BACKGROUND Coronavirus disease 2019(COVID-19)is strongly associated with an increased risk of thrombotic events,including severe outcomes such as pulmonary embolism.Elevated D-dimer levels are a critical biomarker for...BACKGROUND Coronavirus disease 2019(COVID-19)is strongly associated with an increased risk of thrombotic events,including severe outcomes such as pulmonary embolism.Elevated D-dimer levels are a critical biomarker for assessing this risk.In Gabon,early implementation of anticoagulation therapy and D-dimer testing has been crucial in managing COVID-19.This study hypothesizes that elevated Ddimer levels are linked to increased COVID-19 severity.AIM To determine the impact of D-dimer levels on COVID-19 severity and their role in guiding clinical decisions.METHODS This retrospective study analyzed COVID-19 patients admitted to two hospitals in Gabon between March 2020 and December 2023.The study included patients with confirmed COVID-19 diagnoses and available D-dimer measurements at admission.Data on demographics,clinical outcomes,D-dimer levels,and healthcare costs were collected.COVID-19 severity was classified as non-severe(outpatients)or severe(inpatients).A multivariable logistic regression model was used to assess the relationship between D-dimer levels and disease severity,with adjusted odds ratios(OR)and 95%CI.RESULTS A total of 3004 patients were included,with a mean age of 50.17 years,and the majority were female(53.43%).Elevated D-dimer levels were found in 65.81%of patients,and 57.21%of these experienced severe COVID-19.Univariate analysis showed that patients with elevated D-dimer levels had 3.33 times higher odds of severe COVID-19(OR=3.33,95%CI:2.84-3.92,P<0.001),and this association remained significant in the multivariable analysis,adjusted for age,sex,and year of collection.The financial analysis revealed a substantial burden,particularly for uninsured patients.CONCLUSION D-dimer predicts COVID-19 severity and guides treatment,but the high cost of anticoagulant therapy highlights the need for policies ensuring affordable access in resource-limited settings like Gabon.展开更多
The ease of accessing a virtually unlimited pool of resources makes Infrastructure as a Service (IaaS) clouds an ideal platform for running data-intensive workflow applications comprising hundreds of computational tas...The ease of accessing a virtually unlimited pool of resources makes Infrastructure as a Service (IaaS) clouds an ideal platform for running data-intensive workflow applications comprising hundreds of computational tasks. However, executing scientific workflows in IaaS cloud environments poses significant challenges due to conflicting objectives, such as minimizing execution time (makespan) and reducing resource utilization costs. This study responds to the increasing need for efficient and adaptable optimization solutions in dynamic and complex environments, which are critical for meeting the evolving demands of modern users and applications. This study presents an innovative multi-objective approach for scheduling scientific workflows in IaaS cloud environments. The proposed algorithm, MOS-MWMC, aims to minimize total execution time (makespan) and resource utilization costs by leveraging key features of virtual machine instances, such as a high number of cores and fast local SSD storage. By integrating realistic simulations based on the WRENCH framework, the method effectively dimensions the cloud infrastructure and optimizes resource usage. Experimental results highlight the superiority of MOS-MWMC compared to benchmark algorithms HEFT and Max-Min. The Pareto fronts obtained for the CyberShake, Epigenomics, and Montage workflows demonstrate closer proximity to the optimal front, confirming the algorithm’s ability to balance conflicting objectives. This study contributes to optimizing scientific workflows in complex environments by providing solutions tailored to specific user needs while minimizing costs and execution times.展开更多
The release of a new artificial intelligence(AI)model,ironically,evokes a biblical memory.What has transpired in the past few days echoes the biblical story of David versus Goliath,in which the massive and wellarmed G...The release of a new artificial intelligence(AI)model,ironically,evokes a biblical memory.What has transpired in the past few days echoes the biblical story of David versus Goliath,in which the massive and wellarmed Goliath is defeated by the comparatively puny David,who comes to the battle with only his staff and sling.展开更多
Restructuring of power market not only introduces competition but also brings complexity which increases overloading of Transmission Lines(TL).To obviate this complexity,this paper aims to mitigate the overloading and...Restructuring of power market not only introduces competition but also brings complexity which increases overloading of Transmission Lines(TL).To obviate this complexity,this paper aims to mitigate the overloading and estimate the optimal location of Static Synchronous Compensator(STATCOM) by reducing congestion for a deregulated power system.The proposed method is based on the use of Locational Marginal Price(LMP) difference technique and congestion cost.LMPs are obtained as a by-product of Optimal Power Flow(OPF),whereas Congestion Cost(CC) is a function of difference in LMP and power flows.The effiectiveness of this approach is demonstrated by reducing the CC and solution space which can identify the TLs more suitable for placement of STATCOM.Importantly,total real power loss,reactive power loss and total CC are the three main objective functions in this optimization process.The process is implemented by developing an IEEE-69 bus test system which verifies and validates the effectiveness of proposed optimization technique.Additionally,a comparative analysis is enumerated by implementing two optimization techniques:Flower Pollination Algorithm(FPA) and Particle Swarm Optimization(PSO).The comparative analysis is sufficient to demonstrate the superiority of FPA technique over PSO technique in estimating an optimal placement of a STATCOM.The results from the load-flow analysis illustrate the reduction in CC,total real and reactive power loss using FPA technique compared to PSO technique.Overall,satisfactory results are obtained without using complex calculations which verify the effectiveness of optimization techniques.展开更多
With the development of the new energy industry and the depletion of nickel sulfide ore resources,laterite nickel ore has become the main source of primary nickel,and nickel for power batteries has become a new growth...With the development of the new energy industry and the depletion of nickel sulfide ore resources,laterite nickel ore has become the main source of primary nickel,and nickel for power batteries has become a new growth point in consumption.This paper systematically summarizes the processes,parameters,products,recovery rates,environmental indicators,costs,advantages,disadvantages and the latest research progress of mainstream nickel extraction processes from laterite nickel ore.It also provides a comparative analysis of the environmental impact and economic efficiency of different nickel extraction processes.It is found that the current nickel extraction processes from laterite nickel ore globally for commercial production mainly include the RKEF process for producing ferronickel and the HPAL process for producing intermediate products.The former accounts for about 80%of laterite nickel ore production.Compared to each other,the investment cost per ton of nickel metal production capacity for the RKEF is about 43000$,with an operational cost of about 16000$per ton of nickel metal and a total nickel recovery rate of 77%–90%.Its products are mainly used in stainless steels.For the HPAL process,the investment cost per ton of nickel metal production capacity is about 56000$,with an operational cost of about 15000$per ton of nickel metal and a total nickel recovery rate of 83%–90%.Its products are mainly used in power batteries.The significant differences between the two lies in energy consumption and carbon emissions,with the RKEF being 2.18 and 2.37 times that of the HPAL,respectively.Although the use of clean energy can greatly reduce the operational cost and environmental impact of RKEF,if RKEF is converted to producing high Ni matte,its economic and environmental performance still cannot match that of the HPAL and oxygen-enriched side-blown processes.Therefore,it can be inferred that with the increasing demand for nickel in power batteries,HPAL and oxygen-enriched side blowing processes will play a greater role in laterite nickel extraction.展开更多
With the continuous application of new technologies in reconnaissance and attack, false camouflage plays a more important role in improving the survivability of targets, and the number of decoys plays a crucial role i...With the continuous application of new technologies in reconnaissance and attack, false camouflage plays a more important role in improving the survivability of targets, and the number of decoys plays a crucial role in the camouflaging effect. Based on the concept of cost-effectiveness ratio, according to the newly formulated Johnson criterion and the view of discovery and destruction, this paper proposes to take the identification probability as the probability of being destroyed and uses mathematical formulas to calculate the cost of a single use decoy. On this basis, a cost-effectiveness ratio model is established, with the product of the increase in the survival probability of the target and the cost of the target as the benefit, and the sum of the product of the probability of being destroyed and the cost of the decoy and the cost of a single use as the consumption cost. The model is calculated and analyzed, and the number of decoys that conform to the actual situation is obtained.展开更多
Background Tuberculosis(TB)poses a significant social and economic burden to households of persons with TB(PwTB).Despite free diagnosis and care under the National TB Elimination Programme(NTEP),individuals often expe...Background Tuberculosis(TB)poses a significant social and economic burden to households of persons with TB(PwTB).Despite free diagnosis and care under the National TB Elimination Programme(NTEP),individuals often expe-rience significant out-of-pocket expenditure and lost productivity,causing financial catastrophe.We estimated the costs incurred by the PwTB during TB care and identified the factors associated with the costs.Methods In our cross-sectional study,we used multi-stage sampling to select PwTB notified under the NTEP,whose treatment outcome was declared between May 2022 and February 2023.Total patient costs were meas-ured through direct medical,non-medical and indirect costs.Catastrophic costs were defined as expenditure on TB care>20%of the annual household income.We determined the factors influencing the total cost of TB care using median regression.We plotted concentration curves to depict the equity in distribution of catastrophic costs across income quintiles.We used a cluster-adjusted,generalized model to determine the factors associated with cata-strophic costs.Results The mean(SD)age of the 1407 PwTB interviewed was 40.8(16.8)years.Among them,865(61.5%)were male,and 786(55.9%)were economically active.Thirty-four(2.4%)had Drug Resistant TB(DRTB),and 258(18.3%)had been hospitalized for TB.The median(Interquartile range[IQR]and 95%confidence interval[CI])of total costs of TB care was US$386.1(130.8,876.9).Direct costs accounted for 34%of the total costs,with a median of US$78.4(43.3,153.6),while indirect costs had a median of US$279.8(18.9,699.4).PwTB<60 years of age(US$446.1;370.4,521.8),without health insurance(US$464.2;386.7,541.6),and those hospitalized(US$900.4;700.2,1100.6)for TB experienced higher median costs.Catastrophic costs,experienced by 45%of PwTB,followed a pro-poor distribution.Hospitalized PwTB(adjusted prevalence ratio[aPR]=1.9;1.6,2.2)and those notified from the private sector(aPR=1.4;1.1,1.8)were more likely to incur catastrophic costs.Conclusions PwTB in India incur high costs mainly due to lost productivity and hospitalization.Nearly half of them experience catastrophic costs,especially those from poorer economic quintiles.Enabling early notification of TB,expanding the coverage of health insurance schemes to include PwTB,and implementing TB sensitive strategies to address social determinants of TB may significantly reduce catastrophic costs incurred by PwTB.展开更多
One of the most significant annual expenses that a person has is their health insurance coverage. Health insurance accounts for one-third of GDP, and everyone needs medical treatment to varying degrees. Changes in med...One of the most significant annual expenses that a person has is their health insurance coverage. Health insurance accounts for one-third of GDP, and everyone needs medical treatment to varying degrees. Changes in medicine, pharmaceutical trends, and political factors are only a few of the many factors that cause annual fluctuations in healthcare costs. This paper describes how a system may analyse a person’s medical history to display their insurance plans and make predictions about their health insurance premiums. The performance of four ML models—XGBoost, Lasso, KNN, and Ridge—is evaluated using R2-score and RMSE. The analysis of medical health insurance cost prediction using Lasso regression, Ridge regression, and K-Nearest Neighbours (KNN), and XGBoost (XGB) highlights notable differences in performance. KNN has the lowest R2-score of 55.21 and an RMSE of 4431.1, indicating limited predictive ability. Ridge Regression improves on this by an R2-score of 78.38 but has a higher RMSE of 4652.06. Lasso Regression slightly edges out Ridge with an R2-score of 79.78, yet it suffers from an advanced RMSE of 5671.6. In contrast, XGBoost excels with the highest R2-score of 86.81 and the lowermost RMSE of 4450.4, demonstrating superior predictive accuracy and making it the most effective model for this task. The best method for accurately predicting health insurance premiums was XGBoost Regression. The findings beneficial for policymakers, insurers, and healthcare providers as they can use this information to allocate resources more efficiently and enhance cost-effectiveness in the healthcare industry.展开更多
In the process of China’s national economic development,the construction industry is a very important component and has a direct impact on the level of China’s economic construction.Nowadays,the development speed of...In the process of China’s national economic development,the construction industry is a very important component and has a direct impact on the level of China’s economic construction.Nowadays,the development speed of the prefabricated construction industry is constantly accelerating.To effectively ensure the economic benefits of engineering projects,it is necessary to comprehensively strengthen cost budgeting and cost control.This article analyzes the cost budget of prefabricated construction projects,introduces the application advantages of prefabricated construction,and proposes specific cost budgeting and cost control measures,hoping to provide some reference for relevant researchers.展开更多
Accurate software cost estimation in Global Software Development(GSD)remains challenging due to reliance on historical data and expert judgments.Traditional models,such as the Constructive Cost Model(COCOMO II),rely h...Accurate software cost estimation in Global Software Development(GSD)remains challenging due to reliance on historical data and expert judgments.Traditional models,such as the Constructive Cost Model(COCOMO II),rely heavily on historical and accurate data.In addition,expert judgment is required to set many input parameters,which can introduce subjectivity and variability in the estimation process.Consequently,there is a need to improve the current GSD models to mitigate reliance on historical data,subjectivity in expert judgment,inadequate consideration of GSD-based cost drivers and limited integration of modern technologies with cost overruns.This study introduces a novel hybrid model that synergizes the COCOMO II with Artificial Neural Networks(ANN)to address these challenges.The proposed hybrid model integrates additional GSD-based cost drivers identified through a systematic literature review and further vetted by industry experts.This article compares the effectiveness of the proposedmodelwith state-of-the-artmachine learning-basedmodels for software cost estimation.Evaluating the NASA 93 dataset by adopting twenty-six GSD-based cost drivers reveals that our hybrid model achieves superior accuracy,outperforming existing state-of-the-artmodels.The findings indicate the potential of combining COCOMO II,ANN,and additional GSD-based cost drivers to transform cost estimation in GSD.展开更多
BACKGROUND Breast cancer is one of the most common malignant tumors in women worldwide and poses a severe threat to their health.Therefore,this study examined patients who underwent breast cancer surgery,analyzed hosp...BACKGROUND Breast cancer is one of the most common malignant tumors in women worldwide and poses a severe threat to their health.Therefore,this study examined patients who underwent breast cancer surgery,analyzed hospitalization costs and structure,and explored the impact of China Healthcare Security Diagnosis Related Groups(CHS-DRG)management on patient costs.It aimed to provide medical institutions with ways to reduce costs,optimize cost structures,reduce patient burden,and improve service efficiency.AIM To study the CHS-DRG payment system’s impact on breast cancer surgery costs.METHODS Using the CHS-DRG(version 1.1)grouping criteria,4073 patients,who underwent the radical resection of breast malignant tumors from January to December 2023,were included in the JA29 group;1028 patients were part of the CHS-DRG payment system,unlike the rest.Through an independent sample t-test,the length of hospital stay as well as total hospitalization,medicine and consumables,medical,nursing,medical technology,and management expenses were compared.Pearson’s correlation coefficient was used to test the cost correlation.RESULTS In terms of hospitalization expenses,patients in the CHS-DRG payment group had lower medical,nursing,and management expenses than those in the diagnosis-related group(DRG)non-payment group.For patients in the DRG payment group,the factors affecting the total hospitalization cost,in descending order of relevance,were medicine and consumable costs,consumable costs,medicine costs,medical costs,medical technology costs,management costs,nursing costs,and length of hospital stay.For patients in the DRG nonpayment group,the factors affecting the total hospitalization expenses in descending order of relevance were medicines and consumable expenses,consumable expenses,medical technology expenses,the cost of medicines,medical expenses,nursing expenses,length of hospital stay,and management expenses.CONCLUSION The CHS-DRG system can help control and reduce unnecessary medical expenses by controlling medicine costs,medical consumable costs,and the length of hospital stay while ensuring medical safety.展开更多
Introduction: Tuberculosis is closely linked to poverty, with patients facing significant indirect treatment costs. Treating drug-resistant tuberculosis further increases these expenses. Notably, there is a lack of pu...Introduction: Tuberculosis is closely linked to poverty, with patients facing significant indirect treatment costs. Treating drug-resistant tuberculosis further increases these expenses. Notably, there is a lack of published data on the indirect costs incurred by patients with drug-resistant tuberculosis in Mozambique. Objective: To assess the indirect costs, income reduction, and work productivity incurred by patients undergoing diagnosis and treatment for Drug-Resistant Tuberculosis (DRTB) in Mozambique during their TB treatment. Methods: As part of a comprehensive mixed-methods study conducted from January 2021 to April 2023, this research utilized a descriptive cross-sectional approach, incorporating both quantitative and qualitative methods. The primary goal was to evaluate the costs incurred by the national health system due to drug-resistant TB. Additionally, to explore the indirect costs experienced by patients and their families during treatment, semi-structured interviews were conducted with 27 individuals who had been undergoing treatment for over six months. Results: All survey participants unanimously reported a significant decline in labour productivity, with 70.3% experiencing a reduction in their monthly income. Before falling ill, the majority of respondents (33.3%) earned up to $76.92 monthly, representing the minimum earnings range, while 29.2% had a monthly income above $230.77, the maximum earnings range. Among those who experienced income loss, the majority (22.2%) reported a decrease of up to $76.92 per month, and 18.5% cited a loss exceeding $230.77 per month. Notably, patients with Drug-Resistant Tuberculosis (DRTB) have not incurred the direct costs of the disease, as these are covered by the government. Conclusion: The financial burden of treating Drug-Resistant Tuberculosis (DRTB), along with the income reduction it causes, is substantial. Implementing a patient-centred, multidisciplinary, and multisector approach, coupled with strong psychosocial support, can significantly reduce the catastrophic costs DRTB patients incur.展开更多
Thucydides asserts that the occupation of Decelea by the Spartans in 413 BC made the grain supply for Athens costly by forcing the transport from land onto the sea.This calls into question the well-established consens...Thucydides asserts that the occupation of Decelea by the Spartans in 413 BC made the grain supply for Athens costly by forcing the transport from land onto the sea.This calls into question the well-established consensus that sea transport was far cheaper than land transport.This paper contends that the cost of protecting supply lines-specifically the expenses associated with the warships which escorted the supply ships-rendered the grain transported on the new route exceptionally costly.In this paper,the benefits and drawbacks of a maritime economy,including transaction costs,trade dependencies,and the capabilities of warships and supply ships are discussed.展开更多
Background: Schizophrenia is a chronic disease related to long-lasting and tremendous effects on patient’s health in China, which is generally considered as a huge economic burden not only for patients but also for t...Background: Schizophrenia is a chronic disease related to long-lasting and tremendous effects on patient’s health in China, which is generally considered as a huge economic burden not only for patients but also for their caregivers and the whole society. Therefore, it is necessary to conduct an analysis of cost. Previous cost-of-illness (COI) studies have already provided some useful information on the economic burden that schizophrenia brought to global society, including China. Objectives: This systematic review aims to obtain a comprehensive understanding of the economic burden of schizophrenia in China. Method: A literature review was performed through CNKI, Wanfang, CQVIP, EMBASE and Medline databases to identify COI studies published between 2010-2024. The primary outcome of this review was societal cost per schizophrenia patient by cost component, including direct medical costs, non-medical costs and indirect medical costs. Results: 14 COI studies in schizophrenia were identified, covering 7 municipalities and 8 provinces of China. The annual societal cost per patient ranged from 10,765 CNY in Zhejiang province to 406,382 CNY in Xuancheng city (Anhui province). The ratio of indirect cost ranged from 66.6% to 96.8%. The main cost drivers were the productivity losses. There was an enormous heterogeneity between societal cost estimations that could be interpreted by the difference in economic state and regional healthcare resource allocation. Conclusions: This review highlights the large economic burden of schizophrenia in varied areas in China. Substantial cost variation was observed both nationwide and globally, which may be caused by the varied economic situation and healthcare policy. Limitation of this review was summarized, which may provide a useful guidance for the future COI studies in China.展开更多
Life Cycle Cost Analysis (LCCA) provides a systematic approach to assess the total cost associated with owning, operating, and maintaining assets throughout their entire life. BIM empowers architects and designers to ...Life Cycle Cost Analysis (LCCA) provides a systematic approach to assess the total cost associated with owning, operating, and maintaining assets throughout their entire life. BIM empowers architects and designers to perform real-time evaluations to explore various design options. However, when integrated with LCCA, BIM provides a comprehensive economic perspective that helps stakeholders understand the long-term financial implications of design decisions. This study presents a methodology for developing a model that seamlessly integrates BIM and LCCA during the conceptual design stage of buildings. This integration allows for a comprehensive evaluation and analysis of the design process, ensuring that the development aligns with the principles of low carbon emissions by employing modular construction, 3D concrete printing methods, and different building design alternatives. The model considers the initial construction costs in addition to all the long-term operational, maintenance, and salvage values. It combines various tools and data through different modules, including energy analysis, Life Cycle Assessment (LCA), and Life Cycle Cost Analysis (LCCA) to execute a comprehensive assessment of the financial implications of a specific design option throughout the lifecycle of building projects. The development of the said model and its implementation involves the creation of a new plug-in for the BIM tool (i.e., Autodesk Revit) to enhance its functionalities and capabilities in forecasting the life-cycle costs of buildings in addition to generating associated cash flows, creating scenarios, and sensitivity analyses in an automatic manner. This model empowers designers to evaluate and justify their initial investments while designing and selecting potential construction methods for buildings, and enabling stakeholders to make informed decisions by assessing different design alternatives based on long-term financial considerations during the early stages of design.展开更多
Adequacy of structural fire design in uncommon structures is conceptually ensured through cost-benefit analysis where the future costs are balanced against the benefits of safety investment.Cost-benefit analyses,howev...Adequacy of structural fire design in uncommon structures is conceptually ensured through cost-benefit analysis where the future costs are balanced against the benefits of safety investment.Cost-benefit analyses,however,are complicated and computationally challenging,and hence impractical for application to individual projects.To address this issue,design guidance proposes target reliability indices for normal design conditions,but no target reliability indices are defined for structural fire design.We revisit the background of the cost-optimization based approach underlying normal design target reliability indices then we extend this approach for the case of fire design of structures.We also propose a modified objective function for cost-optimization which simplifies the evaluation of target reliability indices and reduces the number of assumptions.The optimum safety level is expressed as a function of a new dimensionless variable named“Damage-to-investment indicator”(DII).The cost optimization approach is validated for the target reliability indices for normal design condition.The method is then applied for evaluating DII and the associated optimum reliability indices for fire-exposed structures.Two case studies are presented:(i)a one-way loaded reinforced concrete slab and(ii)a steel column under axial loading.This study thus provides a framework for deriving optimum(target)reliability index for structural fire design which can support the development of rational provisions in codes and standards.展开更多
The highway engineering process is complex,coupled with a relatively long construction period,hence requires increased coordination between participating units to prevent economic disputes and efficiency losses.The ma...The highway engineering process is complex,coupled with a relatively long construction period,hence requires increased coordination between participating units to prevent economic disputes and efficiency losses.The main body of the construction project needs to strengthen the management and control of funds.In this regard,this paper analyzes the importance of cost management in highway projects by clarifying and analyzing the current cost management status quo problems and causes.The highway engineering cost control strategy and implementation methods are summarized to provide references for improving the quality of highway engineering.展开更多
The whole-process project cost management based on building information modeling(BIM)is a new management method,aiming to realize the comprehensive optimization and improvement of project cost management through the a...The whole-process project cost management based on building information modeling(BIM)is a new management method,aiming to realize the comprehensive optimization and improvement of project cost management through the application of BIM technology.This paper summarizes and analyzes the whole-process project cost management based on BIM,aiming to explore its application and development prospects in the construction industry.Firstly,this paper introduces the role and advantages of BIM technology in engineering cost management,including information integration,data sharing,and collaborative work.Secondly,the paper analyzes the key technologies and methods of the whole-process project cost management based on BIM,including model construction,data management,and cost control.In addition,the paper also discusses the challenges and limitations of the whole-process BIM project cost management,such as the inconsistency of technical standards,personnel training,and consciousness change.Finally,the paper summarizes the advantages and development prospects of the whole-process project cost management based on BIM and puts forward the direction and suggestions for future research.Through the research of this paper,it can provide a reference for construction cost management and promote innovation and development in the construction industry.展开更多
Against the backdrop of rapid development in China’s construction and infrastructure sectors,discrepancies between project budgets and actual costs have become pronounced,manifesting in project overruns and suspensio...Against the backdrop of rapid development in China’s construction and infrastructure sectors,discrepancies between project budgets and actual costs have become pronounced,manifesting in project overruns and suspensions,posing significant challenges.To address inaccuracies in investment targets and operational complexities,this study focuses on a beam-bridge construction project in a district of Shijiazhuang city as a case study.Drawing upon historical analogs,the project employs a Work Breakdown Structure(WBS)to decompose the engineering works.Building on theories of Cost Significant(CS)and Whole Life Costing(WLC),the study constructs Cost Significant Items(CSIs)and develops a CNN-BiLSTM-Attention neural network for nonlinear prediction.By identifying significant cost drivers in engineering projects,this paper presents a streamlined cost estimation method that significantly reduces computational burdens,simplifies data collection processes,and optimizes data analysis and forecasting,thereby enhancing prediction accuracy.Finally,validation with real-world cost fluctuation data demonstrates minor errors,meeting predictive requirements across project execution phases.展开更多
文摘BACKGROUND Coronavirus disease 2019(COVID-19)is strongly associated with an increased risk of thrombotic events,including severe outcomes such as pulmonary embolism.Elevated D-dimer levels are a critical biomarker for assessing this risk.In Gabon,early implementation of anticoagulation therapy and D-dimer testing has been crucial in managing COVID-19.This study hypothesizes that elevated Ddimer levels are linked to increased COVID-19 severity.AIM To determine the impact of D-dimer levels on COVID-19 severity and their role in guiding clinical decisions.METHODS This retrospective study analyzed COVID-19 patients admitted to two hospitals in Gabon between March 2020 and December 2023.The study included patients with confirmed COVID-19 diagnoses and available D-dimer measurements at admission.Data on demographics,clinical outcomes,D-dimer levels,and healthcare costs were collected.COVID-19 severity was classified as non-severe(outpatients)or severe(inpatients).A multivariable logistic regression model was used to assess the relationship between D-dimer levels and disease severity,with adjusted odds ratios(OR)and 95%CI.RESULTS A total of 3004 patients were included,with a mean age of 50.17 years,and the majority were female(53.43%).Elevated D-dimer levels were found in 65.81%of patients,and 57.21%of these experienced severe COVID-19.Univariate analysis showed that patients with elevated D-dimer levels had 3.33 times higher odds of severe COVID-19(OR=3.33,95%CI:2.84-3.92,P<0.001),and this association remained significant in the multivariable analysis,adjusted for age,sex,and year of collection.The financial analysis revealed a substantial burden,particularly for uninsured patients.CONCLUSION D-dimer predicts COVID-19 severity and guides treatment,but the high cost of anticoagulant therapy highlights the need for policies ensuring affordable access in resource-limited settings like Gabon.
文摘The ease of accessing a virtually unlimited pool of resources makes Infrastructure as a Service (IaaS) clouds an ideal platform for running data-intensive workflow applications comprising hundreds of computational tasks. However, executing scientific workflows in IaaS cloud environments poses significant challenges due to conflicting objectives, such as minimizing execution time (makespan) and reducing resource utilization costs. This study responds to the increasing need for efficient and adaptable optimization solutions in dynamic and complex environments, which are critical for meeting the evolving demands of modern users and applications. This study presents an innovative multi-objective approach for scheduling scientific workflows in IaaS cloud environments. The proposed algorithm, MOS-MWMC, aims to minimize total execution time (makespan) and resource utilization costs by leveraging key features of virtual machine instances, such as a high number of cores and fast local SSD storage. By integrating realistic simulations based on the WRENCH framework, the method effectively dimensions the cloud infrastructure and optimizes resource usage. Experimental results highlight the superiority of MOS-MWMC compared to benchmark algorithms HEFT and Max-Min. The Pareto fronts obtained for the CyberShake, Epigenomics, and Montage workflows demonstrate closer proximity to the optimal front, confirming the algorithm’s ability to balance conflicting objectives. This study contributes to optimizing scientific workflows in complex environments by providing solutions tailored to specific user needs while minimizing costs and execution times.
文摘The release of a new artificial intelligence(AI)model,ironically,evokes a biblical memory.What has transpired in the past few days echoes the biblical story of David versus Goliath,in which the massive and wellarmed Goliath is defeated by the comparatively puny David,who comes to the battle with only his staff and sling.
文摘Restructuring of power market not only introduces competition but also brings complexity which increases overloading of Transmission Lines(TL).To obviate this complexity,this paper aims to mitigate the overloading and estimate the optimal location of Static Synchronous Compensator(STATCOM) by reducing congestion for a deregulated power system.The proposed method is based on the use of Locational Marginal Price(LMP) difference technique and congestion cost.LMPs are obtained as a by-product of Optimal Power Flow(OPF),whereas Congestion Cost(CC) is a function of difference in LMP and power flows.The effiectiveness of this approach is demonstrated by reducing the CC and solution space which can identify the TLs more suitable for placement of STATCOM.Importantly,total real power loss,reactive power loss and total CC are the three main objective functions in this optimization process.The process is implemented by developing an IEEE-69 bus test system which verifies and validates the effectiveness of proposed optimization technique.Additionally,a comparative analysis is enumerated by implementing two optimization techniques:Flower Pollination Algorithm(FPA) and Particle Swarm Optimization(PSO).The comparative analysis is sufficient to demonstrate the superiority of FPA technique over PSO technique in estimating an optimal placement of a STATCOM.The results from the load-flow analysis illustrate the reduction in CC,total real and reactive power loss using FPA technique compared to PSO technique.Overall,satisfactory results are obtained without using complex calculations which verify the effectiveness of optimization techniques.
基金This research was jointly supported by the China Geological Survey Project(DD20211404)the Natural Science Foundation of Inner Mongolia Autonomous Region(2019LH05028).
文摘With the development of the new energy industry and the depletion of nickel sulfide ore resources,laterite nickel ore has become the main source of primary nickel,and nickel for power batteries has become a new growth point in consumption.This paper systematically summarizes the processes,parameters,products,recovery rates,environmental indicators,costs,advantages,disadvantages and the latest research progress of mainstream nickel extraction processes from laterite nickel ore.It also provides a comparative analysis of the environmental impact and economic efficiency of different nickel extraction processes.It is found that the current nickel extraction processes from laterite nickel ore globally for commercial production mainly include the RKEF process for producing ferronickel and the HPAL process for producing intermediate products.The former accounts for about 80%of laterite nickel ore production.Compared to each other,the investment cost per ton of nickel metal production capacity for the RKEF is about 43000$,with an operational cost of about 16000$per ton of nickel metal and a total nickel recovery rate of 77%–90%.Its products are mainly used in stainless steels.For the HPAL process,the investment cost per ton of nickel metal production capacity is about 56000$,with an operational cost of about 15000$per ton of nickel metal and a total nickel recovery rate of 83%–90%.Its products are mainly used in power batteries.The significant differences between the two lies in energy consumption and carbon emissions,with the RKEF being 2.18 and 2.37 times that of the HPAL,respectively.Although the use of clean energy can greatly reduce the operational cost and environmental impact of RKEF,if RKEF is converted to producing high Ni matte,its economic and environmental performance still cannot match that of the HPAL and oxygen-enriched side-blown processes.Therefore,it can be inferred that with the increasing demand for nickel in power batteries,HPAL and oxygen-enriched side blowing processes will play a greater role in laterite nickel extraction.
文摘With the continuous application of new technologies in reconnaissance and attack, false camouflage plays a more important role in improving the survivability of targets, and the number of decoys plays a crucial role in the camouflaging effect. Based on the concept of cost-effectiveness ratio, according to the newly formulated Johnson criterion and the view of discovery and destruction, this paper proposes to take the identification probability as the probability of being destroyed and uses mathematical formulas to calculate the cost of a single use decoy. On this basis, a cost-effectiveness ratio model is established, with the product of the increase in the survival probability of the target and the cost of the target as the benefit, and the sum of the product of the probability of being destroyed and the cost of the decoy and the cost of a single use as the consumption cost. The model is calculated and analyzed, and the number of decoys that conform to the actual situation is obtained.
基金United States Agency for International Development(USAID)and supported by Tuberculosis Implementation Framework Agreement(TIFA)implemented through John Snow Research&Training Institute Inc(JSI).
文摘Background Tuberculosis(TB)poses a significant social and economic burden to households of persons with TB(PwTB).Despite free diagnosis and care under the National TB Elimination Programme(NTEP),individuals often expe-rience significant out-of-pocket expenditure and lost productivity,causing financial catastrophe.We estimated the costs incurred by the PwTB during TB care and identified the factors associated with the costs.Methods In our cross-sectional study,we used multi-stage sampling to select PwTB notified under the NTEP,whose treatment outcome was declared between May 2022 and February 2023.Total patient costs were meas-ured through direct medical,non-medical and indirect costs.Catastrophic costs were defined as expenditure on TB care>20%of the annual household income.We determined the factors influencing the total cost of TB care using median regression.We plotted concentration curves to depict the equity in distribution of catastrophic costs across income quintiles.We used a cluster-adjusted,generalized model to determine the factors associated with cata-strophic costs.Results The mean(SD)age of the 1407 PwTB interviewed was 40.8(16.8)years.Among them,865(61.5%)were male,and 786(55.9%)were economically active.Thirty-four(2.4%)had Drug Resistant TB(DRTB),and 258(18.3%)had been hospitalized for TB.The median(Interquartile range[IQR]and 95%confidence interval[CI])of total costs of TB care was US$386.1(130.8,876.9).Direct costs accounted for 34%of the total costs,with a median of US$78.4(43.3,153.6),while indirect costs had a median of US$279.8(18.9,699.4).PwTB<60 years of age(US$446.1;370.4,521.8),without health insurance(US$464.2;386.7,541.6),and those hospitalized(US$900.4;700.2,1100.6)for TB experienced higher median costs.Catastrophic costs,experienced by 45%of PwTB,followed a pro-poor distribution.Hospitalized PwTB(adjusted prevalence ratio[aPR]=1.9;1.6,2.2)and those notified from the private sector(aPR=1.4;1.1,1.8)were more likely to incur catastrophic costs.Conclusions PwTB in India incur high costs mainly due to lost productivity and hospitalization.Nearly half of them experience catastrophic costs,especially those from poorer economic quintiles.Enabling early notification of TB,expanding the coverage of health insurance schemes to include PwTB,and implementing TB sensitive strategies to address social determinants of TB may significantly reduce catastrophic costs incurred by PwTB.
文摘One of the most significant annual expenses that a person has is their health insurance coverage. Health insurance accounts for one-third of GDP, and everyone needs medical treatment to varying degrees. Changes in medicine, pharmaceutical trends, and political factors are only a few of the many factors that cause annual fluctuations in healthcare costs. This paper describes how a system may analyse a person’s medical history to display their insurance plans and make predictions about their health insurance premiums. The performance of four ML models—XGBoost, Lasso, KNN, and Ridge—is evaluated using R2-score and RMSE. The analysis of medical health insurance cost prediction using Lasso regression, Ridge regression, and K-Nearest Neighbours (KNN), and XGBoost (XGB) highlights notable differences in performance. KNN has the lowest R2-score of 55.21 and an RMSE of 4431.1, indicating limited predictive ability. Ridge Regression improves on this by an R2-score of 78.38 but has a higher RMSE of 4652.06. Lasso Regression slightly edges out Ridge with an R2-score of 79.78, yet it suffers from an advanced RMSE of 5671.6. In contrast, XGBoost excels with the highest R2-score of 86.81 and the lowermost RMSE of 4450.4, demonstrating superior predictive accuracy and making it the most effective model for this task. The best method for accurately predicting health insurance premiums was XGBoost Regression. The findings beneficial for policymakers, insurers, and healthcare providers as they can use this information to allocate resources more efficiently and enhance cost-effectiveness in the healthcare industry.
文摘In the process of China’s national economic development,the construction industry is a very important component and has a direct impact on the level of China’s economic construction.Nowadays,the development speed of the prefabricated construction industry is constantly accelerating.To effectively ensure the economic benefits of engineering projects,it is necessary to comprehensively strengthen cost budgeting and cost control.This article analyzes the cost budget of prefabricated construction projects,introduces the application advantages of prefabricated construction,and proposes specific cost budgeting and cost control measures,hoping to provide some reference for relevant researchers.
文摘Accurate software cost estimation in Global Software Development(GSD)remains challenging due to reliance on historical data and expert judgments.Traditional models,such as the Constructive Cost Model(COCOMO II),rely heavily on historical and accurate data.In addition,expert judgment is required to set many input parameters,which can introduce subjectivity and variability in the estimation process.Consequently,there is a need to improve the current GSD models to mitigate reliance on historical data,subjectivity in expert judgment,inadequate consideration of GSD-based cost drivers and limited integration of modern technologies with cost overruns.This study introduces a novel hybrid model that synergizes the COCOMO II with Artificial Neural Networks(ANN)to address these challenges.The proposed hybrid model integrates additional GSD-based cost drivers identified through a systematic literature review and further vetted by industry experts.This article compares the effectiveness of the proposedmodelwith state-of-the-artmachine learning-basedmodels for software cost estimation.Evaluating the NASA 93 dataset by adopting twenty-six GSD-based cost drivers reveals that our hybrid model achieves superior accuracy,outperforming existing state-of-the-artmodels.The findings indicate the potential of combining COCOMO II,ANN,and additional GSD-based cost drivers to transform cost estimation in GSD.
基金Research Center for Capital Health Management and Policy,No.2024JD09.
文摘BACKGROUND Breast cancer is one of the most common malignant tumors in women worldwide and poses a severe threat to their health.Therefore,this study examined patients who underwent breast cancer surgery,analyzed hospitalization costs and structure,and explored the impact of China Healthcare Security Diagnosis Related Groups(CHS-DRG)management on patient costs.It aimed to provide medical institutions with ways to reduce costs,optimize cost structures,reduce patient burden,and improve service efficiency.AIM To study the CHS-DRG payment system’s impact on breast cancer surgery costs.METHODS Using the CHS-DRG(version 1.1)grouping criteria,4073 patients,who underwent the radical resection of breast malignant tumors from January to December 2023,were included in the JA29 group;1028 patients were part of the CHS-DRG payment system,unlike the rest.Through an independent sample t-test,the length of hospital stay as well as total hospitalization,medicine and consumables,medical,nursing,medical technology,and management expenses were compared.Pearson’s correlation coefficient was used to test the cost correlation.RESULTS In terms of hospitalization expenses,patients in the CHS-DRG payment group had lower medical,nursing,and management expenses than those in the diagnosis-related group(DRG)non-payment group.For patients in the DRG payment group,the factors affecting the total hospitalization cost,in descending order of relevance,were medicine and consumable costs,consumable costs,medicine costs,medical costs,medical technology costs,management costs,nursing costs,and length of hospital stay.For patients in the DRG nonpayment group,the factors affecting the total hospitalization expenses in descending order of relevance were medicines and consumable expenses,consumable expenses,medical technology expenses,the cost of medicines,medical expenses,nursing expenses,length of hospital stay,and management expenses.CONCLUSION The CHS-DRG system can help control and reduce unnecessary medical expenses by controlling medicine costs,medical consumable costs,and the length of hospital stay while ensuring medical safety.
文摘Introduction: Tuberculosis is closely linked to poverty, with patients facing significant indirect treatment costs. Treating drug-resistant tuberculosis further increases these expenses. Notably, there is a lack of published data on the indirect costs incurred by patients with drug-resistant tuberculosis in Mozambique. Objective: To assess the indirect costs, income reduction, and work productivity incurred by patients undergoing diagnosis and treatment for Drug-Resistant Tuberculosis (DRTB) in Mozambique during their TB treatment. Methods: As part of a comprehensive mixed-methods study conducted from January 2021 to April 2023, this research utilized a descriptive cross-sectional approach, incorporating both quantitative and qualitative methods. The primary goal was to evaluate the costs incurred by the national health system due to drug-resistant TB. Additionally, to explore the indirect costs experienced by patients and their families during treatment, semi-structured interviews were conducted with 27 individuals who had been undergoing treatment for over six months. Results: All survey participants unanimously reported a significant decline in labour productivity, with 70.3% experiencing a reduction in their monthly income. Before falling ill, the majority of respondents (33.3%) earned up to $76.92 monthly, representing the minimum earnings range, while 29.2% had a monthly income above $230.77, the maximum earnings range. Among those who experienced income loss, the majority (22.2%) reported a decrease of up to $76.92 per month, and 18.5% cited a loss exceeding $230.77 per month. Notably, patients with Drug-Resistant Tuberculosis (DRTB) have not incurred the direct costs of the disease, as these are covered by the government. Conclusion: The financial burden of treating Drug-Resistant Tuberculosis (DRTB), along with the income reduction it causes, is substantial. Implementing a patient-centred, multidisciplinary, and multisector approach, coupled with strong psychosocial support, can significantly reduce the catastrophic costs DRTB patients incur.
文摘Thucydides asserts that the occupation of Decelea by the Spartans in 413 BC made the grain supply for Athens costly by forcing the transport from land onto the sea.This calls into question the well-established consensus that sea transport was far cheaper than land transport.This paper contends that the cost of protecting supply lines-specifically the expenses associated with the warships which escorted the supply ships-rendered the grain transported on the new route exceptionally costly.In this paper,the benefits and drawbacks of a maritime economy,including transaction costs,trade dependencies,and the capabilities of warships and supply ships are discussed.
文摘Background: Schizophrenia is a chronic disease related to long-lasting and tremendous effects on patient’s health in China, which is generally considered as a huge economic burden not only for patients but also for their caregivers and the whole society. Therefore, it is necessary to conduct an analysis of cost. Previous cost-of-illness (COI) studies have already provided some useful information on the economic burden that schizophrenia brought to global society, including China. Objectives: This systematic review aims to obtain a comprehensive understanding of the economic burden of schizophrenia in China. Method: A literature review was performed through CNKI, Wanfang, CQVIP, EMBASE and Medline databases to identify COI studies published between 2010-2024. The primary outcome of this review was societal cost per schizophrenia patient by cost component, including direct medical costs, non-medical costs and indirect medical costs. Results: 14 COI studies in schizophrenia were identified, covering 7 municipalities and 8 provinces of China. The annual societal cost per patient ranged from 10,765 CNY in Zhejiang province to 406,382 CNY in Xuancheng city (Anhui province). The ratio of indirect cost ranged from 66.6% to 96.8%. The main cost drivers were the productivity losses. There was an enormous heterogeneity between societal cost estimations that could be interpreted by the difference in economic state and regional healthcare resource allocation. Conclusions: This review highlights the large economic burden of schizophrenia in varied areas in China. Substantial cost variation was observed both nationwide and globally, which may be caused by the varied economic situation and healthcare policy. Limitation of this review was summarized, which may provide a useful guidance for the future COI studies in China.
文摘Life Cycle Cost Analysis (LCCA) provides a systematic approach to assess the total cost associated with owning, operating, and maintaining assets throughout their entire life. BIM empowers architects and designers to perform real-time evaluations to explore various design options. However, when integrated with LCCA, BIM provides a comprehensive economic perspective that helps stakeholders understand the long-term financial implications of design decisions. This study presents a methodology for developing a model that seamlessly integrates BIM and LCCA during the conceptual design stage of buildings. This integration allows for a comprehensive evaluation and analysis of the design process, ensuring that the development aligns with the principles of low carbon emissions by employing modular construction, 3D concrete printing methods, and different building design alternatives. The model considers the initial construction costs in addition to all the long-term operational, maintenance, and salvage values. It combines various tools and data through different modules, including energy analysis, Life Cycle Assessment (LCA), and Life Cycle Cost Analysis (LCCA) to execute a comprehensive assessment of the financial implications of a specific design option throughout the lifecycle of building projects. The development of the said model and its implementation involves the creation of a new plug-in for the BIM tool (i.e., Autodesk Revit) to enhance its functionalities and capabilities in forecasting the life-cycle costs of buildings in addition to generating associated cash flows, creating scenarios, and sensitivity analyses in an automatic manner. This model empowers designers to evaluate and justify their initial investments while designing and selecting potential construction methods for buildings, and enabling stakeholders to make informed decisions by assessing different design alternatives based on long-term financial considerations during the early stages of design.
基金funded by the Ghent University Special Research Fund under grant 01N01219“Multi-objective societal optimization of structural fire safety investments for uncommon projects using advanced regression techniques”.
文摘Adequacy of structural fire design in uncommon structures is conceptually ensured through cost-benefit analysis where the future costs are balanced against the benefits of safety investment.Cost-benefit analyses,however,are complicated and computationally challenging,and hence impractical for application to individual projects.To address this issue,design guidance proposes target reliability indices for normal design conditions,but no target reliability indices are defined for structural fire design.We revisit the background of the cost-optimization based approach underlying normal design target reliability indices then we extend this approach for the case of fire design of structures.We also propose a modified objective function for cost-optimization which simplifies the evaluation of target reliability indices and reduces the number of assumptions.The optimum safety level is expressed as a function of a new dimensionless variable named“Damage-to-investment indicator”(DII).The cost optimization approach is validated for the target reliability indices for normal design condition.The method is then applied for evaluating DII and the associated optimum reliability indices for fire-exposed structures.Two case studies are presented:(i)a one-way loaded reinforced concrete slab and(ii)a steel column under axial loading.This study thus provides a framework for deriving optimum(target)reliability index for structural fire design which can support the development of rational provisions in codes and standards.
文摘The highway engineering process is complex,coupled with a relatively long construction period,hence requires increased coordination between participating units to prevent economic disputes and efficiency losses.The main body of the construction project needs to strengthen the management and control of funds.In this regard,this paper analyzes the importance of cost management in highway projects by clarifying and analyzing the current cost management status quo problems and causes.The highway engineering cost control strategy and implementation methods are summarized to provide references for improving the quality of highway engineering.
文摘The whole-process project cost management based on building information modeling(BIM)is a new management method,aiming to realize the comprehensive optimization and improvement of project cost management through the application of BIM technology.This paper summarizes and analyzes the whole-process project cost management based on BIM,aiming to explore its application and development prospects in the construction industry.Firstly,this paper introduces the role and advantages of BIM technology in engineering cost management,including information integration,data sharing,and collaborative work.Secondly,the paper analyzes the key technologies and methods of the whole-process project cost management based on BIM,including model construction,data management,and cost control.In addition,the paper also discusses the challenges and limitations of the whole-process BIM project cost management,such as the inconsistency of technical standards,personnel training,and consciousness change.Finally,the paper summarizes the advantages and development prospects of the whole-process project cost management based on BIM and puts forward the direction and suggestions for future research.Through the research of this paper,it can provide a reference for construction cost management and promote innovation and development in the construction industry.
文摘Against the backdrop of rapid development in China’s construction and infrastructure sectors,discrepancies between project budgets and actual costs have become pronounced,manifesting in project overruns and suspensions,posing significant challenges.To address inaccuracies in investment targets and operational complexities,this study focuses on a beam-bridge construction project in a district of Shijiazhuang city as a case study.Drawing upon historical analogs,the project employs a Work Breakdown Structure(WBS)to decompose the engineering works.Building on theories of Cost Significant(CS)and Whole Life Costing(WLC),the study constructs Cost Significant Items(CSIs)and develops a CNN-BiLSTM-Attention neural network for nonlinear prediction.By identifying significant cost drivers in engineering projects,this paper presents a streamlined cost estimation method that significantly reduces computational burdens,simplifies data collection processes,and optimizes data analysis and forecasting,thereby enhancing prediction accuracy.Finally,validation with real-world cost fluctuation data demonstrates minor errors,meeting predictive requirements across project execution phases.
