Inhibition of stress corrosion cracking in 304 stainless steel through titanium ion implantation

The effects of titanium ion implantation on the stress corrosion cracking (SCC) behaviour of 304 austenitic stainless steel were studied. Slow strain rate tests (SSRTs) were conducted on 304 steel in air and in 5?wt-% NaCl solution. The microscopic effects of ion implantation were evaluated by Stopping and Range of Ions in Matter Procedures (SRIM). Fracture morphologies and microstructures were investigated by scanning electron microscopy (SEM) and transmission electron microscopy (TEM). The fracture surfaces illustrate that ion implantation significantly inhibits the corrosion pits that initiate SCC. A dense passive film, which inhibits SCC, was formed during the ion implantation process. SCC initiation was restrained due to the dense dislocation nets that were generated by titanium ion implantation.

Highlights

• Ion implantation inhibits SCC susceptibility.

• The lack of Cr at the grain boundary leads to the expansion of SCC along the grain boundary.

• Implantation-induced damage leads to high-density dislocations.

• The surface was amorphised due to high-density dislocations.

     

Genetic algorithm to simultaneously optimise stope sequencing and equipment dispatching in underground short-term mine planning under time uncertainty

ABSTRACT

The main issue in short-term planning optimisation for underground mining is organising the mining process with limited resources in the form of equipment and materials to satisfy production targets and stable feed grade requirements. In this paper, an integrated optimisation model is proposed based on an individual generation algorithm and an improved Genetic Algorithm to simultaneously optimise stope extraction sequencing and timing, extracted ore grade and equipment dispatching. The model objectives are to shorten the time gap between the stope mining processes and the overall working time. When the uncertainty of equipment working time is taken into account in a short-term scheduling model, the Monte Carlo simulation is applied to evaluate the risk of not meeting the production target. A modification strategy is defined to evaluate equipment failure. Consequently, any available equipment is automatically reassigned to the mining site to replace the broken-down equipment. A case study is used to validate the model in the Sanshandao gold mine of China to formulate an optimal monthly schedule. Compared with the conventional approach, the new model could reduce the variance of ore tonnage and feed grade and improve the equipment allocation efficiency. Discussions are presented to address the uncertainty.     

Analog active filter design using a multi objective genetic algorithm

The selection of the correct values for passive elements, resistors, and capacitors, is an important task in analog active filter design. The classic method of choosing passive elements is a difficult task and can lead to errors. To reduce the incidence of error and human effort evolutionary optimization techniques are used to select the values of capacitors and resistors. However, due to the single objective optimization technique, these are not well suited to optimize different filter parameters. For this reason, the performance of a multi-objective genetic algorithm named non-dominated sorting genetic algorithm II (NSGA-II) against the different single objective algorithms is evaluated. Two analog active filters: A fourth order Butterworth and a second order state variable filter with the operational amplifiers in their cores are used for testing purposes. In both cases two different objects are chosen along with eight components as variables to be optimized. The component values are compatible with the E12, E24 and E96 series using NSGA-II. The computation results are better in terms of design error and allow for better resistor and capacitor choice. To reach the same or better results the NSGA-II needs fewer generations compared with other genetic algorithms for this problem.     

基于改进正弦余弦算法的无线传感器节点部署优化

为了提高无线传感器网络（WSN）的性能，提出了一种基于改进正弦余弦算法（ESCA）的节点部署优化方法。首先，引入双曲正弦调节因子和动态余弦波权重系数，以平衡算法的全局探索与局部开发能力；然后，提出了一种基于拉普拉斯和高斯分布的变异策略，避免算法陷入局部最优。对于基准函数的优化实验结果表明，ESCA相比引力搜索算法、鲸鱼优化算法、基本正弦余弦算法（SCA）及其改进算法具有更高的收敛精度和收敛速度。最后，将ESCA应用于WSN节点部署优化，结果表明其优化覆盖率相比改进粒子群优化算法、外推人工蜂群算法、改进灰狼优化算法和自适应混沌量子粒子群算法分别提高了1.55个百分点、7.72个百分点、2.99个百分点和7.63个百分点，用更少节点便可达到相同目标精度。     

Consumer context-specific sensory acceptance tests: Effects of a cognitive warm-up on affective product discrimination

For simple hedonic ratings, product range effects often limit the reliability and accuracy of consumer affective discriminations among multiple products. In the present study, a cognitive warm-up (CWU) procedure was applied to consumer acceptance tests in order to stabilize consumers’ evaluative criteria by evoking their personal evaluation context. The effects of such procedure on the robustness of the affective product discriminations were investigated using two sets of comparison experiments, each using a related samples design. Consumers with equivalent sensory preference profile for skin lotions were screened and divided into High Reflection Thinkers (HRT) and Low Reflection Thinkers (LRT) using the Cognitive Reflection Test. They were then randomly, but evenly assigned to either a treatment (Group I) or a control (Group II) group, in which hedonic rating was performed either with or without the preceding CWU procedure, thus forming four sub-groups (treatment group-HRT, treatment group-LRT, control group-HRT, and control group-LRT subjects). For each sub-group, discriminability between the two chosen products in terms of d′ value was compared across the two experimental conditions consisting of different product ranges. The results indicated that only in the control group-LRT subjects, the two conditions displayed different product discrimination. It suggests that for LRT subjects who are assumed to be more intuitive and thus might be more vulnerable to such product range effects, the CWU had a stabilizing effect on the evaluative criteria, resulting in more robust product discrimination.     

A modified cross-correlation algorithm for PIV image processing of particle-fluid two-phase flow

PIV (Particle Image Velocimetry) technique for flow field measurement has achieved popular self-identify through over ten years development, and its application range is becoming wider and wider. PIV post-processing techniques have a great influence on the success of particle-fluid two-phase flow field measurement and thus become a hot and difficult topic. In the present study, a Phase Respective Identification Algorithm (PRIA) is introduced to separate low-density solid particles or bubbles and high-density tracer particles from the PIV image of particle-fluid two-phase flow. PTV (Particle Tracking Velocimetry) technique is employed to calculate the velocity fields of low-density solid particles or bubbles. For the velocity fields of high-density solid particles or bubble phase and continuous phase traced by high-density smaller particles, based on the thought of wavelet transform and multi-resolution analysis and the theory of cross-correlation of image, a delaminated processing algorithm (MCCWM) is presented to conquer the limitation of conventional Fourier transform. The algorithm is firstly testified on synthetic two-phase flows, such as uniform steady flow, shearing flow and rotating flow, and the computational results from the simulated particle images are in reasonable agreement with the given simulated data. The algorithm is then applied to images of actual bubble-liquid two-phase flow and jet flow, and the results also confirmed that the algorithm proposed in the present study has good performance and reliability for post-processing PIV images of particle-fluid two-phase flow.     

Machine learning algorithm selection for windage alteration fault diagnosis of mine ventilation system

Machine learning algorithms have been widely used in mine fault diagnosis. The correct selection of the suitable algorithms is the key factor that affects the fault diagnosis. However, the impact of machine learning algorithms on the prediction performance of mine fault diagnosis models has not been fully evaluated. In this study, the windage alteration faults (WAFs) diagnosis models, which are based on K-nearest neighbor algorithm (KNN), multi-layer perceptron (MLP), support vector machine (SVM), and decision tree (DT), are constructed. Furthermore, the applicability of these four algorithms in the WAFs diagnosis is explored by a T-type ventilation network simulation experiment and the field empirical application research of Jinchuan No. 2 mine. The accuracy of the fault location diagnosis for the four models in both networks was 100%. In the simulation experiment, the mean absolute percentage error (MAPE) between the predicted values and the real values of the fault volume of the four models was 0.59%, 97.26%, 123.61%, and 8.78%, respectively. The MAPE for the field empirical application was 3.94%, 52.40%, 25.25%, and 7.15%, respectively. The results of the comprehensive evaluation of the fault location and fault volume diagnosis tests showed that the KNN model is the most suitable algorithm for the WAFs diagnosis, whereas the prediction performance of the DT model was the second-best. This study realizes the intelligent diagnosis of WAFs, and provides technical support for the realization of intelligent ventilation.     

Elp3 modulates neural crest and colorectal cancer migration requiring functional integrity of HAT and SAM domains

Cell migration is a finely tuned biological process that often involves epithelial-mesenchymal transition (EMT). EMT is typically characterized by the upregulation of mesenchymal markers such as Snail1. This process has been shown to be of critical importance to normal developmental processes, including neural crest migration and invasion. Interestingly, similar mechanisms are utilized in disease processes, such as tumor metastasis and migration. Notably, EMT and EMT-like processes confer tumor cells with the ability to migrate, invade, and adopt stem cell-like properties that largely account for immunosuppression and tumor recurrence. Therefore, identifying sensitive EMT markers may contribute to cancer prognosis and diagnosis in many facets. Previously, we showed that Elp3 plays an essential role during neural crest migration by stabilizing Snail1. In the current study, we further elucidate the molecular mechanism underlying colorectal cancer migration. We found that mElp3 exerted an identical function to xElp3 in modulating neural crest migration, and both HAT and SAM domains are imperative during this migratory process. Interestingly, overexpression of mElp3 in SW480 cells promoted cell migration and invasion, and we further showed that Elp3 stabilized Snail1 requiring integrity of both SAM and HAT domains. Our findings warrant further exploration of the specific target of Elp3 in cancer cells.     

Cetyltrimethylammonium bromide inhibits the metastasis of breast cancer to the lungs by inhibiting epithelial–mesenchymal transition

Breast cancer is a highly aggressive cancer in females. Metastasis is a major obstacle to the efficient and successful treatment of breast cancer. Cetyltrimethylammonium bromide (CTAB) has anti-tumor effects on a variety of tumors. We showed that CTAB inhibits the metastasis of breast cancer to the lungs both in vitro and in vivo. Epithelial-mesenchymal transition (EMT) is thought to be one of the major processes mediating breast cancer metastasis. We found that CTAB suppressed EMT and regulated the levels of the classical EMT markers E-cadherin, N-cadherin, vimentin, Snail and Twist1. Moreover, as a candidate anti-tumor agent, CTAB showed primary safety in vivo. Taken together, our results suggest that CTAB inhibits the migration of primary breast cancer to the lungs. Our findings confirm the clinical potential of CTAB for the treatment of breast cancer by targeting EMT. CTAB may thus be a promising novel anti-tumor drug for the treatment of breast cancer metastasis.     

A study on typology of areas densely populated with foreigners in Seoul,South Korea

This study identifies the types of areas in Seoul where foreigners are concentrated by reflecting the characteristics of their region of origin and residence in Seoul. Lack of cultural understanding and uniform policies for various immigrant communities are causing some social issues. Hence, this study aims to prepare the basic data from which the government can predict changes in ethnic areas and prepare policies suitable for each derived type. The 14 most populous immigrant communities in Seoul were investigated, and cluster analysis and ANOVA test were performed. This examination resulted in four immigrant community types. Each type has specific characteristics, which have influenced the creation and change of the built environment. Types 1 and 4 are collectivistic. These types gradually tend to change urban environments to emulate the environment in their region of origin. In addition, they make little effort to improve their degraded environments, and such areas become increasingly backward. In contrast, types 2 and 3 are individualistic and prefer to assimilate into a migrant region. The formation and change of the urban environment for each type can be predicted on the basis of these characteristics. From this information, the government can prepare an appropriate urban architecture policy for each type.