Purchase Cost Research Articles

Efficient Resource Management and Expansion Scheme for Collaborative Edge-Cloud Computing

Integrating the advantages of both the edge and the cloud, the edge-cloud computing system emerges to provide high-quality computing services for mobile users. To improve system efficiency, we investigate a hybrid mode of resource collaboration and expansion for the edge-cloud computing system, in which edge servers not only can collaborate with the cloud by purchasing high-priority computation resources temporarily but also can expand their local computation resources permanently. In such a way, the edge server can maximize its long-term profit by making a trade-off between the purchasing cost and the expanding cost. By formulating the resource management problem as a long-term profit maximization one, we first analyze the relationships among the expected minimal purchasing cost, the computation delay, and the available computation resources. Then, we design an efficient resource reserving and expanding scheme to determine the optimal expected amounts of reserving resources and expansion resources. Next, we propose an efficient real-time resource purchasing scheme to obtain the optimal amount of real-time purchasing resources dynamically. Finally, simulation results show that the proposed efficient resource collaboration and expanding scheme can maximize the long-term profit while guaranteeing the computation delay.

Improving appropriate use of intravenous albumin: results of a single-centre audit and multifaceted intervention

BackgroundIntravenous albumin has limited indications supported by randomised controlled trials, yet it is often prescribed for indications not supported by evidence.AimTo reduce unnecessary transfusion of albumin.InterventionsUnder the leadership of a...

GRU-integrated constrained soft actor-critic learning enabled fully distributed scheduling strategy for residential virtual power plant

In this study, a novel residential virtual power plant (RVPP) scheduling method that leverages a gate recurrent unit (GRU)-integrated deep reinforcement learning (DRL) algorithm is proposed. In the proposed scheme, the GRU- integrated DRL algorithm guides the RVPP to participate effectively in both the day-ahead and real-time markets, lowering the electricity purchase costs and consumption risks for end-users. The Lagrangian relaxation technique is introduced to transform the constrained Markov decision process (CMDP) into an unconstrained optimization problem, which guarantees that the constraints are strictly satisfied without determining the penalty coefficients. Furthermore, to enhance the scalability of the constrained soft actor-critic (CSAC)-based RVPP scheduling approach, a fully distributed scheduling architecture was designed to enable plug-and-play in the residential distributed energy resources (RDER). Case studies performed on the constructed RVPP scenario validated the performance of the proposed methodology in enhancing the responsiveness of the RDER to power tariffs, balancing the supply and demand of the power grid, and ensuring customer comfort.

Tax Evasion Detection: A Fusion of AdaBoost Classifiers and Deep Learning Models

Abstract: Income Tax plays an important role in the developmental activities of a nation. However some companies try to avoid or minimize their tax by showing reduced profits. Such companies can be identified by studying and analyzing the balance sheet indicators like: Sale Volume Vs. Purchase Volume, Employee Growth Vs. Taxpaid, Total Sales Vs. Taxpaid, Claims Crediting, Net Income Vs. Tax Increase, Gross Loss Vs. Tax, Inventories Vs. Direct Income, Gross Revenue Vs. Taxpaid, Raw Material Purchase Cost (RMPC) Vs. Final Product Sales (FPS) and Fixed Assets Vs. Taxpaid. This project aims at Tax Evaders detection by using a dataset, containing the above mentioned identification factors in the balance sheet. The analysis is carried out by using AdaBoost classifier and also by using a Deep learning model

A Study on Replenishment Decision of Vegetable Goods Based on Polynomial Regression and Arima Time Series Forecasting

In a fresh food superstore, the superstore replenishes daily based on the historical sales and demand of vegetable commodities. To formulate the optimal replenishment decision, this paper analyses the sales flow, purchase cost, and loss of a supermarket in a period, and through the use of a variety of models, it can give a reference opinion on the formulation of automatic pricing and replenishment decisions for vegetable commodities. The first text describes the process of analyzing the relationship between total sales volume and cost-plus pricing, including data integration, preprocessing, and multiple regression analysis methods. Through these analyses, a general relationship between total sales volume and merchandise selling price is identified. Univariate linear regression, polynomial regression, random forest regression, and BP neural network regression models were all used to analyze this relationship. The focus is then on the total daily replenishment and pricing strategy for the coming week to maximize the superstore's revenue. The text details the use of ARIMA time series analysis methods, as well as steps for noise reduction and forecasting using Wavelet wavelet functions. Through these methods, the text provides sales forecast results for different vegetable categories for the coming week for the superstore management's reference.

Research on inhibitory effect of mixed suppressants CaCO3, KCl, and K2CO3 on coal dust explosion pressure

To discuss the inhibitory effect of micrometer scale coal dust explosion pressure, three types of explosion suppressants are selected for mixed explosion suppression. The results indicate that the coal dust explosion process includes three stages: accelerated and decelerated energy release, as well as energy dissipation. When using explosive suppressants, K2CO3 has the greatest inhibitory effect on coal dust explosion, followed by KCl, and CaCO3 has the smallest effect. The K2O, K2O2, and KOH generated by the thermal decomposition of K2CO3 can also block the heat transfer of coal dust, playing a good role in suppressing explosions. The explosion suppression effect of mixing CaCO3 and K2CO3 is better than that of mixing CaCO3 and KCl, and is worse than the explosion suppression effect of using K2CO3 alone. The synergistic effect of KCl and K2CO3 mixed explosion suppression makes the suppression effect better than using K2CO3 alone. This is because KCl generates K2O during pyrolysis, promoting the dynamic equilibrium of K2CO3 explosion suppression process. This makes mixed explosion suppression more worthy of attention and adoption when considering purchase costs.

A Circular Economy Approach to Convert Waste Dairy Scum Oil into Biodiesel for the Development of Sustainable Environment

The present study is an effort to examine a technique for evaluating the expenses associated with the production of biodiesel from waste dairy scum oil in Pakistan, to create an economic assessment of this option. A survey was conducted on ten dairies in Gujrat to encourage them participation in the biodiesel supply chain. The survey aimed to collect data on waste dairy scum creation, disposal methods and quantity, as well as purchase cost. The waste dairy scum oil was extracted by solvent extraction method. Acetone solvent has been used to extract the waste dairy scum oil. A two-step acid treatment was done to reduce the FFA value of WDSO from 4.6 to 0.98 mg KOH/g. The independent variables of the transesterification process parameters including catalyst concentration, methanol to oil ratio, temperature, speed, and reaction time were kept 0.25 w/w, 8.50:1, 57.50℃, 600 rpm, and 1h respectively. The maximum biodiesel yield at these operating parameters was observed 93%. The economic feasibility of the conversion of WDSO into biodiesel was assessed using the information obtained from the surveys conducted by the different dairy’s owners. Approximately 70% of diaries produced scum and wanted to convert this waste dairy scum into biodiesel. The net biodiesel production cost was found to be 171 rupees. The logistic cost of biodiesel produced from the WDSO was found to be 22 rupees. The overall cost of 1L biodiesel would be 193 rupees. A two-way sensitivity analysis was performed to determine the profit of the conversion of the WDSO into biodiesel. The environmental analysis revealed that the emissions like CO, HC, and NOx significantly reduced during the combustion of biodiesel as compared to conventional diesel.

Study on time-of-use pricing method for steam heating system considering user response characteristics and thermal storage capacity

Industrial steam heating is a significant aspect of energy consumption in China, playing a crucial role in industrial energy efficiency. Historically, demand response research has predominantly focused on the electricity sector, with limited emphasis on steam supply. This study explores a novel Time-of-Use pricing approach for steam heating systems, anchored in the demand response theory of price incentives. It develops a peak-valley segmentation method by considering both the load differences between supply and demand and the thermal storage capacity of the pipeline system. Additionally, the paper delves into user response characteristics to formulate an optimized Time-of-Use pricing model for steam heating systems. Implemented in a Jiangsu-based case study, the model demonstrates significant efficiency improvements: it reduces the peak-valley load difference by up to 68.3% and cuts steam purchasing costs by ¥135,000. Furthermore, the alignment between the users' total steam load curve and the heat source's steam supply is enhanced by 18.8%, illustrating the model's effectiveness in balancing demand and supply.

Learning-Augmented Online Algorithm for Two-Level Ski-Rental Problem

In this paper, we study the two-level ski-rental problem, where a user needs to fulfill a sequence of demands for multiple items by choosing one of the three payment options: paying for the on-demand usage (i.e., rent), buying individual items (i.e., single purchase), and buying all the items (i.e., combo purchase). Without knowing future demands, the user aims to minimize the total cost (i.e., the sum of the rental, single purchase, and combo purchase costs) by balancing the trade-off between the expensive upfront costs (for purchase) and the potential future expenses (for rent). We first design a robust online algorithm (RDTSR) that offers a worst-case performance guarantee. While online algorithms are robust against the worst-case scenarios, they are often overly cautious and thus suffer a poor average performance in typical scenarios. On the other hand, Machine Learning (ML) algorithms typically show promising average performance in various applications but lack worst-case performance guarantees. To harness the benefits of both methods, we develop a learning-augmented algorithm (LADTSR) by integrating ML predictions into the robust online algorithm, which outperforms the robust online algorithm under accurate predictions while ensuring worst-case performance guarantees even when predictions are inaccurate. Finally, we conduct numerical experiments on both synthetic and real-world trace data to corroborate the effectiveness of our approach.

Optimal scheduling of integrated wind- photovoltaic-hydrogen energy system considering hydrogen application and waste heat recovery

In order to further promote the use of renewable energy sources such as wind-pv-hydrogen and carbon emission reduction in the park integrated energy system, battery fuel cell waste heat recovery is less considered and has a greater economic value, in this context, the paper proposes an optimal scheduling strategy for the park-level integrated energy system with hydrogen fuel cell vehicles (HFCV) taking into account the fuel cell waste heat recovery in order to improve the economic efficiency and stability characteristics of the system. In this paper, a fuel cell waste heat recovery model is constructed, followed by the introduction of a tiered carbon trading model, and then a scheduling model is constructed with the objective of minimizing the sum of the system's energy purchase cost, carbon trading cost, and the penalty cost for abandoning the wind and solar, and solved by CPLEX. Finally, the effectiveness of the proposed strategy is demonstrated through the analysis of a simulation example, which leads to a 30.7% reduction in the total system cost, a reduction in carbon emissions by 18.8% and a 34.2% reduction in the system's purchased energy cost compared to the conventional model.

Global enhanced oil recovery assessment: A comprehensive data-driven analysis using self-organizing maps

This study presents a comprehensive analysis of Enhanced Oil Recovery (EOR) methods' application in the global oil industry, identifying trends and main factors influencing their adoption. A wealth of unstructured and scattered data from public documents covering traditional EOR methods and newer technologies was processed to create a reliable global database, encompassing 1237 EOR projects in reservoirs around the world. Data science techniques and self-organizing maps (SOM) were employed for data analysis, dimensionality reduction, and visualization of complex problems, proving suitable for high-dimensionality and incomplete data. The research integrates various data modalities and data science visualization techniques, enabling a high-level representation of EOR projects' self-contained knowledge, and providing a comprehensive view of EOR application worldwide. The study reveals implicit interrelationships between multiple variables and EOR methods, identifying trends, research and development directions, and better-informed decisions. In-depth analysis showed that factors impacting the choice, application, and success of EOR methods include technical properties of reservoirs, local, commercial, and temporal aspects. Despite the diffusion of secondary and tertiary recovery technologies, a concentration of EOR methods exists in specific regions. Factors influencing EOR projects' implementation, maintenance, and expansion range from technical reasons, deselected reserves, and infrastructure to incentive policies and regulatory aspects of each country, business structure, and global economic scenario. Both petrophysical and non-petrophysical variables influence the application of EOR methods. Availability and costs of purchasing and transporting injection fluids greatly impact decision-making processes. The analysis contributes to understanding the complex network of factors influencing EOR, and this work can aid in making more informed decisions in the industry and research for EOR applications.

PLANO DE GESTÃO DE LOGÍSTICA SUSTENTÁVEL: ESTUDO DE CASO SOBRE AS AÇÕES PROPOSTAS PARA A REDUÇÃO DO CONSUMO DE COPOS DESCARTÁVEIS NO IFPR-FOZ

The aim of this study is to analyze the contribution of actions to mitigate the consumption of disposable cups at the Federal Institute of Paraná – Foz do Iguaçu Campus, proposed in the agency's Sustainable Logistics Management Plan, and which target campus employees. The research had as methodological path the bibliographic and documentary research of the case study, which resulted in a survey of quantitative and qualitative data. The results show that, despite the possibilities of improvement of the actions and that even in the face of their discontinuity, there was still a decrease in the consumption of plastic cups on campus, and no new acquisitions of the product were made. In addition, it was found that the financial impact for the acquisition of plastic cups was derisory in relation to the total cost of purchase of consumables in 2017, thus noting that the motivation of the application of the actions is based on the intention of the agency to outline a management focused on sustainability.

A Cost Analysis of COVID-19 Vaccination: A Comparison between mRNA-based and Inactivated Virus Vaccines

Introduction: COVID-19 vaccination is considered a primary preventive measure to combat the COVID-19 pandemic. Several types of COVID-19 vaccines were developed, among them the mRNA-based vaccine and the inactivated virus vaccine. Different COVID-19 vaccines have different purchase price and require different logistics system, which translate into different estimated vaccination costs. Therefore, this study aims to estimate the cost of COVID-19 vaccination using the mRNA-based vaccine compared to the inactivated virus vaccine to assist future decision-making related to COVID-19 vaccination. Methods: Direct costs for per-dose vaccination for both vaccines were estimated from the health provider’s perspective using the microcosting approach. Vaccination pathways were constructed for both the mRNA-based and the inactivated virus vaccine based on activities involving the acquisition, transportation, storage, and vaccine injection processes. Cost value was obtained from purchase documents, asset records, and market surveys. Results: The estimated costs for COVID-19 vaccination were RM 69.71 per dose for the mRNA-based vaccine and RM 91.68 per dose for the inactivated virus vaccine. The difference in estimated vaccination costs was mainly driven by the difference in the vaccine purchase costs which was higher for the inactivated virus vaccine. Conclusion: COVID-19 vaccination using the mRNA-based vaccine incur was estimated to incur lower total costs to the health provider compared to the inactivated virus vaccine during the pandemic.

Evolution of Crop Planting Structure in Traditional Agricultural Areas and Its Influence Factors: A Case Study in Alar Reclamation

This research provides a comprehensive analysis of the spatiotemporal evolution of the regional cropping structure and its influencing factors. Using Landsat satellite images, field surveys, and yearbook data, we developed a planting structure extraction model employing the classification regression tree algorithm to obtain data on the major crop cultivation and structural characteristics of Alar reclamation from 1990 to 2023. A dynamic model and transfer matrix were used to analyze temporal changes, and a centroid migration model was used to study spatial changes in the cropping structure. Nonparametric mutation tests and through-traffic coefficient analysis were utilized to quantify the main driving factors influencing the cropping structure. During the period of 1990–2023, the cotton area in the Alar reclamation region expanded by 722.08 km2, while the jujube exhibited an initial increase followed by a decrease in the same period. The primary reasons are linked to the cost of purchase, agricultural mechanization, and crop compatibility. In the Alar reclamation area, cotton, chili, and jujube are the primary cultivated crops. Cotton is mainly grown on the southern side of the Tarim River, while chili cultivation is concentrated on the northern bank of the river. Over the years, there has been a noticeable spatial complementarity in the distribution and density of rice and cotton crops in this region. In the Alar reclamation, the main factors influencing the change in cultivated land area are cotton price, agricultural machinery gross power, and population. Consequently, implementing measures such as providing planting subsidies and other policy incentives to enhance planting income can effectively stimulate farmers’ willingness to engage in planting activities.

Production of Galacto‐Oligosaccharides by Enzymatic Membrane Reactors Using Free β‐Galactosidase

AbstractPrebiotic galacto‐oligosaccharides (GOSs) are synthesized by transgalactosylation of lactose using β‐galactosidases. Well‐established, large‐scale production of GOS is performed by stirred‐tank reactors (STR) operating in batch fashion. Main manufacturing expenses are attributed to enzyme purchase costs and the downstream operations required for subsequent enzyme inactivation and removal. In the last decades, several studies and patents have appeared on free enzyme membrane reactor (FEMR) as an economic alternative to STR due to enhanced enzyme recovery and reuse. In this review, we attempt to summarize the key design aspects, the catalytic performance and efficiency of FEMR compared to STR technology, and the main concerns related to stable product quality over long‐term operation.

Analysis of the Economic Soundness and Viability of Migrating from Net Billing to Net Metering Using Energy Cooperatives

In the European Union, increasing attention is already being paid not only to the development of renewable energy sources, but also to the establishment of solutions to achieve local energy self-sufficiency while increasing the role of citizens in managing the energy they generate. This approach is expected both to have a positive impact on the environment and the reduction of greenhouse gas emissions, and to enhance energy security—both in economic and civic terms by, i.a., combating energy poverty. The development of local energy communities promoted in the EU is supported i.a. by energy cooperatives. These contribute to the efficient harnessing of renewable energy potential in rural and urban-rural areas, and have been developing in Poland for several years now. In their previous studies, the authors of this research paper attempted to verify the generation (number, type and capacity of installed sources) and consumption (energy demand) configurations in which an energy cooperative would be a viable solution for prosumers who might establish it. However, over the past few years, the conditions for prosumers and the method of their accounting with the electricity seller have changed radically in Poland (shift from net metering to net billing). This situation has opened up space for further research and encouraged the authors to revisit the problem of analyzing the viability of establishing energy cooperatives in relation to the rules of operation of individual prosumers. This research was carried out for three scenarios, and the horizon of the analyses conducted and described extends to 2045. The comparative analysis included energy consumers without their own generation sources, prosumers with a photovoltaic generation installation covered by the net billing model, as well as a scenario involving prosumers’ cooperation within an energy cooperative, which by law is settled in the net metering model. Conclusions from the research and simulations made it possible to confirm the claim that, despite changes in the rules of prosumer billing, developing energy independence in the energy community formula results in a significant reduction in the cost of purchasing electricity (even several times lower purchase costs in the timeframe analyzed) and can lead to a reduction in the payback time of investments in generation sources even by a factor of two. The results presented in this research paper open up space for further research. The outcomes allow us to assume that energy cooperatives—in the organizational and institutional model in Poland—are a good tool for reducing the phenomenon of energy poverty on a local scale.

Transesterification of Persea americana seed oil to methyl ester using bio-based heterogeneous catalyst: Optimization and techno-economic analysis

The techno-economic analysis of producing biodiesel from agrarian wastes at optimal condition was carried out. The acid activated empty palm fruit bunch ash (AAEPFBA) catalyzed the methyl ester production. The AAEPFBA catalyst was synthesized from waste palm fruit bunch, and modified with phosphoric acid. The activation was best achieved by adding H3PO4 acid in a ratio of 1:2 (g/ml). The modification of the catalyst increased the alkaline properties and surface area and decreased the particle size and adsorption energy. The process variables of temperature 61.9 °C, reaction time of 3.09 h, methanol/oil molar ratio of 10.2:1, catalyst concentration of 3.14wt%, and agitation speed of 321.54 rpm at a desirability of one gave an experimental optimal yield of biodiesel as 90.3%. Based on the experimental study, 3062 litres (2728 kg) of biodiesel was evaluated annually. The annual operating cost, payback period, and internal rate of return are ₦2201288, 1.5 years, and 7.6%, respectively. The sensitivity analysis of oil purchase cost, biodiesel selling price, and labour cost on Return on investment, PBP, IRR, and Net present value were investigated. In this study, the relative biodiesel selling price is a determinant of the profitability of producing avocado pear seed oil methyl ester. The overall results show that investors can embark on biodiesel production using avocado pear seed oil, and AAEPFBA as feedstocks.

Assessment of Energy Self-Sufficiency of Wastewater Treatment Plants—A Case Study from Poland

Currently, one of the main goals is to make municipal wastewater treatment plants (WWTPs) energy-neutral. However, advanced wastewater treatments and sewage sludge processing are still classified as highly energy-intensive. In this study, the energy self-sufficiency potential assessment of the WWTP located in Krosno (Poland) was evaluated. Moreover, the possible paths for improving the energy balance of the analyzed facility are presented in this paper. The performed evaluation indicated that in 2016–2019, the energy consumption at WWTP Krosno varied from 0.25 to 0.71 kWh/m3 of wastewater (average 0.51 kWh/m3), and the highest energy utilization values in each year were recorded around the summer season. An analysis of the data showed that as the pollutant load flowing into the WWTP increased, its energy utilization decreased. Such results indicate that the treatment cost per cubic meter decreases as the load increases due to the capital cost being the same. The estimated self-sufficiency of the facility in the years analyzed was 50.5%. The average energy recovery from 1 m3 of wastewater was 0.27 kWh/m3, and the average energy recovery from 1 m3 of biogas was 1.54 kWh/m3. Since the energy balance of this wastewater treatment plant, determined primarily by the continuously increasing cost of energy purchases, has to be improved, two courses of action were identified that will allow for increasing self-sufficiency. The co-digestion strategy was indicated as the easiest solution to implement, given the on-going anaerobic stabilization of sewage sludge at this WWTP. Moreover, the possible co-substrates that can be obtained from local suppliers were indicated. The second course of action, which requires a thorough economic analysis, is sludge pre-treatment, which might improve sewage sludge properties, resulting in a more favorable biogas yield.

Pellets from mixtures of short rotation coppice with forest-derived biomass: Production costs and energy intensity

The demand for feedstock for pellet production is growing, with a consequent increase in the cost of their purchase and the cost of production. The objective of this study was to determine the cost and energy intensity of pellet production from forest biomass (Scots pine, silver birch) and short rotation coppice – SRC (black locust, poplar and willow) and their mixtures. The lowest cost (339.7 € Mg−1) and energy input (1448 kWh Mg−1) for pellet production were determined for Scots pine. The cost of pellet production from poplar and willow was higher by 5–6% compared with Scots pine pellet. The energy input for pellet production from willow, black locust and poplar was higher by 48.1%, 50.4% and 53.2%, respectively. The feedstock purchase cost (50.3–57.8%) and energy input for drying them (74.6–82.6%) accounted for the largest portion. Increasing the share of SRC biomass from 25% through 50%–75% relative to Scots pine and silver birch biomass resulted in a cost and energy intensity increase and decrease in the energy efficiency of pellet production. Three pellet types: produced from Scots pine and those with an addition of 25% of willow or poplar made up one cluster.

아파트 구매결정 요인이 주거만족도 및 재구매 의도에 미치는 영향

This study aims to analyze the mediating effect of buyer's perceived value on the impact of apartment purchase decision factors on residential satisfaction and repurchase intention among residents of apartments in Jeju Island. To achieve this, a survey was conducted on 437 residents of apartments in Jeju Island, and the results were analyzed.
 The research findings revealed that among the factors influencing apartment purchase decisions, locational factors negatively influence repurchase intentions, economic factors negatively influence purchase satisfaction, while physical and social factors positively influence residential satisfaction. Additionally, economic and social factors among the purchase decision factors positively influence functional value, and social factors positively influence social value.
 Furthermore, functional value positively influences purchase satisfaction, social factors positively influence both purchase satisfaction and repurchase intention. Both functional value and social value play a partial mediating role in the relationship between apartment purchase decision factors and residential satisfaction.
 These research results suggest several implications. When purchasing an apartment, it is necessary to conduct a precise analysis of locational factors and surrounding market prices. From the seller's perspective, active promotion of various amenities, safety, convenience, and accessibility is essential. Moreover, promoting the superiority of functional and social values of apartments compared to the purchase cost is crucial. To enhance apartment purchase and repurchase intentions from consumers, ensuring the adequacy of quality and price, as well as enhancing satisfaction through factors such as social status improvement resulting from apartment purchase, are necessary.