2022년 3월12일 제천시의 발표(구체화)에 따르면 제천지역 내 양봉 농가를 대상으로 꿀벌생육실태를 확인한 결과 전체 벌통에서 절반 수준의 꿀벌이 사라진 것을 확인하였다. 이는 이전부터 국내 남부에서 진행되어 오던 꿀벌집단실종 현상의 한계선이 지구온난화로 인해 북쪽으로 이동하고 있다고 언론에서 집중 조명된 적이 있다. 이러한 현상이 과연 한반도 온난화에 의한 것인지의 여부를 파악하고자 원인분석 및 실험을 진행하였다. 먼저 꿀벌실종이 일어난 연도와 달을 중심으로 제천지역내의 기온, 일교차, 강수, 일조량 등 다양한 환경조건 중 예전 과 비교하여 급격한 변화가 일어난 요인을 조사하였으며 이러한 급격한 변화가 일어나는 요인이 꿀벌의 집단실 종에 미칠 수 있는 가능성을 분석하였다. 다른 요인분석으로 미국, 유럽 등에서 꿀벌실종의 주요 원인으로 주목받 고 있는 네오니코티노이드계(Neonicotinoids) 살충제를 이용해 꿀벌에 미치는 영향을 실험하였으며, 생존한계 농도를 측정하였다. 또한 국내 살충제의 연도별 사용량을 간접 비교함으로써 꿀벌실종의 주요요인을 찾고자 하였다. 분석결과 충북제천 꿀벌의 실종은 기온의 상승보다는 일조량이 큰 영향을 미친 것으로 보이며, 향후 일조량에 따른 벌집내부의 온도변화 및 꿀벌의 활동성 변화에 초점을 맞추어 꿀벌실종에 대한 장기적인 상관관 계를 살펴보아야 할 것으로 생각된다.

최근 국내에서는 꿀벌 대량소실 현상이 2022년부터 전국적으로 발생하고 있다. 우리나라 뿐 만 아니라, 전세계 적으로 양봉산업에 큰 위협이 되고 있는 봉군붕괴현상은 2016년 미국에서 세계 최초로 보고되었다. 국내에서는 2022년 민관 합동조사 결과, 이상기온, 응애, 말벌 등이 주요 원인으로 지목되었다. 대량소실 현상을 보인 양봉농 가와 정상 농가의 병원체 검출 비교 결과, 유의성있게 검출이 증가되는 병원체는 발견되지 않았다. 그러나, Tyrophagus mite, Trypanosome, Lake Sinai virus, Apis mellifera filamentous virus 등의 신종 응애, 원충 및 바이러 스 감염이 추가로 확인되었다. 국내에서 새롭게 감염이 확인된 기생충과 병원체가 대량소실, 나아가 봉군붕괴현 상에 직간접적으로 영향을 주었을 것으로 사료되며, 지속적인 조사와 연구개발을 통해 기후등 환경변화에 따른 신종 질병 검색과 대책을 마련해야 할 것이다.

With the continuous development of science and technology, unmanned ship has gradually become a hot spot in the field of marine research. In practical applications, unmanned ships need to have long-range navigation and high efficiency, so that they can accurately perform tasks in the marine environment. As one of the key technologies of unmanned ship, path planning is of great significance to improve the endurance of unmanned ship. In order to meet the requirements, this paper proposes a path planning method for long distance unmanned ships based on reinforcement learning angle precedence ant colony improvement algorithm. Firstly, canny operator is used to automatically extract navigation environment information, and then MAKLINK graph theory is applied for environment modelling. Finally, the basic ant colony algorithm is improved and applied to the path planning of unmanned ship to generate an optimal path. The experimental results show that, compared with the traditional ant colony algorithm, the path planning method based on the improved ant colony algorithm can achieve a voyage duration of nearly 7 km for unmanned ships under the same sailing environment, which has certain practicability and popularization value.

An Ant Colony Optimization Algorithm(ACO) is one of the frequently used algorithms to solve the Traveling Salesman Problem(TSP). Since the ACO searches for the optimal value by updating the pheromone, it is difficult to consider the distance between the nodes and other variables other than the amount of the pheromone. In this study, fuzzy logic is added to ACO, which can help in making decision with multiple variables. The improved algorithm improves computation complexity and increases computation time when other variables besides distance and pheromone are added. Therefore, using the algorithm improved by the fuzzy logic, it is possible to solve TSP with many variables accurately and quickly. Existing ACO have been applied only to pheromone as a criterion for decision making, and other variables are excluded. However, when applying the fuzzy logic, it is possible to apply the algorithm to various situations because it is easy to judge which way is safe and fast by not only searching for the road but also adding other variables such as accident risk and road congestion. Adding a variable to an existing algorithm, it takes a long time to calculate each corresponding variable. However, when the improved algorithm is used, the result of calculating the fuzzy logic reduces the computation time to obtain the optimum value.

With the development of the global marine transportation industry, marine accidents frequently occur due to the complex and changeable climate environment, and maritime search and rescue work has thus received much attention. To improve marine search and rescue operations, an algorithm for environmental modeling and search path optimization based on an ant colony system is proposed. First, MAKLINK is selected to build an ecological model. Secondly, the relevant parameters of the ant colony system algorithm are established, and the search and rescue route is designed. Finally, simulations of the environmental model and route design are constructed in search and rescue waters in Zhoushan, Zhejiang Province, using MATLAB. Experimental results prove the validity of this algorithm.

It is one of the known methods to obtain the optimal solution using the Ant Colony Optimization Algorithm for the Traveling Salesman Problem (TSP), which is a combination optimization problem. In this paper, we solve the TSP problem by proposing an improved new ant colony optimization algorithm that combines genetic algorithm mutations in existing ant colony optimization algorithms to solve TSP problems in many cities. The new ant colony optimization algorithm provides the opportunity to move easily fall on the issue of developing local optimum values of the existing ant colony optimization algorithm to global optimum value through a new path through mutation. The new path will update the pheromone through an ant colony optimization algorithm. The renewed new pheromone serves to derive the global optimal value from what could have fallen to the local optimal value. Experimental results show that the existing algorithms and the new algorithms are superior to those of existing algorithms in the search for optimum values of newly improved algorithms.

Although somatic cell nuclear transfer (SCNT)-derived embryonic stem cells (ESCs) in pigs have great potential, their use is limited because the establishment efficiency of ESCs is extremely low. Accordingly, we tried to develop in-vitro culture system stimulating production of SCNT blastocysts with high performance in the colony formation and formation of colonies derived from SCNT blastocysts for enhancing production efficiency of porcine ESCs. For these, SCNT blastocysts produced in various types of embryo culture medium were cultured in different ESC culture medium and optimal culture medium was determined by comparing colony formation efficiency. As the results, ICM of porcine SCNT blastocysts produced through sequential culture of porcine SCNT embryos in the modified porcine zygote medium (PZM)-5 and the PZM-5F showed the best formation efficiency of colonies in α-MEM-based medium. In conclusion, appropriate combination of the embryo culture medium and ESC culture medium will greatly contribute to successful establishment of ESCs derived from SCNT embryos.

We investigated the effects of honey bee (Apis mellifera L.) colony size on the pollination of greenhouse-cultivated watermelon grown under the forcing cultivation system. The highest pollination activity of bees was observed (14.3 ± 5.0 honey bees/day) when the bee colony size was 10,000 honey bees, followed by 7,500 and 5,000 honey bees. There was a positive correlation between the bee colony size and pollination activity (R = 0.262). There was no significant difference in the fruit set with different honey bee colony sizes (88%–91%). However, the larger the bee colony size, the higher the number of seeds fertilized and the rate of seed fertilization (p > 0.05). The number of seeds and the content of sugar were negatively correlated (R = -0.714). Overall, we found that a colony size of 5,000 honey bees was the most effective for the pollination of watermelon grown under forcing cultivation.

Ambrosia artemisiifolia is native in North America and an invasive alien species in East Asia and Europe. This plant causes economic losses such as reducing agricultural production and producing severe allergenic pollen. Recently, there was an effort to control this alien plant chemically and mechanically in South Korea, but they are neither sustainable nor environmentally-friendly control strategies. Epiblema sugii Kawabe 1976 (Lepidoptera: Tortricidae) is known as a potential biological control agent of A. artemisiifolia. In order to control this species using a biological control method, we investigated overwintering structures and spatial distributions of E. sugii in A. artemisiifolia colony as an initial step.

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.

To investigate the effectiveness of ventilation in bumblebee colony, we ventilated bumblebee(bombus terrestris) colonies using PC cooling fan to replace the fanning behavior of bumblebees, unventilated colonies were used as controls. The temperature of the colonies that were ventilated using the cooler fan was 3 ℃ lower than the control colonies, and the rate of fanning behavior were 9 times lower in ventilated colonies than in the control colonies. Foraging activity and survival rates of worker bees in ventilated colonies doubled compared to the control colonies. Rates of seed set per flower and number of seeds set by bees in the ventilated colonies was 12 % and –15 % and 1.1 and –1.4 times higher pollination, respectively, than for the control colonies. We conclude that installing a ventilation device on the colony box can improve the internal environment of bumblebee colonies at high temperatures through ventilation and that this is a good option to increase pollination effectiveness for the crops in high temperature greenhouse conditions.

Feeding effects of the honeybee pollen products from both domestic, China, Spain and mixture of different origin on the colony development of earth bumblebee, Bombus terrestris L., were surveyed to evaluate efficient nutritional resources for commercial bombiculture in Korea. As the results, the domestic pollen was most effective to achieve high rates of oviposition (88%), colony foundation (70%), and queen production. While feeding with domestic pollens during the egg-laying period, and domestic+Chinese mixture (5:2) during the breeding period (Plot-2), it revealed high oviposition rate of 75%, colony foundation of 65%, and large numbers of adult queen production, indicating its suitability for generation subculturing. In the Plot-3, the same high oviposition rate (75%) was obtained except for feeding with the domestic+Chinese mixture (2:5) during the breeding period, which produced large number of workers.

A colony of Solenopsis invicta was first intercepted on Gamman pier, Pusan port in Korea at September, 2017 by Animal and Plant Quarantine Agency. The mitochondrial DNA (mt-DNA) of workers was analyzed and compared with vary libraries of mt-DNA haplotypes to elucidate the origin of the introduced colony with the trade pattern of the Gamman pier. The mt-DNA fragment of 768 bp, which is part of the Cytochrome oxidae I gene, was amplified and sequenced. The results showed that the mt-DNA was in the clade of haplotype 5, which is endemic in southern USA, China, Taiwan, and Australia. More than 60% of containers are imported from China into Gamman pier, it may be possible to assume that the colony was inadvertently invaded through containers from China.

Recently a colony of Solenopsis invicta, the red imported fire ant (RIFA), was intercepted on Gamman pier, Pusan port in Korea by Animal and Plant Quarantine Agency. It has been generally known that RIFA has two social forms as monogyne and polygyne, which showing the behavioral differences between the two forms and dictated by a pheromone binding protein, Gp-9. The social forms of the RIFA colony was revealed as polygyne form, when the GP-9 gene was analyzed by three allelic discrimination assays including Real-Time PCR (RT-PCR), rh-Amp SNP Genotyping, and peptide nucleic acid probe-based RT-PCR in this study.

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.