Dynamic Procedure Research Articles

On a Stable Multiplicative Calculus-Based Hybrid Parallel Scheme for Nonlinear Equations

Fractional-order nonlinear equation-solving methods are crucial in engineering, where complex system modeling requires great precision and accuracy. Engineers may design more reliable mechanisms, enhance performance, and develop more accurate predictions regarding outcomes across a range of applications where these problems are effectively addressed. This research introduces a novel hybrid multiplicative calculus-based parallel method for solving complex nonlinear models in engineering. To speed up the method’s rate of convergence, we utilize a second-order multiplicative root-finding approach as a corrector in the parallel framework. Using rigorous theoretical analysis, we illustrate how the hybrid parallel technique based on multiplicative calculus achieves a remarkable convergence order of 12, indicating its effectiveness and efficiency in solving complex nonlinear equations. The intrinsic stability and consistency of the approach—when applied to nonlinear situations—are clearly indicated by the symmetry seen in the dynamical planes for various parameter values. The method’s symmetrical behavior indicates that it produces accurate findings under a range of scenarios. Using a dynamical system procedure, the ideal parameter values are systematically analyzed in order to further improve the method’s performance. Implementing the aforementioned parameter values using the parallel approach yields very reliable and consistent outcomes. The method’s effectiveness, reliability, and consistency are evaluated through the analysis of numerous nonlinear engineering problems. The analysis provides a detailed comparison with current techniques, emphasizing the benefits and potential improvements of the novel approach.

Object detection and tracking for drones: A system design using dynamic visual SLAM

Abstract. Drones can play a quite crucial role in many walks of life today. Enhancing the visual perception ability of drones is crucial to their intelligence level. Among them, it is necessary to focus on strengthening the detection, tracking and mapping capabilities of drones for dynamic objects. However, the existing visual SLAM systems carried by drones do not perform well in dynamic environments. This project designs a monocular visual SLAM system specifically for drones, aiming to achieve efficient three-dimensional mapping and target tracking, surpassing the limitations of simple static mapping and positioning. Besides, this project constructs a drone dynamic SLAM system developed on the ORB-SLAM3 structure, uses drone images to detect, track and map object motion models, and reconstructs environmental maps to obtain motion parameters with real physical scales. This project strives to optimize the input pre-processing module, improve the validity of data and output environmental maps and raster maps. The outcomes demonstrate the system's strong accuracy and adaptability in dynamic installation procedures.

Rapid dynamic load estimation procedure for lifting propellers in forward flight

AbstractLifting propellers operate at oblique inflow and thus encounter severe dynamic loads during forward flight, impacting structural integrity, fatigue, and vibration. Numerical optimization approaches consider aerodynamic, structural mechanical, and aeroacoustic aspects within preliminary design. To also account for dynamic loads during forward flight, a novel procedure allows their rapid estimation. Based on steady-state simulations combining strip theory and finite beam elements, aerodynamic excitation, damping, and stiffness are defined in the frequency domain. Loads are derived through a linear inflow model and quasi-steady aerodynamics. Damping and stiffness loads are linearized and transferred into matrix form to calculate the frequency response. The computationally expensive need for simulations in the time domain is thus avoided. Applicability extends to both fixed and variable pitch lifting propellers utilized in large multicopters for cargo or passenger transportation. Comparisons to time-marching simulations show good agreement with deviations of approximately 10 %. The analytical derivation yields physical insights to understand and reduce dynamic loads and their magnification due to resonance.

A complementary approach for detecting biological signals through a semi-automated feature selection tool.

Untargeted metabolomics is often used in studies that aim to trace the metabolic profile in a broad context, with the data-dependent acquisition (DDA) mode being the most commonly used method. However, this approach has the limitation that not all detected ions are fragmented in the data acquisition process, in addition to the lack of specificity regarding the process of fragmentation of biological signals. The present work aims to extend the detection of biological signals and contribute to overcoming the fragmentation limits of the DDA mode with a dynamic procedure that combines experimental and in silico approaches. Metabolomic analysis was performed on three different species of actinomycetes using liquid chromatography coupled with mass spectrometry. The data obtained were preprocessed by the MZmine software and processed by the custom package RegFilter. RegFilter allowed the coverage of the entire chromatographic run and the selection of precursor ions for fragmentation that were previously missed in DDA mode. Most of the ions selected by the tool could be annotated through three levels of annotation, presenting biologically relevant candidates. In addition, the tool offers the possibility of creating local spectral libraries curated according to the user's interests. Thus, the adoption of a dynamic analysis flow using RegFilter allowed for detection optimization and curation of potential biological signals, previously absent in the DDA mode, being a good complementary approach to the current mode of data acquisition. In addition, this workflow enables the creation and search of in-house tailored custom libraries.

Facial Nerve Disorders: Sociodemographic Predictors and Temporal Trends in Dynamic Facial Reanimation in a National Administrative Claims Database.

Background: Recent surgical innovations have increased treatment options for patients with facial nerve disorders (FNDs), leading to substantial improvements in functional and psychosocial outcomes. However, it is unclear whether sociodemographic factors are associated with the likelihood of undergoing dynamic facial reanimation procedures. Objective: In patients undergoing FND surgical treatment, what sociodemographic variables are associated with undergoing dynamic facial reanimation compared with static facial reanimation within a 16-year period? Methods: This was a retrospective study of adults undergoing surgical management for FND from 2007 to 2022 using the Merative™ Marketscan® Research Databases. Chi-squared and logistic regression analyses were performed. Results: Among 4,730 adults who underwent FND surgical intervention, 1,390 (34.2%) underwent dynamic facial reanimation. In multivariable regression analyses, more recent treatment year, younger age, and living in the Northeast United States were significant predictors of undergoing dynamic reanimation. Secondary analysis demonstrated that FND patients who were younger, female, and living in the Northeast United States were more likely to undergo concurrent selective neurectomy. Conclusions: These analyses demonstrate significant sociodemographic and temporal associations in the surgical management of FND. Future work is needed to evaluate how sociodemographic factors might influence access and decisions to pursue different types of reanimation procedures.

On robust inference in time series regression

Abstract Least squares regression with heteroskedasticity consistent standard errors (“OLS-HC regression”) has proved very useful in cross section environments. However, several major difficulties, which are generally overlooked, must be confronted when transferring the HC technology to time series environments via heteroskedasticity and autocorrelation consistent standard errors (“OLS-HAC regression”). First, in plausible time-series environments, OLS parameter estimates can be inconsistent, so that OLS-HAC inference fails even asymptotically. Second, most economic time series have autocorrelation, which renders OLS parameter estimates inefficient. Third, autocorrelation similarly renders conditional predictions based on OLS parameter estimates inefficient. Finally, the structure of popular HAC covariance matrix estimators is ill-suited for capturing the autoregressive autocorrelation typically present in economic time series, which produces large size distortions and reduced power in HAC-based hypothesis testing, in all but the largest samples. We show that all four problems are largely avoided by the use of a simple and easily-implemented dynamic regression procedure, which we call DURBIN. We demonstrate the advantages of DURBIN with detailed simulations covering a range of practical issues.

Efficient and Lightweight Model-Predictive IoT Energy Management

Multi-sensor IoT devices often rely on renewable energy and batteries to support diverse field deployments. A device’s sensors use significant energy, so careful energy management is needed to maximize its operating time while meeting application requirements. Model Predictive Control (MPC), a successful energy management technique for other application domains, requires significant computational resources usually not available in typical IoT sensing devices. We develop and evaluate a low-complexity approximation to MPC for IoT device operations powered by renewable (solar) energy, where the approximation is guided by the charging characteristics of real batteries. The complex MPC optimization problem is solved by decomposing it into a time-dependent energy allocation problem, and a task-dependent sensor scheduling problem, each of which is solvable with cubic time complexity. We utilize a novel combination of an incremental max-min fair allocation method and a recursive dynamic programming-like procedure. This low-complexity predictive optimization step is integrated with a very simple parabola-based adaptive solar prediction to provide a full system solution, termed Predictive EneRgy Management for IoT (PERMIT) , that can be implemented in lightweight IoT devices. PERMIT is evaluated through experiments on a solar-powered Raspberry Pi device with multiple sensors. We also complement our evaluations with simulations based on real device data traces. Results show that PERMIT’s low-complexity algorithm approximates the exact MPC solution very closely. PERMIT also performs significantly better than Signpost, a comparable IoT energy management solution.

Corruption, Economic Growth, and Non-performing Loans in Sub-Saharan Africa: An Empirical Analysis (2011–2019)

AbstractThis study explores the impact of corruption and economic growth on non-performing loans (NPLs) in Sub-Saharan Africa (SSA) after the global financial crisis. We use the Arellano-Bond Generalized Method of Moments dynamic panel estimation procedure on 493 banks from 31 Sub-Saharan African countries from 2011 to 2019. We find that corruption and economic growth have significantly positively and negatively influenced NPLs in SSA, respectively. We further investigate how corruption affects NPLs and observe that bank size and NPLs have a positive relationship. Simultaneously, well-capitalized banks and the effectiveness of the judicial system reduce the level of NPLs. The study's uniqueness lies in evaluating the impact of corruption on NPLs at the sub-regional level. The results reveal how corruption has not significantly affected Central and Southern African banks' NPLs but vice versa in West and Eastern Africa. The study concludes that NPLs are a dead weight loss to society. The results confirm how anti-corruption campaigns, improvements in judicial regulation, and sustained economic growth can help mitigate non-performing loans. Furthermore, policy implications such as enhancing institutional quality and implementing growth-sustaining measures have been suggested.

Autonomous AI Systems for Continuous Learning and Real-Time Decision Making

Autonomous AI systems are upsetting navigation by constantly learning and pursuing ongoing choices without human oversight. These frameworks have applications across businesses like medical services, money, and assembling, where ongoing information is fundamental for functional achievement. This paper investigates the parts of independent simulated intelligence frameworks, including constant learning models, continuous information handling structures, and dynamic procedures. We likewise look at the difficulties such frameworks face, including information security, reasonableness, and taking care of edge cases. Through contextual analyses in various enterprises, we show the capability of independent computer-based intelligence frameworks to reshape the fate of savvy navigation. At last, we propose future bearings for research, zeroing in on moral structures and half and half learning models to further develop flexibility and straightforwardness in these frameworks.

Research on digital vibration model of typical components in pipe system

The spatial distribution of pipe system is complex, long span and multi-point elastic support, so vibration analysis of pipe system is required in the pipeline design. This paper takes the pipe system as a typical component, based on the Timoshenko beam model, establishes a dynamic procedure for straight pipe elements that considers the axial flow rate of fluid, the pressure on the pipe wall, the Poisson coupling effect of fluid and pipeline structure, and the axial, transverse and torsional vibration. Through Laplace transform, the frequency simplified dynamic model is established. And the frequency transfer matrix model of straight pipe elements and the boundary constraint matrix of common boundary (such as fixed support) is established. Finally, the frequency domain transfer matrix model of different pipeline elements are set to the overall transfer matrix of the flow pipe system, providing a convenient means for rapid optimization of pipe system vibration.

An interprocess communication-based two-way coupling approach for implicit–explicit multiphysics lattice discrete particle model simulations

In this study, the researchers have developed a Multiphysics-Lattice Discrete Particle Model (M-LDPM) framework that deals with coupled-fracture-poroflow problems. The M-LDPM framework uses two lattice systems, the LDPM tessellation and the Flow Lattice Element (FLE) network, to represent the heterogeneous internal structure of typical quasi-brittle materials like concrete and rocks, and to simulate the material’s mechanical and transport behavior at the aggregate scale. The researchers revisited the LDPM governing equations and added the influence of fluid pore pressure. They also derived the Flow Lattice Model (FLM) governing equations for pore pressure flow through mass conservation balances for uncracked and cracked volumes. The M-LDPM framework was implemented using Abaqus user element subroutine VUEL for the explicit dynamic procedure of LDPM and user subroutine UEL for the implicit transient procedure of FLM. The coupling of the two models was achieved using Interprocess Communication (IPC) between Abaqus solvers. The M-LDPM framework can simulate the variation of permeability induced by fracturing processes by relating the transport properties of flow elements with local cracking behaviors. The researchers validated the M-LDPM framework by comparing the numerical simulation outcomes with analytical solutions of classical benchmarks in poromechanics.

Seismic Performance Evaluation of Irregular Auditorium Building based on ASCE 41-23

This study discusses the seismic performance evaluation of an irregular auditorium building. The building is an educational facility which has roof span up to 46 meters, column height up to 10 meters, beam span up to 15 meters, cantilever up to 6.50 meters, 2 inclined columns with the angle up to 54.72°, and 1 transfer column with 1 transfer beam. The evaluation process was carried out using Tier 3 method with linear dynamic procedure which consisted of Response Spectrum Analysis (RSA) and Linear Time History Analysis (LTHA) according to ASCE 41-23 using SAP2000 subjected to short (S s ) and long (S ) period of earthquakes. The building is designed to Risk Category IV, which the target performance levels for structural components are Immediate Occupancy (IO) for Basic Safety Earthquake 1 for New Building (BSE-1N) and Life Safety (LS) for Basic Safety Earthquake 2 for New Building (BSE-2N). Even though the building had a torsional strength irregularity, the percentage of components with a Demand-Capacity Ratio value that did not meet the requirements was 9.87% of the total components; hence the linear procedure was assumed to be still applicable. Analyses showed that the average acceptance criteria ratio of the components with the RSA method was lower than with the LTHA method but the percentage of the components with acceptance criteria ratio exceeding 1 using RSA method was higher than using the LTHA method. In addition, the results indicated that the average performance level of the components was IO for BSE-1N and LS for BSE-2N, which both results had met the expected performance level targets. However, the maximum performance level of the components did not meet the IO performance level target for BSE-1N and did not meet the LS performance level target for BSE-2N.

Sufficiency assessment of intensity measures for natural and spectral-matched ground motion records

A correct Intensity Measure (IM) selection is essential for Performance-Based Earthquake Engineering (PBEE) applications, as any probabilistic seismic demand model (PSDM) depends significantly on the IM. If a single IM can describe the complexity of the corresponding ground motion record, it can be defined as sufficient in an absolute sense. However, this is unlikely because a single number should be able to inform on the frequency content, the amplitude, the duration, the energy content, etc. For this reason, literature studies have defined sufficiency in a relative sense to investigate whether one IM is more sufficient (i.e., more informative) than another in predicting the structural response. This work explores the relative sufficiency of eight scalar IMs through Nonlinear Response History Analyses (NRHAs) using two sets of 20 pairs of ground motion records. Both sets are spectrum-compatible and consist of unscaled natural and spectral-matched records. Also, both Cloud and Incremental Dynamic Analysis procedures are used. This study demonstrates that Cloud analysis cannot be used in its conventional form to study sufficiency when spectral-matched accelerograms are used. When natural accelerograms are employed, the results clearly indicate the existence of a sufficient IM among those selected. Conversely, it is more difficult to define the relative sufficiency of the IMs for spectral-matched records because the operation of record adjusting leads to similar structural demands. This result could question either the validity of using spectral-matched accelerograms for PBEE due to the lack of aleatory variability in the structural demand or the necessity of having a sufficient IM when a PSDM is fitted in a PBEE analysis using spectral-matched accelerograms.

A high-static-low-dynamic-stiffness delayed resonator vibration absorber

Delayed resonator (DR), which enables complete vibration suppression through loop delay tuning, has been extensively investigated as a linear active dynamic vibration absorber since its invention. Besides, the nonlinear high-static-low-dynamic stiffness (HSLDS) has been widely used in vibration isolators for broadband (yet incomplete) vibration reduction. This work combines the benefits of DR and the nonlinear HSLDS characteristics, thus creating a nonlinear DR (NDR). Three unexplored topics are considered: (1). To address the nonlinear dynamics that are made complicated by the introduction of delay and the nonlinearity coupled between the NDR and the primary structure; (2). To tune the control parameters to seek possible complete vibration suppression in the nonlinear case, and accordingly, to determine the operable frequency band; (3). To evaluate how the HSLDS characteristics can enhance the performance of the linear DR (LDR) and how to design an NDR to maximize its benefits. Without loss of generality, a classic nonlinear HSLDS structure with three springs and two links is considered, and mathematical tools and computational algorithms are introduced or proposed to efficiently address theoretical analysis. Using the parameters of an experimental setup, we show that a properly tuned NDR suppresses the vibrations on the primary structure to a sufficiently low level. Besides, the HSLDS characteristics extend the operable frequency band compared with the LDR, while strong nonlinearity limits such extension. The proposed analysis procedures for delay-coupled nonlinear dynamics, alongside the control parameter tuning, determination of operable frequency bands, and structural design rules, establish a basic theoretical framework for the NDR design.

New oscillatory features of electrodermal activity signals evaluated in automated mental workload monitoring

Oscillatory components are discriminative features of mental workload monitoring in electrodermal activity (EDA) signals. Currently, most feature extraction techniques mainly focus on time domain or linear techniques but rarely consider the non-stationary nature, individual differences, and recording noise. This study has proposed a new time–frequency feature extraction method based on Tunable Q-factor Wavelet Transform providing an effective quantification of oscillatory behavior of a single transient. The extracted features have been classified using a recurrent neural network with the advantage of dynamic mapping procedure. The proposed method has been evaluated using EDA data acquired during an arithmetic task of five workload levels with both subtle and distinct differences. In a comparative study, the effects of several decomposition parameters and cognitive factors have been explored. Experimental results have shown that it achieves an average accuracy rate of 98.52% for three workload levels discrimination by optimizing the number of features to 3 (features extracted from a low frequency sub-band) from 15 (features extracted from five frequency sub-bands), i.e., 80% feature reduction. The greatest contribution coming fromthe smallest frequency sub-bands has also been obtained. This method is superior to other existing methods in separating 3 to 5 workload levels with both distinct and subtle differences extracting effective oscillatory characteristics. Considering the merits of a simple feature extraction procedure and cost-effectiveness of a single lead EDA signal as well as high discriminability between several workload levels, it provides a trade-off between performance and computational complexity, thus demonstrating its potential for online human-error prevention systems.

A finite volume parallel adaptive mesh refinement method for solid-liquid phase change

We present a finite volume adaptive mesh refinement method for solid-liquid phase change problems with convection. The refinement criterion consisted of three different error estimators for the solid-liquid interface, the flow field, and the temperature field respectively. For the solid-liquid interface, the cells undergoing phase change were refined based on the maximum difference in the liquid fraction over the cell faces. For the flow field and the temperature field, an error indicator was used based on the cell residual method. To maintain a high parallelization efficiency, a dynamic load balancing procedure was used. The adaptive mesh refinement strategy was verified through three different test cases, these are the gallium melting in both 2D and 3D cavities, and the molten salt reactor freeze valve. For all three cases, very good agreement was obtained between the adaptive mesh results and the reference solutions. In addition, more accurate results were obtained with the adaptive meshes compared to static meshes with a similar amount of mesh cells. This illustrated the potential of the current approach for developing computationally efficient numerical methods for solid-liquid phase change problems.

Near-infrared-based hematocrit determination of dried blood samples collected by volumetric absorptive microsampling: An in-depth evaluation

BackgroundVolumetric absorptive microsampling (VAMS) has gained great interest in the past decade because of its capability to collect a fixed volume of blood independent of the hematocrit (Hct). This alternative microsampling technique tackles the well-known Hct-related area bias, a major issue in conventional partial-punch dried blood spot (DBS) analysis. However, as microsampling typically involves the collection of blood, whereas the ‘gold standard’ matrix used in clinical practice is plasma or serum, a major concern remains: how do we interpret or convert these dried blood microsample-based results? ResultsA method to determine the Hct of a VAMS sample based on non-contact NIR spectroscopy was developed and extensively validated. Using authentic patient samples (n = 102) and a dynamic measurement procedure (turning the VAMS samples in between measurements), accurate (maximum bias of –0.022 L/L) and precise (maximum total imprecision of 10.6 %) Hct determinations were obtained. Sample storage (up till one month at room temperature or lower), operator and lot of VAMS devices did not impact the resulting Hct, confirming the method’s robustness. Significance and noveltyThis is the first non-contact method allowing Hct determination of VAMS samples. Importantly, there is no ‘consumption’ of part of the microsample, which would impact subsequent analysis of target compounds. As one of the issues hampering wide implementation of microsampling is related to interpretation of (dried) blood-based results and conversion into plasma results, a simple and easy way to determine the Hct of a microsample, as presented here, may help to overcome this hurdle.

Theoretical insight into the sulfurated poisoning process of rhenium-doped nickel catalyst

Three rhenium-doped nickel catalyst surfaces including Re-Ni(111), Re-Ni(200) and Re-Ni(220) were built via Re atom substituting one atom of upper layer Ni surfaces. The sulfurated poisoning process involving H2S adsorption and subsequent splitting steps (H2S→HS+H, HS→S+H) on the three surfaces was investigated and further compared using density functional theory (DFT) to understand the crystal-plane effect. For H2S adsorption, the H2S molecule is preferentially adsorbed on the Re top and bridge ReNi1 sites. Re-Ni(200) surface possessed lower H2S adsorption energy compared to Re-Ni(111) and Re-Ni(220) surfaces, due to its smaller coordination number and higher d-orbital intensity at Fermi level. For splitting steps, transition state search showed that the two processes on the three surfaces are all calculated as exothermic while the energy barrier is basically in the range of 0.23 eV∼0.44 eV, which signifies that the poisoning process is favorable both kinetically and thermodynamically. The splitting steps can be described as a dynamic procedure that contains intimate HS fragment activation, S–H bond stretching accompanied by H atom departure and final dissociation into HS+H or S+H co-intermediates. The valence bond changes during dissociation deduced from projected density of states (PDOS) analysis also proved the above procedure.

An algorithm based on B-differentiable equations method for solving problems involving frictional contact coupled with concrete plastic damage

In this paper, an algorithm is proposed to solve the frictional contact problems while considering elasto-plastic material properties. The static and dynamic solution procedures are derived in incremental form, respectively. The proposed method employs a two-layer iteration strategy. In the outer iteration layer, the equilibrium equations are solved, with the stress and stiffness matrix updated at each iteration. Within the inner iteration layer, the contact force is calculated, where the contact equations, expressed as B-differentiable equations, incorporate only contact conditions in normal and tangential directions, and the contact flexibility matrix, obtained by applying unit force pair to all contact node pairs in turn, is introduced. Consequently, the solution for nonlinear equilibrium equations and contact equations is decoupled, requiring only one decomposition of the stiffness matrix for each calculation of the contact flexibility matrix, as it remains constant within the contact iteration layer. In addition, the constitutive model of concrete developed by Lee and Fenves is utilized. This model adopts two independent variables to describe tensile and compressive damage, together considering the strength recovery of the concrete. Numerical examples demonstrate the accuracy of the proposed method by comparing the results from Abaqus. Furthermore, the present algorithm is applied to static and dynamic analyses of a concrete arch dam with several transverse joints, where the effects of gravity, water level, the number of contact surfaces, and seismic load are considered, and some conclusions are obtained.

Propagation of Modelling Uncertainties for Seismic Risk Assessment: The Effect of Sampling Techniques on Low-Rise Non-Ductile RC Frames

ABSTRACT Quantifying the impact of modelling uncertainty on seismic performance assessment of existing buildings is non-trivial when considering the partial information available on material properties, construction details, and the uncertainty in the capacity models. This task is further complicated when uncertainty related to ground motion representation is considered. To address this issue, record-to-record variability, uncertainties in structural model parameters, and fragility model parameters due to limited sample size are propagated herein by employing a nonlinear dynamic analysis procedure based on recorded ground motions. A one-to-one sampling approach is adopted in which each recorded ground motion is paired up with a different structural model realization. Uncertainty propagation is explored by measuring the impact of different sampling techniques, such as Monte Carlo simulation with standard random sampling and Latin Hypercube sampling (with Simulated Annealing) in the presence of three alternative nonlinear dynamic analysis procedures: Incremental Dynamic Analysis (IDA), Modified Cloud Analysis (MCA), and Cloud to IDA (a highly efficient IDA-like procedure). This is all illustrated through application to an existing reinforced-concrete school building in southern Italy. It is shown that with a small subset of records, both MCA and Cloud to IDA can provide reliable structural fragility (and risk) estimates for three considered limit states, comparable to the results of more resource-intensive schemes.