Search Results (1,718)

This study introduces a quantitative approach to evaluating the resilience of oil pipeline systems against various natural and physical disruptions. Resilience is increasingly essential in critical infrastructure to ensure continuous operations and minimize disruption impacts. However, existing quantitative methods often need specific time-dependent data, making measuring resilience in pipeline infrastructure challenging. To address this gap, this paper proposed a comprehensive framework by integrating the existing incident database with key features of assessing failure probabilities based on historical events and developing multi-event resilience indicators based on system performance under various disruptions. The methodology employs event tree analysis to quantify the probabilities of multiple failure scenarios and their impact on pipeline operations and recovery efforts. The practical application of the proposed approach was demonstrated using real-world oil pipeline incident data from across the United States, covering the period from 2010 to 2022. The focus was on multiple event scenarios involving pipeline disruptions, followed by shutdowns, examining how these events collectively impact pipeline resilience. The results indicate that corrosion failure, equipment failure, and natural hazard damage significantly impact oil pipeline resilience. Corrosion and equipment failures affect resilience primarily due to their frequency, while natural hazard damage, despite its lower occurrence rate, is more unpredictable and often requires more frequent shutdowns. Understanding these failure causes and their impacts is essential for enhancing the resilience and sustainable operation of oil pipeline systems. Full article

This study measured the individual and conjoint effects of Argentina’s primaries and first- and second-voting presidential election results, as well as their post-election comparative effects, on the stock market performance of its most relevant economic sectors. Within four different estimation methods, the state-space specification outperformed the rest. The findings suggest that investors can under/overreact compared to post-election sectors performance, the public services sector being the exception. Therefore, those investors who anticipated the election results by liquidating positions in companies in the materials sector and investing more in companies in the energy and other industrial sectors achieved a superior performance. Full article

The Nigua River basin in the Dominican Republic is a critical hydrographic area facing significant environmental challenges, including deforestation, soil erosion and pollution from mining and agricultural activities. This study explores the role of risk perception among local residents in shaping policies for the basin’s sustainable management. The research aims to identify the factors influencing risk perception and propose actionable strategies to improve environmental governance in the region. A “perceived risk profile” methodology was applied, using survey data from 1223 basin residents. The analysis identified key variables that influence risk perception, including demographic factors such as education, gender, and place of residence. The findings reveal that risk underestimation correlates with low awareness of risks, uncertainty about the origins of disasters, fatalism toward natural events, and low trust in institutions. In contrast, risk over-estimation is linked to infrequent risk communication, heightened catastrophism and a strong emphasis on the benefits of environmental protection. The study also highlights significant regional differences in risk perception, with residents of the lower basin exhibiting higher perceptions of risk due to cumulative pollution and frequent disaster impacts. Based on these insights, the study recommends targeted strategies to bridge risk perception gaps, including tailored risk communication, community-based environmental education and stronger institutional trust-building initiatives, all aimed at fostering more effective and inclusive environmental governance in the Nigua basin. Full article

This study analyzes the clean energy infrastructure resilience on Santa Cruz Island, located in the Galapagos archipelago, facing identified multi-natural hazard scenarios such as earthquakes, tsunamis, volcanic eruptions, and extreme weather events. Although Santa Cruz Island has a relatively modern energy infrastructure, its geographic location and lack of clear emergency management actions would significantly affect its performance. Risk assessment components, such as exposure and vulnerability, are also analyzed, highlighting the need for strategic interventions to ensure the continuity of energy supply and other essential services. Proved methodologies are used to propose action plans, including structural and non-structural solutions and simulations based on disaster scenarios. As a result, a series of strategies are revealed to strengthen the response and adaptation capacity of both critical infrastructure and the local community. These strategies hold the potential to ensure the island’s long-term energy security and sustainability, reducing its carbon footprint and instilling hope for a resilient future. Full article

This study investigates the prediction of small and medium-sized enterprise (SME) default rates in Republic of Korea by comparing the performance of three prominent time-series forecasting models: ARIMA, SARIMA, and Prophet. The research utilizes a comprehensive dataset provided by the Korea Credit Guarantee Fund (KODIT), which covers regional and monthly default rates from January 2012 to December 2023, spanning 12 years. By focusing on Republic of Korea’s 17 major cities, the study aims to identify regional and seasonal patterns in default rates, highlighting the critical role that regional economic conditions and seasonality play in risk management. The proposed methodology includes an exploratory analysis of default rate trends and seasonal patterns, followed by a comparative evaluation of ARIMA, SARIMA, and Prophet models. ARIMA serves as a baseline model for capturing non-seasonal trends, while SARIMA incorporates seasonal components to handle recurring patterns. Prophet is uniquely suited for dynamic datasets, offering the ability to include external factors such as holidays or economic shocks. This work distinguishes itself from others by combining these three models to provide a comprehensive approach to regional and seasonal default risk forecasting, offering insights specific to Republic of Korea’s economic landscape. Each model is evaluated based on its ability to capture trends, seasonality, and irregularities in the data. The ARIMA model shows strong performance in stable economic environments, while SARIMA proves effective in modeling seasonal patterns. The Prophet model, however, demonstrates superior flexibility in handling irregular trends and external events, making it the most accurate model for predicting default rates across varied economic regions. The study concludes that Prophet’s adaptability to irregularities and external factors positions it as the most suitable model for dynamic economic conditions. These findings emphasize the importance of region-specific and seasonal factors in tailoring risk forecasting models. Future research will validate these predictions by comparing forecasted default rates with actual data from 2024, providing actionable insights into the long-term effectiveness of the proposed methods. This comparison aims to refine the models further, ensuring robust financial stability and enhanced SME support strategies for institutions like KODIT. Full article

The cloud system characteristics within satellite cloud imagery play a crucial role in the meteorological operational analysis of cold fronts, and integrating satellite cloud imagery into automated frontal identification schemes can provide valuable insights for accurately determining the position and morphology of cold fronts. This study introduces Cloud-DETR, a deep learning identification method that uses the DETR model with satellite cloud imagery, to identify cold fronts from extensive datasets. In the Cloud-DETR method, preprocessed satellite cloud imagery is used to generate training images, which are then put into the DETR model for cold front identification, achieving excellent results. The alignment between the Cloud-DETR cold fronts and weather systems during continuous periods and extreme weather events is assessed. The Cloud-DETR method exhibits high accuracy in both the position and morphology of cold fronts, ensuring stable identification performance. The high matching rate between the Cloud-DETR cold fronts and the manually identified ones in the test set, image dataset and labels from 2017 is verified. This indicates that the Cloud-DETR method can provide an accurate cold fronts dataset. The cold fronts dataset from 2005 to 2023 was obtained using the Cloud-DETR method. It was found that over the past 18 years, the frequency of cold fronts displays distinct seasonal patterns, with the highest occurrences observed during winter, particularly along the mid-latitude storm tracks extending from the east coast of East Asia to the Northwest Pacific. The methodology and findings presented in this study could help advance further research on the characteristics of cold front cloud systems based on long-term datasets. Full article

The rapid detection of landslide phenomena that may be triggered by extreme rainfall events is a critical point concerning timely response and the implementation of mitigation measures. The main goal of the present study is to identify susceptible areas by estimating changes in the Normalized Difference Vegetation Index (NDVI), Normalized Difference Moisture Index (NDMI), Bare Soil Index (BSI), and Synthetic Aperture Radar (SAR) amplitude ratio before and after extreme rainfall events. The developed methodology was utilized in a case study of Storm Daniel, which struck central Greece in September 2023, with a focus on the Mount Pelion region on the Pelion Peninsula. Using Google Earth Engine, we processed satellite imagery to calculate these indices, enabling the assessment of vegetation health, soil moisture, and exposed soil areas, which are key indicators of landslide activity. The methodology integrates these indices with a Weight of Evidence (WofE) model, previously developed to identify regions of high and very high landslide susceptibility based on morphological parameters like slope, aspect, plan and profile curvature, and stream power index. Pre- and post-event imagery was analyzed to detect changes in the indices, and the results were then masked to focus only on high and very high susceptibility areas characterized by the WofE model. The outcomes of the study indicate significant changes in NDVI, NDMI, BSI values, and SAR amplitude ratio within the masked areas, suggesting locations where landslides were likely to have occurred due to the extreme rainfall event. This rapid detection technique provides essential data for emergency services and disaster management teams, enabling them to prioritize areas for immediate response and recovery efforts. Full article

Set in a wide open plain, the monolith of Uluṟu (‘Ayers Rock’) has become an internationally recognizable symbol for the Australian outback, currently attracting hundreds of thousands of tourists each year. Promoted since the 1950s as an exotic tourist destination, one of the major activities has been the ‘conquest’ of Uluṟu by completing the steep climb to the top. Always disapproved by the Aṉangu, the Indigenous Australian community of the area, and actively discouraged since 1990, the climb became an extremely contentious issue in the final two years before it was permanently closed to tourists on 26 October 2019. Given that climbing Uluṟu as a tourist activity has become an event of the past, this paper will examine the nature, materiality, and potential heritage value of the portable material culture associated with the climb. The background to the history of climbing Uluṟu in the context of European invasion (‘exploration’), the nature of tourism at Uluṟu and the role climbing played in this, as well as the management decisions that led to the closure of the climb can be grouped into four thematic periods: the beginnings of settler colonialist ascents (1873–1950), the ‘heroic’ age of Uluṟu tourism (1950–1958), lodges in a National Park (1958–1985), and joint management and the eventual closure of the climb (1985–2019). Based on a description of the material culture associated with the climb, particularly badges, patches and certificates, and drawing on the methodologies of historic and material culture studies, this paper will discuss the various interpretations of climbing Uluṟu and how the portable material culture reflects or exemplifies climbing as a conquest and heroic deed, as a spiritual ritual, and as a violation of cultural rights. After examining the materiality of the wearable material culture, we conclude by exploring which of these portable items are culturally significant and which, if any, should be curated in public collections. Full article

The conditional Gaussian nonlinear system (CGNS) is a broad class of nonlinear stochastic dynamical systems. Given the trajectories for a subset of state variables, the remaining follow a Gaussian distribution. Despite the conditionally linear structure, the CGNS exhibits strong nonlinearity, thus capturing many non-Gaussian characteristics observed in nature through its joint and marginal distributions. Desirably, it enjoys closed analytic formulae for the time evolution of its conditional Gaussian statistics, which facilitate the study of data assimilation and other related topics. In this paper, we develop a martingale-free approach to improve the understanding of CGNSs. This methodology provides a tractable approach to proving the time evolution of the conditional statistics by deriving results through time discretization schemes, with the continuous-time regime obtained via a formal limiting process as the discretization time-step vanishes. This discretized approach further allows for developing analytic formulae for optimal posterior sampling of unobserved state variables with correlated noise. These tools are particularly valuable for studying extreme events and intermittency and apply to high-dimensional systems. Moreover, the approach improves the understanding of different sampling methods in characterizing uncertainty. The effectiveness of the framework is demonstrated through a physics-constrained, triad-interaction climate model with cubic nonlinearity and state-dependent cross-interacting noise. Full article

Risk communication from the perspective of Extreme Event Attribution (EEA), which assesses the extent to which climate change influences various extreme weather events, has significant potential for climate change communication due to its ability to make the phenomenon more relatable to citizens. This study examines the digital conversation generated following the wave of wildfires in Spain in 2022, which was declared the worst year of the 21st century in terms of hectares burned. By using the Social Network Analysis (SNA) methodology, 145,081 tweets were analyzed to construct a mention network, capturing the digital clusters formed around this discussion and highlighting the predominant tones in the debate. The findings reveal that the conversation predominantly adopted a tone of outrage and assigned responsibility. This research study offers a renewed perspective on risk communication, highlighting significant challenges faced by environmental activism on social media and underscoring the need to improve communication strategies to increase awareness and mobilization around climate change. Full article

China’s Dongting Lake area is intertwined with rivers and lakes and possesses many water systems. As such, it is one of the most complicated areas in the Yangtze River Basin, in terms of the complexity of its flood control. Over time, siltation and reclamation in the lake area have greatly weakened the river discharge capacity of the lake area, and whether it can endure extreme floods remains an open question. As there is no effective scenario simulation model for the lake area, this study constructs a hydrological model for the Jingjiang–Dongting Lake system and verifies the model using data from 11 typical floods occurring from 1954 to 2020. The parameters derived from 2020 data reflect the latest hydrological relationship between the lake and the river, while meteorological data from 1954 and 1998 are used as inputs for various scenarios with the aim of evaluating the flood pressure of the lake area, using the water levels at the Chengglingji and Luoshan stations as indicators. The preliminary results demonstrate that the operation of the upstream Three Gorges Dam and flood storage areas cannot completely offset the flood pressure faced by the lake area. Therefore, the reinforcement and raising of embankments should be carried out, in order to cope with potential extreme flood events. The methodology and results of this study have reference value for policy formation, flood control, and assessment and dispatching in similar areas. Full article

Since the beginning of the satellite era, only three tropical cyclones have been recorded over the South Atlantic Ocean. To investigate the potential occurrence of such systems since the 1900s, ERA20C, a centennial reanalysis, was utilised. This study first evaluates the performance of ERA20C in reproducing the climatology of all cyclone types over the southwestern South Atlantic Ocean by comparing it with a modern reanalysis (ERA5) for the period 1979–2010. Despite its simpler construction, ERA20C is able to reproduce key climatological features, such as frequency, location, seasonality, intensity, and thermal structure of cyclones similar to ERA5. Then, the Cyclone Phase Space (CPS) methodology was applied to determine the thermal structure at each time step for every cyclone between 1900 and 2010 in ERA20C. The cyclones were then categorised into different types (extratropical, subtropical, and tropical), and systems exhibiting a warm core at their initial time step were classified as tropical cyclogenesis. Between 1900 and 2010, 96 cases of tropical cyclogenesis were identified over the South Atlantic. Additionally, throughout the lifetime of all cyclones, a total of 1838 time steps exhibited a tropical structure, indicating that cyclones can acquire a warm core at different stages of their lifecycle. The coasts of southeastern and southern sectors of northeast Brazil emerged as the most favourable for cyclones with tropical structures during their lifecycle. The findings of this study highlight the occurrence of tropical cyclones in the South Atlantic prior to the satellite era, providing a foundation for future research into the physical mechanisms that enabled these events. Full article

Background. The 5-year overall survival (OS) rates of T-cell lymphocytic leukemia (T-ALL) are better for children (>90%) compared to adults (~57%). The early T-cell precursor (ETP) T-ALL subtype is prognostically unfavorable in adults, but less significant in pediatric T-ALL, and the diagnosis and prognosis of “near”-ETP is controversial. We compared protein and RNA expression patterns in pediatric and adult T-ALL to identify prognostic subgroups, and to further characterize ETP and near-ETP T-ALL in both age groups. Methods. Protein expression was assessed using RPPA methodology for 321 target proteins in 361 T-ALL patient samples from 292 pediatrics and 69 adults, including 103 ETP-ALL. RNA-sequencing was performed on 81 pediatric T-ALL samples. Results. We identified recurrent protein expression patterns that classified patients into ten protein expression signatures using the “MetaGalaxy” analysis. In adults, Cox regression analysis identified two risk-groups associated with OS (p = 0.0002) and complete remission duration (p < 0.001). Cluster analysis of adults and pediatric-ETP patients identified three ETP-clusters strongly associated with age. Pediatric ETP-patients with a pediatric-dominant expression profile were associated with a shorter OS (p = 0.04) and event-free survival (p = 0.05) compared to pediatric ETP-patients with an ETP expression profile that was also identified in adults. Conclusion. Our study demonstrates that proteomics are predictive of outcome in adult T-ALL and that we can identify a small subset of pediatric ETP with an inferior outcome. The observation that there are age-specific patterns supports the idea that the origin of T-ALL in most pediatric and adult patients is different, while overlapping patterns suggests that there are some with a common pathophysiology. Proteomics could enhance risk stratification in both pediatric and adults with T-ALL. Full article

Landslides are among the most serious and frequent environmental disasters, involving the fall of large masses of rock and soil that can significantly impact human structures and inhabited areas. Anticipating these events is crucial to reduce risks through real-time monitoring of areas at risk during extreme weather events, such as heavy rains, allowing for early warnings. This study aims to develop a methodology to enhance the prediction of landslide susceptibility, creating a more reliable system for early identification of risk areas. Our project involves creating a model capable of quickly predicting the susceptibility index of specific areas in response to extreme weather events. We represent the terrain using cellular automata and implement a random forest model to analyze and learn from weather patterns. Providing data with high spatial accuracy is vital to identify vulnerable areas and implement preventive measures. The proposed method offers an early warning mechanism by comparing the predicted susceptibility index with the current one, allowing for the issuance of alarms for the entire observed area. This early warning mechanism can be integrated into existing emergency protocols to improve the response to natural disasters. We applied this method to the area of Prunella, a small village in the municipality of Melito di Porto Salvo, known for numerous historical landslides. This approach provides an early warning mechanism, allowing for alarms to be issued for the entire observed area, and it can be integrated into existing emergency protocols to enhance disaster response. Full article

Albizia odoratissima Benth is a perennial evergreen tree valued for its medicinal properties and is indigenous to the mountainous regions of southwestern China. The population status of A. odoratissima has been sparsely studied. This study systematically evaluated the population structure and dynamics of A. odoratissima in the central mountainous region of Hainan Island, China, with the objective of informing the development of sustainable conservation strategies for the ecological restoration of its natural populations. Using the methodologies of population ecology, including the development of static life tables, population survival curves, population dynamics analysis, and time-series predictions, the results indicated that the populations of A. odoratissima on Hainan Island were geographically isolated into three groups. The age class distribution revealed that young, middle-aged, and mature individuals accounted for 5.73%, 74.94%, and 19.33%, respectively, suggesting a declining trend in the population. Moreover, the A. odoratissima population on Hainan Island was highly sensitive to anthropogenic disturbances, with significant increases in mortality rates observed at both the juvenile and mature stages. These results were likely due to the intraspecific and interspecific competitions, as well as external factors including human-induced disturbances, climate variability, and extreme weather events, which might potentially lead to the species’ future endangerment. Based on the current status of the A. odoratissima population, we develop adaptive management and forbid anthropogenic deforestation, conserving in situ and expanding populations, protecting ex situ germplasm resources, and replanting artificially, in order to manage the long-term conservation and management of A. odoratissima. Full article