Programming Principle Research Articles

Optimal Consumption and Investment with Income Adjustment and Borrowing Constraints

In this paper, we address the utility maximization problem of an infinitely lived agent who has the option to increase their income. The agent can increase their income at any time, but doing so incurs a wealth cost proportional to the amount of the increase. To prevent the agent from infinitely increasing their income and borrowing against future income, we additionally consider a non-negative wealth constraint that prohibits borrowing based on future income. This utility maximization problem is a mixture of stochastic control, where the agent chooses consumption and investment, and singular control, where the agent chooses a non-decreasing income process. To solve this non-trivial and challenging problem, we derive the Hamilton–Jacobi–Bellman (HJB) equation with a gradient constraint using the dynamic programming principle (DPP). Then, using the guess-and-verify method and a linearization technique, we obtain a closed-form solution to the HJB equation and, based on this, find the optimal strategy.

Feedback Stabilization of Chain-Like MDOF Nonlinear Structural Systems Under Purely Parametric Gaussian White Noises

A feedback control method is proposed to asymptotically stabilize the chain-like multi-degree-of-freedom (MDOF) nonlinear structural system under purely parametric Gaussian white noises. First, the motion equation of a chain-like nonlinear structural system with [Formula: see text] coupled elements and an undetermined control law is derived. It is reduced to a one-dimensional partially averaged Itô stochastic differential equation of the controlled Hamiltonian by applying the stochastic averaging method, which bypasses the challenge of calculating multiple Lyapunov exponents. Next, based on the dynamical programming principle, the dynamical programming equation of the averaged system with an undetermined cost function is established and addressed to obtain the optimal control law. Then, the Lyapunov exponent of the completely averaged Itô equation is derived. For the given requirement of stabilizing the system, the Lyapunov exponent is entirely fixed and used to analyze the stability of the originally chain-like MDOF nonlinear structural system. A controlled chain-like 8-DOF nonlinear structural system is taken as an example to illustrate the application and effectiveness of the proposed procedure and the effect of control forces on stabilizing the system.

Dynamic programming principle for optimal control of uncertain random differential equations and its application to optimal portfolio selection

This study aimed to examine an uncertain stochastic optimal control problem premised on an uncertain stochastic process. The proposed approach is used to solve an optimal portfolio selection problem. This paper’s research is relevant because it outlines the procedure for solving optimal control problems in uncertain random environments. We implement Bellman’s principle of optimality method in dynamic programming to derive the principle of optimality. Then the resulting Hamilton-Jacobi-Bellman equation (the equation of optimality in uncertain stochastic optimal control) is used to solve a proposed portfolio selection problem. The results of this study show that the dynamic programming principle for optimal control of uncertain stochastic differential equations can be applied in optimal portfolio selection. Also, the study results indicate that the optimal fraction of investment is independent of wealth. The main conclusion of this study is that, in Itô-Liu financial markets, the dynamic programming principle for optimal control of uncertain stochastic differential equations can be applied in solving the optimal portfolio selection problem.

Cpvlib: A comprehensive open-source tool for modeling CPV systems

The design and simulation of concentrator photovoltaic (CPV) systems necessitate precise modeling tools, for which some commercial and open-source options exist. However, when new technologies or applications need to be modeled, they can present some limitations: lack of documentation transparency and inability to extend existing models, or little flexibility to do it. For instance, the novel hybrid CPV/flat-plate module, conceived by Insolight and developed within the HIPERION project, required the ability to model integrated tracking and dual use of incident irradiance, which was not possible with existing tools. Addressing these issues, cpvlib is introduced as a comprehensive, open-source tool offering modular and adaptable functionalities for CPV-based systems, built as an extension of the popular pvlib python library.cpvlib's design enables the simulation of various CPV-based configurations, incorporating advanced architectures such as integrated tracking and hybrid CPV-flat plate modules. The library uses PVSyst's utilization factors to model deviations from the single-diode model, accounting for spectral and thermal effects. Its class structure leverages object-oriented programming principles, ensuring ease of use and extension.The validation of cpvlib is carried out through the modeling and long-term monitoring of Insolight's hybrid Si/III-V translucent planar micro-tracking modules, achieving a root mean square error of 3.5 % in case of Si cells and 2.7 % for III-V CPV cells. The tool accounts for complex behaviors like air mass impact on CPV performance, angle of incidence limits, and light spillage. The annual energy yield for a hybrid module is computed using typical meteorological year data, showcasing cpvlib's practical application.

Framework for Implementation of Building Automation Control Programs for Industrial Heating and Cooling Systems

This article proposes a novel framework for the rapid implementation of automation programs in industrial heating and cooling systems. The global push for sustainability necessitates significant infrastructural transformations within these systems, which currently rely heavily on fossil fuels and are responsible for 75% of industrial final energy consumption. Our research highlights the critical role of design patterns and object-oriented programming principles to address the complex integration of additional energy converters and storage into automation programs. By leveraging design patterns, our framework encapsulates the intricacies of various components, such as actuators, sensors, and storage, within a comprehensive object-oriented model that also allows the integration of different control strategies. Qualitatively, this approach enhances the reusability, scalability, and adaptability of automation programs. Therefore, quantitatively, our framework enables a more resilient and efficient energy system. The framework is validated through its application to a complex, cross-linked industrial heating and cooling system at the ETA Research Factory of the Technical University of Darmstadt. Using the developed framework reduces implementation effort significantly due to its consistent and modular structure resulting from the reusable design patterns.

Finite Horizon Stochastic H2/H∞ Control for Continuous‐Time Systems Driven by G‐Brownian Motion

ABSTRACTIn this article, we consider a stochastic control problem under volatility ambiguity. In this framework, the state function is described by a stochastic differential equation driven by ‐Brownian motion. Different from the classical approach, the volatility ambiguity is characterized by a family of non‐dominated probability measures. control can be modeled as a “inf‐sup problem” and control can be modeled as a “sup‐sup problem.” With the help of stochastic maximum principle and dynamic programming principle, we derive the stochastic bounded real lemma and two sets of cross coupled generalized differential Riccati equations, which is associated with the solvability of control.

The relationship between maximum principle and dynamic programming principle for stochastic recursive control problem with random coefficients

The relationship between maximum principle and dynamic programming principle for stochastic recursive control problem with random coefficients

Stochastic representation for solutions of a system of coupled HJB-Isaacs equations with integral–differential operators

In this paper, we focus on the stochastic representation of a system of coupled Hamilton–Jacobi–Bellman–Isaacs (HJB–Isaacs (HJBI), for short) equations which is in fact a system of coupled Isaacs’ type integral-partial differential equation. For this, we introduce an associated zero-sum stochastic differential game, where the state process is described by a classical stochastic differential equation (SDE, for short) with jumps, and the cost functional of recursive type is defined by a new type of backward stochastic differential equation (BSDE, for short) with two Poisson random measures, whose wellposedness and a prior estimate as well as the comparison theorem are investigated for the first time. One of the Poisson random measures μ appearing in the SDE and the BSDE stems from the integral term of the HJBI equations; the other random measure in BSDE is introduced to link the coupling factor of the HJBI equations. We show through an extension of the dynamic programming principle that the lower value function of this game problem is the viscosity solution of the system of our coupled HJBI equations. The uniqueness of the viscosity solution is also obtained in a space of continuous functions satisfying certain growth condition. In addition, also the upper value function of the game is shown to be the solution of the associated system of coupled Isaacs’ type of integral-partial differential equations. As a byproduct, we obtain the existence of the value for the game problem under the well-known Isaacs’ condition.

Development and testing of a resuscitation-specific measure of organizational culture for resuscitation teams

BackgroundThe organizational culture (shared beliefs, perceptions, and values) of teams informs their behaviours and practices. Little is known about organizational culture for resuscitation teams. Our objective was to develop a reliable and valid resuscitation-specific organizational culture instrument (ROCI) with the goal of improving team performance. MethodsUsing Neonatal Resuscitation Program principles, literature review, and discussion of existing culture measures with experts, we identified organizational culture components for resuscitation and adapted existing measures to resuscitation. We developed a ROCI with five subscales (role clarity, shared-mental models, closed-loop communication, team adaptability, and psychological safety) and administered it to neonatal resuscitation team members across a hospital network. Survey psychometric assessment included reliability analyses (Cronbach’s α, Pearson correlation coefficients) and validity testing (confirmatory factor analysis [CFA] and regression models examining the association of culture with implementation outcomes: climate and perceived success). ResultsAcross 11 hospitals there were 318 complete responses (41 % response rate). Of the 22-items tested, 18 were retained after iterative psychometric assessment. The ROCI had excellent overall reliability (Cronbach’s α = 0.994) and very good subscale reliability (Cronbach’s α = 0.789–0.867). The CFA goodness-of-fit statistics confirmed five constructs (subscales). At the individual-level, the ROCI and all subscales were associated with both implementation outcomes. At the hospital-level, the ROCI overall and three subscales were associated with perceived success. ConclusionThe ROCI is a reliable and valid measure of the organizational culture of resuscitation teams. Future ROCI assessments may provide a foundation to inform culture change initiatives to improve resuscitation quality and outcomes across populations and contexts.

A Multilinear HJB-POD Method for the Optimal Control of PDEs on a Tree Structure

Optimal control problems driven by evolutionary partial differential equations arise in many industrial applications and their numerical solution is known to be a challenging problem. One approach to obtain an optimal feedback control is via the Dynamic Programming principle. Nevertheless, despite many theoretical results, this method has been applied only to very special cases since it suffers from the curse of dimensionality. Our goal is to mitigate this crucial obstruction developing a version of dynamic programming algorithms based on a tree structure and exploiting the compact representation of the dynamical systems based on tensors notations via a model reduction approach. Here, we want to show how this algorithm can be constructed for general nonlinear control problems and to illustrate its performances on a number of challenging numerical tests introducing novel pruning strategies that improve the efficacy of the method. Our numerical results indicate a large decrease in memory requirements, as well as computational time, for the proposed problems. Moreover, we prove the convergence of the algorithm and give some hints on its implementation.

NEURO-LINGUISTIC PROGRAMMING ELEMENTS IN ADVERTISING: CRAFTING PERSUASIVE DISCOURSE

This study explores the application of Neuro-Linguistic Programming (NLP) principles inadvertising, focusing on how these elements enhance persuasive discourse. NLP is a psychological approach that examines the interplay between language, behavior, and thought patterns, providing valuable insights into consumer decision-making processes. The research aims to identify specific NLP techniques—such as anchoring, reframing, and the use of sensory language—and analyze their effectiveness in creating compelling advertising messages that resonate with target audiences. Through a mixed-methods approach, this study evaluates a selection of advertising campaigns that successfully employ NLP strategies. Qualitative analysis of consumer responses and quantitative assessments of campaign performance metrics reveal a significant correlation between the useof NLP elements and improved consumer engagement and conversion rates. The findings suggest that advertisements incorporating NLP techniques can effectively influence perceptions, emotions, and behaviors, ultimately leading to higher brand loyalty and sales. This research contributes to the understanding of how language and psychological frameworks can be strategically utilized in marketing to enhance persuasive communication. It highlights the need for advertisers to adopt NLP principles to craft messagesthat not only capture attention but also drive meaningful consumer interactions. The implications of these findings underscore the importance of integrating psychological insights into advertising practices, paving the way for future studies in the intersection of psychology and marketing.

Проблемы поиска и преследования беспилотных летательных аппаратов с использованием теоретико-игрового подхода

Currently, the application of mathematical modeling theory is widely used in various fields and, among other things, can be used to prevent illegal actions. This study examines scenarios involving a single pursuer tracking a single evader, as well as situations involving multiple pursuers pursuing multiple evaders. The authors formulate this problem as a search and pursuit problem for four-rotor unmanned aerial vehicles (UAVs) or quadcopters. The solution to the problem is based on game theory, as it provides a mathematical framework for modeling and studying strategic interactions involving multiple decision makers. The authors consider a game where the set of strategies of the runner is the set of possible combinations of speeds and directions of his movement, and the set of strategies of the pursuer is the set of all possible permutations of the elements of his speeds. The matrix of the resulting game consists of elements that are the time of capture. A mathematical problem on the optimal assignments of searching and pursuing unmanned aerial vehicles is formulated and solved. Purpose: optimizing the effectiveness of strategies for detecting and capturing four-rotor unmanned aerial vehicles (quadcopters). Methods: methods of mathematical modeling, game theory apparatus, Hungarian method of solving the problem of assignments, decision theory, the principle of dynamic programming, the Maple package for solving examples were used. Results: In order to fulfill the conditions for solving the problem of optimal assignments, it is necessary and sufficient that it is balanced. This assignment problem can be balanced by entering the required number of fictitious boats or escaping boats. After that, it is possible to formulate and solve the dual problem of optimal appointments. The resulting game can be solved by any method of solving matrix games. In this way, it is possible to determine the policy of harassment and search between drones. Practical importance: All escaped quadcopter UAVs can be chased and successfully intercepted using the developed models. Examples of the study of mathematical models using the Maple software package are given.

Preventing and Controlling Healthcare-Associated Infections: The First Principle of Every Antimicrobial Stewardship Program in Hospital Settings.

Antimicrobial resistance (AMR) is one of the main public health global burdens of the 21st century, responsible for over a million deaths every year. Hospital programs aimed at improving antibiotic use, referred to as antimicrobial stewardship programs (ASPs), can both optimize the treatment of infections and minimize adverse antibiotics events including the development and spread of AMR. The challenge of AMR is closely linked to the development and spread of healthcare-associated infection (HAIs). In fact, the management of patients with HAIs frequently requires the administration of broader-spectrum antibiotic regimens due to the higher risk of acquiring multidrug-resistant organisms, which, in turn, promotes resistance. For this reason, even before using antibiotics correctly, it is necessary to prevent and control the spread of HAIs in our hospitals. In this narrative review, we present seven measures that healthcare workers, even if not directly involved in the tasks of infection prevention and control, must know, support, and embrace. We hope that this review may raise awareness among all healthcare professionals about the issues with the increasing rate of AMR and the ongoing efforts towards minimizing its rise.

Life-cycle planning with CEV model and time-inconsistent preferences

In this paper, we investigate an optimization problem for a wage earner seeking to maximize expected utilities until retirement by choosing optimal consumption, investment, and life insurance purchase strategies. The constant elasticity of variance (CEV) model is adopted to describe the price process of the risky asset. Additionally, we assume that the wage earner has time-inconsistent preferences. This makes the wage earner discount her payoff by a non-constant discount rate. Applying the dynamic programming principle, we have derived the Hamilton-Jacobi-Bellman (HJB) equation corresponding to the optimization problem. Furthermore, we present semi-analytical expressions for optimal strategies and value functions in three cases: the benchmark model with time-consistent preferences, the naive and sophisticated wage earners with time-inconsistent preferences. Finally, illustrations of the optimal solutions and some economic insights are provided in the numerical examples.

An Innovative Double-Frontier Approach to Measure Sustainability Efficiency Based on an Energy Use and Operations Management Perspective

China’s economic development has achieved great success in recent years, but the problems of energy scarcity and environmental pollution have become increasingly serious. To enhance the reliability and efficiency between energy, the environment and the economy, sustainable development is an inevitable choice. In the context of measuring sustainability efficiency, a network data envelopment analysis model is proposed to formulate the two-stage process of energy use and operations management. A double frontier is derived to optimize the available energy for sustainable development. Due to nonlinearity, previous linear methods are not directly applicable to identify the double frontier and calculate stage efficiencies for inefficient decision-making units. To address this problem, this study develops the primal-dual relationship between multiplicative and envelopment network models based on the Lagrange duality principle of parametric linear programming. The newly developed approach is used to evaluate the sustainability efficiency of 30 administrative regions in China. The results show that insufficient sustainability efficiency is a systemic problem. Different regions should take different measures to conserve energy and reduce pollutant emissions for sustainable development. To increase sustainability efficiency, regions should support energy-saving and emission-reducing technologies in production processes and strengthen their capacity for technological innovation. Compared with energy use efficiency, operations management efficiency in China has a wider range of changes. During the operations management stage, there is not much difference between the capacity and quantity of each region. Based on benchmark regions at the efficiency frontier, there is an opportunity to improve operations management in the near future. Blockchain technology can effectively improve energy allocation efficiency.

Enhanced Dynamic Programming Approaches for Efficient Solutions to the Traveling Salesman Problem

This study aims to enhance dynamic programming techniques for efficiently solving the Traveling Salesman Problem, a fundamental combinatorial optimization challenge. Given its NP-hard classification, traditional exact algorithms become computationally infeasible as the problem size increases. The research revisits foundational dynamic programming principles, notably the Held-Karp algorithm, and identifies existing limitations. The study begins with a comprehensive literature review, followed by an analysis of the dynamic programming challenges specific to TSP. Novel algorithms are then developed, implemented, and rigorously tested against benchmark instances. Performance evaluation is conducted using metrics such as execution time, memory usage, and solution optimality across different problem sizes. Results demonstrate significant improvements in efficiency and scalability, with enhanced algorithms achieving optimal solutions in reduced time and computational resource usage. However, the exponential growth in complexity remains a challenge for larger instances. The study concludes with recommendations for future research, focusing on further algorithmic refinements and exploring hybrid approaches to address large-scale TSPs.

The Selection of Best Open Source Integrated Library Management Software: A Review

Since the first established library in the 7th century B.C., libraries have played a main role in civilization development due to their ability to help humans reach the required information easily. However, in the last decades, after the huge propagation of information from various sources, including the addition of books, other data types such as audio, video, and images have emerged. Therefore, finding an integrated system to manage this huge amount of library data became necessary. As a result, the integrated library system (ILS) improved its efficiency and ability to manage various and enormous amounts of data in addition to following the lending processes and inventory management. There are many commercial and open-source integrated systems used in libraries; selecting the best open-source integrated library system to achieve the library requirements is still challenging due to the variety of available libraries. Although these libraries are open source and freely available, the installation process requires a long time and expert effort, making selecting the best library system a sensitive and important mission. This survey demonstrates the most famous open-source integrated library systems and the essential technologies used to build them, along with the system specifications, to help librarians and educational and academic institutions select the best one for their requirements. Through reviewing the previous academic research, it was noticed that many of them had the task of selecting the right ILS, but most of them did not handle the programming principles based on it, which is the milestone in its building, so we recommended using an automated technique to evaluate ILS and select the best one.

Stochastic Optimal Bounded Parametric Control of Periodic Viscoelastomer Sandwich Plate with Supported Mass Based on Dynamical Programming Principle

Stochastic Optimal Bounded Parametric Control of Periodic Viscoelastomer Sandwich Plate with Supported Mass Based on Dynamical Programming Principle

Post-war reconstruction of Ukraine: analysis of individual program principles

Post-war reconstruction of Ukraine: analysis of individual program principles

Risk-Averse Markov Decision Processes Through a Distributional Lens

By adopting a distributional viewpoint on law-invariant convex risk measures, we construct dynamic risk measures (DRMs) at the distributional level. We then apply these DRMs to investigate Markov decision processes, incorporating latent costs, random actions, and weakly continuous transition kernels. Furthermore, the proposed DRMs allow risk aversion to change dynamically. Under mild assumptions, we derive a dynamic programming principle and show the existence of an optimal policy in both finite and infinite time horizons. Moreover, we provide a sufficient condition for the optimality of deterministic actions. For illustration, we conclude the paper with examples from optimal liquidation with limit order books and autonomous driving. Funding: This work was supported by Natural Sciences and Engineering Research Council of Canada [Grants RGPAS-2018-522715 and RGPIN-2018-05705].