Genomic evaluation has been widely applied to several species using commercial single nucleotide polymorphism (SNP) genotyping platforms. This study investigated the informative genomic regions and the efficiency of genomic prediction by using two Bayesian approaches (BayesB and BayesC) under two moderate-density SNP genotyping panels in Korean Duroc pigs. Growth and production records of 1026 individuals were genotyped using two medium-density, SNP genotyping platforms: Illumina60K and GeneSeek80K. These platforms consisted of 61,565 and 68,528 SNP markers, respectively. The deregressed estimated breeding values (DEBVs) derived from estimated breeding values (EBVs) and their reliabilities were taken as response variables. Two Bayesian approaches were implemented to perform the genome-wide association study (GWAS) and genomic prediction. Multiple significant regions for days to 90 kg (DAYS), lean muscle area (LMA), and lean percent (PCL) were detected. The most significant SNP marker, located near the MC4R gene, was detected using GeneSeek80K. Accuracy of genomic predictions was higher using the GeneSeek80K SNP panel for DAYS (Δ2%) and LMA (Δ2–3%) with two response variables, with no gains in accuracy by the Bayesian approaches in four growth and production-related traits. Genomic prediction is best derived from DEBVs including parental information as a response variable between two DEBVs regardless of the genotyping platform and the Bayesian method for genomic prediction accuracy in Korean Duroc pig breeding.

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The k-Nearest Neighbors (kNN) query is an important spatial query in mobile sensor networks. In this work we extend kNN to include a distance constraint, calling it a l-distant k-nearest-neighbors (l-kNN) query, which finds the k sensor nodes nearest to a query point that are also at l or greater distance from each other. The query results indicate the objects nearest to the area of interest that are scattered from each other by at least distance l. The l- kNN query can be used in most kNN applications for the case of well distributed query results. To process an l-kNN query, we must discover all sets of kNN sensor nodes and then find all pairs of sensor nodes in each set that are separated by at least a distance l. Given the limited battery and computing power of sensor nodes, this l-kNN query processing is problematically expensive in terms of energy consumption. In this paper, we propose a greedy approach for l- kNN query processing in mobile sensor networks. The key idea of the proposed approach is to divide the search space into subspaces whose all sides are l. By selecting k sensor nodes from the other subspaces near the query point, we guarantee accurate query results for l- kNN. In our experiments, we show that the proposed method exhibits superior performance compared with a post-processing based method using the kNN query in terms of energy efficiency, query latency, and accuracy.

Drones, flying Internet of Things (IoT), have been widely used in several industrial fields, including rescue, delivery, military, and agriculture. Motors connected to a drone’s propellers play a crucial role in its movement. However, once the motor is damaged, the drone is at risk of falling. Thus, to prevent the drone from falling, an accurate and reliable prediction of motor vibration is necessary. In this study, four types of time series vibration data collected in the time domain from motors are predicted using long short-term memory (LSTM) and gated recurrent unit (GRU), and the accuracy and time efficiency of the predicted and actual vibration waveforms are compared and examined. According to the simulation results, the coefficient of determination, R2, and the root mean square error values obtained from the actual and predicted vibrations by the LSTM and GRU are similar. Furthermore, both the LSTM and GRU show excellent performance in forecasting future motor vibration, but GRU can predict future vibration about 22.79% faster than LSTM.