Solution Of Problem Research Articles

Towards optimal use of the explicit [formula omitted]-Bathe time integration method for linear and nonlinear dynamics

In an earlier publication, we proposed a new explicit time integration scheme, the β1/β2-Bathe method, which is simple in its formulation and showed remarkable accuracy in the solution of problems [1]. A particular strength of the method is that it can directly be used as a first-order or second-order scheme by a change of the values of β1 and β2. While good results are obtained with reasonable values of β1 and β2, for excellent accuracy better values of the parameters need to be chosen. We propose in this paper values of β1 and β2 for the first-order scheme, best used in wave propagation analyses, and separate values for the second-order scheme, best used in analyses of structural vibrations. In each case, one set of values of (β1,β2) is given and to possibly improve the results only one of the parameters needs to be changed, that is, β1 for wave propagations and β2 for structural vibrations, making the scheme a one-parameter method. Another strength of the procedure is that physical damping can directly be included in the solution, the effect of which on the stability and accuracy of the solutions we analyze in the paper. The use of the solution scheme in nonlinear analysis is, as we show in the paper, a simple extension from linear analysis. Finally, we give various solutions using the explicit β1/β2-Bathe method in linear and nonlinear analyses to illustrate the performance of the method with the given recommendations for its use.

Minimum-Cost Ordering for Selective Assembly

We consider the minimization of input cost for a selective assembly system which features two random inputs and a finite number of matching classes. This setup frequently arises in high-precision manufacturing when input tolerances are not tight enough for the required output precision. We determine optimality conditions for the cost-optimal input portfolio given an expected-output target, first using a normal approximation of the multinomial binning distribution, and second employing a simple upper envelope of the output objective. We show that the relative error tends to zero as the production scale becomes sufficiently large. The envelope optimization problem also yields a closed-form solution for the cost-minimizing inputs as well as total costs, which are easy to understand for managers. A numerical study tests the practicality of the envelope solution. The latter can be used as seed for a numerical solution of the exact problem, as well as a closed-form approximate solution which allows for an analysis of structural properties.

Some auxiliary estimates for solutions to non-uniformly degenerate second-order elliptic equations

We consider a class of second order elliptic equations in divergence form with non-uniform exponential degeneracy. The method used is based on the fact that the degeneracy rates of the eigenvalues of the matrix ||aij(x)|| (function λi(x)) are not the functions of unusual norm |x|, but of some anisotropic distance |x| a−. We assume that the Dirichlet problem for such equations is solvable in the classical sense for every continuous boundary function in any normal domain Ω. Estimates for the weak solutions of Dirichlet problem near the boundary point are obtained, and Green’s functions for second order non-uniformly degenerate elliptic equations are constructed.

Feature selection by Universum embedding

Feature selection in classification is an important task in machine learning. Inspired by the success of Universum support vector machine proposed by Weston et al. on improving the classification ability of classical support vector machine, this paper considers a special type of Universum and further lets it play its role in both useful feature identification and separating hyperplane construction, aiming to improve both the feature selection ability and classification performance of Universum support vector machine. By introducing this special Universum, a redundant feature can be identified by observing whether some Universum sample is useful. In fact, we prove that by observing the dual solution of the optimization problem, useful features can be selected from a set satisfying some properties. Due to the introduction of these extra Universum samples, it needs to cope with a large-scale optimization problem. To improve the training efficiency, we modify the sequential minimal optimization algorithm and further combine it with the coordinate descent technique to solve the proposed model. Experimental results on artificial datasets, benchmark datasets, and text classification datasets demonstrate that the proposed method improves the classification performance of support vector machine and Universum support vector machine, and also has good feature selection ability.

Elastic prolate spheroidal inclusion in an infinite elastic solid—an exact analysis of the inclusion stress by an engineering treatment of the problem based on the corresponding cavity solutions

The paper demonstrates the analysis of the stress and strain states of an inhomogeneous structure with an axial symmetrical spheroidal inclusion in an infinite solid under a remote uniform tension load derived from the conditions of the theory of elasticity. With respect to the inhomogeneous structure, the deformations produce constraints that require a complete 3-dimensional analysis in the z, r-coordinate system. The solution generates the stress state of the inclusion and at the interface of the matrix. Spheroidal inclusions in an uniform outer tension stress field deform self-similarly to a rather elongated spheroid. With respect to the compatibility condition and depending on the different elastic moduli and Poisson’s ratios in the inclusion and matrix, the solution procedure leads to a system of two linear equations with the magnitudes of the two tractions as unknowns. In this way, the solution is shortened to a twofold statically indeterminate system. The analysis performed leads to an exact solution of the general spheroidal problem in a formulation of stress concentration factors considering the different stiffness parameters of inclusion and matrix.

The design of human-artificial intelligence systems in decision sciences: A look back and directions forward

The field of decision sciences is undergoing significant disruption and reinvention because of rapid advances in artificial intelligence (AI) technologies and the design of complex human-artificial intelligence systems (HAIS). The integration of human decision behaviors with cutting-edge AI capabilities is transforming business and society in irreversible ways. In this paper, we examine prior research published in Decision Support Systems that makes contributions to HAIS design science research (DSR). We define synergistic interactions among DSR, AI technology design, and human interaction design, which we use to specify the dimensions for an analysis of the DSS HAIS literature. We identify key challenges, leading to future research directions for the design of HAIS as solutions for complex decision science problems.

Investigating the existence, uniqueness, and stability of solutions in boundary value problem of fractional differential equations

This study uses fixed point theory and the Banach contraction principle to prove the existence, uniqueness, and stability of solutions to boundary value problems involving a Ψ-Caputo-type fractional differential equation. The conclusions are supported by illustrative cases, which raise the theoretical framework’s legitimacy. Fractional calculus is widely used in scientific fields, as seen by its applications in beam deflection analysis, groundwater pollution, and biomedical signal processing.

On Intonation in Chamber Music Ensemble

As an important form of music performance, chamber music ensemble requires a high skill and cooperation of the player. Among them, the pitch is particularly critical, which directly affects the harmony of the performance and the feeling of the audience. This article aims to explore the causes, effects and solutions of the pitch problem in chamber music ensemble. Through the combing of relevant literature, combined with practical experience, it analyzes many factors of the pitch problem, and proposes targeted solutions.

Theoretical foundations of circular business models in the agriculture sphere: classification, components, and functions

The article is dedicated to the practical implications of circular business models in the agro-food sector, classifying their types and explaining their strategic components and functions. We explore resource models, design models, models of extending the product life cycle, platform models, product as a service, and end-of-life models of the product. We also delve into cascade processes, which are circular processes that involve the creation of reverse biological cycles and the use of biomass to produce products with a more significant share of added value. The importance of these models for preserving the value and duration of the life cycle of biological materials in closed agro-product networks is highlighted. We outline the architecture and strategic basis of business models in agri-food production based on three key components (value proposition, generation of social value, and management of social value). We substantiate a set of functions of circular business models of agro-food production, among which the function of preserving value, the function of extending the life cycle, the function of organization, the function of creating and maximizing additional value, and the function of maximizing income streams are highlighted. Circular business models in the agri-food sector should be considered a strategic tool for achieving sustainable development goals and ensuring business entities' economic sustainability. We substantiate that business models of a closed cycle are a promising and priority way of further socio-economic development, contribute to the solution of society's acute socio-economic and environmental problems, and ensure the creation of a basis for further economic development and growth. Keywords: models, functions, circular economy, business models of closed production, ensuring the sustainability of systems, agricultural enterprises.

Multiplicity and concentration properties of solutions for double-phase problem in fractional modular spaces

Multiplicity and concentration properties of solutions for double-phase problem in fractional modular spaces

Selection of waste receiving companies for sustainable industrial symbiosis network: an application a case in Ankara for foundry industry waste

AbstractIn the past twenty years, besides the technological and innovative efforts for production processes, there have been activities for the recovery and quality improvement of waste based on the development of sustainable solutions. These activities bring us closer to zero waste by protecting natural resources and reducing the environmental impact of production facilities. One of the essential approaches in this process is the industrial symbiosis (IS) approach. Efforts to implement the industrial symbiosis approach in an industrial park constitute the motivation of the study. The study aims to identify alternative enterprises that can evaluate the wastes generated in the main production process of the foundry industry with industrial symbiosis and rank them to establish a symbiotic relationship. First, the Pythagorean Fuzzy Analytical Hierarchy Process (PF-AHP) model has been developed to evaluate organizational factors, waste-specific factors, and economic and environmental sustainability criteria for establishing an IS network among alternative businesses. Then, alternative businesses were ranked to establish a symbiosis network with the PF-TOPSIS method. The proposed method contributes to the solution of a real-life problem that may arise when there are multiple alternatives to cooperate in the field of industrial symbiosis. By contributing this framework to the literature, the robustness of the results has been demonstrated through sensitivity analysis and comparative analysis. According to the results obtained, it was seen that the importance levels of economic factors came to the fore. Furthermore, among the alternatives, enterprises with high waste exchange potential, such as cement, took first place in the ranking.

Multi-Objective Portfolio Optimization Using a Quantum Annealer

In this study, the portfolio optimization problem is explored, using a combination of classical and quantum computing techniques. The portfolio optimization problem with specific objectives or constraints is often a quadratic optimization problem, due to the quadratic nature of, for example, risk measures. Quantum computing is a promising solution for quadratic optimization problems, as it can leverage quantum annealing and quantum approximate optimization algorithms, which are expected to tackle these problems more efficiently. Quantum computing takes advantage of quantum phenomena like superposition and entanglement. In this paper, a specific problem is introduced, where a portfolio of loans need to be optimized for 2030, considering ‘Return on Capital’ and ‘Concentration Risk’ objectives, as well as a carbon footprint constraint. This paper introduces the formulation of the problem and how it can be optimized using quantum computing, using a reformulation of the problem as a quadratic unconstrained binary optimization (QUBO) problem. Two QUBO formulations are presented, each addressing different aspects of the problem. The QUBO formulation succeeded in finding solutions that met the emission constraint, although classical simulated annealing still outperformed quantum annealing in solving this QUBO, in terms of solutions close to the Pareto frontier. Overall, this paper provides insights into how quantum computing can address complex optimization problems in the financial sector. It also highlights the potential of quantum computing for providing more efficient and robust solutions for portfolio management.

Dynamical analysis combined with parameter identification for a model of infection in honeybee colonies with social immunity

Several models on honeybee population dynamics have been considered in the past decades, which explain that the growth of beecolonies is highly dependent on the availability of food and social inhibition. The phenomenon of the Colony Collapse Disorder (CCD) and its exact causes remain unclear and here we are interested on the factor of social immunity. We work with the mathematical model in [1]. The core model, consisting of four nonlinear ordinary differential equations with unknown functions: brood and nurses B, iB, N and iN represent the number of healthy brood, infected brood, healthy nurses, and infected nurses, respectively. First, this model implements social segregation. High-risk individuals such as foragers are limited to contact only nectar-receivers, but not other vulnerable individuals (nurses and brood) inside the nest. Secondly, it includes the hygienic behavior, by which healthy nurses actively remove infected workers and brood from the colony. We aim to study the dynamics and the long-term behavior of the proposed model, as well as to discuss the effects of crucial parameters associated with the model. In the first stage, we study the model equilibria stability in dependence of the reproduction number. In the second stage, we investigate the inverse problem of parameters identification in the model based on finite number time measurements of the population size. The conjugate gradient method with explicit Frechet derivative of the cost functional is proposed for the numerical solution of the inverse problem. Computational results with synthetic and realistic data are performed and discussed.

DFT Struggles to Predict the Energy Landscape for Iron Pyridine Diimine-Catalyzed [2 + 2] Cycloaddition of Alkenes: Insights into the Problem and Alternative Solutions.

In recent years, noninnocent pyridine diimine (PDI) complexes featuring first-row transition metals have emerged as prominent catalysts, demonstrating efficacy in a diverse range of vital organometallic transformations. However, the inherent complexity of the fundamental reactivity paradigm in these systems arises from the presence of a noninnocent ligand and the multispin feasibility of 3d metals. While density functional theory (DFT) has been widely used to unravel mechanistic insights, its limitations as a single-reference method can potentially misrepresent spin-state energetics, compromising our understanding of these intricate systems. In this study, we employ extensive high-level ab initio state averaged-complete active space self-consistent field/N-electron valence state perturbation theory (SA-CASSCF/NEVPT2) calculations in combination with DFT to investigate an iron-PDI-catalyzed [2 + 2] cycloaddition reaction of alkenes. The transformation proceeds through two major steps: oxidative cyclization and reductive elimination. Contrary to the predictions of DFT calculations, which suggest two-state reactivity in the reaction and identify reductive elimination as the turnover-limiting step, SA-CASSCF/NEVPT2-corrected results unequivocally establish a single-state reactivity scenario with oxidative cyclization as the turnover-limiting step. SA-CASSCF/NEVPT2-based insights into electronic ground states and electron distribution elucidate the intriguing interactions between the PDI ligand and the iron center, revealing the highly multiconfigurational nature of these species and providing a precise depiction of metal-ligand cooperativity throughout the transformation. A comparative assessment of several widely recognized DFT functionals against SA-CASSCF/NEVPT2-corrected data indicates that single-point energy calculations using the modern density functional MN15 on TPSSh geometries offer the most reliable density functional methodology, in scenarios where SA-CASSCF/NEVPT2 computational cost is a consideration.

Analytic Solutions for Hilfer Type Fractional Langevin Equations with Variable Coefficients in a Weighted Space

In this work, analytic solutions of initial value problems for fractional Langevin equations involving Hilfer fractional derivatives and variable coefficients are studied. Firstly, the equivalence of an initial value problem to an integral equation is proved. Secondly, the existence and uniqueness of solutions for the above initial value problem are obtained without a contractive assumption. Finally, a formula of explicit solutions for the proposed problem is derived. By using similar arguments, corresponding conclusions for the case involving Riemann–Liouville fractional derivatives and variable coefficients are obtained. Moreover, the nonlinear case is discussed. Two examples are provided to illustrate theoretical results.

Expandable-RCNN: toward high-efficiency incremental few-shot object detection.

This study aims at addressing the challenging incremental few-shot object detection (iFSOD) problem toward online adaptive detection. iFSOD targets to learn novel categories in a sequential manner, and eventually, the detection is performed on all learned categories. Moreover, only a few training samples are available for all sequential novel classes in these situations. In this study, we propose an efficient yet suitably simple framework, Expandable-RCNN, as a solution for the iFSOD problem, which allows online sequentially adding new classes with zero retraining of the base network. We achieve this by adapting the Faster R-CNN to the few-shot learning scenario with two elegant components to effectively address the overfitting and category bias. First, an IOU-aware weight imprinting strategy is proposed to directly determine the classifier weights for incremental novel classes and the background class, which is with zero training to avoid the notorious overfitting issue in few-shot learning. Second, since the above zero-retraining imprinting approach may lead to undesired category bias in the classifier, we develop a bias correction module for iFSOD, named the group soft-max layer (GSL), that efficiently calibrates the biased prediction of the imprinted classifier to organically improve classification performance for the few-shot classes, preventing catastrophic forgetting. Extensive experiments on MS-COCO show that our method can significantly outperform the state-of-the-art method ONCE by 5.9 points in commonly encountered few-shot classes.

Numerical simulations for fractional Hirota–Satsuma coupled Korteweg–de Vries systems

Abstract In this investigation, the fractional Hirota–Satsuma coupled Korteweg–de Vries (KdV) problem is solved using two modern semi-analytic techniques known as the Aboodh residual power series method (ARPSM) and Aboodh transform iteration method (ATIM). The two suggested approaches are briefly explained, along with how to use them to solve the fractional Hirota–Satsuma coupled KdV problem. Some analytical approximate solutions for the current problem are derived using the proposed techniques until the second-order approximation. To ensure high accuracy of the derived approximation, they are analyzed numerically and graphically and compared with the exact solutions of the integer cases. The offered techniques demonstrate more accuracy in their outcomes compared to other alternatives. The numerical results show that ARPSM and ATIM are highly accurate, practical, and beneficial for solving nonlinear equation systems. The current results are expected to help many physics researchers in modeling their different physical problems, especially those interested in plasma physics.

Neurorehabilitation after epilepsy surgery in the temporal lobe in patients with specific neurocognitive risk profiles

In case of pharmacoresistant focal epilepsy, surgery is often the only way to achieve seizure freedom. These operations may result in cognitive deficits, especially with surgery in the left temporal lobe. The aim of this study was to determine whether patients operated in the left or right temporal lobe, respectively, have different cognitive outcomes. Furthermore, it was investigated if there was a positive influence of occupational therapy on neurorehabilitation in epilepsy patients. In all patients cognitive performance was assessed preoperatively and six months postoperatively. Patient groups with surgery in the right and left temporal lobe were compared. Additionally, single cases of patients who had undergone extensive pre- and postoperative occupational therapy were analyzed. There was a significantly better cognitive outcome in patients who underwent surgery in their right temporal lobe. Occupational therapy was highly beneficial in patients after left temporal lobe surgery. Occupational therapy after left temporal lobe surgery is adapted to find individual solutions for the patient's problems and to implement effective cognitive training strategies.

Iterative method for the numerical solution of optimal control model for mosquito and insecticide

A linear multistep method is transformed into an iterative method based on Patade and Bhalekar's technique for the numerical solution of the optimal control problem modeled for mosquito and insecticide management using forward-backward sweep methods via Pontryagin's principle. Stability and convergence analysis of the iterative method are carried out, and it is found to be stable, convergent, and of order four. Results obtained by the method clearly show that the population of mosquitoes can be minimized to a large extent using the new iterative method while reducing the harmful effects of the insecticide, subsequently reducing the spread of malaria.

A bi-objective model for location, dispatch and relocation of ambulances with a revision of dispatch policies

PurposeUntimely responses to emergency situations in urban areas contribute to a rising mortality rate and impact society's primary capital. The efficient dispatch and relocation of ambulances pose operational and momentary challenges, necessitating an optimal policy based on the system's real-time status. While previous studies have addressed these concerns, limited attention has been given to the optimal allocation of technicians to respond to emergency situation and minimize overall system costs.Design/methodology/approachIn this paper, a bi-objective mathematical model is proposed to maximize system coverage and enable flexible movement across bases for location, dispatch and relocation of ambulances. Ambulances relocation involves two key decisions: (1) allocating ambulances to bases after completing services and (2) deciding to change the current ambulance location among existing bases to potentially improve response times to future emergencies. The model also considers the varying capabilities of technicians for proper allocation in emergency situations.FindingsThe Augmented Epsilon-Constrained (AEC) method is employed to solve the proposed model for small-sized problem. Due to the NP-Hardness of the model, the NSGA-II and MOPSO metaheuristic algorithms are utilized to obtain efficient solutions for large-sized problems. The findings demonstrate the superiority of the MOPSO algorithm.Practical implicationsThis study can be useful for emergency medical centers and healthcare companies in providing more effective responses to emergency situations by sending technicians and ambulances.Originality/valueIn this study, a two-objective mathematical model is developed for ambulance location and dispatch and solved by using the AEC method as well as the NSGA-II and MOPSO metaheuristic algorithms. The mathematical model encompasses three primary types of decision-making: (1) Allocating ambulances to bases after completing their service, (2) deciding to relocate the current ambulance among existing bases to potentially enhance response times to future emergencies and (3) considering the diverse abilities of technicians for accurate allocation to emergency situations.