Different Types Of Costs Research Articles

Applying deep reinforcement learning to solve the low-carbon economic dispatch problem in smart microgrids

Abstract Smart microgrid (SMG) technology can effectively integrate the advantages of new and renewable energy power generation - distributed generation, and, at the same time, provide a new technical approach for the large-scale application of new and renewable energy grid-connected power generation. Smart microgrids can improve the comprehensive utilization efficiency of energy and serve as an effective complementary power grid of the primary power grid to enhance the reliability of power supply and power quality, which is the latest frontier topic in power engineering research. This paper, aiming at the characteristics of distributed generation such as solar power generation, wind power generation, fuel cell power generation, and micro gas turbine in the smart microgrid system, considering the fuel, efficiency, operation, and maintenance costs of different types and capacities, the different greenhouse gas emissions, and the particularity of solar power generation and wind power generation, a mathematical model of low-carbon economic dispatch of smart microgrid that comprehensively considers the cost of power generation and emission is proposed. This paper presents a scheduling decision model based on an improved dual-delay depth deterministic strategy gradient algorithm. The improved double-delay deep deterministic strategy gradient algorithm is used to construct and train the neural network to avoid overestimation and improve the stability of the network output. At the same time, to improve the efficiency of network training, the historical experience data in the training process were stored, and the experience was replayed and sampled. Examples verify the superiority and practicability of the method proposed in this paper.

Semiparametric copula models applied to the decomposition of claim amounts

In this paper, we develop a conditional copula model to analyze the distribution of a claim that generates different types of costs and/or simultaneously impacts several guarantees. Our methodology is adapted to taking into account the particular structure of our data, since observations are subject to right-censoring. Right-censoring occurs since payment of a claim is not made instantaneously, and therefore unsettled claims only provide a partial information on the phenomenon that one wishes to model. The new methodology that we develop is supported by theoretical results that show the asymptotic normality of our estimators. A simulation study and a real data analysis illustrate the method.

Calculating the social impact of home automation for people with disability: A social return on investment study.

Home automation can deliver important outcomes for people with disabilities, including enhanced independence. Despite the millions of dollars spent on home automation in Australia and other developed nations, to date, there has been no economic evaluation of this type of assistive technology. A social return on investment analysis of home automation study was undertaken. Primary data were collected using qualitative interviews with home automation consumers and other key stakeholders, including occupational therapists, a spinal rehabilitation physician, peer support advocate, and managers and technical personnel from home automation providers (n = 17). The analysis was supported by (1) secondary data from a scoping review on outcomes from home automation and (2) additional literature searches to identify suitable financial proxies and to make estimates of the proportion of home automation users expected to experience each outcome. A scenario approach was used with three home automation scenarios developed with increasing complexity and costs to calculate the social return on investment. Eight outcomes from the use of home automation were identified, including reduced reliance on carers and family members, increased independence, and improved energy and comfort. The social return on investment ranged from $38.80 (low cost) to $15.10 (high cost) for every $1 invested across a 10-year benefit period, with the financial proxy for reduced care attendant hours contributing the most to the social return ratio. Even the highest cost scenario was repaid in social value within the first year of the benefit period. This study suggests that home automation represents a sound investment and has a significant impact on the overall quality of life of people with disabilities. Focusing on the financial savings in care attendant hours alone should be compelling evidence for funders to recognise home automation's value and continue to fund this assistive technology. A consumer representative was a member of the project steering group, which supported the research team at all stages of the project. When people get injured, their disability can stop them doing things around the home that they used to be able to do. Technology can help people with disabilities do things like open and close doors and turn off taps by pressing a button, so they do not have to wait for someone to help them. This technology can be expensive, but no one has looked at if it is worth the money. We spoke to some people with disabilities who used this type of technology, and they told us their lives were better now they used this technology. For example, they told us they were able to do things for themselves, they did not need carers as much, and they had better mental health. We spoke to businesses about the costs of different types of technology that can be used in the home. We then put a dollar value on the ways people with disabilities told us their lives were better. For example, for better mental health, we worked out how much it would cost to see a psychologist for 1 year. We found that the dollar value of the ways in which people with disabilities' lives were improved was at least 15 times more than the costs of the technology. This study therefore shows that this technology is worth the money and improves the lives of people with disabilities following serious injury.

Costs and Benefits of Diverse Plurality in Economics

The literature on pluralism in economics has focused on the benefits expected from the plurality of theories, methods, and frameworks. This overlooks half of the picture: the costs. Neither have the multifarious costs been systematically analyzed in philosophy of science. We begin rectifying this neglect. We discuss how the benefits of plurality and diversity in science presuppose distinct types of plurality and how various benefit and plurality types are associated with different types of costs. Finally, we ponder how the general mechanisms that give rise to the costs of plurality and diversity are aggravated by various disciplinary characteristics of economics.

Strategic Resource Utilization for Enhancing Corporate Value: Dynamics of Exploration and Exploitation in Korea

This study examines the impact of research and development (R&D) expenditures, training expenditures, and entertainment expenditures (business promotion expenditures) on firm value in the Korean electronics and metal industry. Extending the theoretical foundation of James March’s exploration and exploitation theory, this study analyzes the impact of R&D and training investments on firm value to explore new capabilities from a long-term perspective, and the impact of entertainment costs on firm value to achieve short-term organizational goals. Using Tobin’s Q methodology, which uses the ratio of a firm’s market value to its asset replacement cost as a proxy for firm value, this study finds the relationship between these types of expenditures and firm value. The analysis finds that R&D expenditures and training expenditures are significantly correlated with increases in firm value, suggesting that these investments play an important role in enhancing a firm’s competitiveness and performance. On the other hand, while we hypothesize that the balance of exploration and exploitation within an organization will affect firm value, we find that entertainment expenditures, which are business promotion expenditures, do not show a significant relationship with firm value. This suggests that these expenditures by companies in Korea’s electronics and metals industry contribute to the achievement of the organization’s short-term goals but do not have a significant impact on firm value. These findings suggest that resource allocation in the electronics and metal industries where technological innovation is important should be more heavily weighted toward investments in R&D and training for long-term exploration in order to increase firm value. To increase firm value, firms should prioritize investments that drive sustainable growth and enhance competitive advantage. This research allows for a deeper examination of how different types of costs contribute to firm value and underscores the need for strategic clarity in resource allocation decisions.

Cost-effectiveness of prehabilitation of elderly frail or pre-frail patients prior to elective surgery (PRAEP-GO) versus usual care – Protocol for a health economic evaluation alongside a randomized controlled trial

BackgroundPrehabilitation aims to improve patients' functional capacity before surgery to reduce perioperative complications, promote recovery and decrease probability of disability. The planned economic evaluation is performed alongside a large German multi-centre pragmatic, two-arm parallel-group, randomized controlled trial on prehabilitation for frail elderly patients before elective surgery compared to standard care (PRAEP-GO RCT). The aim is to determine the cost-effectiveness and cost-utility of prehabilitation for frail elderly before an elective surgery.MethodsThe planned health economic evaluation comprises cost-effectiveness, and cost-utility analyses. Analyses are conducted in the German context from different perspectives including the payer perspective, i.e. the statutory health insurance, the societal perspective and the health care provider perspective. Data on outcomes and costs, are collected alongside the ongoing PRAEP-GO RCT. The trial population includes frail or pre-frail patients aged ≥70 years with planned elective surgery. The intervention consists of frailty screening (Fried phenotype), a shared decision-making conference determining modality (physiotherapy and unsupervised physical exercises, nutrition counselling, etc.) and setting (inpatient, day care, outpatient etc.) of a 3-week individual multimodal prehabilitation prior to surgery. The control group receives standard preoperative care.Costs include the intervention costs, the costs of the index hospital stay for surgery, and health care resources consumed during a 12-month follow-up. Clinical effectiveness outcomes included in the economic evaluation are the level of care dependency, the degree of disability as measured by the WHO Disability Assessment Schedule 2.0 (WHODAS 2.0), quality-adjusted life years (QALY) derived from the EQ-5D-5L and the German utility set, and complications occurring during the index hospital stay. Each adopted perspective considers different types of costs and outcomes as outlined in the protocol. All analyses will feature Intention-To-Treat analysis. To explore methodological and parametric uncertainties, we will conduct probabilistic and deterministic sensitivity analyses. Subgroup analyses will be performed as secondary analyses.DiscussionThe health economic evaluation will provide insights into the cost-effectiveness of prehabilitation in older frail populations, informing decision-making processes and contributing to the evidence base in this field. Potential limitation includes a highly heterogeneous trial population.Trial registrationPRAEP-GO RCT: NCT04418271; economic evaluation: OSF (https://osf.io/ecm74).

Characteristics and Changes in the Properties of Cereal and Rapeseed Straw Used as Energy Feedstock

Solid biofuels, including straw as production residue, are still the largest energy feedstock in the structure of primary energy production from renewable energy sources. However, the properties of straw as a solid biofuel can vary depending on the species from which it was produced and the harvest period and year. Therefore, this study aimed to assess the thermophysical properties and elemental composition of six types of straw (rye, oat, triticale, wheat, corn, and rapeseed straw) obtained over three consecutive years (2020, 2021, 2022). Rye straw had the lowest moisture (mean: 10.55%), ash (mean: 2.71% DM), nitrogen (mean: 0.54% DM) and chlorine (mean: 0.046% DM) contents and the highest carbon content (mean: 47.93% DM), a higher heating value—HHV (mean: 19.03 GJ Mg−1 DM) and a lower heating value—LHV (mean: 15.71 GJ Mg−1). Triticale straw had similar properties, classifying it into the same cluster as rye straw. Corn straw had a remarkably high moisture content (mean: 48.91%), low LHV and high chlorine content. Rapeseed straw contained high levels of Cl, S, N and ash, and they were 643%, 481%, 104% and 169% higher, respectively, than those in rye straw. The sulfur, chlorine and moisture contents of the six straw types under study were highly variable during the three years of the study. Knowledge of the properties of different types of straw as energy feedstocks facilitates the logistics and organization of the supply of bioenergy installations. However, further research is needed, especially studies assessing the energy intensity and logistical costs of different types of straw used for energy purposes.

Sex-specific effects of immune challenges on green anole lizard metabolism.

Immune responses can increase survival, but they can also incur a variety of costs that may lead to phenotypic trade-offs. The nature of trade-offs between immune activity and other components of the phenotype can vary and depend on the type and magnitude of immune challenge, as well as the energetic costs of simultaneously expressing other traits. There may also be sex-specific differences in both immune activity and trade-offs, particularly with regard to energy expenditure that might differ between males and females during the breeding season. Females are generally expected to invest less in nonspecific immune responses compared to males due to differences in the allocation of resources to reproduction, which may lead to sex differences in the metabolic costs of immunity. We tested for sex-specific differences in metabolic costs of different types of immune challenges in Anolis carolinensis lizards, including lipopolysaccharide (LPS) injection and wounding. We also tested for differences in immune prioritization by measuring bacterial killing ability (BKA). We predicted males would show a greater increase in metabolism after immune challenges, with combined immune challenges eliciting the greatest response. Furthermore, we predicted that metabolic costs would result in decreased BKA. LPS injection increased the resting metabolic rate (RMR) of males but not females. Wounding did not affect RMR of either sex. However, there was an inverse relationship between BKA and wound healing in LPS-injected lizards, suggesting dynamic tradeoffs among metabolism and components of the immune system.

Exploring the costs of phenotypic plasticity for evolvable digital organisms

Phenotypic plasticity is usually defined as a property of individual genotypes to produce different phenotypes when exposed to different environmental conditions. While the benefits of plasticity for adaptation are well established, the costs associated with plasticity remain somewhat obscure. Understanding both why and how these costs arise could help us explain and predict the behavior of living creatures as well as allow the design of more adaptable robotic systems. One of the challenges of conducting such investigations concerns the difficulty of isolating the effects of different types of costs and the lack of control over environmental conditions. The present study addresses these challenges by using virtual worlds (software) to investigate the environmentally regulated phenotypic plasticity of digital organisms. The experimental setup guarantees that potential genetic costs of plasticity are isolated from other plasticity-related costs. Multiple populations of organisms endowed with and without phenotypic plasticity in either the body or the brain are evolved in simulation, and organisms must cope with different environmental conditions. The traits and fitness of the emergent organisms are compared, demonstrating cases in which plasticity is beneficial and cases in which it is neutral. The hypothesis put forward here is that the potential benefits of plasticity might be undermined by the genetic costs related to plasticity itself. The results suggest that this hypothesis is true, while further research is needed to guarantee that the observed effects unequivocally derive from genetic costs and not from some other (unforeseen) mechanism related to plasticity.

Adoption of mechanization alternatives in oil palm crops in the Colombian Orinoquía natural region

The Orinoquía Natural Region in Colombia, particularly the Cumaral and Bajo Upía subzone, serves as the country’s primary palm oil production area. However, it faces with labor shortages and a lack of current mechanization data. This study aims to assess mechanization status to enhance productivity and competitiveness in the oil palm industry. The goal was to determine the mechanization landscape, characterize available technologies, and evaluate their impact on productivity and costs for different types of oil palm producers. Utilizing a semi-structured questionnaire, data was collected from plantation administrators and technical assistants. Analysis focused on identifying mechanization options and assessing machinery adoption, productivity, and costs. Tractors are predominantly used for power, with limited adoption of advanced machinery like grabbers, variable-rate fertilizers, and electrostatic sprayers. Additionally, there’s a notable gap in harvest equipment availability, suggesting a need for further technological development and machinery rental strategies. This study highlights the importance of updated mechanization data and provides insights for decision-makers seeking to improve oil palm productivity and competitiveness in Colombia. By understanding the mechanization landscape, stakeholders can address labor shortages and drive sustainable growth in the oil palm sector.

Not all discounts are created equal: Regional activity and brain networks in temporal and effort discounting

Reward outcomes associated with costs like time delay and effort investment are generally discounted in decision-making. Standard economic models predict rewards associated with different types of costs are devalued in a similar manner. However, our review of rodent lesion studies indicated partial dissociations between brain regions supporting temporal- and effort-based decision-making. Another debate is whether options involving low and high costs are processed in different brain substrates (dual-system) or in the same regions (single-system). This research addressed these issues using coordinate-based, connectivity-based, and activation network-based meta-analyses to identify overlapping and separable neural systems supporting temporal (39 studies) and effort (20 studies) discounting. Coordinate-based activation likelihood estimation and resting-state connectivity analyses showed immediate-small reward and delayed-large reward choices engaged distinct regions with unique connectivity profiles, but their activation network mapping was found to engage the default mode network. For effort discounting, salience and sensorimotor networks supported low-effort choices, while the frontoparietal network supported high-effort choices. There was little overlap between the temporal and effort networks. Our findings underscore the importance of differentiating different types of costs in decision-making and understanding discounting at both regional and network levels.

A-VRPD: Automating Drone-Based Last-Mile Delivery Using Self-Driving Cars

Drone-based last-mile delivery is an emerging technology that uses drones loaded onto a truck to deliver parcels to customers. In this paper, we introduce a fully automated system for drone-based last-mile delivery through incorporation of autonomous vehicles (AVs). A novel problem called the autonomous vehicle routing problem with drones (A-VRPD) is defined. A-VRPD is to select AVs from a pool of available AVs based on crowd sourcing, assign selected AVs to customer groups, and schedule routes for selected AVs to optimize the total operational cost. We formulate A-VRPD as a Mixed Integer Linear Program (MILP) and propose an optimization framework to solve the problem. A greedy algorithm is also developed to significantly improve the running time for large-scale delivery scenarios. Extensive simulations were conducted taking into account real-world operational costs for different types of AVs, traveled distances calculated considering the real-time traffic conditions using Google Map API, and varying load capacities of AVs. We evaluated the performance in comparison with two different state-of-the-art solutions: an algorithm designed to address the traditional vehicle routing problem with drones (VRP-D), which involves human-operated trucks working in tandem with drones to deliver parcels, and an algorithm for the two echelon vehicle routing problem (2E-VRP), wherein parcels are first transported to satellite locations and subsequently delivered from those satellites to the customers. The results indicate a substantial increase in profits for both the delivery company and vehicle owners compared with the state-of-the-art algorithms.

The consideration of cost in evaluations of moral praiseworthiness among Chinese children

AbstractChildren recognise the praiseworthiness of others' moral actions from quite a young age. The work we propose here focuses on whether and how children's judgements of moral praiseworthiness may be affected by cost incurred by the agents. Recent work has revealed developmental changes in how children consider physical and psychological costs in their judgements of moral praiseworthiness. However, this question has only been examined among US children, and little is known about how much the findings can be generalised across cultures. In this article, we examine the consideration of different types of costs (psychological, physical) in moral evaluation by children in China, and compare to the US data. We choose to study children in China not only because it is a non‐WEIRD culture, but also because recent work has suggested that there are cultural differences between the US and China in terms of how children conceptualise desire inhibition and self‐control. This research will have implications for research on children's moral cognition across cultures.

Use of recurrent neural networks considering maintenance to predict urban road performance in Beijing, China.

A correct understanding of the pavement performance change law forms the premise of the scientific formulation of maintenance decisions. This paper aims to develop a predictive model taking into account the costs of different types of maintenance works that reflects the continuous true usage performance of the pavement. The model proposed in this study was trained on a dataset containing five-year maintenance workdata on urban roads in Beijing with pavement performance indicators for the corresponding years. The same roads were matched and combined to obtain a set of sequences of pavement performance changes with the features of the current year; with the recurrent-neural-network-based long short-term memory (LSTM) network and gate recurrent unit (GRU) network, the prediction accuracy of highway pavement performance on the test set was significantly increased. The prediction result indicates that the generalization ability of the improved recurrent neural network model is satisfactory, with the R2 achieving 0.936, and of the two models the GRU model is more efficient, with an accuracy thatreaches almost the same level as LSTMbut with the training convergence time reduced to 25 s. This study demonstrates that data generated by the work of maintenance units can be used effectivelyin the prediction of pavement performance. This article is part of the theme issue 'Artificial intelligence in failure analysis of transportation infrastructure and materials'.

Research on Day-Ahead Optimal Scheduling Considering Carbon Emission Allowance and Carbon Trading

In the context of the marketization of carbon trading in the power system, it is of great theoretical and practical significance to study a scientific and effective carbon emission quota allocation strategy. To solve this problem, under the current situation of large-scale access to new energy, considering the limitations of the carbon emissions from different emission subjects plus the construction of a carbon trading model among the emission subjects, a day-ahead optimal scheduling method that takes carbon emission quotas and carbon trading into account is proposed. Firstly, carbon transaction cost models of thermal power and wind power are constructed, respectively, and a carbon emission quota allocation strategy based on the entropy method is proposed to redistribute the weights of baseline emission factors for the regional power grid. Then, considering the additional carbon emissions of conventional thermal power units caused by wind power access, the carbon trading costs of different types of generation units are calculated on the basis of carbon trading price prediction. Thereafter, a day-ahead optimal scheduling model considering carbon emissions trading is constructed with the objective of minimizing the total cost of the system in the scheduling period. The model is solved as an MINLP problem based on MATLAB 2016a software utilizing CPLEX 12.4. Simulation results verify the correctness and effectiveness of the proposed method.

Carbon capture and utilization: More than hiding CO2 for some time

Carbon capture and utilization: More than hiding CO2 for some time

Fuzzy Decision-Based Energy Management of Energy Grids with Hubs considering Participation of Hubs and Networks in the Energy Markets

With the creation of competitive environments, such as electricity market, it is expected that energy networks and active consumers such as energy hubs participate in the market to promote their economic situation. So, the article proposes the optimal involvement of energy networks and hubs in energy markets in two wholesale and retail designs based on the energy management system at the same time. The proposed scheme is expressed as two-objective optimization. The first objective is to minimize the cost of different types of energy in electricity-gas-thermal networks. In another objective function, the cost of energy (which is the difference between the energy purchase cost and energy sale income) of energy hubs in the retail market is minimized. The scheme is bound to optimal power flow equations of the mentioned networks and operating model of power sources and active loads. Then, the Pareto optimization mixed with the sum of weighted functions helps extract an optimal compromise solution on the basis of fuzzy decision-making. Finally, the scheme is applied to a test system, and the obtained numerical results confirm that energy hubs are improved financially, and economic and operation conditions of the electricity-gas-thermal networks are enhanced simultaneously. So, significant profit can be achieved for EHs in the retail energy market. The economic situation of the networks enhances up to roughly 10% compared with that of power flow studies. Also, operating situation of the networks enhances by about 12% to 53% compared with a case without EHs.

Phenotype–environment mismatch errors enhance lifetime fitness in wild red squirrels

Mismatches between an organism's phenotype and its environment can result in short-term fitness costs. Here, we show that some phenotype-environment mismatch errors can be explained by asymmetrical costs of different types of errors in wild red squirrels. Mothers that mistakenly increased reproductive effort when signals of an upcoming food pulse were absent were more likely to correctly increase effort when a food pulse did occur. However, mothers that failed to increase effort when cues of an upcoming food pulse were present suffered lifetime fitness costs that could only be offset through food supplementation. In fluctuating environments, such phenotype-environment mismatches may therefore reflect a bias to overestimate environmental cues and avoid making the costliest error, ultimately enhancing lifetime fitness.

Comparison of Operational Costs for Fixed-Route Bus Service and Demand Responsive Transport Systems. The Case of Kosice Region - Slovakia

The issue of sustainability of public transport is constantly receiving attention from both government and research. One possible solution, demand responsive transport (DRT), can increase accessibility in rural areas, improve the quality of service as well as reduce the costs. This paper estimates the operational costs for different types of vehicles in DRT in person-kilometers and compares it to the current costs of standard bus transport for year 2021 and analyses the capacity, occupancy and ticket prices of fixed-route bus transport. As the results indicate, in many cases the costs of DRT are higher compared to standard bus transport, but by increasing the occupancy of vehicles the DRT can provide savings up to 66 %. The use of cars, as well as car-sharing, show higher costs, which is mainly related to a low transport capacity. As the most appropriate vehicles of DRT have been identified micro buses and minibuses.

Comparing the Accuracy of Diagnostic Tests When Disease Is Characterized by an Ordinal Scale.

In diagnostic medicine, the true disease status of a patient is often represented on an ordinal scale-for example, cancer stage (0, I, II, III, or IV) or coronary artery disease severity measured using the Coronary Artery Disease Reporting and Data System (CAD-RADS) scale (none, minimal, mild, moderate, severe, or occluded). With advances in quantitation of diagnostic images and in artificial intelligence (AI), both supervised and unsupervised algorithms are being developed to help physicians correctly grade disease. Most of the diagnostic accuracy literature deals with binary disease status (disease present or absent); however, tests diagnosing ordinal-scaled diseases should not be reduced to a binary status just to simplify diagnostic accuracy testing. In this paper, we propose different characterizations of ordinal-scale accuracy for different clinical use scenarios, along with methods for comparing tests. In the simplest scenario, just the proportion of correct grades is considered; other scenarios address the magnitude and direction of misgrading; and at the other extreme, a weighted accuracy measure with weights based on the relative costs of different types of misgrading is presented. The various scenarios are illustrated using a coronary artery disease example where the accuracy of AI algorithms in providing patients with the correct CAD-RADS grade is assessed.