and increasing its fitness value three times (0.0981 to 0.3000). A study of the effects of optimizing the GA parameters shows that the number of swaps during crossover [Formula: see text] and the radius [Formula: see text] were the two most important variables to optimize when executing this GA.

The automatic parameter-exploration with a machine-learning-like approach: Powering the evolutionary modeling on the origin of life

The origin of life involved complicated evolutionary processes. Computer modeling is a promising way to reveal relevant mechanisms. However, due to the limitation of our knowledge on prebiotic chemistry, it is usually difficult to justify parameter-setting for the modeling. Thus, typically, the studies were conducted in a reverse way: the parameter-space was explored to find those parameter values “supporting” a hypothetical scene (that is, leaving the parameter-justification a later job when sufficient knowledge is available). Exploring the parameter-space manually is an arduous job (especially when the modeling becomes complicated) and additionally, difficult to characterize as regular “Methods” in a paper. Here we show that a machine-learning-like approach may be adopted, automatically optimizing the parameters. With this efficient parameter-exploring approach, the evolutionary modeling on the origin of life would become much more powerful. In particular, based on this, it is expected that more near-reality (complex) models could be introduced, and thereby theoretical research would be more tightly associated with experimental investigation in this field–hopefully leading to significant steps forward in respect to our understanding on the origin of life.

A Simulation Analyzing Approach to Estimating the Probability of Airborne Infection Risks in Railway Station Platform Coupling with the Wells-Riley Model and Pathfinder Model

Railway station platforms present a particular challenge, especially during a train departure or arrival where some passengers may have potential conditions that make them vulnerable to airborne infections due to the high density and close proximity of passengers. This study presented a simulation analyzing approach to estimating the probability of airborne infection risks in station platform spaces coupling with the Wells-Riley model and Pathfinder model. We examine the impact of overcrowded area of the station platform on infection rates under various traces of evacuation. The result of the potential risk for three modes is discussed, and the results of the standard model under the same parameter setting are optimised. Next, the impact of the ventilated volume based on uneven distribution of individuals and the exposure time based on evacuation on the infection risk in platform spaces are studied. The relationship between platform spaces overcrowding and the infection risk provided further insights to observe the supporting information.

Modeling and Simulation of Exit Selection Behavior in Pedestrian Evacuation Based on Information Perception and Transmission

This paper aims to present an improved evacuation model, which is capable of simulating individual exit selection behavior based on the acquisition and processing of information, especially in dangerous and unfamiliar environments. Firstly, an evacuation model was improved by the introduction of a floor field of gas concentration and an exit selection model, considering the congestion avoidance and danger avoidance behavior. Secondly, the process of information perception and transmission was studied and introduced into the model with a set of rules. Finally, real experiments in a simple double-exit room were conducted for model validation and parameter setting, and simulation experiments in scenarios with an unknown hazard or unknown exits were conducted to confirm the necessity and rationality of introducing information perception and transmission. The simulation results show that, with the increase in perception distance or trust extent, the pedestrian safety increases. The critical values of perception distance or trust extent, below which some people cannot acquire any new information, vary depending on the pedestrian density. When the density is high, the influence of perception distance or trust extent reduces, and the probability of reselecting an exit increases, which causes the safety of pedestrians to decrease.

Trajectory Recording and Analysis System for Cockroach Robot

In this study, We design a trajectory recording and analysis system to record and analysis the changes in the movement behavior of the cockroach robot after stimulation. The external hardware of this system is an infrared touchpad as the experimental platform for the cockroach robot to crawl freely, and the infrared matrices densely distributed in the X and Y directions of the infrared touchpad are used to detect and locate the position of the cockroach robot. The cockroach robot's movement trajectory is displayed visually through the projector's interface projection on the infrared touchpad. The system software consists of three main parts: the electrical signal parameter setting module, the movement trajectory recording module, and the data analysis module. The electrical signal parameter setting module sets the stimulation parameters and configures the corresponding serial port to independently stimulate the left and right antenna and cercus of the cockroach; the trajectory recording module is used to record the trajectory of the cockroach robot through the coordinate positioning method. The data analysis module explores the change of motion behavior of the cockroach robot with time after receiving the stimulus by using the stage analysis method, and explores the change of motion of the cockroach robot with different voltage stimulus by using the module analysis method. The system is tested in experiments and the results demonstrated its applicability to the recording and analysis of the cockroach robot's trajectories.

On the Impact of Build Envelope Sizes on E-PBF Processed Pure Iron

In additive manufacturing, the thermal history of a part determines its final microstructural and mechanical properties. The factors leading to a specific temperature profile are diverse. For the integrity of a parameter setting established, periphery variations must also be considered. In the present study, iron was processed by electron beam powder bed fusion. Parts realized by two process runs featuring different build plate sizes were analyzed. It is shown that the process temperature differs significantly, eventually affecting the properties of the processed parts.

A conceptional framework for combining brain mapping and brain stimulation

Here, we introduce a conceptual framework for studies that combine non-invasive transcranial brain stimulation (NTBS) with neuroimaging. We outline the type of neuroscientific questions that can be addressed with a combined NTBS-neuroimaging approach and describe important experimental considerations. Neuroimaging methods differ with respect to their spatiotemporal resolution and reflect different neurobiological aspects of brain function, structure or metabolism. These characteristics need to be carefully considered in order to select the most appropriate neuroimaging modality. NTBS and neuroimaging can be combined concurrently (online) or sequentially (offline). The “online” approach applies neuroimaging while NTBS is delivered to the brain and thus, can reveal the immediate functional effects of NTBS on the targeted brain networks, but one has to deal with interfering effects of NTBS on brain mapping. The “offline” approach applies neuroimaging and NTBS in sequence: Offline neuroimaging can be performed BEFORE the stimulation session to inform NTBS parameter setting or AFTER the stimulation session to provide functional, metabolic or structural readouts of NTBS-effects. Since NTBS and neuroimaging can be separated in space and time, NTBS does not interfere with offline brain mapping. Finally, we discuss how NTBS and neuroimaging are gaining importance in clinical NTBS applications and how both techniques can be iteratively combined to create open-loop setups.

Experimental investigation on cladding with metal cored wire using GMAW process and parametric optimization

Cladding is widely used in manufacturing industries for the production of pressure vessel by depositing thick layer of filler material for providing corrosion resistant-surface. The use of metal cored wire in gas metal arc welding (GMAW) process is popular due to its higher deposition rate and productivity. This work investigates the effect of process parameters on the deposition of cladding layer with ER 309L metal core wire (as filler material) on a corrosion resistant material (IS 2062). The welding parameters viz., wire feed rate (WFR), voltage (V), welding speed (S) and nozzle to plate distance (NTD) are employed as process parameters while penetration (P), bead width (W), reinforcement (R), weld penetration, shape factor (WPSF) and weld reinforcement form factor (WRFF) as welding responses. The predictive model developed for P, W, R, WPSF, and WRFF using the response surface methodology (RSM) approach is found adequate at 95% confidence interval. The validation results for the developed model results in a model accuracy (MA) of 92.82%, 96.34%, 91.47% 88.98% and 87.75% for model P, W, R, WPSF, and WRFF respectively and it shows higher predictability and accuracy. The process parameters are optimized simultaneously with integrated optimization approach using RSM with Jaya algorithm and obtain optimal solution in less than 20 number of iterations. The minimum fitness value obtained as 1.3008 at an optimal parameter setting of WFR=12m/min, V=26V, S=280mm/min, NTD=10mm. The validation result at the optimal parameter setting results in an improvement of 6.45%, 11.29%, 13.58%, 16.07%, 15.38% is noted for P, W, R, WPSF, and WRFF respectively.