Miracle-dz/UniFAR-FTUs huggingface dataset:
UWA RESERVOIR MANAGEMENT

Efficiently modeling seismic datasets in complex 3D anisotropic media by solving the 3D elastic wave equation is an important challenge in computational geophysics. Using a stress-stiffness formulation on a regular grid, we present a 3D finite-difference time-domain (FDTD) solver using a 2-order temporal and 8order spatial accuracy stencil that leverages the massively parallel architecture of graphics processing units (GPUs) to accelerate the computation of key kernels. The relatively small memory of an individual GPU limits the model domain sizes that can be computed on a single device. To circumvent this constraint and move toward modeling industry-sized 3D anisotropic elastic data sets, we parallelize computation across multiple GPU devices by using domain decomposition and, for each time step, employing an inter-device communication protocol to exchange data values falling within interior boundaries of each sub-domain. For two or more GPU devices within a single compute node, we use direct peer-topeer (i.e., GPU-to-GPU) communication, while for networked nodes we employ message-passing interface (MPI) directives to route data over the network. Our 2D GPU-based anisotropic elastic modeling tests achieved a 10× speedup relative to an OpenMP CPU implementation run on an eight-core machine, while our 3D tests using dual GPU devices produced up to a 28× speedup. The performance boost afforded by the GPU architecture allows us to model seismic data for 3D anisotropic elastic models at lower hardware cost and in less time than previously possible.
Investment in Education and Health: Lessons for the Growth Potentials in the COVID-19 Era

The negative effect of the Covid-19 pandemic on the growth potential of the economy, as it affects the health and educational sector serves as an inspiration for this paper to examine the effect of government expenditures on education and health on the economic growth of Nigeria. The study utilized data from the Central Bank of Nigeria statistical bulletin, World Development Indicators, and the Nigeria Centre for Disease Control (NCDC). The dependent variable was gross domestic product, while the explanatory variables include gross fixed capital formation, labour force, and government expenditure on education, government expenditure on health, broad money supply, inflation rate, and exchange rate. The data were analysed using the Bounds test for cointegration and error correction mechanism to investigate both the short run and long run relationship. The Bounds test revealed the existence of a long run relationship between government expenditures on education and health and economic growth. Further, the error correction model revealed that both government expenditures on education and health exert positive and significant effect on economic growth both in the short run and in the long run. The coefficient of the error correction term (0.5833) indicates that 58.33% of the short run disequilibrium is corrected annually. The paper concludes by stating that Nigeria needs to invest massively on the education and health sector so as to make provisions for the necessary infrastructures that could be required for their smooth operations in this period of global pandemic and beyond.
SARAFA ADEYEMI AKEEM, OLUWAFEMI YERUMOH, OLUWADAMILOLA LEIGH, KEHINDE BAMGBALA, GABRIEL OKEKE, FADEYINMI SOKUNBI, IBRAHIM OLAYIWOLA

Article history: Received: November 20, 2017 Accepted: June 26, 2018 Yoghurt is a popular fermented milk product, and milk, like other animal protein sources, is scarce and expensive. This study was therefore designed to explore the potentials of coconut milk and cow milk prepared from full cream powdered milk in the production of yoghurts and to determine the physicochemical, colour, and sensory properties of the yoghurts. Yoghurts were produced from milk blends obtained by mixing full-cream cow milk prepared from powdered milk and coconut milk in five different ratios (90:10, 80:20, 70:30, 60:40, and 50:50), based on a completely randomized design, with 100% full-cream cow milk yoghurt as the control. The physicochemical, colour, and sensory properties of the yoghurts were evaluated. Significant differences (p<0.05) existed among the physicochemical properties and colour characteristics of the yoghurts. However, all the evaluated sensory parameters of the yoghurts were not significantly different from one another. Acceptable yoghurts that were highly comparable to the control (100% full-cream milk yoghurt) were produced from full-cream cow milk and coconut milk at the ratios of 90:10 and 70:30. This study demonstrated that delicious and acceptable yoghurts could be prepared from full-cream powdered milk and coconut milk, which could be particularly advantageous to people that are moderately lactose intolerant and those in resource poor settings.
Clause Features for Theorem Prover Guidance ∗

State-of-the-art saturation-based automated theorem provers (ATPs) for first-order logic (FOL), such as E [10], are today’s most advanced tools for general reasoning across a variety of mathematical and scientific domains. Many ATPs employ the given clause algorithm, translating the input FOL problem T ∪ {¬C} into a refutationally equivalent set of clauses. The search for a contradiction is performed maintaining sets of processed (P ) and unprocessed (U) clauses. The algorithm repeatedly selects a given clause g from U , extends U with all clauses inferred with g and P , and moves g to P . This process continues until a contradiction is found, U becomes empty, or a resource limit is reached. The search space of this loop grows quickly and it is a well-known fact that the selection of the right given clause is crucial for success. ENIGMA [5, 6] is an efficient learning-based method for guiding given clause selection in saturation-based ATPs. ENIGMA is based on a simple but fast logistic regression algorithm effectively implemented by the LIBLINEAR open source library [4]. In order to employ logistic regression or a similar machine learning method, all generated first-order clauses need to be translated to fixed-length numeric feature vectors. This is done by translating each clause to a multi-set of features which is in turn translated to a numeric vector. Various possible choices of efficient clause features for theorem prover guidance have been experimented with [5, 6, 7, 8]. So far our goal has been to develop with fast and practically usable methods, allowing E users to directly benefit from our work. Related research in developing neural approaches [2, 9, 11] is of great interest, but so far less practically usable in the context of a fast saturation-style prover. The original ENIGMA [5] uses term-tree walks of length 3 as features, while the second version [6] reaches better results by employing various additional features. Which of the additional features are the main cause of the improvement was however, not investigated. The main goal of this work is to employ experiments with various combinations of features, in order to estimate the value of every possible features choice. Moreover, we propose to use another popular machine learning method instead of logistic regression, namely decision trees and their gradient boosted ensembles, effectively implemented by XGBoost library [3]. In this way, we shall be able to estimate, which of the features are useful across various machine learning methods.
Epileptic Seizure Diagnosis Using EEG Signals

— To measure and track human performance and physiological state, a great number of biomedical data such as the electroencephalographic (EEG) signal is collected from the human body every day. For research and for medical diagnosis and treatment, understanding these signals is critical. There are many research and development projects Within this field, but there are weaknesses point, including their reliance on specific registration technologies, low accuracy and timing of implementations. In This paper a computer-aided system are used for diagnosing epileptic seizures more accurately using electronic imaging data. The framework relies on identifying (EEG) signals for use in linear and non-linear applications. This study was conducted using six algorithms of machine learning algorithms which are logistic regression (LR), support vector machine (SVM), k-Nearest Neighbors (KNN), Gaussian Naive Bayes, Artificial Neural Networks (ANN), Principal Component Analysis(PCA). Also, improve surrogate data technique are used for feature extractions that depend on Fourier transforms (FT). It was concluded that the classifier (SVM) works better and showed high accuracy in the classification of data taken for all epilepsy cases.
Another Time-Complexity Analysis for Maximal Clique Enumeration Algorithm CLIQUES

We revisit the maximal clique enumeration algorithm CLIQUES that appeared in Theoretical Computer Science 2006. It is proved to work in O (3 n/ 3 )-time in the worst-case for an n vertex graph. In this note, we extend the time-complexity analysis with respect to the number of maximal cliques, an issue that was left as an open problem since TCS 2006.
Development Research at High Geographic Resolution: An Analysis of Night-Lights, Firms, and Poverty in India Using the SHRUG Open Data Platform

The SHRUG is an open data platform describing multidimensional socioeconomic development across 600,000 villages and towns in India. This paper presents three illustrative analyses only possible with high-resolution data. First, it confirms that nighttime lights are highly significant proxies for population, employment, per capita consumption, and electrification at very local levels. However, elasticities between night-lights and these variables are far lower in time series than in cross section, and vary widely across context and level of aggregation. Next, this study shows that the distribution of manufacturing employment across villages follows a power law: the majority of rural Indians have considerably less access to manufacturing employment than is suggested by aggregate data. Third, a poverty mapping exercise explores local heterogeneity in living standards and estimates the potential targeting improvement from allocating programs at the village—rather than at the district—level. The SHRUG can serve as a model for open high-resolution data in developing countries.
First Look at the Public Space in Tel Aviv

Foucault states that those in power create the systems of culture in a nation. At first glance, this theory seems to define Tel Aviv. The major intersection between the Shuk Hacaramel and Allenby Street is in the shape of the six point Star of David. The major neighborhood, Neve Sha'an's streets form the eight points of a menorah. In fact immigrant German Jews designed most of the city's architecture in the International style. However, the architecture of the buildings and streets only create the infrastructure of the city's culture. In a true democratic space any individual can have an impact and in a city graffiti can serve to not only allow the poorest citizen to impact their space, it can also serve as a tool to renovate a decaying building and as a way for disenfranchised groups to impact the cities artistic culture.
ONLINE CONSUMER ATTITUDE FORMATION AND CHANGE

Few concepts in the marketing literature have proliferated like the concept of attitude. However, a closer look at studies investigating attitudes as consumers' responses to marketing efforts reveals a considerable diversity in perspectives about the concept of attitude and its formation. Attitudes are considered either relatively stable object–associations, or temporarily constructed evaluations, which are formed through memory (cognitive)based information processing or contextual and affect-based information processing. The current paper reviews the various research papers contextual to online consumer attitude and organizes these different theoretical viewpoints. By approaching the topic through an integrative lens, the paper attempts to have an in-depth analysis of online consumer attitude formation and change and provides a foundational framework for future research in this area. The paper attempts to explore some unavoidable factors which were earlier little known or completely unexplored but can be considered the strong influential factors in online consumer attitude formation and change.
The Sampled Moran Genealogy Process.

We define the Sampled Moran Genealogy Process, a continuous-time Markov process on the space of genealogies with the demography of the classical Moran process, sampled through time. To do so, we begin by defining the Moran Genealogy Process using a novel representation. We then extend this process to include sampling through time. We derive exact conditional and marginal probability distributions for the sampled process under a stationarity assumption, and an exact expression for the likelihood of any sequence of genealogies it generates. This leads to some interesting observations pertinent to existing phylodynamic methods in the literature. Throughout, our proofs are original and make use of strictly forward-in-time calculations and are exact for all population sizes and sampling processes.
Probabilistic Reconstruction of Truncated Particle Trajectories on a Closed Surface

Investigation of dynamic processes in cell biology very often relies on the observation in two dimensions of 3D biological processes. Consequently, the data are partial and statistical methods and models are required to recover the parameters describing the dynamical processes. In the case of molecules moving over the 3D surface, such as proteins on walls of bacteria cell, a large portion of the 3D surface is not observed in 2D-time microscopy. It follows that biomolecules may disappear for a period of time in a region of interest, and then reappear later. Assuming Brownian motion with drift, we address the mathematical problem of the reconstruction of biomolecules trajectories on a cylindrical surface. A subregion of the cylinder is typically recorded during the observation period, and biomolecules may appear or disappear in any place of the 3D surface. The performance of the method is demonstrated on simulated particle trajectories that mimic MreB protein dynamics observed in 2D time-lapse fluorescence microscopy in rod-shaped bacteria.
SEED PRIMING WITH ASFAC-BCO-4 IMPROVES FESTUCA ARUNDINACEA SCHREB. SEED GERMINATION AND SEEDLINGS GROWTH UNDER MOISTURE STRESS

Festuca arundinacea Schreb. is an important cool-season perennial grass, used in parks, home lawns, athletic fields, golf courses and soil conservation sites. Recent researches are directed towards a deeper understanding of its behaviour under different stress conditions and increasing the ability to counteract the effects of global climate change. This study was carried out with a view to evaluate the effects of saline and drought on some standard germination and early seedlings growth indicators, in laboratory conditions. Both types of stress had significant negative effects on physiological indicators, but the influence of saline stress have proven to be more prominent, especially as regard as mean germination time. The effect of seeds priming was also evaluated. Priming with ASFAC-BCO-4 (a Romanian agricultural biostimulator) stimulated the germination process, i.e. it hastened it, compared with the untreated control, and the effects were more noticeable at the beginning of germination. Also, it positively affected early seedling growth, as well as chlorophyll synthesis, even if the saline stress was present.
A SHORT REVIEW ON PREDICTIONS FOR WIND POWER GENERATION { ITS LIMITATION AND FUTURE DIRECTIONS

Controlling and predicting power generation are essential elements for ef-(cid:12)cient smart grid operations. In the case of wind energy, accurate prediction is even more important because humans have no control over its energy source. This study (cid:12)rst reviews the line of prediction studies. Then, we generate point estimates using several machine learning methods and compare the performance and the pattern of the generated forecasts. We discuss the limitations of the point estimates and why probabilistic predictions are desirable. We suggest a few considerations for future studies
Study of Microstructure and Wear Behaviour of Duplex Stainless Steel Hardfacing Deposits on Ferritic Ductile Iron Substrate

In this study, cladding of ferritic ductile iron (DI) was performed using duplex stainless steel wire (25.10.4.L) and gas tungsten arc welding (GTAW). The microstructure and wear behaviour of the clad layer and interface were investigated by optical microscope(OM), scanning electron microscope(SEM) and X-ray diffraction analysis. The results showed that the clad layer had a dendritic structure including austenite, ferrite, carbides and nitrides. Also, the results revealed that the preheating temperature was high enough to prevent the formation of brittle martensite phase in the heat affected zone(HAZ). Wear resistance of the clad layer was much higher than that of base metal (BM), possibly due to the presence of the dendritic structure and many carbides and nitrides. In order to understand the wear life of the coated sample, wear test on interface was performed. The wear resistance of interface was significantly higher than that of the clad layer. ARTICLE HISTORY Received 07-01-2019 Revised 15-09-2020 Accepted 09-10-2020 Published 20-03-2020
Measuring the Relative Similarity and Difﬁculty Between AI Benchmark Problems

There has been an explosion of challenge problems, algorithmic tests and datasets for evaluating AI systems. Yet no methodology exists to objectively measure either the collective difﬁculty of these problems or their similarity. This is an obstacle to creating more general AI systems. We pro-pose a theory for measuring the similarity between pair-wise problems. We evaluate this theory by utilizing a methodology based on a deep neural network to objectively measure these properties between test problems using foundational datasets. An implementation of these methods is then used to measure the similarity between well known datasets. Results show that the proposed measure successfully identiﬁes the difﬁculty and similarity among problems. This can be used to ensure diversity in test suites used to evaluate AI systems.
Design, Analysis and Measurement Results of a Fully- Integrated Low-Power LNA Presenting Faults

The oldest reported integrated radio-frequency (RF) low-noise amplifier (LNA) operating in the UHF band was conceived in the 80’s [1]. Its authors claimed that previously reported LNAs did not have low noise figure (< 3 dB), nor low power consumption (< 150 mW), neither sufficient bandwidth for VHF-UHF applications. In their paper, they described a two-stage LNA using Gallium-Arsenide field-effect transistors (GaAs-FET) with channel lengths of 1 μm. The amplifier’s measured noise figure (NF) was lower than 3 dB and the insertion gain was above 16 dB in a bandwidth spanning from 9 MHz through 3.9 GHz. The power consumption reported was 170 mW and it could be as low as 120 mW if external inductors were used. The circuit occupied a semiconductor area of 0.96 mm2. In the middle of the 90’s, the strong demand for lower power, lower cost, smaller and lighter circuits was driven by the fast growing market of personal wireless communication services [2]. Normally, these requirements were addressed with bipolar or GaAs technology, however the CMOS technology was becoming attractive due to its lower cost and the possibility to come up with full system-on-chip (SOC) solutions, including digital, mixed-signal and RF circuits [3]-[4]. One of the early works covering the design of a LNA in CMOS technology is found in [4]. The circuit was conceived in a 0.5-μm CMOS standard process to operate at 900 MHz, with a 15-dB gain and a 2.2-dB noise figure. Its power consumption was 20 mW and it occupied 0.28 mm2 of silicon area. Thenceforth, the adoption of CMOS standard processes for designing RF LNA has become a standard. As the transistor’s channel length has been reduced, we have witnessed the design of high-performance CMOS low-noise amplifiers with restrictive specifications, as those presented in [5], [6], and [7]. An example of high-performance state-of-the-art CMOS LNA is communicated in [8]. The circuit was designed in a 90-nm CMOS technology and uses external inductors. Its measured noise figure did not exceed 0.2 dB at frequencies between 800 MHz to 1400 MHz. AbsTRACT1
CACS : Closed Anonymous Communication System for MANETs

- In hostile environments, communication anonymity has to be preserved to ensure security. Network members should not reveal their real identities to others but use pseudonyms (identity-free). Their locations also need to be concealed to protect themselves (location anonymity). The source and destination of a flow should be indistinguishable among all the nodes (source/destination anonymity) and their end-to-end relation should be hidden (end-to-end communication relation anonymity). A flow must be untraceable, should not be discovered by malicious adversaries (route anonymity). All these strict requirements become the heavy tasks of anonymous routing protocols. Previously proposed anonymous routing schemes have provided anonymity for MANET communications on certain levels, but they all suffer from different defects. The most common drawback is that they sacrifice the networking performance. Normally, a routing protocol has the route discovery phase and the message transfer phase. During the second phase, most of the prior schemes require the packets to be encrypted and decrypted at each node in the path. These cryptographic operations incur large cost. All the existing schemes suffer from the lack of universal applicability, the conflict with secure routing, and the lack of differentiated anonymity provision.
An introduction to event history analyses for ecologists

Efforts to understand the emergence of an event require our ability to measure and understand the dynamics between time in a state (e.g., being alive or a behavior) and the outcome of the state. Studying the main drivers that affect changes in state over time allows researchers to better understand population dynamics and evolutionary processes. Event history analyses provide a range of theoretical and empirical tools to explore the emergence of an event. Their use is still restricted in ecology; however, they are commonly used in human demography. Event history analysis is a powerful tool for measuring the probability that an event occurs at time t . Here, we provide an introductory guide for ecologists who are interested in exploring event history analyses in their research. In the ﬁ rst part of this article, we outline key concepts in event history analyses and present a decision tree, statistical techniques, and their applications to ecological questions. To introduce practical applications of event history analyses, we provide four detailed tutorials, stemming from observational and longitudinal records of events in mammalian and avian species, along with relevant R scripts. We then explain how to interpret and present results of such analyses. Our results show that event history analyses are useful to quantify the effect of factors on the emergence of events. We conclude by highlighting additional strengths, pitfalls, and limitations researchers should be aware of when using such methods. We foresee the use of event history analyses for ecological studies.