Miracle-dz/UniFAR-FTUs huggingface dataset:
Lipases and Their Applications-An Applied Research in West Asia

Lipases are the enzymes that cleave carboxylic ester bonds and are produced by many living organisms like bacteria, fungi, plants and animals. These are the family of hydrolases. Their action is to hydrolyze triglycerides into monoglycerides, diglycerides, glycerol and fatty acids. As the lipases can perform several reactions like esterification, interesterification and transesterification they are widely used in leather, textile, cosmetic, detergent, fine chemistry, cellulose, pulp, paper, medical, effluent treatment, biodiesel production, food and pharmaceutical industries. These enzymes are low cost and easy to produce because of several sources and new technological developments in purification methods. The present paper discusses the various lipase sources, isolation, characterization and purification methods along with their applications.
Comparison of snow accumulation events on two High-Arctic glaciers to model-derived and observed precipitation

We evaluate how precipitation forcing data used in glacier mass balance models characterize snow accumulation events on synoptic timescales for two glaciers in north-western Svalbard (Kongsvegen and Holtedahlfonna). Using sonic ranger (snow depth) and wind speed data from automatic weather stations located on the glaciers, we distinguish accumulation events occurring under either calm or windy conditions. We show clear differences in the timing and magnitude of snow accumulation events between the two neighbouring glaciers, illustrating the spatial heterogeneity of snow accumulation in this region. The accumulation measurements show that at equivalent elevations, Kongsvegen receives more snowfall than neighbouring Holtedahlfonna, and that Kongsvegen is more affected by wind-driven snow redistribution than Holtedahlfonna. This is consistent with the synoptically-driven precipitation patterns in the region. Accumulation events are then compared to precipitation data from the nearest meteorological station in Ny-Alesund (ca. 30 km distant) and to a downscaled snowfall data product based on the ERA-Interim reanalysis (nearest gridpoint ca. 300 m distant). Evaluation of the synchrony of observed events at the glacier sites and the precipitation products shows that the ERA-Interim precipitation data reproduce more snowfall events than the Ny-Alesund station data, suggesting that the precipitation fields from distributed reanalysis data provide a more reasonable representation of accumulation on the study glaciers, even over short timescales.
Remote sensing of land use/cover changes in South and Southeast Asian Countries

Land-Use/Cover Changes (LU/CC) are occurring rapidly in South/Southeast Asian (S/SEA) countries, generally associated with population growth, economic development and competing demands for land. In the region, the most common LU/CC changes include urban expansion, agricultural land loss, land abandonment, deforestation, logging, reforestation, agricultural expansion, etc. Specific to South Asia, forest cover has been increasing in countries like India, Nepal, and Bhutan due to sustainable afforestation measures; whereas, large-scale deforestation in Southeast Asian countries is continuing, for example, due to oil palm plantation expansion driven by the international market demand in Malaysia and Indonesia. Small-scale deforestation in most of the Southeast Asian countries is due to slash-and-burn agriculture by indigenous people, driven by poverty and population growth. Logging activities are common in Myanmar and Cambodia. In terms of urbanization, South and Southeast Asian countries contain 23 megacities, each with more than 10 million people. Megacities in these countries started as urban clusters which grow rapidly, merging into conurbations. Rapid urbanization is driving agricultural land loss, and agricultural intensification has been increasing due to less availability of land for growing food crops as, for example, in India, Vietnam, and Thailand. The drivers of LU/CC vary widely in the region and include such factors as land tenure, local economic development, government policies, inappropriate land management, land speculation, improved road networks, etc. Also, variability in the weather, climate, and socioeconomic factors drive LU/CC resulting in disruptions of biogeochemical cycles, radiation and surface energy balance. Documenting LU/CC and the associated impacts gains significance in S/SEA as the results can be useful for informing policy and improved land management. Remote sensing, due to its multi-temporal, multi-spectral, synoptic and repetitive coverage capabilities is highly useful to monitor and document LU/CC and the associated impacts. The current special issue articles were part of the output from the LU/CC meeting organized in Chiang Mai, Thailand, 17–19th, July 2017 as part of the South/Southeast Asia Research Initiative (SARI), which is a NASA Land-Cover/Land-Use Change program-funded activity. The goal of the SARI is to promote and support innovative regional research, education, and capacity building, involving state-of-the-art remote sensing, natural sciences, engineering, and social sciences to enrich LCLUC science in South/Southeast Asia. SARI has been organizing focused thematic meetings and workshops in different countries of the region
Geology of the Neoproterozoic – early Cambrian Marwar Supergroup, Rajasthan: A synthesis

The Marwar Supergroup of the western India is one the largest Proterozoic sedimentary sequences of peninsular India. Due to its terminal Neoproterozoicearly Cambrian antiquity, these deposits are believed to hold potential clues to our understanding of the Proterozoic ocean, atmosphere, and origin of the complex multicellular life. Numerous paleobiological investigations in these largely fluvial and shallow marine deposits have brought out some intriguing results over the last decade. Similarly, chemostratigraphic studies have correlated the Bilara Group carbonates with Hanseran Group evaporites, and placed the middle and upper Marwar Supergroup alongside a host of late Ediacaran trans-Gondwanan basins. In this review, I have compiled the available lithostratigraphic, paleobiological, chemostratigraphic, and geochronological data from the Marwar Supergroup. In the wake of new age constraints for the Jodhpur Group, some of the trace fossil assemblages might require revisiting whereas new geochronologic data from within the supergroup would come in handy as we continue to look for the evidence of various Neoproterozoic global events in India.
Research into the Automatic Berthing of Underactuated Unmanned Ships under Wind Loads Based on Experiment and Numerical Analysis

With the continuous improvement of unmanned ship automation requirements, research into the automatic berthing of underactuated unmanned ships has important theoretical significance and practical value. In order to determine the trajectory of unmanned ships, the line of sight (LOS) algorithm was applied due to the characteristics of underactuated unmanned ships without side thrusters. In order to resist wind disturbance, the active disturbance rejection control (ADRC) method was applied to keep the ship moving on its intended trajectory. Then, to carry out the simulation analysis before the tank experiment, a remote-control simulation system based on a user datagram protocol (UDP) communication was built, and the ability of the ADRC controller to make the ship perform completely automatic berthing in both wind and no wind conditions was verified in simulations. Combined with the simulation results, a tank experiment was accomplished at the Japanese National Research Institute of Fishery Engineering. The experimental results also showed that the ADRC controller has good robustness, that the problems of insufficient autonomous route determination and the disturbance rejection ability in the process of the automatic berthing of underactuated unmanned ships are solved, and the safety of ship navigation is improved, which lays a theoretical and experimental foundation for the further development of unmanned ship control.
Implementation of ANN for Predicting the Percentage of Illiteracy in Indonesia by Age Group

Illiteracy is one of the serious problems experienced by Indonesia. The lack of care from the government and the private sector towards illiterate people makes the illiteracy rate quite high. Because of this, this problem must be one of the government’s targets going forward, because directly or indirectly illiteracy plays a role in increasing the number of poverty in Indonesia. The purpose of this study is to predict the percentage of the illiterate population in Indonesia according to the age group 15-44 years because this age group category is a productive age. The results of this prediction are expected to be a reference and benchmark for the government in determining and making policies to reduce illiteracy rates. The data that will be predicted is the illiteracy rate data for each province in Indonesia sourced from the Indonesian Central Bureau of Statistics from 2011 to 2017. The method used for prediction is Backpropagation Artificial Neural Network. Data analysis and calculation were carried out with the help of Matlab and Microsoft Excel software. This study uses 5 architectures, 4-5-1, 4-6-1, 4-9-1, 4-14-1 and 4-18-1. Of the five models, the best network architecture is 4-14-1 with an accuracy rate of 91% and the Mean Squared Error 0.00274166. Using this 4-14-1 network model, a prediction on the percentage of illiteracy in Indonesia will be calculated for 2018 until 2020.
Regurgitalites – a window into the trophic ecology of fossil cephalopods

Fossil oral ejecta that exclusively contain bivalved calcitic lower jaws of ammonites (=aptychi) from Late Jurassic Solnhofen-type deposits of southern Germany are described. Based on the symphysis length of aptychi the ammonite shell diameters were reconstructed, ranging from 5 to 170 mm. To identify the potential ammonite predator published reports on stomach contents, coprolites and ammonite-palaeopathology were critically assessed. Implementation of the actualistic approach, together with some functional considerations, includes the identification of modern cephalopod predators with a focus on the modern Nautilus, and the distribution of vomiting behaviour among them. Stomach fluid pH is also important because species with an alkaline stomach pH are unable to dissolve calcareous hardparts and have to orally eject such material. Herein, we regard coleoids with an opportunistic diet as the most likely producers of the aptychi-bearing regurgitalites. This is based on finds of aptychi in the stomachs of the two coleoids Plesioteuthis and Trachyteuthis. They were likely fast, highly manoeuvrable swimmers. Both share a strong, triangular-shaped jaw apparatus that could possibly crush ammonite shells as well as arms equipped with suckers to hold their prey. Circumstantial evidence for coleoids as the producers comes from the alkaline pH of the stomach fluids of modern coleoids. Supplementary material: Data for lamellaptychi are available at https://doi.org/10.6084/m9.figshare. c.4695002
Change Detection of Surface Water in Remote Sensing Images Based on Fully Convolutional Network

Song, A. ; Kim, Y. , and Kim, Y. , 2019. Change detection of surface water in remote sensing images based on fully convolutional network. In: Lee, J.L. ; Yoon, J.-S. ; Cho, W.C. ; Muin, M. , and Lee, J. (eds.), The 3rd International Water Safety Symposium. Journal of Coastal Research, Special Issue No. 91, pp. 426-430. Coconut Creek (Florida), ISSN 0749-0208.This study presents a new approach based on fully convolutional networks (FCN) to detect changes in surface water. The proposed method can be divided into three steps: (1) training the FCN using color-infrared (CIR) images from the Coastwide Reference Monitoring System (CRMS) dataset with two classes, such as water and land; (2) passing the multitemporal images respectively through the pre-trained FCN and generating a difference image (DI) from score maps of the last prediction layers; and (3) determining optimal threshold values using fuzzy entropy and discriminating between changed and unchanged pixels in the DI. This method has the advantage of effectively learning the spatial and spectral characteristics of water bodies from large remote-sensing datasets, and it would be helpful to analyze and monitor changes in newly obtained images without ground truth. The experimental results obtained using the multitemporal CRMS data demonstrated the effectiveness of this deep-learning approach for detecting changes in remote-sensing images, as compared other traditional methods for change detection.
Practices in Early Intervention for Children with Autism: A Comparison with the National Research Council Recommended Practices

The National Research Council (2001) report was reviewed to identify and document recommended practices for programs serving young children with autism spectrum disorder. Twenty seven surveys inquiring about program practices were sent to educational service districts, school districts, and neurodevelopmental centers in Oregon and Washington that had a program specifically designed to serve young children with autism spectrum disorder. Survey results indicated that the practices currently utilized within autism early intervention programs are in many ways inconsistent with the recommended practices identified in the literature. Implica- tions and future research needs are discussed.
United States Midwest Soil and Weather Conditions Influence Anaerobic Potentially Mineralizable Nitrogen

Nitrogen provided to crops through mineralization is an important factor in N management guidelines. Understanding of the interactive effects of soil and weather conditions on N mineralization needs to be improved. Relationships between anaerobic potentially mineralizable N (PMNₐₙ) and soil and weather conditions were evaluated under the contrasting climates of eight US Midwestern states. Soil was sampled (0–30 cm) for PMNₐₙ analysis before pre-plant N application (PP₀N) and at the V5 development stage from the pre-plant 0 (V5₀N) and 180 kg N ha⁻¹ (V5₁₈₀N) rates and incubated for 7, 14, and 28 d. Even distribution of precipitation and warmer temperatures before soil sampling and greater soil organic matter (SOM) increased PMNₐₙ. Soil properties, including total C, SOM, and total N, had the strongest relationships with PMNₐₙ (R² ≤ 0.40), followed by temperature (R² ≤ 0.20) and precipitation (R² ≤ 0.18) variables. The strength of the relationships between soil properties and PMNₐₙ from PP₀N, V5₀N, and V5₁₈₀N varied by ≤10%. Including soil and weather in the model greatly increased PMNₐₙ predictability (R² ≤ 0.69), demonstrating the interactive effect of soil and weather on N mineralization at different times during the growing season regardless of N fertilization. Delayed soil sampling (V5₀N) and sampling after fertilization (V5₁₈₀N) reduced PMNₐₙ predictability. However, longer PMNₐₙ incubations improved PMNₐₙ predictability from both V5 soil samplings closer to the PMNₐₙ predictability from PP₀N, indicating the potential of PMNₐₙ from longer incubations to provide improved estimates of N mineralization when N fertilizer is applied.
Landscape Approach to Normalized Difference Vegetation Index Forecast by Artificial Neural Network: Example of Diyala River Basin

2019. Т. 19, No 3. С. 5–19 5 Introduction Modern Earth sciences are not conceivable without the analysis of multispectral satellite data. The Normalized Difference Vegetation Index (NDVI) and other proxies of primary biological productivity are important products of this analysis [1]. The values of NDVI are highly depended on environmental conditions, so that NDVI is one of principal indicators for evaluating climate impact onto terrestrial ecosystems [2–12]. Particularly, extent and evolution NDVI are often used to estimate climate changes global and vegetation activity [2, 3] and net primary production and vegetation dynamics overlarge arid regions such as Sahel [4, 5], arid regions of Central Asia and Kazakhstan [6, 7], Mongolia and arid area of China [8, 9], Tibetan Plateau [10]. In addition to monitoring arid zones in the works [11, 12] explores the long-period changes in forest-steppe, forest and tundra vegetation of the Russian Federation. Mapping of NDVI dynamics is one of the main instruments for evaluation and prediction of agricultural productivity [13–18]. First, as in the case of natural biomes, some work explore the impact of climate change on productivity of rain-fed zones [13] and other use NDVI data to model of ecological regimes of rural territory [14]. The changes in vegetation indices of rural areas allows to separate healthy vegetation crops from weak developed fields in irrigated agriculture [15]; to monitor droughts [16, 17] and, with availability of additional surface data, to implement crop forecast [18]. From this point, the capabilities to predict vegetation index under an appropriate spatiotemporal scale [13–18] are critical for decision making to adapt agricultural techniques or to limit socio-economic losses associated with urbanization [19, 20]. Информатика и вычислительная техника
Modelling of rare flood meadow species distribution by a combined habitat surface water–groundwater model

Floodplains are highly complex and dynamic systems in terms of their hydrology. Thus, they comprise a wide habitat heterogeneity and therefore harbour highly specialized species. For future projections of habitat and species diversity, process‐based models simulating ecohydrological conditions and resulting habitat and species distributions are needed. We present a new modelling framework that includes a physically based, surface water–groundwater model coupled with a habitat model. Using the model framework, we simulate the occurrence of 23 flood meadow plant species in a Rhine River floodplain. To benchmark the data, results are compared with a conventional approach with simple spatial hydrological information. Our results show that models with predictors obtained from the surface water–groundwater model are significantly more accurate for rare and endangered species, as well as for typical flood meadow species. A total of 15 hydrological predictors were defined, which look relevant and promising for a good prediction, but at the same time reflect very different hydrological conditions. The standard deviation of the groundwater level, wet soil conditions, and inundation belong to the most relevant predictors for an accurate prediction. Therefore, we recommend including more specific hydrological information in habitat models of species in complex floodplain ecosystems. Such spatial explicit habitat models can also open up further possibilities, such as the projection of global change impact studies or nature conservation planning.
An Earthquake Model Based on Fatigue Mechanism—A Tale of Earthquake Triad

Earthquakes are the result of strain build-up from without and erosion from within faults. A generic co-seismic condition includes merely three angles representing respectively fault geometry, fault strength and the ratio of fault coupling to lithostatic load. Correspondingly, gravity fluctuation, bridging effect, and granular material production/distribution form the earthquake triad. As a dynamic component of the gravity field, groundwater fluctuation is the nexus among the three intervened components and plays a pivotal role in regulating major earthquake irregularity: reducing natural (dry) inter-seismic periods and lowering magnitudes. It may act mechanical-directly (MD) through super-imposing a seismogenic lateral stress field thus aiding plate-coupling from without; or mechanical-indirectly (MI) by enhancing fault fatigue, hence weakening the fault from within. A minimum requirement for a working earthquake prediction system is stipulated and implemented into a well-vetted numerical model. This fatigue mechanism based modeling system is an important supplement to the canonical frictional theory of tectonic earthquakes. For collisional systems (e.g., peri-Tibetan Plateau regions), MD mechanism dominates, because the orographically-induced spatially highly biased precipitation is effectively channeled into deeper depth by the prevalence of through-cut faults. Droughts elsewhere also are seismogenic but likely through MI effects. For example, ENSO, as the dominant player for regional precipitation, has strong influence on the gravity field over Andes. Major earthquakes, although bearing the same 4 - 7 years occurrence frequency as ENSO, have a significant hiatus, tracing gravity fluctuations. That granular channels left behind by seamounts foster major earthquakes further aver the relevance of MI over Andes. Similarly, the stability of the Cascadia fault is found remotely affected by Californian droughts (2011-15), which created a 0.15 kPa/km stress gradient along the Pacific range, which also is the wave guide.
Stress and Environmental Health of Women in different Neighbourhoods of Lagos Metropolis

It has been established that women spend more time in the neighbourhood environment, and therefore, are more vulnerable to the observable poor conditions. The focus of this study is on neighbourhood environmental stressors that affect womens’ health in Lagos metropolis. The factors considered include access to clean water, adequate sanitation, drainage conditions, ventilation and hygiene, type of energy for cooking and nutrition. These factors are exacerbated by poverty and differ across different neighbourhoods in Lagos metropolis. 1150 respondents (high – 50; medium – 328; and low 772) consisting of randomly selected women, aged 18 years and above were selected from all the 17 Local Government Areas (LGAs) in metropolitan Lagos to achieve 100% representation. Focus Group Discussions were held with women from selected different neighbourhoods. A 5-point likert scale was used as a measure of self-reported stress and self-reported health, with higher numbers indicating a greater self-reported stress. From the different survey methods used, results show that women in the low income neighbourhoods are more vulnerable to environmental stressors, and so their health is mostly affected negatively. Women in the other income groups also experience some form of stress but at lower severity levels. Environmental stressors and severity of chronic illness are linked to stress. An improvement in the environmental conditions will reduce the amount of stress experienced by women of different income neighbourhoods.
A Stochastic Representation of Temperature Fluctuations Induced by Mesoscale Gravity Waves

Ubiquitous mesoscale gravity waves generate high cooling rates important for cirrus formation. We make use of long‐duration balloon observations to devise a probabilistic model describing mesoscale temperature uctuations (MTF) away from strong wave sources. We define background conditions based on observed probability distributions of temperature and underlying vertical wind speed fluctuations. We show theoretically that MTF are subject to damping at a rate near the Coriolis frequency when the vertical wind speed fluctuations are autocorrelated over a fraction of a Brunt‐Väisälä period. We find that for background wave activity, a decrease in temperature of 1K translates into cooling rate standard deviations and mean updraft speeds of 4–8Kh−1 and ≈ 10–20 cms−1, respectively, depending on latitude and stratification. We introduce an effective Coriolis frequency to generate cooling rates in equatorial regions consistent with balloon data. Above ice saturation, MTF are large enough to affect ice crystal nucleation. Our results help constrain uncertainty in aerosol‐cirrus interactions, provide insights to better meet challenges in comparing measurement data with model simulations, and support the development of cutting‐edge ice cloud schemes in global models.