Mathematics, Volume 9, Issue 9 (May-1 2021) – 157 articles

We considered an hybridizable discontinuous Galerkin (HDG) method for discrete elliptic PDEs with random coefficients. By an approach of projection, we obtained the error analysis under the assumption that $a(\omega ,x)$ is uniformly bounded. Together with the HDG method, we applied a multilevel Monte Carlo (MLMC) method (MLMC-HDG method) to simulate the random elliptic PDEs. We derived the overall convergence rate and total computation cost estimate. Finally, some numerical experiments are presented to confirm the theoretical results. Full article

In the field of mechanical engineering, conveyors and moving belts are frequently used machine parts. In many working regimes, they are subjected to sudden loading, which can be a source of irregular motion in the impacting bodies and undesirable behavior in the working machine. This paper deals with a mechanical model where colisions between an impact body and a moving belt take place. The impact body is constrained by a flexible rope, the upper end of which is excited by a slider in the vertical direction. The behavior of the system was investigated in terms of its dependence on the amplitude and frequency of excitation given by the movement of the slider, and the eccentricity of the center of gravity of the impact body. Outputs of the computations indicate that different combinations of the analyzed parameters lead to high complexity of the system’s movement. The bifurcation analysis shows multiple periodic areas changed by chaotic regions. The research carried out provides more details about the behavior and properties of strongly nonlinear mechanical systems resulting from impacts and dry friction. The obtained information will enable designers to propose parameters for industrial machines that make it possible to avoid their working at undesirable operating levels. Full article

Nonlinear fractional differential equations reflect the true nature of physical and biological models with non-locality and memory effects. This paper considers nonlinear fractional differential equations with unknown analytical solutions. The Adomian decomposition and the fractional power series methods are adopted to approximate the solutions. The two approaches are illustrated and compared by means of four numerical examples. Full article

With the growth of artificial intelligence in healthcare and biomedical research, many researchers are interested in large amounts of data in hospitals and medical research centers. Then the need for remote medicine services and clinical data utilization are expanding. However, since the misuse and abuse of clinical data causes serious problems, the scope of its use is bound to have a limited range physically and logically. Then a security-enhanced data distribution system for medical deep web environments. Therefore, in this paper, we propose a blockchain-based clinical data management model named DeepBlockshield to prevent information leakage between the deep web and the surface web. Blockchain supports data integrity and user validation to support data sharing in closed networks. Meanwhile, the agent performs integrity verification between the blockchain and the deep web and strengthens the security between the surface web and the deep web. DeepBlockShield verifies the user’s validity through the records of the deep web and blockchain. Furthermore, we wrap the results analyzed by the valid request into a web interface and provide information to the requester asynchronously. In the experiment, the block generation cycle and size on the delay time was analyzed for verifying the stability of the blockchain network. As a result, it showed that the proposed approach guarantees the integrity and availability of clinical data in the deep web environment. Full article

Due to the need to predict traffic congestion during the morning or evening rush hours in large cities, a model that is capable of predicting traffic flow in the short term is needed. This model would enable transport authorities to better manage the situation during peak hours and would allow users to choose the best routes for reaching their destinations. The aim of this study was to perform a short-term prediction of traffic flow in Madrid, using different types of neural network architectures with a focus on convolutional residual neural networks, and it compared them with a classical time series analysis. The proposed convolutional residual neural network is superior in all of the metrics studied, and the predictions are adapted to various situations, such as holidays or possible sensor failures. Full article

In this article, we study uncertainty quantification for flows in heterogeneous porous media. We use a Bayesian approach where the solution to the inverse problem is given by the posterior distribution of the permeability field given the flow and transport data. Permeability fields within facies are assumed to be described by two-point correlation functions, while interfaces that separate facies are represented via smooth pseudo-velocity fields in a level set formulation to get reduced dimensional parameterization. The permeability fields within facies and pseudo-velocity fields representing interfaces can be described using Karhunen–Loève (K-L) expansion, where one can select dominant modes. We study the error of posterior distributions introduced in such truncations by estimating the difference in the expectation of a function with respect to full and truncated posteriors. The theoretical result shows that this error can be bounded by the tail of K-L eigenvalues with constants independent of the dimension of discretization. This result guarantees the feasibility of such truncations with respect to posterior distributions. To speed up Bayesian computations, we use an efficient two-stage Markov chain Monte Carlo (MCMC) method that utilizes mixed multiscale finite element method (MsFEM) to screen the proposals. The numerical results show the validity of the proposed parameterization to channel geometry and error estimations. Full article

An optimal parameter estimation methodology of solid oxide fuel cell (SOFC) using modern optimization is proposed in this paper. An equilibrium optimizer (EO) has been used to identify the unidentified parameters of the SOFC equivalent circuit with the assistance of experimental results. This is presented via formulating the modeling process as an optimization problem considering the sum mean squared error (SMSE) between the observed and computed voltages as the target. Two modes of the SOFC-based model are investigated under variable operating conditions, namely, the steady-state and the dynamic-state based models. The proposed EO results are compared to those obtained via the Archimedes optimization algorithm (AOA), Heap-based optimizer (HBO), Seagull Optimization Algorithm (SOA), Student Psychology Based Optimization Algorithm (SPBO), Marine predator algorithm (MPA), Manta ray foraging optimization (MRFO), and comprehensive learning dynamic multi-swarm marine predators algorithm. The minimum fitness function at the steady-state model is obtained via the proposed EO with value of 1.5527 × 10⁻⁶ at 1173 K. In the dynamic based model, the minimum SMSE is 1.0406. The obtained results confirmed the reliability and superiority of the proposed EO in constructing a reliable model of SOFC. Full article

Although the direct sampling method (DSM) has demonstrated its feasibility in identifying small anomalies from measured scattering parameter data in microwave imaging, inaccurate imaging results that cannot be explained by conventional research approaches have often emerged. It has been heuristically identified that the reason for this phenomenon is due to the coupling effect between the antenna and dipole antennas, but related mathematical theory has not been investigated satisfactorily yet. The main purpose of this contribution is to explain the theoretical elucidation of such a phenomenon and to design an improved DSM for successful application to microwave imaging. For this, we first survey traditional DSM and design an improved DSM, which is based on the fact that the measured scattering parameter is influenced by both the anomaly and the antennas. We then establish a new mathematical theory of both the traditional and the designed indicator functions of DSM by constructing a relationship between the antenna arrangement and an infinite series of Bessel functions of integer order of the first kind. On the basis of the theoretical results, we discover various factors that influence the imaging performance of traditional DSM and explain why the designed indicator function successfully improves the traditional one. Several numerical experiments with synthetic data support the established theoretical results and illustrate the pros and cons of traditional and designed DSMs. Full article

The article brings a brief revision of the two-degree-of-freedom (2-DoF) internal model control (IMC) and the 2-DoF Smith-Predictor-based (SP) control of unstable systems. It shows that the first important reason for distinguishing between these approaches is the limitations of the control action. However, it also reminds that, in addition to the seemingly lucrative dynamics of transients, the proposed approaches can conceal a tricky behavior with a structural instability, which may manifest itself only after a longer period of time. Instead, as one of possible reliable alternatives, two-step IMC and filtered Smith predictor (FSP) design are applied to unstable first-order time-delayed (UFOTD) systems. Firstly, the 2-DoF P controller yielding a double real dominant closed loop pole is applied. Only then the 2-DoF IMC or FSP controllers are designed, providing slightly slower, but more robust transients. These remain stable even in the long run, while also showing increased robustness. Full article

Cyber-Physical System (CPS) applications including human-robot interaction call for automated reasoning for rational decision-making. In the latter context, typically, audio-visual signals are employed. Τhis work considers brain signals for emotion recognition towards an effective human-robot interaction. An ElectroEncephaloGraphy (EEG) signal here is represented by an Intervals’ Number (IN). An IN-based, optimizable parametric k Nearest Neighbor (kNN) classifier scheme for decision-making by fuzzy lattice reasoning (FLR) is proposed, where the conventional distance between two points is replaced by a fuzzy order function (σ) for reasoning-by-analogy. A main advantage of the employment of INs is that no ad hoc feature extraction is required since an IN may represent all-order data statistics, the latter are the features considered implicitly. Four different fuzzy order functions are employed in this work. Experimental results demonstrate comparably the good performance of the proposed techniques. Full article

This paper addresses an approximation-based quantized state feedback tracking problem of multiple-input multiple-output (MIMO) nonlinear systems with quantized input saturation. A uniform quantizer is adopted to quantize state variables and control inputs of MIMO nonlinear systems. The primary features in the current development are that (i) an adaptive neural network tracker using quantized states is developed for MIMO nonlinear systems and (ii) a compensation mechanism of quantized input saturation is designed by constructing an auxiliary system. An adaptive neural tracker design with the compensation of quantized input saturation is developed by deriving an augmented error surface using quantized states. It is shown that closed-loop stability analysis and tracking error convergence are conducted based on Lyapunov theory. Finally, we give simulation and experimental results of the 2-degrees-of-freedom (2-DOF) helicopter system for verifying to the validity of the proposed methodology where the tracking performance of pitch and yaw angles is measured with the mean squared errors of 0.1044 and 0.0435 for simulation results, and those of 0.0656 and 0.0523 for experimental results. Full article

In the past decade, panel data models using time-series observations of several geographical units have become popular due to the availability of software able to implement them. The aim of this study is an updated comparison of estimation techniques between the implementations of spatiotemporal panel data models across MATLAB and R softwares in order to fit real mortality data. The case study used concerns the male and female mortality of the aged population of European countries. Mortality is quantified with the Comparative Mortality Figure, which is the most suitable statistic for comparing mortality by sex over space when detailed specific mortality is available for each studied population. The spatial dependence between the 26 European countries and their neighbors during 1995–2012 was confirmed through the Global Moran Index and the spatiotemporal panel data models. For this reason, it can be said that mortality in European population aging not only depends on differences in the health systems, which are subject to national discretion but also on supra-national developments. Finally, we conclude that although both programs seem similar, there are some differences in the estimation of parameters and goodness of fit measures being more reliable MATLAB. These differences have been justified by detailing the advantages and disadvantages of using each of them. Full article

In the work, a new oscillation condition was created for second-order damped delay differential equations with a non-canonical operator. The new criterion is of an iterative nature which helps to apply it even when the previous relevant results fail to apply. An example is presented in order to illustrate the significance of the results. Full article

Sharing data along the economic supply/demand chain represents a catalyst to improve the performance of a digitized business sector. In this context, designing automatic mechanisms for structured data exchange, that should also ensure the proper development of B2B processes in a regulated environment, becomes a necessity. Even though the data format used for sharing can be modeled using the open methodology, we propose the use of FAIR principles to additionally offer business entities a way to define commonly agreed upon supply, access and ownership procedures. As an approach to manage the FAIR modelled metadata, we propose a series of methodologies to follow. They were integrated in a data marketplace platform, which we developed to ensure they are properly applied. For its design, we modelled a decentralized architecture based on our own blockchain mechanisms. In our proposal, each business entity can host and structure its metadata in catalog, dataset and distribution assets. In order to offer businesses full control over the data supplied through our system, we designed and implemented a sharing mechanism based on access policies defined by the business entity directly in our data marketplace platform. In the proposed approach, metadata-based assets sharing can be done between two or multiple businesses, which will be able to manually access the data in the management interface and programmatically through an authorized data point. Business specific transactions proposed to modify the semantic model are validated using our own blockchain based technologies. As a result, security and integrity of the FAIR data in the collaboration process is ensured. From an architectural point of view, the lack of a central authority to manage the vehiculated data ensures businesses have full control of the terms and conditions under which their data is used. Full article

The Covid-19 pandemic has generated major changes in society, most of them having a negative impact on the quality of life and income obtained by the population and businesses. The negative consequences have been highlighted in the decrease of the GPD level for regions, countries and even continents. Returning to pre-pandemic levels is a considerable effort for both economic and political decision-makers. This article deals with the construction of a mathematical model for economic aspects in the context of variable productivity in time. Through this mathematical model, we propose to maximize revenues in pandemic conditions, in order to limit the economic consequences of the lockdown. One advantage of the proposed model consists in the fact that it is based on units that can be regions, economic branches, economic units or fields of investment. Another strength of the model is determined by the fact that it offers the possibility to choose between two different investment strategies, based on the specific options of the decision makers: the consistent increase of the state revenues or the amelioration of the disparity phenomenon. Furthermore, our model extends previous approaches from the literature by adding some generalization options and the proposed model can be applied in lockdown cases and seasonal situations. Full article

In this paper, we introduce a model-free algorithm, robust mode analysis (RMA), to extract primary constituents in a fluid or reacting flow directly from high-frequency, high-resolution experimental data. It is expected to be particularly useful in studying strongly driven flows, where nonlinearities can induce chaotic and irregular dynamics. The lack of precise governing equations and the absence of symmetries or other simplifying constraints in realistic configurations preclude the derivation of analytical solutions for these systems; the presence of flow structures over a wide range of scales handicaps finding their numerical solutions. Thus, the need for direct analysis of experimental data is reinforced. RMA is predicated on the assumption that primary flow constituents are common in multiple, nominally identical realizations of an experiment. Their search relies on the identification of common dynamic modes in the experiments, the commonality established via proximity of the eigenvalues and eigenfunctions. Robust flow constituents are then constructed by combining common dynamic modes that flow at the same rate. We illustrate RMA using reacting flows behind a symmetric bluff body. Two robust constituents, whose signatures resemble symmetric and von Karman vortex shedding, are identified. It is shown how RMA can be implemented via extended dynamic mode decomposition in flow configurations interrogated with a small number of time-series. This approach may prove useful in analyzing changes in flow patterns in engines and propulsion systems equipped with sturdy arrays of pressure transducers or thermocouples. Finally, an analysis of high Reynolds number jet flows suggests that tests of statistical characterizations in turbulent flows may best be done using non-robust components of the flow. Full article

This article is devoted to the study of the Boolean-valued universe as an algebraic system. We start with the logical backgrounds of the notion and present the formalism of extending the syntax of Boolean truth values by the use of definable symbols, internal classes, outer terms and external Boolean-valued classes. Next, we enrich the collection of Boolean-valued research tools with the technique of partial elements and the corresponding joins, mixings and ascents. Passing on to the set-theoretic signature, we prove that bounded formulas are absolute for transitive Boolean-valued subsystems. We also introduce and study intensional, predicative, cyclic and regular Boolean-valued systems, examine the maximum principle, and analyze its relationship with the ascent and mixing principles. The main applications relate to the universe over an arbitrary extensional Boolean-valued system. A close interrelation is established between such a universe and the intensional hierarchy. We prove the existence and uniqueness of the Boolean-valued universe up to a unique isomorphism and show that the conditions in the corresponding axiomatic characterization are logically independent. We also describe the structure of the universe by means of several cumulative hierarchies. Another application, based on the quantifier hierarchy of formulas, improves the transfer principle for the canonical embedding in the Boolean-valued universe. Full article

Using the Unified Theory of Acceptance and Use of Technology for Innovations in the Public Sector (UTAUT-IPS) model, this study examined the influences on using a specific innovation practice on public managers. We based our analysis on an end-of-2019 sample of 227 Spanish public managers, aiming to answer the question “Are public innovation and project managers driven only by a governance paradigm, influencing their intention and usage of an innovation practice?” Using the Partial Least Squares Structural Equation Modelling (PLS-SEM) algorithm, we singled out the effects of the governance paradigm, performance expectancy, and motivation, among seven other behavioral composite variables. The PLS-Prediction-Oriented Segmentation routine was used to segment our sample into three distinct groups of innovation managers: (i) those driven by nearly all influences; (ii) those driven by results and the governance paradigm; and (iii) those driven by governance and habits. The three groups highlight the different practical approaches to public innovation and co-creation initiatives, which clearly reflect the complex process of deciding which tool (or tools) should be used to implement these. Our UTAUT-IPS model helps visualize this complex decision-making process. Full article

This paper discusses the construction of a type-2 fuzzy B-spline model to model complex uncertainty of surface data. To construct this model, the type-2 fuzzy set theory, which includes type-2 fuzzy number concepts and type-2 fuzzy relation, is used to define the complex uncertainty of surface data in type-2 fuzzy data/control points. These type-2 fuzzy data/control points are blended with the B-spline surface function to produce the proposed model, which can be visualized and analyzed further. Various processes, namely fuzzification, type-reduction and defuzzification are defined to achieve a crisp, type-2 fuzzy B-spline surface, representing uncertainty complex surface data. This paper ends with a numerical example of terrain modeling, which shows the effectiveness of handling the uncertainty complex data. Full article

Various studies have been conducted on object detection, tracking, and action recognition based on thermal images. However, errors occur during object detection, tracking, and action recognition when a moving object leaves the field of view (FOV) of a camera and part of the object becomes invisible. However, no studies have examined this issue so far. Therefore, this article proposes a method for widening the FOV of the current image by predicting images outside the FOV of the camera using the current image and previous sequential images. In the proposed method, the original one-channel thermal image is converted into a three-channel thermal image to perform image prediction using an image prediction generative adversarial network. When image prediction and object detection experiments were conducted using the marathon sub-dataset of the Boston University-thermal infrared video (BU-TIV) benchmark open dataset, we confirmed that the proposed method showed the higher accuracies of image prediction (structural similarity index measure (SSIM) of 0.9839) and object detection (F1 score (F1) of 0.882, accuracy (ACC) of 0.983, and intersection over union (IoU) of 0.791) than the state-of-the-art methods. Full article

This article discusses how to calculate the location of a point on a surface using a mathematical approach on two levels. The first level uses the traditional calculation procedure via Cooper’s iterative method through a spreadsheet editor and a classic result display map. The second level uses the author-created computer-aided location expert system on the principle of calculation using Cooper’s iterative method with the direct graphical display of results. The problem is related to designing a practical computer location expert system, which is based on a new idea of using the resolution of a computer map as an image to calculate location. The calculated results are validated by comparing them with each other, and the defined accuracy for a particular example was achieved at the 32nd iteration with the position optima DC[x⁽³²⁾;y⁽³²⁾] = [288.8;82.7], with identical results. The location solution in the case study to the defined accuracy was achieved at the 6th iteration with the position optima DC[x(⁶);y(⁶)] = [274;220]. The calculations show that the expert system created achieves the required parameters and is a handy tool for determining the location of a point on a surface. Full article

The article explores the mechanisms that affect consumers’ interest in luxury clothing innovations. The actual research aims to investigate the effect of status quo and clothing involvement on consumer brand loyalty. More, it was intended to quantify the influence of the level of engagement concerning clothing acquisition and the status quo tendency on the consumers’ level of interest toward innovative luxury fashion products. The models were analyzed through the partial-least square-path modeling method. The results revealed that status quo bias and consumers’ involvement in fashion influence their loyalty to brands and level of innovativeness. The novelty of the present research comes from the analysis of the impact of the status quo manifest variables on consumers’ innovative tendencies. Moreover, it was found that status consumption fully mediates the relationships among the investigated predictors and considered outcome variables. The mediator manifests the highest effect size of all investigated predictors. The actual paper advances research in a direction that was not sufficiently addressed in the past, introducing the status quo construct as the main predictor of peoples’ inclination to be loyal to a brand or to manifest a tendency toward innovativeness. Moreover, the article emphasizes the essential role manifested by social status in foreseeing a behavioral response. Full article

Framelets have been widely used in narrowband signal processing, data analysis, and sampling theory, due to their resilience to background noise, stability of sparse reconstruction, and ability to capture local time-frequency information. The well-known approach to construct framelets with useful properties is through frame multiresolution analysis (FMRA). In this article, we characterize the frequency domain of bandlimited FMRAs: there exists a bandlimited FMRA with the support of frequency domain G if and only if G satisfies $G\subset 2G$, ${\bigcup }_m{2}^{m}G\cong {\mathbb{R}}^d$, and $\left(G\backslash \frac{G}{2}\right)\bigcap \left(\frac{G}{2}+2\pi \mathit{\nu }\right)\cong \varnothing (\mathit{\nu }\in {\mathbb{Z}}^d)$. Full article

This paper focuses on the analysis of the stability of the GW17 high-voltage disconnecting switch drive system. Firstly, the optimization model of the disconnector is established, and the simulation analysis is carried out by ADAMS (Automatic Dynamic Analysis of Mechanical Systems) and the simulation results are verified by experiments. Afterwards, ADAMS optimization design and particle swarm optimization algorithm (PSO) are used to optimize the drive system of the disconnector, and the results are verified on the experimental platform. After optimization, the space rod is reduced by 15 mm, the minimum corner angle of the lower conductive rod is reduced by 71.0%, the minimum folding arm angle is reduced by 88.7% and the maximum force of the ball pair is reduced by 35.7%, which realizes the lightweight of the rod, reduces the wear of the ball pair, and improves the stability of the equipment operation. Full article

This paper presents the dynamic responses of a fiber-reinforced composite beam under a moving load. The Timoshenko beam theory was employed to analyze the kinematics of the composite beam. The constitutive equations for motion were obtained by utilizing the Lagrange procedure. The Ritz method with polynomial functions was employed to solve the resulting equations in conjunction with the Newmark average acceleration method (NAAM). The influence of fiber orientation angle, volume fraction, and velocity of the moving load on the dynamic responses of the fiber-reinforced nonhomogeneous beam is presented and discussed. Full article

Renewable energies have increased in importance in recent years due to the harm caused to the environment by fossil fuels. As a result, renewable energy companies seem to be profitable investment opportunities given their likely substantial future earnings. However, previous empirical evidence has not always agreed about this likely profitability. In addition, the methodologies employed in the existing empirical literature are complicated and not feasible for most investors to use. Therefore, it is proposed an approach which combines the use of performance measures, screening rules, devolatized returns and portfolio strategies, all of which can be implemented by investors. This approach results in high cumulative returns of more than 200% and other positive ratios, even when transaction costs are considered. This should encourage people to invest in these renewable energies and contribute to improving the environment. Full article

Time series clustering is an essential ingredient of unsupervised learning techniques. It provides an understanding of the intrinsic properties of data upon exploiting similarity measures. Traditional similarity-based methods usually consider local geometric properties of raw time series or the global topological properties of time series in the phase space. In order to overcome their limitations, we put forward a time series clustering framework, referred to as time series clustering with Topological-Geometric Mixed Distance (TGMD), which jointly considers local geometric features and global topological characteristics of time series data. More specifically, persistent homology is employed to extract topological features of time series and to compute topological similarities among persistence diagrams. The geometric properties of raw time series are captured by using shape-based similarity measures such as Euclidean distance and dynamic time warping. The effectiveness of the proposed TGMD method is assessed by extensive experiments on synthetic noisy biological and real time series data. The results reveal that the proposed mixed distance-based similarity measure can lead to promising results and that it performs better than standard time series analysis techniques that consider only topological or geometrical similarity. Full article

One of the important objectives and concerns today is to find efficient means to manage the information security risks to which organizations are exposed. Due to a lack of necessary data and time and resource constraints, very often it is impossible to gather and process all of the required information about an IT system in order to properly assess it within an acceptable timeframe. That puts the organization into a state of increased security risk. One of the means to solve such complex problems is the use of multicriteria decision-making methods that have a strong mathematical foundation. This paper presents a hybrid multicriteria model for the evaluation of critical IT systems where the elements for risk analysis and assessment are used as evaluation criteria. The iterative steps of the design science research (DSR) methodology for development of a new multicriteria model for the objectives of evaluation, ranking, and selection of critical information systems are delineated. The main advantage of the new model is its use of generic criteria for risk assessment instead of redefining inherent criteria and calculating related weights for each individual IT system. That is why more efficient evaluation, ranking, and decision-making between several possible IT solutions can be expected. The proposed model was validated in a case study of online banking transaction systems and could be used as a generic model for the evaluation of critical IT systems. Full article

Secure and compact designs of HIGHT block cipher on representative ARM microcontrollers are presented in this paper. We present several optimizations for implementations of the HIGHT block cipher, which exploit different parallel approaches, including task parallelism and data parallelism methods, for high-speed and high-throughput implementations. For the efficient parallel implementation of the HIGHT block cipher, the SIMD instructions of ARM architecture are fully utilized. These instructions support four-way 8-bit operations in the parallel way. The length of primitive operations in the HIGHT block cipher is 8-bit-wise in addition–rotation–exclusive-or operations. In the 32-bit word architecture (i.e., the 32-bit ARM architecture), four 8-bit operations are executed at once with the four-way SIMD instruction. By exploiting the SIMD instruction, three parallel HIGHT implementations are presented, including task-parallel, data-parallel, and task/data-parallel implementations. In terms of the secure implementation, we present a fault injection countermeasure for 32-bit ARM microcontrollers. The implementation ensures the fault detection through the representation of intra-instruction redundancy for the data format. In particular, we proposed two fault detection implementations by using parallel implementations. The two-way task/data-parallel based implementation is secure against fault injection models, including chosen bit pair, random bit, and random byte. The alternative four-way data-parallel-based implementation ensures all security features of the aforementioned secure implementations. Moreover, the instruction skip model is also prevented. The implementation of the HIGHT block cipher is further improved by using the constant value of the counter mode of operation. In particular, the 32-bit nonce value is pre-computed and the intermediate result is directly utilized. Finally, the optimized implementation achieved faster execution timing and security features toward the fault attack than previous works. Full article

In the present paper, we delve into the study of nodal systems on the unit circle that meet certain separation properties. Our aim was to study the Hermite interpolation process on the unit circle by using these nodal arrays. The target was to develop the corresponding interpolation theory in order to make practical use of these nodal systems linked to certain mechanical models that fit these distributions. Full article