Miracle-dz/UniFAR-FTUs huggingface dataset:
Arti cial Neuronal Networks : A Bayesian Approach Using Parallel Computing

An Arti cial Neural Network (ANN) is a learning paradigm and automatic processing inspired in the biological behavior of neurons and the brain structure. The brain is a complex system; its basic processing unit are the neurons, which are distributed massively in the brain sharing multiple connections between them. The ANNs try to emulate some characteristics of humans, and can be thought as intelligent systems that perform some tasks in a di erent way that actual computer does. The ANNs can be used to perform complex activities, for example: pattern recognition and classi cation, weather prediction, genetic values prediction, etc. The algorithms used to train the ANN, are in general complex, so therefore there is a need to have alternatives which lead to a signi cant reduction of times employed to train an ANN. In this work, we present an algorithm based in the strategy divide and conquer which allows to train an ANN with a single hidden layer. Part of the sub problems of the general algorithm used for training are solved by using parallel computing techniques, which allows to improve the performance of the resulting application. The proposed algorithm was implemented using the C++ programming language, and the libraries Open MPI and ScaLAPACK. We present some application examples and we asses the application performance. The results shown that it is possible to reduce signi cantly the time necessary to execute the program that implements the algorithm to train the ANN.
Matching named entities with the aid of Wikipedia

In this paper we propose a novel framework using features extracted from Wikipedia for the task of matching named entities. We present how the employed features are extracted from Wikipedia and how its structure is utilized. We describe how a term-concepts table constructed from Wikipedia and the redirect links are integrated in the framework. In addition, the internal links within Wikipedia along with the categories structure are also used to compute the relatedness between concepts. We evaluate the built framework and report its performance in the applications of word sense disambiguation and named entities matching. The system performance is compared against other learning-based state-of-the-art systems and its reported results are found to be competitive. We also present a method in this paper for named entities matching that is context-independent and compare its results with those of other systems.
Deformation of Indian Ocean lithosphere: Evidence for a highly nonlinear rheological law

The width of diffuse oceanic plate boundaries is determined by the rheology of oceanic lithosphere. Here we apply thin viscous sheet models, which have been successfully applied to deformation in several continental deforming zones, to investigate the deformation of oceanic lithosphere in the diffuse oceanic plate boundaries between the India, Capricorn, and Australia Plates. We apply kinematic boundary conditions based on the current motion between these plates. We neglect buoyancy forces due to plate thinning or thickening and assume that the thin viscous sheet has the same depth‐integrated nonlinear viscosity coefficient everywhere. Our initial models have only one adjustable parameter, n, the power‐law exponent, with n = 1, 3, 10, 30, and 100. The predicted width of the deforming zone decreases with increasing n, with n ≥ 30 explaining the observations. This n value is higher than has been estimated for continental lithosphere and suggests that more of the strength of oceanic lithosphere lies in layers deforming by faulting or by dislocation glide than for continental lithosphere. To obtain a stress field that better fits the distribution and type of earthquake focal mechanisms in the diffuse oceanic plate boundary, we add a second adjustable parameter, representing the effect of slab pull stretching the oceanic plate near the Sumatra Trench. We show that an average velocity increment on this boundary segment of 5 mm a−1 (relative to the average velocity of the India and Australia Plates) fits the observed distribution of fault types better than velocities of 3.3 mm a−1 or 10 mm a−1.
Interactive comment on “Emission-dominated gas exchange of elemental mercury vapor over natural surfaces in China” by Xun Wang et al

Response: We thank reviewer for the suggestion and have provided additional data in Figures S8-S9 to address the reviewer’s concern regarding model evaluation. We respectfully disagree regarding the organization of the section describing the results. Compared to earlier air-surface models (Bash, 2010;Wang et al., 2014), we built a new scheme for air-soil flux exchange. We think the evaluation for this scheme is necessary before running the air-surface exchange model and choose to present the evaluation results of individual model components in section 3.1, and show the verification results after multiple model components are integrated in section 3.4.
Influence of mechanical anisotropy on shear fracture development

Analogue models have been used to investigate the influence of the orientation of a planar mechanical anisotropy on the development of shear fractures. Anisotropic plasticine models with the anisotropy planes initially oriented at different angles with respect to the deformation axes have been deformed under pure shear boundary conditions. Two sets of shear fractures are formed in all the experiments. Their asymmetry depends on the initial orientation of anisotropy. In oblique cases the local stress field inferred from fracture sets differs from the stress field applied by the deformation apparatus. The degree of anisotropy can be estimated from the differences between both stress fields using a simple analytical method.
The Optimality of Arm's Length, Memoryless Contracts

For most workers, the primary source of incentives is managerial feedback. In this environment, I show why long-term employment relationships are optimally governed by a series of arm’s length, memoryless shorter-term contracts: The worker is evaluated infrequently and his future prospects depend on current evaluations, but not on evaluations from long ago. The setting is a general repeated moral hazard model with costless monitoring and complete contracts, where the manager could, in theory, employ a highly complex, history-dependent long-term contract. The intuition for the optimality of arm’s length, memoryless contracts is robust, applicable, in particular, to settings with risk aversion.
PH triggered sol-gel transition system of ofloxacin for prolongedgastric retention

Gastro retentive drug delivery system has been widely used to prolong retention of dosage forms in stomach. Amongst the various approaches, the Raft formulation offers sustained drug release as well as prolonged gastric retention, along with the added advantage of liquid oral dosage form. The present study was an attempt to formulate and evaluate Raft forming floating drug delivery system for Ofloxacin which undergoes pH dependent sol-gel transition at gastric pH; thereby prolonging the retention of the system in stomach. Gellan gum (Gelrite®) was employed as gelling agent whose gelation is triggered by source of Ca2+ ions in the form of Calcium Carbonate. The evaluation was carried out for both In vitro and In vivo parameters and the results substantiated that the optimized formulation revealed excellent floating characteristics and gastric retention.
Purchasing Power Parity Principle in Latin American Countries

espanolResumen: Sometemos a prueba el principio de paridad del poder de compra (PPP) en una muestra de 17 paises de America Latina en el periodo 1960-2016. Con tal fin empleamos dos pruebas relativamente novedosas de panel propuestas por Pesaran (2007) y Hadri y Rao (2008). La primera permite probar el PPP en todos los posibles pares de tipos de cambio real entre las monedas de los paises considerados mientras que la segunda prueba permite identificar y validar diferentes versiones del PPP que pueden caracterizar el tipo de cambio real bilateral de cada pais con respecto al dolar de los Estados Unidos. Esta ultima prueba contempla la posibilidad de que las series que conforman el panel presenten una ruptura estructural heterogenea entre los individuos, muestren dependencia de seccion cruzada y que los residuos esten correlacionados serialmente. Los resultados de la prueba de Pesaran (2007) sugieren el cumplimiento del PPP en todos los posibles pares de tipos de cambio real analizados mientras que los resultados de la prueba de Hadri y Rao (2008) validan diferentes versiones del PPP en los tipos de cambio reales bilaterales de los paises de la region con respecto al dolar de los Estados Unidos. EnglishAbstract: In this paper we test the purchasing power parity principle (PPP) with a sample of 17 Latin American countries and data from 1960 to 2016. For this purpose, we used two relatively new panel tests proposed by Pesaran (2007) and Hadri and Rao (2008). The first test allows the PPP to be tested on all possible pairs of real exchange rates of the currencies belonging to the countries studied herein. The second test allows us to identify the type of PPP hypothesis that characterizes each country’s bilateral real exchange rate vis-a-vis the U.S. dollar. This latter test takes into account that the series in the panel may contain a breakpoint or structural break and show cross-section dependency, that residuals are serially correlated, and that structural breaks can have diverse forms for each bilateral real exchange rate. Results from the Pesaran (2007) test suggest that the PPP is fulfilled for all possible pairs of real exchange rates studied here. In addition, the results of the Hadri and Rao (2008) test validate distinct versions of the PPP for countries in the region.
Fashion Brand Purity and Firm Performance

A large number of prior empirical research and case studies used qualitative methodology to discuss the fashion brand dilution resulting from consumer base extension from the target group(s) to the nontarget groups and its impacts. From a different perspective, this paper establishes a dynamic brand dilution and performance model, demonstrating how dynamic changes of sales volumes involving the two consumer groups affect the degree of brand dilution and the performance of the brand. We incorporate the factor “brand purity” to the model as a quantitative measure of brand dilution level that affects firm annual revenue and profit change comprehensively in iteration. Our model suggests that fashion brands, especially luxury brands, can be easily diluted under the pressure of firm growth, and the brands suffer the significant negative impact on their revenues and profit. While increasing sales volume can aggravate the negative consequences, brand purity can be increased through limiting the consumer base to the target group only.
Handbook of mathematical cognition

Part 1: Cognitive Representations for Number and Mathematics. M. Fayol, X. Seron, About Numerical Representations: Insights from Neuropsychological, Experimental and Developmental Studies. M. Brysbaert, Number Recognition in Different Formats. W. Fias, M.H. Fischer, Spatial Representation of Numbers. J. Tzelgov, D. Ganor-Stern, Automaticity in Processing Ordinal Information. M. Zorzi, I. Stoianov, C. Umilta, Computational Modeling of Numerical Cognition. E.M. Brannon, What Animals Know about Numbers. R. Nunez, G. Lakoff, The Cognitive Foundations of Mathematics: The Role of Conceptual Metaphor. Part 2: Learning and Development of Numerical Skills. S. Cordes, R. Gelman, The Young Numerical Mind: When Does It Count? J. Bisanz, J.L. Sherman, C. Rasmussen, E. Ho, Development of Arithmetic Skills and Knowledge in Pre-school Children. K.F. Miller, M. Kelly, X. Zhou, Learning Mathematics in China and the United States: Cross-cultural Insights into the Nature and Course of Pre-school Mathematical Development. M. -P. Noel, L. Rousselle, C. Mussolin, Magnitude Representation in Children: Its Development and Dysfunction. R.S. Siegler, J.L. Booth, Development of Numerical Estimation: A Review. K.C. Fuson, D. Abrahamson, Understanding Ratio and Proportion as an Example of the Apprehending Zone and Conceptual-phase Problem-solving Models. T. Ben-Zeev, S. Duncan, C. Forbes, Stereotypes and Math Performance. Part 3: Learning and Performance Disabilities in Math and Number Processing. D.C. Geary, M.K. Hoard, Learning Disabilities in Arithmetic and Mathematics: Theoretical and Empirical Perspectives. M.M. M. Mazzocco, M. McCloskey, Math Performance in Girls with Turner or Fragile X Syndrome. M.A. Barnes, B. Smith-Chant, S.H. Landry, Number Processing in Neurodevelopmental Disorders: Spina Bifida Myelomeningocele. M.H. Ashcraft, K.S. Ridley, Math Anxiety and its Cognitive Consequences: A Tutorial Review. Part 4: Calculation and Cognition. N.J. Zbrodoff, G,D. Logan, What Everyone Finds: The Problem Size Effect. J.I. D. Campbell, L.J. Epp, Architectures for Arithmetic. J.-A. LeFevre, D. DeStefano, B. Coleman, T. Shanahan, Mathematical Cognition and Working Memory. J.A. Dixon, Mathematical Problem Solving: The Roles of Exemplar, Schema, and Relational Representations. S. Duverne, P. Lemaire, Aging and Mental Arithmetic. M. Pesenti, Calculation Abilities in Expert Calculators. Part 5: Neuropsychology of Number Processing and Calculation. S. Dehaene, M. Piazza, P. Pinel, L. Cohen, Three Parietal Circuits for Number Processing. B. Butterworth, Developmental Dyscalculia. A. Lochy, F. Domahs, M. Delazer, Rehabilitation of Acquired Calculation and Number Processing Disorders.
Implications of Climate Variability and Change for Smallholder Crop Production in Different Areas of Zimbabwe

Sustainable food production in the rain-fed smallholder sector of Zimbabwe is imperative in current and future climates given the increasing proportion and role of the sector in food security. Backgrounds of the smallholder sector include low resource bases, declining soil productivity, climate variability and increasing human population. Impacts of climatic and non-climatic variables on crop production vary in different Agro-Ecological Regions (AERs). The study identifies the main climatic opportunities and constraints for sustainable rain-fed smallholder crop production systems in current and future climates. Estimated changes in rainfall and temperature by 2050 are not likely to negatively impact crop production in smallholder areas in AER I, increases in temperature may have negative impacts for most of AER III and IV. Most of AER V is likely to become unsuitable for sustainable maize production. Overall projections indicate increased differences in agricultural potential between smallholder farmers in AER I compared to those in AER II and III as well as those in AER IV and V and different management options. Proposed adaptation and coping strategies in AER I and some in AER II include intensification of agriculture and optimization of both C3 and C4 crop production while in AER III to V adoption of stress tolerant crops for family subsistence may be imperative.
Optimal power tracker for stand-alone photovoltaic system using Artificial Neural Network (ANN) and Particle Swarm Optimisation (PSO)

In recent years, many intelligent techniques and approaches have been introduced into photovoltaic (PV) system for the utilisation of free harvest renewable energy. Generally, the output power generation of the PV system rely on the intermittent solar insolation, cell temperature, efficiency of the PV panel and its output voltage level. Consequently, it is essential to track the generated power of the PV system and utilise the collected solar energy optimally. Artificial Neural Network (ANN) is initially used to forecast the solar insolation level and followed by the Particle Swarm Optimisation (PSO) to optimise the power generation of the PV system based on the solar insolation level, cell temperature, efficiency of PV panel and output voltage requirements. This paper proposes an integrated offline PSO and ANN algorithms to track the solar power optimally based on various operation conditions due to the uncertain climate change. The proposed approach has the capability to estimate the amount of generated PV power at a specific time. The ANN based solar insolation forecast has shown satisfactory results with minimal error and the generated PV power has been optimised significantly with the aids of the PSO algorithm.
Model of neural networks for fertilizer recommendation and amendments in pasture crops

In the production of milk, pastures are the basis of bovine feeding, it is considered that around 50% of the costs to produce a liter of milk correspond to the feeding of the cows. Therefore the cultivation of pastures is an essential factor in the dairy industry. The cultivation of pastures requires the preparation of the soil adding fertilizers and amendments for the increase of biomass. The determination of fertilizers and amendments is an activity that adds costs to the cultivation process. The purpose of this work is to identify that from the basic nutrients in the soil such as Nitrogen(N), Phosphorus(P) and Potassium(K), with an MLP neural network of multiple input layers and multiple output layers trained with the back propagation algorithm, it can determine the fertilizers and amendments required by pasture cultivation. The results indicate that despite using a small sample of data (44), with a threshold of 0.75 and with k-fold =3 it was possible to identify the fertilizers Potassium chloride(Y2), Diammonium phosphate(Y6), Copper sulfate(W3) and Zinc sulfate(W4) for the cultivation of pastures. With these results, our contribution is to initiate the use of ML in pasture cultivation and later extend it to other crops. The importance of this work lies in the fact that the results can contribute to the reduction of milk production costs and that the results of this work will be the baseline for the use of other machine learning algorithms in agriculture.
Political Resource Curse under Authoritarianism: Evidence from China

By analyzing a panel constructed on the political turnovers of 4,390 county leaders in China during 1999{2008, we nd that the revenue windfalls accrued to these ocials from land sales since 1998 have both undermined the eectiveness of the promotion system for government ocials and fueled corruption. Instead of rewarding eorts made to boost GDP growth, promotion is now also positively correlated with signaling eorts or specically increased spending on amboyant (so-called \image") public projects,
Prediction Models for Identification and Diagnosis of Tomato Plant Diseases

Through the ages, pests and plant diseases have remained a constant threat to the quality and quantity of overall crop production. Hence, their timely and accurate identification could greatly extenuate the economic losses worldwide, also denigrating the harmful impact of fertilizers and pesticides on the environment. Once the correct disease is identified, conforming to the characteristic symptoms, proper control measures can be applied. The concept of Precision Agriculture necessitates the timely automation of dominant and crucial agricultural processes by applying the techniques of computational intelligence in the agriculture domain, Machine Learning and Computer Vision being the most researched and implemented technologies. This paper presents a survey on the current techniques and prediction models, based on Image processing and the role of Internet of Things (IoT), being applied for identification, detection as well as quantification of Tomato plant diseases.
Copper-catalyzed N-arylation of heterocycles in ethylene glycol medium

N-Aarylation of imidazole, benzimidazole, pyrazole, and indole was performed in the presence of CuI as the catalyst and NaOH as the base in ethylene glycol medium. Apparently, ethylene glycol played a dual role as both the reaction solvent and a ligand for the catalyst. All reactions proceeded selectively to give the corresponding N-arylated products. In some cases, O-alkylation or dehalogenation was observed which was explained by the proposed catalytic cycle. Conventional heating at 120 oC and microwave irradiation both were used, where the results showed that microwave irradiation could facilitate the reaction by lowering the reaction times and increasing the yields. The reaction medium was recovered and reused in several consecutive runs.
RF Energy Harvesting and Information Transmission Based on Power Splitting and NOMA for IoT Relay Systems

Due to the proliferation of the Internet of Things (loT), an unprecedented expansion of wireless devices and communication among these devices for data transmission is expected over the next few years. IoT devices are rather power constrained and are mostly battery operated devices. Therefore, energy efficiency of such IoT devices or systems is a major concern. In this paper, radio frequency (RF) energy harvesting and information transmission based on Non-orthogonal multiple access (NOMA) protocol is considered for wireless powered IoT relay systems. A source node needs the help of power constrained IoT relay node IoTR to transmit its data. IoTR node first harvests the energy from the RF signal of the source node and process the information in the first stage and then transmits the source node information data along with its data using NOMA protocol in the next stage. Unlike most of the previous works in this domain, this paper also considers the data transmission of the IoTR node along with the source node to its intended destination nodes. Specifically, considering the energy constrained nature of IoT nodes and devices, we have used the combination of power splitting (PS) and NOMA protocol and have mathematically derived the outage probability and sum-throughput for the proposed system. We have also formulated an algorithm to find out the optimal power splitting factor that maximizes the sum-throughput of the proposed system. Our proposed system analytical results are validated by the simulation results.
Business Process Modeling Plural ized

Traditional centralized business process management approaches pose difficulties in coping with rapid changes and evolving process models. We developed the Plural method to allow for decentralized modeling of processes. The Plural method enables process participants, rather than a centralized group of process engineers or managers, to model and maintain their processes. In previous works, we introduced the Plural method and discussed its applications in case studies. This paper elaborates more into the notation component of the Plural method. We describe the diagram types used for capturing process information. We also present a case study performed in a small web application development company. We discuss the results together with a synthesis of the findings and lessons learned from our previous case studies.