Cost Value Research Articles

Unmanned vehicle off-road path-planning method with comprehensive constraints on multiple environmental factors

ABSTRACT In complex off-road environments, different slopes, slope directions, and land types differently affect unmanned vehicle trafficability. Thus, we propose an unmanned vehicle off-road path-planning method with comprehensive constraints on multiple environmental factors. First, the influence of slope, aspect, and surface-coverage type on unmanned vehicle drivability was quantitatively evaluated, and the slope and land type influence layers were formed. Concurrently, a goal guidance layer was formed using the artificial potential field method. Second, the slope influence, land type influence, and goal guidance layers were used to quantitatively evaluate each grid’s pass cost value. Finally, the traditional A* algorithm, A* algorithm considering the impassable area, A* algorithm with comprehensive constraints, and Dijkstra algorithm with comprehensive constraints were used for path planning. The results show that the path slopes planned by A* algorithm with comprehensive constraints and Dijkstra algorithm with comprehensive constraints were smaller than those planned by A* algorithm and A* algorithm considering the impassable area; particularly, the length of the uphill path was shorter and the stability was better. The relevant research results provide methods and technical guidance for obtaining the shortest length, shortest time, and most stable and passable path in off-road environments.

Optimizing hybrid renewable energy based automated railway level crossing in Bangladesh: Techno-economic, emission and sensitivity analysis

The railway system in Bangladesh, particularly the level crossing system, needs significant advancements, including a shift towards using renewable energy to power these crossings. As a solution, this study proposes optimal hybrid systems powered by renewable energy on an automated railway level crossing system, which is reliable, efficient, and sustainable. The main contribution of this study is to introduce an optimal hybrid renewable energy-based automated railway level crossing system in Bangladesh, focusing on technical and economic evaluation, emissions, and sensitivity assessment using HOMER Pro. The proposed system examines the optimal outcome for a 1 kW vertical-axis wind turbine and a 0.440 kW photovoltaic system at five selected locations. The results reveal satisfactory net present cost values amounting to USD 8495, USD 8505, USD 8564, USD 8262, and USD 8357 for Narayanganj, Cox’s Bazaar, Noakhali, Dinajpur, and Rajshahi respectively. Moreover, HOMER Pro indicates that the photovoltaic-wind turbine–grid-connected model offers a lower Cost of Energy which is around 0.03 USD/kWh compared to other configurations. The Internal Rate of Return for the selected locations are 20 %, 33.7 %, 31.5 %, 5.2 %, and 4.6 % for Narayanganj, Cox’s Bazaar, Noakhali, Dinajpur, and Rajshahi, respectively. The developed structure is projected to have a payback period ranging from 4.57 to 13.29 years across the five selected locations. Furthermore, the proposed systems reduce greenhouse gas emissions by up to 44.23 % compared to a grid-only system. Additionally, sensitivity analysis is performed by varying wind speed and solar irradiation to emphasize the robustness of the proposed systems.

SHOULD ANTI-CGRP MONOCLONAL ANTIBODIES ALWAYS BE THE DRUG OF FIRST CHOICE FOR MIGRAINE PROPHYLAXIS IN BRAZIL? - A PHARMACOECONOMIC STUDY

Efficacy (success of therapy under ideal conditions), efficiency (the relationship between the costs and outcomes of a specific intervention), and effectiveness (the balance between efficacy and efficiency in clinical practice) are measures used to evaluate health interventions. Thus, in private practice and the public health system, the knowledge of these pharmacoeconomic data should influence the appropriate treatment choice. Migraine prophylaxis falls within this context. Traditional medications are available in the public health system, while galcanezumab is not routinely available. The present study aims to analyze the efficacy and effectiveness of galcanezumab and traditional therapeutic alternatives (amitriptyline, divalproex sodium, and topiramate). Efficacy data were obtained from the relevant literature (PubMed) and cost values from the ABCFARMA magazine. The economic impact analysis considered the cost of living for an economically active adult in Brazil based on the Brazilian Institute of Geography and Statistics (Instituto Brasileiro de Geografia e Estatística, IBGE) and the Institute for Applied Economic Research (Instituto de Pesquisa Econômica Aplicada, IPEA). Efficacy: amitriptyline: 40%; divalproex sodium: 30%; topiramate: 31%; galcanezumab: 50%. Their annual costs were: amitriptyline R$ 240.00; sodium divalproate R$ 876.00; topiramate R$ 600.00; galcanezumab R$ 13,992.00. Efficiency: amitriptyline 200%; divalproex sodium 41.1%; topiramate 62%; galcanezumab 4%. Ultimately, effectiveness is amitriptyline 120%; divalproex sodium 35.7%; topiramate 46.5%; galcanezumab 27%. Galcanezumab is the most effective; however, in a broader analysis, where payers, availability, and patient conditions are considered, evaluating efficacy alone may not be feasible in practical contexts and, therefore, anti-CGRP antibodies will not always be the first-line medications in migraine prophylaxis.

Incorporating costs and benefits to the evaluation of uncertain research results: applications to cancer research funding

Abstract Correctness is a key aspiration of the scientific process, yet recent studies suggest that many high-profile findings may be difficult to replicate or require considerable evidence for verification. Proposals to fix these issues typically ask for tighter statistical controls—e.g., stricter p-value thresholds or higher statistical power. However, these approaches often overlook the importance of contemplating research outcomes’ potential costs and benefits. Here, we develop a framework grounded in Bayesian decision theory that seamlessly integrates cost-benefit analysis into evaluating research programs with potentially uncertain results. We derive minimally acceptable pre-study odds and positive predictive values for cost and benefit levels. We show that tolerance to inaccurate results changes dramatically due to uncertainties posed by research. We also show that reducing uncertainties (e.g., by recruiting more subjects) may have limited effects on the expected benefit of continuing specific research programs. We apply our framework to several types of cancer research and their funding. Our analysis shows that highly exploratory research designs are easily justifiable due to their potential benefits, even when probabilistic models suggest otherwise. We discuss how the cost and benefit of research could and should always be part of the toolkit used by scientists, institutions, or funding agencies. Peer Review https://www.webofscience.com/api/gateway/wos/peer-review/10.1162/qss_a_00332

The societal cost of 'unwanted' loneliness in Spain.

Unwanted loneliness negatively affects people's health and quality of life, increasing morbidity and the risk of premature death; this situation can generate major social costs. The aim of this study is to estimate the social costs of loneliness in Spain for 2021: both tangible costs -monetary value of health costs and production losses- and intangible costs -Quality Adjusted Life Years (QALYs). To estimate costs not derived from mortality, information from a sample of 400 people with unwanted loneliness was compared with that derived from two samples of the general population obtained from the Spanish National Health Surveys. To estimate the costs associated with premature deaths, the population attributable fraction was calculated using the relative risks estimated in previous survival studies. In the baseline scenario, the tangible costs of unwanted loneliness are estimated to be around 14,129million euros in 2021, representing 1.2% of Spain's GDP. Approximately 56.8% of the tangible costs correspond to production losses due to reduced working time, and 43.2% are due to healthcare costs related with increased consultation frequency in healthcare services and higher consumption of medicines related to loneliness. In addition, loneliness generates a reduction in quality of life equivalent to 1.04million QALYs, which corresponds to 2.8% of the total stock of QALYs of the Spanish population over 15 years of age.

The contributions of direct and indirect selection to the evolution of mating preferences.

Many influential mathematical models of sexual selection have stressed that mating preferences evolve due to correlations that build between mating preferences and preferred display traits - that is, through indirect selection. Nevertheless, there is a perception that indirect selection should generally be overwhelmed by direct selection, for example in the form of search costs. Recent work by Fry has used quantitative genetic models to argue that in many cases, including when there are direct benefits (a fecundity advantage to mating with the preferred male), direct and indirect selection may be of similar magnitude. Here I use population genetic models, in which the strength of the genetic correlation is an emergent property of evolution at mating preference and display trait loci, to assess the relative contributions of direct and indirect selection to the evolution of mating preferences. For the cases of direct benefits and of indirect benefits with fixed and frequency-dependent search costs, I outline parameter values of fecundity benefits, preference strengths, and search costs for which indirect selection on female preferences can potentially predominate. I also analyze male mate choice under polygyny, showing that direct selection will always outweigh indirect selection except when there are direct benefits.

OPTIMIZATION OF LOADING AND UNLOADING WORKS AND PLACEMENT OF TARED AND PIECE CARGOES IN VEHICLES AND WAREHOUSES USING THE METHODS OF ERGONOMICS AND THEORY OF SYSTEMS

Purpose. The tasks of optimizing the placement of tared and piece cargoes (or the task of loading) in a limited volume (capacity) are typical for cases of loading with tared and piece cargoes (mainly large-sized) of transport containers, car bodies, ship holds in the merchant fleet, transport aircraft, railway wagons, areas of the warehouse complex. At the same time, the safe and efficient performance of loading and unloading operations with tared and piece cargoes, including large-sized ones, in a limited space is directly related to taking into account the requirements and using ergonomic methods. The purpose of this paper is to develop a mathematical model for the optimization of the placement of tared and piece cargoes (or about loading) in a limited volume (capacity), which is used to select a subset of cargo units with the maximum relative utility, while observing the limit on the total volume (or mass) of cargo. Methods. The analysis of literary sources made it possible to establish that for modeling and solving the problem of optimizing the placement of cargo (or loading) in a limited volume (capacity), it is rational to use one of the exact solution methods (dynamic programming method), the number of stages of which is the number of types of cargo. The work uses the method of dynamic programming, methods of theory of systems, and linear optimization. Results. The article presents a mathematical model of the problem of optimal placement of tared and piece cargoes (or about loading) in a limited volume, and for the case of placing weapons, ammunition, weapons, it is proposed to maximize not the function of the total cost (value) of the cargo, but the function that would take into account the relative usefulness of each cargo. The relative usefulness of each cargo is proposed to be determined by expert methods of systems theory, for example, the Delphi method, which is well formalized and suitable for computer processing of results. Scientific novelty. The scientific novelty consists in the development of a mathematical model that allows solving the problem of optimal placement of container loads (or loading) in a limited volume, and the target function of the optimization model is proposed to be the total relative usefulness of the cargo. Practical significance. Defined ergonomic requirements for cargo operations with tared and piece cargoes in a limited space (vehicles, warehouses). A method of numerical calculation based on the model is proposed (the case of loading a car body with tared and piece cargoes). In further research, the obtained results will be used for computer simulation according to the developed model using the Solver tool of Microsoft Excel.

Does Managerial Power Explain the Association between Agency Costs and Firm Value? The French Case

Does Managerial Power Explain the Association between Agency Costs and Firm Value? The French Case

Multi-Objective Short-Term Operation of Hydro–Wind–Photovoltaic–Thermal Hybrid System Considering Power Peak Shaving, the Economy and the Environment

In recent years, renewable, clean energy options such as hydropower, wind energy and solar energy have been attracting more and more attention as high-quality alternatives to fossil fuels, due to the depletion of fossil fuels and environmental pollution. Multi-energy power systems have replaced traditional thermal power systems. However, the output of solar and wind power is highly variable, random and intermittent, making it difficult to integrate it directly into the grid. In this context, a multi-objective model for the short-term operation of wind–solar–hydro–thermal hybrid systems is developed in this paper. The model considers the stability of the system operation, the operating costs and the impact in terms of environmental pollution. To solve the model, an evolutionary cost value region search algorithm is also proposed. The algorithm is applied to a hydro–thermal hybrid system, a multi-energy hybrid system and a realistic model of the wind–solar–hydro experimental base of the Yalong River Basin in China. The experimental results demonstrate that the proposed algorithm exhibits superior performance in terms of both convergence and diversity when compared to the reference algorithm. The integration of wind and solar energy into the power system can enhance the economic efficiency and mitigate the environment impact from thermal power generation. Furthermore, the inherent unpredictability of wind and solar energy sources introduces operational inconsistencies into the system loads. Conversely, the adaptable operational capacity of hydroelectric power plants enables them to effectively mitigate peak loads, thereby enhancing the stability of the power system. The findings of this research can inform decision-making regarding the economic, ecological and stable operation of hybrid energy systems.

OPTIMAL DECISION-MAKING FOR NEWSPAPER INVENTORY: BALANCING DEMAND UNCERTAINTY AND PROFITABILITY

Estimating cost-benefit analysis, calculating the real value of anything before buying it is essential in order to know if this purchase is fetching or not. In the industry of selling newspapers, since demand is determined by various unpredictable features like news quality and types of reader interest, it may be difficult to estimate how many copies you should buy daily. On the other hand, this study addresses the classic Newsvendor Problem by analyzing a single-period inventory management model to find the optimal order quantity that maximizes profit. Simulating different demand scenarios up to 20 days into the future, using financial-based assumptions (e.g.: selling price, buying cost, and salvage value) on order quantities in order to investigate profits. The results show that we can identify the optimal level of order quantity in the cost-benefit analysis and profit analysis under different demand situations. This method reduces both excess waste and missed sales opportunities, making an optimal answer for inventory alignment in sought-after uncertainty-based industries. The insights of this analysis are not just relevant to newspaper sales but also to all comparable industries where accurate inventory management becomes challenging.

Research on the Planning and Design of Med–Small-Sized Hospitals in China Based on Patient Perceived Value in the Context of Healthcare Model Transformation

China’s aging society has led to a major transformation of the healthcare model from traditional therapeutic medical care to life support healthcare. Med–small-sized hospitals (with 20–299 beds) are expected to play a major role in this transition compared to large hospitals. However, hospitals often take 3–5 years or more from design to construction. In order to guide architects in planning and designing for future needs, this study aims to identify the key points of planning and design that can enhance the value of med–small-sized hospitals in terms of functional configuration, spatial environment, etc. In this study, a questionnaire survey was conducted on the perceived value of 600 patients in Nanjing, China. The data were used to construct a structural equation model (SEM) of perceived value for med–small-sized hospitals in China. There are 24 observed variables in six dimensions, including cost value, functional value, social value, cognitive value, patient satisfaction, and patient loyalty. The final SEM indicates that optimizing functional configurations and spatial environment design can significantly increase the value of hospitals, while patients’ willingness to go to the inpatient department is quite low. Finally, based on the survey findings from Nanjing, we proposed four optimization strategies: (1) promoting diversified development and collaboration based on regional characteristics, (2) optimizing functional configurations, (3) maintaining an efficient and concise patient experience, and (4) optimizing the inpatient department.

Clinical and economic analysis of the use of dapagliflozin in patients with chronic heart failure with reduced left ventricular ejection fraction in various subgroups of standard therapy in the Russian Federation

To evaluate the clinical and economic effectiveness of dapagliflozin in patients with chronic heart failure (CHF) with reduced left ventricular ejection fraction (HFrEF) in the Russian Federation in various subgroups of standard therapy for CHF. A clinical and economic analysis of the use of the drug dapagliflozin in addition to standard therapy was carried out in comparison with standard therapy in various subgroups of standard therapy for HFrEF using a modeling method. Cost calculations were carried out in a mathematical model adapted to the healthcare conditions of the Russian Federation by using Russian cost indicators and characteristics of the patient population. The present study demonstrates that the addition of dapagliflozin is beneficial in terms of clinical and cost-effectiveness, regardless of the initial regimen (angiotensin receptor-neprilysin inhibitors [ARNI] or angiotensin-converting enzyme inhibitors [ACEi]/angiotensin II receptor blockers [ARB]) of standard drug therapy for HFrEF and in all cases leads to an increase in life expectancy, a decrease in the number of hospitalizations and emergency visits due to CHF, as well as cardiovascular mortality. The obtained values of added value per additional year of life in all cases are significantly lower than the willingness-to-pay threshold, which indicates the clinical and economic effectiveness of the strategy of prescribing dapagliflozin as part of standard therapy for patients with HFrEF. In the case of adding dapagliflozin the values of the additional cost of an added year of life in the 3 considered standard therapy options (ARNI or ACEi/ARB, only ACEi/ARB and only ARNI) were 291,256, 279,571 and 338,374 rubles respectively. Thus, the scenario of using dapagliflozin with standard therapy, which included only ACEi/ARB, is characterized by the lowest additional cost and has the best clinical and economic characteristics. At the same time the scenario of use with standard therapy, which included only ARNI, is characterized by the highest value of the additional value of the added year of life.

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.