A Neural Network Decision Method for Software Maintenance Life Cycle Identification

The software maintenance life cycle concept is a powerful model in helping software maintenance planning. The operationalization of the life cycle concept requires a heuristic decision method. Although the heuristic decision method works most of the time, the method requires integration of different tools and sometimes leads to errors. In this paper, we propose a neural network decision method, which combines data smoothing and maintenance stage identification into one unit

Liver Development, Regeneration, and Carcinogenesis

The identification of putative liver stem cells has brought closer the previously separate fields of liver development, regeneration, and carcinogenesis. Significant overlaps in the regulation of these processes are now being described. For example, studies in embryonic liver development have already provided the basis for directed differentiation of human embryonic stem cells and induced pluripotent stem cells into hepatocyte-like cells. As a result, the understanding of the cell biology of proliferation and differentiation in the liver has been improved. This knowledge can be used to improve the function of hepatocyte-like cells for drug testing, bioartificial livers, and transplantation. In parallel, the mechanisms regulating cancer cell biology are now clearer, providing fertile soil for novel therapeutic approaches. Recognition of the relationships between development, regeneration, and carcinogenesis, and the increasing evidence for the role of stem cells in all of these areas, has sparked fresh enthusiasm in understanding the underlying molecular mechanisms and has led to new targeted therapies for liver cirrhosis and primary liver cancers

Computational Modeling of Pathophysiologic Responses to Exercise in Fontan Patients

Reduced exercise capacity is nearly universal among Fontan patients. Although many factors have emerged as possible contributors, the degree to which each impacts the overall hemodynamics is largely unknown. Computational modeling provides a means to test hypotheses of causes of exercise intolerance via precisely controlled virtual experiments and measurements. We quantified the physiological impacts of commonly encountered, clinically relevant dysfunctions introduced to the exercising Fontan system via a previously developed lumped-parameter model of Fontan exercise. Elevated pulmonary arterial pressure was observed in all cases of dysfunction, correlated with lowered cardiac output (CO), and often mediated by elevated atrial pressure. Pulmonary vascular resistance was not the most significant factor affecting exercise performance as measured by CO. In the absence of other dysfunctions, atrioventricular valve insufficiency alone had significant physiological impact, especially under exercise demands. The impact of isolated dysfunctions can be linearly summed to approximate the combined impact of several dysfunctions occurring in the same system. A single dominant cause of exercise intolerance was not identified, though several hypothesized dysfunctions each led to variable decreases in performance. Computational predictions of performance improvement associated with various interventions should be weighed against procedural risks and potential complications, contributing to improvements in routine patient management protocol

Accretion onto disk galaxies via hot and rotating CGM inflows

Observed accretion rates onto the Milky-Way and other local spirals fall short of that required to sustain star formation for cosmological timescales. A potential avenue for this unseen accretion is an inflow in the volume-filling hot phase ($\sim10^6$ K) of the circumgalactic medium (CGM), as suggested by some cosmological simulations. We derive an approximate axisymmetric analytic solution of such hot CGM accretion flows, and validate it with hydrodynamic simulations. We show that a hot inflow spins up as it approaches the galaxy, while remaining hot, subsonic and quasi-spherical. At the radius of angular momentum support ($\approx15$ kpc for the Milky-Way) the hot flow flattens into a disk geometry and then cools from $\sim10^6$ K to $\sim10^4$ K at the disk-halo interface. Cooling affects all hot gas, rather than just a subset of individual gas clouds, implying that accretion via hot inflows does not rely on local thermal instability in contrast with 'precipitation' models for galaxy accretion. Prior to cooling and accretion the inflow completes $\sim t_{\rm cool}/t_{\rm ff}$ radians of rotation, where $t_{\rm cool}/t_{\rm ff}$ is the cooling time to free-fall time ratio in hot gas immediately outside the galaxy. The ratio $t_{\rm cool}/t_{\rm ff}$ may thus govern the development of turbulence and enhancement of magnetic fields in gas accreting onto low-redshift spirals. We argue that accretion via hot inflows can explain the observed truncation of nearby thin stellar disks at $\approx4$ disk radii. We also show that if rotating hot inflows are common in Milky-Way size disk galaxies, as predicted, then signatures should be observable with X-ray telescopes, kinetic SZ measurements, and FRB surveys.Comment: 19 pages, 11 figures, submitted to MNRA

New hybrid FMADM model for mobile commerce improvement

Internet of things (IoT) can provide an extensive scope of services via smart devices to promote the convenience of life. With advances being made in smart phones, enterprises are increasingly considering expanding their customer base through mobile commerce services. To promote m-commerce improvement, enterprises should organize an excellent m-commerce environment and attempt to realize user needs in the era of IoT. In a fuzzy environment of the real world, objective decision-making for m-commerce improvement is usually a FMADM problem involving feedback-effect and interdependence among the dimensions and criteria. But, many traditional decision models cannot conduct the complicated interrelationships among dimensions and criteria. This study proposes an improvement model that can promote m-commerce improvement towards achieving the aspiration level in fuzzy environment. The proposed hybrid model conducts the feedback-effect and dependence among attributes, and it combines the FDEMATEL technique, FDANP, and MFGRA methods. The empirical case study was conducted to prove the utility of the new hybrid FMADM model in evaluating an m-commerce environment. Comparative results exhibited that the proposed approach is superior to the traditional method and that it can obtain most real grey relational degree that can be used for establishing the best performance improvement strategy in reality

Towards next generation, low cost, hydrogen resilient austenitic steels: Relating composition, microstructure and deformation modes across length

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A data-driven MADM model for personnel selection and improvement

Personnel selection and human resource improvement are characteristically multiple-attribute decision-making (MADM) problems. Previously developed MADM models have principally depended on experts’ judgements as input for the derivation of solutions. However, the subjectivity of the experts’ experience can have a negative influence on this type of decision-making process. With the arrival of today’s data-based decision-making environment, we develop a data-driven MADM model, which integrates machine learning and MADM methods, to help managers select personnel more objectively and to support their competency improvement. First, RST, a machining learning tool, is applied to obtain the initial influential significance-relation matrix from real assessment data. Subsequently, the DANP method is used to derive an influential significance-network relation map and influential weights from the initial matrix. Finally, the PROMETHEE-AS method is applied to assess the gap between the aspiration and current levels for every candidate. An example was carried out using performance data with evaluation attributes obtained from the human resource department of a Chinese food company. The results revealed that the data-driven MADM model could enable human resource managers to resolve the issues of personnel selection and improvement simultaneously, and can actually be applied in the era of big data analytics in the future. First published online 15 May 202