Using data from flies collected with pitfall traps in 365 forests on a nationwide scale in Korea, the abundance and distribution changes of two families (Phoridae and Lauxaniidae) in Korean forests were predicted at the genus level according to two climate change scenarios: RCP 4.5 and RCP 8.5. The most suitable temperature for the 17 major genera was estimated using a weighted average regression model. Stichillus and Anevrina displayed the lowest optimum temperature with 7.6℃ and 8.5℃ in annual mean temperature, respectively, whereas Chonocephalus had the highest optimum temperature with 12.1℃. Among thirty genera, seven genera (four from Phoridae and three from Lauxaniidae), which showed their abundance in a bell-type or linear pattern along the temperature gradient, were used for predicting the distribution changes according to the future climate change scenarios. All the taxa of this study are expected to decrease in abundance and distribution as a function of temperature increase. Moreover, cold-adapted taxa were found to be more affected than warmadapted taxa.

풀명나방과의 Patania brevipennis (Inoue, 1982); Herpetogramma tominagai Yamanaka, 2003; Aethaloessa calidalis (Guenée, 1854); Anania subfumalis (Munroe et Mutuura, 1971) 등 4종을 국내 처음으로 기록한다. Aethaloessa속은 국내에서 처음으로 기록된다. 여기 기록한 종의 외형과 생식기 사진을 제공한다. 필요한 경우, 해당 종의 아종 관계와 국내 발생 상황에 대해 논한다.

본 연구에서는 간벌 이후 지표 절지동물 군집의 장기적인 변화를 확인하기 위해, 잣나무조림지에서 간벌 전후에 조사되었던 과거 지표 절지동물 군집 자료와 간벌처리 10년 후인 2018년에 채집한 군집 자료를 비교하였다. 조사지역에서는 간벌 이후 10년이 지났으나 여전히 간벌처리에 따른 서식 환경 (유기물 깊이, 지표면 식생 피도, 울폐도)의 차이가 나타났다. 2018년 조사된 절지동물 군집은 곤충강 파리목, 벌목, 딱정벌레목, 톡토기강 톡토기목, 거미강 거미목 순으로 우점하였고, 간벌처리 조건 중 강도간벌에서는 거미강 거미목, 곤충강 딱정벌레목과 벌목이, 약도간벌에 서 톡토기강 톡토기목과 곤충강 파리목이 가장 많이 나타났다. 간벌 직후인 2008년에 비해 2018년 곤충강 딱정벌레목과 파리목, 벌목, 갑각강 등각목의 개체수는 감소하였고, 곤충강 노린재목과 메뚜기목의 개체수는 증가하였다. 2018년의 지표 절지동물 군집은 간벌 이전인 2006년 군집에 비해, 간벌 직후인 2008년 군집과 유사한 것으로 나타났다. 본 연구는 지표 절지동물 군집의 변화를 조사지역 및 간벌처리 조건에 따라 나타내어, 차후 산림 내 간벌 계획 작성 또는 간벌을 이용한 산림 자원 관리에 대한 기초 자료를 제공한다. 이후로 다양한 간벌처리지 내 조사 등이 수반된 추가 연구가 수행된다면, 간벌로 인한 산림 절지동물 군집 변화를 더욱 명확하게 규명할 수 있을 것이다.

Fast service access involves keeping track of the location of mobile users, while they are moving around the mobile network for a satisfactory level of QoS (Quality of Service) in a cost-effective manner. The location databases are used to keep track of Mobile Terminals (MT) so that incoming calls can be directed to requested mobile terminals at all times. MT reporting cell system used in location management is to designate each cell in the network as a reporting cell or a non-reporting cell. Determination of an optimal number of reporting cells (or reporting cell configuration) for a given network is reporting cell planning (RCP) problem. This is a difficult combinatorial optimization problem which has an exponential complexity. We can see that a cell in a network is either a reporting cell or a non-reporting cell. Hence, for a given network with N cells, the number of possible solutions is  . We propose a biogeography based optimization (BBO) for design of mobile station location management system in wireless communication network. The number and locations of reporting cells should be determined to balance the registration for location update and paging operations for search the mobile stations to minimize the cost of system. Experimental results show that our proposed BBO is a fairly effective and competitive approach with respect to solution quality for optimally designing location management system because BBO is suitable for combinatorial optimization and multi-functional problems.

In this study, we developed ten microsatellite markers specific to L. angelina using the Illumina NextSeq 500 platform. Forty-three individuals of L. angelina collected from three localities in South Korea were genotyped to validate these markers and to preliminarily assess population genetic characteristics. The observed number of alleles, observed heterozygosity (HO), and expected heterozygosity (HE) at a locus ranged from 4–13, 0.211–0.950, and 0.659–0.871 in the population with the largest sample size (20 individuals), respectively, thereby validating the suitability of the markers for population analyses. Our preliminarily assessment of the population genetic characteristics indicates the presence of inbreeding in all populations, an isolation of the most geographically distant population (Seocheon), and lower HO than HE. The microsatellite markers developed in this study will be useful for studying the population genetics of L. angelina collected from additional sites in South Korea and from other regions. †These authors contributed equally to this paper.

In this study, we developed 11 microsatellite markers specific to A. crataegi using NGS to investigate the genetic relationships of A. crataegi populations from South Korea to circumferential Asian countries (China, Russia, Mongolia, and Japan). Further, two mitochondrial DNA (mtDNA) gene segments (COI and CytB) were sequenced from the samples. The population- and individual-based Principal Coordinates and STRUCTURE analyses collectively suggested that the South Korean population of A. crataegi is most differentiated from the Japanese population, whereas it was closer to Mongolian and Chinese populations. These results collectively suggest that northern populations, in particular, Mongolian populations can be considered as the most genetically compatible one as donee population, when the reintroduction program is launched. †These authors contributed equally to this paper.

A wireless sensor network is emerging technology and intelligent wireless communication paradigm that is dynamically aware of its surrounding environment. It is also able to respond to it in order to achieve reliable and efficient communication. The dynamical cognition capability and environmental adaptability rely on organizing dynamical networks effectively. However, optimally clustering the cognitive wireless sensor networks is an NP-complete problem. The objective of this paper is to develop an optimal sensor network design for maximizing the performance. This proposed Ranking Artificial Bee Colony (RABC) is developed based on Artificial Bee Colony (ABC) with ranking strategy. The ranking strategy can make the much better solutions by combining the best solutions so far and add these solutions in the solution population when applying ABC. RABC is designed to adapt to topological changes to any network graph in a time. We can minimize the total energy dissipation of sensors to prolong the lifetime of a network to balance the energy consumption of all nodes with robust optimal solution. Simulation results show that the performance of our proposed RABC is better than those of previous methods (LEACH, LEACH-C, and etc.) in wireless sensor networks. Our proposed method is the best for the 100 node-network example when the Sink node is centrally located.

한국산 제비나방과 쌍꼬리나방아과의 3미기록종인Europlema nubifasciaria (Leech), Dysaethria meridiana (Inoue), Phazaca theclata (Guenée)을 보고한다. Europlema leleji는 Epiplema nubifasciaria의 동종이명으로 판명되었고, 후자는 Europlema속으로 새롭게 편입된다. Europlema nubifasciaria의 암컷생식기를 처음으로 기재한다. 채집된 종의 성충과 생식기의 사진을 제공한다.

Conductive and dielectric SiC are fabricated using electroless plating of Ni–Fe films on SiC chopped fibers to obtain lightweight and high-strength microwave absorbers. The electroless plating of Ni–Fe films is achieved using a two-step process of surface sensitizing and metal plating. The complex permeability and permittivity are measured for the composite specimens with the metalized SiC chopped fibers dispersed in a silicone rubber matrix. The original noncoated SiC fibers exhibit considerable dielectric losses. The complex permeability spectrum does not change significantly with the Ni–Fe coating. Moreover, dielectric constant is sensitively increased with Ni–Fe coating, owing to the increase of the space charge polarization. The improvements in absorption capability (lower reflection loss and small matching thickness) are evident with Ni–Fe coating on SiC fibers. For the composite SiC fibers coated with Ni–Fe thin films, a -35 dB reflection loss is predicted at 7.6 GHz with a matching thickness of 4 mm.

최근 지구온난화로 인한 기후변화는 육상 및 해양 생태계에 다양한 영향을 미치고 있다. 농업생태계 역시 이들 생태계에 의존하고 있는 생물 및 인간에게 생물학적, 경제학적, 사회학적으로 다양한 영향을 주고 있다. 기후변화를 쉽게 감지할 수 있는 지표종은 기후변화에 비교적 민감하게 반응을 나타내기 때문에 농업생태계와 같은 경제 사회적 영향을 많이 받는 곳에서 다양하게 활용될 수 있다. 2017년 농업과학원에서는 농업생태계에서 기후변화에 따른 영향을 잘 나타낼 수 있는 식물과 무척처동물 30종을 지표종으로 선정하였다. 30종 중 나비목에 속하는 종으로는 배추흰나비(Pieris rapae), 남방노랑나비(Eurema mandarina), 노랑나비(Colias erate), 호랑나비(Papilio xuthus) 4종이다. 이 연구는 나비 지표종 중 농업생태계에서 가장 풍부하고 확인이 쉬운 배추흰나비를 대상으로 전남, 충북, 경기, 강원지역에서 4월부터 월 1-2회 모니터링을 실시하여 생물계절의 차이를 알아보았다. 조사는 각 지역에서 논과 밭, 산림 등을 포함하는 경로를 선정하여 30분간 이동하면서 좌우 5m내외 출현하는 나비를 조사하는 선 조사법을 실시하였다. 4월 이후부터 조사한 결과 전남에서는 4월초인 14째 주에 이미 많은 수가 관찰된 반면 충북, 경기 등지에선 15째 주 이후 관찰되기 시작하였다. 강원도에서는 6월 중순인 24째부터 관찰되어 위도별 출현 양상의 차이를 나타내었다. 9월말까지 관찰된 생활사 수는 전남에서는 5회, 경기도에서는 4-5회, 충북에서는 4회, 강원에서는 2회로 지역별 차이를 나타내었다. 이러한 결과는 농업생태계에서 흔히 볼 수 있는 배추흰나비가 위도별로 출현시기와 출현횟수를 달리하면서 나타나는 것을 통해 기후변화를 나타낼 수 있는 지표종으로 적절하다고 여겨지며 앞으로도 전국적으로 지속적인 모니터링을 통해 정밀한 출현양상과 미래 분포 변화 모델링 작업에 효과적으로 이용될 수 있는 기초자료를 제공할 수 있을 것으로 여겨진다.

K-means algorithm is one of the most popular and widely used clustering method because it is easy to implement and very efficient. However, this method has the limitation to be used with fixed number of clusters because of only considering the intra-cluster distance to evaluate the data clustering solutions. Silhouette is useful and stable valid index to decide the data clustering solution with number of clusters to consider the intra and inter cluster distance for unsupervised data. However, this valid index has high computational burden because of considering quality measure for each data object. The objective of this paper is to propose the fast and simple speed-up method to overcome this limitation to use silhouette for the effective large-scale data clustering. In the first step, the proposed method calculates and saves the distance for each data once. In the second step, this distance matrix is used to calculate the relative distance rate (Vｊ) of each data j and this rate is used to choose the suitable number of clusters without much computation time. In the third step, the proposed efficient heuristic algorithm (Group search optimization, GSO, in this paper) can search the global optimum with saving computational capacity with good initial solutions using Vｊ probabilistically for the data clustering. The performance of our proposed method is validated to save significantly computation time against the original silhouette only using Ruspini, Iris, Wine and Breast cancer in UCI machine learning repository datasets by experiment and analysis. Especially, the performance of our proposed method is much better than previous method for the larger size of data.

Previous phylogenetic results often showed fluctuating positions of Geometroidea in Macroheterocera, particularly from mitochondrial genome (mitogenome)-based analyses. In this study, we sequenced mitogenomes of four geometrid moths to increase taxon diversity for the inference of phylogenetic positions of Geometroidea in Macroheterocera. The general genomic features found in Macroheterocera also were found in the four geometrid moths. Phylogenetic analyses using 71 representative mitogeneome sequences in Macroheterocera yielded the consensus superfamilial relationships (((((Bombycoidea + Lasiocampoidea) + Geometroidea) + Noctuoidea) + Drepanoidea) + Mimallonoidea), confirming the sister relationship of Geometroidea to (Bombycoidea + Lasiocampoidea) in both Bayesian Inference and Maximum-likelihood method.

Limacodidae, also known as slug moths, comprise about 1,000 species worldwide (Epstein et al., 1998). This moth group is characterized by two adult characteristics: the presence of the female legs with the recessed pad of sensilla trichodea underneath and the entire, disk-shaped papillae anales, and four characteristics in immature stages: the absence of the crochets in all instars of larvae; the presence of the extended pupal maxilla, contiguous with the labial palpus; the hard, ovoid cocoons, lid invisible when uneclosed; and the flat, thin eggs (Epstein, 1996). Since Fixsen (1887), a total of 19 genera and 26 species of Limacodidae have been recorded from Korea. In the present paper, we reported three species of Limacodidae new to the Korean fauna: Ceratonema butleri Kawada, 1930, Microleon decolatus Sasaki, 2016, and Isopenthocrates japona Yoshimoto, 2004.

한국산 뿔나방과의 2미기록종인Helcystogramma compositaepictum (Omelko et Omelko)와Paralida triannulata Clarke을 보고한다. Paralida Clarke속은 국내에서 처음 기록된다. 성충과 생식기의 사진을 제공하는 한편, 분포 정보를 논하였다.

Data clustering is one of the most difficult and challenging problems and can be formally considered as a particular kind of NP-hard grouping problems. The K-means algorithm is one of the most popular and widely used clustering method because it is easy to implement and very efficient. However, it has high possibility to trap in local optimum and high variation of solutions with different initials for the large data set. Therefore, we need study efficient computational intelligence method to find the global optimal solution in data clustering problem within limited computational time. The objective of this paper is to propose a combined artificial bee colony (CABC) with K-means for initialization and finalization to find optimal solution that is effective on data clustering optimization problem. The artificial bee colony (ABC) is an algorithm motivated by the intelligent behavior exhibited by honeybees when searching for food. The performance of ABC is better than or similar to other population-based algorithms with the added advantage of employing fewer control parameters. Our proposed CABC method is able to provide near optimal solution within reasonable time to balance the converged and diversified searches. In this paper, the experiment and analysis of clustering problems demonstrate that CABC is a competitive approach comparing to previous partitioning approaches in satisfactory results with respect to solution quality. We validate the performance of CABC using Iris, Wine, Glass, Vowel, and Cloud UCI machine learning repository datasets comparing to previous studies by experiment and analysis. Our proposed KABCK (K-means+ABC+K-means) is better than ABCK (ABC+K-means), KABC (K-means+ABC), ABC, and K-means in our simulations.

Epipleminae is the most species-rich lineage within Uraniidae and can be characterized by its unusual resting posture.Since Leech (1897) first reported two species, Epiplema moza (Butler, 1878) and Gathynia fasciaria Leech, 1897, therehave been 5 genera and 9 species of Epipleminae recorded from Korea (Sohn & Yen, 2005). We reported three speciesof Epipleminae new to Korea: Dysaethria meridiana (Inoue, 1982), Phazaca theclata (Guenee, 1857), and Europlemaleleji Sinev, 2016. Taxonomic status of Europlema leleji Sinev is revised. A revised checklist of the Korean Epipleminaeis provided.

Data clustering determines a group of patterns using similarity measure in a dataset and is one of the most important and difficult technique in data mining. Clustering can be formally considered as a particular kind of NP-hard grouping problem. K-means algorithm which is popular and efficient, is sensitive for initialization and has the possibility to be stuck in local optimum because of hill climbing clustering method. This method is also not computationally feasible in practice, especially for large datasets and large number of clusters. Therefore, we need a robust and efficient clustering algorithm to find the global optimum (not local optimum) especially when much data is collected from many IoT (Internet of Things) devices in these days. The objective of this paper is to propose new Hybrid Simulated Annealing (HSA) which is combined simulated annealing with K-means for non-hierarchical clustering of big data. Simulated annealing (SA) is useful for diversified search in large search space and K-means is useful for converged search in predetermined search space. Our proposed method can balance the intensification and diversification to find the global optimal solution in big data clustering. The performance of HSA is validated using Iris, Wine, Glass, and Vowel UCI machine learning repository datasets comparing to previous studies by experiment and analysis. Our proposed KSAK (K-means+SA+K-means) and SAK (SA+K-means) are better than KSA(K-means+SA), SA, and K-means in our simulations. Our method has significantly improved accuracy and efficiency to find the global optimal data clustering solution for complex, real time, and costly data mining process.