Miracle-dz/UniFAR-FTUs huggingface dataset:
Financial competitiveness of firms: A study of the Indian Automobile Industry

Competitiveness is the ability of firms, to perform better than rivals, where performance is dependent on both financial and non-financial conditions of the firm. This study empirically examined the financial competitiveness of Indian automobile companies, for the period from 2001–02 to 2015–16. Out of 42 variables, 13 factors were extracted and these thirteen factors, put together, explaind 76.122 per cent of the total variance. Factor analysis was applied, to identify the factors that significantly contributed to the financial competitiveness. The result indicated that among the three sectors, passenger cars and multiutility vehicles sector were most dominating, followed by two and three wheelers sector and commercial vehicles sector. The commercial vehicles sector was obviously ruled by Eicher Motors Ltd whereas passenger cars and multi-utility vehicles sector was dominated by Honda Siel Cars India Limited and Maruti Udyog Limited. The result of this study would help consumers, to judge the competitive performance of these firms, from the product quality and investment point of view.
Klasifikasi Citra X-Ray Diagnosis Tuberkulosis Berbasis Fitur Statistis

Tuberkulosis merupakan salah satu penyebab kematian manusia. Hasil pemeriksaan x-ray diagnosis tuberkulosis dapat dijadikan objek pada proses ekstraksi ciri yang merupakan suatu tahapan dalam mengekstrak ciri/ informasi dari objek yang terdapat pada suatu citra diagnosis tuberkulosis. Pada penelitian ini digunakan metode ekstraksi ciri citra berbasis tekstur statistis orde satu (histogram), orde kedua berbasis Gray-Level Co-occurrence Matrix (GLCM), serta Principle Component Analysis (PCA). Data penelitian diperoleh dari RS Dr. Sardjito Yogyakarta sebanyak 33 citra digital x-ray pasien diagnosis tuberkulosis Tahun 2012 masing-masing 6 citra PA (Postero-Anterior) normal, 19 citra abnormal, 4 citra AP (Antero-Posterior) normal, dan 4 citra AP abnormal. Penelitian ini bertujuan mencari ciri terbaik yang terkandung pada citra x-ray diagnosis tuberkulosis menggunakan analisis tekstur statistis yang diperoleh dari fitur ciri yang terdapat pada metode ekstraksi ciri berbasis tekstur. Fitur ciri yang teridentifikasi antara lain: varians, std deviasi, skewness, kurtosis, contrast dan energy. Klasifikasi menggunakan data input berupa 33 data uji yang dibangun dengan metode Multi Layer Perceptron (MLP), sedang output berupa citra normal dan citra abnormal. Hasil penelitian menunjukkan bahwa akurasi klasifikasi menggunakan metode Histogram (81,81%), metode GLCM (96,96%), metode PCA (81,82%), dan metode kombinasi Histogram GLCM (100%).
Privacy-Preserving Authentication Protocols for IoT Devices Using the SiRF PUF

Authentication between IoT devices is important for maintaining security, trust and data integrity in an edge device ecosystem. The low-power, reduced computing capacity of the IoT device makes public-private, certificate-based forms of authentication impractical, while other lighter-weight, symmetric cryptography-based approaches, such as message authentication codes, are easy to spoof in unsupervised environments where adversaries have direct physical access to the device. Such environments are better served by security primitives rooted in the hardware with capabilities exceeding those available in cryptography-only frameworks. A key foundational hardware security primitive is the physical unclonable function or PUF. PUFs are well known for removing the need to store secrets in secure non-volatile memories, and for providing very large sets of authentication credentials. In this article, we describe two PUF-based mutual authentication protocols rooted in the entropy provided by a strong PUF. The security properties of the authentication protocols, called COBRA and PARCE, are evaluated in hardware experiments on SoC-based FPGAs, and under extended industrial-standard operating conditions. A codesign-based system architecture is presented in which the SiRF PUF and core authentication functions are implemented in the programmable logic as a secure enclave, while network and database operations are implemented in software on an embedded microprocessor.
The use of GIS tools for sustainable management of municipal housing stock in Poland

The management of social housing stock by balancing the housing needs of local communities plays a crucial role in the sustainable development of urban municipalities. In the present study, the management of municipal housing stock (MHS) was evaluated for compliance with sustainability principles using GIS tools. Special attention was given to the spatial, technical, tenure-related and organisational aspects of sustainable MHS management. The importance of spatial data and spatial analyses for assessments of MHS management was demonstrated. The research was based on a case study analysing MHS management in the Olsztyn municipality.
Prediction of ultimate tensile strength of friction stir welding joint using deep learning-based-multilayer perceptron and long short term memory networks

Abstract Friction stir welding (FSW) is a solid-state joining technique where the joint strength is mainly influenced by three process parameters, namely, spindle speed (N), welding speed (V), and plunge force (Fz). The modelling of complex relationships between the process parameters and joint strength requires many experiments, which is a challenging, time-consuming, and non-economical affair. To tackle this problem, computational mathematical models such as deep learning (DL) can be employed to predict the joint strength reliably. In this paper, DL techniques, namely, deep multilayer perceptron (DMLP) and long short-term memory (LSTM) networks have been proposed for such a purpose. The DL networks were first trained with the FSW experimental data and then, the pre-trained models were used for predicting the weld strength. It was found that the DMLP and LSTM models provided lower prediction errors, which are RMSE of 3.30 and 7.63, respectively, and can be effectively utilized for determining weld quality. The proposed DL-based techniques were further compared with the traditional models – the shallow artificial neural network (SANN) model having an RMSE of 27.11 and the ANFIS model having an RMSE of 5.31. DMLP was found to be superior in determining the weld strength most accurately.
Wind turbine generator state monitoring based on flower pollination algorithm and neural network

In this paper, new technologies and algorithms such as machine learning and swarm intelligently optimize algorithm are introduced into the condition monitoring of wind turbines, and the method of temperature trend analysis is used to monitor the condition of wind turbines. Firstly, in order to solve the problems of slow convergence speed and easy to fall into local optimum during the training of wavelet neural network, an improved method of Flower Pollination Algorithm optimized wavelet neural network is proposed, and the temperature model of wind turbine is established, and the model is used to carry out temperature prediction, and then the state of the wind turbine is obtained by analyzing the temperature residual, so as to achieve the purpose of online temperature monitoring.
Presidential Attack Risk: The Impact of Trump's Tweets on Financial Markets

The prevalence of social media has inspired multiple researchers to investigate the value of information on various platforms. However, most studies focus on integrating individual views (the wisdom of the crowd), and few studies investigate just one person's effect. To close this gap, this article investigates the impact of Trump's tweets on stock markets. Based on intraday stock market data, this study uses an event study to test the immediate reaction of the stock markets in both the Chinese and U.S. markets. Next, with ordinary least squares (OLS) regression, this study testes the effect of tweets' content features on the returns and volatility of the Chinese and U.S. indices. The results show that Trump's tweets impacted the financial market, especially the returns of the U.S. stock market during the COVID-19 pandemic. With additional analyses based on industry indices and time frequencies, the researchers found that Trump's sentiment on Twitter affected the Chinese financial industry during the trade war and impacted the Chinese pharmaceutical industry during the pandemic.
Efeito do tempo e áreas de uso agrícola no processo de decomposição de celulose

A conservação do solo em áreas agrícolas pode contribuir para a produtividade e produção sustentável. Nosso objetivo foi avaliar a taxa de perda de massa no processo de decomposição da celulose em sistemas agrícolas, em diferentes épocas de cultivo (mais de 30 anos vs. menos de 10 anos) considerando floresta em unidade de conservação e floresta antropizadas próxima, como sistemas de controle. Foram utilizados sacos de duas malhas (0,5 mm vs. 10 mm) na superfície do solo por 30, 60 e 90 dias em todos os sistemas (agrícolas vs. floresta antrópica e floresta em unidade de conservação). A decomposição diminuiu em um quarto (variação o tamanho do efeito ± -22 a -26%) nos sistemas agrícolas estudados em comparação com os sistemas florestais, evidenciando o processo de decomposição como um bom indicador ecológico. A alta riqueza de espécies e estratos vegetais em sistemas florestais aumentaram a perda de massa em comparação com sistemas agrícolas. A diferença entre 10 e 30 anos nos sistemas agrícolas variou de 3% (na decomposição total) a 7% (na decomposição microbiana), diminuindo em sistemas de 30 anos de uso. Além disso, percebemos que fragmentos florestais próximos a sistemas agrícolas podem funcionar como refúgios para macrofauna detritívora e assim ajudar a preservar este serviço ecossistêmico em áreas produtivas.
Multi-Environment Training Against Reward Poisoning Attacks on Deep Reinforcement Learning

: Our research tackles the critical challenge of defending against poisoning attacks in deep reinforcement learning, which have significant cybersecurity implications. These attacks involve subtle manipulation of rewards, leading the attacker’s policy to appear optimal under the poisoned rewards, thus compromising the integrity and reliability of such systems. Our goal is to develop robust agents resistant to manipulations. We propose an optimization framework with a multi-environment setting, which enhances resilience and generalization. By exposing agents to diverse environments, we mitigate the impact of poisoning attacks. Additionally, we employ a variance-based method to detect reward manipulation effectively. Leveraging this information, our optimization framework derives a defense policy that fortifies agents against attacks, bolstering their resistance to reward manipulation.
A Tradeoff in Econometrics

markdownabstractThe scientific method dictates how researchers can make claims about the underlying truth by confronting prior hypotheses to a random sample of data. A fundamental task of Statistics is to allow researchers to balance their hypotheses with data-optimized results. This thesis shows that current statistical approaches make it difficult to control such a tradeoff and proposes an intuitive alternative. In the process, it will be explained how the famous coefficient of determination R2 can be unpacked, so that the individual relevance of deviating from each hypothesis can be quantified. Next to presenting novel techniques for estimating and selecting parameters of the linear regression model, a convenient tradeoff is defined for weighing observations and for the search of an optimal configuration.
First submillimeter observation of CO in the stratosphere of Uranus with Herschel-HIFI

Oxygen-rich deep interiors of the Giant Planets [1] cannot explain the discovery of water vapor and carbon dioxide in the stratospheres of the Giant Planets by [2] because these species are trapped by condensation around their tropopause levels (except CO 2 in Jupiter and Saturn). Therefore, several sources in the direct or far environment of the Giant Planets have been proposed: icy rings and/or satellites [3], interplanetary dust particles [4] and large comet impacts [5]. Infrared Space Observatory (ISO), Cassini, Odin and Herschel observations have proven that the Jovian stratospheric water and carbon dioxide originate from the Shoemaker-Levy 9 comet impacts in July 1994 [6, 7], while Herschel has recently shown the external flux of water at Saturn and Titan is most likely due to the Enceladus geysers and the water torus they feed [8, 9]. As for carbon monoxide (CO), the emerging picture seems to show more uniformity for its sources. Because CO does not condense at the tropopauses of Giant Planets, oxygen-rich interiors are a valid source. An internal component has indeed been observed in the vertical profile of CO in Jupiter by [10] and in Neptune by [11], while an upper limit has been set on its magnitude by [12] for Saturn. In addition to interiors, large comets seem to be the dominant external source of CO in the Giant Planets, as shown by various studies: [10] and [13] for Jupiter, [14] for Saturn and [15] for Neptune. Despite its first detection almost a decade ago by [16], the situation has remained unclear for Uranus ever since. The (sub)millimeter domain with the use of heterodyne spectroscopy has long been considered as promising to determine the vertical profile of CO, and thus its origin, in Uranus (e.g., [17]). However, attempts made to detect the molecule have failed so far in this spectral range, leading only to upper limits [18]. In this paper, we present the first submillimeter detection of CO in Uranus carried out with the HIFI instrument [19] onboard the Herschel Space Observatory [20] in 2011-2012. Using a simple transport model, we review the various possible sources of CO (internal and external) and constrain their magnitude. For instance, we derive an upper limit for the internal source of CO. And with the thermochemical model of [21], adapted to the interior of Uranus, we derive an upper limit on its deep O/H ratio from it.
Opportunities and challenges of quality engineering for additive manufacturing

Abstract Additive manufacturing (AM), commonly known as three-dimensional printing, is widely recognized as a disruptive technology, and it has the potential to fundamentally change the nature of future manufacturing. Through building products layer by layer, AM represents a paradigm shift in manufacturing, with many industrial applications. It enables production of huge varieties of customized products with considerable geometric complexity, extended capabilities, and functional performances. Despite tremendous enthusiasm AM faces major research challenges for widespread adoption of this innovative technology. Specifically, addressing the unique challenges associated with quality engineering of AM processes is crucial to the eventual success of AM. This article presents an overview of quality-related issues for AM processes and products, focusing on opportunities and challenges in quality inspection, monitoring, control, optimization, and transfer learning as well as on building quality into the product through design.
Variable Daily Air Temperature Model for Analysis and Design

An analytical model, TA(t), for the observed outside air temperature change, Ta(t), with time is developed using two components: one for the variation caused by the Earth’s movement, plus any other quasi-stationary thermodynamic effects due to industrialization; and one for the random variation caused by stochastic and/or chaotic, local environmental changes. The first component, TR(t), describes a regular trend, expressed by periodic functions of time and constants unchanged with time. The second component, TS, is a random, stochastic variation. For the observed outside air temperature, the analytical model of TA(t)=TR(t) +TS is such as to give a statistically best approximation for the observed time period with = min. Several versions for the TR(t) functions are defined and tested in the study for an example location for 20 years. The best model for TR(t) t is found as a linear function with time plus a variable-coefficient Fourier series with linearly changing amplitude with time. It is found that the final analytical temperature, TA(t), can be used not only to represent the historical daily mean temperature but also to predict the future daily mean temperature at the given location. The upper and lower boundaries give safety limits for the temperature prediction. The stochastic component identified in the model is stable and stationary. The method of model identification for TA(t) can be used for determining input temperature functions for supporting engineering design; or for an unbiased scientific inquiry of temperature change with time in climate studies.
ANALYSIS OF FINANCIAL INCLUSION THROUGH FINTECH INNOVATION

This study aims to explain the intention in using mobile money through the application of the Unified Theory of Acceptance and Use of Technology 2 (UTAUT 2) model. This research was conducted throughout the Solo Raya community. The number of samples in this study were 100 respondents. The data collection method used non-probability sampling with purposive sampling in which the researcher determines sampling by providing specific criteria. The research data was obtained through a survey method with a questionnaire technique. The analytical tool used to test the hypothesis was path analysis with the help of SmartPLS 3.0. The results show that the variable performance expectations, effort expectations, social influence, hedonic motivation, and facility conditions have no significant effect on the intention to use mobile money. Price value and habit variables have a positive and significant effect on the intention in using mobile money.
Order parameter and detection for a finite ensemble of crystallized one-dimensional dipolar bosons in optical lattices

We explore the ground-state properties of bosons with dipole-dipole interactions in a one-dimensional optical lattice. Remarkably, a crystallization process happens for strong dipolar interactions. Herein, we provide a detailed characterization and a way to measure the resulting crystal phase. Using the eigenvalues of the reduced one-body density matrix we define an order parameter that yields a phase diagram in agreement with an analysis of the density and two-body density. We demonstrate that the phase diagram can be detected experimentally using the variance of single-shot measurements.
SYNTHESIS OF THE SOUTHEAST ATMOSPHERE STUDIES: Investigating Fundamental Atmospheric Chemistry Questions

AbstractThe Southeast Atmosphere Studies (SAS), which included the Southern Oxidant and Aerosol Study (SOAS); the Southeast Nexus (SENEX) study; and the Nitrogen, Oxidants, Mercury and Aerosols: Distributions, Sources and Sinks (NOMADSS) study, was deployed in the field from 1 June to 15 July 2013 in the central and eastern United States, and it overlapped with and was complemented by the Studies of Emissions, Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys (SEAC4RS) campaign. SAS investigated atmospheric chemistry and the associated air quality and climate-relevant particle properties. Coordinated measurements from six ground sites, four aircraft, tall towers, balloon-borne sondes, existing surface networks, and satellites provide in situ and remotely sensed data on trace-gas composition, aerosol physicochemical properties, and local and synoptic meteorology. Selected SAS findings indicate 1) dramatically reduced NOx concentrations have altered ozone production regimes; 2) indica.
2D to 3D conversion using a pair of images “KLT algorithm as a case of development and study”

, This paper investigate how motion between two images is affecting the reconstruction process of the KLT algorithm which we used to convert 2D images to 3D model. The reconstruction process is carried out using a single calibrated camera and an algorithm based on only two views of a scene, the SFM technique based on detecting the correspondence points between the two images, and the Epipolar inliers. Using the KLT algorithm with structure from motion method shows the incompatibility of it with the widely-spaced images. Also, the ability of reducing the rate of reprojection error by removing the images that have the biggest rate of error. The experimental results are consisting from three stages. The first stage is done by using a scene with soft surfaces, the performance of the algorithm shows some deficiencies with the soft surfaces which are have few details. The second stage is done by using different scene with objects which have more details and rough surfaces, the algorithm results become more accurate than the first scene. The third stage is done by using the first scene of the first stage but after adding more details for surface of the ball to motivate the algorithm to detect more points, the results become more accurate than the results of the first stage. The experiments are showing the performance of the algorithm with different scenes and demonstrate the way of improving the algorithm where it found more points from images, so it builds more accurate 3D model.
Fractional-Order Nonlinear Systems: Modeling, Analysis and Simulation

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Robust finite‐time consensus formation control for multiple nonholonomic wheeled mobile robots via output feedback

The finite‐time formation control for multiple nonholonomic wheeled mobile robots with a leader‐following structure is studied. Different from the existing results, the considered mobile robot has the following features: (i) a higher‐order dynamic model, (ii) the robot's velocities cannot be measured, and (iii) there are external disturbances. To solve the problem, a finite‐time consensus formation control algorithm via output feedback is explicitly given. At the first step, some finite‐time convergent observers are skillfully constructed to estimate both the unknown velocity information and the disturbance in finite time by imposing certain assumptions on the disturbances. Then, on the basis of the integral sliding‐mode control method, a disturbance observer‐based finite‐time output feedback controller is developed. Rigorous proof shows that the finite‐time formation can be achieved in finite time. An example is finally given to verify the efficiency of the proposed method.