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

New bounds for bb-Symbol Distances of Matrix Product Codes

Matrix product codes are generalizations of some well-known constructions of codes, such as Reed-Muller codes, $[u+v,u-v]$-construction, etc. Recently, a bound for the symbol-pair distance of a matrix product code was given in \cite{LEL}, and new families of MDS symbol-pair codes were constructed by using this bound. In this paper, we generalize this bound to the $b$-symbol distance of a matrix product code and determine all minimum $b$-symbol distances of Reed-Muller codes. We also give a bound for the minimum $b$-symbol distance of codes obtained from the $[u+v,u-v]$-construction, and use this bound to construct some $[2n,2n-2]_q$-linear $b$-symbol almost MDS codes with arbitrary length. All the minimum $b$-symbol distances of $[n,n-1]_q$-linear codes and $[n,n-2]_q$-linear codes for $1\leq b\leq n$ are determined. Some examples are presented to illustrate these results

Low Resolution Face Recognition in Surveillance Systems

In surveillance systems, the captured facial images are often very small and different from the low-resolution images down-sampled from high-resolution facial images. They generally lead to low performance in face recog-nition. In this paper, we study specific scenarios of face recognition with surveillance cameras. Three important factors that influence face recognition performance are investigated: type of cameras, distance between the ob-ject and camera, and the resolution of the captured face images. Each factor is numerically investigated and analyzed in this paper. Based on these observations, a new approach is proposed for face recognition in real sur-veillance environment. For a raw video sequence captured by a surveillance camera, image pre-processing tech-niques are employed to remove the illumination variations for the enhancement of image quality. The face im-ages are further improved through a novel face image super-resolution method. The proposed approach is proven to significantly improve the performance of face recognition as demonstrated by experiments

Partially coupled gradient estimation algorithm for multivariable equation-error autoregressive moving average systems using the data filtering technique

System identification provides many convenient and useful methods for engineering modelling. This study targets the parameter identification problems for multivariable equation-error autoregressive moving average systems. To reduce the influence of the coloured noises on the parameter estimation, the data filtering technique is adopted to filter the input and output data, and to transform the original system into a filtered system with white noises. Then the filtered system is decomposed into several subsystems and a filtering-based partially-coupled generalised extended stochastic gradient algorithm is developed via the coupling concept. In contrast to the multivariable generalised extended stochastic gradient algorithm, the proposed algorithm can give more accurate parameter estimates. Finally, the effectiveness of the proposed algorithm is well demonstrated by simulation examples

Identifying Destination Image of Rural Areas: The Case of Brookings, South Dakota

A clear understanding of destination image is crucial for developing effective marketing and positioning strategies. The purpose of the study is to examine the images of Brookings perceived by actual tourists. An online questionnaire was developed and sent to email subscribers of the largest local event center. A total of 344 valid responses were received. Overall tourists had positive perceptions of Brookings as a tourism destination. The study identified six Brookings’ image dimensions, including Outdoor Activities and Natural Scenery, Atmosphere, Tourism Infrastructure, Value for Money and Convenience, Historic Attractions, and College Town Style. The social and cultural environment is the most favored element in Brookings. As a college town, Brookings was differentiated from other rural tourism destinations. It is suggested that the city and the university work in partnership to increase visitation both to the campus and the community. To enhance Brookings’ image, destination marketers should focus on the low-rated image items and incorporate them in destination marketing materials

Isolation of AhDHNs from Arachis hypogaea L. and evaluation of AhDHNs expression under exogenous abscisic acid (ABA) and water stress

The peanut (Arachis hypogaea L.) is an important oil and cash crop all over the world. It is mostly planted in arid and semi-arid regions. To determine the mechanism by which dehydrins (DHNs) are regulated by abscisic acid (ABA) in peanuts, three Arachis hypogaea L. dehydrins (AhDHNs) were isolated from peanut plants and sequenced. By blasting the protein sequences of these AhDHNs, AhDHN1 was found belonging to the YnSKn subfamily. AhDHN2 and AhDHN3 were found belonging to the SKn and YnKn types, respectively. 100 μM ABA enhanced AhDHNs expression in peanut leaves. When peanut plants were treated with ABA and then with the ABA synthesis inhibitor sodium tungstate 12 h later, AhDHN expression was suppressed. However, AhDHN2 was inhibited by sodium tungstate at 2 h, though other AhDHNs were not. AhDHNs expressions increased greatly in peanut leaves treated with 30% polyethylene glycol (PEG). Sodium tungstate along with PEG inhibited the expression of AhDHNs. This study found that exogenous and endogenous ABA can both affect the expression of AhDHN independently. The differential expression of AhDHNs to exogenous ABA may be because of differences in the structure of different AhDHNs.Keywords: Arachis hypogaea L. dehydrins (AhDHNs), peanut, abscisic acid (ABA), expression, sodium tungstate, water stres