Miracle-dz/UniFAR-FTUs huggingface dataset:
HEALTH CARE FACILITIES FOR ADOLESCENT GIRLS IN KASTURBA GANDHI BALIKA VIDALAYA (KGBV) OF ASSAM

The adolescent girls of the Kasturba Gandhi BalikaVidalaya (KGBV) need access to information and services related to nutrition, reproductive health, family planning, and general health. The girls need to be taught the ways to keep their bodies clean to avoid any type of infection. As the girls are growing faster at this periods of time than at any time after their first year of life therefore nutritious food should be provided to the girls of KGBV. Methods: The state of Assam in India was selected for the present study. Multi stage sampling design was adopted for the study. The sampling units at different stages were State, District and Kasturba Gandhi BalikaVidyalayas (KGBVs). The six districts namely Dibrugarh, Sibsagar, LakhimpurNagoan, Kamrup and Barpeta District were selected purposively for the study. To elicit information from the sample, an interview schedule was developed. The collected data was organized, coded, consolidated and tabulated by using Microsoft excel sheet and analyzed systematically. The preliminary analytical devices expressed in frequency and percentage was used to assess the health care facilities provided in KGBV. Conclusion: The study indicates that although the food staffs are regularly provided to the girls of KGBV but more nutritious foods are to be incorporated in the diet. The health check up camps is to be organized in convergence with health department in a regular interval. KEYWORDS: Health, care, Facilities, Adolescent, Kasturba Gandhi Balika Vidyalayas.
Key factors of parts logistics for milk-run and cross-docking system:Design and analysis of simulation experiments method

Automobile and logistics are the two critical restructuring and invigorating industries in China.In the context of assembly plant capacity expansion,with the purpose of improving the system performance,this paper elaborates on the optimal design of key operations in the logistics mode of auto parts milk run and cross dock dealt with by a 3PL company.We build a simulation model which integrated the operations of milk run and cross-docking based on practical research data.Further,we propose an optimization algorithm that uses sequential bifurcation and Response surface methodology(SB-RSM) of the design and analysis of simulation experiments theory.The results of specified simulation design and runs indicate that the logistics performances are greatly improved if the 3PL simply optimizes the key operations,which helps to reduce the workload of the 3PL.The proposed SB-RSM shows higher efficiency and efficacy than classical RSM.This study contributes to decision making concerning supporting automobile parts 3PL Company when the automobile assembly plant capacity expands,and guides to solve the optimization issue of complex simulated system.
GRACE constraints on Earth rheology of the Barents Sea and Fennoscandia

Abstract. The Barents Sea is situated on a continental margin and was home to a large ice sheet at the Last Glacial Maximum. Studying the solid Earth response to the removal of this ice sheet (glacial isostatic adjustment; GIA) can give insight into the subsurface rheology of this region. However, because the region is currently covered by ocean, uplift measurements from the center of the former ice sheet are not available. The Gravity Recovery and Climate Experiment (GRACE) gravity data have been shown to be able to constrain GIA. Here we analyze GRACE data for the period 2003–2015 in the Barents Sea and use the data to constrain GIA models for the region. We study the effect of uncertainty in non-tidal ocean mass models that are used to correct GRACE data and find that it should be taken into account when studying solid Earth signals in oceanic areas from GRACE. We compare GRACE-derived gravity disturbance rates with GIA model predictions for different ice deglaciation chronologies of the last glacial cycle and find that best-fitting models have an upper mantle viscosity equal or higher than 3×1020 Pa s. Following a similar procedure for Fennoscandia we find that the preferred upper mantle viscosity there is a factor 2 larger than in the Barents Sea for a range of lithospheric thickness values. This factor is shown to be consistent with the ratio of viscosities derived for both regions from global seismic models. The viscosity difference can serve as constraint for geodynamic models of the area.
Performance Analysis of Routing Protocols for Target Tracking in Wireless Sensor Networks

Wireless sensor networks (WSNs) are large scale integration in large topology deployed with thousands sensor nodes. Nodes sizes are very small, with low cost, low weight, and limited battery, primary storage, processing power. Sensor nodes have wireless communication capabilities with sensor to monitor physical or environmental conditions. This paper study and evaluate performance for localization and target tracking application with proposed hierarchical localization tracking scheme based on hierarchical binary tree structure. The target detected information is stored at multiple sensor nodes (e.g. node, parent node and grandparent node) which deployed using complete binary tree structure to improve fault tolerance. This drastically reduces number of messaging in the network. Performance of proposed scheme and some existing routing scheme is evaluated using NS2. Simulation result proof increased in network lifetime by 25%, target detection probability by 25%, and reduces error rate by 20%, increased energy efficiency by 20%, fault tolerance, and routing efficiency.
Simultaneous Optical Stimulation and Electrophysiological Recordings in Closed-loop Operation

Closed-loop brain computer interfaces are rapidly progressing due to their application in fundamental neuroscience and prosthetics implemented (Hatsopoulos and Donoghue, 2009; Lebedev and Nicolelis, 2006). The integration of optical stimulation and electrophysiological recordings, on one hand, brings the advantage of cell-type selectivity. On the other hand, it provides an alternative solution to the stimulation-induced artifacts, a challenge in electrical stimulation (Zhang et al., 2009; Zhang and Oertner, 2007; Wininger et al., 2009). In this contribution, we describe a prototype allowing simultaneous optical stimulation and electrophysiological recordings in a closed-loop manner. The prototype is implemented with online spike detection and classification for selective cell-type stimulation.
The utility of Sentinel-2 Vegetation Indices (VIs) and Sentinel-1 Synthetic Aperture Radar (SAR) for invasive alien species detection and mapping

The threat of invasive alien plant species is progressively becoming a serious global concern. Alien plant invasions adversely affect both ecological services and socio-economic systems. Hence, accurate detection and mapping of invasive alien species is valuable in mitigating adverse ecological and socio-economic effects. Recent advances in active and passive remote sensing technology have created new and cost-effective opportunities for the application of remote sensing to invasive species mapping. In this study, new generation Sentinel-2 (S2) optical imagery was compared to S2 derived Vegetation Indices (VIs) and S2 VIs fused with Sentinel-1 (S1) Synthetic Aperture Radar (SAR) imagery for detecting and mapping the American Bramble (Rubuscuneifolius). Fusion of S2 VIs and S1SAR imagery was conducted at pixel level and multi-class Support Vector Machine (SVM) image classification was used to determine the dominant land use land cover classes. Results indicated that S2 derived VIs were the most accurate (80%) in detecting and mapping Bramble, while fused S2 VIs and S1SAR were the least accurate (54%). Findings from this study suggest that the application of S2 VIs is more suitable for Bramble detection and mapping than the fused S2 VIs and S1SAR. The superior performance of S2 VIs highlights the value of the new generation S2 VIs for invasive alien species detection and mapping. Furthermore, this study recommends the use of freely available new generation satellite imagery for cost effective and timeous mapping of Bramble from surrounding native vegetation and other land use land cover types.
Dynamics of hydrological model parameters: calibration and reliability

Abstract. It has been demonstrated that the dynamics of hydrological model parameters based on dynamic catchment behavior significantly improves the accuracy and robustness of conventional models. However, the calibration for the dynamization of parameter set involves critical components of hydrological models, including parameters, objective functions, state variables, and fluxes, which usually are ignored. Hence, it is essential to design a reliable calibration scheme regarding these components. In this study, we compared and evaluate five calibration schemes with respect to multi-metric evaluation, dynamized parameter values, fluxes, and state variables. Furthermore, a simple and effective tool was designed to assess the reliability of the dynamized parameter set. The tool evaluates the convergence processes for global optimization algorithms using violin plots (ECP-VP), effectively describes the convergence behaviour in individual parameter spaces. The different types of violin plots can well match to all possible properties of fitness landscapes. The results showed that the reasons for poor model performance included time-invariant parameters oversimplifying the dynamic response modes of the model, the high-dimensionality disaster of parameters, the abrupt shifts of the parameter set, and the complicated correlations among parameters. The proposed calibration scheme overcome these issues, characterized the dynamic behaviour of catchments, and improved the model performance. Additionally, the designed ECP-VP tool effectively assessed the reliability of the dynamic parameter set, providing an indication on recognizing the dominant response modes of hydrological models in different sub-periods or catchments with the distinguishing catchment characteristics.
Numerical simulation of ducted and non-ducted tidal turbines using actuator line method

Electricity generation using tidal turbines is a relatively new technology in the renewable energy field. These turbines, due to their harsh and complex operational environment, must be engineered to operate with low maintenance and high efficiency. Therefore, significant research and development is being undertaken to achieve these operational goals, and high fidelity numerical tools such as CFD are being applied to the problem. However, tidal flows are very site-specific and only in-situ simulations can give a reliable estimate of a turbine’s performance and the imposed forces on its components. In this thesis, the actuator line (AL) method is investigated as a method feasible for in-situ simulation of tidal turbines in high resolution CFD simulations. The AL method does not require a detailed treatment of the turbine blade geometry but incorporates its rotational effects on the flow. It therefore becomes more computationally efficient while also maintaining the ability to include dynamic loads and wakes. Therefore, in this work the AL method has been developed in a heterogeneous CPU/GPU parallel architecture and its implementation has been validated by simulating a well-studied non-ducted tidal
Regional Distribution of Mesospheric Small‐Scale Gravity Waves During DEEPWAVE

The Deep Propagating Gravity Wave Experiment project took place in June and July 2014 in New Zealand. Its overarching goal was to study gravity waves (GWs) as they propagate from the ground up to ~100 km, with a large number of ground‐based, airborne, and satellite instruments, combined with numerical forecast models. A suite of three mesospheric airglow imagers operated onboard the NSF Gulfstream V (GV) aircraft during 25 nighttime flights, recording the GW activity at OH altitude over a large region (>7,000,000 km2). Analysis of this data set reveals the distribution of the small‐scale GW mean power and direction of propagation. GW activity occurred everywhere and during every flight, even over open oceans with no neighboring tropospheric sources. Over the mountainous regions (New Zealand, Tasmania, isolated islands), mean power reached high values (more than 100 times larger than over the waters), but with a considerable variability. This variability existed from day to day over the same region, but even during the same flight, depending on forcing strength and on the middle atmosphere conditions. Results reveal a strong correlation between tropospheric sources, satellite stratospheric measurements, and mesosphere lower thermosphere airglow observations. The large‐amplitude GWs only account for a small amount of the total (~6%), even though they carry the most momentum and energy. The weaker wave activity measured over the oceans might originate from distance sources (polar vortex, weather fronts), implying that a ducted mechanism helped for their long range propagation.
Optimization of Pharmaceutical Product Formulation by Factorial Designs: Case Studies

Optimization of a product or process is determination of best possible composition or operating conditions resulting in its optimal performance. Optimization techniques are relatively new to the practice of pharmacy. Optimization using factorial designs is an efficient technique used in formulation optimization. Factorial experiments with two level factors are used widely because they are easy to design, efficient to run, straight forward to analyze, and full of information. Five case studies, in which optimization was performed by factorial design are discussed.
Person re-identification based on saliency

Two novel salient features for person re-identification are proposed in this paper. Salient feature in image always provides valuable information for pattern recognition, therefore we seek two kinds of salient features to represent pedestrians, a Speeded Up Robust Features (SURF) and a Principal Component Analysis-Scale Invariant Feature Transform (PCA-SIFT) feature are used to describe texture of person image respectively, then texture information and color histogram construct the feature space. In the feature space, salient features are detected according to the difference between person images. The effectiveness of our salient features in people re-identification is validated on the widely used GRID dataset, we get more accurate and efficiency results using SURF and PCA-SIFT based salient features.
Drought Assessment using GRACE Terrestrial Water Storage Deficit in Mongolia from 2002 to 2017

Climate warming can result in increases in the frequency and magnitude of drought events, leading to water shortages and socioeconomic losses. Gravity Recovery and Climate Experiment (GRACE) satellite data have been used to monitor and estimate drought events. However, there is little information on detecting the characteristics of droughts in Mongolia due to sparse observations. In this study, we estimate the drought conditions in Mongolia using GRACE terrestrial water storage data during 2002–2017. Water storage deficit (WSD) is used to identify the drought event and calculate the water storage deficit index (WSDI). The WSDI was compared with the standardized precipitation index (SPI) and the standardized precipitation evapotranspiration index (SPEI). The results showed that there were two turning points of WSD in 2007 and 2012. Eight drought events were identified and the most severe drought occurred in 2007–2009 lasting for 38 months with a WSDI of −0.98 and a total WSD of −290.8 mm. Overall, the WSD and WSDI were effective in analyzing and assessing the drought severity in a region where hydrological observations are lacking.
A Survey Report On Current Research And Development of Data Processing In Web Usage Data Mining

WUM is the part of web mining that identifies usages data from the web log server ,in order to known and improved serve the requirements of the web applications. The WUM involves three types, Data Preprocessing , Pattern Discovery & Pattern Analysis. pattern discovery phase contains many web data mining methods are apply to process the data so as to discover patterns. Once the pattern discovered. Analysis is done using various operation with unique session and unique user. This paper gives a brief overview of WUM and its phases . Keyword: Web mining, web usage , web log, data pre-processing, discovery phase and analysis phase
Constrained trajectory simplification with speed preservation

ABSTRACT The rapid increase in movement trajectory data causes data storage, transmission, computational processing, and visualization problems. These issues can be alleviated through trajectory simplification, which removes unnecessary details from raw trajectories. Most existing studies that focused on trajectory simplification considered freely moving objects, and they attempted to minimize position errors in their simplified representations in a two-dimensional plane. However, a large number of objects move within the constraint of road networks. In such constrained trajectory simplification, position error should be measured in the network space. Moreover, constrained trajectories contain a wealth of speed-change information that reflects the movement patterns of moving objects. In this study, we designed a data model, proposed error measurements, and developed a two-component method to simplify constrained trajectories. The geometric component in our method extended the classic Douglas–Peucker method using network distance to simplify trajectories with a guaranteed position error bound in network space. The semantic component enhanced the simplified representation by employing a data-enrichment strategy that allows users to control speed loss. Real trajectory data were used to assess the effectiveness of the proposed method. Experimental results show that our method has a lower position error than existing algorithms do when road network constrained trajectory data are simplified. The method can also preserve original speed in the simplified representation with a relatively low increase in data size. Our study thus provides an approach to simplifying trajectory data that guarantees error bounds in both location and speed.
Overlooked Genetic Diversity of Ammonia Oxidizing Archaea Lineages in the Global Oceans

In this study, we used miTAG approach to analyze the distributional pattern and fine‐scale genetic diversity of the ammonia oxidizing archaea (AOA) lineages in the global oceans with the metagenomics data sets of the Tara Oceans global expedition (2009–2013). Using the ammonium monooxygenase alpha subunit gene as a biomarker, the AOA communities in the global oceans were recovered with highly diverse operational taxonomic units that affiliated to previously defined clades, including water column A (WCA), water column B (WCB), and SCM1‐like clades. In general, the AOA communities were obviously segregated with depth (except the upwelling regions), and the communities in the euphotic zones were more heterogeneous than in the mesopelagic zones (MPZs). The WCA distributed more evenly and widely in the euphotic zone and MPZs, while WCB and SCM1‐like clade mainly distributed in MPZ and high‐latitude waters, respectively. At fine‐scale genetic diversity, SCM1‐like and 2 WCA subclades showed distinctive niche separations of distributional pattern. We further divided the AOA subclades into ecological significant taxonomic units (ESTUs), which were delineated from the distribution pattern of their corresponding subclades. For example, ESTUs of WCA have different correlations with depth, nitrate to silicate ratio, and salinity; SCM1‐like A was negatively correlated with irradiation, whereas other SCM1‐like ESTUs preferred low‐temperature and high‐nutrient conditions. Our result showed that the previously defined AOA clades and ecotypes consist of highly diverse sublineages, whose diversity might be overlooked in the past. The distribution patterns of different ESTUs imply their ecophysiological characteristics and potential roles in biogeochemical cycling.
The Sand Motor : a nature-based response to climate change. Findings and reflections of the interdisciplinary research program Naturecoast

NatureCoast is the largest research program that focused on the Sand Motor, a large sandy peninsula, constructed in 2011 on the Dutch North Sea coast near The Hague. This unprecedented pilot project involved placing 21.5 million m3 of sand on and in front of the beach with the aim that it would spread along the coast. The Sand Motor is a unique beach nourishment due to its size, the design philosophy behind it, and its multifunctionality. It combines the primary function of coastal protection with the creation of a new natural landscape that also provides new leisure opportunities. From the outset, “learning by doing” has been a crucial part of the project and NatureCoast was an integral part of this. Because of its innovations, the Sand Motor has triggered considerable political and scientific interest from all over the world. Broad research consortia were formed to conduct interdisciplinary research on the Sand Motor. The NatureCoast program was carried out by a consortium of knowledge institutes and universities, and the research was conducted in cooperation with end-users from private companies, research institutes and governmental organizations. The Dutch Technology Foundation (NWO-TTW) provided the largest share of the project funds. The research in NatureCoast focused on six themes: coastal safety, dune formation, marine ecology, terrestrial ecology, hydrology and geochemistry, and governance. This book presents countless facets of the Sand Motor, but we also hope it demonstrates the scientific merits of interdisciplinary research and how, ultimately, societies can benefit from it.
Food Communication System Evaluation of LDPC Codes Combined with Differential Space-time Block Codes

In this study, we propose a multi-antenna food communication system combined Low-Density ParityCheck (LDPC) codes with Differential Space-Time Block Codes (DSTBC), in which Channel State Information (CSI) is unknown to the transmitter and the receiver. The food communication system performance is given through computer simulation, we can see that the proposed food communication system not only provides transmit diversity gain but also coding gain.
A Spatially Enhanced Data‐Driven Multimodel to Improve Semiseasonal Groundwater Forecasts in the High Plains Aquifer, USA

The aim of this paper is to improve semiseasonal forecast of groundwater availability in response to climate variables, surface water availability, groundwater level variations, and human water management using a two‐step data‐driven modeling approach. First, we implement an ensemble of artificial neural networks (ANNs) for the 300 wells across the High Plains aquifer (USA). The modeling framework includes a method to choose the most relevant input variables and time lags; an assessment of the effect of exogenous variables on the predictive capabilities of models; and the estimation of the forecast skill based on the Nash‐Sutcliffe efficiency (NSE) index, the normalized root mean square error, and the coefficient of determination (R2). Then, for the ANNs with low‐ accuracy, a MultiModel Combination (MuMoC) based on a hybrid of ANN and an instance‐based learning method is applied. MuMoC uses forecasts from neighboring wells to improve the accuracy of ANNs. An exhaustive‐search optimization algorithm is employed to select the best neighboring wells based on the cross correlation and predictive accuracy criteria. The results show high average ANN forecasting skills across the aquifer (average NSE > 0.9). Spatially distributed metrics of performance showed also higher error in areas of strong interaction between hydrometeorological forcings, irrigation intensity, and the aquifer. In those areas, the integration of the spatial information into MuMoC leads to an improvement of the model accuracy (NSE increased by 0.12), with peaks higher than 0.3 when the optimization objectives for selecting the neighbors were maximized.tT