Search Results (75)

A food pasteurization or sterilization process was treated as a system comprising a target microorganism, a food medium, and applied lethal agents (both thermal and nonthermal). So, the state of such a system was defined by the target microorganism’s concentration, the food medium parameters (food composition, pH, and water activity), and the magnitudes of temperature and nonthermal lethal agents. Further, a path was defined as a series of profiles that describe the changes in state factors over time when a food process system changes from its initial state to any momentary state. Using the Weibull model as an example, results showed that, if the microbial inactivation rate depends on path, then there exists an infinite number of rate equations that can result in the same algebraic primary model under constant conditions but, theoretically, only one of them is true. Considering the infinite possibilities, there is no way to find the most suitable or true rate equation. However, the inactivation rate equation can be uniquely derived from the algebraic primary model if the inactivation rate does not depend on path, which was demonstrated to be true by most microbial survival data reported in previous studies. Full article

High-frequency surface wave radar (HFSWR) is unable to measure the target’s altitude information due to its limited antenna aperture in the elevation dimension. This paper focuses on the 3-D localization problem for moving targets within the line of sight (LOS) in multistatic HFSWR. For this purpose, the 1-D space angle (SA) measurement is introduced into multistatic HFSWR to perform 3-D joint localization together with bistatic range (BR) and bistatic range rate (BRR) measurements. The target’s velocity can also be estimated due to the inclusion of BRR. In multistatic HFSWR, commonly used azimuth measurements offer no information about the target’s altitude. Since SA is associated with the target’s 3-D coordinates, combining SA measurements from multiple receivers can effectively enhance localization accuracy, particularly in altitude estimation. In this paper, we develop a two-stage localization algorithm that first derives a weighted least-squares (WLS) coarse estimate and then performs an algebraic error reduction (ER) procedure to enhance accuracy. Both stages yield closed-form results, thus ensuring overall computational efficiency. Theoretical analysis shows that the proposed WLS-ER algorithm can asymptotically attain the Cramér–Rao lower bound (CRLB) as the measurement noise decreases. Simulation results demonstrate the effectiveness of the proposed WLS-ER algorithm and highlight the contribution of SA measurements to altitude estimation in multistatic HFSWR. Full article

This study introduces a multivariate simulation framework for assessing climate risks in the Dominican Republic. The simulator operates in two modes—climate risk evaluation and disaster management—using a unified database. This database integrates codified variables associated with global warming, hazards, vulnerabilities (including their interdependencies), and adaptive capacities, facilitating risk assessments across diverse scenarios. Simulations are initiated using predefined variable combinations, interconnected via Boolean algebra, to generate risk levels aligned with the Intergovernmental Panel on Climate Change (IPCC) scales. The key findings underscore the influence of specific variables within the modeled scenarios. For instance, inadequate energy management and insufficient mitigation measures significantly amplify climate risks, particularly in regions with vulnerable infrastructure. Validation against established models, including EN-ROADS and PRECIS, confirms the simulator’s predictive accuracy and reliability. This study highlights the critical role of regionalized risk assessments in developing targeted adaptation and mitigation strategies that address localized vulnerabilities. The proposed simulator provides an innovative tool for real-time climate risk assessment, enabling policymakers to model potential outcomes and optimize decision-making processes. Future improvements should focus on enhancing spatial resolution, integrating real-time data, and refining models of infrastructure interdependencies. This research advances the development of evidence-based climate risk assessment tools, supporting informed policymaking and effective disaster risk management in the Dominican Republic. Full article

This study addresses the urgent need for transitioning to clean energy systems to achieve net-zero emissions and mitigate climate change. It introduces an algebraic modeling framework inspired by the nuclear fission six-factor formula to optimize the construction rates of clean power plants, with a focus on Small Modular Reactors (SMRs). The framework integrates four key factors affecting SMR deployment: Public Acceptance (PA), Supply Chain Readiness (SC), Human Resource (HR) Availability, and Land Availability (LA), including their associated sub-factors. The proposed algebraic formula optimizes projections from the existing Dynamic Integrated Climate-Economy (DICE) model. By capturing socio-economic and environmental constraints, the model enhances the accuracy of clean energy transition scenarios. In the case of Ontario’s pathway to achieving net-zero emissions, the results indicate that incorporating the algebraic formula reduces the SMR construction rate projected by the DICE model from 5.2 to 3.7 units per year by 2050 and from 2.7 to 1.9 units per year by 2100. This reduction highlights the need for accelerated readiness in key deployment factors to avoid delays in reaching net zero targets, reinforcing the importance of strategic investments in PA, SC, HR, and LA. Validation against historical nuclear deployment data from the U.S., Japan, and Canada confirms the model’s ability to reflect real-world trends, with PA and SC emerging as the most influential factors. In addition to informing SMR planning, this approach offers a structured tool for prioritizing policy actions and can be adapted to other clean technologies, enhancing strategic decision making in support of net-zero goals. Full article

The following article details a model predictive control (MPC) to improve grid resilience when faced with variable generation resources. This topic is of significant interest to utility power systems where distributed intermittent energy sources will increase significantly and be relied on for electric grid ancillary services. Previous work on MPCs has focused on narrowly targeted control applications such as improving electric vehicle (EV) charging infrastructure or reducing the cost of integrating Energy Storage Systems (ESSs) into the grid. In contrast, this article develops a comprehensive treatment of the construction of an MPC tailored to electric grids and then applies it integration of intermittent energy resources. To accomplish this, the following article includes a description of a reduced order model (ROM) of an electric power grid based on a circuit model, an optimization formulation that describes the MPC, a collocation method for solving linear time-dependent differential algebraic equations (DAEs) that result from the ROM, and an overall strategy for iteratively refining the behavior of the MPC. Next, the algorithm is validated using two separate numerical experiments. First, the algorithm is compared to an existing MPC code and the results are verified by a numerically precise simulation. It is shown that this algorithm produces a control comparable to existing algorithms and the behavior of the control carefully respects the bounds specified. Second, the MPC is applied to a small nine bus system that contains a mix of turbine-spinning-machine-based and intermittent generation in order to demonstrate the algorithm’s utility for resource planning and control of intermittent resources. This study demonstrates how the MPC can be tuned to change the behavior of the control, which can then assist with the integration of intermittent resources into the grid. The emphasis throughout the paper is to provide systematic treatment of the topic and produce a novel nonlinear control compatible design framework applicable to electric grids and the control of variable resources. This differs from the more targeted application-based focus in most presentations. Full article

Range gate pull-off (RGPO) jamming is an effective method for track deception aimed at radar systems. Nevertheless, enhancing the effectiveness of the jamming strategy continues to pose challenges, restricting the RGPO jamming method from achieving its maximum potential. This paper focuses on addressing the problem of optimizing the strategy for white-box RGPO jamming, serving as a foundational step toward quantitative optimization research on RGPO jamming strategies. In the white-box scenario, it is presumed that the jammer has full knowledge of the target radar’s tracking system, encompassing both the choice of tracking method and its parameter configurations. The intricate interactions between the jammer and the tracking radar introduce three primary challenges: (1) Formulating an algebraic expression for the objective function of the jamming strategy optimization is nontrivial; (2) Direct observation of jamming effects from the target radar is challenging; (3) Noise renders the jamming outcomes unpredictable. To tackle these challenges, this study formulates the optimization of the RGPO jamming strategy as an adversarial stochastic simulation optimization (ASSO) problem and introduces a novel solution for the white-box RGPO jamming strategy optimization: a local simulation-assisted particle swarm optimization algorithm with an equal resampling scheme (PSO-ER). The PSO-ER algorithm searches for optimal jamming strategies while utilizing a localized simulation of the tracking radar to evaluate the effectiveness of candidate jamming strategies. Experiments conducted on four benchmark cases confirm that the proposed approach is capable of generating well-tuned strategies for white-box RGPO jamming. Full article

The frequency and intensity of urban flooding continuously increase due to the dual influences of climate change and urbanization. Conducting individual importance classification of urban stormwater channel networks (USCNs) is of significant importance for alleviating urban flooding and facilitating targeted stormwater management implementation. However, a quantitative classification method is lacking for trellis networks, which are a common type of USCN. This study proposed a novel importance classification methodology for channel segments in most types of USCNs, especially suitable for trellis networks, based on permutation and algebraic graph theory. The concept of permutation was integrated into the methodology to measure the importance of each channel segment to the USCN. Algebraic graph theory was employed to quantify the topological structure and hydraulic characteristics of the USCN. To verify the applicability and rationality of the proposed methodology, a real-world city with trellis USCNs in China (i.e., Huai’an) was selected as the study area. Seventy channel segments in the USCN were efficiently classified into three categories based on individual importance. This study provided a decision-support methodology from the perspective of individual importance classification in the USCN and offered valuable reference for urban flooding managers. Full article

This work employs the Extended Direct Algebraic Method (EDAM) to solve quadratic and cubic nonlinear Klein–Gordon Equations (KGEs), which are standard models in particle and quantum physics that describe the dynamics of scaler particles with spin zero in the framework of Einstein’s theory of relativity. By applying variables-based wave transformations, the targeted KGEs are converted into Nonlinear Ordinary Differential Equations (NODEs). The resultant NODEs are subsequently reduced to a set of nonlinear algebraic equations through the assumption of series-based solutions for them. New families of soliton solutions are obtained in the form of hyperbolic, trigonometric, exponential and rational functions when these systems are solved using Maple. A few soliton solutions are considered for certain values of the given parameters with the help of contour and 3D plots, which indicate that the solitons exist in the form of dark kink, hump kink, lump-like kink, bright kink and cuspon kink solitons. These soliton solutions are relevant to actual physics, for instance, in the context of particle physics and theories of quantum fields. These solutions are useful also for the enhancement of our understanding of the basic particle interactions and wave dynamics at all levels of physics, including but not limited to cosmology, compact matter physics and nonlinear optics. Full article

Game-based learning assessments rely on educational data mining approaches such as stealth assessments and quasi mixed methods that help gather data on student learning proficiency. Rarely do we see approaches where student proficiency in learning is woven into the game’s design. Educational burst games (EBGs) represent a new approach to improving learning proficiency by designing fast-paced, short, repetitive, and skill-based games. They have the potential to be effective learning interventions both during instruction in the classroom and during after-school activities such as assignments and homework. Over five years, we have developed two EBGs aimed at improving linear algebra concepts among undergraduate students. In this study, we provide the results of an in-depth evaluation of the two EBGs developed with 45 participants that represent our target population. We discuss the role of EBGs and their design constructs, such as pace and repetition, the effect of the format (2D vs. 3D), the complexity of the levels, and the influence of prior knowledge on the learning outcomes. Full article

This study examines the interactions between healthy target cells, infected target cells, virus particles, and immune cells within an HIV model. The model exhibits two equilibrium points: an infection-free equilibrium and an infection equilibrium. Stability analysis shows that the infection-free equilibrium is locally asymptotically stable when ${\mathcal{R}}_0<1$. Further, it is unstable when ${\mathcal{R}}_0>1$. The infection equilibrium is locally asymptotically stable when ${\mathcal{R}}_0>1$. The structural and practical identifiabilities of the within-host model for HIV infection dynamics were investigated using differential algebra techniques and Monte Carlo simulations. The HIV model was structurally identifiable by observing the total uninfected and infected target cells, immune cells, and viral load. Monte Carlo simulations assessed the practical identifiability of parameters. The production rate of target cells $\left(\lambda \right)$, the death rate of healthy target cells $\left(d\right)$, the death rate of infected target cells $\left(\delta \right)$, and the viral production rate by infected cells $\left(\pi \right)$ were practically identifiable. The rate of infection of target cells by the virus $\left(\beta \right)$, the death rate of infected cells by immune cells $\left(\mathsf{\Psi }\right)$, and antigen-driven proliferation rate of immune cells $\left(b\right)$ were not practically identifiable. Practical identifiability was constrained by the noise and sparsity of the data. Analysis shows that increasing the frequency of data collection can significantly improve the identifiability of all parameters. This highlights the importance of optimal data sampling in HIV clinical studies, as it determines the best time points, frequency, and the number of sample points required to accurately capture the dynamics of the HIV infection within a host. Full article

Aiming at the salvo-attack problem of multiple missiles, a distributed cooperative guidance law based on the event-triggered mechanism is proposed, which enables missiles with large differences in spatial location and velocity to achieve simultaneous attacks with only a few dozen information exchanges. It effectively reduces the generation of control commands and communication frequency, thereby reducing channel load and improving communication efficiency and reliability. Compared to traditional periodic sampling communication, the number of communications has been reduced by over $90\%$. The guidance process is divided into two stages. The first stage is the cooperative guidance stage, where missiles achieve consensus of the time-to-go estimates through information exchange. In this stage, each missile is designed with an event-triggered function based on its own state error, and the missile only updates and transmits its information in the communication network when the error meets the set threshold, effectively reducing the occupancy rate of missile-borne resources during the cooperation process. The second stage is the independent guidance stage, where missiles can hit the target simultaneously while keeping the communication network silent. This is achieved by ensuring that the time-to-go estimates of missiles can represent the real time-to-go after achieving consensus. By the design of the two-stage guidance law and the replacement of the event-triggered function, the cooperative guidance system can be ensured to remain stable in scenarios where the leader missile is present and destroyed, and this excludes Zeno behavior. The stability of the cooperative guidance law is rigorously proved by algebraic graph theory, matrix theory, and the Lyapunov method. Finally, the numerical simulation results demonstrate the validity of the algorithm and the correctness of the stability analysis. Full article

To robustly and adaptively reconstruct displacement, we propose the amplitude modulation integral reconstruction method (AM-IRM) for displacement sensing in a self-mixing interferometry (SMI) system. By algebraically multiplying the SMI signal with a high-frequency sinusoidal carrier, the frequency spectrum of the signal is shifted to that of the carrier. This operation overcomes the issue of frequency blurring in low-frequency signals associated with continuous wavelet transform (CWT), enabling the precise extraction of the Doppler frequency of the SMI signal. Furthermore, the synchrosqueezing wavelet transform (SSWT) is utilized to enhance the frequency resolution of the Doppler signal. Our experimental results demonstrate that the proposed method achieves a displacement reconstruction accuracy of 21.1 nm (0.89%). Additionally, our simulations demonstrated that this method can accurately reconstruct target displacement under the conditions of time-varying optical feedback intensity or a signal-to-noise ratio (SNR) of 0 dB, with a maximum root mean square (RMS) error of 22.2 nm. These results highlight its applicability in real-world environments. This method eliminates the need to manually determine the window length for time–frequency conversion, calculate the parameters of the SMI system, or add additional optical devices, making it easy to implement. Full article

This study offers an in-depth analysis and optimization of a microgrid system powered by renewable sources, designed for the efficient production of hydrogen and dimethyl ether—key elements in the transition toward sustainable fuel alternatives. The system architecture incorporates solar photovoltaic modules, advanced battery storage solutions, and electrolytic hydrogen production units, with a targeted reduction in greenhouse gas emissions and the enhancement of overall energy efficiency. A rigorous economic analysis was conducted utilizing the HYSYS V12 software platform and encompassing capital and operational expenditures alongside profit projections to evaluate the system’s economic viability. Furthermore, thermal optimization was achieved through heat integration strategies, employing a cascade analysis methodology and optimization via the General Algebraic Modeling System (GAMS), yielding an 83% decrease in annual utility expenditures. Comparative analysis revealed that the energy requirement of the optimized system was over 50% lower than that of traditional fossil fuel-based reforming processes. A comprehensive assessment of CO₂ emissions demonstrated a significant reduction, with the integration of thermal management solutions facilitating a 99.24% decrease in emissions. The outcomes of this study provide critical insights into the engineering of sustainable, low-carbon energy systems, emphasizing the role of renewable energy technologies in advancing fuel science. Full article

Migration flocks have different forms, including single individuals, formations, and irregular clusters. The shape of a flock can change swiftly over time. The real-time clustering of multiple groups with different characteristics is crucial for the monitoring of dynamically changing migratory flocks. Traditional clustering algorithms need to set various prior parameters, including the number of groups, the number of nearest neighbors, or the minimum number of individuals. However, flocks may display complex group behaviors (splitting, combination, etc.), which complicate the choice and adjustment of the parameters. This paper uses a real-time clustering-based method that utilizes concepts from the algebraic graph theory. The connected graph is used to describe the spatial relationship between the targets. The similarity matrix is calculated, and the problem of group clustering is equivalent to the extraction of the partitioned matrices within. This method needs only one prior parameter (the similarity distance) and is adaptive to the group’s splitting and combination. Two modifications are proposed to reduce the computation burden. First, the similarity distance can be broadened to reduce the exponent of the similarity matrix. Second, the omni-directional measurements are divided into multiple sectors to reduce the dimension of the similarity matrix. Finally, the effectiveness of the proposed method is verified using the experimental results using real radar data. Full article

To enhance the safety management of steel-truss-bridge construction, an evaluation method based on the improved DEMATEL–ISM was proposed to analyze the risk factors involved in such construction. Decision Making Trial and Evaluation Laboratory (DEMATEL) is a method for systematic factor analysis that utilizes graph-theory and -matrix tools, allowing for the assessment of the existence and strength of relationships between elements by analyzing the logical and direct impact relationships among various elements in a system. The distinctive feature of Interpretative Structural Modeling (ISM) is the decomposing of complex systems into several subsystems (elements) and constructing the system into a multi-level hierarchical structural model through algebraic operations. Specifically, triangular fuzzy numbers are introduced initially to improve the direct influence matrix in the DEMATEL method, thereby reducing the subjectivity of expert evaluations. The degree of influence, influenced degree, centrality degree, and causality degree of each influencing factor are determined and ranked based on the above analysis. In response to the characteristics of top-push construction, 20 key factors were selected from four aspects: “human, material, environment, and management”. The top five identified influencing factors are displacement during pushing (X10), safety-management qualification (X18), local buckling (X14), overturning of steel beams (X13), and collision with bridge piers during guide beam installation (X7). Subsequently, corresponding solutions were proposed for different influencing factors. The results of the study offer targeted measures to enhance the safety management of steel truss bridge construction and provide a reference for accident prevention. Full article