Month: August 2024

Comparative Analysis of Slope Stability for Kalimpong Region under Dynamic Loading Using Limit Equilibrium Method and Machine Benchmark Learning Classifiers

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Abstract

Significant slope destabilisation may become more likely due to the speed at which urbanisation is occurring, as well as the growing necessity for geoengineering initiatives or the growth of the road network. Slope stability analysis is done to lower the risk of landslides and slope failures. The study area, Kalimpong, is well-known for its lush greenery and stunning views and is situated in the Eastern Himalayas. However, it also constantly confronts the risk of landslides because of its rugged topography, potential seismic zone, and heavy monsoon rains. In this study, the results of the factor of safety computed by limit equilibrium (conventional) method have been compared analytically using computational intelligence and machine learning methodologies for both dry and saturated conditions under dynamic loading. Conventional machine learning techniques are combined with seven prediction models. The following algorithms have been chosen for slope stability analysis: support vector machine, k-nearest neighbours, decision tree, random forest, logistic regression, AdaBoost, and gradient boosting. Random cross-validation is used to assess each model's dependability. The stability condition is the result of the random selection of seven parameters: cohesiveness, unit weight, slope height, angle of the slope, internal friction angle, horizontal and vertical pseudo-static coefficient. Moreover, the coefficient of variation method is employed to assess the importance of every indicator in forecasting slope stability. As per the sensitivity analysis, slope stability is primarily affected by cohesiveness. With an average classification accuracy of 0.878, ensembling approach SVM-Boost demonstrates the best prediction abilities among the models tested using multifold cross-validation. The accuracy ratings of SVM and AdaBoost were 0.865 and 0.834, respectively. When combined with SLOPE/W advances, novel SVM-Boost exhibits the highest exactitude, hegemony, and best outcomes in slope stability prediction. Future earthquakes, strong rainfall, and human activity could cause the slope to collapse. The outcome demonstrates machine learning's enormous potential for enhancing slope stability assessments and provides a means of raising the effectiveness and safety of slope management.

2D Hydrodynamic Model for Flood Analysis in Kinikli Stream Basin (Tekirdağ, Türkiye)

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Abstract

Flood is one of the natural disasters occurring due to excessive rainfall and inappropriate stream arrangements. It is important to prepare flood risk maps appropriately to prevent loss of life and property. In this study, areas under flood risk in the Kınıklı Stream Basin were determined with MIKE 21, 2D hydrodynamic model, for 5-, 50-, 200-, and 500-year recurrence flows. At the junction of the Kınıklı Stream main branches and the outlet of the basin, the flood water levels were read at four important points, and the areas at risk were determined. The areas under flood risk were overlapped with the 1/1000 scaled zoning maps and the sizes of the settlements that would be affected by the flood were calculated for each recurrent flow. For all recurrent flows, the areas that will be most affected by the flood were found to be residential areas. When the residential areas that will be affected by the flood are examined, an increase of approximately 70% was observed for the 500-year flow compared to the 5-year flow.

Comparative assessment of univariate and multivariate imputation models for varying lengths of missing rainfall data in a humid tropical region: a case study of Kozhikode, Kerala, India

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Abstract

Accurate measurement of meteorological parameters is crucial for weather forecasting and climate change research. However, missing observations in rainfall data can pose a challenge to these efforts. Traditional methods of imputation can lead to increased uncertainty in predictions. Additionally, varying lengths of missing data and nonlinearity in rainfall distribution make it difficult to rely on a single imputation method in all situations. To address this issue, our study compared univariate and multivariate imputation models for different lengths of missing daily rainfall observations in a humid tropical region. We used 33 years of weather data from Kozhikode, an urban city in Kerala region, and evaluated the selected models using accuracy measures such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Nash–Sutcliffe Efficiency (NSE) and Mean Absolute Relative Error (MARE). Among the considered univariate and multivariate imputation models, Kalman filter coupled time series models like Kalman–Arima ( \(\overline{\mathrm{RMSE} }\)  = 11.90, \(\overline{\mathrm{MAE} }\)  = 4.46) and Kalman Smoothing with structure time series ( \(\overline{\mathrm{RMSE } }\)  = 11.37, \(\overline{\mathrm{MAE} }\)  = 5.28) were found to be best for small (< 7 days) range imputation of rainfall data. Random Forest ( \(\overline{\mathrm{RMSE} }\)  = 16.57, \(\overline{\mathrm{MAE } }\)  = 8.0) and Kalman Smoothing with structure time series ( \(\overline{\mathrm{RMSE} }\)  = 16.84, \(\overline{\mathrm{MAE} }\)  = 8.09) performed well for medium range (8–15 days) of rainfall imputation. Random Forest technique was found to be suitable for large (≤ 30 days) ( \(\overline{\mathrm{RMSE} }\)  = 15.45, \(\overline{\mathrm{MAE } }\)  = 6.77), and very large (> 30 days) ( \(\overline{\mathrm{RMSE} }\)  = 12.91, \(\overline{\mathrm{MAE } }\)  = 3.42) missing length groups and Kalman–ARIMA performed best for mixed day series (RMSE = 9.7, MAE = 3.52). NSE and MARE values for different gap margins in rainfall data (≥ 1 mm) suggest that Kalman Smoothing (KS) connected models, as a representative univariate model, perform exceptionally well when dealing with a small number of missing observations. Notably, multivariate models like Principal Component Analysis (PCA) and Random Forest outperformed univariate models for medium to large gap margins. Considering these findings, utilizing multivariate techniques is recommended for imputing a large number of missing rainfall values and univariate models can be limited for small range of rainfall missing data imputation. The identified imputation models provide effective solutions for filling missing data of various lengths in all stations' datasets in humid tropical regions, thus enhancing rainfall-related analysis and enabling more accurate weather forecasts and climate change research.

Time-series analysis of remotely sensed biophysical parameters and their effects on land surface temperature (LST): a case study of Aligarh region, India

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Abstract

The temporal behaviors of land surface temperature (LST) coupled with its associated parameters play a crucial role in determining the microclimate at the city scale. The increasing pattern of LST and consequent changes in biophysical parameters (parameters specify the amalgamation of living system with their physical characteristics including vegetation, water, built-up, bareness and drought parameters) at monthly, seasonal and annual time spans and from regional to global scale need to be comprehensively evaluated. The present study deals with LST estimation along with other spectral indices including Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Modified Normalized Difference Water Index (MNDWI), Normalized Difference Built-up Index (NDBI), Normalized Difference Bareness Index (NDBaI), and Normalized Multi-band Drought Index (NMDI) using Landsat series datasets from 1991 to 2022 of Aligarh city, Uttar Pradesh, India. The spatial pattern of LST indicates that the areas having water bodies and dense vegetation are colder such as the Aligarh Muslim University Campus and the catchment of Ganga canal areas, whereas the areas of high urbanization and bare grounds reflect high LST trends. Study finds a positive correlation of LST with NDBI (R2-0.56), NDBaI (R2-0.22) and NDWI (R2-0.22), whereas a negative correlation with NDVI (R2-0.35), MNDWI (R2-0.36) and NMDI (R2-0.41). Land use land cover (LULC)-based change detection in land cover classes was found consistent with the obtained results for spectral indices and LST patterns in the study area. Finally, the cross-validation using Modern-Era Retrospective analysis for Research and Applications (MERRA) reanalysis-based products of earth skin temperature and rainfall showed a good fit between observed and reanalysis products.

Maxillofacial fractures among non-indigenous ethnic groups in the Irish national maxillofacial unit: a review

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Abstract

Background

This study investigates maxillofacial fractures in non-indigenous ethnic groups who were reviewed in the national maxillofacial unit in Ireland. The aim of this study was to highlight any potential trends in presentation of facial fractures in non-indigenous groups in comparison to previous reports which have included all ethnicities. This unique study is based on the fact that Ireland has only recently transformed into a diverse, multi-cultural country. This is unlike countries such as the UK and USA which have a long history of multicultural integration.

Materials and methods

This retrospective study evaluated the trauma database of 4761 patients with 5038 fractures who attended the national maxillofacial unit over a 5-year period from 2015 to 2019. Parameters included age, gender, mechanism of injury, fracture sustained, time of the day, day of the week, month of injury, and the referral source were obtained from patient records.

Results

The study identified 456 patients who did not identify as being born in Ireland, with 384 males and 72 females. The most common fracture seen was of the zygomatic bone, and the most common mechanism of injury was alleged assault for this cohort. Most injuries occurred in late afternoon with Friday being the most common day of the week.

Conclusion

This study shows how maxillofacial units need to adapt to the changing trends in Irish demographics with increased demand for resources such as translation services. A further study could evaluate the rapidly changing demographic with mass migration of people currently seeking refuge in Western Europe.

Indigenous-led designation and management of culturally significant species

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Abstract

Indigenous peoples globally are actively seeking better recognition of plants and animals that are of cultural significance, which encompass both species and ecological communities. Acknowledgement and collaborative management of culturally significant entities in biodiversity conservation improves environmental outcomes as well as the health and wellbeing of Indigenous people. The global diversity and complexity of Indigenous knowledge, values and obligations make achieving a universal approach to designating culturally significant entities highly unlikely. Instead, empowering local Indigenous-led governance structures with methods to identify place-based culturally significant entities will yield culturally supported results. Here we used a structured decision-making framework with objectives and biocultural measures developed by Indigenous experts, with the aim of prioritizing place-based culturally significant entities for collaborative management approaches on Bundjalung Country in coastal eastern Australia. We found some congruence and some important differences between culturally significant entities priorities and management compared with the colonial focus of threatened species management underpinned by current laws and policies. We provide reproduceable methods and a demonstration of successful local culturally significant entities designation and prioritization in an Australian context that highlights opportunities for Indigenous leadership, supported by governments in the designation and management of culturally significant entities.

Ethnic identification of children of new immigrants in Taiwan: the roles of the immediate environment and government policy

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Abstract

In this study, I explore the ethnic identification, which is an important indicator of the assimilation of the immigrant second generation, of adult children of new immigrants in Taiwan. Data were collected through semi-structured interviews and an online survey. Findings reveal that daily experiences in the immediate environment in which ethnic identity has played a trivial role have contributed to the majority of the participants’ strong identification with the larger society and weak identification with their immigrant heritage. For the dual-heritage identifiers, they have often constructed their identification with their immigrant heritage through transnational social connections and relevant courses in college. Government policy has also played a role by creating a friendlier environment in which the participants feel safe to claim a Taiwanese identity and switch between different identity labels in different situations and encouraging higher education institutions to offer relevant programs and courses that have provided opportunities for children of new immigrants to interact with the culture and language of their new immigrant parents’ country of origin, which has helped them strengthen their identification with their immigrant heritage. Both a nonthreatening environment in general and the government’s focus on new immigrants and their children in recent years have resulted in the participants’ positive attitudes toward their immigrant heritage.

Limited Awareness of Long COVID Despite Common Experience of Symptoms Among African American/Black, Hispanic/Latino, and Indigenous Adults in Arizona

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Abstract

Objectives

Communities of color might disproportionately experience long-term consequences of COVID-19, known as Long COVID. We sought to understand the awareness of and experiences with Long COVID among African American/Black (AA/B), Hispanic/Latino (H/L), and Indigenous (Native) adults (18 + years of age) in Arizona who previously tested positive for COVID-19.

Methods

Between December 2022 and April 2023, the Arizona Community Engagement Alliance (AZCEAL) conducted 12 focus groups and surveys with 65 AA/B, H/L and Native community members. Data from focus groups were analyzed using thematic analysis to identify emerging issues. Survey data provided demographic information about participants and quantitative assessments of Long COVID experiences were used to augment focus group data.

Results

Study participants across all three racial/ethnic groups had limited to no awareness of the term Long COVID, yet many described experiencing or witnessing friends and family endure physical symptoms consistent with Long COVID (e.g., brain fog, loss of memory, fatigue) as well as associated mental health issues (e.g., anxiety, worry, post-traumatic stress disorder). Participants identified a need for Long COVID mental health and other health resources, as well as increased access to Long COVID information.

Conclusion

To prevent Long COVID health inequities among AA/B, H/L, and Native adults living in AZ, health-related organizations and providers should increase access to culturally relevant, community-based Long COVID–specific information, mental health services, and other health resources aimed at serving these populations.

Deconstructing the anthropocentrism versus ecocentrism binary through Māori oral fire traditions

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Abstract

At the heart of sustainability is the relationship between humans and the planet. The binary of anthropocentric or ecocentric worldviews appears to be powerful in defining this relationship. Sustainability requires nuanced approaches which go beyond simple binaries, and therefore a dialectic approach which works to synthesise the binaries may be helpful. This paper draws on Māori cultural understandings of fire to trouble the ecocentric versus anthropocentric binary. Māori oral traditions of fire identify the connections between people and the planet and see people as part of fire and fire as part of people. By exploring Māori oral traditions, it is possible to see fire as more than purely an element that contributes to environmental problems and reveals the pedagogical potential of campfires to reignite the relationship between humans and the planet.

Between the Lines: Integrating the Science of Reading and the Science of Behavior to Improve Reading Outcomes for Australian Children

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Abstract

Many Australian students fail to meet an acceptable standard of reading proficiency. This can negatively impact their academic progress, social, and emotional well-being, and increase their risk of developing challenging behaviors. These risks and challenges have been found to compound over the lifetime of the learner. Unfortunately, the proportion of Australian students who fail to meet reading proficiency standards increases as they move through their years of schooling, and reading difficulties disproportionately affect historically marginalized groups. This has raised concerns about the effectiveness of instructional approaches used within the Australian education system, particularly in reading, and prompted discussions of reform. The purpose of this review paper was to examine the contributions of the science of reading and science of behavior to our collective knowledge regarding reading development and effective reading instruction, and how this knowledge is currently being used in the Australian context. We provide a discussion on the current state of reading instruction and achievement in Australia by considering national trends, inequities, and systemic challenges. Implications and recommendations to address inequities in reading outcomes, using both the science of reading and science of behavior, are discussed.