Cost Estimation Research Articles

Enhancing the profitability of pay what you want: A study of suggested prices

AbstractA suggested price is oftentimes utilized in practice to enhance the profitability of pay what you want (PWYW). In this paper, we focus on this suggested price strategy. We conduct five experiments to explore factors that impact buyers' payments after seeing the suggested price. In Study 1, we show a relationship between the suggested price and payments. Intuitively, a higher suggested price results in increased payments. Studies 2A and 2B demonstrate that the relationship between the suggested price and payments is mediated by cost estimation, while Studies 3A and 3B display that the relationship is moderated by the presence of a charitable element. Interestingly, in the case with a charitable element, increasing the suggested price does not stimulate an increment in payments on a par with the case without a charitable element. This paper proposes that sellers who use PWYW could employ a suggested price to furnish buyers with pertinent cost information, but utilizing PWYW with a suggested price may not be suitable for products with a charitable component.

Modeling Shifting Workloads for Learned Database Systems

Learned database systems address several weaknesses of traditional cost estimation techniques in query optimization: they learn a model of a database instance, e.g., as queries are executed. However, when the database instance has skew and correlation, it is nontrivial to create an effective training set that anticipates workload shifts, where query structure changes and/or different regions of the data contribute to query answers. Our predictive model may perform poorly with these out-of-distribution inputs. In this paper, we study how the notion of a replay buffer can be managed through online algorithms to build a concise yet representative model of the workload distribution --- allowing for rapid adaptation and effective prediction of cardinalities and costs. We experimentally validate our methods over several data domains.

An Analytical Exploration of Simulated Intelligence Based Approach in Global Prospects

It is the science and planning of making quick machines, especially shrewd PC programs. It is associated with the relative endeavour of using laptops to get a handle on human knowledge, yet man-made reasoning doesn't have to confine itself to methods that are normally perceivable. Hence, in this paper, we aim to infer the intellectual development of AI and ML in finance research, adopting a scoping review combined with an embedded review to pursue and scrutinize the services of these concepts. While no consensual significance of Mechanized thinking (mimicked knowledge) exists, recreated insight is widely depicted as the examination of computations that consider understanding, reason and movement. Today, how much data that is delivered, by individuals and machines, far overwhelms individuals' ability to hold, unravel, and go with complex decisions considering that data. This paper investigates components of man-made thinking, show, implications of computerized reasoning, history, applications, advancement and achievements. The field of simulated intelligence (ML) is satisfactorily energetic that it is at this point stretching out at an accelerating pace, lying at the intersection of programming and experiences, and at the focal point of modernized thinking (man-made consciousness) and data science. Late progression in ML has been driven both by the improvement of new learning computations speculation, and by the persistent shoot in the availability of huge proportion of data (habitually implied as "enormous data") and negligible cost estimation. The gathering of ML-based approaches can be tracked down all through science, advancement and industry, inciting more confirmation based powerful across various foundations, including clinical benefits, biomedicine, creating, guidance, financial illustrating, data organization, policing, and advancing. As a rule, we entrust that this work will help with getting a handle on the geo examples of ML moves close and their congruity in various real spaces, as well as go about as a sort of viewpoint point for both academic local area and industry specialists, particularly from a specific, moral and managerial point of view. Key Word: Artificial intelligence, Man-made thinking, Investigation designs, Remedial administrations, Data organization

Dimensionality reduction and machine learning based model of software cost estimation

Software Cost Estimation (SCE) is one of the research priorities and challenges in the construction of cyber-physical-social systems (CPSSs). In CPSS, it is urge to process environmental and social information accurately and use it to guide social practice. Thus, in response to the problems of low prediction accuracy, poor robustness, and poor interpretability in SCE, this paper proposes a SCE model based on Autoencoder and Random Forest. First, preprocess the project data, remove outliers, and build regression trees to fill in missing attributes in the data. Second, construct a Autoencoder to reduce the dimensionality of factors that affect software cost. Subsequently, the performance of the model was trained and validated using the XGBoost framework on three datasets: COCOMO81, Albrecht, and Desharnais, and compared with common cost prediction models. The experimental results show that the MMRE, MdMRE, and PRED (0.25) values of the proposed model on the COCOMO81 dataset reached 0.21, 0.16, and 0.71, respectively. Compared with other models, the proposed model achieved significant improvements in accuracy and robustness.

A Policy Gradient Algorithm for the Risk-Sensitive Exponential Cost MDP

We study the risk-sensitive exponential cost Markov decision process (MDP) formulation and develop a trajectory-based gradient algorithm to find the stationary point of the cost associated with a set of parameterized policies. We derive a formula that can be used to compute the policy gradient from (state, action, cost) information collected from sample paths of the MDP for each fixed parameterized policy. Unlike the traditional average cost problem, standard stochastic approximation theory cannot be used to exploit this formula. To address the issue, we introduce a truncated and smooth version of the risk-sensitive cost and show that this new cost criterion can be used to approximate the risk-sensitive cost and its gradient uniformly under some mild assumptions. We then develop a trajectory-based gradient algorithm to minimize the smooth truncated estimation of the risk-sensitive cost and derive conditions under which a sequence of truncations can be used to solve the original, untruncated cost problem. Funding: This work was supported by the Office of Naval Research Global [Grant N0001419-1-2566], the Division of Computer and Network Systems [Grant 21-06801], the Army Research Office [Grant W911NF-19-1-0379], and the Division of Computing and Communication Foundations [Grants 17-04970 and 19-34986].

Comparison of the composition and cost estimation of steam distillation and supercritical fluid extraction from leaves of Lippia graveolens

Lippia graveolens, mostly known as Mexican oregano, is an aromatic plant with great economic im-portance. However, very little is known about the reasons that could explain the variation observed with respect to the yield and composition of the essential oil obtained. The effect of steam distillation and supercritical fluid extraction processes on composition, yield and economic viability were evaluated. The essential oil was obtained by steam distillation, with a laboratory scale equipment and supercritical extraction equipment with a 1L capacity cell. The extracts were analyzed by GC–MS. where Carvacrol was the majority compound in both processes. The essential oil is obtained by steam distillation, with laboratory scale equipment and supercritical extraction equipment with a 1L capacity cell. In the case of steam distillation, a maximum yield of 4.1% on a dry weight basis was obtained, while the supercritical fluid extraction obtained provided a maximum yield of 2.8% on a dry weight basis at the same bed porosity (0.91). The costs that were obtained in a period of one year through. The process of extraction by steam distillation showed good results to obtained essential oil from L. graveolens. where it is observed that steam distillation is still the best option.

Interrelations of the Factors Influencing the Whole-Life Cost Estimation of Buildings: A Systematic Literature Review

The global GDP has witnessed a significant upswing, majorly due to the growth of the construction industry. Embracing the whole-life costing (WLC) approach, the construction sector strategically manages expenses across a construction project’s life cycle. However, despite its widespread adoption, accurate cost forecasting remains a major challenge. The intricate interplay of various influencing factors has not been fully explored, leading to inaccurate cost estimations. A comprehensive understanding of specific factors and their interrelationships is crucial to address this issue. Therefore, it is imperative to conduct further research to identify and explore the subtle nuances of these factors that impact whole-life cost estimation. Our study fills this gap, analysing 51 factors from 84 papers across prominent repositories. We assess interrelationships using a systematic literature review and pairwise comparison as in the analytical hierarchy process. The International Construction Measurement Standards (ICMS) framework structures these relationships and is represented in the causal loop diagrams (CLDs). The pioneering CLDs are a notable contribution, illustrating interrelationships and polarities among the 51 WLC factors. Six reinforcing loops and one balancing loop provide valuable insights into their dynamic nature. Importantly, lower-level factors do not always directly connect with upper-level factors. Instead, they interact within the same level before linking to top-level factors. These findings are significant for professionals, such as cost estimators, quantity surveyors and scholars, offering a comprehensive understanding of the WLC system.

Market-based Estimation of Average Electricity Outage Costs in the United States

Electricity outage cost (EOC) estimates ($ per kWh unserved) are essential input data for optimal reliability planning and efficient pricing of electricity services. Based on the 2019-2020 market data published by two US government agencies for the lower 48 states, this paper’s EOC estimates by census region and year are median values of $1.39 to $2.93 per kWh unserved, well below the estimate of $9 per kWh unserved adopted by Texas for optimal reliability planning. The policy implications of adopting our lower EOC estimate are (a) a reduction in an electric grid’s optimal planning reserve to improve the grid’s cost efficiency; and (b) a decline in the grid’s marginal cost-based retail price to encourage welfare-enhancing end-use consumption.

Assessing Reliability and Economic Viability of Different EV Charging Station Configurations

With the increasing popularity of electric vehicles (EVs) as a mode of transportation, companies are prioritizing the development of charging infrastructure to cater to customer needs. Despite efforts to align charging station designs with distribution system requirements, maintaining reliability for EV charging ports remains challenging. To enhance reliability, a novel 56-ported design incorporating both uniform and non-uniform port arrangements has been proposed. These configurations have been tested with distribution systems ranging from 50 to 500 kW. Reliability assessments were conducted using standards outlined failure rate estimation and monte-carlo functions for evaluating port probability functions in terms of failure rate and reliability. By analyzing the failure rates of individual ports, an evaluation process was introduced to determine the overall success rate of the charging station. The failure rate of the proposed 56-ported charging station was further evaluated using the binomial distribution method. Additionally, cost estimation procedures were implemented considering the failure and success rates of individual port maintenance for the proposed configuration. The findings indicate that achieving lower failure rates and maintenance costs is possible through improved port arrangement reliability and enhanced voltage stability.

Photo-thermal activation of persulfate for the efficient degradation of synthetic and real industrial wastewaters: System optimization and cost estimation

The photo-thermal activation of persulfate (PS) was carried out to degrade various pollutants such as reactive blue-222 (RB-222) dye, sulfamethazine, and atrazine. Optimizing the operating parameters showed that using 0.90 g/L of PS at pH 7, temperature of 90 °C, initial dye concentration of 21.60 mg/L, and reaction time of 120 min could attain a removal efficiency of 99.30%. The degradation mechanism was explored indicating that hydroxyl and sulfate radicals were the prevailing reactive species. The degradation percentages of 10 mg/L of sulfamethazine and atrazine were 83.30% and 70.60%, respectively, whereas the mineralization ratio was 63.50% in the case of real textile wastewater under the optimal conditions at a reaction time of 120 min. The treatment cost per 1 m3 of real wastewater was appraised to be 1.13 $/m3 which assured the inexpensiveness of the proposed treatment system. This study presents an effective and low-cost treatment system that can be implemented on an industrial scale.

Nutrient Removal from Industrial Wastewater Systems Using Controlled Activated Sludge Treatment

Biological treatment with activated sludge as an alternative treatment was studied, and nitrogen and phosphorus were monitored. This scheme involves modifying the treatment tank by dividing it into anoxic and aerobic sections. According to this scheme, the process simulation was performed using Hydromantis' GPS-X 7.0 computer software. Wastewater discharges are usually generated by various industrial activities such as the milk (dairy) processing industry, petroleum processing refineries and slaughterhouses, which were studied in detail. The economic evaluation (cost estimation) of removing nutrients has been done using the CapdetWorks 4.0 computer simulation software. A cost sensitivity analysis has also been performed for the variable influent flows, altering the alpha factor for oxygen transfer during the aeration process and serving as a typical variable. The simulation procedure and economic evaluation have been carried out for a complete wastewater treatment plant, including both treatment lines (the water line and the sludge line simultaneously), and the derived results have been represented through illustrated graphs, tables, and meaningful diagrams.

An innovative approach for equitable urban green space allocation through population demand and accessibility modeling

Urban green space (UGS) plays a pivotal role in enhancing citizens' living standards, well-being, and overall physical and mental health. As urbanization continues to progress, ensuring equitable access to UGS becomes increasingly vital in addressing the social resource disparities. This paper proposes an innovative approach to inform UGS planning and updates, placing a strong emphasis on equitable UGS distribution. In pursuit of a more balanced UGS layout, we present a novel recreational demand model that can evaluate the citizen's recreational preferences by taking into account the population activity levels and the agglomeration effect. Leveraging point of interest (POI) data within the geographic information system (GIS) platform, we quantitatively analyze these factors. Additionally, we propose a refined resistance model, enhancing the accuracy of UGS update cost estimation. Applying these models, we undertake a case study in Changsha City to advance UGS update strategies. Our findings reveal disparities not only across different city sectors but also within individual spaces, underscoring the unequal distribution of diverse UGS resources. The proposed models can effectively guide the incorporation of new UGS points in urban planning, mitigating the challenge of inequitable UGS resource allocation. The study provides a new approach to more comprehensively evaluate the UGS quality and offers additional valuable insights for improved UGS planning, and therefore, contributes to fostering a more just, inclusive and sustainable urban environment.

Establishing a Resident-Driven Three-Dimensional Printing Lab Within an Academic Institution

Background: Three-dimensional (3D) modeling and printing technologies have been increasingly used throughout healthcare for research, educational, and clinical purposes. Despite the benefits associated with their use in surgical training, the literature poorly outlines the value of establishing an in-house, resident-driven 3D printing lab. Herein, we outline our quantifiable experience with our 3D printing lab and outline all necessary components needed to establish a lab such that the reader can replicate our experience at their institution. Additionally, we outline the process of creating a 3D-printed model from patient imaging with supplementary tutorial videos. Methods: A retrospective review of all 3D-printed models created in our in-house 3D printing lab since its creation in 2022 was performed. 3D-printed models that were utilized for formal educational or clinical purposes were included in this study. All 3D-printed models were created using the Raise3D 2 Pro. Cost estimates associated with each print were collected from the printer’s cost estimation function. Results: A total of 14 3D-printed models met the inclusion criteria for our study. Of these, 4 (28.6%) were utilized for patient and/or resident education with the remaining 10 (71.4%) being used in patient care. By and large, the average cost of educational 3D-printed models was greater than clinical models ($12.00 vs $1.50, respectively). Ten 3D-printed models of mandibular fractures were successfully used to pre-bend plates prior to reconstructive surgery, and minimal intraoperative adjustment of the plate was required for each case. The processes of establishing a 3D printing lab along with creating a 3D-printed model from patient imaging are outlined. Conclusion: Establishing a 3D printing lab is an attainable feat for most institutions. High-value clinical and educational tools can be created for a reasonable price once the initial overhead costs of establishing a lab are covered.

Market expansion analysis: Choosing between spain and saudi arabia for Dexiotis Security Solutions

The selected company for the marketing consultancy project was Dexiotis Security Solutions, the company started its journey in Hungary and also have its headquarters in the UK and Cyprus. The company is already operating its business activities on the global scale. And currently is planning to expand its business to a new country for which the company has chosen two countries, Spain and Saudi Arabia. This consultancy report thoroughly analyses the Spain and Saudi Arabia market and industry for suggest the better option for Dexiotis to expand its business. For analysing the market and industry, this report has addressed the macro-micro business environment analysis, industry analysis, integrated marketing plan and an approximate cost estimation to implement the plans in an appropriate order. According to the analyses and assessments of the research findings, Saudi Arabia has been chosen for Dexiotis to expand its business considering that the Saudi Arabia market is much more stable, politically favourable, has stable economic growth and development opportunities. Which would support Dexiotis to attract the potential clients.

Design and Dynamic Analysis of a Co-Existence Solution Between a Semi-Submersible Offshore Fish Cage and a Spar-Type Floating Offshore Wind Turbine

Abstract Clean energy captured by offshore wind turbines has been widely used for supporting onshore activities. In the near future, facilities such as offshore wind turbines can also play an important role in the energy transition of offshore activities. Offshore wind energy can be employed for electrifying operations in offshore fish farms, which are traditionally supplied by diesel-engine barges/generators. Based on this motivation, this study focuses on the design of a shared mooring system between a semi-submersible offshore fish cage and a spar-type floating wind turbine. A numerical model of the proposed shared mooring system is implemented in a global response analysis software sima for performing fully coupled time-domain simulations. The configuration of the shared mooring line is determined using an engineering approach, which comprises Irvine’s formulation, system eigenvalue analysis, and cost estimation. Moreover, relevant case studies by altering the environmental conditions are performed. Extreme operational conditions that may give large relative motions are investigated thoroughly. The dynamic performance of the integrated system is compared with that of individual structures. The global motion of the floating wind turbine and its mooring line’s tension behavior are obviously influenced by the existence of the shared line. In general, the present work investigates the feasibility of a shared mooring system for these types of offshore structures and further gives insights into the engineering design procedure.

Technoeconomic characterisation of low-carbon liquid hydrocarbons production

With growing attention on low-carbon fuels and chemicals to meet climate targets, careful cost and performance evaluations become critical. These hydrocarbons can be derived from biomass or hydrogen with captured CO2 via established pathways such as Fischer-Tropsch and methanol synthesis. Techno-economic studies are influential in identifying the economic viability of promising technologies in the absence of reliable industry data. A considerable number of techno-economic studies that represent same technologies exhibit substantial variability in their estimations, posing challenges in decision-making. This work aims to characterise, based on available data, the investment cost and plant performance for the Nth (i.e. mature) low-carbon liquid hydrocarbon plants. The extent of data variabilities across technologies are presented and the factors contributing to differences in data interpretation and normalisation are determined. Through a full assessment of existing values, central tendencies for prospective techno-economic evaluations are identified. Processes utilising biomass have the greatest ranges in the investment cost, with higher costs from studies with the highest level of detail and proximity to real-world plant cost estimations. Energy efficiency variabilities arise when tied to plants with multiple outputs. Demonstration scale hydrogen-to-hydrocarbons in relation to economies of scale should be carefully evaluated.

Cost Estimation Proxy Models for Economic Evaluations in Petroleum Projects

Petroleum economic evaluation involves estimating revenues from forecasted production profiles and field costs including capital expenditures (CAPEX), drilling expenses (DRILLEX), and operating expenses (OPEX). The existing cost-estimating tool requires several inputs making it time-intensive and difficult to use with few data during the early stages of projects. Majority of the previously developed time saving cost estimations proxy models rely on unrealistic assumptions that include uniform operational costs for different fields with a different number of wells, casings, and drilled depths. This work focused at developing proxy models that consider the variability of the development costs with different parameters. The developed models benefited from a three-step approach for CAPEX, DRILLEX, and OPEX estimations based on datasets from three wells from a gas field in southern coastal Tanzania. Firstly, cost sensitivity analysis was performed using QUE$TOR v15.1.0.18, a cost estimating commercial software to determine the most influential field parameters of the field costs. Secondly, the field cost models were generated based on historical cost data from the gas field using multivariable regression analysis with the help of Statistical Package for the Social Sciences software (SPSS) v22.0. Lastly, errors analysis was done for checking the predictive reliability of the models. Based on the analysis, the CAPEX and OPEX were found to be strongly linearly dependent on the size of processing facilities, number of producing wells in the gas field and production capacity, respectively. On the other hand, a nonlinearity relation was revealed on of the DRILLEX which was strongly dependent on drilled well depth and number of installed well casing. Results show that the developed models are useful and their reliability becomes robust when more data is used. A stochastic modeling approach was further recommended for the models to incorporate uncertainties associated with the parameters used to quantify the cost estimates.

Artificial intelligence support in MR imaging of incidental renal masses: an early health technology assessment

ObjectiveThis study analyzes the potential cost-effectiveness of integrating an artificial intelligence (AI)–assisted system into the differentiation of incidental renal lesions as benign or malignant on MR images during follow-up.Materials and methodsFor estimation of quality-adjusted life years (QALYs) and lifetime costs, a decision model was created, including the MRI strategy and MRI + AI strategy. Model input parameters were derived from recent literature. Willingness to pay (WTP) was set to $100,000/QALY. Costs of $0 for the AI were assumed in the base-case scenario. Model uncertainty and costs of the AI system were assessed using deterministic and probabilistic sensitivity analysis.ResultsAverage total costs were at $8054 for the MRI strategy and $7939 for additional use of an AI-based algorithm. The model yielded a cumulative effectiveness of 8.76 QALYs for the MRI strategy and of 8.77 for the MRI + AI strategy. The economically dominant strategy was MRI + AI. Deterministic and probabilistic sensitivity analysis showed high robustness of the model with the incremental cost-effectiveness ratio (ICER), which represents the incremental cost associated with one additional QALY gained, remaining below the WTP for variation of the input parameters. If increasing costs for the algorithm, the ICER of $0/QALY was exceeded at $115, and the defined WTP was exceeded at $667 for the use of the AI.ConclusionsThis analysis, rooted in assumptions, suggests that the additional use of an AI-based algorithm may be a potentially cost-effective alternative in the differentiation of incidental renal lesions using MRI and needs to be confirmed in the future.Clinical relevance statementThese results hint at AI’s the potential impact on diagnosing renal masses. While the current study urges careful interpretation, ongoing research is essential to confirm and seamlessly integrate AI into clinical practice, ensuring its efficacy in routine diagnostics.Key Points• This is a model-based study using data from literature where AI has been applied in the diagnostic workup of incidental renal lesions.• MRI + AI has the potential to be a cost-effective alternative in the differentiation of incidental renal lesions.• The additional use of AI can reduce costs in the diagnostic workup of incidental renal lesions.

Identification of residential building typologies by applying clustering techniques to cadastral data

Building typologies are usually classified according to their shape, distribution and construction features depending on the time period they were built. As a result, a subjective classification arises which highly depends on the criteria used to differentiate buildings. When this analysis is carried out at the urban scale, data sets become bigger, making patterns difficult to uncover. Mistakes in deciding the variables to classify buildings lead to incorrect typologies and, consequently, wrong results. The aim of this study is to test a new methodology based on clustering techniques to identify typologies related to energy retrofitting, which would allow obtaining a more objective classification and better pattern recognition by reducing human intervention. To that end, a data set from the Spanish cadastre is used, with additional information to reflect the influence of existing standards on constructive solutions. By applying three clustering techniques (Ward's method, Partitioning Around Medoids, and a combination of both), new proposals of building typologies are obtained and discussed in comparison to traditional classifications. The results show that the Ward's method produces building typologies with significantly high quality metrics and meaningfulness. The agglomeration coefficient is 99.8%, which indicates that hardly another hierarchical method could generate a better clusters structure. Six clusters comprise 86% of the dwellings as most occupants do not declare retrofitting works, thus not being reflected in the cadastre database. This research provides a new classification method that can notably influence the estimation of costs, environmental impact and cost effectiveness of energy retrofitting actions at urban scale.

Performance prediction of roadheaders used in coal mines from the needle penetration index and the schmidt hammer value

Coal mine galleries and construction tunnels are commonly excavated using roadheaders. Estimating the performance of roadheaders is crucial for planning and cost estimation when planning tunnel or tunnel projects. The aim of this study is to derive generalized performance prediction models including Schmidt hammer value, needle penetration index, and volumetric joint count for roadheaders used in coal mines. The performance measurements of axial and transverse type roadheaders were carried out in six different coal mines. Schmidt hammer tests, needle penetration index tests, and of volumetric joint count measurements were also performed at the locations where the performance measurements were conducted. The extensive data were evaluated using multiple linear and nonlinear regression analysis. The developed formulas were evaluated using statistical tests. The Equations that include the Schmidt hammer value or needle penetration index value in addition to cutter head power have been shown to be unreliable. However, the equations that include Schmidt hammer value or needle penetration index value in addition to cutterhead power and volumetric joint count have been shown to be reliable. This study concludes that the developed reliable equations can be used for the performance assessment of roadheaders used in coal mines.