Music is the most convenient and easy-to-use stress release tool in modern times. Many studies have shown that listening to appropriate music can release stress. However, since it is getting easier to make music, people only need to make it on the computer and upload it to streaming media such as Youtube, Spotify, or Beatport at any time, which makes it very infeasible to search a huge music database for music of a specific genre. In order to effectively search for specific types of music, we propose a novel method based on the visual Mel spectrum for music genre classification, and apply YOLOv4 as our neural network architecture. mAP was used as the scoring criterion of music genre classification in this study. After ten experiments, we obtained a highest mAP of 99.26%, and the average mAP was 97.93%.

The aging of the world’s population is causing older farmers familiar with agricultural practices to retire, resulting in a growing shortage of related labor. The ever-increasing labor shortage in traditional agriculture has reduced the manual sorting of agricultural products during processing and packaging. Delicate products such as cherry tomatoes pose a severe problem. This study proposes an artificial intelligence of things (AIoT) intelligent production line that leverages deep learning technology, the internet of things, and data analytics for fully automated and efficient 360-degree visual defect detection and classification of cherry tomatoes. By integrating image recognition and AIoT technology, the system can prevent the occurrence of defects during packaging, ensure consistent product quality during grading, and improve the reputation of agricultural producers. We achieved an outstanding mean average precision of 97.4% in the AIoT intelligent production line actual application in defect detection. The results showed that AIoT and intelligent screening plate mechanisms were advantageous for the detection of cherry tomatoes and help promote the development of delicate agricultural products. This study contributed to the development of intelligent agriculture by reducing labor and time costs, quickly detecting defects, effectively controlling sorting quality, and improving crop management through data analysis.

Restriction enzymes are used frequently in biotechnology. However, manual mining of restriction enzymes is challenging. Furthermore, integrating available restriction enzymes into different bioinformatics systems is necessary for many biotechnological applications, such as polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP). Thus, in the present study, we developed the package REHUNT (Restriction Enzymes HUNTing), which mines restriction enzymes from the public database REBASE using a series of search operations. REHUNT is a reliable and open source package implemented in JAVA. It provides useful methods and manipulations for biological sequence analysis centered around restriction enzymes contained in REBASE. All available restriction enzymes for the imported biological sequences can be identified by REHUNT. Different genotypes can be identified using PCR-RFLP based on REHUNT for single nucleotide polymorphism (SNP), mutations, and the other variations. REHUNT robustly recognizes multiple inputs with different formats, e.g. regular DNA sequences, variation-in-sequence indicated by IUPAC code, as well as variation-in-sequence indicated by dNTPs format. Variations including di-, tri-, and tetra-allelic types and indel formats are also acceptable. Furthermore, REHUNT provides classified restriction enzymes output, including IUPAC and general sequence types, as well as commercial and non-commercial availabilities. REHUNT also enables analysis for high throughput screening (HTS) technologies. REHUNT is open source software with GPL v3 license and can be run on all platforms. Its features include: 1) Quick restriction enzymes search throughout a sequence based on the Boyer-Moore algorithm; 2) all available restriction enzymes provided and regularly updated from REBASE; 3) an open source API available of integrating all types of bioinformatics systems and applications; 4) SNP genotyping available for plant and animal marker-assisted breeding, and for human genetics; and 5) high throughput analysis available for Next Generation Sequencing (NGS). REHUNT not only to effectively looks for restriction enzymes in a sequence, but also available for SNP genotyping. Furthermore, it can be integrated into other biological and medical applications. REHUNT offers a convenient and flexible package for powerful restriction enzymes analyses in association studies, and supports high throughput analysis. The source codes and complete API documents are available at SourceForge: https://sourceforge.net/projects/rehunt/ , GitHub: https://github.com/yuhuei/rehunt , and at: https://sites.google.com/site/yhcheng1981/rehunt .