Fooled by facts: quantifying anchoring bias through a large-scale experiment

Through a large-scale online field experiment, we provide new empirical evidence for the presence of the anchoring bias in people’s judgement due to irrational reliance on a piece of information that they are initially given. The comparison of the anchoring stimuli and respective responses across different tasks reveals a positive, yet complex relationship between the anchors and the bias in participants’ predictions of the outcomes of events in the future. Participants in the treatment group were equally susceptible to the anchors regardless of their level of engagement, previous performance, or gender. Given the strong and ubiquitous influence of anchors quantified here, we should take great care to closely monitor and regulate the distribution of information online to facilitate less biased decision making.

Improving the energy efficiency of a 6ChN13/15 gas engine with a Miller cycle by optimizing the valve timing

Introduction (problem statement and relevance). The object of research in this work is an inline six-cylinder gas engine 6ChN13/15 with a Miller thermodynamic cycle. On the basis of its computer model studies minimization of the specific effective fuel consumption has been reached due to variation study of gas distribution and air supply systems parameters.The purpose of the study was to investigate the parameters regulation effect of gas distribution and air supply systems on the performance of a 6ChN13/15 gas engine with a Miller cycle on the external speed characteristic basing on numerical modeling.Methodology and research methods. The research was carried out by the method of computer simulation. Numerical modeling was made on the basis of data obtained during a full-scale experiment of a 6ChN13/15 gas engine with Miller thermodynamic cycle.Scientific novelty and results. A comparative analysis of a gas engine optimization results has been carried out. The results obtained will be used to create a gas engine and its further optimization by controlling the working process and the air supply system.Practical significance. The results obtained may be of interest to truck car manufacturers and engine specialists.

Scellseg: a style-aware cell instance segmentation tool with pre-training and contrastive fine-tuning

Deep learning-based cell segmentation is increasingly utilized in cell biology and molecular pathology, due to massive accumulation of diverse large-scale datasets and excellent performance in cell representation. However, the development of specialized algorithms has long been hampered by a paucity of annotated training data, whereas the performance of generalist algorithm was limited without experiment-specific calibration. Here, we present a deep learning-based tool called Scellseg consisted of novel pre-trained network architecture and contrastive fine-tuning strategy. In comparison to four commonly used algorithms, Scellseg outperformed in average precision on three diverse datasets with no need for dataset-specific configuration. Interestingly, we found that eight images are sufficient for model tuning to achieve satisfied performance based on a shot data scale experiment. We also developed a graphical user interface integrated with functions of annotation, fine-tuning and inference, that allows biologists to easily specialize their own segmentation model and analyze data at the single-cell level.

Finite-element simulation of semi-solid metal processing of tool steel encased in carbon steel

The semi-solid metal processing allows for the production of components with complex geometries allied to lower forming forces. In the case of X210Cr12 tool steel, one big advantage of semi-solid metal processing is that it produces a microstructure free of precipitated chromium carbides, resulting in higher resistance to cyclic stresses. However, the application of this process in steels is limited until now due to technical difficulties, such as high temperatures, the necessity of a precise control of temperature, and the narrow liquidus–solidus range. For that reason, a preliminary numerical assessment of the forming procedure is highly welcome, in order to reduce potential errors in the final component. In this paper, we propose a methodology for the numerical simulation of semi-solid metal processing in steel samples under hot compression. We show that it is possible to use multilinear hardening plasticity models whose only input are the flow stress curves of each material. We also show that it is mandatory that these experimental curves are obtained through tensile tests performed at the same temperature as the working/simulated component. To validate the methodology, a full-scale experiment is undertaken so that the deformed sample can be compared to the numerical results. It is concluded that the methodology is suited for the assessment of mechanical quantities during the finite-element analysis of semi-solid processing of steel.

A Possible Neutron-Antineutron Oscillation Experiment at PF1B at the Institut Laue Langevin

We consider a possible neutron–antineutron (n−n¯) oscillation experiment at the PF1B instrument at Institut Laue Langevin. It can improve the best existing constraint on the transition rate and also allow the testing of the methods and instrumentation which would be needed for a later larger-scale experiment at ESS. The main gain factors over the most competitive experiment, performed earlier at PF1 instrument at ILL, are: a more intense neutron beam and a new operating mode based on coherent n and n¯ mirror reflections. The installation of such an experiment would need a temporary replacement of the existing ballistic neutron guide by a specially designed n/n¯ guide with a gradually increasing cross section and a specially selected coating as well as the development and construction of an advanced n¯ annihilation detector with a high efficiency and low background. The overall gain factor could reach up to an order of magnitude and depends on the chosen experiment configuration.