Miracle-dz/UniFAR-FTUs huggingface dataset:
A Review Paper on Strength and Durability Properties of Stabilised Soils

: With the advent of fast construction and development, the construction of road and building infrastructure has encountered enormous growth, especially since the last 3 decades. This rapid growth and large construction projects have led to the problem of scarcity of good quality natural metal aggregates which could be used in construction. One of the globally accepted methods is to replace the natural aggregates with locally available material by enhancing their characteristics and properties as per the requirements by use of stabilizers. Certain stabilisers have gained more strength than others in this field ad are being widely used around the globe in many countries. The strength is achieved by stabilisation but the durability in the long time frame for stabilised materials is still not being explored. This paper reports some of the recent and relevant work that has been done globally regarding the stabilised soils and its strength and durability characteristics. [3] and GW soils and tested for durability as per ASTM D559 (ASTM They used two different mechanism for brushing the samples i.e. hand brushing and mechanical brushing developed by Sampson (1988) the authors observed that, the stabilised samples of all three soils A, B and C having 2% cement could not sustain more than three cycles, five cycles and four cycles and all the soils having minimum 4% cement (except soil A) passed the limiting mass loss of 10% that is required for the structural layer of any pavement. A good relationship was observed between mass loss (%) and cement percentage for the studies of cement stabilised granular lateritic soil. Authors reported that Mass loss (%) has a non-linear power relationship (as fitted) with UCS values. minimum seven-day UCS of 2.97 MPa or seven-day soaked UCS of 1.08 MPa satisfies the mass loss criteria for cemented granular lateritic soil as per PCA guidelines. The mass loss increases linearly up to the 12 th cycle for 6% cement, however, as the cement percentage decreases, mass loss is more during initial cycles. Therefore, the initial mass loss may be considered to predict the total mass loss after 12 cycles. When mechanical brushing is done, the percentage mass loss should be suitably increased as the mass loss reported by hand brush was approximately 2.7 times of mass loss by mechanical brush. [3] Tested UCS for 2 curing periods of 7 days and 28 days. 2 specimens were tested from which one was tested after 4 hr soaking in water and named as soaked UCS and the other as unsoaked UCS. The relation between UCS and Mass loss % revealed that UCS had a non-linear power relationship UCS values. Authors also reported that seven-day soaked UCS and mass loss have a better fit compared as compared to seven-day UCS. stabilised while MDD MD replacement, cementitious in UCS
ANALYSIS OF THE RED TIDE OCCURRENCE PATTERN WHEN HIGH WATER TEMPERATURE

Abstract. In 1982, the red tide caused by Cochlodimium polykrikodies occurred at first in Jinhae Bay in Korea. Since then, it causes serious fisheries damage every year. In 2018, red tide occurred when high water temperature. We analyzed red tide occurrence pattern when high water temperature. Red tide occurrence date, occurrence area caused by the Cochlodinium polykrikoides were used provided by National Institute of Fisheries Science. SST data were used the GHRSST Level 4 OSTIA data provided by NOAA. The red tide occurred July 14 to September 2 in 2013, July 29 to October 4 in 2014, August 5 to September 23 in 2015, and July 23 to August 8 in 2018 in Korea. The nearest SST data from the red tide occurrence area were extracted. When red tide occurred, SST was 22~28 °C in 2013, 23~25 °C in 2014, 21~27 °C in 2015, 26~28 °C in 2018. SST in 2013 was increasing trend and 2015 was downward trend. SST in 2018 occurred at high water temperatures above 25 °C. The spatial pattern by using the self-organizing map (3x3 map), node 4 was the highest frequency (16.9%). It is considered that it appears at the beginning and end of red tide occurrence. Node 1 was 16.3% frequency, it showed at the end of 2014 and 2015. SST of node 1 and 4 maintained 22~23 °C on the southern sea of Korea. Node 9 was 13.9%, which is the late 2013 and 2018. Node 9 showed high temperature pattern of more than 26 °C in the southern sea of Korea.
Tracking Spread of the Agulhas Leakage Into the Western South Atlantic and Its Northward Transmission During the Last Interglacial

Intensification of the Agulhas Leakage (AL) during glacial terminations has long been proposed as a necessary mechanism for reverting the Atlantic Meridional Overturning Circulation (AMOC) to its interglacial mode. However, lack of records showing the downstream evolution of AL signal and substantial temporal differences between AL intensification and resumption of deep‐water convection have cast doubt on the importance of this mechanism to the AMOC. Here, we analyze a combination of new and previously published data relating to Mg/Ca‐derived temperatures and ice volume‐corrected seawater δ18O records (δ18OIVC‐SW, as a proxy for relative changes in ocean salinity), which demonstrate propagation of AL signal via surface and thermocline waters to the western South Atlantic (Santos Basin) during Termination II and the early Last Interglacial. The saline AL waters were temporally stored in the upper subtropical South Atlantic until they were abruptly released in two stages into the North Atlantic via surface and thermocline waters at ca. 129 and 123 ka BP, respectively. Accounting for age model uncertainties, these two stages are coeval with the resumption of convection in the Labrador and Nordic seas during the Last Interglacial. We propose a mechanism whereby both active AL and a favorable ocean‐atmosphere configuration in the tropical Atlantic were required to allow flux of AL waters into the North Atlantic, where they then contributed to enhancing the AMOC during the Last Interglacial period. Our results provide a framework that connects AL strengthening to the AMOC intensifications that followed glaciations.
Impacts of Lithologically Controlled Mechanisms on Downstream Bedrock Valley Widening

Wide, fluvially carved bedrock valleys are found around the world, but little is known about the processes and mechanisms controlling their formation. We know that rates of valley widening are controlled by stream discharge and bedrock lithology, with larger streams and softer rocks resulting in wider valleys. But lithology also controls widening mechanisms that cause valleys with contrasting bedrock strength to widen in fundamentally different ways. The effect of distinct widening mechanisms on bedrock valley width remains unquantified. We analyze a new topographic data set from various lithologies that demonstrates that bedrock valleys in soft lithologies widen downstream twice as fast as valleys in intermediate lithologies. These downstream valley‐widening rates also emerge from landscape evolution model simulations using two end‐member valley‐widening mechanisms. The topographic data show that lithology and mechanism of valley widening are linked and that different widening mechanisms can control bedrock valley development along river courses.
Interpolation of DHS survey data at subnational administrative level 2

Over the last several years and within the framework of the Sustainable Development Goals, there has been a need to improve the measurement and understanding of local geographic patterns to support more decentralized decision-making and more efficient program implementation. This requires more disaggregated data that are not currently available in a nationally representative household survey. This study explores the potential of model-based geostatistics methodology to model DHS survey indicators. We implement a stacked ensemble modeling approach that combines multiple model algorithmic methods to increase predictive validity relative to a single modeling. The approach captures potentially complex interactions and non-linear effects among the geospatial covariates. Three submodels are fitted to six DHS indicator survey data using the geospatial covariates as exploratory predictors. The model prediction surfaces generated from the submodels are used as covariates in the final Bayesian geostatistical model, which is implemented through a stochastic partial differential equation approach in the integrated nested Laplace approximations. The proposed approach can help to inform the allocation of resources and program implementation in areas that need more attention. Countries can use this approach to model other DHS survey indicators at much smaller spatial scales.
Estimation of turbulent boundary layer induced noise using energy flow analysis for ship hull designs

A turbulent boundary layer develops on the surface of submerged bodies in motion; these layers consist of complex flows and interact with the structure causing turbulent boundary layer induced noise due to fluid-structure interaction. Recently, although the research on such noise has attracted great interest in the naval fields, owing to the focus on the competitive development of low-noise naval ships, the limitations corresponding to the application of methods developed in aeroacoustics for underwater structures having lower convection speed of turbulence and faster sound speed along with insufficient environments to conduct experiments restrained to subjects of simple structures at high frequency. To overcome the abovementioned limitations and study the noise characteristics for ship hull design, in this research, methods to analyze the noise radiated due to turbulent flow on the complex underwater structure are developed using energy flow analysis methods for vibro-acoustic calculations. For estimation of the input hydrodynamic forces, wall pressure fluctuation spectrum on the surface is obtained from turbulent boundary layer properties to acquire sufficient resolutions. The vibrational response of the structure is calculated using energy flow analysis incorporating the finite element method for structural forces estimated as input power. The acoustical response coupled with the vibrational response is obtained using the calculated vibrational energy density with the boundary element method in combination with the energy flow analysis, taking advantages of the fact that the methods share the common energy variables. Developed methods are validated with a case of broadband noise radiated from a plate. Using the procedures, numerical estimation and analysis of acoustic performance are performed for trimaran ship hull designs with steady-state computational fluid dynamics to demonstrate the method’s usability as an assessment tool in the early design stage.
Segmenting Audiences and Tailoring Messages

Half of all pregnancies in young adult women are unintended, but few interventions have been successful in encouraging contraceptive use. The group heterogeneity likely contributes to the lack of success. Segmenting based on theories that provide meaningful information may improve tailoring and targeting of behavioral interventions. Previous research has indicated that threat, efficacy, and fear were important factors in influencing intentions to use contraceptives; therefore, the extended parallel process model (EPPM) was used for this cluster analysis. A telephone survey of randomly selected 18- to 30-year-old women in Iowa was conducted (N = 401). The constructs of EPPM and age were used for conducting a K means cluster analysis with four clusters. The cluster analysis pointed to the importance of fear, perceived susceptibility, and age. All of the clusters had varying degrees of ambivalence about the severity of a pregnancy. Cluster 1 (27.8%) had high susceptibility, with little fear. Cluster 2 (23.8%) had high efficacy and higher fear. The third cluster (34.7%) was not fearful and had low susceptibility. The final cluster (13.8%) was younger than the other groups and had the lowest efficacy. Additional analyses were conducted to explore how the clusters varied on other variables. The clusters help campaign developers prioritize audiences and tailor messages.
Probability and Trend Analysis of Monthly Rainfall in Haryana

Study on rainfall pattern of a region over a number of years is very useful for crop planning and irrigations scheduling. The present study deals with the probability and trend analysis of monthly rainfall in selected rain gauge stations scattered over the entire state of Haryana. Probabilities for drought, normal and abnormal events for monthly rainfall have been worked out using monthly rainfall data for 42 years (1970-2011), covering 27 rain gauge stations in the state of Haryana. Analysis indicated that drought months are more probable than normal months while normal months are more probable than abnormal months. The monotonic trend direction and magnitude of change in rainfall over time have been examined using the Mann-Kendall test and Sen’s slope estimator tests. Using the Mann-Kendall test and Sen’s slope estimator, the significant decrease in annual rainfall was noticed at Ballabgarh and Thanesar, While in monsoon rainfall, a significant decrease was noticed at Thanesar and Narnaul. But Sirsa is the only district which shows a significant increase in annual and monsoon rainfall. In probability analysis the highest per cent of normal, draughts and abnormal months was observed for Ambala, Hassanpur and Dujana respectively.
Joint Mean and Covariance Modeling of Chronic Kidney Disease

Chronic kidney disease (CKD) is defined as abnormalities of kidney structure or function. It is a serious health condition that is associated with premature mortality and decreased quality of life. Most of the existing populationlevel CKD studies focus on either the prevalence of CKD or the associations of CKD with other health problems. Very few studies have examined these quantities jointly, or have examined how the associations between CKD and other health problems vary by demographic characteristics. In this article, we apply a joint mean and covariance regression model to statistically describe how CKD and associations among CKD and other health problems vary according to demographic predictors. We apply the methodology to the data
The Association of Unhealthy Diet with Socioeconomic Inequality in Children: A Study in Kurdistan, West of Iran

Background: The prevalence of obesity is an increasing public health problem. Objectives: The present study aimed at estimating the prevalence of unhealthy diet among children aged 10 12 years in Kurdistan, west of Iran and assessing the association of unhealthy diet with socioeconomic inequality. Methods: The present cross-sectional study was conducted on 2506 children living in Sanandaj city, west of Iran in 2015. The subjects were selected from the schools by multistage sampling method. The required data were collected using the food frequency questionnaire. The socioeconomic status (SES) was calculated by measuring household asset by using principal component analysis technique. The inequality was measured using concentration index, and Blinder-Oaxaca decomposition method was utilized to determine the proportion of different determinants causing inequality. Results: The prevalence of unhealthy diet was 50.75% (95% confidence interval (CI): 48.79 52.71). The concentration index for unhealthy diet was -0.14 (95% CI: -0.18, -0.09), indicative of concentration of unhealthy diet in the group with low SES (P < 0.001). The Blinder-Oaxaca decomposition analysis showed that the poor-rich gap in the prevalence of unhealthy diet was 27.77%, while 59.41% of the observed gap attributed to the explained component. The major causes of inequality were mother’s level of education and place of residence, respectively. Conclusions: The obtained results showed that the prevalence of unhealthy diet was higher in children with low SES. Increasing mothers’ awareness of health literacy and performing place-based interventions can be effective in decreasing socioeconomic inequalities in unhealthy diet and prevention of chronic diseases in children.
Impacts of Subtropical Highs on Summertime Precipitation in North America

Numerous observational studies have found that the Hadley cells are expanding poleward in both the Northern and Southern Hemispheres, and model results suggest that such expansion is likely to continue throughout this century as a result of global warming. This has led to concerns that the subtropical dry zones may also shift poleward. However, precipitation changes associated with Hadley cell width are zonally asymmetric—especially in the Northern Hemisphere—suggesting that a more regional focus may give a clearer picture of these changes. In this study, we consider the influence of the Northern Hemisphere subtropical highs on summertime precipitation in North America and contrast this with the influence of Hadley cell expansion. Specifically, we consider the North Pacific and North Atlantic subtropical highs (NPSH and NASH, respectively) and define, for each of these, three indices representing longitude, latitude, and strength. We find that short‐term (monthly) precipitation variability over North America has comparable sensitivities to all six indices. However, for projections of 21st century climate change, North American summertime precipitation trends are driven more strongly by the longitudes of the highs than by their latitudes or strengths. Since the Hadley cells are defined with zonal‐mean quantities, much of the impact of these subtropical high shifts cannot be captured by Hadley cell metrics.
Comparison of Deep Learning models for Determining Road Surface Condition from Roadside Camera Images and Weather Data

Road maintenance during the Winter season is a safety critical and resource demanding operation. One of its key activities is determining road surface condition (RSC) in order to prioritize roads and allocate cleaning efforts such as plowing or salting. Two conventional approaches for determining RSC are: visual examination of roadside camera images by trained personnel and patrolling the roads to perform on-site inspections. However, with more than 500 cameras collecting images across Ontario, visual examination becomes a resource-intensive activity, difficult to scale especially during periods of snowstorms. This paper presents the results of a study focused on improving the efficiency of road maintenance operations. We use multiple Deep Learning models to automatically determine RSC from roadside camera images and weather variables, extending previous research where similar methods have been used to deal with the problem. The dataset we use was collected during the 2017-2018 Winter season from 40 stations connected to the Ontario Road Weather Information System (RWIS), it includes 14.000 labeled images and 70.000 weather measurements. We train and evaluate the performance of seven state-of-the-art models from the Computer Vision literature, including the recent DenseNet, NASNet, and MobileNet. Also, by integrating observations from weather variables, the models are able to better ascertain RSC under poor visibility conditions.
Stability of the dam "Prevoj Šaška" of the flotation tailing dump "Valja Fundata" in Majdanpek

The existing dam "Prevoj Saska" is located on the active flotation tailing dump "Valja Fundata" in Majdanpek. Since there is no valid documentation for the current maximum height of dam K+538 m, and disposal of tailings into this landfill is continued, the stability of dam has been carried out. Stability is calculated with the software SLIDE v6.0.
Application of density-dependant finite element model for studying seawater intrusion in coastal aquifer of Ras El-Hekma, Northwestern Coast, Egypt

A numerical model of variable-density groundwater flow and miscible salt transport is applied to investigate the extent of seawater intrusion in coastal aquifer of Ras El-Hekma, northwestern coast, Egypt. The SEAWAT 2000 code is used to solve the density-dependent groundwater flow and solute transport governing equations. Seventeen groundwater samples were collected in 2015 for routine chemical analysis and water level monitoring were carried out and interpreted. The model was calibrated using data measured for heads (m) and TDS (mg/L) in 2009 compared with data collected in 2015. Three scenarios of pumping and fourth scenarios of sea level rise by 0.5 m were applied. The results of three scenarios of pumping revealed that there was a general trend of drawdown in water table (~5 m) of observed wells located at about 1 to 1.5 km from the coast at the end of 20 years of simulation. At the same time there was replenishment from rainfall and surface runoff caused a water table rise in some observed wells that were located inland. For the proposed fourth scenario of sea level rise, the seawater/freshwater interface will migrate more inland (0.5 to 2 km) than its current position. The result of the numerical model shows a transgression of seawater inland and along the coast. The model and chemical results recommend a decrease in pumping to the drilled wells should not exceed 40 m3/day in order to avoid further seawater intrusion along the coast and upwelling of deep saline groundwater with continuous monitoring of the groundwater level and salinity measurements in north coast of Mediterranean Sea. Key words: Seawater intrusion, groundwater modeling, SEAWAT code, Ras El-Hekma, Egypt.
Enhanced indicator species performance with increasing contextualization

Indicator species need context to perform optimally for conservation purposes. If indicator potential is context‐dependent, then indicators should improve with increasing spatial‐environmental stratification. We tested this hypothesis by hierarchically stratifying a species combinations matrix (plants and wetlands) using regionalization and site typology and analyzing class specificity and occupancy rate for indicators shared across strata. Performance of indicators collectively improved with increased sample stratification providing greater spatial‐environmental context. Carefully considered sample classification schemes could strengthen the value of indicator species for monitoring biodiversity loss, environmental change, and management progress. However, the better accuracy of context‐specific indicators will have to be weighed against the practical need for fewer broad‐based indicators.
Automatic Estimation of Cephalometric Landmarks for Lateral Skull X-Rays using CNN

Introduction Automatic detection of landmarks in lateral skull cephalograms is considered as a Computer-Aided Diagnostics (CAD) tool. It helps physicians to detect pathologies such as craniofacial growth, orthodontic diagnosis, and oralmaxillofacial treatment planning. This study aims to find 19 landmarks out of 300 images (previously labeled by experts, as it is shown in Figure 1), which form the public dataset. The dataset was proposed by the 2014 IEEE International Symposium on Biomedical Imaging (ISBI-2014) [5]. Deep Convolutional Neural Networks (CNN) were used as a direct regression framework for the probabilistic estimation of 19 landmarks. A total of 50% from the dataset was used for training and the other 50% for testing. The obtained outcomes show up to 1.2 mm of average accuracy per image. The results were calculated between the 19 landmarks proposed by the expert and the estimated points.