User churn in games often arises due to inadequate game difficulty. To address this, non-player characters (NPCs) has been utilized to modulate difficulty according to individual game skill. Nevertheless, the effectiveness of solely NPC-based adjustments is limited since game difficulty is influenced by both NPCs and environmental factors. This paper introduces a novel method for dynamically tailoring game difficulty by adjusting in-game environments based on player behavior patterns in top-down shooter game. Through analysis of diverse user game play data, we find that factors within the game environment, such as the distribution of enemy characters and the arrangement of terrain, have a substantial influence on the level of difficulty. Furthermore, it has been observed that behavioral patterns of players show variations according to changes in the game environment. Using these analytical result, we devise an artificial neural network model that configures an environment that suit player behavior patterns. With the model, we figure out the user player pattern and control the difficulty dynamically by changing the environment factors. Through the experiments, we show that our method provides an appropriate level of difficulty for users to prevent user churn.

The toxicity of methanol extract from 28 plants toward four agricultural insect pests were examined using contact toxicity bioassay with spray methods. At a concentration of 1,000 ppm Achyranthes japonica whole body was observed strong activity against Myzus persicae. Paeonia lactiflora root and Corydalis turtschaninovii rhizome methanol extract were observed strong activity toward Tetranychus urticae. Catalpa ovata leaf and Ginkgo biloba leaf and stem methanol extracts were revealed high activity toward Nilaparvata lugens. Actium lap root methanol extract was observed moderate activity against Plutella xylostella. High or moderate insecticidal activity of plant extracts were listed in Fig.1. These plant resource, particularly plant methanol extract or insecticidal constituents, merit further as potential insect pest control agents or leads because of their great activity as a insecticide.

Mungbean (Vigna radiata) is a fast-growing, warm-season legume crop that is primarily cultivated in developing countries of Asia. We constructed a draft genome sequence of mungbean to facilitate genome research into the subgenus Ceratotropis and to enable a better understanding of the evolution of leguminous species. The draft genome sequence covers 80% of the estimated genome, of which 50.1% consists of repetitive sequences. In total, 22,427 high confidence protein-coding genes were predicted. Based on the de novo assembly of additional wild mungbean species, the divergence of what was eventually domesticated and the sampled wild mungbean species appears to have predated domestication. Moreover, the de novo assembly of a tetraploid Vigna species (Vigna reflexo-pilosa var. glabra) provided genomic evidence of a recent allopolyploid event. To further study speciation, we compared de novo RNA-seq assemblies of 22 accessions of 18 Vigna species and protein sets of Glycine max and Cajanus cajan. The species tree was constructed by a Bayesian Markov chain Monte Carlo method using highly confident orthologs shared by all 24 accessions. The present assembly of V. radiata var. radiata will facilitate genome research and accelerate molecular breeding of the subgenus Ceratotropis.