Due to cognitive differences, traditional perceptual engineering (KE) frequently relies too heavily on designers' experience in analyzing customers' emotional demands, which can result in product designs that deviate from users' expectations. This work suggests a thorough evaluation approach that combines the particle swarm optimization-support vector regression (PSO-SVR) model and perceptual engineering to increase the scientificity and precision of design choices. The approach first determines the subjective weights of users' emotional needs using spherical fuzzy hierarchical analysis (SFAHP). Next, it uses the entropy weighting method to determine the objective weights. Finally, it combines the subjective and objective data using game theory to produce a more rational evaluation system. Finally, the emotional prediction model based on PSO-SVR is constructed to realize the accurate mapping between emotional needs and design features. The empirical study shows that“speed”, “dynamic”and“luxury” are the core emotional demands of users, and the algorithm's prediction results are highly consistent with users' actual evaluations, which strongly verifies the accuracy of the model. Compared with the traditional KE method, the model better integrates subjective experience and objective data and provides more practical support for the design of flybridge yachts.

Malaria remains a significant public health issue, particularly in regions such as the Korean Demilitarized Zone (DMZ). Effective malaria control and prevention require precise prediction of mosquito density across both monitored and unmonitored areas. This study aimed to develop predictive models to estimate the abundance of malaria vector mosquitoes by integrating meteorological and geographical data. Data from mosquito surveillance sites and NASA MODIS land cover datasets acquired between 2009 and 2022 were utilized. Two predictive models, the Gradient Boosted Model (GBM) and Principal Component Regression (PCR), were employed and evaluated. Model performance was assessed using the coefficient of determination (R²). Results showed that PCR outperformed GBM in predictive accuracy, suggesting that PCR is more robust in handling multicollinearity among variables. However, both models did not show practically-usable level of prediction performance. This study provides a preliminary but foundational framework for extending predictive modeling to broader regions, thereby supporting malaria prevention efforts through improved risk mapping.

A tick survey was conducted to monitor ticks using tick traps attached dry ice method at each four sites in Ulju and Gimhae counties, Gyeongsangnam-do from April to November, 2023. Two species belonging to one genera were collected with tick traps. A total of 1,064 ticks were collected as Haemaphysalis longicornis (Trap Index; TI 11.0), Haemaphysalis flava (TI <0.1) in Ulju and A total of 843 ticks were collected as Haemaphysalis longicornis (Trap Index; TI 8.7), Haemaphysalis flava (TI 0.1) in Gimhae 2023. Haemaphysalis longicornis was the most frequently collected, representing 99.2% in Ulju, 98.9% in Gimhae. In the collection environments, a total number of 685, 268, 64, and 47 ticks were collected from a glassland, a copse, a mountain path, and a grave of Ulju a total number of 469, 216, 83, and 75 ticks were collected from a glassland, a copse, a Grave, and a mountain path of Gimhae respectively. In the results of the isolation of Severe Fever with Thrombocytopenia Syndrome (SFTS) from the ticks, no pathogens were detected from RNA of 101 pools (Ulju), 98 pools (Gimhae) of the ticks using a Polymerase Chain Reaction method in 2023.

Climate change has made outbreaks of insect-transmitted plant viruses increasingly unpredictable. Understanding spatio-temporal dynamics of insect vector migration can help forecast virus outbreaks, but the relationship is often poorly characterized. The incidence of Beet curly top virus (BCTV) was examined in 2,196 tomato fields in California from 2013-2022. In addition, we experimentally showed dispersal of the beet leafhopper, the only known vector of BCTV is negatively correlated with plant greenness, and we estimated spring migration timing using a vegetation greenness-based model. Potential environmental factors and spring migration time of beet leafhoppers were associated with BCTV incidence. We found BCTV incidence is strongly associated with spring migration timing rather than environmental factors themselves. In addition, the vegetation greenness-based model was able to accurately predict the severe BCTV outbreaks in 2013 and 2021 in California. The predictive model for spring migration time was implemented into a web-based mapping system, serving as a decision support tool for management purposes.

Recent advances in artificial intelligence and machine learning, such as the use of convolutional neural networks (CNNs) for image recognition, have emerged as a promising modality with the capability to visually differentiate between mosquito species. Here we present the first performance metrics of IDX, Vectech’s system for AI mosquito identification, as part of Maryland’s mosquito control program in the USA. Specimens were collected over fourteen weeks from twelve CDC gravid trap collection sites, identified morphologically by an entomologist, and imaged using the IDX system. By comparing entomologist identification to the algorithm output by IDX, we are able to calculate the accuracy of the system across species. Over the study period, 2,591 specimens were collected and imaged representing 14 species, 10 of which were available in the identification algorithm on the device during the study period. The micro average accuracy was 94.9%. Of these 10 species, 7 species consisted of less than 30 samples. The macro average accuracy when including these species was 79%, while the macro average when excluding these species was 93%. In the next iteration of this technology, Vectech is translating the vector identification capabilities of IDX into systems capable of processing greater numbers of specimens at large public health facilities, and remote sensing systems that will allow public health organizations to monitor vector abundance and diversity from the office. These advances demonstrate the utility of artificial intelligence in entomology and its potential to support vector surveillance and control programs around the world.

Due to climate change and the rise in international transportation, there is an emerging potential for outbreaks of mosquito-borne diseases such as malaria, dengue, and chikungunya. Consequently, the rapid detection of vector mosquito species, including those in the Aedes, Anopheles, and Culex genera, is crucial for effective vector control. Currently, mosquito population monitoring is manually conducted by experts, consuming significant time and labor, especially during peak seasons where it can take at least seven days. To address this challenge, we introduce an automated mosquito monitoring system designed for wild environments. Our method is threefold: It includes an imaging trap device for the automatic collection of mosquito data, the training of deep-learning models for mosquito identification, and an integrated management system to oversee multiple trap devices situated in various locations. Using the well-known Faster-RCNN detector with a ResNet50 backbone, we’ve achieved mAP (@IoU=0.50) of up to 81.63% in detecting Aedes albopictus, Anopheles spp., and Culex pipiens. As we continue our research, our goal is to gather more data from diverse regions. This not only aims to improve our model’s ability to detect different species but also to enhance environmental monitoring capabilities by incorporating gas sensors.

IPCC가 발간한 “지구온난화 1.5℃ 특별보고서에서는 전 지구적인 경제피해, 생태계, 종다양성에대한 피해를 언급하고 있다. 우리나라를 기준으로 보았을 때 평균 기온이 상승하는 경우 가장 우려되는 현상 중 한가지는 매개체들의 서식범위가 북쪽으로 확대되어지고 이에 따라 매개체가 옮기는 질병들에 대한 우려가 매우 크다. 특히 국내에 서식하지 않던 매개체들의 유입 위험이 증가되고 있다. 이에 질병관리청은 16개의 매개체감시거점 센터를 운영하고 있다. 하지만 최근 5년간 매개체 관련 과제 숫자는 감소하고 있어 매개체 감시의 중요성이 대두 되어질 필요가 있다.

In light of global climate change, Korea faces significant challenges with indigenous mosquito-borne diseases, notably malaria and Japanese encephalitis. Moreover, there is a growing incidence of imported arboviral diseases attributable to the increasing number of international travelers. Dengue fever emerges as the predominant mosquito-borne ailment among Korean travelers, while cases of Japanese encephalitis and chikungunya are also seeing an upward trend. Many countries have witnessed arboviral infections transmitted by pathogens-carrying mosquitoes, primarily due to the introduction of viruses by travelers. Additionally, the ongoing processes of global warming and urbanization are creating increasingly favorable environments for mosquitoes and the proliferation of mosquito-borne pathogens. This underscores the urgency of assessing both the current status and future projections of mosquito-borne diseases in Korea.

A force-free field (FFF) is determined solely by the normal components of magnetic field and current density on the entire boundary of the domain. Methods employing three components of magnetic field suffer from overspecification of boundary conditions and/or a nonzero divergence-B problem. A vector potential formulation eliminates the latter issue, but introduces difficulties in imposing the normal component of current density at the boundary. This paper proposes four different boundary treatment methods within the vector potential formulation. We conduct a comparative analysis of the vector potential FFF solvers that we have developed incorporating these methods against other FFF codes in different magnetic field representations. Although the vector potential solvers with the new boundary treatments do not outperform our poloidal-toroidal formulation code, they demonstrate comparable or superior performance compared to the optimization code in SolarSoftWare. The methods developed here are expected to be readily applied not only to force-free field computations but also to time-dependent data-driven simulations.

본 논문에서는 대규모 실시간 매칭의 생존 게임에서 플레이를 위한 유저들의 소셜 관계에 대해 연구한다. 특 히 “사전 팀 구성”을 통한 자의적인 팀 구성이 어떤 방식으로 유저들을 연결하는 지 연구하고자 한다. 다수 의 사람 간 집단 역학에서 나타나는 특성이나 패턴에 대한 조사를 중심으로 하였으며, 개인의 특성은 보조적 인 수단으로만 사용된다. 이번 연구에서는 게임을 플레이하는 유저들의 익명화 된 대규모 데이터를 활용하며 이에 대한 간소화된 집계 방법을 제안한다. 데이터 세트에는 사전 팀 구성에 관한 11,259만 줄의 속성이 포 함되어 있으며, 데이터에서 우리는 250만개의 노드와 1,182만개의 무방향 에지가 있는 협업 네트워크를 구성 하여 대규모 게임 내 협동 네트워크를 만듭니다. 연결 정도, 경로 길이, 클러스터링 및 소속 하위 컴포넌트의 크기 등 네트워크에 관한 수치를 통해 게임내 소셜 활동에 대한 이해를 높이고자 한다. 본 논문에서는 다음 의 두가지 특성을 중심으로 결론을 제시한다. 첫째, 네트워크 내에는 대규모로 연결된 2개(전체의 44% 및 2%)와 나머지의 파편화된 하위 컴포넌트로 구성 되어있다. 이 대규모 컴포넌트 중 작은 쪽은 한국 유저로만 구성되어 있다. 둘째, 컴포넌트 크기 별 평균 연결 거리와 군집화 계수, k-core를 확인함으로써 기타 다른 네 트워크 대비 이웃 간 연결이 강하면서 전체적으로는 비교적 멀리 떨어져 있음을 확인한다.

본 연구는 네트워크 이상 감지 및 예측을 위해 벡터 자기회귀(VAR) 모델의 사용을 비교 분석한다. VAR 모 델에 대한 간략한 개요를 제공하고 네트워크 이상 체크로 사용 가능한 두 가지 버전을 검토하며 두 종류의 VAR 모델을 통한 경험적인 평가를 제시한다. VAR-Filtered moving-common-AR 모델이 단일 노드 이상 감지 성능에서 우수하며, VAR-Adaptive Learning 버전은 몇 개의 노드 간 이상을 효과적으로 식별하는 데 특히 효 과적이며 두 가지 주요VAR 모델의 전반적인 성능 차이에 대한 근본적인 이유도 분석한다. 각 기술의 장단점 을 개요로 제공하고 성능 향상을 위한 제안도 제시하고자 한다.