Miracle-dz/UniFAR-FTUs huggingface dataset:
Relationship between Manure Management Application Practices and Phosphorus and Nitrogen Export in Snowmelt Run-off Water from a Black Chernozem Saskatchewan Soil

In Saskatchewan, soil nutrients released from land-applied solid cattle manure (SCM) and liquid hog manure (LHM) could be subject to off-field export via spring surface run-off water and/or subsurface leaching from melting snow. The objective of this study was to determine how the placement of SCM and LHM using surface and subsurface application methods affects the amounts of soluble reactive phosphorus (SRP), nitrate-nitrogen (NO3-N) and ammonium nitrogen (NH4-N) exported in simulated snowmelt run-off. Intact soil slabs were collected post-harvest in Oct. 2008 and Oct. 2009 from an annually cropped Black Chernozem in east-central Saskatchewan having treatments of: 1) a control, with no SCM or urea fertilizer added and 2) SCM applied at a rate of 60.6 t ha-1 for 2 years as: surface broadcast, broadcast and incorporated and subsurface banded. For comparison purposes, intact soil slab monoliths were collected post-harvest in Oct. 2009 from an annually cropped Black Chernozem in east-central Saskatchewan having treatments of: 1) a control, with no LHM or urea fertilizer added; 2) LHM broadcast and incorporated at a rate of 37,000 L ha-1 for 12 years; and 3) LHM subsurface banded at rates of 37,000 L ha-1 and 148,000 L ha-1 for 12 years. Run-off water and leachate were collected under two different simulated prairie spring melt conditions: 1) thawing soil slabs containing snow that slowly melted on the surface; and 2) frozen soil slabs with run-off water applied to the surface and allowed to run-off across the frozen soil surface. Export of SRP in the thawing soil slabs that had SCM applied in subsurface bands was 0.51 kg P ha-1 and was significantly higher than the non-manured control (0.07 kg P ha-1). Dissolved NO3-N exported in water running across the frozen soil slabs was highest in the broadcast and incorporated treatment (0.30 kg N ha-1). All SCM manured treatments had higher export of nitrate (0.2-0.25 kg NO3-N ha-1) compared to the non-manured control (0.07 kg ha-1). There was no significant (P ≤ 0.10) effect of placement method on SRP, NO3-N and NH4-N export on thawing or frozen SCM soil slabs. Export of SRP was less in LHM treatments than SCM treatments. In thawing soil slabs with 148,000 L ha-1 LHM treatment, the P export was 0.05 kg P ha-1 and was greater than the control treatment (0.01 kg ha-1). Rate of application and manure type appears to be more important than method of placement in influencing P and N transport in melt water on these soils.
STRIDE LENGTH REGULATION AT THE APPROACH PHASE OF LONG JUMP IN VISUALLY IMPAIRED (F12 CLASS) ATHLETES

The present study examined whether long jumpers with visual impairment (class F12) demonstrate at the approach run the same pattern of footfall variability across trials as athletes without visual impairment. Five male and five female elite F12 class long jumpers were recorded during a competition. The athletes demonstrated an initial ascending footfall variability followed by a descending one, suggesting the existence of stride length regulation. This regulation emerged on the fourth and the third stride prior to take-off area and at a mean distance of 9.09 ± 0.26 m and 6.28 ± 0.26 m for the males and the females respectively from the take-off line. Results indicated that the stride pattern of F12 class long jumpers was similar to that reported in the literature for athletes without visual impairment, with stride regulation commencing one stride closer to the take-off board.
Applying a Deep Learning Convolutional Neural Network (CNN) Approach for Building a Face Recognition System: A Review

-- Deep learning is a new advanced research area of machine learning. The Deep learning come up with a successful learning approach to make the machine learning closer to Artificial intelligence. In Deep learning, multiple processing layers are organized for simulating mathematical models that learn characteristics of data with various levels of abstraction. These techniques have greatly improved the state-of-the-art in pattern recognition. Deep learning finds out complex structure in large datasets by the help of backpropagation technique to show how the AI supposed to change its internal parameters that are used to calculate the characterization in each layer from the characterization in the previous layer. Basically, the Deep Convolutional nets have been used in the development to deal with images, speech, video, and audio. In contrast, recurrent nets give out a bright way on sequential data like text and speech. Recently, the deep learning CNN has reached propitious results in face recognition. In this survey paper, we are going to study about CNN-based face recognition systems. One of the objective of this study is to know more about the field of pattern recognition in our case face recognition, which is very interesting and have a tremendous benefits in real life applications that require peoples identity, such as Payments, Passport immigration, Access and security, Criminal identification, Advertising, Healthcare, Students attendance, and etc.
FORCE OUTPUT AND HIP MOVEMENT WHILE FLUTTER KICKING WITH VARIATIONS OF KICKBOARD SUPPORT

Nine experienced swimmers completed two trials of 30 s of all-out kicking while holding onto one of three types of kickboards or without a board. Kicking force was measured via a force platform and hip roll via kinematic video anaylsis. The findings of the current study indicate that not using a kickboard results in greater kicking force than any of the three kickboards, which did not differ. Hip roll did not differ across the conditions (p>0.05). Based on the results of the present study, when seeking to overload the legs during kicking training, use of a kickboard may not be indicated.
Hydrothermal Liquefaction of Several Food Residues

Agricultural and food processing wastes are one of the most abundant resources of biomass worldwide. For instance in 2010, it was estimated that 89 Mt of food waste were generated in the EU-27. Unlike other traditional biomasses, such as wood, these wastes usually contain more than 50 wt. % water. In comparison with thermochemical processes such as incineration or pyrolysis, Hydrothermal Liquefaction (HTL) does not require the costly preliminary step of drying the biomass: water is a solvent and a reactant for this process. It is therefore possible to economically treat wet wastes. HTL typically generates 4 phases: an oily product known as 'bio-oil', an aqueous phase containing dissolved organics, a solid residue and a gaseous phase. In this study, several agricultural products were tested, including fruit residues and potato starch. All of the feedstocks were subjected to a detailed chemical analysis. These residues were then exposed to subcritical water in a batch autoclave. Operating conditions were 280-350°C, 8.5-20 MPa, and a reaction time between 15 and 60 min. For each experiment, the aqueous, solid and oily fractions were analysed. This paper focuses on the carbon distribution in the different phases recovered, and the organic compounds contained in the aqueous phase, as regards to the operating conditions. Raw resources are mainly acidic (3 < pH < 5), and initial pH values were modified by addition of NaOH or Na 2 CO 3. The results show that increasing the initial pH of the feed has a significant influence on the fraction of organic carbon remaining in the aqueous phase: it increases from 10% at pH = 4, to 35% at pH = 12.5. The nature of the biomass is also important: for example, more organic carbon is transferred from grape to the aqueous phase, compared to starch. This paper mainly consists in exploratory runs: further studies should focus on model compounds, in order to unveil the mechanisms leading to the products. Especially, the knowledge on the dependency to acidity of the aqueous phase will allow us to optimize the liquefaction process, and the role of alkali catalysts would be clarified.
Evolutionary Game Analysis of Competitive Information Dissemination on Social Networks: An Agent-Based Computational Approach

Social networks are formed by individuals, in which personalities, utility functions, and interaction rules are made as close to reality as possible. Taking the competitive product-related information as a case, we proposed a game-theoretic model for competitive information dissemination in social networks. The model is presented to explain how human factors impact competitive information dissemination which is described as the dynamic of a coordination game and players’ payoff is defined by a utility function. Then we design a computational system that integrates the agent, the evolutionary game, and the social network. The approach can help to visualize the evolution of % of competitive information adoption and diffusion, grasp the dynamic evolution features in information adoption game over time, and explore microlevel interactions among users in different network structure under various scenarios. We discuss several scenarios to analyze the influence of several factors on the dissemination of competitive information, ranging from personality of individuals to structure of networks.
Semi-automated Cleaning of Laser Scanning Campaigns with Machine Learning

Terrestrial laser scanning campaigns provide an important means to document the 3D structure of historical sites. Unfortunately, the process of converting the 3D point clouds acquired by the laser scanner into a coherent and accurate 3D model has many stages and is not generally automated. In particular, the initial cleaning stage of the pipeline—in which undesired scene points are deleted—remains largely manual and is usually labour intensive. In this article, we introduce a semi-automated cleaning approach that incrementally trains a random forest (RF) classifier on an initial keep/discard point labelling generated by the user when cleaning the first scan(s). The classifier is then used to predict the labelling of the next scan in the sequence. Before this classification is presented to the user, a denoising post-process, based on the 2D range map representation of the laser scan, is applied. This significantly reduces small isolated point clusters that the user would otherwise have to fix. The user then selects the remaining incorrectly labelled points and these are weighted, based on a confidence estimate, and fed back into the classifier to retrain it for the next scan. Our experiments, across 8 scanning campaigns, show that when the scan campaign is coherent, i.e., it does not contain widely disparate or contradictory data, the classifier yields a keep/discard labelling that typically ranges between 95% and 99%. This is somewhat surprising, given that the data in each class can represent many object types, such as a tree, person, wall, and so on, and that no further effort beyond the point labeling of keep/discard is required of the user. We conducted an informal timing experiment over a 15-scan campaign, which compared the processing time required by our software, without user interaction (point label correction) time, against the time taken by an expert user to completely clean all scans. The expert user required 95mins to complete all cleaning. The average time required by the expert to clean a single scan was 6.3mins. Even with current unoptimized code, our system was able to generate keep/discard labels for all scans, with 98% (average) accuracy, in 75mins. This leaves as much as 20mins for the user input required to relabel the 2% of mispredicted points across the set of scans before the full system time would match the expert’s cleaning time.
The engagement of various stakeholder groups into the process of e-Health development in Lithuania

New technologies change our social–economic life by making considerable impact on its quality. Healthcare in the meantime is becoming more and more dependent on information and communication technologies (ICT), which enable the development of high quality healthcare services. The deployment of new ICTs has the potential to increase organizational efficiency of healthcare providers, change the processes of work organization and create the conditions for electronic patient information exchange between healthcare providers according to the nationally agreed standards. The engagement of various stakeholder groups into the process of e-Health development has the potential to pave a more effective way of introducing innovation in the healthcare sector and contribute to a greater sustainability of achieved changes. The main idea of the paper is a proposition that the engagement of various stakeholder groups into the process of e-Health development has the potential to pave a more effective way of introducing innovation in the healthcare sector and contribute to a greater sustainability of achieved changes. Quantitative research explored the extent and trends of the engagement and participation of stakeholder groups in the process of e-Health development in Lithuania.
Destination Information Sources for Itinerant Holidaymakers: A Study of Island Hoppers in Greece

This article explores information sources for destination decision-making in the underresearched context of mobile tourism with a quite low level of pre-tour decisions. The questionnaire-based study attends to this gap through updated documentation of vacationer use of online networking sites, review sites, and other information sources. The results show that conventional word-of-mouth was still decisive for destination choices in this context of island hopping in the Aegean, Greece. Websites with traveller evaluations proved more popular than guidebooks, indicative of the advance of usergenerated content (eWOM). Nevertheless, guidebooks were nearly as important as review sites, contrasting several studies and indicative of a continued or renewed guidebook interest. Despite some earlier assertions, there were only small differences in employment of internet-based information sources between younger “digital natives” and older “digital immigrants”. Information conveyed through social networking sites was of little importance for destination decision-making for these tourists, contradicting some of the literature. Particularly to attract younger holidaymakers and first-time visitors, destination marketing organisations might encourage more vacationers to post photographs and videos on the internet (visual eWOM).
Implementation of Underwater Communication System powered by Solar Energy Harvesting

Underwater communication (UWC) has become a fast-growing research because two-thirds of earth is covered by water. UWC is a promising field that is expected to revolutionise some applications such as; oil industry, pollution monitoring. Data collection from oceans, seas and rivers can play a vital role in detecting anomalies and discovering new resources. This paper proposes an Underwater Monitoring System (UMS) whereas the system has solar/light energy harvesting equipped with the nodes. These nodes are placed underwater to report the temperature of the water from time to another. UMS is tested on one master and two slave nodes whereas the master node is onshore of the beach, while the other two are submerged underwater. Moreover, the success rate and a number of transmitted packets per second are used to evaluate the system performance. Furthermore, two different types of solar panels are examined namely; amorphous and mono-crystalline. Further, different light sources are considered such as sunlight, LED and Halogen illumination. In addition, UMS is examined in terms of different containers materials as some Radio Frequency (RF) reflection signals might occur.
Study of Collaborative Cultivation in Liquor Industry

Excellent talents are the support and guarantee for the transformation and upgrading of the liquor industry, while talents separate training can hardly form a system, coordination and pertinence model by different cultivation subject, which needs to strengthen the cooperation among government, liquor enterprises, universities, research institutes. This article proposed "the establishment of industrial classification of talent training mode, implement on effective training complementary resources between the trainer, basic ideas for a long time mutual benefit among the trainers in the core of co-create, co-share, co-work, to put forward the co-cultivation path and countermeasures from four parts of university, enterprises, liquor enterprises and liquor industry. The purpose is to improve the quality of human resources through complementary resource exchanges of different trainers, and to realize the increase of human resource capital and promote the healthy and stable development of the liquor industry.
Student Performance Prediction using Multi-Layers Artificial Neural Networks: A Case Study on Educational Data Mining

In recent years, Neural Network (NN) has seen widespread and successful implementations in a wide range of data mining applications, often surpassing other classifiers. This study plans to research of NN that are a fitting classifier to foresee understudy execution from Learning Management System information with regards to Educational Data Mining. The dataset utilized for this examination is a Moodle log document containing log data around 900 understudies more than 10 college classes. To assess the applicability of Neural Networks, two case studies compare their predictive performance on this dataset. The features used for training originate from LMS data obtained during the length of each course, and range from usage data like time spent on each course page, to grades obtained for course assignments and quizzes. After training, the Neural Network outperforms all six classifiers in terms of accuracy and is on par with the best classifiers in terms of recall. We also assessed the effect course predictors have on predictive performance by leaving out the course identifiers in the data. This does not affect predictive performance of the classifiers. Furthermore, the Neural Network is trained on individual course data to assess difference in classification performance between courses. The results show that half of these course classifiers better generally trained classifiers. The importance of individual predictors used for classification was also investigated, with previously obtained grades contributing most to successful predictions. We can conclude that the proposed neural network architecture works well with the selecting the feature data sets. It seems to the results, accuracy in student performance prediction in feature vector has been achieved and satisfactory through appropriate classification to take better decision for efficient prediction of student performance.
Sources of IT dynamic capability in the context of data genesis capability

Nowadays Knowledge is regarded as a strategic resource in organizations, and thus the leverage of knowledge is a key managerial issue. Knowledge creation, sharing and dissemination are the main activities in knowledge management. This study examines the influence the social and technological factors such as learning culture and IT use, could have on knowledge sharing of King Fahd University of Petroleum and Minerals (KFUPM) students. A cross-sectional survey was used as a methodology for data collection and 137 valid responses were collected from all the three categories of students that include graduates, undergraduates and preparatory students. The study shows that there is a significant positive relationship between the student learning culture and IT use on student knowledge sharing. The study limitations, practical implications, along with directions for further research are discussed.
Financialization of Commodity Markets: Non- linear Consequences from Heterogeneous Agent Behavior

Up to the financial slump of the second quarter of 2008 commodity prices grew fast for several consecutive years in a highly volatile context. Recent commodity fluctuations have raised both policy concerns and a prolific academic debate. This paper offers a coherent theoretical and empirical framework aimed at improving our knowledge of those elements driving commodity prices in the long run once the so-called process of “financialization of commodities” is incorporated into the analysis. To this end, we employ a smooth transition vector autoregressive model which is suitable for testing the hypothesis derived from a heterogeneous agent model in the commodity markets. The empirical methodology allows us to distinguish among those variables that influence prices in the long run –obtaining in this way an “equilibrium” or “fundamental” price; and the mechanisms that generate, strengthen and eventually correct short run deviations with respect to that equilibrium. The results suggest that high discrepancies between spot and fundamental prices tend to be corrected relatively fast, while small misalignments tend to persist over time without any endogenous correcting force taking place. JEL Classification Numbers: C32, D84, Q11. ∗ We are especially grateful to Eric Santor for his insightful comments as discussant in the Workshop “Commodity prices: drivers and economic impacts” organized by Bank of England and European Central Bank (London, February 2009). We thank to Jose Fanelli, Gustavo Canonero, Federico Traverso, Horacio Aguirre, Agusto Mercadier and Fernando Toledo for helpful comments and discussion. We are indebted with Mariano Sardi and Emilio Blanco for their helpful research assistance. We are responsible for remaining errors.
Ethanol as a solvent and hot extraction technique preserved the antioxidant properties of tamarind (Tamarindus indica) seed

The influence of two extraction solvents (ethanol and acetone) and two extraction techniques i.e., hot extraction at 400C and cold extraction at 260C were investigated on the phenolic content and antioxidant activity of extracts from Tamarindus indica seed. The antioxidant activity of T. indica was determined by evaluating 1,1-Diphenyl-2-picrylhydrazyl (DPPH) scavenging activity, ferric reducing power assay (FRAP) and ascorbic acid equivalent content (AAC). The tested sample showed appreciable amounts of total phenolic contents (51.45-71.68 mg GAE/gm of dry extract), DPPH scavenging capacity (61.18-71.17%), IC50 values (98.30-248.60), reducing power (0.6377-0.7702) and total antioxidant capacity (22.75-43.80 AAE/gm) at different solvents and techniques. Current study data shown higher extract yields, phenolic contents, scavenging activity, reducing power and antioxidant activity using ethanol solvent compared to the respective acetone solvent. In addition, higher extract yields and other properties were obtained by hot extraction at 400C compared to the cold extraction at 260C. Present study suggests that ethanol as a solvent and hot extraction technique could be better to preserve the antioxidant properties of tamarind seed.