Programming Approach Research Articles

Optimizing Regulatory Floodways: Dynamic Programming Approach

Optimizing Regulatory Floodways: Dynamic Programming Approach

Value-at-risk constrained portfolios in incomplete markets: a dynamic programming approach to Heston’s model

Value-at-risk constrained portfolios in incomplete markets: a dynamic programming approach to Heston’s model

An alternative approach to biokinetic modelling for phenol pollutant degradation and microbial growth using Genetic Programming

ABSTRACT Biokinetic models can optimise pollutant degradation and enhance microbial growth processes, aiding to protect ecosystem protection. Traditional biokinetic approaches (such as Monod, Haldane, etc.) can be challenging, as they require detailed knowledge of the organism's metabolism and the ability to solve numerous kinetic differential equations based on the principles of micro, molecular biology and biochemistry (first engineering principles) which can lead to discrepancies between predicted and actual degradation rates. More recently, data-driven machine-learning techniques have emerged as a promising alternative for modelling microbial systems. A few machine learning models (such as ANN, SVM, RF, DT, XG BOOST, etc.) have been used recently for modelling phenol degradation, but they lack the robustness of generating mathematical models. This gap is addressed in this study using Genetic Programming (GP) as the modelling approach for modelling the phenol degradation. This study utilises the microalgae Acutodesmus Obliquus, finding that phenol degradation of 98% required 216 hours. Both the traditional kinetic approach and the Genetic Programming (GP) approach were used to determine the specific growth rate (µ max) and saturation constant (Ks ). It is noted that without any a priori information on the form of the mathematical mode, GP can evolve a model which closely fits the Monod kinetics, thus demonstrating that data-driven models can bring out the first engineering principles on which biokinetic models are dependent or framed in a most swift and effective way. Performance was assessed using root mean square error (RMSE) and correlation coefficient (R), with the GP model showing superior predictive accuracy.

A Mathematical Programming Approach for Joint Optimization of Wind Farm Layout and Cable Routing Based on a Three‐Dimensional Gaussian Wake Model

ABSTRACTWind energy is currently one of the most promising alternative energy sources. The optimization of the wind farm layout and the cable layout are two important elements in the design of wind farms. Since increasing the distance between turbines can reduce wake loss but increase cable cost, these two optimizations are coupled and jointly affect the revenue of wind farms. In this paper, we propose a novel nonlinear mathematical programming model based on the 3D Gaussian wake model and use a mathematical programming approach to optimize the layout of the wind farm and the cable layout together, considering both power generation and cable cost. In this method, some of the constraints were linearized to facilitate the solution process. The optimization results show that profit increased by 9.07% when using annual economic efficiency as the objective function, compared with using energy production as the objective function.

Market-based or policy strategies to increase the cultivation of legumes for livestock? Effects on the cropping pattern and economic performance

Legume crops, the production of which is related to their use in animal nutrition (referred to in this study as “legumes for livestock—LL” and include species such as bean, lupin, pea, vetch and clover), have positive effects on the economic performance of farms and environmental outcomes, although their relation to sustainable weed management practices (SWMP) has received limited attention. The expansion of LL in Europe is limited due to market constraints, despite important policy incentives. This study was designed to investigate whether a policy-oriented Scenario (S1) or a market-based Scenario (S2) could induce the expansion of the cultivation of LL as well as to examine the possible impact on the economic performance of farming systems, including cost savings related to pest management. A farm typology was developed according to the level of expenses of farms for crop protection and their implemented SWMP. Using technical and economic data from 45 farms that cultivate annual arable crops in Greece, with special emphasis on LL, a Linear Programming model was constructed to examine the potential role of each one of the three identified farm types in the expansion of LL in Central Greece (Baseline Solution—BS). Following the BS, the effects of the two Scenarios were examined through a Price Parametric Programming approach. The solutions revealed that the valorization of LL had a significant economic potential for all farm types. In addition, it was found that a substantial policy incentive of increasing the coupled payments of LL by 128% (S1) can have an equivalent impact on the expansion of LL as modest levels of market interventions—through the achievement of higher yields or/and higher selling prices—to increase the revenues of LL by 59% (S2). Farms that relied more on chemical pesticides could play a more decisive role in expanding LL. The novel aspect is related to the investigation of the economic performance of different farm types cultivating LL with a specific focus on potentially higher profitability related to SWMP. Furthermore, this study contributed to the “policy versus market solutions” debate toward the expansion of LL.

Purchasing and remanufacturing decisions with different quality returned material and finished goods

To transition from a linear to a circular value chain, an effective refurbishment policy is crucial to preserve material value and functionality at the end of a product's life-cycle. This study examines a refurbisher that processes first- and second-quality returned materials to produce first- and second-quality products in a make-to-stock system. The refurbisher makes purchasing decisions for the returned materials and determines whether to refurbish or remain idle, and if refurbishing, how to convert them into finished goods of varying quality. There are five refurbishment decisions (converting first-quality to first- or second-quality, second-quality to first- or second-quality, or no production) and two purchasing decisions for the materials. With production and arrival times modelled as exponential random variables, the optimal control problem is formulated as a Markovian Decision Process, using a long-run average profit criterion to identify optimal decisions. A linear programming approach is employed for numerical optimisation. Results show that the most profitable option based solely on sales prices, purchasing, and conversion costs may not be optimal. Instead, the optimal policy is influenced by per-unit profit differences, returned material availability, demand rates, and production times across various refurbishment scenarios.

Exploring Nutrient-Adequate Sustainable Diet Scenarios That Are Plant-Based but Animal-Optimized.

Background: Transitions toward more sustainable food systems may become rather polarized, particularly in the plant-based vs. animal-based debate. These discussions, however, are often based on environmental impact data from individual products or product groups and do not consider that the products together should form a nutrient-adequate diet that is also affordable. Linear programming approaches are ideal for exploring the impact of changes in diet composition on environmental impact and price while ensuring nutrient adequacy. Methods: In this study, we used a linear programming system, Optimeal 2.0, based on the diet in the Netherlands to explore the impact of changing the contributions of specific food groups on the environmental impact and price of the diet. Results: Reducing the amount of meat products in the diet reduced the environmental impact but increased the price. On the other hand, when dairy products were reduced or even omitted, the environmental impact of the nutrient-adequate optimized diet did not change notably, but prices increased notably. This could be attributed to the fact that the products required to compensate for the nutrient gaps by removing dairy products have equal environmental impact and higher prices. Increasing vegetable or fruit consumption increased price but did not affect the environmental impact nor did it increase the consumption of beans and pulses when kept within realistic levels. Conclusions: Overall, this work strongly highlights that consideration of 'sustainability' at a food product level is insufficient and that their role in nutrient-adequate diets needs to be considered. Furthermore, dietary shifts should be considered from the perspective of affordability and consumer preferences.

Availability-based maintenance prioritization for data centres: a dynamic programming approach

Digital infrastructures and Data Centres (DCs) have played a significant role in society for several years in conducting digital transformation. Recently after the global COVID-19 crisis, the importance of DCs has greatly increased because of facing new realities. High demand for digital commerce, online meetings, and other online and cloud services in the digitisation process has given the DC industry special attention. Regarding the critical components of DCs, special requirements, such as minimum availability, reliability, quality, and performance levels, should be addressed in DC operations. Therefore, implementing optimised maintenance management is beneficial for these infrastructures to reduce the risk of failure and ensure a minimum level of reliability and availability while minimising maintenance costs. This paper presents a novel, availability-based model for optimising maintenance prioritisation in DCs. The model leverages a Multiple 0-1 Knapsack problem, solved using Dynamic Programming (DP), to determine the optimal set of components for maintenance actions, considering both DC availability requirements and budget limitations. By incorporating reliability and availability analysis, the DP-based approach generates a maintenance prioritisation plan that maximises DC availability within budgetary constraints. This study provides a practical framework for enhancing maintenance decision-making in DCs, ultimately contributing to more efficient and resilient digital infrastructures.

Sufficient approximate optimality condition for the inverse one-phase Stefan problem

Abstract We investigate the identification problem for the one-phase Stefan problem. As the inverse Stefan problem is not well posed, an optimal control problem is considered instead. In the paper we develop a dual dynamic programming approach to derive sufficient approximate optimality conditions for that optimal control problem. As a next step we formulate and prove a verification theorem for approximate solution. The verification Theorem 4.1 is the basis for the development of a numerical algorithm. Having the verification theorem we do not need the convergence of our algorithm.

Net present value comparison of different mathematical programming models for multi-element open pit mines

ABSTRACT This paper compares two well-known mathematical programming approaches aiming to maximise multi-element open-pit mines’ net present value (NPV). The first approach, called production scheduling-based (PS-based), decides the optimum schedule of the individual blocks within the pit limit, whereas the second approach, called cut-off grade-based (COG-based), decides the partial extraction of bins derived from a reprehensive grade-tonnage curve of the orebody. A modified COG-based approach is also presented to accentuate the importance of blending requirements in multi-element deposits. The blending constraints in the PS-based model are also considered selectively to understand the paramount effect of the accessibility rules and to compare the two types of strategy fairly. The models were then applied to the Chadormalu iron ore open-pit mine in central Iran. The results were almost identical for those deposits with low stripping ratios and where excessive mining capacity is available. On the other hand, the results were entirely different for deposits with high stripping ratios and insufficient mining capacity. It was also shown that the COG-based models overestimate the NPV, in this special case, about 4.3%, and discriminate the ore-waste untruly. Also, the conventional COG-based models consistently violate the rigid bounds of elements at destinations. This comparison provides a guideline for knowing where the fast COG-based approaches can be used for the project evaluations instead of time-consuming PS-based approaches.

Feasibility and resource utilization of nurse-administered subcutaneous immunoglobulin therapy in antibody deficiency: A cross-sectional study.

Primary and secondary antibody deficiencies (PAD and SAD) are amongst the most prevalent immunodeficiency syndromes, often necessitating long-term immune globulin replacement therapy (IRT). Both intravenous immunoglobulin (IVIG) and subcutaneous immunoglobulin (SCIG) have demonstrated efficacy in antibody deficiency. Comparative analyses of these two routes of administration are limited to nurse-administered IVIG and home therapy with self-administered SCIG. A third programmatic approach, SCIG administered by nurses in hospital-based outpatient infusion clinics, combines certain advantages of IVIG and home SCIG such as the provision of nursing support and the reduced risk of systemic reactions associated with the subcutaneous route. This cross-sectional study aimed to compare the viability and resource utilization of nurse-administered SCIG with IVIG in patients with antibody deficiency. We hypothesized that nurse-administered SCIG would require a similar amount of infusion clinic time per month as IVIG, despite more frequent dosing, due to shorter individual appointments, while maintaining high patient satisfaction. Information on infusion duration, time in the infusion chair, direct nursing time, and treatment satisfaction using the Life Quality Index was collected. Time measures for each patient were expressed as minutes/4 weeks to account for the more frequent dosing of nurse-administered SCIG compared to IVIG. We determined that the total time, infusion time, and nursing time needed to provide nurse-administered SCIG was comparable to IVIG. The more frequent dosing of SCIG was offset by the shorter times required per infusion. Patients reported favorable treatment satisfaction with both nurse-administered SCIG and IVIG. We conclude that nurse-administered SCIG may be a useful treatment modality for well-selected individuals.

Grass Weed Control and Rice Response with Tetflupyrolimet-containing Programs

ABSTRACT Tetflupyrolimet is the first herbicide with a novel site of action labeled preemergence (PRE) and early postemergence for use in agronomic crops in the last three decades. Direct-seeded paddy rice field experiments were conducted near Stuttgart on a silt loam soil, AR, and Keiser on a clay soil, AR, to evaluate tetflupyrolimet-containing herbicide programs in comparison to commercial standards in conventional, imidazolinone-, and quizalofop-resistant rice systems. Additionally, a furrow-irrigated rice experiment was conducted near Colt, AR, on a silt loam soil, and Keiser to ensure clomazone and tetflupyrolimet mixtures compared to commercial standards. Twelve commonly planted rice cultivars were also evaluated in response to a single PRE or postemergence (POST) (2- to 3-leaf rice) application of tetflupyrolimet at 200 and 400 g ai ha-1 in a paddy rice system, near Colt. When averaged over soil texture and site-year, all herbicide programs, provided ≥98% barnyardgrass control at 56 d after (DA) the last application. Visible rice injury varied for each rice system. Still, injury rarely differed amongst herbicide programs, except at a single evaluation timing in the conventional (7 DA 3- to 4-leaf applications) and quizalofop-resistant (preflood) systems. All 12 rice cultivars displayed a high tolerance level to a single PRE or POST application of tetflupyrolimet at 200 and 400 g ai ha-1. No visible injury, stand loss, or negative impact on rice maturity or reduced grain yield was observed for any cultivar. Tetflupyrolimet will be an effective alternative SOA in a program approach for barnyardgrass while maintaining excellent rice crop safety.

A mathematical programming approach for a wildfire suppression problem

A mathematical programming approach for a wildfire suppression problem

Implementasi Information Reminder System Menggunakan Data Sharing Pada Keluarga Berbasis Web

The adaptation of technology in an instantaneous era with all its sophistication makes individuals rely more on technology to help them in doing their daily work. Almost all aspects have been facilitated through the help of the system. However, it cannot be denied that there are still many families who record their monthly expenditure information needs manually, resulting in limited collaboration and effectiveness between members. Limited collaboration in the family can be overcome with a digital reminder system that can be implemented to help family members to remember information related to monthly expenses so that families can see, monitor and control their financial flow. The system can be implemented using React as the main framework and Firebase as a database service that is directly integrated with the data sharing model using the internet as a platform to manage and access data in a single-entry manner. In practice, the researcher applied field research (qualitative method) as the main method to obtain data. The researcher also chose the Personal Extreme Programming (PXP) approach as a method as well as a technique for measuring data in this research. The final solution of this research is producing a web-based family recording and reminder application. This application has features to record monthly spending information, schedule monthly and annual bills and record daily spending needs. Through a directly synchronized cloud service, users can access their accounts from any internet-connected device. The conclusion obtained from this research is that the development of this application can assist families in recording and reminding information directly related to monthly expenses in one website container that can be accessed on various devices with only an internet connection.

Development of Internal Quality Audit Information System with Personal Extreme Programming (PXP) Approach

Internal Quality Audit (IQA) is an important process to ensure the quality of implementation of activities in educational institutions in accordance with established standards. However, the implementation of IQA that is still carried out manually or semi-digitally can hamper the effectiveness, efficiency, and accuracy in data management and reporting of audit results. Therefore, an information system is needed that can support the audit process in a more systematic and integrated manner. This research examines the development of an internal quality audit system using the Personal Extreme Programming (PXP) approach that is carried out iteratively. The purpose of this study was to evaluate the effectiveness and efficiency of the system developed through four iterations, each of which focused on developing key features such as user management, document management, audit process, and assessment. The PXP methodology was applied to provide flexibility in responding to user feedback, with each iteration resulting in rapid improvements and adjustments according to emerging needs. The results showed that the developed system met the audit management objectives with high functionality, ease of use, and improved efficiency at each iteration. In addition, this iterative approach allows the system to be more responsive to changing user needs compared to conventional development methods. Overall, this research supports the use of PXP as an effective methodology in the development of internal quality audit systems, demonstrating that it can produce solutions that are adaptive, efficient, and suited to user needs.

Optimization of cropping patterns in the Bhimsagar canal irrigation scheme using linear programming approach

ABSTRACT Optimization techniques can be employed to determine the best cropping patterns to ensure optimum net benefits, food production, and labor employment. Therefore, the current investigation employed the linear programming module in LINGO 14.0 software to develop optimal crop area allocation plans in the Right Main Canal (RMC) of the Bhimsagar irrigation project. The optimal cropping patterns (OCPs) were generated for 2013–2014 under different canal run (CR) periods in a month, i.e., 30, 24, and 21 days using three objective functions, viz., net benefit, food production, and labor-days maximization. In addition, the OCP scenarios were generated for long-term agriculture sustainability scenarios, viz., 2015, 2020, and 2025. Results revealed that the net benefit was increased by 122.2, 93.4, and 95.5% for Ratanpura (RT), Chaplada (CP), and Maraita II (MT) minor, respectively, under OCP estimated for 30-day CR in 2013–2014, whereas food production was increased by 22.4, 33.8, and 25.9% for RT, CP, and MT, respectively, under OCP over existing cropping patterns. Similarly, under OCP, labor employment had increased by 40.9, 33.8, and 33.1% for RT, CP, and MT, respectively, for 30-day CR. These findings infer that higher net benefits, food production, and labor employment may be achieved by shifting to OCPs.

Accelerating Deep Learning-Based Morphological Biometric Recognition with Field-Programmable Gate Arrays

Convolutional neural networks (CNNs) are increasingly recognized as an important and potent artificial intelligence approach, widely employed in many computer vision applications, such as facial recognition. Their importance resides in their capacity to acquire hierarchical features, which is essential for recognizing complex patterns. Nevertheless, the intricate architectural design of CNNs leads to significant computing requirements. To tackle these issues, it is essential to construct a system based on field-programmable gate arrays (FPGAs) to speed up CNNs. FPGAs provide fast development capabilities, energy efficiency, decreased latency, and advanced reconfigurability. A facial recognition solution by leveraging deep learning and subsequently deploying it on an FPGA platform is suggested. The system detects whether a person has the necessary authorization to enter/access a place. The FPGA is responsible for processing this system with utmost security and without any internet connectivity. Various facial recognition networks are accomplished, including AlexNet, ResNet, and VGG-16 networks. The findings of the proposed method prove that the GoogLeNet network is the best fit due to its lower computational resource requirements, speed, and accuracy. The system was deployed on three hardware kits to appraise the performance of different programming approaches in terms of accuracy, latency, cost, and power consumption. The software programming on the Raspberry Pi-3B kit had a recognition accuracy of around 70–75% and relied on a stable internet connection for processing. This dependency on internet connectivity increases bandwidth consumption and fails to meet the required security criteria, contrary to ZYBO-Z7 board hardware programming. Nevertheless, the hardware/software co-design on the PYNQ-Z2 board achieved an accuracy rate of 85% to 87%. It operates independently of an internet connection, making it a standalone system and saving costs.

Automatic Hardware Pragma Insertion in High-Level Synthesis: A Non-Linear Programming Approach

Automatic Hardware Pragma Insertion in High-Level Synthesis: A Non-Linear Programming Approach

Optimal investment problem with multiple risky assets and correlation between risk model and financial market for an insurer under the CEV model

This article investigates the optimal investment problem with multiple risky assets and the correlation between risk model and financial market for an insurer. The insurer’s claim process is described by a Brownian motion with drift under the mean-variance premium principle. The insurer is allowed to invest in one risk-free asset and multiple risky assets whose price processes follow the constant elasticity of variance (CEV) model. Moreover, the correlation between the claim process and each risky asset’s price is taken into account. The insurer’s objective is to maximize the exponential utility of the terminal wealth. By applying dynamic programming approach, we propose a new form of the solution to the Hamilton-Jacobi-Bellman (HJB) equation and derive the optimal investment strategy explicitly. This is the first time that an explicit result of the optimal investment strategy is given under the framework of the exponential utility function for the general CEV model based on the correlation between the financial market and the insurance market. In addition, we provide some special cases of our model, i.e., the optimal investment problem with one risky asset and that with two risky assets. We also consider the case that the risk model and the financial market is independent. The results show that ignoring the correlation between the claim process and the risky asset’s price will misestimate the value of risky assets and has a significant effect on the insurer’s investment decision. Finally, numerical simulations are presented to analyze the effects of model parameters on the optimal investment strategy.

CSI-GEP: A GPU-based unsupervised machine learning approach for recovering gene expression programs in atlas-scale single-cell RNA-seq data.

Exploratory analysis of single-cell RNA sequencing (scRNA-seq) typically relies on hard clustering over two-dimensional projections like uniform manifold approximation and projection (UMAP). However, such methods can severely distort the data and have many arbitrary parameter choices. Methods that can model scRNA-seq data as non-discrete "gene expression programs" (GEPs) can better preserve the data's structure, but currently, they are often not scalable, not consistent across repeated runs, and lack an established method for choosing key parameters. Here, we developed a GPU-based unsupervised learning approach, "consensus and scalable inference of gene expression programs" (CSI-GEP). We show that CSI-GEP can recover ground truth GEPs in real and simulated atlas-scale scRNA-seq datasets, significantly outperforming cutting-edge methods, including GPT-based neural networks. We applied CSI-GEP to a whole mouse brain atlas of 2.2 million cells, disentangling endothelial cell types missed by other methods, and to an integrated scRNA-seq atlas of human tumors and cell lines, discovering mesenchymal-like GEPs unique to cancer cells growing in culture.