Dynamic Approach Research Articles

Optimal control design for power system stabilizer using a novel crow search algorithm dynamic approach

This paper proposes the first application of a novel improved metaheuristic optimization technique, dynamic crow search algorithm (DCSA) to find the optimal design of the power system stabilizer (PSS). Using the novel DCSA adaptive approach provides a global search capability as well as fastness and effectiveness to get the PSS best parameters under the search space constraints and local optimums. Moreover, PSS parameters has a crucial effect on power system stability that is a major nowadays engineering concern, where its best parameters could make a difference by damping small-signal stability oscillations effectively and in the smallest amount of time if they are obtained through a robust algorithm. As a result, PSS will prevent large types of instabilities which could reach up to some generation stations shedding. The novel proposed algorithm is for first time used and tested under different power system instabilities scenarios by using single-machine infinity bus model over multiple operating conditions to damp out perturbation signals effectively by means of getting optimal PSS design parameters, where Integral absolute error (IAE) performance index is used as an objective function to be minimized. Additionally, results obtained by the proposed approach are compared with those obtained by other methods. It is observed that the proposed method has better convergence characteristics and robustness compared to the original version and other comparison methods. It is revealed that the proposed adaptive method is able to improve the power system stability dynamics and damp out perturbation oscillations successfully.

Optimization of Crystal Structures in Polylithionite Concentrate: A Molecular Dynamics Approach to Lithium Extraction Efficiency.

Molecular dynamics (MD) techniques offer significant potential for optimizing mineral extraction processes by simulating economically or physically restrictive conditions at the laboratory level. Lithium, a crucial metal in the electromobility era, exemplifies the need for ongoing re-evaluation of extraction techniques. This research aims to simulate the crystal structures of mineral species present in a polylithionite mineral concentrate [KLi2Al(Si4O10)(F,OH)2] using crystallographic data obtained from X-ray diffraction analysis. This study focuses on optimizing these structures, validating them through density comparisons, and determining the interaction parameter between the identified phases and lithium oxide (Li2O). The X-ray diffraction analysis revealed five predominant mineral phases: quartz (SiO2), calcite [Ca(CO3)], pyrite (FeS2), cassiterite (SiO2), and a compound Pb6O2(BO3)2SO4. Structural data, including lattice parameters, space groups, and atomic coordinates, were used to construct the crystal structures with Materials Studio 8.0, employing the Crystal Builder module. Optimization was performed using the Forcite module with the Smart optimization algorithm and the Universal force field. The interaction parameter (χ) indicated an affinity between lithium oxide and pyrite, as well as between calcite and quartz.

Institutions, Human Capitals and Emerging Nations' Economic Expansion

This paper looks at institutions, economic growth and human capital in emerging nations. By utilizing a piece of 120 developing nations for the years 2000–2023, the study simultaneously applies Quantile regression and the Generalized Method of Moments (GMM). The results demonstrate that institutions and human development do have a major beneficial influence on the expansion of the economy. It's interesting to note that human development and institutions combine negatively to significantly hinder the economic progress of emerging nations. In this article argues that incremental investments in human development would have a detrimental influence on economic growth since more capital is often used for rent-seeking and socially useless endeavors. Policymakers should keep in mind the vital role that institutions play in a nation's early stages of development in order to improve the efficacy of its health and education programs. The biggest novelty is the examination of many routes of transmission— financial institutions, political and economic—that human development uses to impact economic progress in developing countries. This study examines the notion of economic development and conducts an empirical investigation to determine whether Muslim nations differ from non-Muslim nations. Furthermore, this work expands on the body of research on empirical growth by combining quantile regression and dynamic system GMM approaches

Politics of local fiscal discipline with vertical fiscal imbalance

AbstractWe examine the political conditions mediating the effect of vertical fiscal imbalance (VFI) on local government fiscal discipline in Ghana. Based on a panel dataset of 216 local governments in Ghana over the period 1994–2018, we adopt both a static and dynamic approach, in addition to a regression discontinuity design. From the results, while alignment may give “free pass,” local governments could leverage same for effective tax efforts. Further, the benefits of lower political competition may reduce where local governments place reliance on central government grants. There is consistent evidence of a negative effect of VFI on fiscal discipline.

A Local Macroscopic Conservative (LoMaC) Low Rank Tensor Method for the Vlasov Dynamics

In this paper, we propose a novel Local Macroscopic Conservative (LoMaC) low rank tensor method for simulating the Vlasov-Poisson (VP) system. The LoMaC property refers to the exact local conservation of macroscopic mass, momentum and energy at the discrete level. This is a follow-up work of our previous development of a conservative low rank tensor approach for Vlasov dynamics (arXiv:2201.10397). In that work, we applied a low rank tensor method with a conservative singular value decomposition to the high dimensional VP system to mitigate the curse of dimensionality, while maintaining the local conservation of mass and momentum. However, energy conservation is not guaranteed, which is a critical property to avoid unphysical plasma self-heating or cooling. The new ingredient in the LoMaC low rank tensor algorithm is that we simultaneously evolve the macroscopic conservation laws of mass, momentum and energy using a flux-difference form with kinetic flux vector splitting; then the LoMaC property is realized by projecting the low rank kinetic solution onto a subspace that shares the same macroscopic observables by a conservative orthogonal projection. The algorithm is extended to the high dimensional problems by hierarchical Tuck decomposition of solution tensors and a corresponding conservative projection algorithm. Extensive numerical tests on the VP system are showcased for the algorithm’s efficacy.

Real-time volume rendering for three-dimensional fetal ultrasound using volumetric photon mapping

Three-dimensional (3D) fetal ultrasound has been widely used in prenatal examinations. Realistic and real-time volumetric ultrasound volume rendering can enhance the effectiveness of diagnoses and assist obstetricians and pregnant mothers in communicating. However, this remains a challenging task because (1) there is a large amount of speckle noise in ultrasound images and (2) ultrasound images usually have low contrasts, making it difficult to distinguish different tissues and organs. However, traditional local-illumination-based methods do not achieve satisfactory results. This real-time requirement makes the task increasingly challenging. This study presents a novel real-time volume-rendering method equipped with a global illumination model for 3D fetal ultrasound visualization. This method can render direct illumination and indirect illumination separately by calculating single scattering and multiple scattering radiances, respectively. The indirect illumination effect was simulated using volumetric photon mapping. Calculating each photon’s brightness is proposed using a novel screen-space destiny estimation to avoid complicated storage structures and accelerate computation. This study proposes a high dynamic range approach to address the issue of fetal skin with a dynamic range exceeding that of the display device. Experiments show that our technology, compared to conventional methodologies, can generate realistic rendering results with far more depth information.

Thermoelectric transport in molecular crystals driven by gradients of thermal electronic disorder.

Thermoelectric materials convert a temperature gradient into a voltage. This phenomenon is relatively well understood for inorganic materials but much less so for organic semiconductors (OSs). These materials present a challenge because the strong thermal fluctuations of electronic coupling between the molecules result in partially delocalized charge carriers that cannot be treated with traditional theories for thermoelectricity. Here, we develop a quantum dynamical simulation approach revealing in atomistic detail how the charge carrier wave function moves along a temperature gradient in an organic molecular crystal. We find that the wave function propagates from hot to cold in agreement with the experiment, and we obtain a Seebeck coefficient in good agreement with experimental measurements that are also reported in this work. Detailed analysis reveals that gradients in thermal electronic disorder play an important role in determining the magnitude of the Seebeck coefficient, opening unexplored avenues for the design of OSs with improved Seebeck coefficients.

Scheduling of e-commerce packaging machines: blocking machines and their impact on the performance–waste tradeoff

AbstractTo streamline their fulfillment processes, many e-commerce retailers today use automated packaging machines for their outbound parcels. An important performance–waste tradeoff is associated with these machines: To reduce packaging waste when handling different sized goods, packaging machines should be able to handle different carton sizes. However, more carton sizes lead to a more involved scheduling process, so that the throughput performance deteriorates (and vice versa). To investigate this tradeoff, this paper develops scheduling procedures for a specific type of packaging machine, called blocking machines. These packaging machines provide multiple back-to-back packaging devices, each continuously processing a dedicated carton size, but blocking each other whenever incoming goods are not properly ordered according to carton sizes on the infeed conveyor. To reduce the resulting throughput loss, we derive various scheduling problems for optimizing the inflow of goods, provide a thorough analysis of the computational complexity, and derive an exact dynamic programming approach that is polynomial in the number of orders to be packed. This allows us to solve even large real-world instances to proven optimality with which we can analyze the performance–waste tradeoff of blocking machines.

Real-time localization with probabilistic maps and unscented kalman filtering: a dynamic sensor fusion approach

This paper presents a robust position estimation technique for autonomous vehicles navigating urban and highway environments. It employs a probabilistic framework, integrating Monte Carlo simulation and Bayesian filtering via particle filters to represent position estimates beyond Gaussian distributions. To improve accuracy, Unscented Kalman Filtering (UKF) is used to fuse radar and lidar data, facilitating the detection of static, pole-like objects. These objects are matched with landmarks from a detailed reference map generated through 3D lidar scans. The proposed method addresses both lateral and longitudinal localisation challenges, ensuring precise vehicle positioning. Comprehensive simulation tests validate the system's effectiveness, demonstrating reliable performance across various driving conditions.

A Nonlinear Approach in the Quantification of Numerical Uncertainty by High-Order Methods for Compressible Turbulence with Shocks

This is a comprehensive overview on our research work to link interdisciplinary modeling and simulation techniques to improve the predictability and reliability simulations (PARs) of compressible turbulence with shock waves for general audiences who are not familiar with our nonlinear approach. This focused nonlinear approach is to integrate our “nonlinear dynamical approach” with our “newly developed high order entropy-conserving, momentum-conserving and kinetic energy-preserving methods” in the quantification of numerical uncertainty in highly nonlinear flow simulations. The central issue is that the solution space of discrete genuinely nonlinear systems is much larger than that of the corresponding genuinely nonlinear continuous systems, thus obtaining numerical solutions that might not be solutions of the continuous systems. Traditional uncertainty quantification (UQ) approaches in numerical simulations commonly employ linearized analysis that might not provide the true behavior of genuinely nonlinear physical fluid flows. Due to the rapid development of high-performance computing, the last two decades have been an era when computation is ahead of analysis and when very large-scale practical computations are increasingly used in poorly understood multiscale data-limited complex nonlinear physical problems and non-traditional fields. This is compounded by the fact that the numerical schemes used in production computational fluid dynamics (CFD) computer codes often do not take into consideration the genuinely nonlinear behavior of numerical methods for more realistic modeling and simulations. Often, the numerical methods used might have been developed for weakly nonlinear flow or different flow types other than the flow being investigated. In addition, some of these methods are not discretely physics-preserving (structure-preserving); this includes but is not limited to entropy-conserving, momentum-conserving and kinetic energy-preserving methods. Employing theories of nonlinear dynamics to guide the construction of more appropriate, stable and accurate numerical methods could help, e.g., (a) delineate solutions of the discretized counterparts but not solutions of the governing equations; (b) prevent numerical chaos or numerical “turbulence” leading to FALSE predication of transition to turbulence; (c) provide more reliable numerical simulations of nonlinear fluid dynamical systems, especially by direct numerical simulations (DNS), large eddy simulations (LES) and implicit large eddy simulations (ILES) simulations; and (d) prevent incorrect computed shock speeds for problems containing stiff nonlinear source terms, if present. For computation intensive turbulent flows, the desirable methods should also be efficient and exhibit scalable parallelism for current high-performance computing. Selected numerical examples to illustrate the genuinely nonlinear behavior of numerical methods and our integrated approach to improve PARs are included.

Modelling interrelations between C-ITS impact categories: a system-dynamics approach using causal loop diagrams

The growing number of connected vehicles has led to an increased focus on Vehicle-to-Everything (V2X) communication in the field of transport research. This communication paradigm facilitates cooperation between vehicles and infrastructure to address traffic challenges such as efficiency, sustainability and safety. The development and standardisation of such Cooperative Intelligent Transport Systems (C-ITS) has been pursued in several projects. Beyond technical considerations, assessing the effect of these applications in terms of various impact categories is of paramount importance. However, existing research tends to examine impact categories such as efficiency, sustainability, safety, psychological or socioeconomic impacts separately, often overlooking potential interactions and interdependencies. This approach is inadequate as impacts on one category can have both cascading effects on others and rebound effects. To address this gap, this paper proposes a system dynamics approach using Causal Loop Diagrams (CLD) to illustrate the interconnectedness of impact categories and the potential impacts of C-ITS services. By depicting general relationships, interdependencies and feedback loops between impact category elements, the model accommodates the introduction of single or multiple C-ITS services as separate modules, allowing an analysis of their combined effects on the overall system. To this end, two use cases demonstrate the applicability of the developed CLD and illustrate some of the multiple interrelations between the effects of C-ITS services. The results of this paper support road operators and researchers when setting up the impact assessment of C-ITS services by revealing the dynamic and intertwined nature of different impact categories.

Exchange rate fluctuations and industrial production in Malaysia: evidence from dynamic simulated ARDL analysis

Contemporary literature argues that the exchange rate serves as an important instrument of industrial policy to boost economic growth. However, it is difficult to design effective exchange rate policies owing to the uncertainty shrouding the outcomes of the exchange rate movement for industrial growth. Therefore, this study uses a dynamic simulated autoregressive distributive lags approach to study the impact of positive and negative changes in the real effective exchange rate on industrial production of Malaysia for the period 1970–2022. We employ an extended production function to study this relationship by incorporating capital, labour and globalisation as additional explanatory variables. The findings suggest that positive (negative) changes in real effective exchange rate are followed by positive (negative) changes in economic growth in the long run. Furthermore, positive changes in capital affect economic growth positively and significantly in both the short and long run. Finally, positive (negative) movement in labour and globalization positively (negatively) and significantly affect industrial production in the long run respectively. Policymakers should depreciate currency to expand industrial production in Malaysia. Moreover, to boost industrial production, short-term loans should be introduced for capital accumulation.

On Isaac’s War Game of Attrition and Attack Using Dynamic Programming Approach

On Isaac’s War Game of Attrition and Attack Using Dynamic Programming Approach

Enhancing shipboard waste heat management with advanced technologies

The complexity of the energy systems onboard ships, combined with the different operating/weather conditions and the availability of cutting-edge technologies, makes analyses for improving the energy and environmental performances of ships time consuming and challenging. From this point of view, this article provides new criteria for the sustainable design and management of energy systems of existing or new ships. In particular, the impact of the adoption of organic Rankine cycle units, wet steam volumetric expanders, and single or double effect absorption chillers is here investigated. Two types of ships are examined as suitable case studies, evaluating the impact of each technology and their combinations by varying the shipping cruises. By using a dynamic simulation approach, potential savings and optimal solutions are assessed for different energy system layouts by also comparing their economic, energy and environmental impact performance. Results are reported in specific performance matrices for helping stakeholders in preliminary energy efficiency analyses.In particular, outcomes show that high cooling demands of ships in the Caribbean Sea enable primary energy savings close to 4.5 %, compared to 3.5 % in the Mediterranean Sea and 3 % in the North Sea. In the latter case, cooling needs can be almost fully balanced through the examined energy recovery technologies. Screw expanders integrate best in all operating conditions with short paybacks, whilst organic Rankine cycles (with electrical efficiency above 8%) are advantageous especially in cold climate routes. Benefits on environmental impact are significant, with avoided CO2 emissions around 6 kt/y, depending on the selected ship cruise.

Artificial Intelligence in Cybersecurity : Advancing Threat Modeling and Vulnerability Assessment

This article examines the transformative role of Artificial Intelligence (AI) in revolutionizing cybersecurity practices, with a particular focus on threat modeling and vulnerability assessment. As cyber threats grow increasingly sophisticated, traditional security measures often fall short in providing comprehensive protection. We explore how AI and machine learning algorithms enhance the accuracy and efficiency of threat modeling by analyzing vast datasets to identify patterns and anomalies indicative of potential security risks. The article also investigates AI's contribution to vulnerability assessment, highlighting its capacity for continuous monitoring and real-time analysis of diverse data sources, including network traffic, system logs, and threat intelligence feeds. By simulating various attack scenarios and prioritizing security vulnerabilities, AI-driven tools enable organizations to adopt a more proactive and dynamic approach to cybersecurity. While acknowledging the challenges and ethical considerations associated with AI implementation in this domain, this article underscores the significant potential of AI in fortifying cyber defenses and safeguarding digital assets in an ever-evolving threat landscape.

Gradient dynamics approach to reactive thin-film hydrodynamics

Wetting and dewetting dynamics of simple and complex liquids is described by kinetic equations in gradient dynamics form that incorporates the various coupled dissipative processes in a fully thermodynamically consistent manner. After briefly reviewing this, we also review how chemical reactions can be captured by a related gradient dynamics description, assuming detailed balanced mass action type kinetics. Then, we bring both aspects together and discuss mesoscopic reactive thin-film hydrodynamics illustrated by two examples, namely, models for reactive wetting and reactive surfactants. These models can describe the approach to equilibrium but may also be employed to study out-of-equilibrium chemo-mechanical dynamics. In the latter case, one breaks the gradient dynamics form by chemostatting to obtain active systems. In this way, for reactive wetting we recover running drops that are driven by chemically sustained wettability gradients and for drops covered by autocatalytic reactive surfactants we find complex forms of self-propulsion and self-excited oscillations.

Boolean Delay Equations: a dynamical approach to modeling complex systems

Boolean delay equations (BDEs) are equations with discrete variables evolving in continuous time. They serve as exploratory tools in the study of nonlinear and complex systems. In this review paper, we outline their formulation and illustrate their properties by application to a solid-earth problem and a climate one. The first problem is the seismotectonic description and prediction of earthquakes and of their clustering. The second one is that of the coupled atmosphere-ocean phenomenon of the El Niño-Southern Oscillation (ENSO). Both involve irregular behavior that is hard to predict, although some form of cyclicity is present in both, especially in ENSO. The paper concludes with broad perspectives on the further use of BDEs in the geosciences and elsewhere.

Searching for differential effects of the dynamic approach to teacher professional development: a study on promoting formative assessment

ABSTRACT The Dynamic Approach (DA) to Teacher Professional Development (TPD) stresses the importance of identifying specific needs and improvement priorities for each teacher. A framework measuring teacher assessment skills was used and teachers were classified into three stages. The skills identified at each stage were used to make decisions in relation to the focus and content of the TPD course. A study in four European countries (i.e. Belgium, Cyprus, Greece and the Netherlands) was conducted. Teachers (N = 206) were randomly allocated into two groups (experimental and control group). A questionnaire measuring teachers’ skills in assessment and tests measuring students’ learning outcomes in mathematics (cognitive and metacognitive) were administered at the beginning and the end of the course. The DA was effective in improving the assessment skills of teachers at all stages. Differential effects of DA on promoting student learning outcomes in mathematics were identified. Implications of the findings are discussed.

The role of process monitoring devices and digital line balancing in achieving operational excellence in garment manufacturing

This study investigates the transformative potential of integrating digital technologies with lean manufacturing principles to address the evolving challenges in the garment industry. The industry demands high-quality products, rapid delivery, cost efficiency, and production flexibility to maintain competitiveness and sustainability. While traditional lean methods have focused on waste reduction and process optimization, the advent of digitalization offers unprecedented opportunities for enhancing operational excellence. This research specifically examines the role of Internet of Things-based Process Monitoring Devices (PMDs) and digital line balancing in achieving these goals. PMDs, integrated within a digital lean framework, facilitate continuous monitoring of machine performance and operator efficiency, providing real-time insights into operational processes. This real-time visibility enables data-driven decision-making and centralized analysis, fostering agility and responsiveness. Furthermore, this study presents a dynamic digital line balancing algorithm that optimizes sewing line configurations and workload distribution. This dynamic approach allows for real-time adaptations in production flows to mitigate bottlenecks and boost productivity. By exploring the synergistic relationship between PMDs, digital line balancing, and lean principles, this paper contributes to a comprehensive understanding of how digital solutions can revolutionize operational efficiency in the garment manufacturing sector.

Change in Femoral Offset after Closed Reduction and Dynamic Hip Screw Osteosynthesis Via Lateral Approach in Patients with Medial Femoral Neck Fracture: A Retrospective Analysis.

Closed reduction and dynamic hip screw (DHS) osteosynthesis are preferred as joint-preserving therapy in case of medial femoral neck fractures (MFNFs). A change in the femoral offset (CFO) can cause gait abnormality, impingement, or greater trochanteric pain syndrome. It is unknown whether the femoral offset (FO) can be postoperatively fully restored. The aim of the study was to investigate the extent of a possible CFO in hip joints after DHS osteosynthesis in the case of an MFNF. In this retrospective study, 104 patients (mean age: 71.02 years, men: n = 50, women: n = 54) with MFNF who underwent closed reduction and DHS osteosynthesis were analyzed by postoperative x-rays to assess CFO between the operated (OS) and nonoperated joint side (NOS). The studies covered the time period 2010-2020. A statistical comparison was performed between the mean values of FO between OS and NOS, taking into account patient age, gender, and fracture severity. All operated hip joints showed a CFO. In 76.0% (79 of 104), the FO decreased (FOD), and in 24.0% (25 of 104), the FO increased (FOI). A critical CFO (>15% CFO) was detected in 52.9% (55 of 104). In hip joints with postoperative FOD, the mean FO between NOS (49.15 mm [±6.56]) and OS (39.32 mm [±7.87]) and in hip joints with postoperative FOI the mean FO between NOS (41.59 [±8.21]) and OS (47.27 [±6.68]) differed significantly (p < 0.001). Preoperative FO (rS: -0.41; p > 0.001) and caput-collum-diaphyseal angle (CCD; rS: 0.34; p > 0.001) correlated with postoperative CFO. FOD was found in hip joints with a preoperative FO >44 mm and CCD <134° vice versa FOI in hip joints with a preoperative FO <44 mm and CCD >134°. Closed reduction and DHS osteosynthesis in patients with MFNF result in a clustered significant CFO. The individual FO should be taken into account pre- and intraoperatively to avoid a postoperative extensive CFO.