Multi-Slice DCE-MRI Data Using P760 Distinguishes Between Metastatic and Non-Metastatic Rodent Prostate Tumors

An intermediate molecular weight contrast agent P760 was used to investigate the ability of multi-slice dynamic contrast-enhanced MRI (DCE-MRI) to distinguish metastatic from non-metastatic rodent prostate tumors. Non-metastatic AT2.1 and metastatic AT3.1 prostate tumors originally derived from the Dunning prostate cancer model were implanted on the hind leg of Copenhagen rats. Multi-sliced DCE-MRI data were acquired on a SIGNA 1.5 T scanner and analyzed using a recently developed empirical mathematical model. The P760 multi-slice DCE-MRI data in combination with the empirical mathematical model successfully distinguished between metastatic and non-metastatic rodent prostate tumors. Specifically, fitting the data with the empirical model showed that metastatic tumors had significantly faster contrast media uptake (p<0.001) and slower washout rates (p<0.01) than non-metastatic tumors     

Topology optimization for hybrid additive-subtractive manufacturing

Hybrid additive-subtractive manufacturing is gaining popularity by making full use of geometry complexity produced by additive manufacturing and dimensional accuracy derived from subtractive machining. Part design for this hybrid manufacturing approach has been done by trial-and-error, and no dedicated design methodology exists for this manufacturing approach. To address this issue, this work presents a topology optimization method for hybrid additive and subtractive manufacturing. To be specific, the boundary segments of the input design domain are categorized into two types: (i) Freeform boundary segments freely evolve through the casting SIMP method, and (ii) shape preserved boundary segments suppress the freeform evolvement and are composed of machining features through a feature fitting algorithm. Given the manufacturing strategy, the topology design is produced through additive manufacturing and the shape preserved boundary segments will be processed by post-machining. This novel topology optimization algorithm is developed under a unified SIMP and level set framework. The effectiveness of the algorithm is proved through a few numerical case studies.     

Enrollment Management Model: Artificial Neural Networks versus Logistic Regression

This paper presents an enrollment management model by applying artificial neural network (ANN). The aim of the research, which has been presented in this paper, is to show that ANNs are more successful in predicting than the classical statistical method – regression analysis (logistic regression). Both predictive models, no matter whether they are based on ANNs or logistic regression, offer satisfactory predictive results, and they can offer support in the decision-making process. However, the model based on neural networks shows certain advantages. ANNs demand understanding of functional connection between independent and dependent variables in order to evaluate the model. Also, they adapt easily to related independent variables, without the appearance of the problem of multicollinearity. In contrast to logistic regression, neural networks can recognize the appearance of nonlinearity and interactions in input data, and they can react on time.     

Adaptive passive control with varying time delay

This paper presents a passive control scheme for a force reflecting bilateral teleoperation system with a varying time communication delay. To improve the stability and performance of the system, the master and slave must be coupled dynamically via a transmission network through which the force and velocity are communicated bilaterally. However, the time delay caused by various factors, such as the transmission distance, network congestion, and communication bandwidth, is a long-standing impediment to bilateral control that can destabilize the system. In this study, we investigated how a varying time delay affects the stability of a teleoperation system. A new optimal adaptive approach based on a passive control scheme was designed bilaterally for both the master and slave sites. Extra variables were transmitted together with the wave variables in the scattering system. The proposed scheme achieved both passive control, and an acceptable tracking performance. The tracking performance was demonstrated using a computer simulation of varying time delays in a bilateral teleoperation system.     

Influence of Carbon Content and Solidification Condition on Incipient Melting of DS Superalloy MAR-M200+2Hf

Hf lowers the incipient melting temperatureof superalloy. As carbon content in Hf-bearingalloy decreases, the incipient melting temperaturedrops furthey. PD ingots have stronger tendencyto incipient melting than HRS ones. Even thoughin PD ingot, the sensitivities at both endsof the ingot are quite different. The meltingof Ni_5Hf phase may be considered as one of themain factors affecting incipient melting. Themore Ni_5Hf the alloy contains, the more seriousthe incipient melting becomes. The results ofdifferential thermal analysis (DTA) have provedthat the peak of 1135-1160℃ corresponds tothe melting range or Ni_5Hf. By means of apretreatment at 1150℃, 8h, Ni_5Hf phase can beeliminated in two ways: the reactionNi_5Hf+γ(C)→MC_(2)+γ and solid solution, andtherefore the final solid solution treatmentcan be carried out at 1260℃. This brings abouta high homogenized structure and further increasesthe stress rupture properties of the alloy at1040℃, 140 MPa.     

Fracture Analysis of Composites in High-speed Tension

In this paper the results of a high-speedtension experiment of the SiC_w/Al compositehave been reported and a simplified theoreticalmodel has been developed to study the fracturemechanism of composites in high-speed tension.This theoretical model provides a new explanationfor the increase of dynamic fracture strengthof composites in high-speed tension.     

An adaptive clinical decision support system for serving the elderly with chronic diseases in healthcare industry

With the increasing ageing population worldwide, providing effective nursing care planning in nursing homes is important in meeting the expectations of elderly patients and in streamlining the healthcare information process, hence maintaining high‐quality services. Instead of the traditional manual nursing care planning formulation based on expert experience and subjective judgement, this paper describes an adaptive decision support system, namely, the cloud‐based nursing care planning system, to enable decision making in formulating nursing care strategies. By integrating cloud computing technology and the case‐based reasoning (CBR) technique, medical records and documents pertaining to the elderly can be captured in real time, whereas appropriate treatment plans based on past similar treatment records can be formulated. However, the current case adaptation processes in CBR rely on domain experts to modify retrieved cases, which may not satisfy the needs of the elderly. Therefore, text mining is integrated in the case adaptation process of CBR for extracting up‐to‐date medical information from the Internet so that its efficiency can be improved. By conducting a pilot study in a nursing home, it was shown that the time for formulating applicable treatment plans for elderly patients can be reduced, and the service satisfaction level can be enhanced.     

A slip model for micro/nano gas flows induced by body forces

A slip model for gas flows in micro/nano-channels induced by external body forces is derived based on Maxwell’s collision theory between gas molecules and the wall. The model modifies the relationship between slip velocity and velocity gradient at the walls by introducing a new parameter in addition to the classic Tangential Momentum Accommodation Coefficient. Three-dimensional Molecular Dynamics simulations of helium gas flows under uniform body force field between copper flat walls with different channel height are used to validate the model and to determine this new parameter.