Cost Value Research Articles

Coyote and Badger Optimization (CBO): A natural inspired meta-heuristic algorithm based on cooperative hunting

Optimization techniques play a pivotal role in refining problem-solving methods across various domains. These methods have demonstrated their efficacy in addressing real-world complexities. Continuous efforts are made to create and enhance techniques in the realm of research. This paper introduces a novel technique that distinguishes itself through its clarity, logical mathematical structure, and robust mathematical equations, particularly in the second phase. This study presents the development of a new metaheuristic algorithm named Coyote and Badger Optimization (CBO). CBO draws inspiration from the cooperative behaviors observed in honey badgers and coyotes, with a specific focus on their intriguing communication process. Utilizing the inherent traits of these animals, the proposed CBO algorithm offers an intuitive and effective solution for addressing engineering optimization challenges by providing the best fitness values. To validate CBO's effectiveness in real-time applications, complex engineering problems called pressure vessel design, feature selection in medical system, and tension-compression spring design are used as case studies for testing the proposed CBO compared to other recent algorithms. Additionally, ten benchmark functions and also statistical analysis methods (mean, standard deviation, confidence intervals, t-test, and Wilcoxon test) are used. Experimental results demonstrate that the CBO algorithm surpasses eleven recent algorithms when subjected to common ten benchmark functions. Additionally, CBO outperforms other recent eleven algorithms according to three different case studies. According to the ten benchmark functions (F1 to F10), CBO provides the minimum fitness values which are closed to the exact (standard) values; 0, 0, 0.003, 0.0002, -1.0316, 3.0058, 0.398, 0.02, 0.00076, and 0.000725 respectively. Related to statistical analysis, CBO provides the best mean, standard deviation, confidence intervals, t-test, and Wilcoxon test values. According to case studies, CBO provided the minimum cost value for pressure vessel design, the maximum accuracy value for feature selection, and the minimum cost value for spring design. Hence, CBO superiors other recent algorithms.

Machine learning with analysis-of-variance-based method for identifying rice varieties

Rice, which has the highest production and consumption rates worldwide, is one of the main nutrients in our country because it is economical and nutritious. Rice undergoes various stages of production from the field to the dinner tables, with the cleaning phase involving separation of rice from unwanted materials. There are different varieties of rice such as arborio, basmati, aracadaga, ipsala, and jasmine. Therefore, it is necessary to identify rice varieties based on customer demands. Rice variety identification is primarily based on its outer appearance, including color, size, and shape. In this study, 75,000 rice grain images, including 15,000 images for each variety, were considered. Using a combination of 12 morphological features, four shape features, and 90 color features obtained from five different color spaces, 106 features were extracted from the images. An analysis of variance (ANOVA) was employed to select high-rank features, which were then fed to a support vector machine (SVM) for classification. The experimental results demonstrated that the utilization of 40 high-rank features yielded impressive outcomes, with a validation accuracy of 99.89 %, cost value of 16, test accuracy of 99.81 %, and cost value of 28. These high accuracy rates are particularly noteworthy, as they indicate the robustness and reliability of the proposed methodology in distinguishing between different rice varieties with minimal error. This high level of precision is critical for practical applications, such as quality control in rice production, to ensure that customers receive the specific rice variety they demand. Implementing this methodology in real-world scenarios could significantly enhance the efficiency and accuracy of the rice variety identification processes, benefiting both producers and consumers.

Cost-effectiveness analysis of first-line sintilimab plus chemotherapy vs. chemotherapy alone for unresectable advanced or metastatic gastric or gastroesophageal junction cancer in China.

The Chinese Society of Clinical Oncology (CSCO) has recommended sintilimab plus chemotherapy (SINT + Chemo) as a standard first-line therapy for advanced gastric cancer or gastroesophageal junction cancer (GC/GEJC), based on the proven effectiveness and safety in the ORINT-16 trail. Its cost-effectiveness, however, remains to be evaluated. We established a partitioned survival approach (PartSA) model with a 10-year time horizon to determine whether SINT + Chemo (vs. chemotherapy) was more cost-effective as a first-line treatment for unresectable advanced or metastatic GC/GEJC. Survival data was generated from the ORIENT-16 trail. Cost calculation was limited to direct medical costs. Database of Hunan Public Resources Trading Service Platform was used as the source for obtaining drug prices. Other cost and utility values were gathered from established literature. Incremental cost-effectiveness ratio (ICER) was the primary output. Additionally, we conducted sensitivity analysis, subgroup analysis, and scenario analysis. In the base-case analysis, group SINT + Chemo showed an increase in utility value by 0.32 quality-adjusted life-years (QALYs) at an extra cost of $7988.43, resulting in an ICER of $25239.29/QALY, below the Chinese cost-effective willingness-to-pay (WTP) threshold of $38223.34. Upon further subgroup analysis according to patients' programmed death 1 ligand (PD-L1) combined positive score (CPS), the ICERs were $26341.01/QALY for patients highly expressing PD-L1 (CPS ≥5) and $17658.26/QALY for patients lowly expressing PD-L1 (CPS <5). Based on the sensitivity analysis, we found the PFS utility was the parameter that had the most significant impact on the model's outcomes. Moreover, in scenario analysis, the results remained consistent despite variations in the model's time frame. In China, SINT + Chemo is a more cost-effective option (vs. chemotherapy) as a first-line therapy for unresectable advanced or metastatic GC/GEJC, irrespective of PD-L1 expression levels.

REDUCTION OF FOOD COSTS AND WASTE IN PRE-PREPARATION OF MEALS BY IMPLEMENTING TECHNICAL TOKENS IN A FORTALEZA-CEARÁ FOOD AND NUTRITION UNIT

The Food and Nutrition (HFS) is a unit which develops activities related to food and nutrition, providing safe meals. To reduce waste, it can be used to produce sheet (FTP) that it is an effective tool for the control of production of foodstuffs both in financial as well as nutrition. This work aims to standardize and reduce waste in the pre-preparation and cost of preparations by deploying factsheets on a Power Unit Nutrition business in Fortaleza-Ceará. This is an observational study, prospective, descriptive quantitative, with six days of execution. The fact sheets were made of five (5) dishes present in the unit in which menu was composed of entrance, main dishes, base plate, trim and desserts. Among the FC obtained in this study there was a significant variation in the results available in the literature, making it possible to compare the losses, evaluation of raw materials, storage, receipt and hand labor used. With the reduction of factors, therefore there was a reduction in the cost of the preparation of the meals, as FC interfere with cost values, the sales price is an important factor in determining the profit of the company. Based on the data presented, it is concluded that this data sheet is a very important tool for the units of food and nutrition, it provides a reduction in waste and cost of preparations, and contribute to the choice of supplier, upon receipt, storage , handling, assisting in the planning of purchasing, control production costs and providing a standard and quality of meals.

Factors Affecting Work Efficiency’sEmployee ofChampalao Deposit Taking Micro Finance Institution

This Researchaimedto study the motivation, efficiency in the operation of microfinance institution employees who receive deposits in Lao Pagoda and the factors that affect the performance of employees of microfinance institutions who receive deposits in Lao Pagoda.By using 50 sets of questionnaires to collect data from 50 target groups and the program SPSS software was used to analyze all the data. The research results found that the employee's work motivation is the motivation to monitor and evaluate the performance frequently with the value of X̅= 4.47;SD=0.46, job responsibility is appropriate with value X̅= 4.23;SD=0.57, types ofwork has a value of X̅= 4.07;SD=0.58, Work improvement has value X̅= 4.02;SD=0.72 and acceptance of each other within the organization staff has a value of X̅= 4.01;SD=0.64.There is also a good relationship between colleagues with a value of X̅= 4.22;SD=0.56, the level of productivityin the work function, value X̅= 4.14;SD= 0.60, policy and organizational management has a value of X̅= 4.01;SD= 0.65, working environment value X̅= 3.96 ;SD= 0.71 and lastly labor cost and welfare value X̅= 3.73;SD=0.73. The efficiency in the performance of the employee's work wasin a highlevels, such as good time management with a value of X̅= 4.37;SD= 0.53,the quality of work performed X̅= 4.31;SD= 0.48, in terms of the amount of work that can be done, the value of X̅= 4.03;SD=0.54. Factors that affect the performance of microfinance institutions that accept deposits inLaos have only one factor such asjobstability (X10) with a statistical significance level of 0.01.

Cost misperception and voting for public goods

AbstractPublic good provision is often determined through referendums by voters, who weigh benefits against costs. However, perceptions of benefits and costs may be incorrect, which could in turn lead to voter error and misallocation of public goods. Using real‐world referendums, we evaluate voter perceptions of the private costs of providing public goods by conducting three exit polls of New England voters and an online survey of California voters. By comparing cost perceptions to actual tax incidence, we find pervasive evidence that voters misperceive costs. Fewer than 20% of voters in our samples reported perceived costs within 25% of estimated actual costs. These findings are unsurprising given the ubiquity of opaque language explaining the financial consequences of public good referendums. In addition, our analysis suggests that actual costs have no statistical bearing on voter choice, but at least in the New England sample, voter approval is affected by perceived costs. Thus, a substantial proportion of voters are making decisions based in part on inaccurate costs, which in some cases lead to people voting against their preferences and potential misallocation of public funds. Further, researchers who match voter approval with estimated actual cost are unlikely to obtain accurate cost responsiveness or valuation estimates.

Implementation of Value Engineering For Optimization of Road Reconstruction Project Financing Case Study: Cibadak – Padasuka Road Section Project, Lebak Regency, Banten Province

Road reconstruction is one of the important infrastructure projects in supporting economic growth and community mobility. However, such projects are often faced with budget constraints, which require the application of effective methods for cost management. One method that can be used is Value Engineering (VE), which aims to optimize costs without reducing the quality or function of the project. This study focuses on the implementation of VE in the Cibadak - Padasuka road reconstruction project in Lebak Regency, Banten. The study was conducted through an analysis of several project components that have the potential to be optimized, using methods such as Pareto analysis, and Life Cycle Cost (LCC) analysis. The results of the study indicate that 1. The work item that is the subject of Value Engineering study in this study is Road Paving work; 2. The Value Engineering study obtained 3 alternatives, namely the Rigid Pavement Method PPCP, SPRigWP &amp; Flexible Pavement; 3. The construction cost value of Value Engineering is: • Alternative 1 PPCP Method against alternative 0 (Conventional Concrete/Cast In Situ) experienced an increase in construction costs of Rp. 324,545,518.51 or 3.30%, • Alternative 2 SPRigWP Method against alternative 0 (Conventional Concrete/Cast In Situ) experienced an increase in construction costs of Rp. 2,693,845,581.45,- or 30.75%, • Alternative 3 Flexible Pavement compared to alternative 0 (Conventional Concrete/Cast In Situ) experienced a decrease in construction cost of Rp. 1,368,807,868.21, or 15.63%; 4. The Life Cycle Cost (LCC) value from Value Engineering is: • Alternative 1 PPCP Method compared to alternative 0 (Conventional Concrete/Cast In Situ) experienced a decrease in Life Cycle Cost (LCC) of Rp. 1,020,655,389.33,- or 9.55%, • Alternative 2 SPRigWP Method against alternative 0 (Conventional Concrete/Cast In Situ) experienced an increase in Life Cycle Cost (LCC) of Rp. 1,375,850,167.98 or 12.87%, • Alternative 3 Flexible Pavement against alternative 0 ( Conventional Concrete/Cast In Situ) experienced an increase in Life Cycle Cost (LCC) of Rp. 10,697,757,430.34 or 100.07%. So that in the study conducted on the Cibadak - Padasuka Road Reconstruction Project, Warunggunung District, Lebak Regency, Banten Province Alternative 1 Precast Prestressed Concrete Pavement (PPCP) Rigid Pavement Method is the recommendation with the lowest LCC value.This research is expected to provide practical recommendations that can be applied to similar projects in the future, as well as contributing to the development of science in the field of civil engineering and project management.

Intelligent building construction cost optimization and prediction by integrating BIM and elman neural network

This study aims to address the challenges of capturing design changes, supply chain fluctuations, and labor cost variations to improve the accuracy and real-time nature of intelligent building construction cost predictions. It seeks to accurately forecast and optimize project costs. The study innovatively constructs an intelligent building construction cost prediction model based on Building Information Modeling (BIM) and Elman neural networks (ENNs), denoted as the BIM-ENN model. The BIM-ENN model first introduces BIM technology to digitize and visualize information related to building structures, electromechanical systems, and pipelines. The digitized data obtained through BIM technology is then used as input data for the ENN, which optimizes the neural network parameters to predict and optimize intelligent building construction costs. Finally, the BIM-ENN model is experimentally evaluated. The results demonstrate that the prediction value of the construction cost of the intelligent building by this model closely matches the original information price, with a prediction accuracy of 95.83 %. Compared with the single ENN, the root mean squared error of the BIM-ENN model is less than 75, and the determination coefficient is above 0.95. This indicates that this model can explain more than 95 % of the construction cost prediction results, making it a feasible solution for actual prediction problems and achieving satisfactory results. The intelligent building construction cost prediction model reported here exhibits high accuracy and reliability. It can successfully forecast construction costs, providing robust decision support for the digitalization and intelligent development of construction enterprises. The practical significance of this study lies in providing the construction industry with an accurate cost management tool that helps enterprises optimize budget control and resource allocation, enhancing risk assessment and management capabilities. Moreover, the potential impact of the BIM-ENN model is its ability to elevate prediction standards within the construction industry, promote technological integration and innovation, enhance enterprise competitiveness, and drive the industry's transition towards digitalization and intelligent sustainable development.

Optimal Scheduling of Renewable Sources Based Micro Grid With PV and Battery Storage Using Crayfish Optimization Algorithm

ABSTRACTEnvironmental concerns and energy security are pressing issues of the 21st century, with a heavy reliance on fossil fuels causing significant environmental pollution and resource depletion. To mitigate these problems, it is crucial to explore and implement alternative clean energy sources. This manuscript proposes a novel crayfish optimization algorithm (COA) for optimal scheduling in a hybrid power system that incorporates various renewable energy sources, like battery energy storage systems (BESS), fuel cells (FC), wind turbines (WT), micro turbines (MT) and photovoltaic (PV) panels. The importance of the work lies in its ability to optimize the entire operating costs of a grid‐connected microgrid while improving the accuracy and efficiency of energy management. The COA method addresses economic dispatch problems and manages energy within the grid‐connected microgrid, accounting for high levels of uncertainty. The proposed approach, tested using MATLAB Simulink, achieved a cost value of 252, outperforming existing methods such as GTO, PSO, SSA, and ALO. This illustrates the potential of the proposed technique to provide more cost‐effective and efficient energy management solutions in hybrid power systems.

Energy Maximization Absorption of Wave Energy Converter Based on Fourier Pseudo-Spectral Method and Adaptive Dynamic Programming

In this paper, a novel noncausal control framework to address the energy maximization problem of wave energy converters (WECs) with physical constraints is proposed. The energy maximization problem of WECs is a constrained optimal control problem. The proposed control framework converts this problem into a reference trajectory-tracking problem through the Fourier pseudo-spectral method (FPSM) and utilizes the online tracking adaptive dynamic programming (OTADP) algorithm to realize real-time trajectory tracking for practical use in the ocean environment. Using the wave prediction technique, the optimal trajectory is generated online through a receding horizon (RH) implementation. A critic neural network (NN) is applied to approximate the optimal cost value function and calculate the error-tracking control by solving the associated Hamilton-Jacobi-Bellman (HJB) equation. The proposed WEC control framework improves computational efficiency and makes it feasible for the online control of the WEC in practice. Simulation results show the effects of the receding-horizon implementation of FPSM with different window lengths and window functions, while verifying the performances of tracking control and energy absorption of WECs in two different sea conditions.

The Effect on Business Strategy on Accounting Method for R&amp;D Cost and Firm Value

The Effect on Business Strategy on Accounting Method for R&amp;D Cost and Firm Value

Modified Harris Hawks optimization for the 3E feasibility assessment of a hybrid renewable energy system

The off-grid Hybrid Renewable Energy Systems (HRES) demonstrate great potential to be sustainable and economically feasible options to meet the growing energy needs and counter the depletion of conventional energy sources. Therefore, it is crucial to optimize the size of HRES components to assess system cost and dependability. This paper presents the optimal sizing of HRES to provide a very cost-effective and efficient solution for supplying power to a rural region. This study develops a PV-Wind-Battery-DG system with an objective of 3E analysis which includes Energy, Economic, and Environmental CO2 emissions. Indispensable parameters like technical parameters (Loss of Power Supply Probability, Renewable factor, PV fraction, and Wind fraction) and social factor (Human Developing Index) are evaluated to show the proposed modified Harris Hawks Optimization (mHHO) algorithm’s merits over the existing algorithms. To achieve the objectives, the proposed mHHO algorithm uses nine distinct operators to obtain simultaneous optimization. Furthermore, the performance of mHHO is evaluated by using the CEC 2019 test suite and the most optimal mHHO is chosen for sizing and 3E analysis of HRES. The findings demonstrate that the mHHO has achieved optimized values for Cost of Energy (COE), Net Present Cost (NPC), and Annualized System Cost (ASC) with the lowest values being 0.14130 $/kWh, 1,649,900$, and 1,16,090$/year respectively. The reduction in COE value using the proposed mHHO approach is 0.49% in comparison with most of the other MH-algorithms. Additionally, the system primarily relies on renewable sources, with diesel usage accounting for only 0.03% of power generation. Overall, this study effectively addresses the challenge of performing a 3E analysis with mHHO algorithm which exhibits excellent convergence and is capable of producing high-quality outcomes in the design of HRES. The mHHO algorithm attains optimal economic efficiency while simultaneously minimizing the impact on the environment and maintaining a high human development index.

Evaluation of perceived value and behavioral intentions of citizens in Chinese commercially open communities: A case study of Liuyun Community

As urban development shifts from the incremental era to the stock era, urban design is becoming increasingly focused on high-quality living and human needs. However, the opening of traditional communities has been insufficiently examined. This study took Liuyun Community, a typical open community in Guangzhou (China) that has undergone a transformation from residential to commercial use, as its research object. First, more than 300 residents, tourists, and merchants were surveyed via questionnaires administered in field investigations and interviews. Next, the data from the completed questionnaires were used to construct a structural equation model consisting of five dimensions, namely commercial service value, cost value, risk value, perceptual value, and social value. The data were then analyzed to determine the correlations between perceived value and behavioral intention, satisfaction, and other psychological attributes. The following results were obtained: (1) As commercial activities in the community increased, the influence of social value, cost value, and commercial service value on behavioral intention decreased. (2) The open community inevitably had negative impacts on its citizens. However, these negative impacts were mitigated to some extent by high levels of social value. (3) Perceptions of commercial service value varied between groups.

Optimized the locations and sizes of FACTS devices on electrical network involving wind power using a new hybrid stochastic algorithm

This article focuses on utilizing a new hybrid optimization algorithm called the Fitness-Distance Balance-based Archimedes Optimization Algorithm (FDB-AOA) to solve the Optimal Power Flow (OPF) problems within a recently adopted electrical transmission grid, specifically the modified IEEE-30 bus system. This system integrates thermal and wind -based generating units, including various types of Flexible AC Transmission System (FACTS) devices. Several tests are performed where the stochastic wind energy is modeled using probability density functions. The optimization goal takes into account the cost of thermal generation, the direct cost of scheduled wind power, and the penalty cost for underestimating wind power. The locations and sizing of FACTS devices are optimized with aim of reducing several fitness functions. The optimization results achieved by the proposed method in solving single objective functions were more effective in finding the optimal solution compared to several well-known algorithms. The results show the superiority of the proposed method in the majority of case studies, as it achieved a better optimum solution with a total generation cost ( Cgen ) value of 806.9817 $/h, and a real power loss ( Ploss ) value 1.7619 MW, also yields a competitive gross cost ( Cgross ) value of 1104.6652 $/h compared to those obtained by the other algorithms. In contrast, the statistical analysis proven the superiority of this algorithm where the standard deviations (SD) required in solving the single objective problem ( Cgen ) is 0.0996, which are better compared to other techniques. the simulation results demonstrate that the FDB-AOA optimizer is robust than other approaches, like the Success history based-adaptive differential evolution (SHADE) algorithm, MSA (Moth Swarm Algorithm), and ABC (artificial bee colony) integrated with the SF (superiority of feasible solutions) approach, in solving OPF problems involving the integration of both thermal and wind power plants along with FACTS devices.

Neural quantile optimization for edge–cloud networking

We seek the best traffic allocation scheme for the edge–cloud networking subject to SD-WAN architecture and burstable billing. First, we formulate a family of quantile-based integer programming problems for a fixed network topology with random parameters describing the traffic demands. Then, to overcome the difficulty caused by the discrete feature, we generalize the Gumbel-softmax reparameterization method to induce an unconstrained continuous optimization problem as a regularized continuation of the discrete problem. Finally, we introduce the Gumbel-softmax sampling neural network to solve optimization problems via unsupervised learning. The neural network structure reflects the edge–cloud networking topology and is trained to minimize the expectation of the cost function for unconstrained continuous optimization problems. The trained network works as an efficient traffic allocation scheme sampler, outperforming the random strategy in feasibility and cost value. Besides testing the quality of the output allocation scheme, we examine the generalization property of the network by increasing the time steps and the number of users. We also feed the solution to existing integer optimization solvers as initial conditions and verify the warm-starts can accelerate the short-time iteration process. The framework is general, and the decoupled feature of the random neural networks is adequate for practical implementations.

Navigating Reversed Valuation Dilemmas: SME Enterprises Nationalized in 1950s Poland - A Case Study

The publication describes a problem that often arises in court cases regarding the valuation of SME forcibly nationalized in the 1950s. Valuation of compensation to previous SME owners is an ongoing court debate in Poland. The authors proposed an approach that took into account the economic conditions at that time and complied with the principle of paying compensation for the actual losses incurred, and not for the potential losses that could be incurred as a result. At the same time, the publication analysed other similar judgments in cases regarding compensation for nationalized enterprises. The practical and scientific value of the publication lies in the fact that it addresses an existing practical problem and its solution. Research Design &amp; Methods: the paper presents a conceptual proposal of the SME valuation method in unique circumstances, complemented by a detailed case study illustrating the application of this valuation approach. Findings: When calculating the compensation for nationalization or other forms of expropriation courts use various methods of valuing net assets (fair market value, book value, recreation value) plus separately determine the value of intangibles, such as licenses, based on the discounted average profits from such assets for a reasonable period of time. If indexation was employed (for historical cost valuation), it used either average interest rates in the economy, the cost of capital of a company, or the cost of lost opportunities for a particular company. Using discounted cash flow valuation for the entire business was uncommon and even if it was included in the bilateral investment treaties (which were meant to protect investors from expropriation) it was considered by the tribunals as speculative and unreliable. Our approach proposes net asset value approach with the use of inflation or average salaries for indexation (to calculate current values of assets) and a combined approach of historical values if they were known at the moment of nationalization and modern estimates of values of remaining assets (that belonged to the company but their value was not provided in the original documentation). Implications &amp; Recommendations: The analysis of historical compensation decisions and bilateral investment treaties suggests that the most popular approach to valuing a loss from expropriation is the fair market value approach and net assets approach with a separate valuation of significant intangible assets provided that they gave certainty of profits for the investor. Contribution &amp; Value Added: The practical and scientific value of the publication lies in the fact that it presents an existing practical problem of court calculation of compensations to owners of forcibly nationalized SMEs in Poland and its solution together with methodological framework that can be used in the future

Techno-Economic Assessment of Industrial Vanadium Flow Batteries Based on Experimental Data

This work presents a techno-economic model based on experimental and market data to provide forecasts of the profitability of vanadium flow batteries (VFBs), which are emerging as a promising technology for specific stationary energy services [1‒2]. Each component affecting the capital and operative costs was analyzed and the impact of side phenomena on capacity losses was considered. Relevant economic parameters were taken from real market data: discount rate was calculated based on the Capital Asset Pricing Model (CAPM) to account for real market conditions; the electricity price follows an arbitrage strategy to profit of the daily price fluctuations taken from the historical data of the Italian energy market [3‒4]. Technical parameters are taken from large-area multi-cell stack, rather than from small single cell experiments, thus allow to characterize the behavior of real industrial reactors [5‒6]. The economic performance indicators obtained are the capital cost, the operative cost, the Levelized Cost of Storage (LCOS) and the Net Present Value (NPV). The resulting values are: a discount rate of 9.12% and electricity prices of 200 € MWh–1 in selling and of 100 € MWh–1 in buying. Capital costs and LCOS were calculated for different system power and energy (E/P) ratings. At E/P = 2 h, the values of capital costs and LCOS were in the range of 800 – 900 € kWh–1 and 0.50 – 0.55 € kWh–1, respectively, whereas at E/P = 10 h they reduced to 350 – 380 € kWh–1 and 0.29 – 0.32 € kWh–1, respectively. In addition, a NPV analysis was carried out in order to evaluate whether these capital costs could provide a profit. The analysis suggests that only at capital cost < 105 € kWh–1 the break-even point (NPV = 0) was reached. The result is that the assumed techno-economic scenario VFBs were not profitable for every considered E/P. A perspective analysis was developed to reveal when VFBs can become profitable. Different screenings were made on both technical and economic parameters. At a technical level, a power density of 0.25 W cm–2, a RTE = 85%, a SOC = 90%, and a reduced number of regenerating processes in the lifespan (Nreg = 4) were considered. Regarding the economic parameters, a discount rate of 7 % was assumed, an electricity selling price of 250 € MWh–1 and a purchasing price of 75 € MWh–1 were used, compatible with a more pronounced Duck Curve of the daily electricity price, induced by the expansion of renewable energy sources expected in the near future. Under this scenario, the system costs decreased considerably: at E/P = 2 h, the Capital Costs and LCOS ranged as 530 – 570 € kWh–1 and 0.25 – 0.27 € kWh–1, respectively, whereas at E/P = 10 h, the Capital Costs and LCOS ranges as 260 – 270 € kWh–1 and around 0.17 € kWh–1, respectively. The latter figures made VFBs profitable for E/P in the range of 4 – 10 hours, as shown in the figure below.

The Development of a New Approach for the Harmonized Multi-Sectoral and Multi-Country Cost Valuation of Services: The PECUNIA Reference Unit Cost (RUC) Templates.

Increasing healthcare costs require evidence-based resource use allocation for which assessing costs rigorously and comparably is crucial. Harmonized cross-country costing methods for evaluating interventions from a societal perspective are lacking. This study presents the development process and content of the service costing templates developed as part of the European project PECUNIA. The six developmental steps towards technological readiness of the templates included (1) a common conceptual costing framework and review of methodological costing issues, (2) harmonization strategy formulation, (3) proof-of-concept with expert feedback, (4) piloting, (5) validation, and (6) demonstration in six European countries. The PECUNIA Reference Unit Cost (RUC) Templates for service costing are three new self-completion tools to be used with secondary or primary data for top-down micro-costing or top-down gross-costing approaches. Complementary data collection and unit cost aggregation/weighting templates are available. The applications leading to the final versions including (4) piloting through calculation of 15-unit costs, (5) validation within a Health Technology Assessment framework, and (6) RUC calculations mostly based on secondary data demonstrated the templates' general feasibility, with feedback for improved usability incorporated and a supplementary user guide developed. The validated PECUNIA RUC Templates for multi-sectoral and multi-country service costing allow for harmonized RUC development while incorporating flexibility and transparency in the choice of costing approaches, data sources and magnitude of remaining heterogeneity. The templates are expected to significantly improve the quality, comparability and availability of unit costs for economic evaluations, and promote the transferability of service cost information across Europe.

Analisis Rantai Nilai Kopi untuk Meningkatkan Nilai Tambah Petani Kopi di Kecamatan Paguyangan Brebes

The current coffee potential in Brebes Regency has not been accompanied by high income for coffee farmers. This is because farmers' knowledge of how to maintain coffee plants is still very low, farmer organizations are weak and government support is limited. Until now, coffee has not been considered a crop of strategic importance. The purpose of this study was to identify coffee commodity resources, analyze the value chain and determine cost drivers. This study used a mixed research approach of both qualitative and quantitative research. The qualitative research approach was to answer the research questions posed. Quantitative research in this study is to calculate the cost value of each process activity in coffee processing. Based on the value chain analysis, the determinants of downstream coffee processing products are greenbean processing, roasting and grinding. Greenbean processing is able to add value by 47.5% for robusta type and 71.47% for arabica coffee type. While roasting and grinding activities are able to increase the added value by 65.36% for robusta type and 85.12% for arabica type.

Optimal STI controls for HIV patients based on an efficient deep Q learning method

We investigate an efficient computational tool to suggest useful treatment regimens for people infected with the human immunodeficiency virus (HIV). Structured treatment interruption (STI) is a regimen in which therapeutic drugs are periodically administered and withdrawn to give patients relief from an arduous drug therapy. Numerous studies have been conducted to find better STI treatment strategies using various computational tools with mathematical models of HIV infection. In this paper, we leverage a modified version of the double deep Q network with prioritized experience replay to improve the performance of classic deep learning algorithms. Numerical simulation results show that our methodology produces significantly more optimal cost values for shorter treatment periods compared to other recent studies. Furthermore, our proposed algorithm performs well in one-day segment scenarios, whereas previous studies only reported results for five-day segment scenarios.