This study proposes a statistical modeling framework for estimating the daily number of bus stops at highway transfer facilities (ex-HUBs) where demand information is often uncertain during the early planning stages. Accurate estimation of the daily number of bus stops is critical for efficient design and operation; however, reliable demand data are rarely available in the initial planning phase. Using pooled data from 16 facilities, a direct demand estimation approach was implemented, based on facility characteristics, transportation connectivity, highway traffic conditions, and socioeconomic factors. Log-linear model (LLM) and negative binomial model (NBM) were developed to capture the count data characteristics. Ensemble models using arithmetic and weighted means were also constructed to improve predictive reliability. The analysis revealed that the arithmetic mean ensemble of NBM and LLM produced the most accurate predictions. The daily number of bus stops was significantly influenced by the distance from bus terminals, highway traffic volume, public transportation connectivity, economically active population, and level of urbanization. The framework proposed in this study provides a practical tool for estimating the daily number of bus stops at highway transfer facilities, and can support more reliable feasibility analyses and infrastructure planning under demand uncertainty.

Odorous compounds from the operation of wastewater treatment plants (WWTPs) have increasingly become public health concerns and civil complaints. This study identifies priority management stages in WWTPs by quantitatively analyzing the distribution of complex odor and designated odor substances across key processes using a dimensionless tool (the Odor Activity Value, OAV), while providing a statistical basis and operational strategies for efficient odor mitigation in public facilities. Although there was a very poor correlation between designated odorous concentrations and complex odor level (dilution ratio), the application of the OAV yielded much more accurate results with a strong correlation (R2 = 0.7) specifically at high-temperature condition. Odor potential in the wastewater treatment processes fluctuates substantially depending on the specific unit. Statistical analysis using Kruskal–Wallis tests demonstrated that influent and sludge treatment process (return flow and centrate) produce much higher odor intensities compared to the biological reactor and secondary clarifier. Based on PERMANOVA analysis, differences in the profiles of complex odor and the OAVs of designated odorants across 6 treatment stages explained 64.1% of the total variance. Principal Coordinates Analysis (PCoA) showed that sludge treatment processes form a distinct, unique cluster, whereas sewage treatment streams present a more gradual transition of odor profiles. Statistical assessment using the Mann-Whitney U test demonstrated that mean odorants levels did not have considerable shift under high-temperature and low-temperature conditions. However, the sensory perception in higher temperatures enhanced relative to the OAVs. In conclusion, the OAV is an effective dimensionless tool, as it establishes priorities in odor management and control, offering a practical supplementary indicator for addressing civil complaints. These findings provide a robust foundation for optimizing deodorization systems designs and operational efficiency of odor mitigation systems within WWTPs.

2000년대 초중반 국내 고속도로 교량에 LMC계 교면포장이 도입된 이후, 우수한 수밀성 및 내구성과 기존 바닥판 콘크리 트와 유사한 열팽창 특성에 기반한 구조적 일체성 확보의 장점으로 신설 및 유지관리 현장에서 폭넓게 활용되어 왔다. 이후 조강·초속경 시멘트를 적용한 다양한 공법이 개발되면서 초기 개방 시간 단축 및 교통 통제 최소화를 위한 기술적 확장이 이루어졌으며, 국내 교면포장 기술은 재료 및 시공 측면에서 지속적으로 발전해왔다. 그러나 준공 후 일정 기간이 경과한 교량에서 들뜸, 탈락, 균열 등 손상이 반복적으로 보고되고 있으며, 이에 따른 장기 공용성 저하와 유지관리 비용 증가 문제 가 제기되고 있다. 기존 연구는 실내 물성시험 및 단기 성능 평가에 집중되어 왔으며, 실제 공용 중인 다수 교량을 대상으 로 교통·환경 인자를 통합 고려한 장기 성능 분석은 제한적인 실정이다. 이에 본 연구에서는 실교량 기반의 공용성 데이터 베이스를 구축하고, 누적 교통하중과 환경하중을 포함한 다양한 인자와 손상지표 간의 통계적 분석을 수행함으로써 LMC 교 면포장의 장기 성능 특성을 정량적으로 평가하고자 한다. 이후 통계적 유의성 검정과 함께, 향후 성능 예측 모델 개발 및 고도화를 통해 성능기반 유지관리 의사결정 체계를 위한 기초 자료로 사용될 수 있다.

This study develops a scientific fishing-ground exploration framework for the Korean large purse-seine fishery, where traditional experience-based searching has become increasingly unreliable under rapid climate variability. AIS-derived fishing locations from 2021 to 2023 were integrated with HYCOM-based temperature and salinity fields and MODIS-Aqua chlorophyll-a data to construct a unified environmental – fishing dataset. After multicollinearity screening and principal component analysis, temperature and salinity at 30 m depth and chlorophyll-a were selected as representative predictors. Using these variables, a generalized additive model (GAM) with background-sampled pseudo-absence data and monthly maximum entropy (MaxEnt) models were developed to quantify nonlinear habitat – environment relationships and predict monthly and seasonal mackerel fishing occurrences. Model performance was evaluated using independent data from 2024. GAM exhibited relatively stable predictive performance across months with generally high AUC and TSS values whereas MaxEnt showed pronounced seasonal variability and was effective in identifying potential habitat structures based on presence-only environmental conditions. Spatial predictions from both models showed good agreement with observed fishing-ground distributions during specific seasons, reproducing high-suitability zones associated with seasonal thermal – salinity fronts and productivity gradients. These results provide insights into the environmental mechanisms governing purse-seine fishing grounds and demonstrate the complementary roles of GAM for operational prediction and MaxEnt for potential habitat exploration.

본 연구는 재해방지형, 생활환경보전형, 경관보전형, 수원함양형, 산림유전자원보호림 등 다섯 가지 산림보호구역 유형 간 생태계서비스 구조의 차이를 정량적으로 분석하였다. 이를 위해 InVEST 모형을 활용하여 수원함양(WY), 탄소저장(CS), 서식지질(HQ), 토양유실(SDR) 및 통합 생태계 서비스 지수(CES)를 산정하고, 유형 간 차이를 통계적으로 검증하였다. 분석 결과, 모든 지표에서 산림보호구역 유형 간 평균값은 통계적으로 유의한 차이를 보였으며(p < 0.001), 특히 산림유전자원보호구역이 전반적으로 가장 높은 생태기능을 나타냈다. 사후검정(Games–Howell) 결과 또한 산림보호구역 유형별 생태계서비스 수준이 뚜렷하게 구분됨을 확인하였다. 본 연구는 산림보호구역 유형에 따라 생태계서비스 수준이 구조적으 로 달라짐을 실증적으로 제시하였으며, 유형별 맞춤형 관리 전략 수립을 위한 근거자료를 제공한다.

Crash risk in metropolitan areas arises from the interaction among driver behavior, enforcement infrastructure, and urban environmental conditions; however, microspatial evidence remains scarce. This study examines the effects of automated speed-enforcement cameras on the crash risk in Seoul at the legal-dong level using the accident risk index, which accounts for both crash frequency and injury severity. The dataset combines crash records, enforcement infrastructure, school-zone information, demographic indicators, and land-use characteristics. Methodologically, a Bayesian negative binomial regression model was employed to address overdispersed crash data, whereas gradient-boosting machine and eXtreme Gradient Boosting models with SHAP interpretations were applied to capture nonlinear effects, heterogeneity, and variable interactions. The results reveal that the crash risk is spatially concentrated, with a small proportion of districts contributing disproportionately to the overall exposure. Regression analysis highlights enforcement and behavioral factors as significant predictors, whereas machine-learning models emphasize the added importance of structural and demographic characteristics, such as road area and floating population. This divergence reflects the sensitivity of the regression to collinearity and linearity assumptions in contrast to the flexibility of tree-based methods in uncovering nonlinear and context-dependent influences. In general, the findings reflect the value of integrating statistical and machine-learning approaches for a more comprehensive understanding of crash risk at the microspatial scale. This study advances the methodological diversity in traffic-safety research and provides practical evidence that cameradeployment strategies should be context sensitive while targeting areas with concentrated risks and distinct structural and demographic profiles.

본 연구는 새만금 방조제 준공(2010년)을 전후한 20년간의 기상자료를 분석하여 새만금 방조제 건설 전후 군산 공항의 기상 변화를 고찰하였다. 방조제의 영향을 파악하기 위해, 유사한 기상 특성을 갖지만 방조제의 영향을 받지 않는 서산공항의 관측값을 대조군으로 활용하였다. 분석 결과, 방조제 완공 이후 군산공항에서는 월평균 해무 발생일수가 2.6 일에서 1.9일로 약 27% 감소하였으며, 저고도 구름 유입시 운고가 203 m에서 264 m로 약 30% 상승하는 유의미한 기상 변화를 보였으나, 서산공항에서는 변화가 미미했다. 그 원인을 찾기 위한 기상요소별 분석에서 기온, 풍속, 상대습도에서 특별한 사항은 없었으나, 해수면 온도 분석 결과, 방조제 내부 해역은 외부보다 평균 수온이 높고(내측 16.4oC vs 외측 15.4oC), 연간 변동폭도 크며(내측 24.9oC vs 외측 22.1oC), 담수화 특성이 뚜렷하게 나타났다. 이러한 결과는 방조제를 포 함한 주변환경의 변화(간척, 담수화, 수온 상승 등)가 군산공항의 해무 소산에 실질적으로 기여했음을 알 수 있었다.

Distant metastasis is an uncommon but critical determinant of prognosis in oral squamous cell carcinoma (OSCC). This study aimed to evaluate clinicopathological risk factors associated with distant metastasis and overall survival (OS) in surgically treated patients with OSCC. A retrospective review was conducted on 116 patients who underwent surgery for oral cancer at Samsung Medical Center between 2018 and 2024. Clinicopathological variables—including depth of invasion (DOI), extranodal extension (ENE), lymphovascular invasion (LVI), perineural invasion (PNI), and worst pattern of invasion (WPI)—were analyzed. Kaplan–Meier survival analysis and Cox proportional hazards regression were used to assess prognostic factors. Distant metastasis occurred in 9.1% of patients and was significantly associated with inferior OS (P < 0.0001). In univariate analysis, LVI, ENE, WPI, and multiple metastatic lymph nodes were significantly associated with poor prognosis. Multivariate analysis identified focal LVI as an independent predictor of OS (HR = 14.23, 95% CI: 1.85–109.67, P = 0.011). Subgroup analysis showed a higher frequency of distant metastasis among patients without neck dissection and those with deeper tumor invasion, although statistical significance was not consistently achieved due to limited events. The lung was the most common site of metastasis, and median post-metastatic survival was 5 months. LVI, ENE, WPI, and nodal burden are significant prognostic factors for OSCC. Focal LVI was independently associated with survival. These findings support the integration of high-risk pathological features into postoperative surveillance and treatment planning.

Bayesian techniques are vital in mechanical manufacturing for uncertainty quantification and process optimization. This review explores their diverse applications, highlighting advantages in handling small data and incorporating expertise for improved decision-making in quality control, reliability, and machining. It also discusses integration with machine learning and applications in specialized areas. Future research should focus on Industry 4.0 integration and user-friendly tools, emphasizing Bayesian methods' role in intelligent manufacturing.

Solar energetic particle (SEP) events, driven by solar flares and coronal mass ejections (CMEs), are occasionally accompanied by ground level enhancements (GLEs), detected by neutron monitors. While GLEs represent only a subset of SEP events, their occurrence may provide insight into the acceleration and propagation mechanisms of SEPs. In this study, we conducted a statistical analysis of 122 SEP events from 1997 to 2023, including 16 events associated with GLE and 106 without, using elemental composition data from the ACE/SIS instrument and X-ray fluence data from GOES/XRS. The results show that SEP events with GLE exhibit significantly higher fluences of SIS elements (He, C, N, O, Ne, Mg, Si) than those without, particularly at high energy channels. Notably, the fluences of carbon and oxygen were particularly enhanced in SEP events associated with GLE, suggesting a potential role for these elements in the generation of GLEs. A strong correlation (average r ≈ 0.75) was observed between the X-ray fluence of associated solar flares and the elemental fluences in SEP events with GLE, whereas a weaker correlation (average r ≈ 0.32–0.40) was found for SEP events without GLE. These findings imply that the presence of a GLE is linked to distinct acceleration conditions and enhanced ion production, particularly of light ions with large charge-to-mass ratios. This study contributes to a better understanding of SEP composition, GLE-associated mechanisms, and their relevance to space weather forecasting and radiation hazard assessments.

This study aimed to develop and evaluate goji berry (Lycium barbarum) beverages with enhanced sensory quality through the addition of fruit extracts. Five formulations were prepared using hot water extracts of dried goji berries, blended with varying proportions of concentrated apple and aronia extracts. Each beverage was adjusted to 7 of °Brix and standardized with citric acid and stevia to ensure consistent sweetness and acidity. Sensory evaluation was conducted by 20 panelists using a 9-point hedonic scale to assess sweetness, sourness, bitterness, color, aroma, and overall acceptability. Significant differences among samples were confirmed through one-way ANOVA and Tukey’s HSD post-hoc test. Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA) were performed to explore patterns in the sensory data. The beverage formulation containing 70% goji berry extract, 15% apple extract, and 14.93% aronia extract, along with sodium citric acid and stevia, demonstrated the highest overall acceptability among all samples. This was followed by the formulation composed of 70% goji berry extract, 15% apple extract, and 15% aronia extract. Principal component analysis (PCA) identified sweetness, aroma, and overall acceptability as the primary sensory attributes influencing consumer preference, with the first two principal components accounting for 95.61% of the total variance. Hierarchical cluster analysis (HCA) further confirmed these findings by grouping the two formulations together based on their sensory profiles. In conclusion, blending goji berry extract with complementary fruit extracts such as apple and aronia significantly improves the sensory quality and consumer acceptability of functional beverages. These findings suggest that optimized fruit blending may be a promising strategy for enhancing the palatability and marketability of goji-based health beverages.

This study analyzed the emission characteristics of major air pollutants (dust, nitrogen oxides, hydrogen chloride, and carbon monoxide) emitted from domestic public waste incineration facilities based on their operating elements. Using automatic measuring equipment for smokestacks (TMS), data was collected from 97 facilities from 2015 to 2023. The emission source unit (kg/ton) was evaluated based on the facility’s capacity, aging level, and incineration type. Emissions were calculated, and descriptive statistical analysis was performed based on the mean, standard deviation, and coefficient of variation. As a result of the analysis, it was found that the larger the facility capacity, the lower the average emission and volatility, which suggests that the operational stability of large facilities is high. On the other hand, facilities that had deteriorated for 10 to 15 years had the highest emission rates, and emissions decreased in facilities that were aged more than 20 years. In addition, the pyrolysis and high-temperature melting incineration facilities had lower NOx and HCl emissions than the conventional incineration type. Furthermore, CO showed the greatest volatility overall, which was found to be particularly difficult to manage in facilities in the early to mid stages of aging. These results provide empirical evidence that the structural characteristics and incineration type of incineration facilities have a significant impact on air pollutant emissions and can serve as useful basic data for policy-making, including for implementing region-wide initiatives and planning major repairs in the future.

This study aimed to improve the accuracy of road pavement design by comparing and analyzing various statistical and machine-learning techniques for predicting asphalt layer thickness, focusing on regional roads in Pakistan. The explanatory variables selected for this study included the annual average daily traffic (AADT), subbase thickness, and subgrade California bearing ratio (CBR) values from six cities in Pakistan. The statistical prediction models used were multiple linear regression (MLR), support vector regression (SVR), random forest, and XGBoost. The performance of each model was evaluated using the mean absolute percentage error (MAPE) and root-mean-square error (RMSE). The analysis results indicated that the AADT was the most influential variable affecting the asphalt layer thickness. Among the models, the MLR demonstrated the best predictive performance. While XGBoost had a relatively strong performance among the machine-learning techniques, the traditional statistical model, MLR, still outperformed it in certain regions. This study emphasized the need for customized pavement designs that reflect the traffic and environmental conditions specific to regional roads in Pakistan. This finding suggests that future research should incorporate additional variables and data for a more in-depth analysis.

도로 결빙이란 도로 표면에 형성된 얼음층으로 도로 결빙으로 인한 교통사고의 치사율은 결빙이 원인이 아닌 교통사고의 치사율과 비교하여 1.5배 높은 수치인 2.3으로 나타났다. 현재 국토교통부에서는 결빙사고 취약구간을 선정하고 관리하기 위하여 결빙 취약구간 평가기준표를 제시하였다. 그러나 도로 결빙은 노면 온도와 수분 공급에 따라 형성되며 기온, 구름량, 풍속, 풍향, 상대습도, 강수량 등 의 기상인자들이 복합적으로 작용하여 발생하며, 기존의 평가 기준은 이와 같은 인자들을 충분히 반영하지 못하여 결빙 형성을 예측 하고 평가하는 능력이 부족하다고 판단된다. 따라서 본 연구는 결빙 교통사고 데이터의 통계적인 분석을 통하여 결빙이 형성되는 기 상 조건을 구체화하고 결빙사고 및 결빙 형성을 예측하기 위한 기상학적 기준을 마련하는 것을 목적으로 진행되었다. 2018년 1월 1 일～2024년3월 15일 동안 발생한 결빙 사고와 사고 발생 당시 및 이전 6시간동안의 기상 데이터를 분석 데이터로 사용하였다. 이때, 역거리 가중법, 기온감률 등 공간보간기법을 적용하였다. 이후, 박스도표, 히스토그램, 경험적 누적분포함수 등의 통계분석을 적용하여 결빙사고의 기상 분포 특성을 확인하였다. 최종적으로 결빙사고의 몬테카를로 시뮬레이션을 활용하여 기온 및 습도에 따른 결빙사고 의 발생 확률을 계산하였다. 이와 같은 연구 결과는 결빙 형성을 예측하는 기온 및 습도의 기준점으로 제시할 수 있으며 더 나아가, 추후 결빙사고 예방 및 예보의 기준으로 활용이 가능할 것으로 기대된다.