Miracle-dz/UniFAR-FTUs huggingface dataset:
Symbolic-based Optimal Operation of Flexible Reconfigurable Networks (FRNs) Using Movable/Changeable Resources

: Most types of networks depend on fixed resources in their configuration, but these fixed resources are not effective, especially in the case of a problem in any of these resources. This may lead to a defect in the entire network until the problem is resolved and therefore there must some alternatives for such situations. In this paper, the concept of addressing optimal operations of Flexible Reconfigurable Networks (FRNs) with movable or changeable resources is thoroughly presented. The methodology is represented in a symbolic-based mathematical form by solving three FRN optimization problems using quadratic programming, nonlinear programming and multi-objective programming. Implementation of the proposed technique is carried out to several illustrative examples with a single or multiple movable resource. The implemented FRN examples demonstrate the efficacy of the proposed technique and its strong capability in presenting many scenarios for system operation under movable resources. The same approach can be extended for more general classes of FRNs when the optimal solution could be expressed in the form of matrix representation and manipulation. Application of the concept is demonstrated to a selected agricultural and irrigation systems. Finally, the applications of the new FRN concept to real life systems in the area of electricity, water, oil and gas, communication, computer, transportation
Modified Clover Trap for Capturing Mountain Goats in Northwest British Columbia

Safe, humane, and effective capture methods are a critical component of mountain goat (Oreamnos americanus) research and management. Clover traps are widely used as an effective method for ground capturing mountain goats. The traps, which consist of a metal frame covered with heavy mesh netting, are simple to use, require only 2 people to operate, and can be used in a wide range of terrain, including open alpine ridges. Combined with a skilled capture and handling crew, use of the clover trap can also minimize adverse capture effects such as stress and physical injury. This paper discusses our experience using clover traps to capture mountain goats in Northwest BC and a number of design improvements made to improve trap efficacy. During capture, periods of snow, sleet and very strong winds were frequently experienced. During these periods, failed captures were frequently reported (i.e. goats entering traps were not captured, as confirmed by observations or tracks). Failures appeared to be due to freezing or icing of the trap mesh, the trap release mechanism (a snap-trap), or the trap door drop-bar. Improvements were subsequently made to both the trap-release mechanism and the vertical uprights of the door frame to minimize failed captures. We present an evaluation of these improvements in terms of capture efficacy and discuss other critical factors that influenced capture success in our work.
Electronic Banking Services and Customers’ Satisfaction in Deposit Money Banks in Rivers State, Nigeria

Electronic banking is increasingly becoming very dominant in the Nigerian banking sub-sector due to quest for improved service delivery, thus, making it a subject of interest in both business and academic cycles. This study assessed the impact of electronic banking services on customers’ satisfaction in selected Deposit Money Banks in Rivers State, Nigeria. Three hundred and seven (307) copies of questionnaire were utilized to elicit the required information from the customers and staff of four (4) systematically Important (Too Big to Fail) Banks and eight (8) National Banks. The data gathered focused mainly on the socio-economic characteristics of the respondents, nature and types of customers’ complaints regarding electronic banking and other challenges that influence effective implementation of electronic banking services as perceived by the bank staff. A combination of descriptive and inferential statistics was employed as techniques for data analysis. The findings reveal that customers’ complaints regarding electronic banking include debiting of their accounts by the ATM without payment, non-availability of cash at the ATM, resolution of disputes on the electronic banking products and trapping of customer’s card by the ATM. Aside the customers’ complaints, it was equally found that the bank staff identified network failure, limited personnel, mishandling of the naira note and CBN’s directive on trapped cards as other key challenges that obstruct effective implementation of electronic banking services. Thus, it was recommended that access to improved network via maximum link-up time and periodic review of cash withdrawal and transaction limits on the e-channels should command top priority in the operations of Deposit Money Banks to meet customers’ needs.
The Lucas Paradox and Institutional Quality: Evidence from Emerging Markets

The neoclassical theory illustrates that the capital will flow from the capital-rich economies towards the capital-poor states. However, it is generally observed that the capital does not move from high-income to low-income economies. This contradictory behavior of global capital flows is called the Lucas paradox. According to Alfaro, Kalemli-Ozcan, & Volosovych (AKV) model, the Lucas paradox can be entirely explained by the institutional quality. In the light of AKV notion, this paper examines the role of institutional quality in explaining the Lucas paradox. The empirical analysis involves 17 major emerging economies of the world by using panel data for the period 1999-2018. The GMM estimation reveals that the Lucas paradox is explained by the institutional quality in case of FDI flows only. However, institutional quality indicators generally remain unable to explain the paradox in case of portfolio equity capital flows. Moreover, financial development is found to be a significant determinant of portfolio equity flows in the emerging markets. Finally, we suggest that the financial flows to the emerging markets are sensitive to the nature of the capital flows.
Automated Trading System for Stock Index Using LSTM Neural Networks and Risk Management

Financial time series predictions are a challenge due to their nonlinear and chaotic nature. In recent decades, many researchers and investors have studied methods to improve quantitative analysis. In the field of artificial intelligence, sophisticated machine learning techniques, such as deep learning showed better performance. In this paper, an automated trading system is built to predict future trends of stock index prices. Using an LSTM-based agent to learn temporal patterns in the data, the algorithm triggers automatic trades according to the historical data, technical analysis indicators, and risk management. The results demonstrate that the proposed method, called LSTM-RMODV, shows better performance when compared with other methods, including the buy-and-hold technique. The proposed method also works in bear or bull market conditions, showing a rate over net income based on invested capital of 228.94%. That is, despite the low accuracy, the algorithm is capable of generating consistent profits when all the transaction costs are considered.
Extraction of Total RNA and Protein by the Method of TRIzol

Total RNA and protein was extracted from the same cell sample by the methods of TRIzol.The results showed that the extracted RNA was integrated enough for the further experiments,and the extracted protein is equal in quality with the protein estracted by the RIPA lysis buffer.So,it is fesible to shorten the period of time and ensure the same experimental conditions.
Research on Multijoint Rock Failure Mechanism Based on Moment Tensor Theory

To reveal the influence of the number and location of joints on rock failure mechanism, using Particle Flow Code (PFC) to simulate the calculation of a large amount of acoustic emission data generated during breeding, development, and penetration of rock cracks, the fracture parameters such as the spatial location, rupture azimuth, rupture type, stress state, and moment magnitude of acoustic emission events in various fracture stages of multijoint rock were studied based on the moment tensor theory, the P-T diagram method, and the T-k diagram method. It will be of great importance in the geotechnical engineering field.
Legal Document Classification: An Application to Law Area Prediction of Petitions to Public Prosecution Service

In recent years, there has been an increased interest in the application of Natural Language Processing (NLP) to legal documents. The use of convolutional and recurrent neural networks along with word embedding techniques have presented promising results when applied to textual classification problems, such as sentiment analysis and topic segmentation of documents. This paper proposes the use of NLP techniques for textual classification, with the purpose of categorizing the descriptions of the services provided by the Public Prosecutor’s Office of the State of Paraná to the population in one of the areas of law covered by the institution. Our main goal is to automate the process of assigning petitions to their respective areas of law, with a consequent reduction in costs and time associated with such process while allowing the allocation of human resources to more complex tasks. In this paper, we compare different approaches to word representations in the aforementioned task: including document-term matrices and a few different word embeddings. With regards to the classification models, we evaluated three dif-ferent families: linear models, boosted trees and neural networks. The best results were obtained with a combination of Word2Vec trained on a domain-specific corpus and a Recurrent Neural Network (RNN) architecture (more specifically, LSTM), leading to an accuracy of 90% and F1-Score of 85% in the classification of eighteen categories (law areas).
Critical Factors Affecting Bank Management Policies

We suggest some managerial implications following the identification of the determinants of the bestefficiency levels achieved by banking groups in the main Euro Area countries in the period 2009-2018, using acluster analysis. Differently from previous works, this study analyzes the banking groups’ behavior in aparticularly interesting period characterized by the sequence of three very different crises: the sub-prime, thesovereign debt and the NPLs crises. The results show that the best performances are linked to a particular businessmodel, to specific managerial decisions, such as the achievement of a medium size along with a rational valuationof the number of firms in the same banking group and to suitable cost rationalization strategies and liquidityreserve optimization policies. All the results obtained support empirical evidences already highlighted in theexisting literature.
Climate Risk and Beliefs in New York Floodplains

Applying a difference-in-differences framework to a census of residential property transactions in New York City 2003–17, we estimate the price effects of three flood risk signals: (1) the Biggert-Waters Flood Insurance Reform Act, which increased premiums; (2) Hurricane Sandy; and (3) new floodplain maps reflecting three decades of climate change. Estimates are negative for all three signals, and some are large: properties included in the new floodplain after escaping flooding by Sandy experienced 11% price reductions. We investigate possible mechanisms, including selection of properties into the market and residential sorting. Finding no evidence for these, we develop a parsimonious theoretical model that allows decomposition of our reduced-form estimates into the effects of insurance premium changes and belief updating. Results suggest the new maps induced belief changes comparable to those from insurance reform.
Classical Heritage and Text-Based Second Language Learning in Three-Dimensional Virtual Library Environment

In our paper we would like to discuss in detail the present status of our three-dimensional virtual library model (3DVLM) built on the hierarchical scheme of the ancient Library of Alexandria worked out in the so-called Pinakes by Callimachus in the 3rd century BC. The full content of the model, once completed, would provide a comprehensive overview of the classical heritage our culture is originated from. In this tradition Callimachus is considered to be one of the most talented Hellenistic scholar-poets. His brief epigrams were outstanding which reflected his allusive mind and erudition. In general, it is a great challenge for us to deliver the message of ancient times to the present-day culture, but it seems to be very difficult to convey this highly sophisticated content to the members of the young (and especially the y and z) generations of our times who have been brought up and educated in a completely different social and cultural environment. Our basic idea is that language learning can serve as a common basis to transform and offer the ancient knowledge for the young generations of the internet era . Moreover, we are aware that nowadays one of the preferred sources of information is Wikipedia. So we chose and carefully preprocessed some Wikipedia texts (in English) about Callimachus’ life and works for the 3DVLM to assist our users in language learning and, at the same time, provide them short but valuable texts which are worth learning and memorizing. In order that the selected texts could be easily understood and memorized we provided additional items which are necessary for language learners (e.g. vocabulary and thesaurus of rare or special words, expressions and idioms,
Nonlinear water wave problems over a general bathymetry: A unified theory

Under the assumptions of incompressibility and ir rotationality, the problem of evolution of water waves over a variable bathymetry region admits of at least two different variational formulations. A Hamiltonian one, proposed by Petrov (1964) and exploited further by Zakharov (1968), and an unconstrained one, proposed by Luke (1967). The present developm ent is based on Luke's variational principle , in which the admissible fields are free of essential conditions, except for smoothness and com pleteness prerequisites. A complete local-mode series expansion of the wave po t ntial is constructed, which represents exactly the vertical structure of the wave field. This series contains the usual propagating and evanescent modes , plus two additional modes, called the free-surface mode and the sloping-bottom mode , introduced in order to consistently treat the non-vertical end -conditions at the free-surface and the bottom boundaries, respectively. Using this expansion, in conjunction with the variational principle, the original proble m is reformulated as an infinite, coupled-mode, non-linear system of second-order differential equations in the propagation (horizontal) space, fu lly accounting for the effects of non-linearity and dispersion. The main features of this approach are the following: (i) Various standard models of water-wav e propagation are recovered by appropriate simplifications of the cou pled-mode system. Among them are included the mild-slope equation(s), the s econd-order Stokes solutions, and the Boussinesq equation. (ii) In all c ses examined, a small number of modes (up to 5 or 7) are enough for the p recise numerical solution, provided that the two new modes (the free-surface a nd the sloping-bottom ones) are included in the local-mode series.
Non-parametric generalized linear model

A fundamental problem in statistical neuroscience is to model how neurons encode information by analyzing electrophysiological recordings. A popular and widely-used approach is to fit the spike trains with an autoregressive point process model. These models are characterized by a set of convolutional temporal filters, whose subsequent analysis can help reveal how neurons encode stimuli, interact with each other, and process information. In practice a sufficiently rich but small ensemble of temporal basis functions needs to be chosen to parameterize the filters. However, obtaining a satisfactory fit often requires burdensome model selection and fine tuning the form of the basis functions and their temporal span. In this paper we propose a nonparametric approach for jointly inferring the filters and hyperparameters using the Gaussian process framework. Our method is computationally efficient taking advantage of the sparse variational approximation while being flexible and rich enough to characterize arbitrary filters in continuous time lag. Moreover, our method automatically learns the temporal span of the filter. For the particular application in neuroscience, we designed priors for stimulus and history filters useful for the spike trains. We compare and validate our method on simulated and real neural spike train data.
U(1)×SU(2) gauge invariance made simple for density functional approximations

A semirelativistic density-functional theory that includes spin-orbit couplings and Zeeman fields on equal footing with the electromagnetic potentials, is an appealing framework to develop a unified first-principles computational approach for noncollinear magnetism, spintronics, orbitronics, and topological states. The basic variables of this theory include the paramagnetic current and the spin-current density, besides the particle and the spin density, and the corresponding exchange-correlation (xc) energy functional is invariant under local U(1) × SU(2) gauge transformations. The xc-energy functional must be approximated to enable practical applications, but, contrary to the case of the standard density functional theory, finding simple approximations suited to deal with realistic atomistic inhomogeneities has been a long-standing challenge. Here we propose a way out of this impasse by showing that approximate gauge-invariant functionals can be easily generated from existing approximate functionals of ordinary density-functional theory by applying a simple minimal substitution on the kinetic energy density, which controls the short-range behavior of the exchange hole. Our proposal opens the way to the construction of approximate, yet nonempirical functionals, which do not assume weak inhomogeneity and therefore may have a wide range of applicability in atomic, molecular, and condensed matter physics.
Combination of Statistical and Neural Approaches in the Japanese Question Generation System

1 Abstract Question generation is the task of generating a nat-ural question from a given input sentence. This often requires hand-crafted templates or sophisticated NLP pipelines which require extensive labor and expertise to morphologically analyze the sentences and create the NLP framework. In order to simplify these labors, contrastive experiment between two types of sequence learning: statistical-based machine translation and attention-based sequence neural network have been done. Going further we investigate, compares, and observe the combination of both approaches for question generation task. Combination of both approaches is done based on the voting mechanism. This way we aim to overcome both approaches and establish a system that excels in terms of content quality and (cid:13)uency according to a subjective human test.
On Fault Diagnosis for High-G Accelerometers via Data-Driven Models

Shock test is a pivotal stage for designing and manufacturing space instruments. As the essential components in shock test systems to measure shock signals accurately, high-g accelerometers are usually exposed to hazardous shock environment and could be subjected to various damages. Owing to that these damages to the accelerometers could result in erroneous measurements which would further lead to shock test failures, accurately diagnosing the fault type of each high-g accelerometer can be vital to ensure the reliability of the shock test experiments. Additionally, in practice, an accelerometer in one malfunction form usually outputs mutable signal waveforms, so that it is difficult to empirically judge the fault type of the accelerometer based on the erroneous readings. Moreover, traditional hardware diagnosis approaches require disassembling the sensor’s package shell and manually observing the damage of the elements inner the sensor, which are less-efficient and uneconomical. Aiming at these problems, several data-driven approaches are incorporated to diagnose the fault types of high-g accelerometers in this work. Firstly, several high-g accelerometers with most frequent types of damage are collected, and a shock signal dataset is gathered by conducting shock tests on these faulty accelerometers. Then, the obtained dataset is used to train several base classifiers to identify the fault types in a supervised fashion. Lastly, a hybrid ensemble learning model is established by integrating these base classifiers with both heterogeneous and homogeneous models. Experimental results show that these data-driven methods can accurately identify the fault types of high-g accelerometers from their mutable erroneous readings.
Improving Relation Classification by Incorporating Dependency and Semantic Information

Relation classification is the task of identifying relations between two entities in a sentence, which is an essential step in the standard NLP pipeline. Most of the previous models only make use of dependency or semantic features, which may result in the loss of vital information. In this paper, we propose a novel model that incorporates dependency and semantic information for relation classification. This is a neural network model using long short-term memory(LSTM), graph convolutional networks(GCN), and convolutional neural networks(CNN), named LGCNN. Concretely, it utilizes self-attention and LSTM to capture the local context in sentences. What’s more, it uses graph convolution network to encode dependency information and takes advantage of convolution neural network to encode semantic information from the local context. Experiments on the SemEval-2010 Task 8 and KBP37 dataset demonstrate that our model is very effective in relation classification.
An exploratory study of turnaround in Chinese and Malay firms in the Malaysian context

While research on turnaround in the Asian context is a recent phenomenon, there is little research on firms of different cultural and ethnic background within Asian countries. This paper reports the findings of an exploratory study on the impact of different ethnic background, ownership types and role of government on corporate recovery and turnaround using case studies of a Chinese and a Malay firm in Malaysia. The findings reveal turnaround characteristics similar to those reported in recent literature on Asian turnaround as well as differences between the Chinese and Malay firm. These findings are discussed and implications for future research indicated.
Hazard Identification, Risk Assessment and Risk Control in Foundry

Foundry work is the process of producing metal castings by melting the metal into a liquid state then pouring the metal into a mould which contains a hollow cavity of desired shape. A pattern is used to make the mould of the required article. Sometimes, the core inside the mould determines the internal cavity’s dimension. Foundries are of two types. Ferrous foundries produce steel and iron castings. Non ferrous foundries produce copper based alloys casting, aluminium-based alloys casting and other alloys. Foundry industry is considered to be a high risk area since it contains the hazardous operations. Assessing the risk and controlling the risk according to the aspects of occupational health and safety is very hard in foundry. The motive of this project is identifying the hazard and risk associated with the various steps involved in each process and taking all the possible measure to control the risk.