Solution Process Research Articles

Mathematical Model of Fungal Growth with Hyphae Death and Substrate

One of the most crucial subjects in mathematics education is mathematical modeling, which involves connecting situations from the real world to mathematical formulas and vice versa. Our study will focus on how substrate affects fungal growth and lateral branching behaviors, hyphal death, and tip death due to overcrowding. In order to solve a mathematical model, we will employ the system's three-dimensional of equations (PDEs). using numerical solutions, traveling wave theory, stability, and non-dimensionality in our mathematical approach as well as we use trace and determine to find stable states.Computer programs, such as the Maple program and Pdep code in MATLAB, will also be used to aid in the solution process.

APPLICATION OF THE THEORY OF PETRI NETS IN THE DEVELOPMENT OF SIMULATION MODELS OF BUSINESS PROCESSES BASED ON THE IDEF3 METHODOLOGY

In this study, we propose a model of an artificial neural network used as a specialized superstructure over a genetic algorithm, which allows influencing the process of finding solutions directly during the synthesis of solutions. Such a combination of methods will allow controlling the trajectory of the population in the solution space, which is especially important when working with big data processing technology, when stopping the solution search process due to the attenuation of the evolutionary procedure or finding the population in a local extremum requires stopping the genetic algorithm, performing additional adjustment of operators and restarting, the use of such an approach is ineffective, especially when working with big data and labor-intensive calculations. This article proposes a model of an artificial neural network that allows recognizing the state of the population of the genetic algorithm and making a decision to change the operating parameters of the genetic algorithm operators. The proposed model allows recognizing the processes of attenuation of the evolutionary procedure when solving the problem of structural and parametric synthesis of large discrete systems and determining measures of influence on the operating parameters of the genetic algorithm. This model recognizes the state of the population with an accuracy of more than 95%, which allows to significantly reduce the time for finding solutions in problems of applying a genetic algorithm to work with big data.

A High-Efficiency Two-Layer Path Planning Method for UAVs in Vast Airspace

In response to the challenges associated with the inefficiency and poor quality of 3D path planning for Unmanned Aerial Systems (UAS) operating in vast airspace, a novel two-layer path planning method is proposed based on a divide-and-conquer methodology. This method segregates the solution process into two distinct stages: heading planning and path planning, thereby ensuring the planning of both efficiency and path quality. Firstly, the path planning phase is formulated as a multi-objective optimization problem, taking into account the environmental constraints of the UAV mission and path safety. Subsequently, the multi-dimensional environmental data is transformed into a two-dimensional probabilistic map. An improved ant colony algorithm is proposed to efficiently generate high-quality sets of headings, facilitating the preliminary heading planning for UAVs. Then, the three-dimensional environment of the heading regions is extracted, and an improved Dung Beetle algorithm with multiple strategies is proposed to optimize the three-dimensional path in the secondary layer accurately. The efficacy and quality of the proposed path planning methodology are substantiated through comprehensive simulation analysis.

Expanding the Accessible Forest Areas by Improving Forest Road Standards and Utilizing Mobile Fire-fighting Teams

In Turkey, most of the roads (66%) in forests consist of Type-B secondary forest roads with limited technical standards. The lack of technical standards used on these roads and the lack of road structures and surface material limit the fire truck’s speed and increase the travel time of the firefighting team. Improving forest road standards will contribute to driving safety and increasing the design speed, thereby expanding the forest areas that can be reached within the critical response period, reducing possible wood raw material losses. There is no study in the national and international literature investigating the contribution of forest road standards improvement to firefighting activities and the effects on possible wood raw material losses due to forest fires. In this study, it was aimed to investigate the effects of improving forest road standards on expanding accessible forest areas within the critical response time, especially in forests with high fire risk, and thus reducing potential wood raw material losses. Antalya Forestry Regional Directorate, where forest fires are seen most frequently and the amount of burned area is the highest in Turkey, was selected as the study area as one of the forest regional directorates where forest fires are seen most frequently and the amount of burned area is the highest in Turkey. In the solution process, primarily the forest areas that can be reached by the firefighting team (including mobile teams used in emergencies) within the critical response time were determined by considering the existing road network in the study area. Then, the possible increase in the accessible forest areas was investigated when the road standards are improved. Within the scope of the study, the impact of mobile teams used in emergency situations on forest areas reached during the critical response period was also evaluated. According to the results, in the scenario where current road standards and stationary teams were evaluated, it was determined that only 59.54% of the forest areas could be reached by initial response teams during the critical response time, and if road standards were improved, this rate increased to 71.69%. On the other hand, in the scenario where current road standards and stationary and mobile teams were evaluated together, it was determined that 70.40% of the forest areas could be reached by initial response teams during the critical response time, and if road standards were improved, this rate increased to 78.17%. The results obtained have shown that improvements in road standards and the presence of mobile teams have a very effective role in combating forest fires.

Novel theoretical studies based on CIELab and deconvolution methods to characterize AuNPs aggregation processes

The study of the interaction of spherical gold nanoparticles when they undergo aggregation phenomena due to the presence of a ligand in solution is presented, based on both colorimetric parameters (using CIELab space) and traditional deconvolution analysis. The feasibility of using CIELab space for this purpose can be an important method to simplify the required instrumentation (colorimeter versus UV spectrophotometer) as well as being less time consuming compared with the traditional computational analysis. For these reasons, in this work we present the first experimental approach of the proposed methodology for its application in this field of research. The synthetized AuNPs employed in this work have been previously characterized by means of the maxima of their LSPR bands, zeta potential and electron microscopy images. The aggregation of spherical AuNPs (with a diameter of 12 nm) by an inert electrolyte (NaCl) is investigated in a first step with successfully results, allowing to obtain the characteristic CIELab parameters (a*, b* and L*). At a later stage, the color changes induced in the optical properties of the solution due to the interaction of AuNPs (with increasing diameters of 12, 25 and 33 nm) with an amino acid such as lysine at neutral pH, have been also described using the CIELab spatial framework. The obtained a*, b* and L* parameters of the CIELab space allow to characterize satisfactorily the color change from red to blue which describes the aggregation phenomena, relying on deconvolution analysis of the LSPR bands. The results obtained support the validity of the CIELab space as a valid tool for the investigation of the aggregation process of colloidal solutions. It will allow simplifying the whole process using simpler and cheaper instrumentation as well as a decrease in the required analysis time.

Unsupervised learning for efficiently distributing EVs charging loads and traffic flows in coupled power and transportation systems

With the escalating adoption of electric vehicles (EVs), the intricate interplay between power and traffic systems becomes increasingly pronounced. Understanding the distribution of charging loads and traffic flows are paramount for effective coordination. Traditionally, the distribution of EVs charging loads and traffic flows are obtained via solving the EVs traffic assignment problem with User Equilibrium (TAP-UE). Despite the general convexity of TAP-UE, the iterative nature of the prevailing solution process and the nonlinear objective function pose challenges, leading to prolonged solution times. This paper introduces a novel unsupervised learning-based framework aimed at efficiently distributing EVs charging loads and traffic flows without off-the-shelf solvers or a large dataset. Firstly, feasible paths are identified for each OD pair, eliminating the need for iterative procedures. Subsequently, the convexity-preserving reformulation of TAP-UE converts it into an unconstrained nonlinear optimization problem, leading to a properly designed loss function to guide neural networks in directly learning a legitimate OD demands-EVs loads-traffic flows mapping which satisfies the UE conditions. The incorporation of the Hessian matrix into the gradient update of network parameters, facilitated by the Limited-memory Broyden–Fletcher–Goldfarb–Shanno (L-BFGS) algorithm, enhances the convergence speed of the unsupervised learning process. Case studies are conducted to demonstrate the efficacy of the proposed framework.

Optimal Design of Shear Wall Sections Considering Different Stress-Strain Relationships of Steel According to Eurocode 2

The present paper highlights the usefulness of bi-linear stress strain relationship of steel with inclined plastic branch with respect to horizontal plastic branch for slender rectangular shear wall design. The inclined branch refers to strain hardening of steel for which, stresses also increase with strain beyond the yield point. This significantly complicates the solution process for load-based design approach as adopted in flexure design. For design of sections subjected to axial forces with flexure, capacity-based approach of design is adopted instead of load-based design. Hence, the myth that the inclined plastic planch will lead to cumbersome and tedious calculations does not seems to hold good for capacity-based design approach which is adopted for design of columns and shear walls. In the present paper using different strength classes of concrete as permitted by the latest version of Eurocode2 (2023) and strength of steel, fyk= 500 MPa along with different ductility classes have been used for the development of P-M interaction charts for rectangular shear walls with uniformly distributed vertical reinforcements. An average of 12.26% and 3.42% increment in moment capacity values have been obtained in under reinforced and over reinforced failure conditions respectively between charts prepared by inclined and horizontal plastic branch.

Solution-Processed CsPbBr3 Perovskite Photodetectors for Cost-Efficient Underwater Wireless Optical Communication System.

Underwater wireless optical communication (UWOC) has attracted increasing attention due to its advantages in bandwidth, latency, interference resistance, and security. Photodetectors, as a crucial part of receivers, have been continuously developed with the great progress that has been made in advanced materials. Metal halide perovskites emerging as promising optoelectronic materials in the past decade have been used to fabricate various high-performance photodetectors. In this work, high-performance CsPbBr3 perovskite PDs were realized via solution process, with low noise, a high responsivity, and a fast response. Based on these perovskite PDs, a cost-efficient UWOC system was successfully demonstrated on an FPGA platform, achieving a data rate of 6.25 Mbps with a low bit error rate of 0.36%. Due to lower background noise under environment illumination, perovskite PDs exhibit better communication stability before reaching a data rate threshold; however, the BER increases rapidly due to the long fall time, resulting in difficulty in distinguishing switching signals. Reducing the fall time of perovskite PDs and using advanced coding techniques can help to further improve the performance of the UWOC system based on perovskite PDs. This work not only demonstrates the potential of perovskite PDs in the application of UWOC, but also improves the development of a cost-effective UWOC system based on FPGAs.

Crystallization Morphology and Self-Assembly of Polyacrylamide Solutions During Evaporation

This study investigates the crystallization and self-assembly phenomena of polyacrylamide (PAM) solutions during evaporation. While traditional thin-film fabrication methods such as spin coating and drop casting are commonly used, this study utilizes a simple evaporation approach to gain insights into the self-assembly processes of PAM solutions. We examined PAM solutions with varying concentrations (1%, 2%, and 5%) and observed the resulting crystalline structures and morphological changes during evaporation. Spectroscopic absorbance measurements were employed to analyze concentration gradients and crystallization patterns. The findings reveal that the concentration gradient during evaporation significantly impacts the crystallization behavior of PAM solutions. The 2% solution exhibited the clearest and most regular crystalline patterns, whereas the 1% and 5% solutions displayed more complex and irregular morphologies. These observations provide new insights into how solution concentration influences PAM crystallization, contributing to a better understanding of polymer thin-film self-assembly. The novelty of this study lies in exploring PAM self-assembly through controlled evaporation, offering valuable insights for the development of polymer films.

Microstructure Evolution and Mechanical Properties of In Situ TiB2/Al–Zn–Mg–Cu Composites by Electropulsing‐Assisted Solution Treatment

The traditional solution treatment is not completely applicable for in situ TiB2/Al–Zn–Mg–Cu composite due to the influence of TiB2 particles. This study investigates the impact of the electropulsing‐assisted solution (EAS) process on the microstructure evolution and mechanical properties of TiB2/Al–Zn–Mg–Cu composites. The nonuniform microstructure evolution related to the difference in temperature and current density is supported by simulative and experimental results. EAS treatment significantly enhances the degree of supersaturation, allowing phases surrounding TiB2 particles to dissolve without extensive overburning, unlike traditional solution treatments. This improvement is attributed to the reduced thermodynamic barrier and accelerated atomic diffusion, facilitated by both localized and average Joule heating effects and enhanced thermal electron/phonon scattering. Meanwhile, grain size remains stable during the temperature rise induced by EAS treatment. Furthermore, the mechanical properties of composites increase, among which the increase in strength is mainly ascribed to solid–solution strengthening, and the increase in elongation is related to multiple factors referring to TiB2 particles fracture and interface crack between the undissolved phases and matrix. This study provides an approach to achieve an efficient dissolution of phases aggregated with particles and without widespread overburning in particle‐reinforced Al matrix composites via electropulsing techniques.

Algorithm Portfolios for Solving the Quadratic Assignment Problem

Introduction. The quadratic assignment problem is a well-established NP-hard problem in combinatorial optimization with applications in diverse fields like economics, archaeology, and chemistry. Due to its complexity, research on efficient solution methods remains active, including efforts for parallelization on multiprocessor computing systems. However, effective parallel algorithms are crucial to fully leverage these computational resources. In this context, algorithm unions (portfolios and teams) play a significant role in achieving parallel execution for solving such problems. Research objectives. This work investigates the application of portfolios constructed from modifications of the repeated iterated tabu search algorithm to the quadratic assignment problem. The effectiveness of these portfolios was evaluated through experimental computations. Results. The portfolios, derived from modifications of the repeated iterated tabu search algorithm, were applied to the quadratic assignment problem. For the most demanding test instances, the proposed algorithms were evaluated on the SCIT-4 supercomputer, alongside previously published results from the authors, confirming their competitive performance. Additionally, we assessed the parallel efficiency of these portfolios in solving instances of the quadratic assignment problem. The results demonstrate their ability to accelerate the optimization process (with speedup dependent on problem size and utilized processors), enabling the solution of large-scale problems. Conclusions. The conducted studies demonstrate that employing algorithm portfolios significantly accelerates the solution process for the quadratic assignment problem. Analysis of the results reveals a near-linear speedup factor achieved by the portfolio. For the challenging test instance tai100a, a new best solution value of 21040996 was obtained using a portfolio of 16 algorithms. Keywords: quadratic assignment problem, algorithm portfolios, experimental research, algorithm portfolios efficiency.

Physicochemical study of the adsorption of chlorophenols on activated carbon using an advanced double layer model

An advanced theoretical analysis was performed to elucidate the adsorption of 2,4-dichlorophenol and 4-chlorophenol on an activated carbon obtained from pine sawdust. In particular, a double layer adsorption model was used to estimate the number of chlorophenols molecules adsorbed per activated carbon functional group, the concentration of active sites from activated carbon surface involved in the adsorption mechanism, the saturation adsorption capacities and interaction energies to form the adsorbate layers on activated carbon. This theoretical investigation showed that the saturation adsorption capacities for the removal of 2,4-dichlorophenol and 4-chlorophenol were 167 and 130 mg/g, respectively. The adsorption of 2,4-dichlorophenol on activated carbon outperformed the 4-chlorophenol adsorption under tested experimental conditions. The adsorption of these compounds was endothermic and governed mainly by van der Waals interactions and hydrogen bonding. The adsorption of 2,4-dichlorophenol on activated carbon was a multimolecular process with the presence of an aggregation process in the aqueous solution, while a multi-docking adsorption mechanism occurred for the removal of 4-chlorophenol. Overall, this study contributes with theoretical insights on the removal of two emerging water pollutants by using activated carbon.

Personalized pricing decisions through adversarial risk analysis

AbstractPricing decisions stand out as one of the most critical tasks a company faces, particularly in today's digital economy. As with other business decision‐making problems, pricing unfolds in a highly competitive and uncertain environment. Traditional analyses in this area have heavily relied on game theory and its variants. However, an important drawback of these approaches is their reliance on common knowledge assumptions, which are hardly tenable in competitive business domains. This paper introduces an innovative personalized pricing framework designed to assist decision‐makers in undertaking pricing decisions amidst competition, considering both buyer's and competitors' preferences. Our approach (i) establishes a coherent framework for modeling competition mitigating common knowledge assumptions; (ii) proposes a principled method to forecast competitors' pricing and customers' purchasing decisions, acknowledging major business uncertainties; and (iii) encourages structured thinking about the competitors' problems, thus enriching the solution process. To illustrate these properties, in addition to a general pricing template, we outline two specifications—one from the retail domain and a more intricate one from the pension fund domain.

Optimal avoidance strategy based on nonlinear approximate analytic solution of non-cooperative differential game

Abstract This study examines the pursuit-evasion game involving unmanned aerial vehicles (UAVs), with a specific focus on the scenario of N-pursuers-one-escapee. The primary objective is to develop an optimal strategy for the escapee when the pursuers possess superior capabilities. To obtain this objective, we conduct the following study. Firstly, to enhance realism, a non-cooperative differential game model is formulated, incorporating multiple motion characteristics, including aerodynamics, overloading, and imposed constraints. Secondly, the end-value performance index is subsequently converted to an integral one, simplifying the solution process of the Hamilton-Jacobi-Bellman (HJB) equation. An iterative method is utilised to determine the covariates using the Cauchy initial value problem, and its convergence and uniqueness are established. The optimal avoidance strategy is subsequently derived from the covariates. Finally, the superiority of the proposed strategy is validated through simulation experiments and compared to three advanced optimal avoidance strategies. A total of 1,000 anti-jamming simulation experiments are conducted to verify the robustness of the proposed strategy.

Global optimization for large‐scale water network synthesis based on dynamic partition and adaptive bound tightening

AbstractThe synthesis of large‐scale integrated water networks is typically formulated as nonconvex mixed‐integer quadratic constrained programming (MIQCP) or QCP problems. With the complexity arising from bilinear terms in modeling mass flows of contaminants and binary variables representing the presence of units or streams, numerous local optima exist, thus presenting a significant optimization challenge. This study introduces a deterministic global optimization algorithm based on mixed‐integer programming (MIP) to tackle such problems. The approach involves dynamically strengthening the relaxed problems to converge towards the original problems. A simultaneous partition strategy is proposed combining locally uniform division with dynamic partitioned variables choosing. Furthermore, several adaptive bound contraction schemes are introduced to efficiently manage the size of the relaxed problems, assisting in accelerating the solution process. The algorithm's effectiveness and robustness are demonstrated with a large test set, showing superior performance compared to commercial solvers specifically on MIQCP problems.

Sorption decontamination of aqueous and organic media from TBP and acidic products of its decomposition

The processes of sorption decontaminationof aqueous solutions from TBP on the polymeric sorbent Polysorb-1,as well as of a 70% solution of TBP indodecane from butylphosphoric acids using layered double oxides and Mg-Alhydroxides were studied. It has been established that the useof the polymeric sorbent Polysorb-1 makes it possible to decontaminateaqueous solutions from TBP in static and dynamic modes, andthe use of the LDH-Mg-Al-CD-OH sorbent allows the decontamination ofTBP solutions in dodecane from acid products of decomposition andhydrolysis of TBP.

Optimal approximation of a large matrix by a sum of projected linear mappings on prescribed subspaces

We propose and justify a matrix reduction method for calculating the optimal approximation of an observed matrix $A \in \mathbb{C}^{m \times n}$ by a sum $\sum_{i=1}^p \sum_{j=1}^q B_iX_{ij}C_j$ of matrix products where each $B_i \in \mathbb{C}^{m \times g_i}$ and $C_j \in \mathbb{C}^{h_j \times n}$ is known and where the unknown matrix kernels $X_{ij}$ are determined by minimizing the Frobenius norm of the error. The sum can be represented as a bounded linear mapping $BXC$ with unknown kernel $X$ from a prescribed subspace ${\mathcal T} \subseteq \mathbb{C}^n$ onto a prescribed subspace ${\mathcal S} \subseteq \mathbb{C}^m$ defined, respectively, by the collective domains and ranges of the given matrices $C_1,\ldots,C_q$ and $B_1,\ldots,B_p$. We show that the optimal kernel is $X = B^{\dagger}AC^{\dagger}$ and that the optimal approximation $BB^{\dagger}AC^{\dagger}C$ is the projection of the observed mapping $A$ onto a mapping from ${\mathcal T}$ to ${\mathcal S}$. If $A$ is large, $B$ and $C$ may also be large and direct calculation of $B^{\dagger}$ and $C^{\dagger}$ becomes unwieldy and inefficient. The proposed method avoids this difficulty by reducing the solution process to finding the pseudo-inverses of a collection of much smaller matrices. This significantly reduces the computational burden.

온라인 동영상 수업 후 서술형 평가상의 쓰기에서 나타나는 오류 분석: 공과대학 1학년 기초미적분학 강좌를 중심으로

Objectives This study attempted to qualitatively examine the errors that appear in the descriptive type assessment after online video classes. Methods For this purpose, we qualitatively analyzed the errors appearing in 5 descriptive type questions in the mid-term and final exams for first-year engineering students at a local college. They are taking basic precalculus in the first semester of 2022. The 5 questions were divided into drawing a graph(3 questions) and descriptive type writing(2 questions), and the types of errors that emerged as a result are as follows. Results First, in the drawing a graph questions, there were errors in conceptual errors, errors regarding the domain, asymptote notation errors, coordinate notation errors, writing explanation errors, omitting convexity mentions, omitting inflection points mentions, and errors of omitting algebraic expressions for determining graph shape). Second, in the descriptive type writing questions, errors in problem recognition, incorrect use of problem conditions, substitution errors, rationalization errors, technical errors, omission of the solution process, logically inappropriate reasoning, and errors due to mistakes or carelessness appeared. Conclusions In addition, as a result of a detailed analysis according to the type of error shown in each question, it was emerged that college students also had errors similar to those made by high school students in drawing graphs and solving descriptive type problems.

Nanoparticle uptake by a semi-permeable, spherical cell from an external planar diffusive field. II. Numerical study of temporal and spatial development validated using FEM

In this paper, we present a mathematical study of particle diffusion inside and outside a spherical biological cell that has been exposed on one side to a propagating planar diffusive front. The media inside and outside the spherical cell are differentiated by their respective diffusion constants. A closed form, large-time, asymptotic solution is derived by the combined means of Laplace transform, separation of variables, and asymptotic series development. The solution process is assisted by means of an effective far-field boundary condition, which is instrumental in resolving the conflict of planar and spherical geometries. The focus of the paper is on a numerical comparison to determine the accuracy of the asymptotic solution relative to a fully numerical solution obtained using the finite element method. The asymptotic solution is shown to be highly effective in capturing the dynamic behaviour of the system, both internal and external to the cell, under a range of diffusive conditions.

Reduced dielectric loss of MXene/PVDF composites with adding MnO2 nanorods

To reduce the dielectric loss of MXene/PVDF composites near the percolation threshold, a MnO2/MXene hybrid structure was successfully synthesized via an ultrasonic-assisted solution process, and MnO2/MXene/PVDF composite films were prepared by a solution casting method. The effects of different amounts of MnO2 nanorods on the microstructure, thermal stability, dielectric properties and mechanical properties of the composite films were investigated. At 1 kHz, the dielectric constant of 7 wt% MnO2/MXene/PVDF is 37.3, and the dielectric loss is as low as 0.05. The electric modulus curve, Nyquist curve and equivalent analog circuit of the composites revealed strong interface polarization and microcapacitance effects in the composites. In addition, the composite films exhibit excellent mechanical properties, and the storage modulus of the composite film loaded with 10 wt% MnO2 is 1990.5 MPa, which is 3.44 times that of pure PVDF. This study confirms that incorporating MnO2 nanorods into MXene/PVDF composites is an effective approach for fabricating high-performance dielectric composites.