Earticle

The endangered species of Cyathea spinulosa (tree ferns) are among the least concerned ferns of Nepal that bring threats to them and their habitat. A way to reduce such threats is by maintaining a database of species’ whereabouts and generating a scientific understanding the habitat preferences. This will eventually help in the formulation of conservation plans for the species. This research aimed to characterize the suitable habitat of C. spinulosa by enumerating the location of species in the Panchase Forests of central Nepal. The statistical index method was applied to relate the occurrence locations of species with various environmental factors for the development of indices. The suitable habitat of C. spinulosa (more and most suitable categories) covered 119 km2 and accounted for 43% of the total area studied. 74.4% of occurrence locations of C. spinulosa were recorded from these habitats. The habitat characteristics suitable for C. spinulosa were: proximity to streams (high moisture), land covered by forested area (shady area), mid-elevations of hills about 1,000 m to 2,000 m (sub-tropical climate), slope gradient of 20° to 40° (steep slopes), and northern to eastern aspects. These habitat characteristics could be considered for in-situ protection of tree ferns and designating the conservation plots.

Accurate channel modeling is crucial for optimizing direct-to-satellite Internet of things (DtS-IoT) communications via low Earth orbit (LEO) nanosatellites. Traditional channel models for land-mobile satellite systems often overlook the significant impact of shadowing at low elevation angles, limiting their applicability to DtS-IoT scenarios. This paper presents an enhanced finite-state Markov channel with two-sectors (FSMC-TS) model that integrates shadowing effects into the bad sector (B-Sector) by using the K-distribution for modeling. This enhancement captures the combined effects of multipath fading and shadowing, providing a more accurate representation of the channel conditions experienced in DtS-IoT applications. Simulation results show that the enhanced model aligns closely with analytical bit error rate (BER) predictions, particularly at higher signal-to-noise ratios (SNRs), with less than 1% deviation from theoretical values. The Enhanced FSMC-TS model offers a valuable tool for reliable DtS-IoT communication systems, addressing a critical gap in existing channel modeling approaches.

Continuous global warming affects distribution patterns of various animal species. In South Korea, Gekko japonicus has found only in Busan, Gimhae, Masan and Mokpo city which are located at southern coastal places of South Korea. In this study, we determined current distribution of G. japonicus in South Korea and Japan based on 19 bioclimate, altitude and aspect data, which were obtained from Worldclim and Vegetation index (NDVI) from USGS, using MaxEnt (Maximum Entropy Modeling). We also subsequently predicted future distribution (2050 and 2070) of G. japonicas in South Korea based on a climate projection model (HadGEM2-ES: rcp 8.5). As distribution data, we used total 910 location data, which were collected from both South Korea and Japan. The each AUC values of the three models were all 0.963, suggesting that our model are highly reliable. Precipitation of driest quarter (35.6%) at present (1960-1990), precipitation of driest quarter (22.5%), annual precipitation (16.9%) and NDVI (15.4%) in 2050 and temperature seasonality (21.8%) and annual precipitation (18.2%) in 2070 explained each more than 15% of its distribution, respectively. In South Korea, current distribution of G. japonicus mainly occurred at westcoast side of Jeolla-do and southand eastcoast side of Gyeonsang-do. Following climate change, the distribution of G. japonicus further expanded to the north, but only along those westcoast and eastcost distribution lines.

지난 수십 년에 걸쳐, 전 지구적 환경변화와 인위적 요인에 의한 생물 종 유입 등으로 인한 파충류 분류 군의 개체 수 감소 및 토착종의 높아진 절멸 위험 등이 문제시 되고 있다. 도마뱀부치 (Gekko japonicus) 는 한국, 중국, 일본에 걸쳐서 분포하고 있으며, 국내에서는 1907년 처음 서식이 보고되고, 자연적 또는 인위적 확산 가능성만 언급된 채 실질적인 종 관리를 위한 자료 마련에 필요한 관련 연구들은 미흡한 실정 이다. 본 연구는 2015년부터 2018년까지 4년 동안 한국, 중국, 일본 내 도마뱀부치 종의 분포 현황과 분 포지 내 미소서식지 이용 특성을 구명하고 개체군 간 형태적 변이 비교를 통하여 도마뱀부치 종의 생태적 이해를 증진하여 장기적인 종 관리에 필요한 기본정보를 제공하고자 하였다. 첫 번째 종 분포 현황 및 잠재서식지 예측 연구결과, 도마뱀부치는 중국 중동부 해안 대도시와 중부 내 륙 중·소도시, 일본 홋카이도 남부의 일부 지역을 포함한 혼슈와 규슈 북부 및 남부 해안 도심지, 한국 남 해안 일부 지역에서 확인되었다. 모델링을 이용한 현재 서식적합지역은 실제 도마뱀부치 서식이 확인된 지 역과 유사하였고, 중국 산둥반도 남부와 일본 북동부, 한국 동부 해안지역은 추가로 서식이 적합한 지역으 로 예측되었다. 서식적합지 예측에 주요하게 작용한 환경요인은 계절 간 기온편차, 고도, 도시와의 거리 순 으로 확인되었다. 미래 기후변화 시나리오를 토대로 미래 서식적합지 변화 양상을 예측한 결과, 현재에 비 하여 2050년과 2070년에 서식적합지 면적이 2.2 – 18.2 % 감소하는 것으로 예측되었다. 특히, 서식적합지 면적은 도심지 인근에서 크게 감소할 것으로 예측되었고, 일본 혼슈의 북부 해안가와 같이 육지 가장자리 의 서식적합지 면적이 크게 감소할 것으로 예측되었다. 두 번째 미소서식지의 일반적인 이용 및 계절적 이용 차이에 대한 연구결과, 도마뱀부치들은 벽돌을 포 함한 콘크리트 기질로 된 평면의 흰색 수직 벽과 각종 틈새를 가장 빈번하게 이용하였는데, 그러한 범주형 미소서식지 이용이 봄과 여름 간 차이가 있지는 않았다. 반면 여름철 기질온도, 조도, 잠재 은신처까지 거 리가 봄철 보다 더 크게 나타나, 수치형 변수의 경우에는 계절적으로 차이를 보였다. 이러한 연구결과는 본 연구지역에서 도마뱀부치는 계절적 환경변화에 주로 수치형 미소서식지 변수를 조절하여 환경변화에 대 응한다는 것을 나타낸다. 마지막으로 한국, 중국, 일본에 서식하는 도마뱀부치 11개체군의 형태 변이 확인 결과에서 개체군 간 변이는 주로 코끝부터 총배설강까지 길이, 코끝부터 앞다리까지 길이, 옆구리 길이, 아랫입술 비늘 수 변수 에서 나타나고, 각 변이는 각 개체군 내 차이가 개체군 간 차이 보다 더 큰 것으로 나타났다. 개체군 간 판별력은 약 45%로 나타났으며, 군집분석 결과, 11개체군은 크게 두 개 그룹으로 묶여서 나타났다. 이러 한 연구결과는 한국, 중국, 일본 개체군들이 최소 두 개 이상의 기원 개체군으로부터 최근에 분화되었을 가능성과 최근까지 개체군 간 빈번한 이동이 있을 가능성을 제시한다. 본 연구에서 확인된 도마뱀부치의 종 분포 양상과 서식지 이용 특성 및 형태학적 변이 비교 결과는 추 가적인 개체군 예측 및 확인, 개체군 변화 양상 확인, 추후 기후변화 속 장기적인 도마뱀부치 종 관리 방 안 마련 등을 위한 자료로 이용될 수 있을 것이다.

비디오에서 행동의 위치를 추정하기 위해 행동의 시작과 끝에 모두 주석을 다는 것은 많은 비용을 요구한다. 따라서 주석 생성 비용을 줄이기 위해 비디오 단위의 행동 카테고리만 존재하는 약지도 학습이 활발히 진행되고 있지만 약 지도 학습은 행동단위 주석의 부재에 따른 많은 한계가 존재한다. 최근에는 주석의 생성 비용은 줄이면서 약지도 학 습에 비해 훨씬 개선된 성능을 낼 수 있는 포인트 주석 기반 약지도 학습에 대한 연구가 진행되고 있다. 하지만 이는 행동구간 내의 포인트가 존재하는 위치에 많이 의존한다는 한계가 있다. 본 연구진은 이런 점을 보완하고자, 행동구 간을 나타낼 수 있는 확률분포를 설계하고 포인트 주석을 샘플링하여 학습이 진행됨에 따라 점차 포인트 주석을 행 동구간의 가운데 방향으로 움직일 수 있게끔 하는 포인트 업데이트 방식을 제안한다. 실험은 제안하는 방법이 기존 의 포인트 주석 기반 학습에서 치우친 포인트가 주어진 경우 성능의 한계를 개선함을 보여준다.

Labeling the starting and ending points of interesting actions in a video is labor-intensive and expensive. To reduce the labeling costs, many studies have been conducted on weakly supervised learning, where only video-level action labels exist, but it has limitations due to the absence of frame-level annotations. Recently, research has been conducted on point annotation-based learning, which can achieve much improved performance while reducing labeling costs. However, this has a limitation in that the performance depends the location of annotated points in the action interval. To compensate for this, we design a probability distribution that can represent action intervals, which allows us to sample point annotations and update them to gradually move point annotations toward the center of action intervals as learning progresses. Experiments show that the proposed method improves the performance limitation given biased points in a point annotation-based learning scheme.

A hierarchical computational method has been developed and used with finite element method based on dislocation density multiple-slip crystalline formulation to predict how nanoindentation affects behavior in face-centered cubic crystalline aggregates. Using displacement profiles which were obtained from molecular dynamics(MD) nanoindentation simulation, scaling relations based on indentation depths, grain-sizes, and grain aggregate distributions were obtained. These relations then applied to coarsen grains in micros- tructurally based FE formulation which accounts for dislocation density evolution, crystalline structures. This computational regime was validated with a several experimental results related to single gold crystals. This hierarchical model provides a tool to link nanosacle level with a microstructurally based FEM formulation that can be to ascertain inelastic effects such as dislocation density evolution. With the above certainty temperature distribution during the nanoindentation simulation also was investigated along with the different indentation depth.

A score evaluation method is the most common deterioration evaluation technique used in Korea for the efficient maintenance of the water distribution network which is a major society-based facility. This study carried out modeling for complementing the limitations of the existing score evaluation method.

A Radio Environment Map (REM) maps its geolocation information to the propagation characteristics as complementary information for an adaptive communication of cognitive radio (CR). In this paper we propose a novel theoretical three dimension (3D) Multiple Input Multiple Output (MIMO) channel model. The proposed model parameterizes the geolocation information of interacting obstacles referred as scatterers. An efficient response distribution function (ERDF) is developed to interpolate the array response for an arbitrary scatterer location. It is shown that the mutual information of the proposed model is consistent with a conventional geometry-based model with the lower simulation time. It allows us to generate channels with all possible scatterer distribution on the surface of a building effectively.

This paper reported the measurements and modeling of noise on the 22.9kV Medium-Voltage (MV) underground power distribution cable for Broadband Power Line Communication (BPLC). The proposed measurement system was composed of inductive coupler and Digital Phosphor Oscilloscope (DPO). The empirical noise data with the measurement system was obtained from thirty-two pad mounted transformers in the test field located in Choji area of Ansan city. After conducting analysis of noise characteristics in time and frequency domain, the noise model are presented. In order to analyze the noise in frequency domain, Power Spectral Density (PSD) was computed with empirical data using Welch’s method. The model of the power line noise at each frequency carried out using Cumulative Probability Distribution (CPD) of computed noise power. It compared with common Cumulative Distribution Function (CDF) of Nakagami-m distribution, Gaussian distribution, Gamma distribution. Below 20MHz frequency range, gamma distribution was fitted with the CPD. Nakagami-m distribution provides a good fitting to the noise CPD above 20MHz frequency range.

We investigate incumbent customers’ store switching behaviors following new store entry in a retail franchise setting. Using customer-level transaction and store characteristics data from a retail franchise in a major urban district, we model switching behavior using a zero-one inflated beta (ZOIB) regression with Bayesian estimation. This approach distinguishes customers who do not switch, partially switch, or completely switch to new stores. We find that new store entry generates more partial switchers than complete switchers. Among pre-entry behavioral variables, purchase frequency and purchase quantity are the strongest predictors of non-switching, suggesting the roles of travel costs and inventory optimization. Conversely, bulk purchasers are most vulnerable to complete switching, as their infrequent, large-quantity trips may imply high travel costs which a nearby new entrant may reduce. Multi-category shoppers are prone to partial switching, likely optimizing category-level purchases across stores. Among store characteristics, greater inter-store distance helps retain incumbent customers. Our findings reframe franchise encroachment as a share-of-visit or share-of-purchase problem rather than a customer-loss problem, offering franchisees a segmentation framework for targeting efforts after new store entry.

A power distribution network (PDN) is a network that provides connection between the voltage source supply and the power/ground terminals of a microprocessor chip. It consists of a voltage regulator module, a printed circuit board, a package substrate, a microprocessor chip as well as decoupling capacitors. For power integrity analysis, the board and package layouts have to be transformed into an electrical network of resistor, inductor and capacitor components which may be expressed using the S-parameters models. This modeling process generally takes from several hours up to a few days for a complete board or package layout. When the board and package layouts change, they need to be re-extracted and the S-parameters models also need to be re-generated for power integrity assessment. This not only consumes a lot of resources such as time and manpower, the task of PDN modeling is also tedious and mundane. In this paper, a block-based PDN modeling is proposed. Here, the board or package layout is partitioned into sub-blocks and each of them is modeled independently. In the event of a change in power rails routing, only the affected sub-blocks will be reextracted and re-modeled. Simulation results show that the proposed block-based PDN modeling not only can save at least 75% of processing time but it can, at the same time, keep the modeling accuracy on par with the traditional PDN modeling methodology.

In this study, a new method of building a dynamic load model for dynamic stability analysis was proposed. This method is as follows: First, inject some small electric power disturbance of known pattern to power system and measure the response in distribution system. Next, analyze the measured response using system identification and create the load model. Simulation was performed with PSCAD, and a dynamic load model was build and evaluated. In the simulation, the availability of the proposed method was confirmed. The model could reproduce the response of the stable system precisely. In addition to it, the stability of the system could evaluate with proposed method.

Parametric method of flood frequency analysis involves fitting of a probability distribution to observed flood data. When record length at a given site is relatively shorter and hard to apply the asymptotic theory, an alternative distribution to the generalized extreme value (GEV) distribution is often used. In this study, we consider the beta-P distribution (BPD) as an alternative to the GEV and other well-known distributions for modeling extreme events of small or moderate samples as well as highly skewed or heavy tailed data. The L-moments ratio diagram shows that special cases of the BPD include the generalized logistic, three-parameter log-normal, and GEV distributions. To estimate the parameters in the distribution, the method of moments, L-moments, and maximum likelihood estimation methods are considered. A Monte-Carlo study is then conducted to compare these three estimation methods. Our result suggests that the L-moments estimator works better than the other estimators for this model of small or moderate samples. Two applications to the annual maximum stream flow of Colorado and the rainfall data from cloud seeding experiments in Southern Florida are reported to show the usefulness of the BPD for modeling hydrologic events. In these examples, BPD turns out to work better than $beta-{\kappa}$, Gumbel, and GEV distributions.

Construction labor productivity, which is the greatest source of variation in overall construction productivity, is the critical factor for determining the project performance in terms of time and cost, especially during scheduled overtime when extra time and cost are invested. The objective of this research is to select an appropriate type of probability distribution function representing the variability of daily labor productivity during overtime. Based on the results of statistical data analysis of labor performance during different weekly work hours, lognormal distribution is selected in order to take advantage of easiness of generating correlated random numbers. The selected lognormal distribution can be used for development of a simulation model in construction scheduling, cost analysis, and other applications areas where representation of the correlations between variables are essential.

Construction labor productivity, which is the greatest source of variation in overall construction productivity, is the critical factor for determining the project performance in terms of time and cost, especially during scheduled overtime when extra time and cost are invested. The objective of this research is to select an appropriate type of probability distribution function representing the variability of daily labor productivity during overtime. Based on the results of statistical data analysis of labor performance during different weekly work hours, lognormal distribution is selected in order to take advantage of easiness of generating correlated random numbers. The selected lognormal distribution can be used for development of a simulation model in construction scheduling, cost analysis, and other applications areas where representation of the correlations between variables are essential.

In this paper, a new algorithm of optimal voltage control is proposed for the Distribution Automation System (DAS) based on constants of four terminal network modeling. In the proposed method, the voltage profiles along feeders are estimated from the measurement of the current and power factor by a Feeder Remote Terminal Unit (FRTU) installed at each node. Whenever the voltage profile violates the restriction, the voltage control strategy is applied to keep the voltage levels along the feeders within the pre-specified range through the modification and coordination of the transformer under-load tap changers (ULTC), step voltage regulator (SVR), as well as shunt condenser. In the case studies, the estimation and control of the voltages have been testified in a radial distribution system with 11 nodes.

Ainsliaea acerifolia, a perennial herbaceous plant endemic to forested regions of the Korean Peninsula, has drawn attention for its bioactive properties and potential as a functional food and medicinal resource. Despite its ecological and economic value, little is known about its spatial distribution or habitat preferences under changing climatic conditions. This study aims to quantify the current and future habitat suitability of A. acerifolia in southeastern Korea?including Gyeongsangnam-do, Busan, and Ulsan?using the MaxEnt (Maximum Entropy) species distribution model. A total of 217 presence records were compiled from both GBIF data and field surveys, and seven key environmental variables were selected through multicollinearity screening. Model performance was robust (mean AUC = 0.845), and key predictors included topographic roughness (TRI), precipitation of the driest month (Bio14), and mean diurnal range (Bio2). Under current climate conditions, 31.5% of the study area was identified as suitable habitat (≥0.222), with only 4.7% categorized as core habitat (≥0.6). Future projections under SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios indicated substantial habitat contraction, particularly in coastal and lowland areas, with refugial zones likely to persist in mid-elevation inland mountains (500?900?m). These findings suggest that A. acerifolia is highly sensitive to climatic variability and emphasizes the urgent need for conservation strategies focused on climate-resilient refugia and ecological corridors. The study provides a spatial framework for species conservation planning under future climate uncertainty.

본 연구에서는, L-moments와 Harmony Search 파라미터 추정법을 사용하여 지역빈도해석을 통한 한국 68개소의 설계풍속을 도출하는 연구를 실시하였다. 기존에 일반적으로 풍속 산정에 적용된 Generalized Logistic (GLOG), General Extreme Value (GEV), Generalized Pareto (GPAR), Weibull의 분포형에 적용하여 설계 풍속값을 산정하였을 뿐만 아니라 Gamma와 Gumbel의 분포를 조합시킨 혼합분포형을 이용하여서도 설계값을 산정하였다. 산정결과, 한국에서는 Weibull분포가 가장 적절한 분포모형임을 알 수 있었다. 기존에 적용되지 않았던 새로운 혼합형 분포형도 특정지역에서 충분히 사용가능한 분포형이 될 수 있음을 알 수 있었다.

In this study, we tried to determine and investigate meteorologically homogeneous regions and to derive regional wind frequency estimates for 68 gauged sites in South Korea using an index wind method with L-moments and harmony search parameter estimation. Comparative regional wind frequency estimates were made for the tested region using various distributions, namely the Generalized Logistic (GLOG), General Extreme Value (GEV), Generalized Pareto (GPAR), Weibull. Furthermore, we applied three types of two-component mixture distributions, built by combination of Gumbel and Gamma distributions with five-parameters such as Gumbel-Gumbel (GumGum), Gamma-Gamma (GamGam), and Gamma-Gumbel (GamGum) distribution models. Their parameters are estimated by harmony search method (HS). The results of the current study showed that the Weibull distribution is identified as the bestfit distribution in Korea. In other words, Weibull as a suitable distribution with three parameters for other Korean stations.