Solution Method Research Articles

Innovative Strategies for Bio-Waste Collection in Major Cities during the COVID-19 Pandemic: A Comprehensive Model for Sustainable Cities—The City of Athens Experience

This paper introduces an innovative model for the organization and management of municipal bio-waste collection networks in major cities, particularly relevant in the context of the COVID-19 pandemic. Embracing circular economy principles and sustainable city practices, the proposed model addresses the urgent need for sustainable urban bio-waste management systems. Delving into the dynamic urban landscape, with a focus on the city of Athens, the study highlights the necessity of a robust decision-making methodology, the implementation of resilient processes, and the evaluation of their efficacy, especially during challenging times. The model centers on the effective collection, transportation, and monitoring of bio-waste, with a strategic aim to moderate environmental impacts, limit greenhouse gas emissions, and advance sustainable development goals. Utilizing the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method, this paper thoroughly examines critical components of an innovative bio-waste collection network, including infrastructure, technology, and human resources. By merging best practices from global urban centers and accounting for the unique characteristics of Athens, the model envisions a transition toward a circular economy. Notably, the proposed municipal bio-waste collection network at the source anticipates substantial contributions to achieving Sustainable Development Goals in major cities. The study concludes by showcasing the successful application of these methodologies in the Municipality of Athens, providing tangible evidence of their positive impact.

Hazard Identification and Risk Assessment for Sustainable Shipyard Floating Dock Operations: An Integrated Spherical Fuzzy Analytical Hierarchy Process and Fuzzy CoCoSo Approach

Background: This study investigated the process of selecting sustainable safety protocols for floating dock operations in shipyards by identifying potential workplace risks in emergency situations. Thirteen occupational hazards for shipyard floating dock operations were identified through a literature review and expert discussions. Methods: We incorporated four risk elements (consequence: C, frequency: F, probability: P, and number of people at risk: NP) from the Fine–Kinney and Hazard Rating Number System (HRNS) approaches as the risk assessment criteria. We obtained the importance weights of the risk assessment criteria via the Spherical Fuzzy Analytical Hierarchy Process (SF-AHP) and extended the Combined Compromise Solution (CoCoSo) method within the fuzzy framework to prioritize occupational hazards. This study demonstrated the practicality and efficiency of the proposed emergency risk assessment model for shipyard floating dock operations through a case example of occupational risk assessment. Results: The analysis results show that H4 is the occupational hazard with the highest priority, with a score of 3.553. H4 represents the hazard associated with insufficient access to the entire pool area. The second and third most important hazards are the inability of cranes to move freely in and out of the berthing dock and the inability to dispatch emergency teams. These hazards, denoted H1 and H12, follow closely behind with scores of 3.391 and 3.344, respectively. H10 is deemed the least concerning hazard, with a score of 1.802. Conclusions: Professionals can handle complex and uncertain risk assessment data more flexibly using the proposed system, which excels in accurately organizing occupational hazards.

Special Methods Controllability and Observability in Optimal Control Systems

This article explores a specific case study that examines controllability and observability in general and in specific so that it is easy for the reader to understand these two concepts, which are fundamental in optimal control theory. Algorithms have been written to determine the controllability and observability of optimal control systems using the MATLAB programming language, and new techniques have been developed to deal with them. In addition, a critical test was created in which the state variables of the system or, more precisely, their corresponding states were split together, illustrating this with more than one example. Divided into four groups in a linear manner, as the article explains. To understand the controllability and observability of some more complex systems, this article is a starting point for the future expansion of these two concepts through the development of new algorithms or other applied solution methods or the creation of new algorithms.

Capacitated Mobile Facility Location Problem with Mobile Demand: Efficient relief aid provision to en route refugees

As a humanity crisis, the tragedy of forced displacement entails relief aid distribution efforts among en route refugee to alleviate their migration hardships. This study aims to assist humanitarian organizations in cost-efficiently optimizing the logistics of capacitated mobile facilities utilized to deliver relief aid to transiting refugees in a multi-period setting. The problem is referred to as the Capacitated Mobile Facility Location Problem with Mobile Demands (CMFLP-MD). In CMFLP-MD, refugee groups follow specific paths, and meanwhile, they receive relief aid at least once every fixed number of consecutive periods, maintaining continuity of service. To this end, the overall costs associated with capacitated mobile facilities, including fixed, service provision, and relocation costs, are minimized. We formulate a mixed integer linear programming (MILP) model and propose two solution methods to solve this complex problem: an accelerated Benders decomposition approach as an exact solution method and a matheuristic algorithm that relies on an enhanced fix-and-optimize agenda. We evaluate our methodologies by designing realistic instances based on the Honduras migration crisis that commenced in 2018. Our numerical results reveal that the accelerated Benders decomposition excels MILP with a 46% run time improvement on average while acquiring solutions at least as good as the MILP across all instances. Moreover, our matheuristic acquires high-quality solutions with a 2.4% average gap compared to best-incumbents rapidly. An in-depth exploration of the solution properties underscores the robustness of our relief distribution plans under varying migration circumstances. Across several metrics, our sensitivity analyses also highlight the managerial advantages of implementing CMFLP-MD solutions.

Distributionally robust chance-constrained kernel-based support vector machine

Support vector machine (SVM) is a powerful model for supervised learning. This article addresses the nonlinear binary classification problem using kernel-based SVM with uncertainty involved in the input data specified by the first- and second-order moments. To achieve a robust classifier with small probabilities of misclassification, we investigate a distributionally robust chance-constrained kernel-based SVM model. Since the moment information in the original problem becomes unclear/unavailable in the feature space via kernel transformation, we develop a data-driven approach utilizing empirical moments to provide a second-order cone programming (SOCP) reformulation for efficient computation. To speed up the required computations for solving large-size problems in higher dimensional space and/or with more sampling points involved in estimating empirical moments, we further design an alternating direction multipliers-based algorithm for fast computations. Extensive computational results support the effectiveness and efficiency of the proposed model and solution method. Results on public benchmark datasets without any moment information indicate that the proposed approach still works and, surprisingly, outperforms some commonly used state-of-the-art kernel-based SVM models.

Computation of three-dimensional incompressible flows using high-order weighted essentially non-oscillatory finite-difference lattice Boltzmann method

In the present work, an accurate and robust solution methodology based on the high-order weighted essentially non-oscillatory (WENO) finite-difference lattice Boltzmann method (LBM) in the three-dimensional generalized curvilinear coordinates is presented and applied for simulating the three-dimensional incompressible flows over complicated configurations with curved boundaries. Here, the incompressible form of the lattice Boltzmann equation in three dimensions is considered and the discretization of the spatial derivative terms is performed with the fifth-order WENO finite-difference method and the temporal derivative term is discretized with the fourth-order Runge–Kutta scheme to ensure the accuracy and stability of the solution method for both the steady and unsteady problems. The three-dimensional lattice Boltzmann equation applied here is based on a nineteen discrete velocity model for transforming the microscopic properties to the macroscopic ones. To assess the accuracy and robustness of the present three-dimensional high-order finite-difference LBM solver, different incompressible flow benchmarks and practical test cases are studied that are the cavity flow, the Beltrami flow, the flow in the curved ducts of rectangular cross sections, and the flow over a sphere for different flow conditions. The decay of the homogeneous isotropic turbulence is also computed to examine the suitability of the present solution method to be applied as the direct numerical simulation of turbulent flows. It is demonstrated that the solution methodology presented based on the high-order WENO finite-difference LBM in the three-dimensional generalized curvilinear coordinate can be used for accurately and effectively computing the three-dimensional practical incompressible flow problems.

Transparent boundary condition and its effectively local approximation for the Schrödinger equation on a rectangular computational domain

The transparent boundary condition for the free Schrödinger equation on a rectangular computational domain requires implementation of an operator of the form ∂t−i△Γ where △Γ is the Laplace-Beltrami operator. It is known that this operator is nonlocal in time as well as space which poses a significant challenge in developing an efficient numerical method of solution. The computational complexity of the existing methods scale with the number of time-steps which can be attributed to the nonlocal nature of the boundary operator. In this work, we report an effectively local approximation for the boundary operator such that the resulting complexity remains independent of number of time-steps. At the heart of this algorithm is a Padé approximant based rational approximation of certain fractional operators that handles corners of the domain adequately. For the spatial discretization, we use a Legendre-Galerkin spectral method with a new boundary adapted basis which ensures that the resulting linear system is banded. A compatible boundary-lifting procedure is also presented which accommodates the segments as well as the corners on the boundary. The proposed novel scheme can be implemented within the framework of any one-step time marching schemes. In particular, we demonstrate these ideas for two one-step methods, namely, the backward-differentiation formula of order 1 (BDF1) and the trapezoidal rule (TR). For the sake of comparison, we also present a convolution quadrature based scheme conforming to the one-step methods which is computationally expensive but serves as a golden standard. Finally, several numerical tests are presented to demonstrate the effectiveness of our novel method as well as to verify the order of convergence empirically.

A method for calculating two-phase equilibrium: Constrained gray prediction evolutionary algorithm with a surrogate model based on quadratic interpolation

Traditional methods, including direct solution methods based on Newton's method and indirect solution methods based on thermodynamic principles, are the mainstream methods used to solve the volume-temperature flash calculation (called NVT-flash), even though they suffer from drawbacks such as sensitivity to initial value and complexity of derivative calculations. A constrained backtracking search algorithm (CBSA), proposed in 2024, was the first and only metaheuristic algorithm to successfully tackle the NVT-flash problem, which overcomes shortcomings of traditional methods. Considering the advantages of metaheuristic algorithms, a constrained gray prediction evolutionary algorithm with a surrogate model based on quadratic interpolation (CGPE-QI) is proposed in this paper to deal with the NVT-flash problem. CGPE-QI considers total Helmholtz free energy as the objective function, moles vector, and volume of a single phase as variables. Constraints to solve the NVT-flash problem are addressed by using a direct search method and an exterior point method. Numerical experiments on two-phase equilibrium of pure substance and mixtures are carried out employing CGPE-QI. Experimental results are the same as those obtained by traditional methods, which confirms that CGPE-QI can effectively tackle the NVT-flash problem and possesses energy decay property. In particular, the results demonstrate that CGPE-QI is more competitive than CBSA in terms of convergence speed, stability, and calculation cost. CGPE-QI proposed in this paper is the second metaheuristic algorithm to successfully solve the NVT-flash problem, illustrating that metaheuristic algorithms have great potential in solving phase equilibrium calculation problems.

Assessing hydropower capability for accommodating variable renewable energy considering peak shaving of multiple power grids

Hydropower can play a transformative role in supporting the substantial increase in variable renewable energy (VRE) via flexible regulation ability, but the capability of cascade hydropower to buffer VRE is indistinct. This study proposes a systematic framework to assess hydropower capability for integrating VRE considering the peak shaving demands of multiple power grids. A chance-constrained multi-time aggregate model for the integrated hydro-wind-solar operation is established to determine the optimal wind and solar capacity and coordinated operational strategies. An independent hydropower peak shaving operation model is proposed for comparison. The proposed models are recast as mixed integer linear programming formulations based on the proposed solution method. Comparative situation experiments divided by different influencing factors are implemented to analyze hydropower capability for integrating VRE. The results revealed the following: (1) Integrating VRE would reduce the peak shaving effectiveness of hydropower—the lowest load rate decreases to 0.899. (2) Hydropower capability for integrating wind and solar power is affected by hydrological conditions. Hydropower capability is limited under fixed hydrological conditions, and it prioritizes expanding wind power and restricting solar power. (3) Expanding the transmission capacity will increase hydropower capability from 13300 to 17000 MW and enhance peak shaving performance through the increased hydropower contribution.

Talfīq as A Method for Legal Solutions in Contemporary Islamic Law

Talfīq, or the combination of various opinions of jurisprudence madhhab, is frequently used to arrive at solutions in Islamic law. It was used extensively in the tajdīd era, the early 20th century AD, namely through the Islamic law reforms in family and personal matters in most Muslim countries. The concept is controversial and not universally accepted by ulama. Most classical ulama opposed it, although most contemporary ulama believe that talfīq is acceptable as a method for legal solutions. This research investigated the reasons or factors for this disagreement by analyzing the related views of such ulama. The qualitative research used a literature review from classical and contemporary books about talfīq. This research concluded that although the classical ulama appeared to be grounded firmly in their opposition to the practice of talfīq, subsequent development of Islamic law showed practical consideration made inroads in accepting talfīq as a method for legal solution. This acceptance is made by putting conditions and controls on how and when talfīq can be used to solve modern Muslim legal problems. This modification ensures the traditionalists that the danger of talfīq, as previously envisaged, would be avoided. Abstrak: Talfīq atau gabungan berbagai pendapat mazhab fikih merupakan metode yang sering digunakan untuk mencari solusi dalam hukum Islam. Hal ini digunakan secara luas pada zaman tajdīd, yaitu awal abad XX Masehi, melalui reformasi hukum Islam dalam masalah hukum privat dan keluarga di sebagian besar negara Muslim. Konsepnya kontroversial dan tidak disetujui secara universal oleh para sarjana. Mayoritas ulama Islam klasik telah menentangnya meskipun sebagian besar ulama kontemporer berpandangan bahwa talfīq dapat diterima sebagai metode penyelesaian hukum. Penelitian ini bertujuan untuk menyelidiki alasan atau faktor ketidaksepakatan ini dengan melihat sampel dari pandangan terkait dari para ulama tersebut. Penelitian ini merupakan penelitian kualitatif dengan menggunakan tinjauan pustaka. Data diperoleh dari buku-buku klasik dan kontemporer yang menulis tentang talfīq. Penelitian ini menyimpulkan bahwa meskipun para ulama klasik pada mulanya tampak berpijak kuat pada penentangan mereka terhadap praktik talfīq, perkembangan hukum Islam selanjutnya menunjukkan pertimbangan praktis yang membuat terobosan dalam menerima talfīq sebagai metode penyelesaian hukum. Penerimaan ini dilakukan dengan meletakkan kondisi dan kontrol tentang bagaimana dan kapan talfīq dapat digunakan dalam memberikan solusi atas masalah hukum Islam modern. Modifikasi ini memastikan kaum tradisionalis menghindari bahaya talfīq seperti yang dibayangkan sebelumnya.

Strategi Dekarbonisasi Untuk Industri Pertambangan Batu Bara di Indonesia Menggunakan Metode TOPSIS

The coal mining industry is one of the largest contributors to Indonesia’s economy and greenhouse gas emissions at the same time. Indonesian coal mining companies will inevitably expand their business to meet the demand and targets. But Indonesia already set its ambition to become a net-zero country by 2060 or earlier. Without a robust decarbonization strategy at the company level, Indonesia will face difficulties meeting the net-zero target. This study proposes to use one of the Multi-Criteria Decision Analysis, Technique for Order of Preference by Similarity to Ideal, Solution method within the Political, Economic, Social, Technology, Environmental, and Legal criteria to assess five prioritized decarbonization strategies, which are renewable energy, equipment electrification, business shifting, process efficiency, carbon offset. The analysis will evaluate essential sub-criteria such as regulation compliance, investment, social acceptance, technological readiness, and emissions reduction. The data was acquired from a survey of coal mining stakeholders, other journal reviews, and company disclosures. The final results show that process efficiency scores highest on the selected criteria due to high economic and technological value. Meanwhile, other criteria like carbon offset, renewable energy use, and equipment electrification receive lower scores.

Free Vibration of Ogival Circular Arch

This study aims to analyze the free vibration of an ogival arch in which two symmetrical arched shapes are discontinuously met at the mid-arc. Differential equations governing the free vibration of an ogival circular arch were derived, where the rotatory inertia and shear deformation effects are included. The governing equations were numerically solved to calculate natural frequencies and mode shapes. In numerical solution methods, the symmetric and anti-symmetric boundary conditions at the mid-arc were focused rather than the boundary conditions of supported end due to the discontinuity of the mid-arc. For the first time, the free vibration problem for discontinuous arches, such as ogival arches, is solved in this study. Calculation results of this study for natural frequencies are compared well with those of the finite element method. The effects of various arch parameters on natural frequencies were highlighted and discussed in detail.

Vibration Analysis of Perovskite Solar Cells Resting on Porous Nanocomposite Substrate in Thermal Environment

The free vibration analysis of a perovskite solar cell (PSC) within thermal environments is studied through a quasi-3D plate model. This model is designed to account for the stretching effects and non-uniform shear strains throughout the thickness. The PSC film is represented in the model as a thin laminated plate, comprising five distinct plies: ITO, PEDOT:PSS, perovskite, PCBM, and Au. A graphene platelets reinforced composite (GPLRC) substrate, made of Poly (methyl methacrylate), is assumed to be located under the noted five layers. The GPLRC substrate is conceptualized in the model as a porous foundation with finite depth. The foundation stiffnesses are determined using a modified Vlasov model, and the equivalent Young modulus of the foundation is evaluated using a modified Halpin–Tsai model, introducing the porosity coefficient. It is also considered that the material properties of GPLRC substrates exhibit temperature dependency. For the PSC with all edges simply supported, Navier solution method is applied to obtain the frequencies and associated mode numbers. Based on the results presented in this study, an increase in the porosity and thickness of the substrate can negatively impact the frequencies of the structure. However, natural frequencies experience almost no change if the temperature rises.

Dynamic response strength of integrated foundation for multiple wind turbines

The focus of this paper is on the conceptual design of offshore multi wind turbine integrated floating foundation (MWF) and the strength analysis method of floating structures considering motion response. Firstly, the maximum environmental load and ultimate motion attitude of MWF were used as boundary conditions to solve the problem of imprecise constraint loads in traditional analysis methods. Then, a stress solving equation based on motion state and inertia release was established to solve the equivalent accuracy problem of floating body motion state. Finally, through theoretical analysis, numerical simulation, and experimental testing, it was found that the stress solution method considered the motion state of the floating body in this paper to be more accurate and reliable, with an error of less than 10% compared to the stress results of experimental testing. Improved calculation accuracy by 30%. These studies provide more accurate analytical methods and theoretical references for predicting the strength of floating structures in actual oceans.

Integrated reinforcement and imitation learning for tower crane lift path planning

Reinforcement learning (RL) has emerged as a promising solution method for crane-lift path planning. However, designing appropriate reward functions for tower crane (TC) operations remains particularly challenging. Poor design of reward functions can lead to non-executable lifting paths. This paper presents a framework combining imitation learning (IL) and RL to address the challenge. The framework comprises three steps: (1) designing a virtual environment consisting of construction site models and a TC model, (2) collecting expert demonstrations through virtual reality (VR) and pretraining through behavioral cloning (BC), and (3) refining the BC policies via generative adversarial imitation learning (GAIL) and proximal policy optimization (PPO). Using the paths generated by a PPO model as the baseline, the proposed BC + PPO + GAIL model exhibited better performance in both blind and nonblind lifting scenarios. This framework has been proven to generate realistic lifting paths mirroring crane operator behavior while ensuring efficiency and safety.

Fourier–Gegenbauer Pseudospectral Method for Solving Periodic Higher-Order Fractional Optimal Control Problems

Optimal control (OC) problems involve determining the best control strategy for a system to achieve the desired outcome. Fractional-order OC models have emerged in recent decades to represent complex systems that exhibit memory effects and nonlocal interactions. These models offer advantages over the traditional integer-order models. However, a key challenge with existing fractional calculus methods to define fractional derivatives, such as Riemann–Liouville, Caputo, and Grünwald–Letnikov derivatives, is that they often fail to maintain the periodic nature of dynamical systems. This limitation hinders their application to real-world scenarios, in which periodicity is crucial. This study proposes a novel model for periodic higher-order fractional OC problems (PHFOCPs). We achieve this by employing recently developed periodic fractional derivatives that specifically preserve the periodicity within the model. This study outlines also a robust numerical solution method for this new class of problems. The numerical method is applicable for any positive fractional order [Formula: see text] and leverages several key techniques, including a useful change of variables, Fourier collocation, and Fourier and Gegenbauer quadratures, to retain numerical stability and high accuracy. We theoretically prove the exponential convergence of the proposed numerical method for sufficiently smooth periodic functions. The effectiveness of the proposed approach is further validated through various numerical simulations. In essence, this work presents a novel framework for the OC of periodic systems using fractional calculus, overcoming the limitations of classical methods and offering a powerful tool for various scientific and engineering applications.

A Novel Fast Iterative STAP Method with a Coprime Sampling Structure.

In space-time adaptive processing (STAP), the coprime sampling structure can obtain better clutter suppression capabilities at a lower hardware cost than the uniform linear sampling structure. However, in practical applications, the performance of the algorithm is often limited by the number of training samples. To solve this problem, this paper proposes a fast iterative coprime STAP algorithm based on truncated kernel norm minimization (TKNM). This method establishes a virtual clutter covariance matrix (CCM), introduces truncated kernel norm regularization technology to ensure the low rank of the CCM, and transforms the non-convex problem into a convex optimization problem. Finally, a fast iterative solution method based on the alternating direction method is presented. The effectiveness and accuracy of the proposed algorithm are verified through simulation experiments.

Ultrafast Visual Detection of a Trace Amount of Water by Highly Efficient Hybrid Manganese Halides.

A quantitative water detection method is urgently needed in storage facilities, space exploration, and the chemical industry. Although numerous physical techniques have been widely utilized to determine the water content, they still suffer from many disadvantages such as highly expensive special instruments, complicated analysis processes, etc. Hence, a convenient, rapid, and sensitive water analysis method is highly desirable. Herein, we developed a visual fluorescence sensing technology for water detection based on reversible PL off-on switching of organic-inorganic hybrid zero-dimensional (0D) manganese halides. In this work, a family of hybrid manganese halides were synthesized through a facile solution method, namely, [NH4(18-Crown-6)]2MnBr4, [Ca(18-Crown-6)·3H2O](18-Crown-6)MnBr4, [NH4(dibenzo-18-Crown-6)]2MnBr4, and [Ca(dibenzo-18-Crown-6)·2H2O]MnBr4. Excited by UV light, these highly crystalline manganese halides exhibit strong green light emissions from the d-d electron transition of Mn2+ with near-unity photoluminescence quantum yield and submillisecond lifetime. Benefiting from the dynamic and weak ionic bonding interactions, these 0D manganese halides display reversible water-response on/off luminescence switching but fail in any other aprotic solvents. Therefore, these 0D hybrid manganese halides can be explored as ultrafast visual fluorescence probes to detect the trace amount of water in organic solvents with multiple superiorities of rapid response time (< 2 s), ultralow detection limit (9.71 ppm), excellent repeatability, etc. The reversible water-response luminescent on/off switching also provides a binary optical gate with advanced applications in anticounterfeiting and information security, etc.

Discrete cutting path problems: a general solution framework and industrial applications

The optimal tool routing for cutting machines, also known as cutting path optimisation is an important problem in production research. This problem is relevant in various manufacturing environments such as aeronautic, automotive, garment and semiconductor industries. In this paper, we introduce a general solution framework for the discrete Cutting Path Problem which includes: (i) the universal approach to reduce numerous settings of this problem to the appropriate auxiliary instances of the well-known Precedence Constrained Generalized Traveling Salesman Problem; (ii) the proposition of efficient solution methods for finding (sub-) optimal solutions. We carry out extensive computational experiments in order to evaluate performance of the proposed framework and the obtained results demonstrate its efficiency for real-life industrial instances.

Employing Machine Learning for Seismic Intensity Estimation Using a Single Station for Earthquake Early Warning

An earthquake early-warning system (EEWS) is an indispensable tool for mitigating loss of life caused by earthquakes. The ability to rapidly assess the severity of an earthquake is crucial for effectively managing earthquake disasters and implementing successful risk-reduction strategies. In this regard, the utilization of an Internet of Things (IoT) network enables the real-time transmission of on-site intensity measurements. This paper introduces a novel approach based on machine-learning (ML) techniques to accurately and promptly determine earthquake intensity by analyzing the seismic activity 2 s after the onset of the p-wave. The proposed model, referred to as 2S1C1S, leverages data from a single station and a single component to evaluate earthquake intensity. The dataset employed in this study, named “INSTANCE,” comprises data from the Italian National Seismic Network (INSN) via hundreds of stations. The model has been trained on a substantial dataset of 50,000 instances, which corresponds to 150,000 seismic windows of 2 s each, encompassing 3C. By effectively capturing key features from the waveform traces, the proposed model provides a reliable estimation of earthquake intensity, achieving an impressive accuracy rate of 99.05% in forecasting based on any single component from the 3C. The 2S1C1S model can be seamlessly integrated into a centralized IoT system, enabling the swift transmission of alerts to the relevant authorities for prompt response and action. Additionally, a comprehensive comparison is conducted between the results obtained from the 2S1C1S method and those derived from the conventional manual solution method, which is considered the benchmark. The experimental results demonstrate that the proposed 2S1C1S model, employing extreme gradient boosting (XGB), surpasses several ML benchmarks in accurately determining earthquake intensity, thus highlighting the effectiveness of this methodology for earthquake early-warning systems (EEWSs).