Cost Model Research Articles

Budget-aware Query Tuning: An AutoML Perspective

Modern database systems rely on cost-based query optimizers to come up with good execution plans for input queries. Such query optimizers rely on cost models to estimate the costs of candidate query execution plans. A cost model represents a function from a set of cost units to query execution cost, where each cost unit specifies the unit cost of executing a certain type of query processing operation (such as table scan or join). These cost units are traditionally viewed as constants, whose values only depend on the platform configuration where the database system runs on top of but are invariant for queries processed by the database system. In this paper, we challenge this classic view by thinking of these cost units as variables instead. We show that, by varying the cost-unit values one can obtain query plans that significantly outperform the default query plans returned by the query optimizer when viewing the cost units as constants. We term this cost-unit tuning process 'query tuning' (QT) and show that it is similar to the well-known hyper-parameter optimization (HPO) problem in AutoML. As a result, any state-of-the-art HPO technologies can be applied to QT. We study the QT problem in the context of anytime tuning, which is desirable in practice by constraining the total time spent on QT within a given budget-we call this problem budgetaware query tuning. We further extend our study from tuning a single query to tuning a workload with multiple queries, and we call this generalized problem budgetaware workload tuning (WT), which aims for minimizing the execution time of the entire workload. WT is more challenging as one needs to further prioritize individual query tuning within the given time budget. We propose solutions to both QT and WT and experimental evaluation using both benchmark and real workloads demonstrates the efficacy of our proposed solutions.

NHPP Software Reliability Model with Rayleigh Fault Detection Rate and Optimal Release Time for Operating Environment Uncertainty

Software is used in diverse settings and depends on development and testing environments. Software development should improve the reliability, quality, cost, and stability of software, making the software testing period crucial. We proposed a software reliability model (SRM) that considers the uncertainty of software environments and the fault detection rate function as a Rayleigh distribution, with an explicit mean value function solution in the model. The goodness-of-fit of the proposed model relative to several existing nonhomogeneous Poisson process (NHPP) SRMs is presented based on three software application failure datasets. Further, a cost model is also presented that addresses the error-removal risk level and required time. The optimal testing release policy for minimizing the expected total cost (ETC) is also determined for NHPP SRMs. The impact of the software environment is studied by varying it, and the optimal release times and minimum ETCs are compared. The goodness-of-fit comparison confirmed that the proposed model has more accurate prediction values than other models. Further, whereas the existing models applied to the cost model do not change after a certain operation period, the proposed model yields changes in release time even for long operating periods.

Two-Stage Locating and Capacity Optimization Model for the Ultra-High-Voltage DC Receiving End Considering Carbon Emission Trading and Renewable Energy Time-Series Output Reconstruction

With the load center’s continuous expansion and development of the AC power grid’s scale and construction, the recipient grid under the multi-feed DC environment is facing severe challenges of DC commutation failure and bipolar blocking due to the high strength of AC-DC coupling and the low level of system inertia, which brings many complexities and uncertainties to economic scheduling. In addition, the large-scale grid integration of wind power, photovoltaic, and other intermittent energy sources makes the ultra-high-voltage (UHV) DC channel operation state randomized. The deterministic scenario-based timing power simulation is no longer suitable for the current complex and changeable grid operation state. In this paper, we first start with the description and analysis of the uncertainty in renewable energy (RE) sources, such as wind and solar, and reconstruct the time-sequence power model by using the stochastic differential equation model. Then, a carbon emission trading cost (CET) model is constructed based on the CET mechanism, and the two-stage locating and capacity optimization model for the UHV DC receiving end is proposed under the constraint of dispatch safety and stability. Among them, the first stage starts with the objective of maximizing the carrying capacity of the UHV DC receiving end grid; the second stage checks its dynamic safety under the basic and fault modes according to the results of the first stage and corrects the drop point and capacity of the UHV DC line with the objective of achieving safe and stable UHV DC operation at the lowest economic investment. In addition, the two-stage model innovatively proposes UHV DC relative inertia constraints, peak adjustment margin constraints, transient voltage support constraints under commutation failure conditions, and frequency support constraints under a DC blocking state. In addition, to address the problem that the probabilistic constraints of the scheduling model are difficult to solve, the discrete step-size transformation and convolution sequence operation methods are proposed to transform the chance-constrained planning into mixed-integer linear planning for solving. Finally, the proposed model is validated with a UHV DC channel in 2023, and the results confirm the feasibility and effectiveness of the model.

ENKIE: A package for predicting enzyme kinetic parameter values and their uncertainties.

Relating metabolite and enzyme abundances to metabolic fluxes requires reaction kinetics, core elements of dynamic and enzyme cost models. However, kinetic parameters have been measured only for a fraction of all known enzymes, and the reliability of the available values is unknown. The ENzyme KInetics Estimator (ENKIE) uses Bayesian Multilevel Models to predict value and uncertainty of KM and kcat parameters. Our models use five categorical predictors and achieve prediction performances comparable to deep learning approaches that use sequence and structure information. They provide calibrated uncertainty predictions and interpretable insights into the main sources of uncertainty. We expect our tool to simplify the construction of priors for Bayesian kinetic models of metabolism. Code and Python package are available at https://gitlab.com/csb.ethz/enkie and https://pypi.org/project/enkie/. Supplementary data are available at Bioinformatics online.

Cost modeling for the GWh-scale production of modern lithium-ion battery cells

Battery production cost models are critical for evaluating the cost competitiveness of different cell geometries, chemistries, and production processes. To address this need, we present a detailed bottom-up approach for calculating the full cost, marginal cost, and levelized cost of various battery production methods. Our approach ensures comparability across research fields and industries, reflecting capital and imputed interest costs. We showcase the model with case studies of a prismatic PHEV2 hardcase cell and a cylindrical 4680 cell in four different chemistries. Our publicly available browser-based modular tool incorporates up-to-date parameters derived from literature and expert interviews. This work enables researchers to quickly assess the production cost implications of new battery production processes and technologies, ultimately advancing the goal of reducing the cost of electrified mobility.

Budget-Aware Local Influence Iterative Algorithm for Efficient Influence Maximization in Social Networks

The budgeted influence maximization (BIM) problem aims to identify a set of seed nodes that adhere to predefined budget constraints within a specified network structure and cost model. However, it is difficult for the existing algorithms to achieve a balance between timeliness and effectiveness. To address this challenge, our study initially proposes a refined cost model through empirical scrutiny of Weibo's quote data. Subsequently, we introduce a proxy-based algorithm, i.e., the budget-aware local influence iterative (BLII) algorithm tailored for the BIM problem, aimed at expediently identifying seed nodes. The algorithm approximates the global influence by leveraging the user’s one-hop influence and circumvents influence overlap among seed nodes via iterative influence updates. Comparative experiments involving eight algorithms across four real networks demonstrate the effectiveness, efficiency, and robustness of the BLII algorithm. In terms of influence spread, the proposed algorithm outperforms other proxy-based algorithms by 20%–255% and reaches the state-of-the-art simulation-based approach by 96%. In addition, the running time of the BLII algorithm is reasonable. Generally, the proposed cost model and BLII algorithm provide novel insights and potent tools for studying BIM problems.

Cost and Efficiency analysis of the Secondary electric machine in a CRAFT wind turbine

Abstract Floating offshore wind turbines are ideal for deeper waters, providing access to stronger and more stable winds. The Counter-Rotating Axis Floating Tilting (CRAFT) turbine features a unique design with two counter-rotating turbines on a tilted vertical shaft and two independent electrical machines submerged below sea level. The primary generator, connected to both turbines, includes counter-rotation which doubles the relative torque, while the secondary machine controls the upper turbine. This study examines the impact of primary and secondary machine efficiency on electricity generation. The findings indicate that the primary generator’s efficiency is crucial for system stability, whereas the secondary machine’s efficiency is less critical. Reducing the secondary machine’s efficiency from 97% to 83% resulted in a 0.1% reduction in annual electricity generation. Despite the asynchronous machine’s lower efficiency, it is the economically favorable choice as the secondary machine over its synchronous counterpart due to its reduced design complexity and lower magnet costs, leading to lower overall expenses. Future research investigate how turbulent flow effects and airflow interactions between the turbines influences the model. Incorporating the cooling factor, a more comprehensive cost model and a refined dynamic stall model will also further improve the simulation’s accuracy and robustness.

Hidden costs of first choice alternatives: A financial model of thromboprophylaxis and prosthetic joint infection prophylaxis in total knee arthroplasty

BackgroundAspirin has been shown to be equally or more effective than factor Xa inhibitors for thromboprophylaxis following total knee arthroplasty (TKA). Cefazolin has been proven more effective than vancomycin in preventing prosthetic joint infection (PJI) after TKA. This study aimed to compare costs between different drug combinations for prevention of venous thromboembolism (VTE) and PJI following TKA, focusing on costs associated with PJI management. MethodsWe used published PJI rates for TKA patients treated with aspirin or factor Xa inhibitors for thromboprophylaxis, as well as for those who received prophylactic cefazolin or vancomycin. Unit prices for each drug and labor costs associated with vancomycin administration were obtained from our hospital's pharmacy service. The PJI cost model included the price of 2-stage septic TKA revision and national projections of future TKA volume. ResultsThe least expensive average per-patient cost resulted from the combination of aspirin and cefazolin, equating to $521.19 given a 0.8 % PJI rate. The most expensive average per-patient cost was the combination of a factor Xa inhibitor and vancomycin, equaling $5,714.96 given a 1.8 % PJI rate. This extrapolates to an annual cost burden of $19.5 billion by 2040. ConclusionThe average per-patient cost of using a combination of a factor Xa inhibitor and vancomycin is 711 % greater than the combination of aspirin and cefazolin. In this era of value-based care, aspirin and cefazolin should be considered gold standards for TKA thromboprophylaxis and PJI prophylaxis, as they reduce costs and improve patient outcomes.

On-Farm Production of Renewable Energy in 2014–2022

The main purpose of this study is to present family farms as consumers and producers of renewable energies which provide them with an opportunity to reduce operating costs. The time scope of the study is 2014–2022, and the Farm Accountancy Data Network is used as the data source. The following research methods were employed: comparative and descriptive analysis, intensity indicators, ranking assignment and panel regression. Based on the values of energy output and energy costs, the rankings revealed a strong position of the Netherlands and Germany. As demonstrated by the study, energy production and consumption volumes depend on the farms’ economic size, but are not impacted by production type. Another finding is that energy production covers only one-third of its costs. Also, both production volumes and costs were on a growth path on a year-over-year basis, with similar growth ratios. The European Union’s leaders in energy consumption and production are the Netherlands, Germany, Belgium, Denmark, Czech Republic, Hungary, Luxembourg, Slovakia and Sweden. The study included the structuring of panel models for energy output and costs and identified their determinants. Energy output depends on total inputs and grows as they grow. Energy costs, in turn, are related to utilized agricultural area, total output and family farm income. An important limitation of this study is that FADN is a provider of high-level data. Hence, it is impossible to tell what specific sources of renewable energy are used by farms, and how they are affected by such exogenous factors as climate, earmarked subsidies or energy policy. The findings from this study are discussed in the context of the European Commission’s recommendations laid down in the Bioeconomy Strategy of the EU (2013), the Seventh Environment Action Program, the New Innovation Agenda of the European Union, the Report “Transforming Our World: the United Nations 2030 Agenda for Sustainable Development” and the Circular Economy Action Plan.

Structure Equation Model for Costs and Benefits of Distribution Pipe Rehabilitation in Reactive Maintenance

Some water distribution pipe networks were constructed several decades ago and have been damaged. The old pipe network has experienced many leaks and should be rehabilitated. In old cities like Jakarta, with high leak rates (1.6 leaks/km/year), pipeline network managers must frequently repair leaks or perform reactive maintenance. Far too many pipes need to be rehabilitated compared to the available budget. This research will develop an optimization model for selecting rehabilitated pipes under reactive maintenance conditions, which has never been performed before. The selection of pipe segments to be rehabilitated must be optimal to obtain maximum benefits using minimum costs. The variables that influence rehabilitation costs and benefits and the relationship between these variables need to be known to optimize the selection. The influencing variables and their model structure were obtained from literature reviews and surveys of respondents with sufficient experience in water pipe network management. These influential variables have varying characteristics. Data processing and analysis uses the confirmatory factor analysis (CFA) method with a formative–formative measurement model. In total, 22 variables were found to be valid and significantly influenced the cost and benefits of rehabilitation. The analysis results are a structural model of the relationship between variables that influence the costs and benefits of rehabilitation in reactive maintenance with a limited budget, which can be used to optimize rehabilitation models.

Decision-analytic modelling of costs & benefits of adding sigmoidoscopy to CRC screening in Germany

Abstract Background In Germany, a colorectal cancer (CRC) screening programme is in place that entitles individuals to a stool test or colonoscopy. However, less than half of those eligible participate. The inclusion of an additional screening option, such as the evidence-based sigmoidoscopy, could lead to a higher overall participation rate, but also to individuals preferring the additional option to the more effective colonoscopy. The objective of this modelling study is to estimate the impact of offering sigmoidoscopy as an additional option for CRC screening in Germany. Methods A life-time mathematical model (Markov model) is developed to analyse the long-term effects of the additional offer of sigmoidoscopy on benefits (prevalence, mortality, health-related quality of life (HRQoL)), costs, and cost-effectiveness. The model is based on systematic literature reviews and real-world healthcare data. It incorporates disease development, sensitivity and specificity of the options, compliance rates, survival probabilities and background mortality. A discrete choice experiment (DCE) was conducted to estimate the future uptake of the options (stool test, colonoscopy, sigmoidoscopy, no screening) based on stated preferences of n = 666 individuals. Results Adding sigmoidoscopy leads to greater effectiveness of the CRC screening programme in terms of increased screening participation, increased adenoma detection, decreased disease-specific mortality, and improved discounted HRQoL. However, it also incurs higher costs. The incremental cost-effectiveness ratios (ICER) for men and women are €507 per quality-adjusted life year gained (QALYg) and €74 per QALYg, respectively. Conclusions The results indicate that the inclusion of sigmoidoscopy in the German CRC screening programme would be cost-effective and that sigmoidoscopy should be piloted in practice. The expanded programme has the potential to enhance the program’s effectiveness, improve health outcomes, and reduce the burden of CRC. Key messages • Expanding the CRC screening programme may increase overall participation, resulting in higher costs, but also greater effectiveness in detecting more adenomas, reducing mortality, improving HRQoL. • Introducing sigmoidoscopy in Germany appears cost-effective and justifies practical piloting to enhance CRC screening efficacy and health outcomes.

Analysis of the Impact of PP&E Two Subsequent Measurement Models on Enterprise Financial Reporting and Financial Performance

Abstract: Measurement based on PP&E has been controversial in accounting in recent years. This paper analyzes the contributions and risks of two subsequent measurement models of PP&E to the financial reporting and performance of enterprises. It also explores the relationship between the two models. This study aims to provide a reference for companies in the selection of subsequent measurement model of PP&E. According to previous research, the cost model provides more timely and understandable information for financial reporting and reduces the cost of preparing reports. It also improves the objectivity and verifiability of financial performance. However, the cost model may not show the true value of a company's PP&E. The revaluation model provides more relevant information for financial reporting and reduces information asymmetry. It also positively affects the company's future financial performance. However, revaluation models are highly subjective, susceptible to bias manipulation, and can lead to misleading information due to value reversal. The relationship between the two models can be explained as a slow evolution from the cost model to the revaluation model, but not a complete replacement.

A comprehensive quantitative lifecycle cost and environmental impact analysis model for computing infrastructure

This paper presents a comprehensive quantitative model for quantitative assessment of the lifecycle costs and environmental impacts of computing infrastructure, with a focus on internet data centers (IDCs) and high-performance computing (HPC) facilities. The key innovation lies in the integration of interdisciplinary cost evaluation and carbon emission methods for the establishment of this quantitative model. This framework, which outlines key cost components and carbon emission factors, enables the calculation of total costs, electricity expenses, and greenhouse gas emissions throughout the lifecycle of infrastructure. With IDCs as a case study, the research clarifies the intricate cost structure associated with equipment procurement, energy usage, land acquisition, and operational expenses. This paper provides an in-depth understanding of the cost structure and environmental impact of computing infrastructure in support of sustainable decision-making in its development.•Based on established cost estimation methods, such as Lifecycle Cost Analysis (LCCA) and the Analogous Estimating Method, this study examines costs across construction and operation phases.•The Emission Factor Method is used to quantify environmental impact, emphasizing the significance of regional energy mix and power usage effectiveness (PUE).

Cost-Effectiveness of Treating Hepatitis C in Clients on Opioid Agonist Therapy in Community Pharmacies Compared to Primary Healthcare in Australia.

Meeting the World Health Organisation 2030 target of treating 80% of people with hepatitis C virus (HCV) in Australia requires accessible testing and treatment services for at-risk populations. Previous clinical trials, including those in Australia, have demonstrated the efficacy of outreach programmes to community pharmacies offering opioid agonist therapy (OAT). This analysis evaluates the potential cost-effectiveness of introducing an outreach programme in community pharmacies. Using a decision analytic model, we estimated the impact of adding a temporary hepatitis C outreach and treatment programme in community pharmacies to the standard treatment pathway available through general practice. We compared the expected number of tests, diagnoses, cures and costs occurring through the addition of this outreach and treatment programme to those expected through general practice alone over a 12-month time horizon. We examined costs from the perspective of the health system and conducted one-way and probabilistic sensitivity analyses to assess uncertainty in model parameters and test key assumptions. In the model adding the outreach programme pathway increased the number of tests from 4178 to 8737, the number of diagnoses from 615 to 1285 and the number of cures from 223 to 777 among people on OAT over a 12-month period. Each additional cure achieved through the addition of the outreach programme was estimated to incur $48,964 (AUD 2023) to the health system, with > 85% of these costs attributable to medication and dispensing expenses. The average cost per cure was estimated to be $49,152 through routine care and $49,018 in the outreach programme. Although outreach models of care incur large upfront costs, they can capture otherwise unreached populations and result in comparable or favourable cost per cure, due to higher levels of engagement and lower rates of loss to follow-up.

Platelet-rich plasma applications in the larynx: snake oil or stupendous?

To explore and summarize recent work examining the use of platelet-rich plasma (PRP) applications for vocal fold disorder. Intrachordal PRP injections have recently emerged as a treatment option for patients with damage to the lamina propria of the vocal fold (scar, atrophy, sulcus and inflammatory lesions). Studies support significant improvements in patient-reported and clinician-reported outcomes, and objective phonatory parameters. PRP demonstrates a good safety profile, absence of immune reactions, affordable cost model and improvement in mucosal wave features with resultant improved voice quality up to 12 months following treatment. PRP injection of the vocal folds is a novel repurposing of a previously validated technique, and emerging data shows consistent results across multiple centres, increasing confidence in use of this freely available material. Current published studies confirm improved voice outcomes compared with pretreatment measures and high patient satisfaction. Further research is required to properly assign the role of and ideal candidate for PRP applications, and to delineate durability of treatment.

Revolutionizing Textile Manufacturing: Sustainable and Profitable Production by Integrating Industry 4.0, Activity-Based Costing, and the Theory of Constraints

The textile industry, a cornerstone of daily life and a highly globalized sector, faces significant environmental challenges due to its high water and energy consumption and extensive chemical usage. This study proposes a comprehensive green production planning and control model integrating Industry 4.0 concepts, activity-based costing (ABC), and the theory of constraints (TOC). The model utilizes mathematical programming to optimize product mix, maximize profitability, and minimize environmental impact. It leverages real-time sensing technologies and ERP systems to facilitate waste recovery, reduce carbon emissions, and achieve energy savings. Various carbon emission cost models, including continuous and discontinuous tax functions, are explored to balance corporate profitability with environmental sustainability. The findings demonstrate the model’s potential in optimizing resource utilization, reducing the environmental footprint, and enhancing profitability.

Mitigating Medical Adverse Events Following Spinal Surgery: The Effectiveness of a Postoperative Quality Improvement (QI) Care Bundle.

Spine surgery is associated with a high incidence of postoperative medical adverse events (AEs). Many of these events are considered "minor" though their cost and effect on outcome may be underestimated. We sought to examine the clinical and cost-effectiveness of a postoperative quality improvement (QI) care bundle in mitigating postoperative medical AEs in adult surgical spine patients. We collected 14-year prospective observational interrupted time series (ITS) with two historical cohorts: 2006 to 2008, pre-implementation of the postoperative QI care bundle; and 2009 to 2019, post-implementation of the postoperative QI care bundle. Adverse Events were identified and graded (Minor I and II) using the previously validated Spine AdVerse Events Severity (SAVES) system. Pearson Correlation tested for changes across patient and surgical variables. Adjusted segmented regression estimated the effect of the postoperative QI care bundle on the annual and absolute incidences of medical AEs between the two periods. A cost model estimated the annual cumulative cost savings through preventing these "minor" medical AEs. We included 13,493 patients over the study period with a mean of 964 per year (SD±73). Mean age, mean Charlson Comorbidity Index (CCI), and mean spine surgical invasiveness index (SSII) increased from 48.4 to 58.1years; 1.7 to 2.6; and 15.4 to 20.5, respectively (p<0.001). Unadjusted analysis confirmed a significant decrease in the annual number of all medical AEs (p<0.01). When adjusting for age, CCI and SSII, segmented regression demonstrated a significant absolute reduction in the annual incidence of cardiac, pulmonary, nausea and medication-related AEs by 9.58%, 7.82%, 11.25% and 15.01%, respectively (p<0.01). The postoperative QI care bundle was not associated with reducing the annual incidence of delirium, electrolyte levels or GI AEs. Annual projected cost savings for preventing Grade I and II medical AEs were $1,808,300 CAD and $11,961,500 CAD. Postoperative QI care bundles are effective for improving patient care and preventing medical care-related AEs, with significant cost savings. Postoperative QI care bundles should be tailored to the specific vulnerability of the surgical population for experiencing AEs.

A soft-linking method for responsive modelling of decarbonisation scenario costs

A soft-linking method for responsive modelling of decarbonisation scenario costs

The costs and the potential allocation of costs of bouillon fortification: The cases of Nigeria, Senegal, and Burkina Faso.

Food and condiment fortification programs are needed to address micronutrient deficiencies and their health, developmental, and mortality consequences; but these programs are never free. Knowing program costs and their allocation across stakeholders is essential to design and manage effective, efficient, and sustainable programs. We developed 10-year hypothetical bouillon fortification program cost models for Nigeria, Senegal, and Burkina Faso that included start-up and operational costs for government and industry, as well as premix costs generated by an embedded premix cost calculator to allow for alternative premix formulas in cost calculations. The main drivers of total costs were total bouillon consumption and the types and amounts of fortificants in the micronutrient premix. For a premix that meets 30% of Codex Nutrient Reference Values in 2.5g of bouillon for vitamin A, folate, vitamin B12, zinc, and iron, the cost per metric ton of fortified bouillon was ∼$325 for all countries (∼$0.01 per 2.5g serving). Annual start-up costs ranged from ∼$324k (Burkina Faso) to ∼$455k (Nigeria); nonpremix annual operating costs ranged from ∼$108k (Burkina Faso) to ∼$3.9m (Nigeria); and annual premix costs varied from ∼$2.4m (Burkina Faso) to ∼$76m (Nigeria). In policy discussions, program costs should be set alongside nutritional benefits.

A data-driven minimum cost consensus model for group decision making with personality traits prediction

The minimum cost consensus model (MCCM) proposes an effective method for reaching group consensus in group decision-making problems. Conventional MCCM and its advanced models focus on the different behaviors and psychologies of decision-makers, but, it ignores the heterogeneity of decision-makers that activated them. Therefore, they need to assume the compromise limits and unit adjustment costs of decision-makers, which may be difficult to achieve in practice. To resolve this problem, this study will propose a novel data-driven minimum cost consensus model of different compromise limits and unit costs based on online Big Five personality traits prediction. First, this study uses the Convolutional Neural Network (CNN) and Bi-directional Long-Short Term Memory model (BiLSTM) to obtain the decision-maker's probability of agreeableness based on their Weibo online reviews. Second, a novel minimum cost consensus model considering the decision-maker's personality traits (MCCM-P) is established. To do that, the unit adjustment cost and the personalized compromise limits of decision-makers and their interrelations are defined based on the personality traits prediction. Finally, the MCCM-P is applied in a real group decision-making case study of a university student club activity selection. The result and comparative analysis show that the proposed MCCM model can obtain lower consensus reaching costs than the traditional method.