Solution Of Problem Research Articles

Refining potential energy surface through dynamical properties via differentiable molecular simulation

Recently, machine learning potential (MLP) largely enhances the reliability of molecular dynamics, but its accuracy is limited by the underlying ab initio methods. A viable approach to overcome this limitation is to refine the potential by learning from experimental data, which now can be done efficiently using modern automatic differentiation technique. However, potential refinement is mostly performed using thermodynamic properties, leaving the most accessible and informative dynamical data (like spectroscopy) unexploited. In this work, through a comprehensive application of adjoint and gradient truncation methods, we show that both memory and gradient explosion issues can be circumvented in many situations, so the dynamical property differentiation is well-behaved. Consequently, both transport coefficients and spectroscopic data can be used to improve the density functional theory based MLP towards higher accuracy. Essentially, this work contributes to the solution of the inverse problem of spectroscopy by extracting microscopic interactions from vibrational spectroscopic data.

Modieslab Innovative Concrete Pavement Structure: From Idea through Research to Implementation

In 1996 the Dutch Ministry of Transport, Public Works and Water Management launched the innovation program ‘Roads to the Future’. One of the developed innovative pavement structures is a Modular Pavement Structure called Modieslab. This structure is designed as a bridge and it merely consists of precast concrete elements. Since 2001 several researches were performed. Along the motorway A50 in the Netherlands a low-risk and not heavily loaded test section was constructed and monitored during 16 months. On this test section mainly the functional properties as well as construction and maintenance techniques were investigated. The main findings of this test section are reported. The structural integrity of the structure was investigated by Accelerated Load Testing of another instrumented test section by means of LINTRACK. Some characteristic measurement results are presented. Insight into the structural behaviour of both the total pavement structure and the most critical upper part of it was further obtained by means of 3D finite element modelling. Some typical calculation results are compared to measurement results. Finally the materials research is briefly described. This research concentrates on the porous concrete wearing course. Research is ongoing to find solutions for skid resistance and ravelling problems that occurred to some extent on the test sections. Given the rather positive experiences, both from a technical and an economical point of view, it was decided to actually construct an in-service road section of the radically innovative Modieslab pavement structure after only 4 to 5 years of research. Probably this project will be realized in 2005 or 2006 in an area with very poor subsoil.

Existence and Uniqueness of a Solution of a Boundary Value Problem Used in Chemical Sciences via a Fixed Point Approach

In this paper, we present Proinov-type fixed point theorems in the setting of bi-polar metric spaces and fuzzy bi-polar metric spaces. Fuzzy bi-polar metric spaces with symmetric property extend classical metric spaces to address dual structures and uncertainty, ensuring consistency and balance. We provide different concrete conditions on the real-valued functions Ω,Π:0,∞→R for the existence of fixed points via the (Ω,Π)-contraction in bi-polar metric spaces. Further, we define real-valued functions Ω,Π:(0,1]→R to obtain fixed point theorems in fuzzy bi-polar metric spaces. We apply Ω,Π fuzzy bi-polar version of a Banach fixed point theorem to show the existence of solutions. Furthermore, we provide some non-trivial examples to show the validity of our results. In the end, we find the existence and uniqueness of a solution of integral equations and boundary value problem used in chemical sciences by applying main results.

Inventory Optimization with Several Resources

Inventory optimization is extended with additional constraints that affect production efficiency using estimated inventory. The extension of the inventory problem is in a special formal form with an additional optimization problem. The latter is aimed at minimizing production costs while using optimal volumes of material stocks. Such integration of inventory and production is formalized through a bi-level optimization problem. The case of simultaneous delivery of different types of goods is strongly related to the production process of final goods. A bi-level optimization problem is numerically defined and solved. Delivery costs are minimized for a given production plan. The bi-level problem is applied to prepare feed with the required nutrient content while minimizing the inventory costs of supplying the required raw materials. Empirical results give advantages for the obtained solution of the bi-level optimization problem.

Stability of Traveling Wave Solutions in a Credit Rating Migration Free Boundary Problem

Stability of Traveling Wave Solutions in a Credit Rating Migration Free Boundary Problem

An Artificial Intelligence Approach for the Kinodynamically Feasible Trajectory Planning of a Car-like Vehicle

This work investigates the possibility to improve the computational efficiency of a set-based method for the trajectory planning of a car-like vehicle through artificial intelligence. Planning is performed on a graph that represents the operating scenario in which the vehicle moves, and the kinodynamic feasibility of the trajectories is guaranteed through a series of set-based arguments, which involve the solution of semi-definite programming problems. Navigation in the graph is performed through a hybrid A* algorithm whose performance metrics are improved through a properly trained classificator, which can forecast whether a candidate trajectory segment is feasible or not. The proposed solution is validated through numerical simulations, with a focus on the effects of different classificators features and by using two different kinds of artificial intelligence: a support vector machine (SVM) and a long-short term memory (LSTM). Results show up to a 28% reduction in computational effort and the importance of lowering the false negative rate in classification for achieving good planning performance outcomes.

Islamic Fintech Development Strategy in Indonesia: Analytic Network Process (ANP) Approach

This study aims to analyze problem priorities, solutions and strategies in the development of Islamic fintech in Indonesia by using Analytical Network Process (ANP) method. The background of this study is the rapid growth of the Islamic fintech industry in Indonesia, but until now the map has not been found in its development. This study involved stakeholders from the government/ regulators, practitioners and academics as the research respondents. The results of this study showed that the problem priorities of Islamic fintech development in Indonesia respectively are Regulation, Literacy, Human Resources (HR) and Capital. While the solution priorities respectively are Literacy, Human Resources (HR), Regulation and Capital. The alternative priorities for the Islamic fintech development strategy in Indonesia respectively are Increasing Literacy, Strengthening Government Support, Improving Quality and Quantity of Human Resources (HR), Expanding Access to Capital and Creating Ecosystems. This study shows that collaborative efforts are needed so that the Islamic fintech industry in Indonesia can develop and be sustainable.

Classification of stable solutions of p-polyharmonic problem

Classification of stable solutions of p-polyharmonic problem

Survey of interactive evolutionary decomposition-based multiobjective optimization methods.

Interactive methods support decision-makers in finding the most preferred solution for multiobjective optimization problems, where multiple conflicting objective functions must be optimized simultaneously. These methods let a decision-maker provide preference information iteratively during the solution process to find solutions of interest, allowing them to learn about the trade-offs in the problem and the feasibility of the preferences. Several interactive evolutionary multiobjective optimization methods have been proposed in the literature. In the evolutionary computation community, the so-called decomposition-basedmethods have been increasingly popular because of their good performance in problems with many objective functions. They decompose the multiobjective optimization problem into multiple sub-problems to be solved collaboratively. Various interactive versions of decomposition-based methods have been proposed. However, most of them do not consider the desirable properties of real interactive solution processes, such as avoiding imposing a high cognitive burden on the decision-maker, allowing them to decide when to interact with the method, and supporting them in selecting a final solution. This paper reviews interactive evolutionary decomposition-based multiobjective optimization methods and different methodologies utilized to incorporate interactivity in them. Additionally, desirable properties of interactive decomposition-based multiobjective evolutionary optimization methods are identified, aiming to make them easier to be applied in real-world problems.

A study of a thermoelastic body possessing microtemperatures

In our study we approach a Cosserat thermoelastic body in which we take into account both the usual temperature and the microtemperature, that is, the temperature of the microparticles of the body. After constructing the mixed problem with initial and boundary values, in this context, we define an appropriate Hilbert space in which we obtain a temporal evolutionary equation which is equivalent to the already constructed mixed problem. With the help of some known results from the theory of contraction semigroups we prove both the existence and the uniqueness of the solution of the evolution equation, therefore of the considered mixed problem. Furthermore, the same semigroup theory allows us to obtain the continuous dependence of the solution of the mixed problem, both with respect to the initial data and with respect to the loading.

CodeContrast: A Contrastive Learning Approach for Generating Coherent Programming Exercises

Generating high-quality programming exercises with well-aligned problem descriptions, test cases, and code solutions is crucial for computer science education. However, current methods often lack coherence among these components, reducing their educational value. We present CodeContrast, a novel generative model that uses contrastive learning to map programming problems, test cases, and solutions into a shared feature space. By minimizing the distance between matched components and maximizing it for non-matched ones, CodeContrast learns the intricate relationships necessary to generate coherent programming exercises. Our model architecture includes three encoder networks for problem descriptions, test cases, and solutions. During training, CodeContrast processes positive triplets (matching problem, test case, solution) and negative triplets (non-matching combinations) and uses a contrastive loss to position positive triplets close in the feature space while separating negative ones. Comprehensive evaluations of CodeContrast—through automatic metrics, expert ratings, and student studies—demonstrate its effectiveness. Results show high code correctness (92.3% of test cases passed), strong problem–solution alignment (BLEU score up to 0.826), and robust test case coverage (85.7% statement coverage). Expert feedback and student performance further support the pedagogical value of these generated exercises, with students performing comparably to those using manually curated content. CodeContrast advances the automated generation of high-quality programming exercises, capturing relationships among programming components to enhance educational content and improve the learning experience for students and instructors.

Uniqueness theorems in the steady vibration problems of the Moore–Gibson–Thompson thermoporoelasticity

Abstract In this paper, the linear model of Moore–Gibson–Thompson thermoporoelasticity is considered and the governing equations of motion and steady vibrations are given. The basic system of equations of steady vibrations with respect to the displacement vector, the changes of temperature and fluid pressure are proposed. Then the radiation conditions are established and Green’s first identity is obtained. Finally, on the basis of this identity, the uniqueness theorems for classical solutions of the boundary value problems of steady vibrations in the theory of MGT thermoporoelaticity are proved.

Thermal Distribution in Fins of Different Geometries With Temperature and Wavelength Dependent Properties

ABSTRACTIn this research work, we developed a mathematical model of heat transfer for different profiles of fins. This paper investigates heat transfer dynamics in continuously moving fins (rectangular, trapezoidal and concave parabolic), focusing on temperature‐dependent thermal conductivity, heat transfer coefficients, internal heat generation and emissivity that varies with temperature and wavelength. The different values of the heat transfer coefficient capture various types of convection, nucleate boiling, condensation, and radiation effects, while treating thermal conductivity as a linear function of temperature. This problem is converted into a dimensionless form, and we adopted the Legendre wavelet collocation method (LWCM) to get the solution of the fin problem for various profiles. An exact solution in specific cases shows congruence up to seven to eight decimal places with LWCM results. Moreover, our analysis explores the effect of numerous non‐dimensional parameters such as thermal conductivity parameter , Peclet number , surface emissivity parameter B, convention‐conduction parameter , radiation‐conduction parameter , internal heat generation , D fin taper ratio on the temperature profile and fin efficiency were studied in detail. As , , and B, D increases in magnitude, the temperature inside the fin decreases, while higher values of Peclet number (), A, m, and Q cause lower heat transfer rate inside the fin. The results provide significant insights into the complex interplay of thermal processes across different fin geometries, emphasizing the importance of these dependencies for accurate modeling and optimization in thermal management systems.

A Novel Prescribed-Time Convergence Acceleration Algorithm with Time Rescaling

In machine learning, the processing of datasets is an unavoidable topic. One important approach to solving this problem is to design some corresponding algorithms so that they can eventually converge to the optimal solution of the optimization problem. Most existing acceleration algorithms exhibit asymptotic convergence. In order to ensure that the optimization problem converges to the optimal solution within the prescribed time, a novel prescribed-time convergence acceleration algorithm with time rescaling is presented in this paper. Two prescribed-time acceleration algorithms are constructed by introducing time rescaling, and the acceleration algorithms are used to solve unconstrained optimization problems and optimization problems containing equation constraints. Some important theorems are given, and the convergence of the acceleration algorithms is proven using the Lyapunov function method. Finally, we provide numerical simulations to verify the effectiveness and rationality of our theoretical results.

Noninvasive assessment of renal dysfunction in patients with end-stage liver disease: new solutions for old problems?

Noninvasive assessment of renal dysfunction in patients with end-stage liver disease: new solutions for old problems?

Enhancing Quantum Algorithms for Quadratic Unconstrained Binary Optimization via Integer Programming

To date, research in quantum computation promises potential for outperforming classical heuristics in combinatorial optimization. However, when aiming at provable optimality, one has to rely on classical exact methods like integer programming. State-of-the-art integer programming algorithms can compute strong relaxation bounds even for hard instances, but may have to enumerate a large number of subproblems for determining an optimum solution. If the potential of quantum computing realizes, it can be expected that in particular finding high-quality solutions for hard problems can be done fast. Still, near-future quantum hardware considerably limits the size of treatable problems. In this work, we go one step into integrating the potentials of quantum and classical techniques for combinatorial optimization. We propose a hybrid heuristic for the weighted maximum-cut problem and for quadratic unconstrained binary optimization. The heuristic employs a linear programming relaxation, rendering it well-suited for integration into exact branch-and-cut algorithms. For large instances, we reduce the problem size according to a linear relaxation such that the reduced problem can be handled by quantum machines of limited size. Moreover, we improve the applicability of depth-1 QAOA, a parameterized quantum algorithm, by deriving a parameter estimate for arbitrary instances. We present numerous computational results from real quantum hardware.

The role of Latin America medicinal plants in wound healing

Wound healing represents a global public health problem when it is not treated correctly, which can cause complications for the patient, such as functional loss of an organ, amputation, and even death. At a biological level, wound healing involves a complex mechanism in which the immune system and cellular biochemical cascades intervene in a coordinated manner, whose development occurs in stages such as inflammation, proliferation, and remodeling. Therefore, therapies have been developed to accelerate wound healing and have proven effective. However, factors such as diabetes mellitus limit the healing process because it causes alterations in microvascular dysfunction, as well as in the inflammatory response and greater oxidative stress. This is reflected in an abnormal healing process; therefore, the search for healing compounds has become an area of interest. In this regard, medicinal plants have been used for centuries to treat wounds in different cultures in the world. Hence, this review documents the main plant species used in Latin America due to its great biodiversity and numerous species that are potentially important for the development of new active healing compounds. In this review, 62 plant families with wound healing studies were found, highlighting Fabaceae, Asteraceae and Euphorbiaceae family. Additionally, 32 natural compounds with diverse structural nature were found, whose effects have been evaluated in in vivo and in vitro models, which are essential for studying the pathogenesis of the tissue repair mechanism, detecting new biomarkers, and evaluate new treatments. Currently, several models are used to study the wound healing process, including in silico, in vitro, and in vivo models. On the other hand, there is no appropriate model to determine the wound healing effect, and, in many cases, they are combined to provide sufficient scientific evidence. Therefore, this review demonstrates that Latin America is a potential region for research into sources of healing molecules. Nevertheless, other species are still being studied whose scientific findings allow generating viable alternatives for the solution of health problems associated with wound healing.

Existence and multiplicity of non-radial sign-changing solutions for a semilinear elliptic equation in hyperbolic space

Abstract In this article, we consider the following problem: - Δ 𝔹 N ⁢ u - λ ⁢ u = | u | p - 1 ⁢ u , u ∈ H 1 ⁢ ( 𝔹 N ) , -\Delta_{\mathbb{B}^{N}}u-\lambda u=|u|^{p-1}u,\quad u\in H^{1}(\mathbb{B}^{N}), where 𝔹 N {\mathbb{B}^{N}} represents the Poincaré ball model of the hyperbolic space, 1 < p < 2 * - 1 = N + 2 N - 2 {1<p<2^{*}-1=\frac{N+2}{N-2}} , λ < ( N - 1 ) 2 4 {\lambda<\frac{(N-1)^{2}}{4}} , N ≥ 4 {N\geq 4} , and H 1 ⁢ ( 𝔹 N ) {H^{1}(\mathbb{B}^{N})} denotes the Sobolev space on 𝔹 N {\mathbb{B}^{N}} . In [D. Ganguly and S. Kunnath, Sign changing solutions of the Brezis–Nirenberg problem in the hyperbolic space, Calc. Var. Partial Differential Equations 50 2014, 1–2, 69–91], Ganguly and Sandeep establish the existence of radial sign-changing solutions for the critical case. In this work, we will discuss the existence and multiplicity of non-radial sign-changing solutions for the sub-critical problem. To establish the presence of sign-changing solutions that are not radial, we will demonstrate the existence of an isometric group action on 𝔹 N {\mathbb{B}^{N}} and operate inside a permissible space of functions that change signs. We shall also discuss the multiplicity of sign-changing solutions.

The method of lower and upper solutions for second order periodic Stieltjes differential equations

As an application of the Schauder Fixed Point Theorem, an existence theory is developed for second order nonlinear differential equations with Stieltjes derivative related to a left-continuous, nondecreasing function. By the method of lower and upper solutions, under a Nagumo-type assumption we get a very general result which can be further applied to deduce the existence of solutions for second order nonlinear problems in the settings of impulsive differential equations, time scale analysis or generalized differential equations.

Convection-diffusion problems involving measure data and p(.)-anisotropic operator in variable exponent space

In this paper, we investigate a nonlinear diffusion-convection problem with measure data, involving a general anisotropic operator with variable exponent and a maximal monotone graph. Utilizing Yosida’s regularization, we apply an approximation technique to formulate a regularized problem. We then employ the theory of maximal monotone operators in Banach spaces to demonstrate the existence of at least one solution for the approximate problem. Subsequently, we show that the sequence of solutions to the approximate problem converges, in the limit, to a renormalized and/or entropic solution of the original problem. Finally, we establish the uniqueness of the solution under certain additional assumptions regarding the convection term.