Application of artificial neural network method to predict the breakage properties of PGE bearing chromite ore

In the present investigation, systematic grinding experiments were conducted in a laboratory ball mill to determine the breakage properties of low-grade PGE bearing chromite ore. The population balance modeling technique was used to study the breakage parameters such as primary breakage distribution (B_{i, j}) and the specific rates of breakage (S_i). The breakage and selection function values were determined for six feed sizes. The results stated that the breakage follows the first-order grinding kinetics for all the feed sizes. It was observed that the coarser feed sizes exhibit higher selection function values than the finer feed size. Further, an artificial neural network was used to predict breakage characteristics of low-grade PGE bearing chromite ore. The predicted results obtained from the neural network modeling were close to the experimental results with a correlation of determination R² = 0.99 for both product size and selection function.     

Electronic synaptic characteristics and simulation application of Ag/CeO2/Pt memristor

With the development of science and technology, artificial neural networks (ANNs) have become a research spot. Furthermore, two-terminal oxide memristors with adjustable resistance have attracted extensive attention due to their simple structure, low power consumption, and easy integration, among other attractive features. Additionally, among many oxides, ceria has exhibited good performance, such as longer retention and better stability in resistive devices; however, it was currently rarely used in artificial neural synapses. In this work, a self-designed Ag/CeO₂/Pt memristor was found to realize the slow transition between the high-resistance state (HRS) and low-resistance state (LRS) at a very small working voltage. It was also found to exhibit very good retention performance and cyclic characteristics. The conductivity of the device was analyzed by the current-voltage (I–V) characteristics curve. Furthermore, its artificial synaptic function was explored, and a series of neuromorphic systems simulations were carried out. Additionally, the relationships between the pulse sequence parameters and the resistance state of the device were explored, and an electrical signal simulation of Pavlov's dog experiment was designed. The findings demonstrated that the device could be used to realize the application of artificial neural synapse simulation.     

人工智能赋能在线实验教学行为分析

教学行为分析作是教学质量分析的重要组成部分，也是教学引导与反馈机制的重要依据。文章阐述了人工智能（AI）的含义及发展历程，重点分析总结了教学行为分析方法及AI在教学行为分析上的应用。文章以东南大学电工电子在线实验为研究平台，探索分析了AI技术在实验教学行为分析上的可行性，梳理了基于专家系统的在线实验分析系统的设计思路，充分探讨了“智能”教育在实验教学中的深刻内涵。     

Predicting the mechanical properties of Cu–Al2O3 nanocomposites using machine learning and finite element simulation of indentation experiments

Micromechanics model, finite element (FE) simulation of microindentation and machine learning were deployed to predict the mechanical properties of Cu–Al₂O₃ nanocomposites. The micromechanical model was developed based on the rule of mixture and grain and grain boundary sizes evolution to predict the elastic modulus of the produced nanocomposites. Then, a FE model was developed to simulate the microindentation test. The input for the FE model was the elastic modulus that was computed using the micromechanics model and wide range of yield and tangent stresses values. Finally, the output load-displacement response from the FE model, the elastic modulus, the yield and tangent strengths used for the FE simulations, and the residual indentation depth were used to train the machine learning model (Random vector functional link network) for the prediction of the yield and tangent stresses of the produced nanocomposites. Cu–Al₂O₃ nanocomposites with different Al₂O₃ concentration were manufactured using insitu chemical method to validate the proposed model. After training the model, the microindentation experimental load-displacement curve for Cu–Al₂O₃ nanocomposites was fed to the machine learning model and the mechanical properties were obtained. The obtained mechanical properties were in very good agreement with the experimental ones achieving 0.99 coefficient of determination R2 for the yield strength.     

The Age-Sensitive Efficacy of Calorie Restriction on Mitochondrial Biogenesis and mtDNA Damage in Rat Liver

Calorie restriction (CR) is the most efficacious treatment to delay the onset of age-related changes such as mitochondrial dysfunction. However, the sensitivity of mitochondrial markers to CR and the age-related boundaries of CR efficacy are not fully elucidated. We used liver samples from ad libitum-fed (AL) rats divided in: 18-month-old (AL-18), 28-month-old (AL-28), and 32-month-old (AL-32) groups, and from CR-treated (CR) 28-month-old (CR-28) and 32-month-old (CR-32) counterparts to assay the effect of CR on several mitochondrial markers. The age-related decreases in citrate synthase activity, in TFAM, MFN2, and DRP1 protein amounts and in the mtDNA content in the AL-28 group were prevented in CR-28 counterparts. Accordingly, CR reduced oxidative mtDNA damage assessed through the incidence of oxidized purines at specific mtDNA regions in CR-28 animals. These findings support the anti-aging effect of CR up to 28 months. Conversely, the protein amounts of LonP1, Cyt c, OGG1, and APE1 and the 4.8 Kb mtDNA deletion content were not affected in CR-28 rats. The absence of significant differences between the AL-32 values and the CR-32 counterparts suggests an age-related boundary of CR efficacy at this age. However, this only partially curtails the CR benefits in counteracting the generalized aging decline and the related mitochondrial involvement.     

Perception of Artificial Intelligence in Spain

This paper analyses the perception of Artificial Intelligence (AI) by individuals in Spain and the factors associated with it. It uses data from 6308 individuals from a 2018 Spanish survey. A binary logit regression model is formulated and estimated for the attitude towards robots and AI and its possible determinants. As main results are that a gender gap is detected, and that people have a negative attitude if they are not interested in scientific discoveries and technological developments and if AI and robots are not useful at work.     

Optimizing the stability and electrical transport properties of CeNbO4+δ-based oxide ceramics by regulating oxygen ion content

CeNbO_4+δ ceramics have attracted extensive research interest because of their unique mixed ion-electron transport characteristics and interesting structure-functional characteristics caused by the difference in oxygen ion content. Although the change of oxygen ion content brings rich redox properties, it also causes serious crystal transformation and abnormal electrical transport properties. In order to obtain stable structure and excellent electrical transport properties, the directional regulation of the oxygen ion content has been realized through introducing Al₂O₃ and high temperature aging. After 600 h of aging at 1073 K, the prepared composite ceramics not only obtain a stable structure without crystal transformation, but also show good negative temperature coefficient (NTC) thermistor characteristics in the temperature range of 473 K–1273 K, in which the linear fitting maximum Pearson's r of the relationship between lnρ and 1000/T can reach 99.97%. The proposed method provides a new thought for the design and application of high-temperature electronic ceramics.     

Corrosion behaviour of an industrial shot-peened and coated automotive spring steel AISI 9254

A high-strength low-alloy steel, AISI 9254 (54SiCr6), is widely used for suspension spring production in the automotive industry. In this work, industrially manufactured zinc phosphate coated helical springs are subjected to detailed microstructural and surface analysis for better understanding of corrosion evolution. The material’s free corrosion potential and anodic/cathodic behaviour were investigated in NaCl solutions and corrosion propagation mechanisms were studied using potentiostatic polarisation on cross-sectional and external surfaces. The bulk material is fully martensite with uniformly distributed MnS inclusions, while the spring surface has a 2–3?μm mechanically deformed region introduced by shot-peening and a thin zinc phosphate coating. The corrosion open circuit potential of bulk material and shot-peened spring surface was about –0.7V_SCE without significant difference, while phosphated surface is more noble (more positive potential). MnS inclusions, stimulating the anodic attack in the steel, influence corrosion propagation and pit morphology to a large extent that can have an impact on the spring performance.     

Sarcopenia: Molecular Pathways and Potential Targets for Intervention

Aging is associated with sarcopenia. The loss of strength results in decreased muscle mass and motor function. This process accelerates the progressive muscle deterioration observed in older adults, favoring the presence of debilitating pathologies. In addition, sarcopenia leads to a decrease in quality of life, significantly affecting self-sufficiency. Altogether, these results in an increase in economic resources from the National Health Systems devoted to mitigating this problem in the elderly, particularly in developed countries. Different etiological determinants are involved in the progression of the disease, including: neurological factors, endocrine alterations, as well as nutritional and lifestyle changes related to the adoption of more sedentary habits. Molecular and cellular mechanisms have not been clearly characterized, resulting in the absence of an effective treatment for sarcopenia. Nevertheless, physical activity seems to be the sole strategy to delay sarcopenia and its symptoms. The present review intends to bring together the data explaining how physical activity modulates at a molecular and cellular level all factors that predispose or favor the progression of this deteriorating pathology.