Solution Method Research Articles

Application of Digital Technology for Oil and Gas Fields

The paper investigates the problem of isothermal filtration using an approximate method for solving the theory of partial differential equations. The mathematical model under consideration is nonlinear and does not lend itself to analytical methods of solution. The results obtained indicate the need for wide application in the development of oil and gas fields in the Republic of Kazakhstan. In particular, the results of the study make it possible to solve the problems of adapting mathematical models and evaluating changes in technological indicators, which are necessary attributes in the digital technology ""Information System for the Analysis of oil and Gas Field Development"" (ISAR). Many problems and mathematical problems of filtration theory arose while working on specific oil and gas fields in the western region of the Republic of Kazakhstan. The above approximate solution methods have found applications not only in filtration theory, but also in other problems (geophysics, ecology, etc.)"

A Double Optimum New Solution Method Based on EVA and Knapsack

A Double Optimum New Solution Method Based on EVA and Knapsack

Optimal allocation model of port emergency resources based on the improved multi-objective particle swarm algorithm and TOPSIS method

The busy maritime traffic and occurrence of ship accidents have led to a growing recognition of the necessity to maritime emergency resources allocation. The port emergency resource allocation is of significant importance for the maritime safety. This paper presents an optimized allocation model for port emergency resources based on the improved multi-objective particle swarm optimization (IMOPSO). The model introduces the crowding distance and improves the external archive update strategy. The particle inertia weight is adjusted and a dynamic mutation operator is incorporated. The entropy-weighted technique for order preference by similarity to an ideal solution method is also employed to identify the optimal solution. A comprehensive comparison with MOPSO has been presented and discussed. Three metrics of generational distance (GD), spacing (SP) and delta indicator (Δ) were employed for performance evaluation. The results demonstrated that the proposed IMOPSO algorithm exhibited superior performance and robustness, with average values of GD = 0.0386, SP = 0.0023 and Δ = 0.6468 for ZDT test functions. The model efficacy is further validated by a case study of oil spill dispersant configuration at Zhanjiang Port, China. Seven alternative schemes have been obtained, among which the optimal scheme is selected by the entropy-weighted TOPSIS method. The overall cost is potentially to be reduced by approximately 33.03 %. The present study would provide a reference for the water pollutant control and environmental management in port waters.

A hybrid GRA-PCA-TOPSIS Technique Optimization of EDD Process Parameter on Drilling of AL8011/B4C/aloevera composites

A class of cutting-edge materials called hybrid aluminum metal matrix composites has been produced for applications such as the automobile, aerospace, and electronics industry and where weight and strengths are crucial factors. In the present research work, AA8011 composite reinforced with boron carbide particles and green aloevera has been fabricated using an economical stir casting process. In this 1% to 3% of boron carbide and 1% to 2% aloevera were used to fabricate the three different combinations of composites. The electrical discharge drilling process variables such as discharge current, pulse-on time, pulse-off time, and dielectric fluid pressure are investigated using the tubular copper electrode on drilling of hybrid composites. Performance aspects including drilling rate, roundness error, taper angle, and electrode wear rate have been analyzed, and the effect of process variables with the response surface methodology–central composite experimental design. The process parameters during the drilling of composites were optimized using a novel multi-criteria decision-making technique called the hybrid grey relational analysis–pulse component analysis–technique for order of preference by similarity to ideal solution method in order to maximize the drilling rate while concurrently minimizing other aspects. The optimum conditions were reached using the hybrid optimization method and a 1.5 mm tubular copper electrode with an input current of 9 Ampere , pulse on time 25 [Formula: see text]s, pulse off time 12 [Formula: see text]s, and pressure 60 kg/cm2. In comparison to the initial settings, the optimal condition shows a 25% faster drilling rate, with a 23% better roundness value, 14% lesser taper angle, and 7% lesser electrode wear rate.

Toward robust decision-making under multiple evaluation scenarios with a novel fuzzy ranking approach: green supplier selection study case

In the evolving field of decision-making, the continuous advancement of technologies and methodologies drives the pursuit of more reliable tools. Decision support systems (DSS) provide information to make informed choices and multi-criteria decision analysis (MCDA) methods are an important component of defining decision models. Despite their usefulness, there are still challenges in making robust decisions in dynamic environments due to the varying performance of different MCDA methods. It creates space for the development of techniques to aggregate conflicting results. This paper introduces a fuzzy ranking approach for aggregating results from multi-criteria assessments, specifically addressing the limitations of current result aggregation techniques. Unlike conventional methods, the proposed approach represents rankings as fuzzy sets, providing detailed insights into the robustness of decision problems. The study uses green supplier selection as a case study, examining the performance of the introduced approach and the robustness of its recommendations within the sustainability field. This study offers a new methodology for aggregating results from multiple evaluation scenarios, thereby enhancing decision-maker awareness and robustness. Through comparative analysis with traditional compromise solution methods, this paper highlights the limitations of current approaches and indicates the advantages of adopting fuzzy ranking aggregation. This study significantly advances the field of decision-making by enhancing the understanding of the stability of decision outcomes.

A Comprehensive Exploration of Hellwig’s Taxonomic Measure of Development and Its Modifications—A Systematic Review of Algorithms and Applications

This paper presents an original and comprehensive investigation into the Taxonomic Measure of Development (TMD), introduced by Hellwig in 1968, enriching both its theoretical foundations and practical applications. It provides an overview of various variants of the Hellwig method, including their extensions and applications, while also exploring recent trends across multiple research domains. Primarily developed as a method for multidimensional analysis, TMD has evolved into a pivotal tool in multi-criteria decision-making. It is widely used for evaluating and ranking alternatives, particularly in the analysis of complex socio-economic phenomena and decision-making scenarios involving multiple criteria. This study systematically reviews the original algorithm and its subsequent extensions and modifications, including adaptations for fuzzy sets, intuitionistic fuzzy sets, and interval-valued fuzzy sets. Furthermore, it explores an integrated multi-criteria approach based on Hellwig’s method and its practical applications across various domains. This paper introduces an original approach by conducting a detailed, step-by-step analysis of the TMD framework. This process-oriented analysis is a novel contribution that sets this study apart from typical reviews based on statistical or bibliometric data. By examining key steps in the TMD framework—such as data collection, criterion weighting, data normalization, ideal value determination, distance calculation, and normalization factor—this paper highlights the method’s versatility in addressing complex, real-world decision-making problems. Although similar to the widely used Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method in its reliance on distance to evaluate alternatives, Hellwig’s approach is unique in focusing exclusively on proximity to an ideal solution, without considering distance from a negative ideal. This distinctive emphasis has led to numerous adaptations and extensions that address specific issues such as criterion dependencies, uncertainty, and rank reversal. The findings underscore the continued relevance of the Hellwig method, its recent extensions, and its growing international recognition.

Modeling and analysis of fluid-solid coupling heat transfer and fluid flow in transonic nozzle

Solid rocket motors (SRMs) are the power units of modern aerospace transportation, and it is of great significance to predict their flow and heat transfer characteristics by numerical simulation. This numerical study is based on a self-developed code framework MHT (Multi-X Heat Transfer, where X represents the region, scale, physical field, and other aspects) and presents an effective method of analyzing fluid–solid coupling heat transfer problem in an axisymmetric supersonic nozzle. For the nozzle wall heat conduction and nozzle-in fluid flow there are two kinds of solution methods, the detached (solved separately in solid and fluid region and them coupling at the interface) and the whole field method (solve the conduction and convection simultaneously). The whole field method is more efficient but few scholars use it to solve the nozzle coupled problem. This paper introduces the numerical details of the whole field solution method, and provides a new idea for the coupling solution of the nozzle. In addition, a mixed grid system is proposed around the interface region of fluid and solid, in which the quadrilateral structured mesh is used at the fluid side, and the triangular unstructured mesh is used for the outer fluid region and the whole solid region to ensure the accuracy of the 1st-oder normal derivatives calculation. The self-developed code is validated by comparison with well-known commercial soft-ware. Transient results of temperature and velocity are presented for the instants of 1st 20 and 40 s of a practical nozzle.

Inherently interpretable machine learning for credit scoring: Optimal classification tree with hyperplane splits

An accurate and interpretable credit scoring model plays a crucial role in helping financial institutions reduce losses by promptly detecting, containing, and preventing defaulters. However, existing models often face a trade-off between interpretability and predictive accuracy. Traditional models like Logistic Regression (LR) offer high interpretability but may have limited predictive performance, while more complex models may improve accuracy at the expense of interpretability. In this paper, we tackle the credit scoring problem with imbalanced data by proposing two new classification models based on the optimal classification tree with hyperplane splits (OCT-H). OCT-H provides transparency and easy interpretation with ‘if-then’ decision tree rules. The first model, the cost-sensitive optimal classification tree with hyperplane splits (CSOCT-H). The second model, the optimal classification tree with hyperplane splits based on maximizing F1-Score (OCT-H-F1), aims to directly maximize the F1-score. To enhance model scalability, we introduce a data sample reduction method using data binning and feature selection. We then propose two solution methods: a heuristic approach and a method utilizing warm-start techniques to accelerate the solving process. We evaluated the proposed models on four public datasets. The results show that OCT-H significantly outperforms traditional interpretable models, such as Decision Trees (DT) and Logistic Regression (LR), in both predictive performance and interpretability. On certain datasets, OCT-H performs as well as or better than advanced ensemble tree models, effectively narrowing the gap between interpretable models and black-box models.

Train assignment and handling capacity arrangement in multi-yard railway container terminals: An enhanced adaptive large neighborhood search heuristic approach

Expanding terminal scale and constructing multiple railway handling yards have become popular strategies for leading railway container terminals globally to cope with the ever-increasing handling volume. Although such an approach greatly enhances the terminal’s productivity, it also intensifies handling operations and introduces additional inter-yard interactions, complicating terminal management. To address these challenges, this paper investigates an integrated optimization approach for multi-yard railway container terminals, which feature both rail-road and rail-rail container transshipment operations. The train assignment plan for each yard and the handling capacity arrangement for each train are jointly optimized while considering inter-yard container transits, workload allocation for yards, and safety requirements in train shunting. This problem is formulated as a nonlinear programming model, with the objective to minimize operational delay and service time for each incoming train, and to minimize workload differences and container transit volume among yards. To efficiently solve this problem, this research develops an enhanced adaptive large neighborhood search (EALNS) heuristic, which includes several customized operators and feasibility repair methods, and is further enhanced with a local search method and backtracking mechanism compared to the standard ALNS framework. Computational experiments with different data scales and problem settings demonstrate the superiority of the EALNS in terms of solution quality and stability compared with three other solution methods. Additionally, practical insights for terminal operations are drawn through detailed analysis of different infrastructure configurations, transshipment train characteristics, and unit cost settings.

A Machine Learning and Fuzzy Logic Model for Optimizing Digital Transformation in Renewable Energy: Insights into Industrial Information Integration

The most essential criteria to improve digital transformation in renewable energy projects should be identified. This situation helps the companies to use limited financial budgets and human resources in the most efficient way. Therefore, a new study is needed to analyze the performance indicators of the digital transformation process in renewable energy projects. Accordingly, this study aims to identify the most significant performance indicators of digital transformation for these projects. A three-stage machine learning and fuzzy logic-based decision-making model has been constructed in this process. The first stage includes the weight calculation of the experts by dimension reduction methodology. Secondly, essential factors of digital transformation in renewable energy projects are examined via Fermatean fuzzy criteria importance through intercriteria correlation (CRITIC). The final part consists of the ranking of emerging seven countries with Fermatean fuzzy weighted aggregated sum product assessment (WASPAS). On the other side, combined compromise solution (CoCoSo) method is also taken into consideration in this process to make a comparative evaluation. The main contribution of this study is the generation of novel machine learning and fuzzy logic integrated decision-making model to make evaluation related to the digital transformation of renewable energy projects. In this model, machine learning technique is used to determine the importance weights of the experts. Similarly, integrating Fermatean fuzzy numbers with CRITIC and WASPAS techniques also contributes to the literature by minimizing the uncertainty and identifying the relationship between the items. The findings demonstrate that employing qualified personnel plays the most critical role in increasing digital transformation in renewable energy projects. Additionally, government support is very critical in the successful implementation of digital transformation processes in renewable energy projects.

Multi-attribute group decision-making method using single-valued neutrosophic credibility numbers with fairly variable extended power average operators and GRA-MARCOS

Data trading platform (DTP) selection is a classic multi-attribute group decision-making (MAGDM) problem. As an extension of intuitionistic fuzzy sets (IFSs), single-valued neutrosophic credibility numbers (SvNCNs) can express both fuzzy evaluation information and the credibility level of the information, offering better expressiveness in describing fuzzy decision-making information. However, existing studies on aggregation operators and decision-making methods in the SvNCN environment are inadequate. Therefore, this paper proposes a MAGDM technique based on the fairly weighted variable extended power average (SvNCNFWVEPA) operators of SvNCNs and grey relational analysis (GRA)-Measurement of Alternatives and Ranking according to Compromise Solution (MARCOS) method. The main contributions are as follows: (1) we propose fairly operation rules for aggregating SvNCNs in an unbiased manner; (2) addressing the lack of SvNCN measurement research, we introduce preference distance and entropy measures for SvNCNs, then utilize the entropy measure to compute the objective weights of the attributes; (3) to effectively aggregate SvNCN information, inspired by the variable power geometric (VPG) operators and the extended power average (EPA) operators, we propose the variable extended power average (VEPA) operator for scientifically handling extreme values, extending it to SvNCNs with the SvNCNs fairly variable extended power average (SvNCNFVEPA) operators and their extended form; (4) we introduce the GRA method to calculate the degree of utility of alternatives relative to ideal and anti-ideal alternatives, forming the GRA-MARCOS method. This method can reflect both indicator differences and the similarity of alternatives, thereby rendering the evaluation results more scientific and objective; (5) to illustrate the application of the method to the MAGDM problem, we apply it to the example of DTP selection. Parameter sensitivity analysis and comparative analysis with other existing methods demonstrate that our proposed method is more scientific and flexible.

Gelatin microcarriers as an effective adipose-derived stem cells delivery strategy in osteoarthritis treatment

Despite their clinical success, stem cells (SCs) in the treatment of osteoarthritis (OA) are limited by lower retention efficiency and restricted paracrine function. The development of effective SCs culture and delivery systems to maintain or promote SCs paracrine function is urgently needed. In this study, we focused on gelatin microcarriers with different sizes as SCs culture scaffold for OA therapy. The effect of culturing adipose-derived stem cells (ADSCs) on different size microcarriers (180 μm and 320 μm) to promote paracrine function was evaluated. The secretome of ADSCs cultured on microcarriers more effectively regulated macrophages and chondrocytes in a direction favorable to OA healing compared to culture plates. In particular, microcarriers with ADSCs effectively reduced the coefficient of friction at the cartilage interface. Injection low-dose ADSCs without and with different size microcarriers into the knee joints in rats' OA model was achieved. Even better OA therapeutic effects were achieved by using smaller size (higher curvature) microcarriers to deliver ADSCs at low doses. Microcarrier-based cell delivery strategies offer potential solution method for OA therapy.

Nanocubic transition metal sulfides (FeMn)S2@NC synthesised by MOFs facily for supercapacitors

Transition metal sulfides have attracted considerable attention as supercapacitor materials due to their favorable electrochemical properties and low cost. However, challenges such as slow electrochemical kinetics and significant volume changes still persist. In this study, (FeMn)S2@NC nanocubes were synthesized by utilizing MOFs as precursors, employing a solution method to encapsulate Ppy around the periphery, followed by a one-step annealing process. The electrode prepared from (FeMn)S2@NC exhibits a high specific capacitance of 1154 F/g at 1 A/g and retains a specific capacitance of 777 F/g even at a high current density of 5 A/g. After 8000 charge-discharge cycles at 10 A/g, it still maintains a high capacity retention of 94% and close to 100% Coulombic efficiency. The (FeMn)S2@NC//AC capacitor exhibite excellent electrochemical performance with good rate capability and practical value 39.73F/g at 1A/g, 38.26F/g at 2A/g, 26.67F/g at 5A/g, 20.53F/g at 7A/g and 14F/g at 10A/g. Electrochemical testing at 5A/g show 93.3% high capacity retention after 180,000 cycles.

Growth and spectral properties of Er3+/Yb3+/Ce3+:YPO4 crystal

Er3+/Yb3+/Ce3+ tir-doped YPO4 crystals were successfully synthesized by high temperature solution method with Pb2P2O7 as flux. The growth, structures and spectral characterizations, including polarization absorption spectra, polarization fluorescence spectra and lifetime, were systematically discussed. It has broad absorption band around 973 nm with a full width at half maximum of 20.76 nm and 14.71 nm for π- and σ-polarizations, respectively, which is suitable for efficient pumping of InGaAs laser diodes. Based on J-O theory, spectral parameters such as oscillator strength, line strength, J-O intensity, fluorescence branching ratio, spontaneous transition rate and radiation lifetime were calculated systematically. In the polarization fluorescence spectra, four strong emission peaks were observed at 1505, 1537, 1561 and 1586 nm. The results show that Er3+/Yb3+/Ce3+:YPO4 crystal may be a promising laser crystal suitable for 1.5–1.6 μm.

Synthesis of high-entropy perovskite metal fluoride anode materials for lithium-ion batteries via a one-pot solution method

Synthesis of high-entropy perovskite metal fluoride anode materials for lithium-ion batteries via a one-pot solution method

Lippmann-Schwinger equation representation of Green's function and its preconditioned generalized over-relaxation iterative solution in wavelet domain

The calculation of Green's function is the core of seismic forward and inverse methods based on integral operators. When the Lippmann-Schwinger (L-S) equation is used to calculate Green's function in strongly scattering media, both the Born scattering series and the numerical iterative method encounter issues of slow convergence or divergence. Although the renormalization method derived from quantum mechanics can effectively address the convergence problem of Born scattering series in strong scattering problems, it is acknowledgeed that the convergence conditions and rates of convergence of different reformulation series may vary, and no universal convergence reformulation scattering series exists. Numerical methods for solving integral equations tend to be more general and mathematically robust. In this work, we focus on the numerical solution method of L-S equations. By using a wavelet-domain preconditioner to a reformulated or equivalent Lippmann-Schwinger (L-S) equation, we present an iterative method for numerically solving the equivalent L-S equation aimed at improving the rate of convergence and iteration efficiency in strongly inhomogeneous media. Following Jakobsen et al. (2020), we first introduce a small imaginary component into the background wave number，then rewrite the L-S equation to derive the equivalent complex wave number L-S equation. This reformulation ensures that the coefficient matrix exhibits a banded structure after numerical discretization, allowing the wavelet coefficient matrix to maintain good sparsity. We employ a multi-level fill-in incomplete LU (ILU) factorization method along with a block ILU-based algebraic recursive multilevel solve (ARMS) method in the wavelet domain to generate sparse approximate inverses as preconditioning operators, thereby accelerating the convergence of the generalized successive over-relaxation (GSOR) iterative method. This method is applied to compute numerical Green's functions in strongly inhomogeneous media. Numerical results demonstrate that our method yields simulation outcomes consistent with those obtained from the direct method for solving the original real wave number L-S equation. By testing various preconditioners, we find that the ARMS preconditioner offers significant advantages in operator generation efficiency and non-zero element filling ratio, effectively accelerating the convergence of the GSOR iterative method while achieving higher computational efficiency.

Enhancing hole-transport efficiency in (CH3NH3)2CuClxBr4-x perovskite solar cells through functionalization of multi-walled carbon nanotubes

This study addresses the conductivity limitation of conventional hole-transporting layers (HTLs) like Spiro-OMeTAD in perovskite solar cells. We integrate functionalized multi-walled carbon nanotubes (MWCNTs) into Spiro-OMeTAD and examine the impact of chemical oxidation/functionalization of MWCNTs on their application in perovskite solar cells. Various ratios of H2SO4/HNO3 mixture were employed to functionalize MWCNTs, with FTIR analysis confirming functional group attachments on the nanotube walls. Raman spectroscopy was utilized to analyze defect formation in the MWCNTs, while FESEM images displayed both the tubular structure and morphological alterations resulting from functionalization. XRD analysis was conducted to examine the structural variances resulting from different functionalization ratios. A lead-free copper-based perovskite (CH3NH3)2CuBr4-xClx (x = 0.5), synthesized via a straightforward solution method, served as the active material for solar cells. The XRD assessment of the active material confirmed perovskite formation, with morphological analysis revealing a layered structure. Diffuse Reflectance Spectroscopy (DRS) measurements yielded a bandgap of approximately 1.86 eV for the perovskite. Dark I–V measurements were executed for Spiro-OMeTAD films incorporated with pure and functionalized MWCNTs, showcasing improved conductivity attributed to charge carrier conduction via defect passivation. Hybrid solar cells were fabricated with structures such as TiO2/(CH3NH3)2CuBr4-xClx (x = 0.5)/Spiro-OMeTAD/Ag, and TiO2/(CH3NH3)2CuBr4-xClx (x = 0.5)/functionalized MWCNTs/Spiro-OMeTAD/Ag. These cells demonstrated enhanced stability, retaining over 65 % of their initial efficiency after 10 days of storage, attributed to improved moisture permeability inferred from contact angle measurements and XRD analysis. Photovoltaic characterization revealed significant improvements in fill factor (∼70 %), short-circuit current density (2.94 mA), open-circuit voltage (0.70 V), and power conversion efficiency (PCE) (1.6 %) for the FTO/TiO2/(CH3NH3)2CuCl0·5Br3.5/Spiro-MWCNT-1/Ag device.

Down and Upconversion NGQDs and CuInS/ZnS QDs Nanocomposite for Ascorbic Acid Sensing

Copper indium sulfide/zinc sulfide (CuInS/ZnS) QDs and Nitrogen graphene quantum dots (NGQDs) were synthesized via aqueous solution methods and facile hydrothermal, respectively. Herein, according to the photoluminescence (PL) properties of QDs, a fluorescent nanocomposite of CuInS/ZnS QDs and NGQDs was synthesized. This nanocomposite was characterized by high-resolution transmission electron microscopy (HRTEM) and PL spectroscopy. This fluorescent nanocomposite was developed as a highly sensitive and selective ascorbic acid optical biosensor based on the luminescent quenching in the range from 10 to 500 μM with a correlation coefficient (R2) of 0.9964 and limit of detection (LOD) of 16.8 µM. HRTEM micrographs confirmed the preparation of CuInS/ZnS QDs with an average diameter size of 3 nm and NGQDs with an average diameter size of 5 nm.

Exact solution methods for the Resource Constrained Project Scheduling Problem with a flexible Project Structure

Exact solution methods for the Resource Constrained Project Scheduling Problem with a flexible Project Structure

Target-point interpolation of a program control in the approach problem

For a no Krasovskii Institute of Mathematics and Mechanics, Ural Branch, Russian Academy of Sciences, 620108, Yekaterinburg, Russianlinear controlled system, a fixed-time approach problem is considered in which the target point location becomes known only at the start of motion. According to the proposed solution method, node resolving program controls corresponding to a finite collection of target points from the set of their admissible locations are computed in advance and a refined control for the target point given at the start of motion is determined via linear interpolation of the node controls. The procedure for designing such a resolving control is formulated in the form of two algorithms, one of which is run before the start of the motion, and the other is executed in real time while the system is moving. The error in the transfer of the system’s state to the target point by applying these algorithms is estimated. As an example, we consider the approach problem for a modified Dubins car model and a target point about which only a compact set of its admissible locations is known before the start of motion.