No abstract

Building energy consumption accounts for a considerable proportion on energy consumption. To reduce building energy consumption, building energy efficiency retrofitting (BEER) based on Energy Performance Contracting mechanism is the most feasible and cost-effective method. With the increase number of BEER projects, BEER project selection has become an essential problem for energy service companies. In this paper, a multi-criteria group decision-making (MCGDM) method is proposed to deal with BEER project selection problem. First, picture fuzzy sets are employed to describe the evaluation information under the complex and uncertain environment. Subsequently, picture fuzzy weighted average operator and Laplace distribution-picture fuzzy order weighted average operator are proposed based on convex combination to aggregate individual evaluations into the overall evaluations. Furthermore, picture fuzzy TOPSIS-based QUALIFLEX method is developed to identify the optimal ranking of alternatives. Moreover, the practicality, effectiveness and advantages of the proposed MCGDM method are illustrated using a case study of hotel BEER project selection and comparative analysis. Finally, conclusions about primary contributions, and future discussions of the proposed method are demonstrated.Mathematics Subject Classification. 62C86.

Agricultural products need to be inspected for quality and safety, and the issue of safety of agricultural products caused by quality is frequently investigated. Safety testing should be carried out before agricultural products are consumed. The existing technologies for inspecting agricultural products are time-consuming and require complex operation, and there is motivation to develop a rapid, safe, and non-destructive inspection technology. In recent years, with the continuous progress of THz technology, THz spectral imaging, with the advantages of its unique characteristics, such as low energies, superior spatial resolution, and high sensitivity to water, has been recognized as an efficient and feasible identification tool, which has been widely used for the qualitative and quantitative analyses of agricultural production. In this paper, the current main performance achievements of the use of THz images are presented. In addition, recent advances in the application of THz spectral imaging technology for inspection of agricultural products are reviewed, including internal component detection, seed classification, pesticide residues detection, and foreign body and packaging inspection. Furthermore, machine learning methods applied in THz spectral imaging are discussed. Finally, the existing problems of THz spectral imaging technology are analyzed, and future research directions for THz spectral imaging technology are proposed. Recent rapid development of THz spectral imaging has demonstrated the advantages of THz radiation and its potential application in agricultural products. The rapid development of THz spectroscopic imaging combined with deep learning can be expected to have great potential for widespread application in the fields of agriculture and food engineering.

Specific length amplified fragment sequencing (SLAF-seq) is a recently developed high-resolution strategy for the discovery of large-scale de novo genotyping of single nucleotide polymorphism (SNP) markers. In the present research, in order to facilitate genome-guided breeding in potato, this strategy was used to develop a large number of SNP markers and construct a high-density genetic linkage map for tetraploid potato. The genomic DNA extracted from 106 F1 individuals derived from a cross between two tetraploid potato varieties YSP-4 × MIN-021 and their parents was used for high-throughput sequencing and SLAF library construction. A total of 556.71 Gb data, which contained 2269.98 million pair-end reads, were obtained after preprocessing. According to bioinformatics analysis, a total of 838,604 SLAF labels were developed, with an average sequencing depth of 26.14-fold for parents and 15.36-fold for offspring of each SLAF, respectively. In total, 113,473 polymorphic SLAFs were obtained, from which 7638 SLAFs were successfully classified into four segregation patterns. After filtering, a total of 7329 SNP markers were detected for genetic map construction. The final integrated linkage map of tetraploid potato included 3001 SNP markers on 12 linkage groups, and covered 1415.88 cM, with an average distance of 0.47 cM between adjacent markers. To our knowledge, the integrated map described herein has the best coverage of the potato genome and the highest marker density for tetraploid potato. This work provides a foundation for further quantitative trait loci (QTL) location, map-based gene cloning of important traits and marker-assisted selection (MAS) of potato.