Search Results (177)

Background: Claustrophobia is defined as the fear of enclosed spaces, and it is a rather common specific phobia. Although the Claustrophobia Questionnaire (CLQ) is a valid questionnaire to measure claustrophobia, there have been no studies validating this tool in Greek. Thus, our aim was to translate and validate the CLQ in Greek. Methods: We applied the forward–backward translation method to translate the English CLQ into Greek. We conducted confirmatory factor analysis (CFA) to examine the two-factor model of the CLQ. We examined the convergent and divergent validity of the Greek CLQ by using the Fear Survey Schedule-III (FSS-III-CL), the NEO Five-Factor Inventory (NEO-FFI-NL-N), and the Spielberger’s State-Trait Anxiety Inventory (STAI). We examined the convergent validity of the Greek CLQ by calculating Pearson’s correlation coefficient between the CLQ scores and scores on FSS-III-CL, NEO-FFI-NL-N, STAI-S (state anxiety), and STAI-T (trait anxiety). We examined the divergent validity of the Greek CLQ using the Fisher r-to-z transformation. To further evaluate the discriminant validity of the CLQ, we calculated the average variance extracted (AVE) score and the Composite Reliability (CR) score. We calculated the intraclass correlation coefficient (ICC) and Cronbach’s alpha to assess the reliability of the Greek CLQ. Results: Our CFA confirmed the two-factor model of the CLQ since all the model fit indices were very good. Standardized regression weights between the 26 items of the CLQ and the two factors ranged from 0.559 to 0.854. The convergent validity of the Greek CLQ was very good since it correlated strongly with the FSS-III-CL and moderately with the NEO-FFI-NL-N and the STAI. Additionally, the Greek CLQ correlated more highly with the FSS-III-CL than with the NEO-FFI-NL-N and the STAI, indicating very good divergent validity. The AVE for the suffocation factor was 0.573, while for the restriction factor, it was 0.543, which are both higher than the acceptable value of 0.50. Moreover, the CR score for the suffocation factor was 0.949, while for the restriction factor, it was 0.954. The reliability of the Greek CLQ was excellent since the ICC in test–retest study was 0.986 and the Cronbach’s alpha was 0.956. Conclusions: The Greek version of the CLQ is a reliable and valid tool to measure levels of claustrophobia among individuals. Full article

This manuscript investigates the applicability and behavior of the Generalized Extreme Value (GEV) distribution in flood frequency analysis, comparing it with the Pearson III and Wakeby distributions. Traditional approaches often rely on a limited set of statistical distributions and estimation techniques, which may not adequately capture the behavior of extreme events. The study focuses on four hydrometric stations in Romania, analyzing maximum discharges associated with rare and very rare events. The research employs seven parameter estimation methods: the method of ordinary moments (MOM), the maximum likelihood estimation (MLE), the L-moments, the LH-moments, the probability-weighted moments (PWMs), the least squares method (LSM), and the weighted least squares method (WLSM). Results indicate that the GEV distribution, particularly when using L-moments, consistently provides more reliable predictions for extreme events, reducing biases compared to MOM. Compared to the Wakeby distribution for an extreme event (T = 10,000 years), the GEV distribution produced smaller deviations than the Pearson III distribution, namely +7.7% (for the Danube River, Giurgiu station), +4.9% (for the Danube River, Drobeta station), and +35.3% (for the Ialomita River). In the case of the Siret River, the Pearson III distribution generated values closer to those obtained by the Wakeby distribution, being 36.7% lower than those produced by the GEV distribution. These results support the use of L-moments in national hydrological guidelines for critical infrastructure design and highlight the need for further investigation into non-stationary models and regionalization techniques. Full article

China’s urban residential building stock is extensive and spans a wide range of construction periods. With the continuous enhancement of building energy efficiency standards, the chronological characteristics and variability of residential building envelopes are evident. Through field research and typological analysis of residential buildings in Jinan, a cold region of China, three construction eras were classified: Period I (1980–1985), Period II (1986–1995), and Period III (1996–2005). Building performance and economic benefits across these periods are modeled using Rhino 7.3 and Grasshopper. The NSGA-II algorithm, as the core of Wallacei2.7, is employed for multi-objective optimization. Through K-means clustering, TOPSIS comprehensive ranking, and Pearson correlation analysis, the optimized processes and solutions are provided for urban residential renovation decisions in different periods and target preferences. The results show that the optimal comprehensive performance solutions for Period I, Period II, and Period III achieve energy savings of 40.92%, 29.62%, and 15.81%, respectively, and increase annual indoor comfort hours by 872.64 h/year, 633.57 h/year, and 564.11 h/year. For Period I and II residential buildings, the most effective energy efficiency retrofit measures include increasing exterior wall and roof insulation, replacing exterior window types, and reducing exterior window k-value. The overall trend in energy savings rates and economic benefits across the three periods shows a decline. For Period III residential buildings, systematic strategies, such as solar thermal collector systems and photovoltaic technology, are required to enhance energy efficiency. Full article

Accurately estimating storm hydrographs for various return periods is crucial for planning and designing hydrological infrastructure, such as dams and drainage systems. A key aspect of this estimation is the separation of baseflow from storm runoff. This study proposes a method for deriving storm hydrographs for different return periods based on hydrological station records. The proposed approach uses three baseflow separation methods: constant, linear, and master recession curve. A significant advantage of the proposed method over traditional rainfall–runoff approaches is its minimal parameter requirements during calibration. The methodology is tested on records from the Lengupá River watershed in Colombia, using data from the Páez hydrological station, which has a drainage area of 1090 km². The results indicate that the linear method yields the most accurate hydrograph estimates, as demonstrated by its lower root mean square error (RMSE) of 0.35%, compared to the other baseflow separation techniques, the values of which range from 2.92 to 3.02%. A frequency analysis of hydrological data was conducted using Pearson Type III and Generalized Extreme Value distributions to identify the most suitable statistical models for estimating extreme events regarding peak flow and maximum storm hydrograph volume. The findings demonstrate that the proposed methods effectively reproduce storm hydrographs for return periods ranging from 5 to 200 years, providing valuable insights for hydrological design, which can be employed using the data from stream gauging stations in rivers. Full article

Background: High rates of vitamin D deficiency have been reported in population-based studies, including those conducted in Germany. The goal of this study was to evaluate vitamin D levels and associated factors in a clinical cohort of German patients presenting to a rheumatology clinic. Methods: We conducted a retrospective observational study of electronic health record data from patients presenting to a rheumatology clinic in southern Germany. Data included demographic characteristics and vitamin D levels as measured by the Elecsys^® Vitamin D total III assay (Roche). Associations between vitamin D levels and patient characteristics were evaluated by Pearson correlation analyses, t-tests, and multiple regression analyses. We also explored seasonal changes. Results: A total of 4979 patients were included; 3230 (64.9%) were female and the mean (standard deviation [SD]) age was 53.6 (15.2) years. The mean (SD) vitamin D level was 27.4 (14.0) ng/mL (range, 3–240 ng/mL). Overall, 1540 (30.9%) had vitamin D levels in the deficient range (<20 ng/mL), 1774 (35.6%) had sufficient vitamin D (20 to 30 ng/mL), 1597 (32.1%) had optimal vitamin D levels (>30 to 70 ng/mL), and 68 (1.4%) had levels >70 ng/mL. Lower vitamin D levels were significantly associated with younger age, male sex, and higher body mass index. Mean levels were significantly lower during winter months and the percentages of patients with vitamin D deficiency were higher. Conclusions: Our data indicate that low levels of vitamin D are common in clinical cohorts, particularly in men, younger adults, overweight individuals, and during winter months. Patient education and/or supplementation may help to address this issue and potentially improve patient health. Full article

Coastal tourism benefits the sustainability of destinations and includes a wide range of experiences related to sun and sand, culture, nature, and social interactions. This study aimed to (i) identify the motivations driving tourists to coastal destinations, (ii) determine the tourist segments in these destinations, and (iii) examine the relationship between these tourist segments and satisfaction and loyalty. The research was conducted in Montañita, Ecuador, a renowned surfing and water sports destination frequented by both national and international tourists. The sample consisted of 380 valid questionnaires, analyzed using factor analysis, K-means clustering, and Pearson’s chi-square test. The findings revealed five motivational dimensions: Culture and Nature, Novelty and Social Interaction, Sun and Beach, Sports, and Entertainment. Two distinct tourist segments were also identified: Multiple Motives tourists and Sun and Beach tourists. Among these, the Multiple Motives group exhibited higher levels of satisfaction and loyalty. These insights are valuable for destination managers and tourism service providers, offering practical applications for enhancing visitor experiences. Additionally, this study contributes to the existing academic literature on coastal tourism. Full article

Statistical distributions play a crucial role in water resources management and civil engineering, particularly for analyzing data variability and predicting rare events with extremely long return periods (e.g., T = 1000 years, T = 10,000 years). Among these, the Pearson III (PE3) distribution is widely used in hydrology and flood frequency analysis (FFA). This study aims to provide a comprehensive guide to the practical application of the PE3 distribution in FFA. It explores five parameter estimation methods, presenting both exact and newly developed approximate relationships for calculating distribution parameters and frequency factors. The analysis relies on data from four rivers with varying morphometric characteristics and record lengths. The results highlight that the Pearson III distribution, when used with the L-moments method, offers the most reliable quantile estimates, characterized by the smallest biases compared to other methods (e.g., 31% for the Nicolina River and, respectively, 5% for the Siret and Ialomita Rivers) and the highest confidence in predicting rare events. Based on these findings, the L-moments approach is recommended for flood frequency analysis to improve the accuracy of extreme flow forecasts. Full article

The current replication crisis relating to the non-replicability and the untrustworthiness of published empirical evidence is often viewed through the lens of the Positive Predictive Value (PPV) in the context of the Medical Diagnostic Screening (MDS) model. The PPV is misconstrued as a measure that evaluates ‘the probability of rejecting $H_0$ when false’, after being metamorphosed by replacing its false positive/negative probabilities with the type I/II error probabilities. This perspective gave rise to a widely accepted diagnosis that the untrustworthiness of published empirical evidence stems primarily from abuses of frequentist testing, including p-hacking, data-dredging, and cherry-picking. It is argued that the metamorphosed PPV misrepresents frequentist testing and misdiagnoses the replication crisis, promoting ill-chosen reforms. The primary source of untrustworthiness is statistical misspecification: invalid probabilistic assumptions imposed on one’s data. This is symptomatic of the much broader problem of the uninformed and recipe-like implementation of frequentist statistics without proper understanding of (a) the invoked probabilistic assumptions and their validity for the data used, (b) the reasoned implementation and interpretation of the inference procedures and their error probabilities, and (c) warranted evidential interpretations of inference results. A case is made that Fisher’s model-based statistics offers a more pertinent and incisive diagnosis of the replication crisis, and provides a well-grounded framework for addressing the issues (a)–(c), which would unriddle the non-replicability/untrustworthiness problems. Full article

Background and Objectives: In coronary artery bypass grafting (CABG) surgeries, monitoring intracranial pressure (ICP) is crucial due to neurological risks. Although pulsatile flow (PF) during cardiopulmonary bypass (CPB) is considered more physiological than non-pulsatile flow (NPF), its impact on ICP remains unclear. This study aimed to compare preoperative and postoperative optic nerve sheath diameter (ONSD) measurements between PF and NPF techniques to evaluate their effect on ICP changes. Materials and Methods: Sixty patients undergoing elective CABG (aged 45–75 years, ASA II-III-IV) were enrolled and divided into two groups depending on the cardiopulmonary bypass technique determined by the surgeon: PF (Group P, n = 30) and NPF (Group NP, n = 30). ONSD measurements were performed with ultrasound before surgery (Tpreop) and after surgery (Tpostop). Hemodynamic parameters and jugular and carotid vessel diameters were also recorded. Statistical analysis included t-tests, Mann–Whitney U-tests, chi-square tests, and Pearson correlation. Results: Both groups demonstrated significant increases in ONSD postoperatively compared to preoperative values (p < 0.001). However, no statistically significant difference in the magnitude of ONSD change was observed between the PF and NPF groups (p > 0.05). Group P showed lower ejection fractions and higher total inotrope requirements compared to Group NP (p < 0.01), but these factors did not translate into differences in postoperative ICP dynamics. Conclusions: ONSD measurements increased significantly after CABG surgery, regardless of perfusion type. PF and NPF strategies were comparable in terms of their effects on ICP as reflected by ONSD changes. ONSD ultrasonography appears to be a simple, rapid, and non-invasive tool for perioperative ICP monitoring in cardiac surgery. Further studies are needed to confirm these findings with dynamic intraoperative monitoring and neurocognitive assessments. Full article

Typhoons generate extreme waves that pose significant threats to offshore wind power platforms in deep-sea areas, a challenge not fully addressed in current design standards. This study investigates wind–wave coupling processes during typhoon events to provide guidance for typhoon selection in deep-sea wind power platform design. Using Pearson Type III frequency analysis of typhoon data from 1949 to 2019, the 50-year return period typhoon intensity was determined for the study area. The validated SWAN model was employed to simulate typhoon-induced waves, revealing that wave height contours align parallel to the coastline and increase sharply from nearshore to deep-sea areas. The maximum significant wave height reaches 7.78 m when a 50-year return period typhoon passes the engineering site. These findings offer critical insights for offshore wind farm design in typhoon-prone regions, providing a robust basis for wave load assessment, structural fatigue analysis, and safety optimization. Full article

This paper examines the essential role of intervehicle spacing on two-lane rural roads, highlighting its significance for traffic safety and management. Recent technological advancements have enabled the precise positioning of vehicles on highways through video recordings and image processing techniques. However, these systems are less applicable to rural roads due to the absence of extensive sensor networks. This study bridges this gap by proposing a simulation-based model to evaluate the probability density of intervehicle spacing under varying traffic conditions. The simulation model integrates macroscopic traffic flow theories with microscopic car following models, simulating intervehicle spacings over a considerable highway segment. Calibration and validation were conducted using data from a two-lane road in Northern Italy. The simulation results identify key characteristics of spacing distribution, including positive skewness (i.e., a longer tail toward higher values), high kurtosis (a peaked distribution with frequent extreme values), non-zero minimum values, and autocorrelation at high traffic densities (indicative of platooning behavior). The Pearson type III distribution was determined to be the most suitable fit for the experimental data. Thus, future research should focus on parameter estimation for the Pearson type III distribution to further understand intervehicle spacing under varying traffic conditions and to expand applications to various road types and traffic scenarios. Full article

Recent progress in Artificial Intelligence and Machine Learning (AIML) has accelerated improvements in the prediction performance of many hydrological processes. Yet, flood prediction remains a challenging task due to its complex nature. Two common challenges afflicting the task are flood volatility and the sensitivity and complexity of flood generation attributes. This study explores the application of Recurrent Neural Networks (RNNs)—specifically Vanilla Recurrent Neural Networks (VRNNs), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU)—in flood prediction and assessment. By integrating catchment-specific hydrological and meteorological variables, the RNN models leverage sequential data processing to capture the temporal dynamics and seasonal patterns characteristic of flooding. These models were employed across diverse terrains, including mountainous watersheds in the state of South Carolina, USA, to examine their robustness and adaptability. To identify significant hydrological events for flash flood analysis, a discharge frequency analysis was conducted using the Pearson Type III distribution. The 1-year and 2-year return period flows were estimated based on this analysis, and the 1-year return flow was selected as a conservative threshold for flash flood event identification to ensure a sufficient number of training instances. Comparative benchmarking with the National Water Model (NWM v3.0) revealed that the RNN-based approaches offer notable enhancements in capturing the intensity and timing of flood events, particularly for short-duration and high-magnitude floods (flash floods). Comparison of predicted disharges with the discharge recorded at the gauges revealed that GRU had the best performance as it achieved the highest mean NSE values and exhibited low variability across diverse watersheds. LSTM results were slightly less consistent compared to the GRU albeit achieving satisfactory performance, proving its value in hydrological forecasting. In contrast, VRNN had the highest variability and the lowest NSE values among the three. The NWM model trailed the machine learning-based models. The study highlights the efficacy of the RNN models in advancing hydrological predictions. Full article

The control of rainfall extremes is essential in the design of hydro-agricultural works, as their performance depends on it. This study aims to determine the best-fit probability model suited to current climatic conditions in the Mono-Couffo basin in Benin. To this end, daily rainfall data from six rainfall stations from 1981 to 2021 were used. The application of the Decision Support System (DSS) with graphical and numerical performance criteria (such as RMSE, SD, and CC represented by the Taylor diagram; AIC and BIC) made it possible to identify the best distribution class and then to select the most suitable distribution for this basin. The results indicate that class C distributions, characterized by regular variations, are the most appropriate for the modeling maximum annual daily precipitation at all stations (78% of cases). Of these, the Inverse Gamma distribution proved to be the most suitable, although its estimation errors ranged from 16.47 mm/d at Aplahoué to 39.80 mm/d at Grand-Popo. The second most appropriate distribution is the Log-Pearson Type III. The use of the Inverse Gamma distribution is, therefore, recommended for hydro-agricultural development studies in the Mono-Couffo basin. Full article

It is essential to understand the characteristics of surface runoff generating processes under different heavy rainfall events in mountainous areas. The intensity and duration of precipitation play an important role in surface runoff processes. In this study, annual rainfall characteristics from 1987 to 2023 in the Taihang Mountains were analyzed using the Pearson-III frequency curve, homogeneity tests, and the Mann–Kendall (MK) test. Four surface runoff generation events between 2014 and 2023 were monitored. The contribution of rainfall to runoff variations was quantified through the double mass curve method. Results indicate a significant increase in the frequency of moderate and heavy rainfall events over the last decade. Spatial variability of rainfall and elevation effects in the Taihang Mountains becomes less pronounced when 24 h rainfall is below 50 mm. The two surface runoff processes in 2016 and 2023 were typical runoff resulting from excess rain, which belonged to the storm runoff. The two surface runoff processes in 2021 were runoff generation under saturated conditions. For runoff generation under saturated conditions, the contribution of rainfall was only 58.17%. When the runoff coefficient exceeded 0.5, the surface runoff generating processes were entirely determined by rainfall. This study suggested that for semi-arid regions, where rainfall is unevenly distributed over the seasons, more soil water is needed to maintain local and downstream water demand during the non-rainy season. The limitations of the study are the lack of research on factors other than rainfall that intrinsically affect the surface runoff generating process. Full article

The geological and environmental conditions of the northern Shaanxi Loess Plateau are highly fragile, with frequent landslides and collapse disasters triggered by rainfall and human engineering activities. This research addresses the limitations of current landslide hazard assessment models, considers Zhuanyaowan Town in northern Shaanxi Province as a case study, and proposes an integrated model combining the information value model (IVM) with ensemble learning models (RF, XGBoost, and LightGBM) employed to derive the spatial probability of landslide occurrences. Adopting Pearson’s type-III distribution with the Bayesian theorem, we calculated rainfall-induced landslide hazard probabilities across multiple temporal scales and established a comprehensive regional landslide hazard assessment framework. The results indicated that the IVM coupled with the extreme gradient boosting (XGBoost) model achieved the highest prediction performance. The rainfall-induced hazard probabilities for the study area under 5-, 10-, 20-, and 50-year rainfall return periods are 0.31081, 0.34146, 0.4, and 0.53846, respectively. The quantitative calculation of regional landslide hazards revealed the variation trends in hazard values across different areas of the study region under varying rainfall conditions. The high-hazard zones were primarily distributed in a belt-like pattern along the Xichuan River and major transportation routes, progressively expanding outward as the rainfall return periods increased. This study presents a novel and robust methodology for regional landslide hazard assessment, demonstrating significant improvements in both the computational efficiency and predictive accuracy. These findings provide critical insights into regional landslide risk mitigation strategies and contribute substantially to the establishment of sustainable development practices in geologically vulnerable regions. Full article