Solving multiple-criteria R&D project selection problems with a data-driven evidential reasoning rule

In this paper, a likelihood based evidence acquisition approach is proposed to acquire evidence from experts'assessments as recorded in historical datasets. Then a data-driven evidential reasoning rule based model is introduced to R&D project selection process by combining multiple pieces of evidence with different weights and reliabilities. As a result, the total belief degrees and the overall performance can be generated for ranking and selecting projects. Finally, a case study on the R&D project selection for the National Science Foundation of China is conducted to show the effectiveness of the proposed model. The data-driven evidential reasoning rule based model for project evaluation and selection (1) utilizes experimental data to represent experts' assessments by using belief distributions over the set of final funding outcomes, and through this historic statistics it helps experts and applicants to understand the funding probability to a given assessment grade, (2) implies the mapping relationships between the evaluation grades and the final funding outcomes by using historical data, and (3) provides a way to make fair decisions by taking experts' reliabilities into account. In the data-driven evidential reasoning rule based model, experts play different roles in accordance with their reliabilities which are determined by their previous review track records, and the selection process is made interpretable and fairer. The newly proposed model reduces the time-consuming panel review work for both managers and experts, and significantly improves the efficiency and quality of project selection process. Although the model is demonstrated for project selection in the NSFC, it can be generalized to other funding agencies or industries.Comment: 20 pages, forthcoming in International Journal of Project Management (2019

Understanding the in vivo Uptake Kinetics of a Phosphatidylethanolamine-binding Agent \u3csup\u3e99m\u3c/sup\u3eTc-Duramycin

Introduction 99mTc-Duramycin is a peptide-based molecular probe that binds specifically to phosphatidylethanolamine (PE). The goal was to characterize the kinetics of molecular interactions between 99mTc-Duramycin and the target tissue. Methods High level of accessible PE is induced in cardiac tissues by myocardial ischemia (30 min) and reperfusion (120 min) in Sprague–Dawley rats. Target binding and biodistribution of 99mTc-duramycin were captured using SPECT/CT. To quantify the binding kinetics, the presence of radioactivity in ischemic versus normal cardiac tissues was measured by gamma counting at 3, 10, 20, 60 and 180 min after injection. A partially inactivated form of 99mTc-Duramycin was analyzed in the same fashion. A compartment model was developed to quantify the uptake kinetics of 99mTc-Duramycin in normal and ischemic myocardial tissue. Results 99mTc-duramycin binds avidly to the damaged tissue with a high target-to-background radio. Compartment modeling shows that accessibility of binding sites in myocardial tissue to 99mTc-Duramycin is not a limiting factor and the rate constant of target binding in the target tissue is at 2.2 ml/nmol/min/g. The number of available binding sites for 99mTc-Duramycin in ischemic myocardium was estimated at 0.14 nmol/g. Covalent modification of D15 resulted in a 9-fold reduction in binding affinity. Conclusion 99mTc-Duramycin accumulates avidly in target tissues in a PE-dependent fashion. Model results reflect an efficient uptake mechanism, consistent with the low molecular weight of the radiopharmaceutical and the relatively high density of available binding sites. These data help better define the imaging utilities of 99mTc-Duramycin as a novel PE-binding agent

Cloud computing resource scheduling and a survey of its evolutionary approaches

A disruptive technology fundamentally transforming the way that computing services are delivered, cloud computing offers information and communication technology users a new dimension of convenience of resources, as services via the Internet. Because cloud provides a finite pool of virtualized on-demand resources, optimally scheduling them has become an essential and rewarding topic, where a trend of using Evolutionary Computation (EC) algorithms is emerging rapidly. Through analyzing the cloud computing architecture, this survey first presents taxonomy at two levels of scheduling cloud resources. It then paints a landscape of the scheduling problem and solutions. According to the taxonomy, a comprehensive survey of state-of-the-art approaches is presented systematically. Looking forward, challenges and potential future research directions are investigated and invited, including real-time scheduling, adaptive dynamic scheduling, large-scale scheduling, multiobjective scheduling, and distributed and parallel scheduling. At the dawn of Industry 4.0, cloud computing scheduling for cyber-physical integration with the presence of big data is also discussed. Research in this area is only in its infancy, but with the rapid fusion of information and data technology, more exciting and agenda-setting topics are likely to emerge on the horizon

Modeling, Analysis, and Optimization of Grant-Free NOMA in Massive MTC via Stochastic Geometry

Massive machine-type communications (mMTC) is a crucial scenario to support booming Internet of Things (IoTs) applications. In mMTC, although a large number of devices are registered to an access point (AP), very few of them are active with uplink short packet transmission at the same time, which requires novel design of protocols and receivers to enable efficient data transmission and accurate multi-user detection (MUD). Aiming at this problem, grant-free non-orthogonal multiple access (GF-NOMA) protocol is proposed. In GF-NOMA, active devices can directly transmit their preambles and data symbols altogether within one time frame, without grant from the AP. Compressive sensing (CS)-based receivers are adopted for non-orthogonal preambles (NOP)-based MUD, and successive interference cancellation is exploited to decode the superimposed data signals. In this paper, we model, analyze, and optimize the CS-based GF-MONA mMTC system via stochastic geometry (SG), from an aspect of network deployment. Based on the SG network model, we first analyze the success probability as well as the channel estimation error of the CS-based MUD in the preamble phase and then analyze the average aggregate data rate in the data phase. As IoT applications highly demands low energy consumption, low infrastructure cost, and flexible deployment, we optimize the energy efficiency and AP coverage efficiency of GF-NOMA via numerical methods. The validity of our analysis is verified via Monte Carlo simulations. Simulation results also show that CS-based GF-NOMA with NOP yields better MUD and data rate performances than contention-based GF-NOMA with orthogonal preambles and CS-based grant-free orthogonal multiple access.Comment: This paper is submitted to IEEE Internet Of Things Journa

Multiple genetic switches spontaneously modulating bacterial mutability

Background: All life forms need both high genetic stability to survive as species and a degree of mutability to evolve for adaptation, but little is known about how the organisms balance the two seemingly conflicting aspects of life: genetic stability and mutability. The DNA mismatch repair (MMR) system is essential for maintaining genetic stability and defects in MMR lead to high mutability. Evolution is driven by genetic novelty, such as point mutation and lateral gene transfer, both of which require genetic mutability. However, normally a functional MMR system would strongly inhibit such genomic changes. Our previous work indicated that MMR gene allele conversion between functional and non-functional states through copy number changes of small tandem repeats could occur spontaneously via slipped-strand mis-pairing during DNA replication and therefore may play a role of genetic switches to modulate the bacterial mutability at the population level. The open question was: when the conversion from functional to defective MMR is prohibited, will bacteria still be able to evolve by accepting laterally transferred DNA or accumulating mutations? Results: To prohibit allele conversion, we "locked" the MMR genes through nucleotide replacements. We then scored changes in bacterial mutability and found that Salmonella strains with MMR locked at the functional state had significantly decreased mutability. To determine the generalizability of this kind of mutability 'switching' among a wider range of bacteria, we examined the distribution of tandem repeats within MMR genes in over 100 bacterial species and found that multiple genetic switches might exist in these bacteria and may spontaneously modulate bacterial mutability during evolution. Conclusions: MMR allele conversion through repeats-mediated slipped-strand mis-pairing may function as a spontaneous mechanism to switch between high genetic stability and mutability during bacterial evolution.Evolutionary BiologyGenetics & HereditySCI(E)9ARTICLEnull1