In-Vitro Analysis of FeMn-Si Smart Biodegradable Alloy

Special materials are required in many applications to fulfill specific medical or industrial necessities. Biodegradable metallic materials present many attractive properties, especially mechanical ones correlated with good biocompatibility with vivant bodies. A biodegradable iron-based material was realized through electric arc-melting and induction furnace homogenization. The new chemical composition obtained presented a special property named SME (shape memory effect) based on the martensite transformation. Preliminary results about this special biodegradable material with a new chemical composition were realized for the chemical composition and structural and thermal characterization. Corrosion resistance was evaluated in Ringer’s solution through immersion tests for 1, 3, and 7 days, the solution pH was measured in time for 3 days with values for each minute, and electro-corrosion was measured using a potentiostat and a three electrode cell. The mass loss of the samples during immersion and electro-corrosion was evaluated and the surface condition was studied by scanning electron microscopy (SEM) and energy dispersive spectroscopy (EDS). SME was highlighted with differential scanning calorimetry (DSC). The results confirm the possibility of a memory effect of the materials in the wrought case and a generalized corrosion (Tafel and cyclic potentiometry and EIS) with the formation of iron oxides and a corrosion rate favorable for applications that require a longer implantation period.

Retrieval context determines whether event boundaries impair or enhance temporal order memory

Meaningful changes in context create "event boundaries", segmenting continuous experience into distinct episodes in memory. A foundational finding in this literature is that event boundaries impair memory for the temporal order of stimuli spanning a boundary compared to equally spaced stimuli within an event. This seems surprising in light of intuitions about memory in everyday life, where the order of within-event experiences (did I have coffee before the first bite of bagel?) often seems more difficult to recall than the order of events per se (did I have breakfast or do the dishes first?). Here, we aimed to resolve this discrepancy by manipulating whether stimuli carried information about their encoding context during retrieval, as they often do in everyday life (e.g., bagel-breakfast). In Experiments 1 and 2, we show that stimuli inherently associated with a unique encoding context produce a "flipped" order memory effect, whereby temporal memory was superior for cross-boundary than within-event item pairs. In Experiments 3 and 4, we added context information at retrieval to a standard laboratory event memory protocol where stimuli were encoded in the presence of arbitrary context cues (colored frames). We found that whether temporal order memory for cross-boundary stimuli was enhanced or impaired relative to within-event items depended on whether the context was present or absent during the memory test. Taken together, we demonstrate that the effect of event boundaries on temporal memory is malleable, and determined by the availability of context information at retrieval.

Modelling of natural growth with memory effect in economics: An application of adomian decomposition and variational iteration methods

The power-law memory effect is taken into consideration in a generalisation of the economic model of natural growth. The memory effect refers to a process's reliance on its current state and its history of previous changes. However, the study that focuses on natural growth in economics considering the memory effect with fractional order-linear differential equation model is still limited. The current investigation seeks to solve the natural growth with memory effect in the economics model and decide the best model using fractional differential equation (FDE), namely Adomian Decomposition and Variational Iteration Methods. Also, this study assumes the level of consumer loss memory during a certain time interval denoted by a parameter (α). This study showed the model of loss memory effect with 0 < α ≤ 1 given a slowdown in output growth compared to a model without memory effect. Besides that, this study also found that output Y(t) is growing faster with the Variational Iteration method compared to the Adomian decomposition method. Also, using graphical simulation, this study found the output Y(t) is closer to the exact solution with α=0.4 and α=0.9. In conclusion, this study successfully solved natural growth with memory effect in economics and decided the best model between FDE, namely Adomian decomposition and Variational iterative methods using numerical analysis.