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Abstract Introduction It is well known that social contact patterns differ from country to country. This variation coincides with significant socioeconomic heterogeneity that complicates the design of effective nonpharmaceutical interventions. This study examined how socioeconomic heterogeneity in selected African countries might be factored in to explain better patterns of social contact mix between countries. Methods We used a standardized contact matrix for 32 African countries, estimated in (Prem et al. in PLoS Comput Biol 17(7):e1009098, 2021). We scaled the matrices using an epidemic model from (Röst et al. in Viruses 12(7):708, 2020). We also analyzed aggregated data from the World Bank country website. The data includes 28 variables; social, economic, environmental, institutional, governance, health and well-being, education, gender inequality, and other development-related indicators that describe countries. Principal component analysis was used to visualize socioeconomic similarities between countries and identify the indicators for maximum variation. The ( 2 D ) 2 P C A $(2D)^{2} PCA$ approach was used to reduce the dimension of synthetic contact matrices for each country to avoid the dimensionality curse. Then, hierarchical aggregate clustering was used to identify groups of countries with similar social patterns, taking into account the country’s socioeconomic performance. Results Our model yielded four meaningful clusters, each with a few distinguishing features. Social contacts varied between groups, but were generally similar within each set. The socioeconomic performance of the country influenced the clusters. Conclusions Our results suggest that integrating socioeconomic factors into social contacts can better explain infectious disease transmission dynamics and that similar interventions can be implemented in countries within the cluster

Abstract While interactive simulations have been mostly limited to Computer Graphics applications, new generations of Graphics Processing Units (GPUs) allow the realization of industrial-grade interactive 3D physics simulations. By combining an immersed boundary method with efficient GPU-based MINRES and CG solvers using a GPU-based geometric multigrid preconditioner, we demonstrate a fast industrial 3D computational mechanics solver. The various implementation aspects - specifically how they differ from similar concepts used in the Computer Graphics community - are discussed in detail. The proposed concept opens up new classes of industrial simulation applications allowing a democratization beyond today’s expert users, from designer centric simulation to operational and service decisions based on 3D simulations. To support this, we provide various benchmark cases including a real-world study of a simulation-based service decision for a damaged gear-box mount.

Abstract Diabetes Mellitus is a metabolic disorder which may result in severe and potentially fatal complications if not well-treated and monitored. In this study, a quantitative analysis of the data collected using CGM (Continuous Glucose Monitoring) devices from eight subjects with type 2 diabetes in good metabolic control at the University Polyclinic Agostino Gemelli, Catholic University of the Sacred Heart, was carried out. In particular, a system of ordinary differential equations whose state variables are affected by a sequence of stochastic perturbations was proposed and used to extract more informative inferences from the patients’ data. For this work, Matlab and R programs were used to find the most appropriate values of the parameters (according to the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC)) for each patient. Fitting was carried out by Particle Swarm Optimization to minimize the ordinary least squares error between the observed CGM data and the data from the ODE model. Goodness of fit tests were made in order to assess which probability distribution was best suitable for representing the waiting times computed from the model parameters. Finally, both parametric and non-parametric density estimation of the frequency histograms associated with the variability of the glucose elimination rate from blood were conducted and their representative parameters assessed from the data. The results show that the chosen models succeed in capturing most of the glucose fluctuations for almost every patient.

This paper proposes an impedance-based approach for locating short-circuit faults in active distribution networks (DNs). This topic is a crucial task for operators, especially in grids with inverter-based distributed generators (IBDGs). Various methods have been proposed in this research area, including traveling waves, impedance-based methods, and artificial intelligence (AI) techniques. Among them, the impedance-based scheme provides a simple and efficient feature that could be used in AI-based techniques. In this paper, an enhanced fault localization method based on impedance estimation is introduced. This method comprises two main components: (i) fault distance determination and (ii) faulty section identification. When developing the proposed method, the modeling of inverter-based resources under symmetrical and asymmetrical faults is considered, which includes the impact and behavior of such sources in the proposed approach. Unlike conventional impedance-based methods, the proposed approach does not require network information such as structure, lines, load data, or voltage and current measurements along the feeder at multiple points. The proposed method can be utilized as a feature in AI-based techniques, significantly enhancing accuracy and reducing the complexity of such techniques. To validate the efficacy of the proposed approach, various series of time-domain case studies are performed, in addition to mathematical proofs. The results demonstrate the effectiveness of the proposed scheme in accurately locating faults with varying resistances at different positions in the presence of inverter-based distributed generators.

In the context of frequency stability in a two-area microgrid, it is crucial to address the fluctuations in frequency caused by load disturbances. To achieve this, an effective load-frequency control (LFC) system, which serves as the secondary control, must be implemented. However, the presence of renewable energy sources such as wind turbines and photovoltaic systems adds complexity to the operation of the LFC system due to their inherent uncertainty. To enhance the performance of the LFC system in the two-area microgrid, this paper proposes a reduction in the number of controllers employed, aiming for a less complex structure. Specifically, Model Predictive Control (MPC) is utilized for LFC, and the weight parameters of the MPC controller are determined using Craziness-based Particle Swarm Optimization (CRPSO). The proposed method is compared with alternative approaches, including PID controller optimized with Social Spider Optimization (SSO), Fractional Order Fuzzy PI (FOFPI), and conventional MPC. The effectiveness of the proposed method is evaluated in various scenarios, considering load variations and the presence of distributed microgrid generation resources. The results demonstrate that the proposed method outperforms the other controllers in terms of speed of response, reduction of overshoot and undershoot, and overall complexity. Importantly, the proposed method significantly improves the frequency stability of the two-area microgrid. The simulation and analysis are conducted using MATLAB software, providing a comprehensive understanding of the system dynamics and the performance of the proposed controller.

Regulating voltage and current signals in microgrids (MG) is essential to ensure stability, optimize power quality, support grid integration, enhance operational efficiency, and promote safety within the system. This paper introduces a novel Linear Matrix Inequalities (LMI)-based approach aimed at regulating voltage and current signals within microgrids through the utilization of sliding mode control. The MG under examination in this paper is composed of a voltage source inverter (VSI) for DC to AC voltage conversion, a filter to ensure sinusoidal signal quality, and an array of loads, including those with uncertain characteristics. The objective of this study is to regulate the output voltage and current in a short period of time in the presence of diverse loads. By promptly adjusting voltage and current levels, the microgrid can effectively accommodate fluctuations in demand and maintain optimal performance under changing conditions. The presented controller consists of two parts: a state feedback gain calculated from the LMI and a sliding mode-based controller to maintain system stability. This controller is intended to reject disturbances, track reference signals, and minimize steady-state errors in a limited time. The satisfactory performance of the microgrid will have a significant impact on various parameters, such as frequency, active power, reactive power, and power factor. Simulating the voltage source inventor and presenting numerical results demonstrate the effectiveness of the proposed controller to provide high robustness against uncertainty and nonlinear loads while maintaining system stability.

In this article, the robust scheduling of the distribution network is presented considering electric vehicles, distributed generation, and energy storage, in which the energy management of the mentioned elements is considered, and also only one scenario is needed. The proposed deterministic problem is an optimization problem whose objective function is equal to minimizing energy cost. Also, the limitations of the problem are equal to the power flow equations of the network, the limitations of the technical indicators of the network such as the voltage of the buses and the passing power of the lines, the operation equations of electric vehicles, energy storages, and distributed generation. It is worth mentioning that the mentioned problem is non-linear. In the following, to achieve the global optimal point with a high solution speed, the linear model of the mentioned problem is presented with a very low calculation error. In this research, the uncertainty parameters of the problem are equal to active and reactive loads, energy prices, parameters of electric vehicles, and renewable productions. Finally, to simplify the decision-making of the distribution network operator, a robust model of the mentioned problem was presented. Finally, the proposed problem is applied to the IEEE standard 33-bus radial distribution network using GAMS optimization software, and then the capabilities of the proposed design are evaluated.

This paper presents the design and hardware implementation of an efficient and optimal induction heating circuit for induction sealing. The circuit has a low implementation cost, so the proposed system with a simple and efficient structure can cover the needs of this technology field. Here, the focus is on the implementation process and practical tips in this field. The proposed induction sealing circuit uses zero-voltage switching technology with parallel MOSFETs. In this structure, by using inductors and capacitors with appropriate tolerable current and voltage, it can provide the power and frequency of the output signal applied to the induction coil for various applications. The use of transistors with suitable current capability makes the circuit appropriate for applications that require stronger magnetic fields. In addition, the induction coil has an elliptical structure, which makes it efficient for sealing a wide range of bottles. It is constructed using Litz wire to reduce power loss in the coil. In the presented structure, the frequency of the output signal is equal to 31 kHz, which is suitable for creating an eddy current in the aluminum foil in the bottle caps. The circuit has been tested and investigated and has acceptable sealing for various industries.

This paper deals with the problem of signal modeling using fractional-order linear prediction. In this research, we obtain the closed-form expression of the optimum sampling frequency of the One-Parameter Fractional-order Linear Prediction (OPFLP) and examine the performance when the fractional order (alpha) is in (0

Active power filters (APFs) play a vital role in reducing the current harmonics and improving power quality. This work studies a shunt APF (SAPF) based on the three-phase voltage source converter (VSC). This paper investigates the new robust control approach using error dynamics. Stable first-order error dynamics are considered when designing the control inputs. Three control inputs are obtained to control the currents at any phase by choosing and optimizing the appropriate parameters. This strategy is also simple to implement in practical applications because it is the same as the proportional-derivative controller design. In addition, the new control method can be utilized for any system with low dynamic information so that the destructive effects of lumped uncertainties in the output channels of the SAPF can be alleviated. The minimum voltage and current measurements are used to control the SAPF, and the grid current harmonics will be reduced by achieving the stabilization of tracking error dynamics. Some numerical simulations are performed by MATLAB software to confirm the proposed method.

A type of converter called Quasi-Z-source converters (QZSC) is becoming more popular due to its benefits, such as operating in a single stage, having smaller components, and maintaining continuous input current and a common ground. This converter is widely used in various applications that need a DC-DC converter. The small-signal analysis and linearization method are often used to control the QZSC. The linear model of QZSC does not provide sufficient stability control over a wide range. Sliding Mode Control (SMC) has become widely used for electronic power converters because of their variable structure .This paper presents a SMC for a QZSC with three objectives:1) to achieve stability across a wide range of QZSC; 2) to systematically select the proposed controller coefficients; and 3) to enable tracking of the reference voltage in spite of changes in input voltage, reference voltage, and output load. The simulations have been done with the help of MATLAB/Simulink and show the effectiveness of the proposed method.

Abstract This paper addresses the challenging task of guide wire navigation in cardiovascular interventions, focusing on the parameter estimation of a guide wire system using Ensemble Kalman Inversion (EKI) with a subsampling technique. The EKI uses an ensemble of particles to estimate the unknown quantities. However, since the data misfit has to be computed for each particle in each iteration, the EKI may become computationally infeasible in the case of high-dimensional data, e.g. high-resolution images. This issue can been addressed by randomised algorithms that utilize only a random subset of the data in each iteration. We introduce and analyse a subsampling technique for the EKI, which is based on a continuous-time representation of stochastic gradient methods and apply it to on the parameter estimation of our guide wire system. Numerical experiments with real data from a simplified test setting demonstrate the potential of the method.

Abstract In the design process of large adaptive mirrors numerical simulations represent the first step to evaluate the system design compliance in terms of performance, stability and robustness. For the next generation of Extremely Large Telescopes increased system dimensions and bandwidths lead to the need of modeling not only the deformable mirror alone, but also all the system supporting structure or even the full telescope. The capability to perform the simulations with an acceptable amount of time and computational resources is highly dependent on finding appropriate methods to reduce the size of the resulting dynamic models. In this paper we present a framework developed together with the company Microgate to create a reduced order structural model of a large adaptive mirror as a preprocessing step to the control system simulations. The reduced dynamic model is then combined with the remaining system components allowing to simulate the full adaptive mirror in a computationally efficient way. We analyze the feasibility of our reduced models for Microgate’s prototype of the adaptive mirror of the Giant Magellan Telescope.

Concerning the increasing application of plug-in electric vehicles (PEVs), planning PEV fast-charging stations (PEVF-CS) has become an important research topic. Regarding the reactive power compensation capability, the optimal planning of PEVF-CS reduces voltage deviation and power loss in the distribution network. Also, one of the basic requirements for expanding electric transportation is the optimal placement of accessible PEVF-CSs, considering the geographic information data. Therefore, the optimal placement of PEVF-CS requires attention to different geographical criteria and power distribution network constraints. In this sense, this paper aims to propose an approach that integrates the Geographic Information System (GIS) technique, Multi-Criteria Decision-Making (MCDM) method, and Mixed-Integer Nonlinear Programming to find the optimal location of a PEVF-CS in Kabul city. The first stage is decision analysis based on the GIS technique and the MCDM approach. The second stage is suitability analysis of the power distribution network constraints to improve power quality. This paper considers ten different suitability criteria, and the Technique for Order Preference Similarity to Ideal Solution (TOPSIS) is applied to rank the different candidate locations. The analysis identified Junction 4 as the optimal choice and demonstrated a significant 3.6% reduction in power loss during peak hours, decreasing from 1071 kW to 1032 kW. These results demonstrate the effectiveness of our approach in optimizing PEVF-CS placement to enhance power quality and reduce the power loss.

— In this paper, a single switch transformer-less DC-DC converter with continuous input current for photovoltaic applications is proposed. The suggested configuration utilizes a CL1C2D2 structure to achieve a high voltage gain and alleviate voltage pressure on the semiconductor components. This design enables a significant increase in voltage output with a minimal turns ratio, leading to reduced size, weight, and cost of the converter system. The reduction in turns ratio of the coupled inductor results in lowered voltage stress on the semiconductor elements. Additionally, employing just one power switch in the converter simplifies control and reduces expenses. With its continuous input current, this converter is particularly well-suited for integration in photovoltaic systems. Simulation results conducted using PSCAD/EMTDC software validate the efficacy of the proposed power converter. Furthermore, the maximum power output of the photovoltaic module through an MPPT (Maximum Power Point Tracking) controller under varying irradiance levels is determined, and simulations are executed using PSCAD/EMTDC.

Objective: The Micro-Expression (ME), which automatically reveals genuine human emotions, has gained significant attention. Recognizing the ME is crucial for many real-time applications. However, there are significant challenges to overcome. For instance, the number of ME frames are limited due to their short duration, and the subtle facial movements can be hard to detect due to their low intensity. These challenges need to be addressed to improve ME recognition. Materials and Methods: We propose a novel method for the ME recognition in real-time. In this method, first, the apex frame is spotted using the rotated local binary pattern from six planes (RLBPS) and correlation coefficient (CC). Next, three hand-crafted methods such as the multi-color rotated local binary pattern from six planes (MRLBPS), the histograms of directed gradients from six planes (HDGS), and the histogram of image gradient direction from six planes (HIGDS) extract the features from the apex frame and its surrounding frames. Finally, the stacks of features as matrixes are fed into a three-dimensional convolutional neural network (3D-CNN), and the output is the maximum recognition rate by voting three results. Results: The proposed method has shown promising results when compared to most state-of-the-art methods. According to the results, an average precision of 99% has been obtained using our proposed method. Conclusion: The combination of the RLBPS and the CC creates a strong method for spotting the apex frame. Also, feeding the stacks of spatiotemporal features into the 3D-ResNet increases the ME recognition rate in real-time.

A new framework for controlling load frequency in a complex, interconnected power system with multiple sources has been developed. This framework combines a fuzzy logic controller (FLC) and a tilted integral derivative (TID) controller, creating a self-tuning fuzzy tilted integral derivative (STFTID) controller. The purpose of this controller is to conduct and reduce load frequency perturbations during the operation of a multi-area interconnected multi-source power system. The STFTID controller is optimized using a particle swarm optimization algorithm to minimize the frequency fluctuations effectively. Investigations of the proposed STFTID controller were performed for power systems with generation units of a conventional system and renewable energy sources. In the design process of the STFTID controller, various nonlinearities, uncertainties, and fluctuations are considered to simulate practical challenges. These challenges include generation rate constraints, governor deadband, and communication time delays (as the sources of nonlinearity), as well as fluctuations caused by step load switching and the connection of renewable power plants to the system. The STFTID controller is compared with the proportional integral derivative (PID), titled integral derivative (TID), and integral tilted-derivative (I-TD) controllers. Simulation results show that the developed STFTID controller significantly enhances the system frequency control under various applied conditions, including multi-step load perturbation, renewable power plant integration, communication time delays, and generation rate constraints.

In this paper, we propose the problem of connectivity maintenance for a group of robots that are connected with wireless communication. The communication between the agents is modeled by a fading channel, and the exchange of information is possible at permissible distances. The distributed controller is designed to maintain the global connectivity of the network communication graph. On the other hand, obtaining the best communication quality between agents is the definition of good network connectivity. Therefore, the Laplacian matrix is defined as a weighted graph according to the communication parameters. Initially, the controller uses the supergradation algorithm to remake the network Laplacian matrix for having a bigger second eigenvalue. While each agent in the network only has access to the neighbor's information. The control algorithm uses a multi-agent estimator to find the eigenvector corresponding to the second small eigenvalue. The formation problem has been considered in the second step after the reference topology has been obtained. Because of the nonlinear dynamics of the agents, the sliding mode controller has been used for this purpose. This robust controller could be a suitable choice due to the modeling uncertainty and sensor measurement uncertainty. Finally, an example of multi-agent robots is provided to evaluate the algorithm.

The delay-and-sum (DS) beamforming and delay-weight-and-sum (DWS) beamforming are primary methods in ultrasonic imaging with phased arrays. Total focusing method (TFM) and Minimum Variance (MV) based adaptive beamforming are well-known methods within DS and DWS beamforming, respectively. The MV-based adaptive beamforming significantly reduces interferences and provides high-resolution image compared to TFM beamforming, at the cost of high computational complexity, and sensitivity to input statistics for matrix inversion. To address these challenges, recently, iterative MV (IMV) has been proposed to alleviate computational burdens without the need for matrix inversion. The delay-multiply-and-sum (DMAS) beamforming enhances TFM beamforming performance by employing spatial information of the array signals. However, the resulting images remain susceptible to speckle and background noises in all of these beamformers. In this paper, we aim to improve these beamforming methods so that speckle and Gaussian background noise are reduced while preserving the quality of the reflective echoes in ultrasonic images. In the proposed method, a wavelet transform with a novel threshold function is applied to the received signals to initially reduce the noise, followed by the application of the beamformer. Subsequently, the coherence weighting using the denoised signals is derived, and the obtained coherence weighting is then integrated into the beamforming process. Simulation results demonstrate that the proposed method achieves a significant reduction in background noise and speckles of the above beamformer, and particularly reduces background noise and speckles of beamformer up to approximately -27dB while preserving the detection capability of reflective points.

Deriving an accurate and simple current-voltage (I-V) characteristic for photovoltaic (PV) module is highly significant for condition monitoring, fault detection and maximizing power production in PV systems. Equivalent circuits consist of one or more diodes are mostly utilized for I-V curve modelling. However, these models are inherently implicit, relatively complex and nonlinear in their parameters. Here, a piecewise quadratic function with four different intervals is generated from the measured I-V data at standard test condition (STC). The intervals are chosen such that the best model performance can be achieved, especially at maximum power point. Each quadratic function is obtained from least square technique according to the experimental data in the corresponding interval. It is easy to obtain the voltage value at MPP from the extracted model, analytically. Also, a suggestion is provided for extending the generated I-V model to the real environmental condition by utilization of artificial neural network. The derived PV module model is highly suitable for maximum power point tracking, monitoring and fault detection due to its simplicity, explicit structure and accuracy.

This paper presents a new high step-up DC-DC switched-mode converter in which not only the input and output ports can be connected to a common ground, but also the service time of the input source is extended due to continuous current injection to the proposed power converter. Furthermore, the fast dynamic response is offered by minimum phase characteristics. This key feature is achieved by making the proposed converter’s control to output transfer function, free from the right half plane zero (RHPZ). Full description of the operation principles, theoretical analysis related to steady-state operation, and also the small-signal modelling derivation of the proposed converter, are presented in this paper. In the end, to confirm all the merits of the proposed converter and accuracy of theoretical analysis, a sample 25 V - 100 V laboratory prototype DC-DC converter with 100W output power has been implemented, and the main experimental results have also been outlined.

Abstract In this paper we propose an algorithm for testing whether the independent observations come from finite-variance distribution. The preliminary knowledge about the data properties may be crucial for its further analysis and selection of the appropriate model. The idea of the testing procedure is based on the simple observation that the empirical cumulative even moment (ECEM) for data from finite-moments distribution tends to some constant whereas for data coming from heavy-tailed distribution, the ECEM exhibits irregular chaotic behavior. Based on this fact, in this paper we parameterize the regular/irregular behavior of the ECEM and construct a new test statistic. The efficiency of the testing procedure is verified for simulated data from three heavy-tailed distributions with possible finite and infinite variances. The effectiveness is analyzed for data represented in time domain. The simulation study is supported by analysis of real vibration signals from rotating machines. Here, the analyses are provided for data in both the time and time-frequency domains.

Abstract In this work we focus on the development of a numerical algorithm for the inverse elastography problem. The goal is to perform an efficient material parameter identification knowing the elastic displacement field induced by a mechanical load. We propose to define the inverse problem through a quadratic optimization program which uses the direct problem formulation to define the objective function. In this way, we end up with a convex minimization problem which attains its minimum at the solution of a linear system. The effectiveness of our method is illustrated through numeral examples.

Abstract Training defect detection algorithms for visual surface inspection systems requires a large and representative set of training data. Often there is not enough real data available which additionally cannot cover the variety of possible defects. Synthetic data generated by a synthetic visual surface inspection environment can overcome this problem. Therefore, a digital twin of the object is needed, whose micro-scale surface topography is modeled by texture synthesis models. We develop stochastic texture models for sandblasted and milled surfaces based on topography measurements of such surfaces. As the surface patterns differ significantly, we use separate modeling approaches for the two cases. Sandblasted surfaces are modeled by a combination of data-based texture synthesis methods that rely entirely on the measurements. In contrast, the model for milled surfaces is procedural and includes all process-related parameters known from the machine settings.

Abstract The molecular movement in single particle tracking (SPT) experiments shows a crucial role of diffusion in many biological processes such as signaling, cellular organization, transport mechanisms, and more. The SPT analysis detects not only classical Brownian motion but diffusion with other features. These include directed diffusion and confined motion. The behavior remains a challenging problem for several reasons. Due to the action of many physical processes, random trajectories of cellular molecules are segmented in different diffusive modes. Often their study requires sophisticated algorithms for the analysis of statistical properties. In this paper we consider the segment analysis for trajectories of G proteins and coupled-receptors in living cells. Their movement is often transient and switches among free diffusion, confined diffusion, directed diffusion, and immobility. Moreover, the confined segments can have both Gaussian and non-Gaussian statistics. The types of alternation of diffusive modes along the trajectories of G proteins and coupled-receptors are analyzed.

Abstract The chain rule is a standard tool in differential calculus to find derivatives of composite functions. Faà di Bruno’s formula is a generalization of the chain rule and states a method to find high-order derivatives. In this contribution, we propose an algorithm based on Faà di Bruno’s formula and Bell polynomials (Bell in Ann Math 29:38–46, 1927; Parks and Krantz in A primer of real analytic functions, 2012) to compute the structure of derivatives of function compositions. The application of our method is showcased using trajectory planning for the heat equation (Laroche et al. in Int J Robust Nonlinear Control 10(8):629–643, 2000).

Abstract In this paper we build a methodology for pricing of insurance-linked securities which are tied to multiple natural catastrophe perils. As a representative example, we construct a multi-peril catastrophe (CAT) bond which can be linked to the industry loss indices or actual losses incurred by an insurer. We provide pricing formulas for such CAT bonds. We illustrate the introduced methodology on the US natural catastrophe data obtained from Property Claim Services (PCS). Within this dataset, we specifically examine two types of risks: losses associated with wind and thunderstorm events, and those linked to winter storm events. Then, we fit and validate the underlying compound non-homogeneous Poisson processes taking into account the fact that the data are left-truncated. The best fitted loss distributions appear to be Burr and Generalised Extreme Value and for the first peril and log-normal for the second. Finally, we visualise the zero-coupon CAT bond prices for the selected best-fitted models.

Abstract Functional data analysis is typically performed in two steps: first, functionally representing discrete observations, and then applying functional methods to the so-represented data. The initial choice of a functional representation may have a significant impact on the second phase of the analysis, as shown in recent research, where data-driven spline bases outperformed the predefined rigid choice of functional representation. The method chooses an initial functional basis by an efficient placement of the knots using a simple machine-learning algorithm. The knot selection approach does not apply directly when the data are defined on domains of a higher dimension than one such as, for example, images. The reason is that in higher dimensions the convenient and numerically efficient spline spaces use tensor bases that require knots located on a lattice. This fundamentally limits flexible knot placement which is fundamental for the approach. The goal of this research is two-fold: first, to propose modified approaches that circumvent the issue by coding the irregular knot selection into the topology of the spaces of tensor-based splines; second, to apply the approach to a classification problem workflow for functional data that utilizes knot selection. The performance is preliminarily accessed on a benchmark dataset and shown to be comparable to or better than the previous methods.

Abstract This paper employs a statistical mechanical model as a framework to investigate how socioeconomic factors of individuals such as gender and place of residence influence their decision when deciding between comprehensive and third-party motor insurance policies in Ghana. Data from a general insurance firm was used for this investigation taking five years’ worth of transactions into account. The methods of partial least squares and the ordinary least squares are, respectively, used to estimate the parameters of the interacting and the non-interacting models in the Multipopulation Currie-Weiss model in a discrete choice framework. The findings showed that both location and gender have discernible influences on how people choose their motor insurance. We encourage insurance companies to intensify their campaign on the importance of motor insurance to all vehicle/car owners, especially those in rural areas in order to reduce the risk and associated losses in vehicular accidents on Ghanaian roads.

Abstract Concatenated backward ray mapping is an alternative for ray tracing in 2D. It is based on the phase-space description of an optical system. Phase space is the set of position and direction coordinates of light rays intersecting a surface. The original algorithm (Filosa, ten Thije Boonkkamp and IJzerman in J Math Ind 11(1):4, 2021) is limited to optical systems consisting of only straight surfaces; we generalize it to accommodate curved surfaces. The algorithm is applied to a standard optical system, the compound parabolic concentrator. We compare the accuracy and speed of the generalized algorithm, the original algorithm and Monte Carlo ray tracing. The results show that the generalized algorithm outperforms both other methods.

Abstract In the present paper the high-frequency induction welding process is studied numerically. The mathematical model comprises a harmonic vector potential formulation of the Maxwell equations and a quasi-static, convection dominated heat equation coupled through the Joule heat term and nonlinear constitutive relations. Its main novelties are a new analytic approach which permits to compute a spatially varying feed velocity depending on the angle of the Vee-opening and additional spring-back effects. Moreover, a numerical stabilization approach for the finite element discretization allows to consider realistic weld-line speeds and thus a fairly comprehensive three-dimensional simulation of the tube welding process.

Abstract In epidemic modeling, interpretation of compartment quantities, such as s, i, and r in relevant equations, is not always straightforward. Ambiguities regarding whether these quantities represent numbers or fractions of individuals in each compartment rise questions about significance of the involved parameters. In this paper, we address these challenges by considering a density-dependent epidemic modelling by a birth-death process approach inspired by Kurtz from 1970s’. In contrast to existing literature, which employs population size scaling under constant population condition, we scale with respect to the area. Namely, under the assumption of spatial homogeneity of the population, we consider the quantities of susceptible, infective and recovered per unit area. This spatial scaling allows diffusion approximation for birth-death type epidemic models with varying population size. By adopting this approach, we anticipate to contribute to a clear and transparent description of compartment quantities and parameters in epidemic modeling.

Abstract Mathematical modelling of a dengue epidemic with two serotypes including a temporary cross-immunity yields a nonlinear system consisting of ordinary differential equations (ODEs). We investigate an optimal control problem, where the integral of the infected humans is minimised within a time interval. The controls represent human actions to decrease the number of mosquitos in the model. An integral constraint is added, which takes a limitation on the sum of the human actions into account. On the one hand, we derive and apply a direct approach to solve the optimal control problem. Therein, a discretisation of the controls is constructed using spline interpolation in time. Consequently, a finite-dimensional constrained minimisation problem can be solved. On the other hand, we employ an indirect approach, where necessary conditions for an optimal solution are considered. This technique yields a multipoint boundary value problem of a larger system of ODEs including adjoint equations. We present results of numerical computations, where the two methods are compared.

Ergonomics is one of the main requirements for clothing at present. The task of ensuring and improving the ergonomic properties of clothing materials is relevant and requires an integrated approach. Ergonomic indicators of clothing encompass anthropometric, psychophysiological, and hygienic properties. Artificial leather finds application in second-layer clothing, demanding optimal microclimate creation within the under-garment space to maximize user comfort. In order to objectively assess the quality of newly developed materials with a polyurethane coating produced in the Republic of Belarus and compare them with imported analogues of similar materials, studies were conducted on the hygienic properties of fabrics with a polyurethane coating from various manufacturers, declared as clothing materials with good vapor and air permeability. Despite manufacturers endowing artificial leather with porosity, the challenge of inadequate hygienic properties persists due to excessively large pore sizes. In contrast, natural leather features medium-sized pores that allow vapor diffusion while preventing moisture ingress. Belarusian fabrics, incorporating a porous polyurethane coating, achieve microporosity akin to natural leather. Unlike previous substitutes, these fabrics exhibit favorable hygienic properties, including vapor and air permeability as well hygroscopicity. The cotton twill fabric forming the material base contributes to elevated performance indicators. Notably, the samples demonstrate satisfactory dirt-repellent properties, and no shrinkage occurs after wet treatment. Belarusian fabrics, featuring a microporous polyurethane coating, outperform imported counterparts with a monolithic and porous coating. Their ergonomic indicators closely align with those of natural leather, establishing their advantage as clothing materials for the second layer.

The relevance of research on obtaining innovative wound dressings from bioinert polymers by electrospinning is associated with the features of their structure and properties, such as the ability to imitate the structure of the extracellular matrix, delivery of active components to the wound surface, increased sorption, hemostatic and healing properties. The aim of this study is to evaluate the effect of nanofibrous coatings of different composition and structure on the vapor permeability of wound dress sings. The article describes the production of samples of wound dressings with a transdermal drug delivery system – a material with nanofibers made of a bioinert polymer to maintain the material's shape, as well as a biocompatible polymer with an added active component. Polyamide-6 was chosen as a bioinert polymer for creating the inner layer due to its chemical stability, biocompatibility, biodegradability, and acceptable mechanical properties. The outer layer is made from a biocompatible water-soluble polymer polyvinyl alcohol with the addition of a functional medicinal component. Nanofibrous coatings were produced by electrospinning method using Fluidnatek LE-50. The structure of the samples was assessed using a LEO 1420 scanning electron microscope from Carl Zeiss. Vapor permeability was determined by comparative testing of material samples using a MAC-50 humidity analyzer from Radwag, according to the PN EN ISO 14268:2005 standard. The analysis of the obtained results allowed us to establish that the vapor permeability of two-layer nanofibrous materials made of polyamide-6 and polyvinyl alcohol depends on the composition and thickness of the nanofibrous coating. Increasing the coating thickness and adding hydrophobic components to its composition leads to an insignificant decrease in vapor permeability.

Prolonged exposure to water negatively affects human health, leads to various diseases and reduces labor productivity. One of the main roles in ensuring waterproof properties of products is played by the materials from which these products are made. The acquisition of special properties by textile materials is achieved through the special finishing. Each type of such finishing serves to improve a specific consumer property. The most common ways of imparting waterproof properties are water-repellent impregnation and coating. The range of modern waterproof materials allows clothing manufacturers to significantly ease the package of materials while maintaining a comfortable microclimate in the undergarment space and a high degree of protection from unfavorable weather conditions. The aim of the work is to evaluate the properties of moisture-protective materials for the manufacture of special clothing using a comprehensive assessment methodology, which makes it possible to determine the quality of materials at the stage of incoming inspection. The method differs in that it ensures the selection of the most suitable material to perform the protective function of the product. Based on the current technical regulations and analysis of literature sources, the single quality indicators were selected. To determine the weight of the indicators, an expert method was used, which is based on assigning ranks to individual indicators. To determine the quality indicators we used both standard methods and author's methods that exclude the shortcomings of standard ones. As objects of the study were chosen moisture-protective materials having a woven base of polyester yarns, made by plain weave and containing in its structure a membrane layer made of polyetherurethane with various additives. In the course of the study, the effectiveness of application of the methodology of complex evaluation of properties of moisture-protective materials of similar structures and raw material composition for selecting a sample with the maximum degree of protection was established.

One of the main objectives of weaving production is the production of high quality fabrics and the search for ways to increase the productivity of weaving machines in order to improve the economic performance and competitiveness of the enterprise. In the production conditions of RUPTE "Orsha Linenmill" weaving machines Picanol OptiMax 190 are installed for the production of a range of household fabrics from low linear density yarn, most of which are exported. To ensure the quality of pure linen fine fabrics and the production of fine yarn, long flax fibre No. 11–14 and above is required. However, due to a number of reasons, the quality and quantity of incoming domestic raw materials cannot guarantee stable technological efficiency of production preparation and weaving processes, which is provided by the uniformity of flax and flax-containing yarn and its tensile properties. The aim of the research is to reduce warp breakage in weaving by determining rational production parameters on the loom and the choice of the sizing agent in order to increase the productivity of the loom in the production of linen fabrics with low surface density. In the article the properties of wet spinning yarn of 30 tex linear density made of long linen fibre are studied, the features of Karl Mayer SMR-SP-10-1800/800 SMR-SP-10-1800/800 sizing machine and Picanol OptiMax 190 rapier weaving machine are studied, the selection of modern sizing agent was made in order to increase machine productivity and reduce warp breakage during weaving of the most elite and marketable article – linen fabric with surface density of 120 g/m2. The changes in the technology made it possible to reduce the equipment downtime by reducing the number of warp breaks from 0.95 to 0.76 breaks per 1 running meter of fabric, to increase the coefficient of utilisation and to increase the annual volume of fabrics produced.

There is currently a renewed interest in research and development in the production and application of natural dyes for dyeing textile materials due to the growing popularity of eco-friendly lifestyles based on the use of environmentally friendly products. The article presents the research results into an environmentally friendly, energy-efficient technology for dyeing wool yarn using natural dyes. The approach aims to reduce the load on wastewater while enhancing color intensity and maintaining the color range of the textile material through the use of biomordants. A shortened, environmentally friendly dyeing scheme is proposed, the distinctive features of which are the replacement of the many-hour process of preparing natural raw materials for dyeing with ultrasonic treatment for 40 minutes at a generator power of 70W. This treatment intensifies the extraction of dyes and reduces the consumption of natural raw materials, resulting in vibrant colors during subsequent dyeing. The integrated processes of extraction, dyeing and pickling contribute to efficiency. Dying substances were extracted from the crushed root of Potentilla erecta; the plant is non-toxic and is widely used for medicinal purposes. Biomordants such as Aloe Vera juice, sauerkraut juice, and tartaric acid were used. The study reveals that it is advisable to dye wool yarn with an aqueous solution of cinquefoil roots at a working bath temperature of 80 °C, which will ensure maximum diffusion of dye molecules into the fiber. By combining dye extraction from natural raw materials and dyeing processes, while reducing the working bath temperature from 100 to 80 °C, the energy intensity of the technology for coloring wool yarn can be significantly reduced.

The aim of the work is to study the effect of the acid comonomer content and forming conditions on the dyeability of gel fibers based on copolymers of acrylonitrile (AN), methyl acrylate (MA) and 2-acrylamide-2-methylpropanesulfonic acid (AMPS). For this purpose, model copolymers AN, MA and AMPS were synthesized with an acid comonomer content in the monomer mixture from 0 to 2 % (wt.) by changing the proportion of MA. It has been established that the actual content of the acidic monomer in the fiber-forming copolymer is higher than expected based on the composition of the initial monomer mixture. Analysis of the dyeability of model samples of polyacrylonitrile fibers formed from synthesized copolymers by the dimethylformamide method with varying technological regimes showed that the amount of dye selected from the dye bath by the gel structure of the fibers is proportional to the content of the acid comonomer in the fiber-forming copolymer. The most significant decrease in the whiteness index (lightness) of the finished fibers is observed with an increase in the AMPS content in the copolymer from 0 to 0.7 % (wt.). Microscopy of model fibers was carried out. Based on the experimental data obtained in the course of the work, it was suggested that in order to achieve an acceptable intensity of dyeing and ensure a soft handle of polyacrylonitrile fibers, the optimal content of acid comonomer during the synthesis of fiber-forming copolymers based on AN, MA and AMPS should be in the range of 0.9 up to 1.3 % (of the mass of monomers in the reaction mixture).

The article presents the results of a study on methods for obtaining labour market data from web sources. The relevance of the research stems from the need to explore innovative approaches to collecting and analyzing labour market data, ensuring comprehensive, reliable, and timely information in the context of the digital transformation of society. The purpose of the study is to compare the methods of collecting and analyzing information, to identify their advantages and disadvantages. Methodologically, an interdisciplinary approach combines the methods of data science, machine learning, natural language processing and human resource management. Empirically, the study draws from expert surveys conducted with heads of Belarusian IT companies and data sourced from the Artificial Analysis platform. Key findings reveal that large language models (LLM) outperform other methods in terms of speed, quality and ease of use. Among the top 10 models, GPT-4o is recognized as the most effective, prioritized for business tasks and economic analysis. The advantages and disadvantages of data collection and analysis methods are revealed. The scientific novelty of the research consists in obtaining new theoretical and empirical results that reveal the potential of using LLM to obtain data on the labour market, as well as in identifying priority areas for the development of methodology and tools in this area. The practical significance lies in the development of a methodology for comparative analysis of the effectiveness of methods for collecting and analyzing information about the labour market from web sources; substantiation of promising directions for the development of information collection methods that open up opportunities for creating innovative solutions in the field of human resource management based on artificial intelligence and big data processing. Financial support for the research was provided by the Belarusian Republican Foundation for Basic Research within the framework of the scientific project No Г24-013 “Theoretical approaches and methodological support for labour market analysis in the Republic of Belarus using big data”.

In contemporary conditions, the role of youth in the labour market is increasing. Economic growth depends on the extent to which it is possible to preserve and enhance the intellectual capital of young people. Consequently, young individuals are the basis of the workforce in the future, and the development of our state depends on the formation of favorable work conditions for their engagement. Nowadays, youth play a pivotal role in shaping the political, economic, scientific, technical, industrial and social fabric of society. Therefore, fostering patriotic work attitudes among young people to ensure their effective transition from school to work is a primary aim for the economic and social security of the state, as well as maintaining stable socio-economic dynamics in the future. The purpose of the study is to develop theoretical and methodological approaches for establishing a digital ecosystem within the youth labour market. It involves a comparative assessment of youth employment in the Republic of Belarus and the regional labour market (specifically, Vitebsk region). Additionally, the study outlines requirements for data processing, classification and visualization within the digital ecosystem of the youth labour market in the region. The object of the study is the youth labour market in the Vitebsk region. The subject of the study encompasses the socioeconomic relations that emerge during youth employment, ensuring its effectiveness. The article argues for the necessity of creating a digital ecosystem of the youth labour market and proposes its own definition. Theoretical approaches to the analysis of the youth labour market, which serves as the methodological basis for creating its digital ecosystem, have been studied and summarized. Furthermore, an analysis of key indicators related to the youth labour market in the Vitebsk region was carried out, comparing it with the youth labour market in the Republic of Belarus. Notably, the study highlights the factors influencing demand in the youth labour market of the Vitebsk region. Finally, basic principles for data processing and visualization within the digital ecosystem of the youth labour market in the region have been developed.

The article describes the use of digital integrated platforms for supply chain management in neoclusters. The article examines the evolution of the cluster concept in economic literature and identifies the distinctive features of a neocluster (inter-industry cooperation, active use of the latest technologies and research for business development, use of elements of artificial intelligence, big data, automation and smart systems; implementation of the concept of sustainable development in business) as an improved form of a business cluster that can be formed in new industries or transformed from a classical cluster as a result of digitalization. It has been established that joint supply chain management through digital integration platforms is an important condition for the formation and development of neoclusters, process optimization, improving interaction between participants and increasing competitiveness in neoclusters. Digital platforms and neoclusters have become powerful engines of modern logistics and supply chain management and play a key role in creating innovative ecosystems that stimulate the growth and development of industries and regions. Mechanisms for coordination and management of supply chains have been developed, such as the formation of a cluster core and filtering cluster participants. The criteria for selecting the core and participants of the neocluster have been determined. Foreign experience in the implementation of digital platforms for supply chain management, examples of their successful application in neoclusters in the fields of healthcare, education, and green urban planning have been studied. An approach is proposed for creating a platform for supply chain management in neoclusters of the Republic of Belarus using domestic developments: Belarusian IT solutions, technologies and services that will contribute to the development of the national technological base and ensure the technological sovereignty of the Republic of Belarus, ensure security and data protection, maintain the confidentiality of information using certified security measures. The tasks for implementing a platform solution for supply chain management in neoclusters are formulated.

The relevance of studying the processes of digitalization and digital transformation occurring in organizations is due to the fact that these processes are accompanied not only by the expansion of business and an increase in its competitiveness, but also by the development of the potential of the organization’s employees. The digital transformation of economic entities is accompanied by the creation of new professions, changing competencies in education and other transformations of the entire socio-economic system. The aim of this study is to assess the dynamics of digitalization processes in organizations of the Republic of Belarus and the Vitebsk region, the structure of digital transformation of industrial organizations in the country and to substantiate the directions for the development of statistical observations to form an information base for managing these processes. The solution to the research problems was carried out using general scientific methods: observation, comparative analysis, quantitative and statistical analysis, deduction and induction. As a result of the study of the essence of the processes of digitalization and digital transformation, their distinctive features were identified. A differentiation of statistical observation indicators into indicators for assessing the level of digitalization and indicators for assessing the level of digital transformation of organizations according to the purposes of using the Internet and the digital technologies used is proposed. A conclusion is made about the need to expand the program of statistical monitoring of digital transformation processes in organizations of the Republic of Belarus.

Abstract Thermal spray coating is a critical process in many industries, involving the application of coatings to surfaces to enhance their functionality. This paper proposes a framework for modelling and predicting critical target variables in thermal spray coating processes, based on the application of statistical design of experiments (DoE) and the modelling of the data using generalized linear models (GLMs) with a particular emphasis on gamma regression. Experimental data obtained from thermal spray coating trials are used to validate the presented approach, demonstrating that it is able to accurately model and predict critical target variables. As such, the framework has significant potential for the optimization of thermal spray coating processes, and can contribute to the development of more efficient and effective coating technologies in various industries.

Power semiconductor devices are the most important components in power electronics applications. They are also the most fragile components of electronic circuits. A power semiconductor device's switching performance and protection depend on the gate drive circuit specifications. Therefore, choosing an appropriate gate-driver and designing its corresponding circuits is necessary. This paper is a technical review of the proper design of gate drivers for silicon power switches (like Si IGBT and Si MOSFET) in industrial power electronics applications. In this paper first conducts an overview of the main specifications of gate drivers for industrial power electronics applications. Then, concerning the protective role that a gate-driver can provide, crucial points of an effective design are discussed. Finally, a circuit is proposed to test the gate driver's short-circuit protection. The circuit is experimentally evaluated for three gate drives, and the results are discussed. A practical comparison of the protection performance of commercial gate drives ACPL-330J, ACPL331, and PC929 is also conducted.

A novel technique for creating logic gates and digital circuitry at the nanoscale is quantum cellular automata (QCA). The sensitivity of the circuit is enhanced and quantum circuits are more susceptible to unfavorable external conditions when component size are reduced. In this article, we offer a five-input majority gate with fault-tolerant feature in QCA technology, taking into account the significance of constructing circuits that can withstand flaws. We also assess all potential defects in the process of arranging cells in specific locations on the surface. These errors consist of extra cells, rotation, deletion, and displacement. The gate under study is subjected to the aforementioned four failure categories in the first stage. The QCADesigner simulator engine is then used to assess the accuracy of the circuit performance in the second step. 41 quantum cells have been used to make the gate of this five-input majority gate with fault-tolerant feature in QCA technology. Several techniques are explored to discover such a majority gate, such as adding cells (i.e., introducing redundancy into the circuit) and particular cell layout techniques. The goal is to come up with a design that can ideally withstand possible faults with the least amount of overhead on the circuit for fault-tolerant through a certain cell layout. The findings demonstrate the implemented majority gate's notable advantage over comparable scenarios.

In this paper, we present a practical encoding and decoding scheme for the binary Wyner-Ziv problem based on graph-based codes. Our proposed scheme uses low-density generator-matrix (LDGM) codes in lossy source coding part and low-density parity-check (LDPC) codes in syndrome generation and decoding part. Actually, we apply Bias-Propagation algorithm for lossy source coding or binary quantization and Sum-Product algorithm for syndrome-based channel decoding. Using appropriate degree distributions for LDGM codes and optimized degree distributions for LDPC codes, we will be able to achieve close rate-distortion performance to the theoretical Wyner-Ziv bound. Also, we extend our proposed scheme for presenting a practical coding scheme for the binary Chief Executive Officer (CEO) problem. In our scheme, encodig is based on binary-quantization and Slepian-Wolf coding using source-splitting technique. It is shown that, source-splitting technique is an efficient strategy for achieving non-corner points in Slepian-Wolf rate region. We show that, this technique along with iterative message-passing algorithms can be efficient for having close rate-distortion performance to the Berger-Tung inner bound of binary CEO problem for non-corner points too.

Optimization algorithms are widely used in various fields to find the best solution to a problem by minimizing or maximizing an objective function, subject to certain constraints. This paper introduces the development and application of an innovative optimization algorithm (WOADD) designed to address the challenges posed by constrained optimization problems with dependent data. Unlike traditional algorithms that struggle with data dependencies and valid range constraints, WOADD incorporates a novel normalization process and a dynamic updating mechanism that accurately considers the interdependencies among features. Specifically, it adjusts the search strategy by calculating a scaling parameter to maneuver within feasible regions, ensuring the preservation of data dependencies and adherence to constraints, thus leading to more efficient and precise optimization outcomes. Our extensive experimental analysis, comparing WOADD against other swarm-based optimization methods on a suite of benchmark functions, illustrates its superior performance in terms of faster convergence rates, improved solution quality, and enhanced determinism in outcomes.

Radiofrequency microelectromechanical system (RF-MEMS) switches are utilized across a broad spectrum of industries, telecommunications, aerospace and smartphone technology.Herein, we proposed a new numerical and simulation analysis approach for spring constant (k) values as the characteristic mechanical parameters of RF MEMSs using the modified energy method (MEM). The proposed RF-MEMS switch was analyzed and simulated using the COMSOL package, and the findings confirmed that the alteration in the position and length (L) of the beams not only diminishes k significantly but also provides actuation-voltage VAC ultrasensitive structures and great concomitance between numerical and simulation k and VAC values. VAC value for the L-dependent numerical k (0.07 N m-1) was calculated to be 1.61 V which was validated with simulation outputs at 0.08 N m-1 and 1.80 V for k and VAC, respectively. Additionally, the switching time (ts), Von Mises Stress (VMS), natural frequency (fn) and mass (m) characteristic mechanical parameters were found to be 25.60 µs, 4.50 MPa, 3118.60 Hz , and0.21 ng, respectively. RF analysis was conducted in HFSS, revealing promising simulation results for the studied RF-MEMS switches. The return loss demonstrates excellent performance, registering better than -1 dB at 46 GHz. Furthermore, the insertion loss is noteworthy, exceeding expectations with values better than -0.7 dB at 46 GHz. Importantly, the isolation is impressive, exceeding -25 dB across the frequency range from 40 GHz to 35 GHz, all achieved with a modest actuation voltage of 1.8 V. This study contributes valuable insights intothe design and application of low-actuation-voltage RF-MEMS switches.

Battery Management System (BMS) including measurements errors that causes decrease in ‎the quality of ‎calculated State of the Charge (SOC). It will limit the accurate estimation of ‎the SOC that is a critical challenge in ‎some of the engineering fields such as medical science, ‎robotics, navigation and industrial applications. These ‎facts implies on the significance of ‎SOC estimation from battery measurements that is the matter of the literature ‎through the ‎recent years. Due to the dependency of the EKF to the system model, the change in the ‎battery ‎parameters and noise information cause losing performance in the SOC estimation ‎over the time. In this paper, we ‎assume that the battery parameters including internal ‎resistance and capacitor and also the noise information are ‎varying over the time. To solve ‎that, two separate on-line identification algorithms for parameters and noise ‎information are ‎introduced. In more details, a Recursive Least Square (RLS) algorithm is used to identify ‎the ‎resistance and capacitor values. Moreover, the process and measurement noise covariance are ‎estimated based ‎on iterative noise information identification algorithm. Then all of the ‎updated values are used in the EKF ‎algorithm. This paper aims to address the issue of uncertainty in SOC estimation by proposing two algorithms. ‎The first algorithm focuses on identifying deterministic uncertainty, which refers to uncertainty in model ‎parameters. To address the challenge of uncertain model parameters, RLS is introduced.