Miracle-dz/UniFAR-FTUs huggingface dataset:
Diagonal recurrent neural networks for control of dynamic systems

A new neural network architecture called Diagonal Recurrent Neural Network (DRNN) is presented. The architecture of DRNN is a modified model of fully connected recurrent neural network with one hidden layer. The hidden layer is comprised of self-recurrent neurons, each feeding its output only into itself and not to other neurons in the hidden layer. Two DRNNs are utilized in a control system, one as an identifier called Diagonal Recurrent Neuroidentifier (DRNI) and the other as a controller called Diagonal Recurrent Neurocontroller (DRNC). A controlled plant is identified by the DRNI, which then provides the sensitivity information of the plant to the DRNC. A generalized dynamic backpropagation algorithm (DBP) is developed and used to train both DRNC and DRNI. To guarantee convergence and for faster learning, an approach that uses adaptive learning rates is developed by introducing a Lyapunov function. Convergence theorems for the adaptive backpropagation algorithms are developed for both DRNI and DRNC. Convergence and the closed-loop stability are established for the DRNN based control system when the plant is BIBO stable. The proposed DRNN model is tested on a number of examples. Two different approaches for selecting learning rates, namely the fixed learning rate and the adaptive learning rate approaches, are investigated. The generalization ability, a BIBO nonlinear control, a non-BIBO nonlinear control, the on-line adapting ability of the DRNN based control, and an unstable plant control are investigated. The results show that the DRNN based control system requires much fewer neurons and weights, and that the number of training cycles required is reduced considerably. Moreover, the convergence, which is guaranteed for a BIBO plant, is also fast and the result is a closed-loop system which tracks reference input very well. An unstable plant was also controlled successfully by incorporating temperature and scaling schemes. The DRNN is also applied in a practical problem: nuclear reactor temperature control, and the simulation results demonstrated that the DRNN based control is very promising for future real-time applications.
Exploitation de la décomposition arborescente pour guider la recherche VNS

Tree decomposition introduced by Robertson and Seymour aims to decompose a problem into clusters constituting an acyclic graph. There are works exploiting tree decomposition for complete search methods. In this paper, we show how tree decomposition can be used to efﬁciently guide the exploration of local search methods that use large neighborhoods like VNS. We introduce tightness dependant tree decomposition which allows to take advantage of both the structure of the problem and the constraints tightness. Experiments performed on random instances (GRAPH) and real life instances (CELAR and SPOT5) show the appropriateness and the efﬁciency of our approach.
Small‐signal stability analysis of Energy Internet through differential inclusion theory

In this study, small-signal stability issue associated with uncertainty excitation is investigated by using differential inclusion theory. Specifically, a polytopic linear differential inclusion (PLDI) model for Energy Internet is developed, in which a modified non-sequence Monte Carlo method is introduced to identify a series of time-variant operation states. Additionally, a simplified small-signal model of renewable energy sources (RES) with virtual synchronous generator control is proposed, and the outputs of RES are modelled as time-varying elements in PLDI model to reflect the inner stochastic excitation in the linearised matrix. The stability criterion for the stochastic time-varying system is mathematically deduced based on convex hull Lyapunov function. Simulation demonstrates the benefits of the proposed model in describing system stochastic characteristics and reducing computational burden.
Development of GUI-based deep learning and image processing system for legume nodule segmentation and classification

Quantification of legumes biological nitrogen fixation (BNF) is normally done via analytical methods that require sampling, drying, grinding, and laboratory processing. These methods are time-consuming, expensive, and are not accessible to growers. The correlation between the BNF quantity and nodule number and nodule mass can be used to develop tools that allow rapid assessments of the BNF. In this work, we developed a graphical user interface (GUI) based deep learning and image processing system for legume nodule segmentation and classification to determine the characteristics associated with legume nodules using digital images. During image acquisition, the legume root samples were imaged using a smartphone camera and lab-made imaging setup. A total of 1468 digital images were collected from 367 root systems. After the first run of imaging, nodules were separated from the roots, and another image was obtained from the nodules of each sample. For comparison and validations, nodules were manually counted, dried, and weighed. In this study, a categorized image data library was developed and utilized for deep learning and image processing. Digital image processing filters, and image segmentation method were applied to process the digital images of the root systems and determine the number of nodules and provide their characteristics. Deep learning models were used to classify the images into different legume classes. Furthermore, a GUI was successfully developed to simplify the utilization and application of deep learning/digital image processing algorithms. The preliminary results of this study demonstrate that our deep learning/image analysis system has a great potential to accurately quantify, characterize, and count the nodules and that could be extremely valuable to growers.
Iteration of a certain continued fraction map

Define a map ν on the rationals by and extend it over the irrationals by setting . We prove that, under the iterates of this map, all rational points return to themselves with period for some ; some quadratic irrational points eventually get to rational points; other irrational points do not eventually return to themselves but return periodically to their -neighborhoods, however small is.
Accounting reform and value relevance of financial reporting from non-financial listed firms on the Vietnam stock market

Abstract The research questions that are solved in this paper are as follows: (1) how the value relevance of accounting information evolved from a developing country perspective and (2) how the accounting reform impacts on value relevance. This study aims to assess the value relevance of accounting information released by non-financial firms listed on the Vietnam stock exchange for the period of 2010–2020. Our paper will contribute more supportive empirical evidence about the usefulness of value relevance research through doing research in Vietnam as a developing country, where experiences switching from a planned economy to an open economy and the regulations change continuously. We provide useful literature overview and important implications for both equity investors and standard setters. Based on usefulness information theory and asymmetric information theory, research hypotheses are designed. The study then uses the Ohlson price model to test these hypotheses. In the model, the explanatory variables, namely, book value per share, earnings per share, and share prices as independent variable, are examined; panel corrected standard error estimator (PCSE) is applied. The findings reveal that both earnings and book value of equity exhibit a positive and significant effect on stock prices. Earnings explain the higher variation in stock market values on the Vietnam Stock Exchange compared to book value of equity. The study, however, finds a decreasing trend in the change of the value relevance over the period 2010–2020 and accounting reforms in 2014 did not improve the value relevance.
The cerebral cortex: An evolutionary breakthrough

Living systems have to establish models of the world in which they evolve in order to be able to predict the outcome of actions and to thereby assure survival and reproduction. Establishing a good model of the world requires the detection of relevant and consistent relations between features of the environment and the efficient storage of these relations (rules). The simplest solution, found in virtually all neuronal systems, are relation encoding feed-forward circuits. Neurons tuned to respond to particular features of the environment converge on common target cells and these respond selectively to particular conjunctions of features provided that the gain of the input connections to these conjunction specific neurons are appropriately adjusted (Barlow, 1972). A particular relation among features gets encoded in the discharge rate of a neuron responding selectively to this relation. Because this neuron encodes always the same relation, one talks about a “labelled line code”. In order to evaluate and encode combinations of relations (relations of higher order) this process of input recombination and gain adjustment is iterated across successive layers. This basic principle for the evaluation, encoding and classification of relational constructs has been implemented in numerous versions of artificial neuronal networks (Rosenblatt, 1958; Hopfield, 1987; DiCarlo and Cox, 2007; LeCun et al., 2015). The highly successful recent developments in the field of “deep learning” (LeCun et al., 2015), capitalize on the iteration of this principle in large multilayer architectures. As far as feed-forward connections are concerned, these artificial multilayer systems resemble the organization of sensory systems in the brain. Marked differences exist, however, with respect to other essential features. Feedback or recurrent lateral connections are prominent in brains (Markov et al., 2014; Bastos et al., 2015) but implementation of these architectural features is still rare in artificial systems. Moreover, the training mechanism used in technical systems for the supervised adjustment of the synaptic gain of connections, the so called “back-propagation algorithm” is biologically implausible and differs from both unsupervised and supervised learning mechanisms implemented in brains (Feldman, 2012; Singer, 2016).
Application of a Graph Model to HypertextQuerying

We present a model for data organized as graphs with typed nodes and edges. Regular expressions over the types of the node and edge labels are used to select walks in the hypertext graph. An outline of the application of this model and its query language to the implementation of hypertext documents is given by using an extended example of a travel agency application.
Effectiveness of Cognitive Orientation to Daily Occupation Performance (Co-Op) Approach in Order to Improve Instrumental Activity of Daily Living (IADL) Skills in Dementia

The main purpose of this study is to determine the effect of Cognitive orientation to activity living of daily occupation Performance (CO-OP) to improve IADL skills among Geriatric patients with dementia. The study was done using a quasi-experimental design and convenient sampling technique was adapted to select the patient with dementia at a mild level in the age group of 60 to 70 years. Totally thirty patients were selected and divided into 15 in the control group and 15 in the experimental group. The experimental group underwent cognitive orientation to daily occupational performance (CO-OP) to improve IADL skills and the control group underwent conventional occupational therapy to improve handwriting. Total intervention is given for 3 months consisting of 30minutes sessions for each 30 sessions, weekly 3 days. To measure IADL for old age people with dementia, Lawton Brody IADL scale has been used. The statistical analysis showed significant changes in the experimental group after CO-OP intervention. Analysis revealed that, Occupational Therapist can provide CO-OP-based activities for the geriatric condition with dementia during the instruction and treatment of IADL, which helps to conduct better results of IADL.
Associations Between Biofuel Production and Sustainable Development Goals 2, 6, 7, 13, and 15

Total biofuel demand in areas like transport and energy is set to grow by almost 30% annually to reach 50 billion gallons by 2026. Biofuels' association with sustainable development has been a subject of discussion among researchers and policymakers. Even the most robust studies on this topic examine only a small number of sustainable development indicators across a limited number of countries. This study analyzes whether biofuel production could advance, impede, or have no association with targets of Sustainable Development Goals (SDG) 2, 6, 7, 13, and 15, which relate to biofuel production on a global scale. Correlations between ethanol, biodiesel, and total biofuel production datasets and 23 indicators of the selected targets in 34 of the world's largest biofuel-producing countries for 2009–2019 were compiled and analyzed in the context of the existing literature. Findings suggest that biofuel production has varying associations with SDG targets.
Multicomponent seismic reconstruction via modified inner product operator and weighted k-SVD

In this abstract, we introduce a novel algorithm to reconstruct the missing multicomponent seismic data. This method belongs to the sparse denoising framework of machine learning and the key points are defining a modified inner product operator for the Orthogonal Matching Pursuit (OMP) and scaling the missing seismic data with a weighted operator while the missing data can be treated as a special kind of nonhomogeneous noise. The corresponding functions of these two processes are building the relationship of each component and uniforming the deviation of the noise, respectively. Two examples, a synthetic multicomponent data reconstruction and a color pictrue inpainting, demonstrate that the new method is feasible and show its potential to deal with real data.
A Combined Entropy and Output-based Adjoint Approach for Mesh Refinement and Error Estimation

This paper presents a strategy for mesh refinement driven by a new indicator that combines two previously-investigated indicators: one based on a user-specified engineering output such as drag or lift coefficient, and the other based on entropy variables. Using the entropy-variable indicator to adapt a mesh is computationally advantageous since it does not require the solution of an auxiliary adjoint equation, which for unsteady problems is particularly costly. However, the entropy-variable indicator targets any region of the domain where spurious entropy is generated, regardless of whether or not this region affects an engineering output of interest. On the other hand, an indicator computed from an engineering output generally targets only those regions important for the chosen output, though it is more computationally taxing because of the required adjoint solution. Approximations in the adjoint calculation reduce this cost, at the expense of indicator accuracy. In combining these indicators, our objective is to maintain the low cost of approximate adjoint solutions while achieving improved indicator accuracy from the entropy variables. We demonstrate the potential for this method through several simulations governed by the compressible Navier-Stokes equations using both hanging-node refinement and MOESS. In general, MOESS will provide more optimal meshes, but often it is not a viable option. This demonstrates the need to
Numerical prediction of pressure loss of fluid in a T-junction

This work presents a prediction of pressure loss of fluid with turbulent incompressible flow through a 90° tee junction was carried out and compared with analytical and experimental results. One turbulence model was used in the numerical simulations: k-ε model for two different values of area ratio between the main pipe and the branch pipe were 1.0 and 4.0, and flow rate ratios. The continuity, momentum and energy equations were discredited by means of a finite volume technique and the SIMPLE algorithm scheme was applied to link the pressure and velocity fields inside the domain. A three dimensional steady state flow was solving by using CFX 5 code ANSYS FLUENT13. The effect of the flow rate ratio q (ratio between the flow rate in the branch and outlet pipes) on pressure drop and velocity profile was predicted at different Reynolds numbers. The results show that increasing the flow rate ratio the pressure and total energy losses increase because the presence of recirculation and the strong streamline curvature. Copyright © 2013 International Energy and Environment Foundation All rights reserved.
Emergency Blended Synchronous Learning Model during the COVID-19 Pandemic: A Qualitative Analysis

Abstract The COVID-19 pandemic impacted various aspects of our lives causing economic, social and psychological challenges all around the world. A sector that immediately responded to this unprecedented change was education, which migrated to online platforms. This study explored the attitudes, perceptions, and experiences of a group of students participating in an emergency blended synchronous learning model during the pandemic in Northern Cyprus. The study adopted a qualitative study by surveying a group of 68 undergraduate students’ experiences through analysing their reflective accounts regarding the instructional mode of education that they received over the fall academic semester in 2020. The results of the study indicated that the new mode of instruction adopted in the context of the study not only presented technological, social, and psychological challenges for the students but also provided them with various learning opportunities and options during the COVID-19 pandemic. The findings of the study offer some implications for the use of technology in education in general and language teaching in particular.
Wave-nature and intrinsic stability of emergent crystals in chiral magnets

Here, we prove that 2-D emergent crystals in helimagnets has a wave-nature. According to variational analysis, long range crystalline order requires the equilibrium magnetization in any emergent crystals to be expressed as a series of plane waves. We formulate the Fourier representation for any emergent crystals, and analytically describe within it three types of nontrivial 2-D emergent crystals in helimagnets: the skyrmion crystal (skX), the biskyrmion crystal (biskX), and the in-plane single-Q phase (IPSQ). Based on the Fourier representation, we establish the method of soft mode analysis, which determines the critical condition of intrinsic stability for any emergent crystals and their phase transition path when unstable. Using this method, we explain the robustness of skX in the temperature-magnetic field phase diagram by its intrinsic stability, and reproduce the dynamical evolution path when skX becomes unstable both at low magnetic field and high magnetic field. Our result explains well the two-phase states (skX/IPSQ and skX/ferromagnetic) appearing near the critical points in Fe0.5Co0. 5Si7 and FeGe thin films. In particular, the IPSQ-skX phase transition, explained by nonlinear spin-density-wave coupling, is found irrelevant to the local stability of an isolated skyrmion. As a result, emergent crystals should be regarded as novel magnetic states with properties go beyond their isolated emergent particles.
Precision Pulsar Timing with NASA's Deep Space Network

Abstract Millisecond pulsars (MSPs) are a class of radio pulsars with extremely stable rotation. Their excellent timing stability can be used to study a wide variety of astrophysical phenomena. In particular, a large sample of these pulsars can be used to detect low-frequency gravitational waves. We have developed a precision pulsar timing backend for the NASA Deep Space Network (DSN), which will allow the use of short gaps in tracking schedules to time pulses from an ensemble of MSPs. The DSN operates clusters of large dish antennas (up to 70-m in diameter), located roughly equidistant around the Earth, for communication and tracking of deep-space spacecraft. The backend system will be capable of removing entirely the dispersive effects of propagation of radio waves through the interstellar medium in real-time. We will describe our development work, initial results, and prospects for future observations over the next few years.
10-POINT RECOMMENDATION PACKAGE FOR EMPLOYEE MOTIVATION & PERFORMANCE IN A LEGALISED REMOTE WORKING ENVIRONMENT: A LITERATURE REVIEW

As the world went into a compelled lockdown in 2020-21as a result of the pandemic, remote working (flexibility to work from anywhere, anytime) came as a rescue for the survival of the companies and the employees during unprecedented times but it removed the boundaries between work and family, work and home, professional and personal routine etc (termed widely as ‘work boundary conflict’) which has created challenges to employees and management alike. Despite the challenges and the fading away of the pandemic in 2022-23, several companies especially the multinationals with work force across the world are considering having remote working as the norm in their ‘future of work’ model. It is in this context that ensuring motivation and productivity amongst employees in such a work-boundary conflict has become the key focus point for managements across the world. Managers need to quickly adapt to the changing work model and are in a befuddled state as to what is expected from them, how to steer their actions etc. This literature review focusses to identify the key issues faced due to teleworking (time lag in recognition, stress & anxiety for employees, social isolation, skewed appraisals, implementation barriers etc.) and comes up with a recommendation package to the management to ensure motivation and productivity amidst this paradigm shift. Key recommendations include autonomy to workers, use of modern communication tools, management by objectives, focus on emotional intelligence of employees, constant feedback to employees, re-defining training needs etc. Objective of this paper is to act as a ready reckoner for management personnel. This paper also identifies certain gaps in the past researches viz. lack of focus on industry-specific or generation specific attributes of teleworking, limited use of quantitative techniques etc. These raise certain interesting questions and opportunities for future research in this area.
Lobular Breast Carcinoma Mimicking a Primary Gastric Malignancy

Invasive lobular carcinoma (ILC) is the second most common subclass of breast cancer and adds to the breast malignancy burden in women. Studies focused on metastatic patterns of ILC have reported bone, gynecologic organs, the peritoneum, and the gastrointestinal tract as potential sites of metastasis. Metastatic spread to the stomach has been reported, but generally remains an infrequent finding. Due to vague symptomatology and the visual limitations of endoscopic examination, metastatic lesions can often mimic a primary gastric malignancy. Metastasis in the stomach can be challenging to diagnose and requires a multimodal, thorough endoscopic and immunohistochemical evaluation. It is important to distinguish the primary origin of malignant lesions as treatment can range from systemic chemotherapy to surgical resection based on the diagnosis. We present a case of an underlying ILC metastatic lesion mimicking a primary gastric adenocarcinoma.