Arsenic Removal and Iron Recovery from Arsenic-Bearing Iron Ores by Calcification-Magnetic Roasting and Magnetic Separation Process.

Extracting iron while minimizing the health and environmental risks associated with arsenic contamination necessitates the removal of arsenic from arsenic-bearing iron ores to ensure a safe and sustainable supply of this metal for industries. The beneficiation of iron minerals and arsenic-bearing minerals from arsenic-bearing iron ores with a calcification-magnetizing roasting and low-intensity magnetic separation (CMR-LMS) process is investigated in this work. The results show that the process is successful in extracting iron minerals and eliminating arsenic-containing minerals. The roasting involves two key steps: calcification and magnetizing, which change hematite and goethite into magnetite and arsenic-bearing minerals into calcium arsenates. The process's separation efficiency of the CMR-LMS is closely linked to the parameters such as roasting temperature, roasting time, coke, alkalinity, and the liberation of gangue minerals from iron minerals. Through grinding and secondary magnetic separation, the iron minerals and gangue components, as well as arsenic, in roasted sand can be further separated. The optimum procedure results in a high-grade iron concentrate with an iron assay of 65.65%, an Fe recovery rate of 80.07%, and an arsenic content of 0.085%, while achieving a 93.29% As removal rate from the original ore that has 45.32% Fe and 0.70% As.

Low Latency and Sparse Computing Spiking Neural Networks With Self-Driven Adaptive Threshold Plasticity.

Spiking neural networks (SNNs) have captivated the attention worldwide owing to their compelling advantages in low power consumption, high biological plausibility, and strong robustness. However, the intrinsic latency associated with SNNs during inference poses a significant challenge, impeding their further development and application. This latency is caused by the need for spiking neurons to collect electrical stimuli and generate spikes only when their membrane potential exceeds a firing threshold. Considering the firing threshold plays a crucial role in SNN performance, this article proposes a self-driven adaptive threshold plasticity (SATP) mechanism, wherein neurons autonomously adjust the firing thresholds based on their individual state information using unsupervised learning rules, of which the adjustment is triggered by their own firing events. SATP is based on the principle of maximizing the information contained in the output spike rate distribution of each neuron. This article derives the mathematical expression of SATP and provides extensive experimental results, demonstrating that SATP effectively reduces SNN inference latency, further reduces the computation density while improving computational accuracy, so that SATP facilitates SNN models to be with low latency, sparse computing, and high accuracy.

Fast Simulation of Lipid Vesicle Deformation Using Spherical Harmonic Approximation.

Lipid vesicles appear ubiquitously in biological systems. Understanding how the mechanical and intermolecular interactions deform vesicle membranes is a fundamental question in biophysics. In this article we develop a fast algorithm to compute the surface configurations of lipid vesicles by introducing surface harmonic functions to approximate the membrane surface. This parameterization allows an analytical computation of the membrane curvature energy and its gradient for the efficient minimization of the curvature energy using a nonlinear conjugate gradient method. Our approach drastically reduces the degrees of freedom for approximating the membrane surfaces compared to the previously developed finite element and finite difference methods. Vesicle deformations with a reduced volume larger than 0.65 can be well approximated by using as small as 49 surface harmonic functions. The method thus has a great potential to reduce the computational expense of tracking multiple vesicles which deform for their interaction with external fields.