Historical Occurrence Research Articles

Probabilistic back analysis for landslide susceptibility assessment in data-scarce regions using a Bayesian approach

Incomplete knowledge about geotechnical conditions, owing to limited data availability, leads to uncertainties in slope stability analysis. Therefore, probabilistic back analysis has been adopted to obtain additional information and control the uncertainty, but the back analysis has previously been adopted only for individual slope problems, not for regional landslide analysis, in which the limited data causes serious inaccurate analysis results. In this study, the probabilistic back analysis was applied to a broad area where limited data is available for input parameters but numerous historical occurrence records are available. To address this issue, Bayesian back analysis was used to obtain supplementary data on input parameters based on observations of multiple landslide occurrence. These supplementary data were used to update the shear strength parameters, and landslide susceptibility was evaluated using the updated parameters. The results of the analysis conducted with the updated input parameters exhibited superior performance in comparison to those obtained with limited data. Moreover, the proposed method demonstrated robust performance regardless of the random selection and proportion of back analysis landslide locations. Consequently, the acquisition of supplementary data by applying probabilistic back analysis led to a reduction in uncertainty, thereby enhancing the accuracy of the landslide susceptibility analysis.

Evaluation of liquefaction potential in central Taiwan using random forest method

Liquefaction is a significant geotechnical hazard in seismically active regions like Taiwan, threatening infrastructure and public safety. Accurate prediction models are essential for assessing soil susceptibility to liquefaction during seismic events. This study evaluates liquefaction potential in central Taiwan using the random forest (RF) method. The RF models were developed with a dataset of 540 soil and seismic parameter sets, including depth, effective and total overburden stresses, SPT-N values, fine soil content, earthquake magnitude, peak ground acceleration, and historical liquefaction occurrences. Rigorous validation techniques, such as cross-validation and comparisons with observed liquefaction events, confirm the RF model’s effectiveness, achieving an accuracy of 98.89%. The model also quantifies predictor importance, revealing that the SPT-N value is the most critical soil factor, while peak ground acceleration is the key seismic factor for liquefaction prediction. Notably, the RF model outperforms simplified procedures in accuracy, even with fewer input factors. Our case studies show that an accuracy of over 95% can still be achieved, highlighting the RF model’s superior performance compared to conventional methods, which struggle to reach similar levels.

Long‐Term Foehn Reconstruction Combining Unsupervised and Supervised Learning

ABSTRACTFoehn winds, characterised by abrupt temperature increases and wind speed changes, significantly impact regions on the leeward side of mountain ranges, e.g., by spreading wildfires. Understanding how foehn occurrences change under climate change is crucial. As foehn is a meteorological phenomenon, its prevalence has to be inferred from meteorological measurements employing suitable classification schemes. Hence, this approach is typically limited to specific periods for which the necessary data are available. We present a novel approach for reconstructing historical foehn occurrences using a combination of unsupervised and supervised probabilistic statistical learning methods. We utilise in situ measurements (available for recent decades) to train an unsupervised learner (finite mixture model) for automatic foehn classification. These labelled data are then linked to reanalysis data (covering longer periods) using a supervised learner (lasso or boosting). This allows us to reconstruct past foehn probabilities based solely on reanalysis data. Applying this method to ERA5 reanalysis data for six stations across Switzerland and Austria achieves accurate hourly reconstructions of north and south foehn occurrence, respectively, dating back to 1940. This paves the way for investigating how seasonal foehn patterns have evolved over the past 83 years, providing valuable insights into climate change impacts on these critical wind events.

Big Epidemiology: The Birth, Life, Death, and Resurgence of Diseases on a Global Timescale.

Big Epidemiology represents an innovative framework that extends the interdisciplinary approach of Big History to understand disease patterns, causes, and effects across human history on a global scale. This comprehensive methodology integrates epidemiology, genetics, environmental science, sociology, history, and data science to address contemporary and future public health challenges through a broad historical and societal lens. The foundational research agenda involves mapping the historical occurrence of diseases and their impact on societies over time, utilizing archeological findings, biological data, and historical records. By analyzing skeletal remains, ancient DNA, and artifacts, researchers can trace the origins and spread of diseases, such as Yersinia pestis in the Black Death. Historical documents, including chronicles and medical treatises, provide contextual narratives and quantitative data on past disease outbreaks, societal responses, and disruptions. Modern genetic studies reveal the evolution and migration patterns of pathogens and human adaptations to diseases, offering insights into co-evolutionary dynamics. This integrative approach allows for temporal and spatial mapping of disease patterns, linking them to social upheavals, population changes, and economic transformations. Big Epidemiology also examines the roles of environmental changes and socioeconomic factors in disease emergence and re-emergence, incorporating climate science, urban development, and economic history to inform public health strategies. The framework reviews historical and contemporary policy responses to pandemics, aiming to enhance future global health governance. By addressing ethical, legal, and societal implications, Big Epidemiology seeks to ensure responsible and effective epidemiological research and interventions. This approach aims to profoundly impact how we understand, prevent, and respond to diseases, leveraging historical perspectives to enrich modern scientific inquiry and global public health strategies.

A Spatial Landslide Risk Assessment Based on Hazard, Vulnerability, Exposure, and Adaptive Capacity

Landslides threaten human life, property, and vital infrastructure in most mountainous regions. As climate change intensifies extreme weather patterns, the landslide risk is likely to increase, resulting in challenges for disaster management, sustainability development, and community resilience. This study presents a comprehensive framework for assessing landslide risk, integrating advanced machine learning models with the Iyengar–Sudarshan method. Our case study is Son La province, the Northwest region of Vietnam, with data collected from 1771 historical landslide occurrences and fifteen influencing factors for developing landslide susceptibility maps using advanced ensemble machine learning models. The Iyengar–Sudarshan method was applied to determine the weights for landslide exposure, vulnerability, and adaptive capacity indicators. The resulting landslide risk map shows that the highest-risk districts in Son La province are located in the central and northeastern regions, including Mai Son, Phu Yen, Thuan Chau, Yen Chau, Song Ma, and Bac Yen. These districts experience high landslide hazards, exposure, and vulnerability, often affecting densely populated urban and village areas with vulnerable populations, such as young children, the elderly, and working-age women. In contrast, due to minimal exposure, Quynh Nhai and Muong La districts have lower landslide risks. Despite having high exposure and vulnerability, Son La City is situated in a low-susceptibility zone with high adaptive capacity, resulting in a low landslide risk for this region. The proposed framework provides a reference tool for mitigating risk and enhancing strategic decision making in areas susceptible to landslides while advancing our understanding of landslide dynamics and fostering community resilience and long-term disaster prevention.

Research on UK Unemployment Rate Forecast Based on ARIMA Model

The objective of this study is to predict the unemployment rate in the UK and the Eurozone for the years 2014 to 2024 using the time series analysis model known as ARIMA. It is essential to be informed of changes to employment rates in order to address them and changes in the economic environment including historical occurrences such as Brexit, COVID-19, and shifts in the labor market. The ARIMA model was used because it fits non-stationary time series, which is a feature present in the employment rates of both time series. The monthly unemployment rates for each state were seasonally adjusted and the ARIMA parameters were estimated by the automatic method. The study observed that while the interior portion of the trading cycle was effectively predicted by the ARIMA model, it failed to do so for the short-term UK unemployment in the post-2023 period because of exogenous shocks. However, the model was equally successful in predicting a marginal contraction in unemployment rates in the Euro zone after 2023. By performing the Ljung-Box test, it was established that the residuals of the models were random and therefore, the models were fit. Accordingly, the results suggest that the estimates derived from the ARIMA model are beneficial in economic predictions, which helps governments to design better social security programs and other economic plans to address the changing employment landscape.

Prioritization of chemical food safety hazards in the European feed supply chain.

Extensive monitoring programs of chemical hazards in the animal feed chain are in place, both organized by public and private organizations. The objective of this review was to prioritize chemical hazards for monitoring in the European animal feed supply chain. A step-wise approach was designed for the prioritization, based on: historical occurrence of the chemicals in animal feed ingredients and animal feeds (in relation to European guidance values or maximum limits in feed); information on transfer of the chemical to edible animal products, and; the extent of human dietary intake of the products and possible adverse human health effects of the chemical. Possible prioritization outcomes were: high (H), medium (M), or low (L) priority for monitoring, or classification not possible (NC) because of limited available data on the transfer of the chemical to edible animal tissues. The selection of chemicals included (with results in parentheses): dioxins and polychlorinated biphenyls (H); brominated flame retardants (H); per- and polyfluorinated alkyl substances (H); the heavy metals arsenic (H) and cadmium (H) as well as lead (M) and mercury (M); aflatoxins (H), ochratoxin A (NC), and other mycotoxins (L); pyrrolizidine alkaloids (H) and other plant toxins (NC); organochlorine pesticides (H) and other pesticides (L); pharmaceutically active substances (M); hormones (NC); polycyclic aromatic hydrocarbons (L), heat-induced processing contaminants (NC), and mineral oils (NC). Results of this study can be used to support risk-based monitoring by food safety authorities and feed-producing companies in Europe.

Contemporary Narratives on Yoruba History, Modern Society in Nigeria, and the Meaning of Decolonized Humanity in the African Context

Instead of being merely a historical occurrence, colonization is a structural feature of civilizations that have been touched by colonialism, which affects the prospects for the colonial subjects. This process is still ongoing in various forms among the so-called postcolonial societies today. Therefore, decolonization is a response to both the negative stereotypes and falsehoods about Indigenous peoples and cultures as well as the structural injustices of colonially impacted society that disproportionately afflict Indigenous peoples. Even though decolonization is a hot issue in academia right now, Indigenous peoples have been fighting against colonialism for millennia and claiming their own spaces, sovereignty, and right to self-determination ever since they first came into contact with colonizers.

Geospatial Modelling of Flood Susceptibility of the Calabar City, Cross River State, Nigeria

Flooding remains a major environmental problem in many parts of the world including Nigeria, causing untold losses. This study modelled the flood susceptibility of Calabar Metropolis using frequency ratio and geographical information system (GIS). Direct field observation and key informant approaches were used to obtain 127 previous and current flood locations in the study area from which a database of historical flood occurrence was developed. Using the geostatistical analyst tool within a geographical information environment the flood locations were split into two parts in the ratio of 70:30 percent for the training and testing processes, respectively. Eight flood conditioning factors (elevation, slope, aspect, curvature topographic position index, topographic wetness index, land cover and normalized difference vegetation index) were extracted from SRTM-30m DEM and Landsat 8 data accordingly and used in the geospatial analysis of flood occurrence within the GIS. The frequency ratio model was then developed using the training dataset. The final product was the flood susceptibility map of the study area. The results revealed that 9.3 percent of the study area was considered to have very high flood susceptibility, 19.97 percent was classified as high flood susceptibility while 31.79 percent was under moderate risk area. About 39 percent of the study area was classified at least under low flood susceptibility. The order of contribution of the conditioning factors to the model were topographic position index (TPI), elevation, normalized difference vegetation index (NDVI), land cover, curvature, aspect, slope, and topographic wetness index (TWI). The validation of the model using the receiver operating characteristics (ROC) curve returned an AUC value of 74.81 percent. Hence, the result was acceptable for the prediction of flood locations in the study area. The findings would assist urban planners, environmental managers, risk reduction and management agencies, land developers, policy makers, and indeed the general public.

Reconstructing the biological invasion of noxious invasive weed Parthenium hysterophorus and invasion risk assessment in China.

Invasive alien plants (IAPs) present a severe threat to native ecosystems and biodiversity. Comprehending the potential distribution patterns of these plant invaders and their responses to climate change is essential. Parthenium hysterophorus, native to the Americas, has become an aggressively invasive species since its introduction to China in the 1930s. This study aims to collect and reconstruct the historical occurrence and invasion of P. hysterophorus. Using the optimal MaxEnt model, the potential geographical distributions of P. hysterophorus were predicted based on screened species occurrences and environmental variables under the current and three future scenarios in the 2030s, 2050s, and 2070s (i.e., SSP1-2.6, SSP2-4.5, and SSP5-8.5), and the invasion risk of P. hysterophorus in Chinese cities, croplands, forests, and grasslands was assessed. The results show that: (1) The species initially invaded highly suitable areas and further spread to regions with non-analogous climate conditions. (2) Under the current climatic conditions, the overall potential distribution of P. hysterophorus is characterized by more in the southeast and less in the northwest. Climate variables, including mean annual temperature (bio1), precipitation in the wettest month (bio13), isothermality (bio3), and temperature seasonality (bio4), are the primary factors influencing its distribution. (3) The potential distribution of P. hysterophorus will expand further under future climate scenarios, particularly toward higher latitudes. (4) Forests and crop lands are the areas with the most serious potential invasion risk of P. hysterophorus. Therefore, we suggest that the government should strengthen the monitoring and management of P. hysterophorus to prevent its spread and protect agro-ecosystems and human habitats. Depending on the potential risk areas, measures such as quarantine, removal, and publicity should be taken to mitigate the threat of P. hysterophorus invasion and to raise awareness of P. hysterophorus invasion prevention.

Evaluation of Floods Susceptibility Models Based on Different Pairwise Parameters in the Analytical Hierarchy Process: Case Study Cilemer and Ciliman Watersheds

This study investigated flood vulnerability in the Ciliman and Cilemer Watersheds, situated in Banten Province, and employs a spatial multi-criteria-integrated approach, with a specific focus on the Analytical Hierarchy Process (AHP). Two distinct scenarios, which have different parameter priority, were compared: one based on expert judgment for pairwise parameter comparisons (scenario-1) and the other derived from historical flood occurrences in high and very high vulnerability areas (scenario-2). Seven parameters, including elevation, slope, precipitation, geologic, soil type, land use, and distance to streams were weighted substantially different between the two scenarios. The study validated the flood vulnerability scenarios by contrasting them with historical flood data. Scenario-2 exhibited a closer agreement with the historical flood points during validation, particularly in very high vulnerability areas. Elevation and slope are identified as pivotal factors influencing flood vulnerability: low elevations and gentle slopes increased vulnerability, while higher slopes decreased flood susceptibility.

Tracing the fate of hay meadows with haylofts in Slovakia: A geographical perspective

Abstract The paper focuses on the historical distribution of haylofts in Slovakia, geographical conditions related to hay meadows with haylofts, and land cover changes of former hayloft localities. Moreover, the current state of preserved haylofts and the land use of their surrounding area were documented at a regional scale, during the field research. A map of the historical occurrence of haylofts was digitised based on the Czechoslovakian military topographic maps from 1952–1957 (1:25,000). Altogether, 9,742 haylofts were recorded. The haylofts were usually built in mountain and sub-mountain areas on places with low soil quality, mostly at higher elevations, on moderate or moderately steep slopes and in more distant and isolated areas. About half of former hayloft meadows now exist as meadows or pastures with different intensity of grassland management. Forest or shrubs already cover the other 38% of the sites. To a lesser extent, the areas have been converted into arable land or recreational areas. Only a few haylofts have survived to this day; for example, in Upper Liptov Region, it is only 1% of their former abundance in this area (48 haylofts). Apart from a few positive cases where they have been restored or preserved, those that have survived continue to decay.

Toward the first documented extinction of a marine macroalga in the Mediterranean Sea?

Fucus virsoides is a glacial relict-species endemic to the Adriatic that was widespread from northern Italy to southern Albania. In recent decades, however, it has suffered an alarming decline. In this study, all available records were reviewed to reconstruct its historical and current occurrence and to investigate the possible causes of its decline. Comprehensive mapping revealed a continuing decline with a significant shift, leaving only about 20 fragmented populations. While the species is already classified as critically endangered in Albania, F. virsoides could be considered functionally extinct in Istria (Croatia), critically threatened with extinction in Italy and Montenegro and locally extinct in Slovenia. The status of the species in the rest of Croatia is still unclear. The remaining populations are located in areas characterised by low temperatures, low salinity and high nutrient concentrations. Long-term analyses of these key abiotic factors revealed profound changes that suggest a link to the species' decline. This study provides a thorough assessment of the overall status of F. virsoides and argues for its inclusion on the IUCN Red List. Immediate conservation measures are needed for the long-term survival of this species.

Prehistorical and historical occurrence and range dynamic of the Eurasian Spoonbill (Platalea leucorodia) and the White Stork (Ciconia ciconia) in Europe

AbstractThe Eurasian Spoonbill (Platalea leucorodia) and the White Stork (Ciconia ciconia) are emblematic birds of high conservation interest. This article examines their range history in Europe since the end of the last Glacial by analyzing subfossil bone finds of both species, mostly from archaeological excavations. It is shown that two thousand years ago, White Storks occurred exclusively in the territory of the Roman Empire, i.e. the northern border of their distribution ran along the Rhine and Danube rivers. The north-east part of the continent, with its high population density today, was only colonized in the last few centuries. The Spoonbill’s patchy occurrence in Europe today appears to be a typical distribution pattern for this species. The subfossil findings also do not indicate a large, closed distribution area for any phase of the last 10,000 years. Some of its distribution islands (e.g. those in the Netherlands) have existed for thousands of years.

Analysis of the Mitochondrial COI Gene and Genetic Diversity of Endangered Goose Breeds.

The mitochondrial cytochrome c oxidase subunit I (COI) genes of six endangered goose breeds (Xupu, Yangjiang, Yan, Wuzong, Baizi, and Lingxian) were sequenced and compared to assess the genetic diversity of endangered goose breeds. By constructing phylogenetic trees and evolutionary maps of genetic relationships, the affinities and degrees of genetic variations among the six different breeds were revealed. A total of 92 polymorphic sites were detected in the 741 bp sequence of the mtDNA COI gene after shear correction, and the GC content of the processed sequence (51.11%) was higher than that of the AT content (48.89%). The polymorphic loci within the populations of five of the six breeds (Xupu, Yangjiang, Yan, Baizi, and Lingxian) were more than 10, the haplotype diversity > 0.5, and the nucleotide diversity (Pi) > 0.005, with the Baizi geese being the exception. A total of 35 haplotypes were detected based on nucleotide variation among sequences, and the goose breed haplotypes showed a central star-shaped dispersion; the FST values were -0.03781 to 0.02645, The greatest genetic differentiation (FST = 0.02645) was observed in Yan and Wuzong breeds. The most frequent genetic exchange (Nm > 15.00) was between the Wuzong and Yangjiang geese. An analysis of molecular variance showed that the population genetic variation mainly came from within the population; the base mismatch differential distribution analysis of the goose breeds and the Tajima's D and Fu's Fs neutral detection of the historical occurrence dynamics of their populations were negative (p > 0.10). The distribution curve of the base mismatches showed a multimodal peak, which indicated that the population tended to be stabilised. These results provide important genetic information for the conservation and management of endangered goose breeds and a scientific basis for the development of effective conservation strategies.

Utilizing Marxist-Leninist Viewpoints to Construct the Vietnamese People's Army in the Present Era

The formation of the military is a historical and social occurrence that possesses significant class attributes. According to the Marxist-Leninist texts, the armed forces are considered as a tool of organized violence controlled by the ruling class. They are trained, taught, and utilized to fulfill the purposes and ideals of the ruling class. Following the October Revolution, the establishment of the socialist state gave rise to a new kind of military known as the socialist army or the army of the working class. This army possessed inherent distinctions from prior armed formations, since it operated in alignment with the objectives of the proletariat and the socialist state. In accordance with Marxist-Leninist ideas, the construction of the socialist army must encompass all aspects of politics, ideology, organization, discipline, weapons, technological equipment, and science. This complete approach is necessary to safeguard the revolutionary accomplishments and the socialist rule. The directives outlined by the 13th National Congress of the Communist Party of Vietnam for developing a "streamlined, strong, and modern" army demonstrate the Party's ongoing adherence to Marxist-Leninist principles in establishing the Vietnamese People's Army at present. This study examines the essential elements and original aspects of applying these rules, which contribute to the protection of the Socialist Republic of Vietnam.

Reconstructing the timing of South Africa’s influenza season over the past century from newspaper records

ABSTRACT While the contemporary influenza season in South Africa is understood, its historical occurrence and dynamics are not. This study applies methods in historical geography, using archival newspaper reports to reconstruct the historical influenza season in South Africa over the past century (1904–2019) to examine changes in the seasonality of influenza in South Africa, and to compare this to contemporary records. A 20% seasonal threshold was calculated and applied to the data to return this approximate 19-week season, with the start of the influenza season ranging from week one in 1917 to week 41 of the year in 1918. On average, over the past century, the South African influenza season has started in the first week of May and ended in the last week of August, lasting 19 weeks. This outcome aligns with the contemporary influenza season described in other studies. These records reveal considerable interannual variability in length and timing with no unimodal shifts. The most linked climate phenomenon to influenza transmission rate were cold and winter conditions. This has important implications in advancing the timing of influenza vaccines to the late summer and early autumn weeks. Moreover, this shows how archival newspaper proxy records can help us reconstruct the past.

Spatio-Temporal Parallel Transformer based model for Traffic Prediction

Traffic forecasting problems involve jointly modeling the non-linear spatio-temporal dependencies at different scales. While Graph Neural Network models have been effectively used to capture the non-linear spatial dependencies, capturing the dynamic spatial dependencies between the locations remains a major challenge. The errors in capturing such dependencies propagate in modeling the temporal dependencies between the locations, thereby severely affecting the performance of long-term predictions. While transformer-based mechanisms have been recently proposed for capturing the dynamic spatial dependencies, these methods are susceptible to fluctuations in data brought on by unforeseen events like traffic congestion and accidents. To mitigate these issues we propose an improvised Spatio-temporal parallel transformer (STPT) based model for traffic prediction that uses multiple adjacency graphs passed through a pair of coupled graph transformer-convolution network units, operating in parallel, to generate more noise-resilient embeddings. We conduct extensive experiments on 4 real-world traffic datasets and compare the performance of STPT with several state-of-the-art baselines, in terms of measures like RMSE, MAE, and MAPE. We find that using STPT improves the performance by around \(10-34\%\) as compared to the baselines. We also investigate the applicability of the model on other spatio-temporal data in other domains. We use a covid-19 dataset to predict the number of future occurrences in different regions from a given set of historical occurrences. The results demonstrate the superiority of our model for such datasets.

Pemetaan Daerah Rawan Tanah Longsor di Daerah Desa Sanggang, Kecamatan Bulu, Kabupaten Sukoharjo

<p>Landslides are one of the frequent natural disasters occurring in various regions of Indonesia, including the Village of Sanggang in Bulu District, Sukoharjo Regency, Central Java. This area boasts diverse topography with numerous steep slopes, rendering it susceptible to landslides. The Bulu District is located in a hilly region with a high vulnerability to landslides, where from 2019 to 2020, there were recorded 11 landslide disaster events, with 10 of them occurring in the Bulu District. This study aims to map landslide-prone areas in the Village of Sanggang using a spatial analysis approach and relevant risk factors. The analytical methods include the use of topographical data, soil types, rainfall patterns, and historical landslide occurrences. The mapping results will provide a deeper understanding of locations vulnerable to landslides in this area. The outcomes of this research are expected to serve as a foundation for the development of more effective mitigation strategies, including recommendations for prevention measures and early warnings. A better understanding of these susceptible areas will assist local government authorities and stakeholders in safeguarding their communities and assets from the potential landslide hazards in the Village of Sanggang, Bulu District, Sukoharjo Regency.</p>

What can impact on the presence of carious lesions in first permanent molars? Revisiting the association between MIH and caries.

Assess whether the independent variables (IV) such as number of MIH-affected molars, MIH severity, past caries experience, visible plaque index (VPI), gingival bleeding index (GBI), age, and gender affect the presence of caries lesion (DMF_s) in first permanent molars, considering or not atypical restoration in MIH-affected molars as a previous caries lesion. A sample of 476 schoolchildren, aged 6-10years, were evaluated for MIH and caries diagnosis, using the Severity Scoring System (MIH-SSS) and the International Caries Detection and Assessment System (ICDAS), respectively. From the ICDAS, the DF-s/ D-s and df-s were calculated. The Zero-inflated Negative Binomial Regression was used to evaluate the impact of the IV on the dependent variable, considering or not the restorative component in MIH-affected molars. When the presence of caries was evaluated with the restorative component, age, MIH severity and past caries experience had a significant impact on the dependent variable (R2 = 0.176). Without the restorative component in MIH-affected molars, only age and past caries experience were statistically significant (R2 = 0.167). Since in the case of MIH teeth restoration may be attributed to post-eruptive breakdown rather than previous caries lesions, in the present study MIH did not influence the presence of caries lesions in the MIH-affected molars showing that restoration is not an adequate parameter for measuring the historical occurrence of caries. The first permanent molars may not necessarily be at an increased risk of caries due to MIH if the etiological factors for caries development are effectively managed.