Miracle-dz/UniFAR-FTUs huggingface dataset:
M-Centrality: identifying key nodes based on global position and local degree variation

Identifying influential nodes in a network is a major issue due to the great deal of applications concerned, such as disease spreading and rumor dynamics. That is why, a plethora of centrality measures has emerged over the years in order to rank nodes according to their topological importance in the network. Local metrics such as degree centrality make use of a very limited information and are easy to compute. Global metrics such as betweenness centrality exploit the information of the whole network structure at the cost of a very high computational complexity. Recent works have shown that combining multiple metrics is a promising strategy to quantify the node’s influential ability. Our work is in this line. In this paper, we introduce a multi-attributes centrality measure called M-Centrality that combines the information on the position of the node in the network with the local information on its nearest neighborhood. The position is measured by the K-shell decomposition, and the degree variation in the neighborhood of the node quantifies the influence of the local context. In order to examine the performances of the proposed measure, we conduct experiments on small and large scale real-world networks from the perspectives of transmission dynamics and network connectivity. According to the empirical results, the M-Centrality outperforms its alternatives in identifying both influential spreaders and nodes essential to maintain the network connectivity. In addition, its low computational complexity makes it easily applied to large scale networks.
Interference elimination in terahertz imaging based on inverse image processing

We propose a novel image restoration approach to eliminate the interference fringes in terahertz (THz) imaging. By making use of the information in the background of a THz image, inverse image processing is introduced under a priori knowledge about the degradation mechanism to suppress the interference. Based on a simulated interference image and the original THz image of a piece of paper coated with graphite powder, taken from our continuous THz imaging system, the interference fringes are successfully eliminated. The enhancement of the peak signal-to-noise ratio and structural similarity index is achieved, compared with the frequency domain filtering. Moreover, the inverse image processing is performed on a THz image of fresh rat brain tissue. It presents an excellent result with accurate information for the brain tissue after interference elimination.
Application of single photon counter module in weak signal detection for RF chip scanning using a quantum near field probe

Diamond based quantum sensing for on‐chip electromagnetic field imaging is an emerging field. While smaller diamond probes offer higher spatial resolution, their lower fluorescence intensity poses a challenge for the traditional Avalanche Photo Diode. In this work, we propose a tapered fiber diamond probe containing Nitrogen‐vacancy center and utilize a single photon counter module as the detector. A time domain differential pulse sequence is introduced to suppress noise caused by laser intensity fluctuation. By analyzing the relationship between the signal‐to‐noise ratio (SNR) and parameters of differential pulse sequence, we derived the formula of SNR and the basic cycle ( M $M$ ), and obtained the optimal SNR in the same measurement time through trade‐off between two different kinds of noise. The technique was applied to a waveguide microstrip filter chip, accurately imaging the current density distribution. Our results have potential applications in on‐chip testing and failure analysis.
Improved Nelder-Mead algorithm for output voltage control of a synchronous generator

It is known that the standard Nelder-Mead algorithm (NMA) is subject to the problem of local minimum. This paper proposes an improved Nelder-Mead algorithm (INMA) based on dynamically varying the reflection, expansion, contraction and the shrinkage coefficients, following a cosine function. Dynamical coefficients intensify the search space exploration and also increase the ability of quickly finding the optimal solution. Firstly, the effectiveness and validity of this approach are confirmed by using some benchmark functions. Secondly, the approach is also verified by applying it for tuning a Proportional–Integral–Derivative (PID) controller used to regulate the voltage of a synchronous generator (SG) under different loading conditions. The PID controller parameters are obtained by a minimisation of the quadratic output error between the reference and the SG output voltage calculated from the adopted model. A comparison is established between INMA, standard NMA, genetic algorithm (GA) and particle swarm optimisation (PSO). The Obtained results show that the INMA has a better performance compared to other algorithms.
IMAGE RETRIEVAL FOR 3D MODELLING OF ARCHITECTURE USING AI AND PHOTOGRAMMETRY

Abstract. This research is intended to provide an initial solution to the problem of finding images for processing by photogrammetry in special cases where these do not exist. An overview of existing artificial intelligence-based algorithms that enable the extension of source image dataset is reported. In particular, this research focused on the use of prompt-to-image systems for obtaining images to be used in reconstruction and then in the next step of 3D modelling. Thus, the combined use of these three techniques, AI, photogrammetry, and modelling allowed the creation of a model of a building that never existed except in the collective imagination, which is the tower of Babel. In particular, the case study chosen is the illustration in Kircher book present in the library of the Brixen seminary that is closed to the public and for which it was necessary to create a tool to enhance the value and knowledge of this heritage for external users. Therefore, the creation of an augmented reality app enabled the visualization of the model created by offering possibilities for immersive experiences and dissemination of the research to a wide audience.
: Current

The purpose of taxation is to raise revenue to pay for public goods. The question of how tax mix affects the size of the underground economy is one of the most important issues for its application to tax policy. However, the empirical evidence on this subject is limited and also somewhat ambiguous. Empirically, this study specifically aims to evaluate the effect of corporate tax rate and net taxes on the product on shadow economy growth in Nigeria during the period 1996 – 2020. It employed augmented Dickey – Fuller (ADF) and Phillips and Perron tests to check for unit roots. Thereafter, the Autoregressive Distributed Lag model was employed to determine the short- and long-run effects. The findings of this study show that corporate tax rates and net taxes on products induced much less shadow economy in the long-run while corporate tax rates have a positive impact on shadow economy in the short-run.
Gen X: The Cro-Magnon of Digital Natives

We a abstract As members of the generation that straddles the inventions of the personal computer, video games, and the Internet, Gen Xers have a unique perspective on technology—comfortable using digital tools, while being aware of their limitations. Gen X evolved from uncritical optimism about technology to a more nuanced, outcomes-based understanding of its uses and value. This progression mirrors the development of strategies employed by Gen X nonprofit leaders as they have sought to apply technology solutions to challenges of aging and other social purposes. The work of Older Adults Technology Services (OATS) is highlighted as an example of these trends. | key words : Gen X, technology, technology and aging, OATS
Selective surface smoothening of 3D micro-optical elements

We have established a non-contact polishing process for thermoplastic, polymeric microlenses and –prisms with dimensions of up to 50 μm, including sharp convex tips and rims with sub-μm details. The required 3D master structures were fabricated using direct laser-writing lithography with two-photon absorption. Master structures were replicated into poly(methyl methacrylate) through a poly(dimethyl siloxane) intermediate copying step and exposed with 172 nm UV light. Due to the reduction of glass transition temperature in a surface-confined layer, roughness in the range of more than 100 nm could subsequently be smoothed-out to below 10 nm by annealing the surface by heating.
Methods of spectral density estimation for airfield pavements

The paper gives an analysis of methods for estimating the spectral density of airfield pavements. The spectral density of the surface of the airfield pavement is the main criterion for estimating the roughness. This characteristic is used for the dynamic strength calculation of pavement. The paper presents analysis and comparison of the most popular methods for estimating the spectral density, and considers Burg's perspective parametric method, previously not used in aerodrome design practice.
A delay-range-dependent stabilization of uncertain singular time-delay systems with one-sided Lipschitz nonlinearities subject to input saturation

This paper investigates the problem of delay-range-dependent robust stabilization for nonlinear singular systems with time-delay subject to some constraints. In practice, the control problem of dynamic systems faces a variety of constraints such as: presence of input saturation; one-sided Lipschitz nonlinearities; model uncertainties; and time-varying delay. The interaction of both algebraic and differential equations in singular systems with delayed state variables adds some complexities and difficulties in the procedure of analysis and design of singular time-delay systems. Moreover, the one-sided Lipschitz nonlinearity condition, which is less conservative than the well-known Lipschitz condition, is considered while the presence of actuator saturation also imposes additional complexity in the procedure of controller design. In this regard, by choosing an appropriate Lyapunov–Krasovskii functional with applying the free-weighting matrices approach, the sufficient conditions are derived as linear matrix inequalities which guarantee the asymptotic stability of the resulting uncertain closed-loop singular system. Finally, computer simulations are provided to verify the theoretical results.
Identification of N-Acyl Hydrazones as New Non-Zinc-Binding MMP-13 Inhibitors by Structure-Based Virtual Screening Studies and Chemical Optimization

Matrix metalloproteinase 13 plays a central role in osteoarthritis (OA), as its overexpression induces an excessive breakdown of collagen that results in an imbalance between collagen synthesis and degradation in the joint, leading to progressive articular cartilage degradation. Therefore, MMP-13 has been proposed as a key therapeutic target for OA. Here we have developed a virtual screening workflow aimed at identifying selective non-zinc-binding MMP-13 inhibitors by targeting the deep S1′ pocket of MMP-13. Three ligands were found to inhibit MMP-13 in the µM range, and one of these showed selectivity over other MMPs. A structure-based analysis guided the chemical optimization of the hit compound, leading to the obtaining of a new N-acyl hydrazone-based derivative with improved inhibitory activity and selectivity for the target enzyme.
Assessment of anthropometric measurements and body composition of selected beginner South West Ethiopian soccer players

This study attempts to determine the anthropometric measurement and body composition quality of south west Ethiopian beginner soccer players’ considering playing position. In so doing, three soccer teams were selected by employing a cluster sampling techniques that consisted of a total forty eight players, and those who attended less than seventeen years of age. Depending on their playing position players were classified in to four categories as goalkeeper (GK), defense (DF), midfielder (MF) and striker (SK). To achieve the stated purpose, cross sectional descriptive research design was employed. The international Society for the Advancement of Kinanthropometry (ISAK) protocol was considered to measure the following anthropometric variables : weight measurements, n_/1; girths, n_/10; lengths, n_/6, skin folds, n_/2;(body fat% and lean mass)and body mass index n-1.The data was analyzed by SPSS version 19, moreover, the level of significance was set at (P < 0.05). The finding of this study depicted that significant difference of anthropometric measurement among players based on their playing position. DF players possess larger lower limp, interior body and upper limp girth. Whereas, SK player exhibited smaller lower limp, interior body and upper limp girth. In lower limp length GK and SK have larger lower limp length but in upper limp GK possess greater. GK (176.6cm) are taller than all other players of team while MF (170cm) is sorter in overall body height. There is no significant difference in BMI(GK 19, DF 20.4, MD 18 and SK 18), lean mass and body fat% But GK(64.2 kg) are heavier while SK (58kg) lighter in their weight. Whereas, DF (60.7 kg) and MF (60 kg) players are average in their weight. It is found that mean anthropometric measurement of south west Ethiopian youth soccer players was slightly lower than that of top world class players of similar age group players. It is also obtained that lack of significant differences among playing positions reflect on BMI, lean mass and body fat% indicate as coaches are not give playing position specific training for players. In this study, within-position variation was quite large in some cases, which could indicate that a team that does not have the opportunity to hand-pick players, based on anthropometric characteristics, may be at a disadvantage therefore the respective soccer coaches, sport science professionals should take into account the principle of morphological optimization in talent detection, identification and selecting soccer players.
Identification of TLE Focus from EEG Signals by Using Deep Learning Approach

Temporal lobe epilepsy, a neurological disease that causes seizures as a result of excessive neural activities in the brain, is the most common type of focal seizure, accounting for 30–35% of all epilepsies. Detection of epilepsy and localization of epileptic focus are essential for treatment planning and epilepsy surgery. Currently, epileptic focus is decided by expert physician by examining the EEG records and determining EEG channel where epileptic patterns begins and continues intensely during seizure. Examination of long EEG recordings is very time-consuming process, requires attention and decision can vary depending on physician. In this study, to assist physicians in detecting epileptic focus side from EEG recordings, a novel deep learning-based computer-aided diagnosis system is presented. In the proposed framework, ictal epochs are detected using long short-term memory network fed with EEG subband features obtained by discrete wavelet transform, and then, epileptic focus identification is realized by using asymmetry score. This algorithm was tested on EEG database obtained from the Ankara University hospital. Experimental results showed ictal and interictal epochs were classified with accuracy of 86.84%, sensitivity of 86.96% and specificity of 89.68% on Ankara University hospital dataset, and 96.67% success rate was obtained on Bonn EEG dataset. In addition, epileptic focus was identified with accuracy of 96.10%, sensitivity of 100% and specificity of 93.80% by using the proposed deep learning-based algorithm and university hospital dataset. These results showed that proposed method can be used properly in clinical applications, epilepsy treatment and surgical planning as a medical decision support system.
Myopic Best-Response Learning in Large-Scale Games

This dissertation studies multi-agent algorithms for learning Nash equilibrium strategies in games with many players. We focus our study on a set of learning dynamics in which agents seek to myopically optimize their next-stage utility given some forecast of opponent behavior; i.e., players act according to myopic best response dynamics. The prototypical algorithm in this class is the well-known fictitious play (FP) algorithm. FP dynamics are intuitively simple and can be seen as the \natural" learning dynamics associated with the Nash equilibrium concept. Accordingly, FP has received extensive study over the years and has been used in a variety of applications. Our contributions may be divided into two main research areas. First, we study fundamental properties of myopic best response (MBR) dynamics in large-scale games. We have three main contributions in this area. (i) We characterize the robustness of MBR dynamics to a class of perturbations common in real-world applications. (ii) We study FP dynamics in the important class of large-scale games known as potential games. We show that for almost all potential games and for almost all initial conditions, FP converges to a pure-strategy (deterministic) equilibrium. (iii) We develop tools to characterize the rate of convergence of MBR algorithms in potential games. In particular, we show that the rate of convergence of FP is \almost always" exponential in potential games. Our second research focus concerns implementation of MBR learning dynamics in large-scale games. MBR dynamics can be shown, theoretically, to converge to equilibrium strategies in important classes of large-scale games (e.g., potential games). However, despite theoretical convergence guarantees, MBR dynamics can be extremely impractical to implement in large games due to demanding requirements in terms of computational capacity, information overhead, communication infrastructure, and global synchronization. Using the aforementioned robustness result, we study practical methods to mitigate each of these issues. We place a special emphasis on studying algorithms that may be implemented in a network-based setting, i.e., a setting in which inter-agent communication is restricted to a (possibly sparse) overlaid communication graph. Within the network-based setting, we also study the use of so-called \inertia" in MBR algorithms as a tool for learning pure-strategy NE.
On Induction, Coinduction and Equality in Martin-Löf and Homotopy Type Theory

Martin Lof Type Theory, having put computation at the center of logical reasoning, has been shown to be an effective foundation for proof assistants, with applications both in computer science and constructive mathematics. One ambition though is for MLTT to also double as a practical general purpose programming language. Datatypes in type theory come with an induction or coinduction principle which gives a precise and concise specification of their interface. However, such principles can interfere with how we would like to express our programs. In this thesis, we investigate more flexible alternatives to direct uses of the (co)induction principles. As a first contribution, we consider the n-truncation of a type in Homo- topy Type Theory. We derive in HoTT an eliminator into (n+1)-truncated types instead of n-truncated ones, assuming extra conditions on the underlying function. As a second contribution, we improve on type-based criteria for termination and productivity. By augmenting the types with well-foundedness information, such criteria allow function definitions in a style closer to general recursion. We consider two criteria: guarded types, and sized types. Guarded types introduce a modality ”later” to guard the availability of recursive calls provided by a general fixed-point combinator. In Guarded Cu- bical Type Theory we equip the fixed-point combinator with a propositional equality to its one-step unfolding, instead of a definitional equality that would break normalization. The notion of path from Cubical Type Theory allows us to do so without losing canonicity or decidability of conversion. Sized types, on the other hand, explicitly index datatypes with size bounds on the height or depth of their elements. The sizes however can get in the way of the reasoning principles we expect. Our approach is to introduce new quantifiers for ”irrelevant” size quantification. We present a type theory with parametric quantifiers where irrelevance arises as a “free theorem”. We also develop a conversion checking algorithm for a more specific theory where the new quantifiers are restricted to sizes. Finally, our third contribution is about the operational semantics of type theory. For the extensions above we would like to devise a practical conversion checking algorithm suitable for integration into a proof assistant. We formal- ized the correctness of such an algorithm for a small but challenging core calculus, proving that conversion is decidable. We expect this development to form a good basis to verify more complex theories. The ideas discussed in this thesis are already influencing the development of Agda, a proof assistant based on type theory.
Adaptive inner iteration processes in pressure-based method for viscous compressible flows

Abstract In some pressure-based methods, inner iteration processes are introduced to achieve efficient solutions. However, number of the inner iteration is fixed as 2 or 4 for different computations. In this paper, a mechanism is proposed to control inner iteration processes to make the number of inner iterations vary adaptively with different problems. The adaptive inner iteration processes are used in viscous compressible flows. Results reveal that by introducing inner iteration processes, computational efficiency is highly improved compared with that of the solution without inner iteration. In addition, adaptive inner iteration solutions have better robustness than fixed inner iteration solutions.
Validity, Reliability, and Sensitivity of Mobile Applications to Assess Change of Direction Speed

This study aimed to assess the validity, reliability, and sensitivity of mobile applications for assessing change-of-direction speed (CODS) performance. Thirty college athletes performed two Illinois CODS tests during one session. Assessments were carried out simultaneously using six devices (the CODTimer app, Seconds Count app, StopwatchCamera app, two analog stopwatches, and timing gates). Validity analyses included Pearson's product-moment correlation analysis, a linear regression model, and Bland-Altman plots. Reliability analyses included the intraclass correlation coefficient (ICC), the coefficient of variation (CV%), and the paired-sample t test. Sensitivity analyses included the typical error and smallest worthwhile change (SWC). The results showed that validity, reliability, and sensitivity values were higher for the CODTimer app (r = 0.99, R2 = 0.99, mean bias = −0.03 ± 0.10, CV% = 3.21, ICC = 0.89, SWC rating: good, p = 0.84) and the Seconds Count app (r = 0.99, R2 = 0.99, mean bias = −0.03 ± 0.08, CV% = 3.28, ICC = 0.88, SWC rating: good, p = 0.84) relative to the StopwatchCamera app (r = 0.98, R2 = 0.97, mean bias = −0.11 ± 0.22, CV% = 3.43, ICC = 0.86, SWC rating: marginal, p = 0.10), Analog Stopwatch 1 (r = 0.98, R2 = 0.96, mean bias = −0.09 ± 0.42, CV% = 2.95, ICC = 0.90, SWC rating: good, p = 0.91), and Analog Stopwatch 2 (r = 0.99, R2 = 0.97, mean bias = −0.12 ± 0.88, CV% = 3.51, ICC = 0.87, SWC rating: marginal, p = 0.96). In conclusion, compared to timing gates, the CODTimer app and Seconds Count app provided lower measurement bias and higher sensitivity for assessing CODS performance.