Miracle-dz/UniFAR-FTUs huggingface dataset:
QUANTITY AND QUALITY OF CITRUS ON A CITROMELO ROOTSTOCK AS AFFECTED BY DIFFERENT TYPES OF N FERTILIZATION (CHEMICAL AND ORGANIC)

With respect to the important effects of nitrogen (N) on plant growth and fruit production, a five-year experiment was performed to evaluate the effects of different sources of N fertilization including chemical and organic on the quantity and quality of citrus fruit. Using five-year old trees, different types of fertilization including ammonium sulfate, urea coated with sulfur, ammonium nitrate and manure were tested using seven treatments in five replicates from 2002 to 2007. Different plant quantitative and qualitative parameters were determined. The most effective strategy on fruit yield production was the use of urea coated with sulfur and manure with 92.46 kg ha−1 fruit yield followed by ammonium sulfate and manure (87.06 kg ha−1) and ammonium sulfate (86.43 kg ha−1). The combination of mineral and organic fertilization may be the most suitable fertilization strategy for citrus production.
Residual effect of organic fertilization on maize yield in an agroforestry system

The objective of the present study was to evaluate, during a period of two years, the residual effects of organic fertilization with animal manure or gliridicia prunings on the fertility of soil and on productivity and nutrient accumulation by maize cultivated either isolated (SA) or intercropped within alleys of gliricidia (CA). The study was conducted during 2006 and 2007 in the Centro Agroecologico Sao Miguel, headquarters of the NGO AS-PTA, in Esperanca, PB, Brazil. The experiment was established in a split-plot design, arranged in randomized blocks, with the type of cropping system (SA or CA) as the main treatments and the organic fertilization (animal manure, gliricidia prunings or control plots) as the secondary treatments, with four replications. The organic fertilization with animal manure led to a residual effect increasing maize productivity during the two years, but the fertilization with gliricidia prunings had no residual effect. The residual effect on maize productivity was more pronounced in the SA plots, compared to the plots with gliricidia alleys (CA). Probably, the lower residual effect within the CA system was due to the competition between maize and gliricidia for resources (nutrients, water and light), which indicates the need for higher rates or more frequent fertilizer applications in these systems. The contents of P, K, Ca and Mg in the soil was higher in the plots that received animal manure within both types of cropping systems.
How do firms manage knowledge sharing while avoiding knowledge expropriation in interfirm collaborations

Interfirm collaborations can raise a fundamental dilemma. To create value, collaborators may have to adopt a variety of practices to facilitate the transfer of knowledge, but in doing so may increase the likelihood that knowledge which is (1) beyond the scope of the collaboration, and (2) difficult to legally protect, is expropriated. How can firms manage this dilemma? The purpose of this paper is to empirically examine the veracity of a chain of propositions addressing this dilemma based on a joint knowledge-based view/transaction cost economics framework. A plausible chain of relationships is proposed and explored empirically using two unrelated datasets. The chain links two knowledge-based attributes of a collaboration—knowledge tacitness and problem solving complexity—to the use of knowledge management practices— high-bandwidth communication channels and co-specialized communication codes. These practices are economizing responses to the knowledge sharing difficulties rated by tacitness and complexity. However, increasing knowledge transparency gives rise to opportunism hazards, which must be safeguarded against via economizing governance choice. The empirical effort examines the effects that two knowledge attributes of collaborations have on governance choice, first directly and then indirectly through the intervening links. Empirical results from both datasets indicate substantial support for the proposed chain of propositions.
EXIF Estimation With Convolutional Neural Networks

In this paper, we explore a novel application of convolutional neural networks (CNNs): the task of estimating photographic metadata. Given the raw pixels of a digital photograph, we attempt to predict the camera settings it was taken with, including shutter speed, aperture size, and ISO level. We present some baseline results on this task using the MIRFLICKR dataset. We further contextualize these re-sults in the realm of photography.
Real-time Detection and Adaptive Mitigation of Power-based Side-Channel Leakage in SoC

Power-based side-channel is a serious security threat to the System on Chip (SoC). The secret information is leaked from the power profile of the system while a cryptographic algorithm is running. The mitigation requires efforts from both the software level and hardware level. Currently, there is no comprehensive solution that can guarantee the whole complex system is free of leakage and can generically protect all cryptographic algorithms. In this paper, we propose a real-time leakage detection and mitigation system which enables the system to monitor the side-channel leakage effects of the hardware. Our proposed system has extensions that provide a real-time monitor of power consumption, detection of side-channel leakage, and real-time adaptive mitigation of detected side-channel leakage. Our proposed system is generic and can protect any algorithm running on it.
CTHULHU: The Design and Implementation of Duke Robotics Club’s 2019 AUVSI Competition Entry

— In 2019, the Duke Robotics Club returns for their third year in recent history to the AUVSI RoboSub Competition with a brand new, smaller, leaner, and more maneuverable robot: Cthulhu. Successor to the team’s previous robot, Leviathan, Cthulhu is the brainchild of the dozens of Duke engineers that make up the student-run Duke Robotics Club. Work on Cthulhu, split between three different subsystem teams (subteams), began in fall of 2018. The new robot features a more compact design (2/3 the size of Leviathan), a redesigned electronics stack, and modularity for greater interaction with the environment. Additionally, the software team successfully transitioned from an unmaintainable legacy codebase to Docker and ROS, which are industry standards. The team is also an active member of the Durham, NC community, mentoring high school FTC and FRC teams.
Investigations of a microwave plasma source operating with air, N2, CO2 and argon gases

The present contribution will report on the development of a Microwave Plasma Torch (MPT) at the von Karman institute and on the characterization of subsonic plasma flow of various plasmagene gases (air, N2, CO2 and Ar) at atmospheric pressure. The aim of this work is to explore plasma flow regimes for a large envelope of operating conditions. Using a conventional arrangement for MPT, including cylindrical open-ended dielectric quartz tube, plasma generated with molecular gases have been found to be homogeneous while with argon filamentary structures have been observed. Electrical characterization of the source has been performed for some operating conditions using forwarded and reflected microwave power monitoring. Comprehensive characterization of the flow by means of high speed imaging has been performed to estimate the effect of the oscillation of the delivered power on plasma jet unsteadiness. Analysis in time and frequency domains of the light intensity recorded at 1 kHz rate is presented. Low resolution Optical Emission Spectroscopy (OES) diagnostic was performed in order to identify typical radiative signature of air, N2 and CO2 plasma jet. High resolution OES was performed on pure N2 plasma suitable for thermodynamic characterization using N2+ First Negative and N2 2nd Positive systems. Making the common assumption that the internal energy levels of molecular species are populated according to a Boltzmann distribution and making no assumption concerning the total species concentration similarly to [1], comparisons between measured and calculated radiative signature of molecular systems evidence for the achievement of thermal equilibrium and slight departure to Saha equilibrium.
Sentiment analysis using BERT (pre-training language representations) and Deep Learning on Persian texts.

Nowadays, due to the massive amount of text data, natural language processing has become more prevalence . Sentiment analysis is one of the text classiﬁcation applications which can be used in some cases to evaluate products, make market decisions, monitor brands and prioritize customer service. sentiment analysis has taken place in English, while in other languages such as Persian, there has not been much attempts. Our goal is to evaluate attention models and BERT -a pre-trainined language representation-in the Persian language. one of the versions of BERT is Un-normalized multilingual model that contains 104 languages and Persian language is one of its languages.
The Impact of Gender Diversity On Boards Composition On the Corporate Environmental Disclosure in Nigerian Listed Firms

Methodology: This research work adopts the qualitative approach using data from the annual reports of the sampled firms listed on the Nigerian stock exchange. The theoretical framework will be supported by the stakeholders theory, agency theory and social role theory. The data will be analyzed using descriptive, correlation and multiple regressions. The estimation results would be evaluated based on individual statistical significance test (t-test) and overall statistical significance test (F-test). The goodness of fit of the model would be tested using the coefficient of determination (R-squared).
DEEP LEARNING AIDED CT DIAGNOSIS ON COVID-19

With the global pandemic of COVID-19 worsening, both people’s lives and countries’ economies are under unprecedented threat. It is essential to develop a method that can diagnose possible patients rapidly. We explore some Convolutional Neural Network (CNN) architectures to establish a deep-learning-based diagnosis method of COVID-19. Given the fact that the current dataset of COVID-19 data is relatively small, we also apply transfer learning to improve the performance of our models. Furthermore, we use Tucker Decomposition and pruning to explore the possibility of improving the model’s efﬁciency and performance.
Image Steganography Using K-Means and DES Algorithm

: Steganography is that the process which involves hiding of image, text or any sensitive information inside another image, video or audio in such some way that an attacker won't be ready to detect its presence. Steganography hides the info so nothing appears out of ordinary. it's done to extend the protection against various malicious attacks. Image steganography uses a picture because the cover media to cover the key message. This project uses image steganography method which clusters the image into various segments and hides data in each of the segment. K-means clustering algorithm is employed for image segmentation. Segmentation involves huge set of knowledge within the style of pixels, where each pixel further has three components namely red, green and blue. K-means clustering technique is employed to induce the accurate leads to small period. Segmented images are used for hiding the data using DES algorithm.
The Effect of Earnings Management on Stock Liquidity of Listed Companies in Tehran Stock Exchange

Stock liquidity providers (market makers or specialists) for firms that manage earnings face greater uncertainty about the true value of their inventory of stocks of such firms and a possible of loss while trading against more informed traders. This study examines the effect of earnings management on stock liquidity of listed companies in Tehran stock exchange. To investigate this effect, panel date techniques is applied on a sample of 81 companies, over the period of 20042009. Earnings management is identified using Jones model. The empirical analysis shows that firms that manage earnings have wider bid-ask spreads. So the empirical results indicate that companies with higher earnings management suffer lower stock liquidity.
Thermal Expansion of Zirconium between 298°K and 1600°K

The average coefficients of thermal expansion of hexagonal zirconium between 298°K and 1143°K are 5.5×10−6 deg−1 along the a axis, 10.8×10−6 deg−1 along the c axis, and 7.2×10−6 deg−1 for a randomly oriented polycrystalline sample. The average value of the linear coefficient of expansion of cubic zirconium between 1143°K and 1600°K is 9.7×10−6 deg−1. At the transition there is a decrease in volume of 0.66 percent.
Remove of lead from the polluted water by phytoremediation technique ( Eclipta Alba plant )

One of the major environmental problems is the pollution of water and soils by toxic heavy metals. The aim of current study was estimation the ability of Eclipta alba plant for the removal of (Pb) from the polluted water by phytoremediation technique and study the effect of a number of parameters(Pb concentration, contact time and pH values). The plant showed higher removal efficiency (99.2%) after 7 days of treatment at 50 ppm. The best removal efficiency of Pb 98.95% at 100 ppm was occurred at pH7. The root ability to accumulate Pb was more than the shoot ability, after 7 days treatment of water and pH=7, they were 2852.5 mg/kg, 2497.5 mg/kg and 502.5 mg/kg in roots, stems and leaves respectively. These results showed that Eclipta Alba can be used in biological treatment of polluted water. Copyright © 2017 International Energy and Environment Foundation All rights reserved.
CONCRETE BASED ON ALKALI ACTIVATED FLY ASH FROM ONE POWER PLANT IN SERBIA

Alkali activated fly ash concrete (AAFAC) prepared with a class F fly ash from power plant „Nikola Tesla B“ and a mixed alkali activator of sodium silicate and sodium hydroxide solutions was investigated. Firstly, eight alkali activated fly ash pastes (AAFAP) with different activator concentration were tested in order to investigate the effect of this concentration on the compressive strength and workability. These pastes were prepared with different mass ratios of sodium silicate and sodium hydroxide solutions and a mass ratio of total alkali activator to fly ash equal to 0.6. Analyzing the compressive strength, cost and environmental effect of these mixtures, the mixture made with 10M sodium hydroxide solution, and a mass ratio of sodium silicate to sodium hydroxide equal to 10 was chosen as the optimal one for further analysis on concrete. To analyze the influence of additional water on the slump and compressive strength of AAFAC six different mixtures were made and tested. All samples were cured in the oven at the temperature of 80°C for six hours. The results of compressive strength and workability of tested concretes showed that fly ash from power plant „Nikola Tesla B“ can be used as a binder in AAFAC made with sodium silicate and sodium hydroxide as alkali activators.
High‐Throughput Monitoring of Interconverting Stereoisomers and Catalytic Reactions

The present article describes enantioselective dynamic chromatography to study the dynamics of interconverting stereoisomers and on-column reaction chromatography to investigate catalytic reactions in a high-throughput format by separation techniques. Reaction rate constants and physico-chemical parameters are obtained from temperature dependent measurements. The unified equation implemented in the DCXplorer software gives direct access to reaction rate constants of any first-order reaction taking place during a chromatographic separation, making tedious computer simulations superfluent. On-column reaction chromatography integrates separation selectivity and catalytic activity in the same stationary separation phase. This technique represents a powerful tool to perform high-throughput kinetic studies of substrate libraries under comparable reaction conditions without competing reactions.
Prevalence of dementia in elderly age population of Barangay Bangkal, City

BackgroundDementia, a significant cause of disability and dependency among older adults. The growing population of the elderly in the Philippines is expected to increase the prevalence of dementia in the country. PurposeThis study aims to determine the prevalence of dementia in the elderly population of Barangay Bangkal, Makati City. MethodsDescriptive cross-sectional community-based study was conducted to determine the prevalence of dementia in the elderly population of Barangay Bangkal, Makati City, aged 60 years and above over one month from mid-October to mid-November 2019. Data was collected with help of Mini-Mental State Examination - Philippines version (MMSE-P) to determine the cognitive status and diagnose dementia in elderly population. ResultsA total of 266 elderly adults participated in the study. Representatives of the study population were male (59.0%), married (68.0%), with an income of less than 5,000 peso (51.1%), working (64.3%), and with high school education (42.1%). The average age of the study population was 68.02 (+ 6.76) years. The average MMSE score of the participants was 27.05 (+ 3.94). The prevalence of dementia in the sample was 18.8%. Age, income, and level of education were associated with the MMSE score (r{square} = - 0.26, n = 266, p < 0.001, r{square} = 0.23, n = 266, p < 0.001, and rs = 0.19, n = 266, p = 0.002, respectively). The findings for statistical significance do resonate with clinical significance as evident during administration of MMSE score. ConclusionAdvancing age increases the risk for cognitive decline while higher income and education level prevent or delays the onset of dementia. Collaborative management between the medical education faculty & students, researchers, and local state health officials might address dementia in the region.
Design of Sieve Plate Spargers for Bubble Columns: Role of Weeping

Weeping through perforated plates occurs when the pressure drop of the gas/vapor passing through it is insufficient to support the liquid. The critical weep velocities (VO, crit) were measured for the plates having hole diameter range of 1 to 6mm. The thickness to hole diameter ratio (t/dO ) and the pitch to hole diameter ratio (P/dO) were varied in the range of 1 to 6 and 3.9 to 25.8, respectively. It was found that the VO, crit, crit is a strong function of plate parameters and the clear liquid height. A mathematical model for "No weep" condition was developed under the following two extreme conditions: (i) holes are partially open, (ii) holes are completely active. The results obtained from the experimentation and the mathematical correlation were put into a coherent form. The weeping criterion in terms of the Froude number was correlated with the plate parameters and the clear liquid height.
HDMI: High-order Deep Multiplex Infomax

Networks have been widely used to represent the relations between objects such as academic networks and social networks, and learning embedding for networks has thus garnered plenty of research attention. Self-supervised network representation learning aims at extracting node embedding without external supervision. Recently, maximizing the mutual information between the local node embedding and the global summary (e.g. Deep Graph Infomax, or DGI for short) has shown promising results on many downstream tasks such as node classification. However, there are two major limitations of DGI. Firstly, DGI merely considers the extrinsic supervision signal (i.e., the mutual information between node embedding and global summary) while ignores the intrinsic signal (i.e., the mutual dependence between node embedding and node attributes). Secondly, nodes in a real-world network are usually connected by multiple edges with different relations, while DGI does not fully explore the various relations among nodes. To address the above-mentioned problems, we propose a novel framework, called High-order Deep Multiplex Infomax (HDMI), for learning node embedding on multiplex networks in a self-supervised way. To be more specific, we first design a joint supervision signal containing both extrinsic and intrinsic mutual information by high-order mutual information, and we propose a High-order Deep Infomax (HDI) to optimize the proposed supervision signal. Then we propose an attention based fusion module to combine node embedding from different layers of the multiplex network. Finally, we evaluate the proposed HDMI on various downstream tasks such as unsupervised clustering and supervised classification. The experimental results show that HDMI achieves state-of-the-art performance on these tasks.
Conversations Gone Alright: Quantifying and Predicting Prosocial Outcomes in Online Conversations

Online conversations can go in many directions: some turn out poorly due to antisocial behavior, while others turn out positively to the benefit of all. Research on improving online spaces has focused primarily on detecting and reducing antisocial behavior. Yet we know little about positive outcomes in online conversations and how to increase them—is a prosocial outcome simply the lack of antisocial behavior or something more? Here, we examine how conversational features lead to prosocial outcomes within online discussions. We introduce a series of new theory-inspired metrics to define prosocial outcomes such as mentoring and esteem enhancement. Using a corpus of 26M Reddit conversations, we show that these outcomes can be forecasted from the initial comment of an online conversation, with the best model providing a relative 24% improvement over human forecasting performance at ranking conversations for predicted outcome. Our results indicate that platforms can use these early cues in their algorithmic ranking of early conversations to prioritize better outcomes.
High linearity InGaP/GaAs power HBTs by collector design

Improved power linearity of InGaP/GaAs heterojunction bipolar transistors (HBTs) with collector design is reported. The collector design is based on nonuniform collector doping profile which is to employ a thin high-doping layer (5/spl times/10/sup 17/ cm/sup -3//200 /spl Aring/) inside the collector (1/spl times/10/sup 16/ cm/sup -3//7000 /spl Aring/). The additional thin high-doping layer within the collector shows no obvious effects and impacts in dc characteristics and device fabrication if the layer was inserted close to the subcollector. For an HBT with a thin high-doping layer being inserted 4000 /spl Aring/ from the base-collector junction, the experimental result on third-order intermodulation demonstrates the significant reduction by as large as 9 dBc and improved IIP3 by 5 dB under input power of -10 dBm at frequency of 1.8 GHz.
An AutoML-based Approach to Multimodal Image Sentiment Analysis

Sentiment analysis is a research topic focused on analysing data to extract information related to the sentiment that it causes. Applications of sentiment analysis are wide, ranging from recommendation systems, and marketing to customer satisfaction. Recent approaches evaluate textual content using Machine Learning techniques that are trained over large corpora. However, as social media grown, other data types emerged in large quantities, such as images. Sentiment analysis in images has shown to be a valuable complement to textual data since it enables the inference of the underlying message polarity by creating context and connections. Multimodal sentiment analysis approaches intend to leverage information of both textual and image content to perform an evaluation. Despite recent advances, current solutions still flounder in combining both image and textual information to classify social media data, mainly due to subjectivity, inter-class homogeneity and fusion data differences. In this paper, we propose a method that combines both textual and image individual sentiment analysis into a final fused classification based on AutoML, that performs a random search to find the best model. Our method achieved state-of-the-art performance in the B-T4SA dataset, with 95.19% accuracy.