Miracle-dz/UniFAR-FTUs huggingface dataset:
Agroclimatic potential for cultivation of two sorghum cultivars in mixtures with okra and maize in a forest-savanna transition zone of Nigeria

(ii) it presents a relatively high protein content (15%) compared with rice (Oryza sativa) (13%) and maize (11%) and (iii) it is a drought tolerant crop. Sorghum is well-known for its capacity to tolerate conditions of limited moisture and to grow during periods of extended drought that would impede production for most other grain crops. It is one of the most drought-tolerant grain crops and by this point of view it is both an excellent ideotype to Agroclimatic potential for cultivation of two sorghum cultivars in mixtures with okra and maize in a forest-savanna transition zone of Nigeria
Wisdom in sum of parts : Multi-platform activity prediction in social collaborative sites

In this paper, we proposed a novel framework which uses user interests inferred from activities (a.k.a., activity interests) in multiple social collaborative platforms to predict users’ platform activities. Included in the framework are two prediction approaches: (i) direct platform activity prediction, which predicts a user’s activities in a platform using his or her activity interests from the same platform (e.g., predict if a user answers a given Stack Overflow question using the user’s interests inferred from his or her prior answer and favorite activities in Stack Overflow), and (ii) cross-platform activity prediction, which predicts a user’s activities in a platform using his or her activity interests from another platform (e.g., predict if a user answers a given Stack Overflow question using the user’s interests inferred from his or her fork and watch activities in GitHub). To evaluate our proposed method, we conduct prediction experiments on two widely used social collaborative platforms in the software development community: GitHub and Stack Overflow. Our experiments show that combining both direct and cross platform activity prediction approaches yield the best accuracies for predicting user activities in GitHub (AUC=0.75) and Stack Overflow (AUC=0.89).
Self-Driving Cars: Exploring the Potential of Using Convolutional Neural Network to Overcome Road Variation

The use of self-driving cars can benefit the society in many ways, such as reducing traffic accidents and enabling disabled people to travel independently. The potential of reducing traffic accidents can be considered most important, since in 2017, mistakes made by human drivers were the cause of over 90% of the traffic accidents, leading to 40,100 people’s deaths in the United States. If human drivers were replaced by autonomous systems, the number of traffic accidents would decrease. Although the concept of self-driving car was raised since at least the 1920s, a commonly accepted development of self-driving car has not yet appeared. A significant challenge is the creation of a system that can accurately detect the environment around itself and then form the right driving command. Recent progress in deep learning suggested that convolutional neural networks are a form of machine learning that can be trained to extract features and use those features to control a car. This project focuses on extending the network model in the paper published by NVIDA in 2016 [1]. The aim of the project is to evaluate how well a convolutional neural network could perform on a simple, simulated roadway with road varying and missing road edges.
' s response to reviews Title : Prevalence and Risk Factors of Depressive Symptoms in a Canadian Palliative Home Care Population : A Cross-Sectional Study

Revisions to the analysis included restricting the sample to clients without significant cognitive impairment, employing polychoric correlations (and Spearman’s correlations) to address multicollinearity, and using multiple imputation to address missing data. Although these revisions did not result in significant changes to the results (i.e., most of the risk factors were the same in the original and revised manuscript), they nevertheless strengthen the methodology and the results.
The Impact of Nature of Institute on Entrepreneurial Marketing : The Moderating Role of Organizational Structure

This paper investigates the effect of nature of institute on entrepreneurial marketing in higher education institutions. Nature of institute characteristics such as, age, and size are studied to find whether they affect entrepreneurial marketing or not. Organizational structure is a moderating variable in this study. This investigation has been conducted in Tehran-Iran, between higher education institutions. Data were collected with questionnaire instruments. Model was developed and tested with structural equation model using data collected from the 103 active managers of these institutions. The results show that age of institute has a positive effect on organizational structure. And organizational structure has a positive effect on entrepreneurial marketing while age of institute doesn't have any effect on entrepreneurial marketing and organizational structure.
Iodine indirect effect on thyroid gland : structure and hormone receptor ( TRα ) in common sole , Solea solea larvae

The effect of feeding Solea solea with Artemia enriched in iodine during metamorphosis was evaluated. In the present study, three treatment groups were used; i) control fish fed with untreated Artemia, ii) low dose (TLow), in which sole were fed with instar Ӏ Artemia exposed to potassium iodate (0.1g IKO3/L) for 3 hours and iii) high dose (Thigh), in which sole were fed with instar Ӏ Artemia exposed to potassium iodate® (0.2 g IKO3/L,). Growth performance, thyroid follicles (localization and morphometric analysis), and thyroid hormone receptor (TRα) were measured. A significant increase in the expression of thyroid hormone receptor type 1 (TRα) occurred in the iodine supplemented larvae and was 3.55±1.13 fold higher than the control. The thyroid gland was not significantly modified in the group fed on Artemia supplemented with iodine; indicating that iodine-supplementation for sole post-larvae is unnecessary in open circuit seawater systems.
FLUORESCENCE ENERGY TRANSFER STUDY OF THE LIPID BILAYER INTERACTIONS WITH TRUNCATED APOLIPOPROTEIN A-I MUTANTS

M.S. Girych, I.L. Maliyov, M.V. Romanova, G.P. Gorbenko, E. Adachi, C. Mizuguchi, J. Molotkovsky, H. Saito V. N. Karazin Kharkiv National University, 4 Svobody Sq. , 61022, Kharkiv, Ukraine Institute of Health Biosciences and Graduate School of Pharmaceutical Sciences, The University of Tokushima, 1-78-1 Shomachi, Tokushima 770-8505, Japan Institute of Bioorganic Chemistry, Russian Academy of Sciences, 16/10 Miklukho-Maklaya, Moscow, 117871, Russia email:girichms@gmail.com Submitted May 7, 2013 Accepted June 2, 2013
Ju l 2 01 9 Uniform Orderings for Generalized Coloring Numbers ∗

The generalized coloring numbers scolr(G) and wcolr(G) of a graph G were introduced by Kierstead and Yang as a generalization of the usual coloring number, and have found important theoretical and algorithmic applications. For each distance r, these numbers are determined by an “optimal” ordering of the vertices of G. We study the question of whether it is possible to find a single “uniform” ordering that is “good” for all distances r. We show that the answer to this question is essentially “yes”. Our results give new characterizations of graph classes with bounded expansion and nowhere dense graph classes.
Towards Building Deep Neural Network Models for Mobile Continuous Vision Sensing Applications

Deep learning has revolutionized vision sensing applications in terms of accuracy comparing to other techniques. Its breakthrough comes from the ability to extract complex high-level features directly from sensor data. However, deep learning models are still yet to be natively supported on mobile devices due to high computational requirements. In this paper, we presents DeepMon, a framework to enable deep learning models on conventional mobile devices (e.g. Samsung Galaxy S7) for continuous vision sensing applications. DeepMon leverages several techniques to achieve fast inferences on mobile devices. Firstly, offloading computation onto integrated mobile GPU significantly reduces execution time of large models. Secondly, approximating large model into smaller one using decomposition techniques such as Tucker-2 lowers enormous amount of computational operations. Lastly, exploiting the similarity between consecutive video frames for intermediate data caching within model’s processing pipeline also enhances inference latency for continuous vision sensing.
Mining Location and User Information from Users' Trajectories

Title: Mining Location and User Information from Users’ Trajectories Author: Marcello Tomasini Major Advisor: Ronaldo Menezes, Ph.D. The understanding of human mobility is integral to the advancement of many fields including public health, city planning, economic forecasting, and it attracts the interest of researchers from a broad number of fields. The work of such researchers would not have been possible without the availability of large localized datasets such as mobile phone Call Detail Records (CDRs) provided by telco operators and Location Based Social Networks (LBSNs) check-ins provided by popular applications such as Twitter, Facebook and Foursquare. However, such datasets are often incomplete, anonymized, and/or inaccurate. Hence there is a need to partially reconstruct or extract more information from such data. In this dissertation, we propose a framework to analyze and augment spatio-temporal data related to human trajectories that could be used to further the understanding of regularities in human mobility. In particular, the aim is to be able to augment the data to make it possible to classify users and places.
Visualizing information in the records and archives management (RAM) disciplines: Using Engelhardt’s graphical framework

Purpose This paper aims to explore the concept of information in records and archives management (RAM) from a fresh, visual perspective by using arts-informed methodology and the draw-and-write technique. Design/methodology/approach Students and practitioners of RAM in Australia were asked to answer the question, “what is information? ” in a drawing and then to describe the drawing in words. This produced a data set of 255 drawings of information or “iSquares”, for short. Compositional interpretation and a framework of graphic representations by Engelhardt were applied to determine how participants envision information and what the renderings imply for RAM. Findings The images reveal an overwhelming recognition in RAM of the diversity of media formats of information and the hyperconnectivity of information in networked information systems; and illustrate the central place of human beings within these systems. These findings offer striking, accessible illustrations of major concepts in RAM and enable new understandings through the construction of stories. Practical implications There are both pedagogical applications and practical implications of this work for students, practitioners and knowledge workers. The graphical representations of information in this research deepen the understanding of textual definitions of information. The data set of iSquares provides opportunities to create new storyboards to explain information definitions, practices and phenomena in RAM disciplines, and, to explain related concepts such as data, information, knowledge and wisdom hierarchy. Originality/value This is the first study in RAM disciplines to provide visual illustrations of information using graphical image representations.
Urdu Characters Recognition Using Multilayer Perceptron

In recent years, Artificial Neural Networks are found to have a wide range of applications in many fields and Character recognition is one among them. In this paper, we intend to make use of artificial neural network in recognition of handwritten Urdu characters in isolated form with an aim of improving efficiency. The proposed method is based on the use of feed forward back propagation method to classify the Urdu characters. The Multilayer Perceptron neural network is trained using the Back Propagation algorithm in which handwritten Urdu letters represented in binary form are made ready as input to the neural network. An optimal architecture of the network with the limited sized training data is developed by balancing between model performance and training costs.
Transmit optimization techniques for physical layer security

Information security is one of the most critical issues in wireless networks as the signals transmitted through wireless medium are more vulnerable for interception. Although the existing conventional security techniques are proven to be safe, the broadcast nature of wireless communications introduces different challenges in terms of key exchange and distributions. As a result, information theoretic physical layer security has been proposed to complement the conventional security techniques for enhancing security in wireless transmissions. On the other hand, the rapid growth of data rates introduces different challenges on power limited mobile devices in terms of energy requirements. Recently, research work on wireless power transfer claimed that it has been considered as a potential technique to extend the battery lifetime of wireless networks. However, the algorithms developed based on the conventional optimization approaches often require iterative techniques, which poses challenges for real-time processing. To meet the demanding requirements of future ultra-low latency and reliable networks, neural network (NN) based approach can be employed to determine the resource allocations in wireless communications. This thesis developed different transmission strategies for secure transmission in wireless communications. Firstly, transmitter designs are focused in a multiple-input single-output simultaneous wireless information and power transfer system with unknown eavesdroppers. To improve the performance of physical layer security and the harvested energy, artificial noise is incorporated into the network to mask the secret information between the legitimate terminals. Then, different secrecy energy efficiency designs are considered for a MISO underlay cognitive radio network, in the presence of an energy harvesting receiver. In particular, these designs are developed with different channel state information assumptions at the transmitter. Finally, two different power allocation designs are investigated for a cognitive radio network to maximize the secrecy rate of the secondary receiver: conventional convex optimization framework and NN based algorithm.
Challenge C: Increasing freight capacity and services A Robust Decision Support Framework for Long-Term Railway Capacity Planning

The potential growth of the railway traffic is expected to be substantial worldwide; hence railway companies and agencies are looking for better tools to allocate their capital investments on capacity planning in the best possible way. In this research we develop a robust decision support framework for long-term railway capacity planning by using robust optimization and multi-commodity flow techniques. This problem is then solved by using the Benders Decomposition method. Based on the estimated future demand, available budget and expansion options, this decision support tool can determine the multi-period optimal investment plan regarding which portions of the network need to be upgraded with what kind of capacity improvements at each defined period in the decision horizon. Using this decision support tool will help railroads their return from capacity expansion projects and minimize the risk in strategic capacity planning subject to demand uncertainty.
Pharmacological application of caffeine inhibits TGF-b-stimulated connective tissue growth factor expression in hepatocytes via PPAR c and SMAD 2 / 3-dependent pathways q

Background/Aims: Epidemiological studies suggest that coffee drinking is inversely correlated with the risk of development of liver fibrosis but the molecular basis is unknown. Methods: We investigated the pharmacological mechanisms involved in caffeine-dependent regulation of CTGF expression, an important modulator protein of fibrogenic TGF-b, in rat hepatocytes using Western-blot, co-immunoprecipitations, reporter-gene-assays and ELISAs. Results: It is demonstrated that caffeine, similar to 8-Br-cAMP, suppresses CTGF expression, decreases SMAD2 protein levels and inhibits SMAD1/3-phosphorylation. The SMAD2 level can be restored by a proteasome inhibitor. Additionally, caffeine leads to an up-regulation of PPARc expression, that enhances the inhibitory effect of the natural PPARc agonist 15-PGJ2 on CTGF expression by inducing a dissociation of the SMAD2/3-CBP/p300-transcriptional complex. Conclusions: We show that caffeine strongly down-modulates TGF-b-induced CTGF expression in hepatocytes by stimulation of degradation of the TGF-b effector SMAD 2, inhibition of SMAD3 phosphorylation and up-regulation of the PPARc-receptor. Long-term caffeinization might be an option for anti-fibrotic trials in chronic liver diseases. 2008 European Association for the Study of the Liver. Published by Elsevier B.V. All rights reserved.
AODVB: Ad hoc On-Demand Distance Vector with Black Hole Avoidance

A Wireless ad-hoc network is a temporary network set up by nodes moving arbitrary in the places that have no network infrastructure. The nodes find a path to the destination node using routing protocols. However, due to security vulnerabilities of the routing protocols, wireless ad-hoc networks are unprotected to attacks of the malicious nodes. One of these attacks is the Black Hole Attack against network integrity absorbing all data packets in the network. Since the data packets do not reach the destination node on account of this attack, data loss will occur. In this paper work, we propose A AODVB (Ad hoc On-Demand Distance Vector with Black-hole Avoidance) protocol for avoiding black-hole attack. AODVB forms link disjoint multi-path during path discovery to provide greater path selection in order to avoid malicious nodes in the path using legitimacy table maintained by each node in the network. Non-malicious nodes gradually isolate the black-hole nodes based on the values collected in their legitimacy table and avoid them while making path between source and destination. We simulated AODV protocol with and without Black-hole attack and our solution AODVB protocol. From our simulation results AODV network has normally 3.21 % data loss and if a Black Hole Node is introducing in this network data loss is increased to 92.59 %. When we used AODVB protocol in the same network, the data loss decreased to 65 %.
Growth and respiration of microbes subjected to nutrient or oxygen pulses

There are an estimated 4-6x10 prokaryotes worldwide and they represent a huge fraction of total organic carbon— 35-55x10 kg of C or about 60%-100% of that found in plants (Whitman et al 1998). This means that “inclusion of prokaryotic carbon in global models will almost double estimates of the amount of carbon stored in living organisms” (Whitman et al 1998). Microbes are found in every ecosystem on earth and in extremely hostile environments, such as fluid inclusions in ancient salt crystals (Vreeland et al 2000), arctic tundra (McMahon et al 2009), within vesicles in basalts (Jorgensen and D’Hondt 2006), and granitic rock 6.7 km beneath the surface (Szewzyk et al 1994). Microbes are omnipresent in ocean sediments, the largest repository of microbes on earth, reaching densities of up to 10 cells per gram of sediment just below the water-sediment interface (Delong & Edward 2004; Pearson 2008; D’Hondt et al University of Southern California, Department of Earth Sciences, Los Angeles, CA
Texture aware learning-based image fusion method for fixed focal-length cameras

This paper aims to develop a novel approach of image fusion for an asymmetrical camera system when multiple images are acquired with cameras which have large differences in focal lengths but similar sensor size with an overlapping field of view. The fused image usually becomes perceptually unpleasant because the high-frequency components of a wideview image will be quite inadequate comparing to the tele-view images. Four steps are consisted in the proposed work: (i) image upscaling of the wide-view image, (ii) texture identification on the upscaled image, (iii) the performance evaluation of image upscaling, and (iv) the image inpainting for the high-frequency components of the wide-view image. The field of view of tele-view camera is set to be 4 times smaller than the wide-view camera in spatial angle in the experiment. The experiment result illustrates that the proposed algorithm brings significantly perceptual improvement to the wide-view image.
Geodesic Approach for an Efficient Trajectory Planning of Mobile Robot Manipulators

In this paper, the geodesic approach has been employed for an effective, optimal, accurate and smooth trajectory planning of a mobile robot manipulator mechanism. Generally, geodesic can be described as the shortest curvature between two loci on a Riemannian manifold. In order to attain the planned end-effector motion, Riemannian metrics has been consigned to the forward kinematics of mobile robot wheel as well as the mobile robot manipulator workspace. The rotational angles of wheel and joint kinematic parameters are chosen as local coordinates of spaces to represent Cartesian trajectories for mobile wheel rotation trajectories and joint trajectories respectively. The geodesic equalities for a given set of boundary conditions are evaluated for the chosen Riemannian metrics and the computational results of the geodesic equations have been shown. So as to verify and validate the efficiency of the chosen geodesic scheme, the method has been implemented for the motion planning and optimization of a mobile robot with a simple 3R manipulator installed upon its platform.
HOWLING DETECTION AND PREVENTION CIRCUIT AND A LOUDSPEAKER SYSTEM EMPLOYING THE SAME BACKGROUND OF THE INVENTION

A howling detection and prevention circuit which receives an output of a microphone as its input Signal and detects howling therein includes a computing Section which divides frequency of the input Signal into a plurality of frequency bands on the basis of a predetermined Sampling period and computes power of each of the frequency bands, an identi fying Section which Sequentially shifts the frequency band and identifies whether howling exists or not in accordance with a predetermined condition by employing a value of the computed power of each frequency band, and again adjust ing Section which, when howling has been detected as a result of the identifying, adjusts gain of the frequency band in which the howling has been detected to prevent the howling. A loudspeaker System employing this howling detection and prevention circuit is also provided.