Contract Costs Research Articles

Coordination mechanism of wooden furniture supply chains with consideration of carbon footprint

This study emphasizes a three-level wooden furniture supply chain, which involves one supplier, one manufacturer, and one retailer. Focusing on maximizing the profit of the supply chain while adhering to low-carbon principles, the Three-level Leader-follower Game (TLG) model, Stackelberg Game Model I (SGI model), Stackelberg Game Model II (SGlI model), and Cooperative Decision-making (CD) model were established by using game theory. The carbon emission reduction cost and benefit sharing contract was introduced into the model with the maximum profit, and the ranges of sharing coefficients for a solid wood bed supply chain and the optimal decision-making process for each supply chain member were discussed. Results showed that the profit for the solid wood bed supply chain reached maximum under the CD model, followed by the SGII model, and then the SGI model, and the TLG model showed the lowest profit. A higher preference for low-carbon products can lead to lower demand for products and higher retail prices. Through introducing the cost and benefit-sharing contract into the CD model, the profit of the supply chain can be guaranteed with different sharing coefficients, and the profit of each member was improved compared to the TLG model.

Comparison of neural network, Gaussian regression, support vector machine, long short-term memory, multi-gene genetic programming, and M5 Trees methods for solving civil engineering problems

In this study, it was investigated that how machine learning (ML) methods show performance in different problems having different characteristics. Six ML approaches including Artificial neural networks (ANN), gaussian process regression (GPR), support vector machine regression (SVMR), long short-term memory (LSTM), multi-gene genetic programming (MGGP) and M5 model tree (M5Tree) were utilized to analyze three independent civil engineering problems belonging to construction management, geotechnical engineering, and hydrological engineering sub-disciplines. Mean absolute percentage error (MAPE), root mean square error (RMSE), coefficient of determination (R2), relative root means square error (RRMSE), Nash–Sutcliffe efficiency (NSE), Kling–Gupta efficiency (KGE), and overall index of model performance (OI) criteria were used to evaluate the performances of the models. Besides performance criteria, the relative performances of the six ML models were assessed using Taylor diagram, Violin diagram and One-Tailed Wilcoxon Signed-Rank Test. For each of the problem considered in this study, the effectiveness of the input parameters on the output parameter has been defined using the Relief Method and Correlation Coefficient. The results show that ANN and MGGP models yielded the most successful estimations for three different problems considered. The best prediction was achieved by MGGP model for hydrological engineering problem. For the construction management, geotechnical engineering problems, the best results were obtained using the ANN model. All models were reliable to solve the geotechnical engineering and hydrological engineering problems while LSTM and SVMR models are not reliable to solve the construction management problem. The most and least effective input parameters on output parameter were contract cost (CC) and work definition number (WDN) for the managerial data set. On the other hand, the most and least effective input parameters on the output parameters for the experimental and natural data sets have been obtained as width of the pile (B), rotation degree (R) and minimum temperature (Tmin), streamflow (Q) data, respectively. The number of data and data selection have a significant effect on the homogeneity of the data set and its representativeness of the problem. The error values obtained in test stage are affected from this condition. The equations to calculate the outputs of each of the problem considered were obtained using MGGP and M5Tree models.

Contract Management and Cost Control of Highway Projects

Contract Management and Cost Control of Highway Projects

How does contract flexibility affect the sustainability performance of public–private partnership projects? A serial multiple mediator model

PurposeThe risks and uncertainties of public–private partnership (PPP) projects threaten their sustainability. Contract flexibility, which is based on the theory of incomplete contract and transaction cost, may be a viable solution to this issue. The purpose of this study is to investigate the relationship between contract flexibility and the sustainability performance of PPP projects. The multiple mediating roles of justice perception and cooperation efficiency are assessed, thereby allowing the pathways and conditions to be understood more comprehensively for improving the sustainability performance of PPP projects.Design/methodology/approachNine hypotheses in the proposed research model are tested via structural equation modeling using data acquired from 218 Chinese PPP professionals.FindingsResults show that contract flexibility positively affects PPP project sustainability performance. Justice perception and cooperation efficiency play direct and sequential mediating roles in this effect.Originality/valueThis study validates that contract flexibility positively impacts the sustainability performance of PPP projects, where justice perception and cooperation efficiency serve direct and sequential mediating roles. The findings of this study contribute to an improved understanding of the effect of contract flexibility on the sustainability performance of PPP projects. Furthermore, they provide important theoretical and practical insights into contract management as well as beneficial information and valuable initiatives for improving the sustainability of PPP projects.

Blockchain technology prospects in transforming Ghana’s economy: a phenomenon-based approach

ABSTRACT A phenomenon-based approach is used to learn more about how blockchain technology could improve Ghana’s economic sectors in terms of cost savings, efficiency, and reliability with reduced risks. With our proposed blockchain-enabled frameworks, we describe how blockchains’ Internet-of-Things (IoTs) and Distributed Ledger Technologies (DLTs) might reduce transaction, contract, and monitoring-related costs in the Agriculture & Agro-processing sector’s supply chains. We also demonstrate how Smart Contracts (SMCs) and Distributed Ledger Technologies (DLTs) can improve time and cost-based efficiencies in local procurements, logistic contract execution, and supply chains across the Mining & Minerals processing sector. With the help of IoTs, DLTs, and SMCs, information asymmetries in the finance sector can be reduced to improve the financing for Small and Medium-sized Enterprises (SMEs). The Technological-Organizational-Environmental (TOE) elements remain crucial in the adoption of blockchain technology. As a result, it’s critical to provide adequate frameworks for blockchain adoption.

An Intelligent Automated System for Detecting Malicious Vehicles in Intelligent Transportation Systems.

The exponential growth of intelligent vehicles(IVs) development has resulted in a complex network. As the number of IVs in a network increases, so does the number of connections. As a result, a great deal of data is generated. This complexity leads to insecure communication, traffic congestion, security, and privacy issues in vehicular networks (VNs). In addition, detecting malicious IVs, data integration, and data validation are major issues in VNs that affect network performance. A blockchain-based model for secure communication and malicious IV detection is proposed to address the above issues. In addition, this system also addresses data integration and transaction validation using an encryption scheme for secure communication. A multi-chain concept separates the legitimate and malicious data into two chains: the Integrity chain (I-chain) and Fraud chain (F-chain). This multi-chain mechanism solves the storage problem and reduces the computing power. The integration of blockchain in the proposed model provides privacy, network security, transparency, and immutability. To address the storage issue, the InterPlanetary File System (IPFS) is integrated with Certificate Authority (CA). A reputation mechanism is introduced to detect malicious IVs in the network based on ratings. This reputation mechanism is also used to prevent Sybil attack. The evaluation of the proposed work is based on the cost of smart contracts and computation time. Furthermore, two attacker models are presented to prevent the selfish mining attack and the Sybil attack. Finally, a security analysis of the proposed smart contracts with their security vulnerabilities is also presented.

P-022: THERAPEUTIC EFFECTS OF GBT1118 ON SKELETAL MUSCLE ENERGETICS AND FUNCTION IN SICKLE CELL MICE

Purpose: Sickle cell disease (SCD) is an hemoglobinopathy ultimately resulting red blood cells sickling. SCD patients suffer from anemia, arterial oxyhemoglobin desaturation, hematologic and hemodynamic abnormalities, all of which disrupt oxygen supply and can reduce exercise capacity and quality of life. Strenuous exercise in SCD patients may induce hemolysis, an early lactate accumulation, an intracellular acidosis, all of which are potential triggering factors for sickling and vaso-occlusive crises. GBT1118 is a HbS polymerization inhibitor which can increase hemoglobin affinity for oxygen and the corresponding supply to tissues. On that basis, we hypothesized that GBT1118 could dampen skeletal muscle energetics and function. Materials and methods: Skeletal muscle energetics and function of sixteen Townes mice (2 ± 0.5 month-old) were assessed in response to a standardized Rest-Stimulation(1Hz)-Recovery protocol before and after an 8-week period (8WP). Investigations were conducted using a setup designed to be operational inside a 7T horizontal MRI scanner (PharmaScan-Bruker). The setup allows (i) magnetic resonance imaging, (ii) muscle force measurements and (iii) 31P-magnetic resonance spectroscopy. Assessments conducted before and after the 8WP were identified with number 1 (SCDGBT1) and two (SCDGBT2). Ten mice received a chronic supplementation of GBT1118 (4g/kg in chow) during the 8WP (SCDGBT), whereas six mice had access to not supplemented chow (SCDCON). Results: The specific peak force (sPf) measured at the beginning of stimulation significantly increased over the 8WP (SCDCON2 +68%, p < 0.05). This increased initial sPf was also present in SCDGBT2 as compared to SCDGBT1 (+ 30%, p < 0.05). Over the entire stimulation session, the overall specific [TM1] force production (sTFP) significantly increased over the 8WP (SCDCON2 +32%, p < 0.05, Figure 1). The increased sTFP was also present in SCDGBT2 as compared to SCDGBT1 (+ 25 %, p < 0.05). However, the sTFP-normalized pHi reduction and sTFP-normalized phosphocreatine (PCr) breakdown significantly decreased in SCDGBT2 as compared to SCDGBT1 (p < 0.05, Table 1). Conclusion: In the control condition, the 8-week period was accompanied by a significant increase in specific total force production. Such an increase has been previously reported from 3 to 10 weeks of age (+ 33%). Overall, the increased specific total force production we reported was related to time and cannot be accounted for by GBT supplementation. In SCDGBT, the expected increased force production related to time was associated to a reduced acidosis and PCr breakdown which were not found in the control condition. For a given amount of force, SCDGBT mice muscle would experience a lower net proton accumulation and a reduced net PCr consumption. This reduction of both acidosis and PCr consumption could illustrate a diminished cost of contraction in response to GBT1118 supplementation which could be related to an improved contractile efficiency as previously described in humans. SCD mice treated with GBT1118 exhibit a reduced acidosis for a given force. Given that acidosis is a potential triggering factor of sickling, GBT would protect SCD muscle against the consequences of sickling. One could speculate that SCD patients receiving GBT1118 might be able to perform exercise with a reduced exposure to sickling.Specific total force production in SCDCON and SCDGBT before and after the 8-week period. Each point represents a single measurement and lines are linking consecutive points. p-values are mentioned on the top of the figure.Metabolic indices in SCDCON and SCDGBT before and after the 8-week period. The authors do not declare any conflict of interest

ANALISIS KOMPARASI TINGKAT RISIKO PEMBIAYAAN JUAL BELI DENGAN BAGI HASIL PADA BANK MUAMALAT KCP. BONE

Despite a fairly rapid development of Islamic Banking in Indonesia, but the composition of cost is still imbalance. This is proved by the nomination of skim murabahah (consumptive sale and purchase) compared with skim mudarabah (products division/productive). The cost of mudarabah is the cost of real sector and considered has high risk. Therefore, syariah bank needs to apply risk management and evaluate the level of maximum risk that might happen on the costs spent. The data were analyzed using credit risk method. Credit risk is a model of risk measurement of costs portfolio. This model was introduced by Credit Suisse Financial Products (CSFP) in 1997.This approach wasmaximum loss measurement from default credit portfolio. Data input credit risk are as follow; (exposure of default, default rates, probability of default, and expected loss) after having results of data analysis and then were analyzed using trend analysis to measure the frequency of risk level happened in the period of 2013-2014 and used analysis of hypothesis test one sample T test to obtain the comparison of risk level of sale and purchase of product division. The results of trend analysis express that the frequency of the occurrence of financial risks is more dominant on the sale and purchase contract, it can be seen in June 2013 until December 2014 NPF murabahah continuously improved, it is different with mudarabah (product division) that still run well. While the results of comparative test analysis One sample T test state that the cost of murabahah contract has higher risk measured from the high costs of financing bogged down experienced in this case the collectability category 5 at the end has impact on the level of achievement of profitability of Muamalat banks.

The Impact of Meeting the International Financial Reporting Standards (IFRS) No. 15 on the Quality of Financial Reports In the Jordanian Construction Companies

The present study explored the impact of meeting the international financial reporting standards (IFRS) on the quality of financial reports in Jordanian construction companies. Those standards are represented in recognition, measurement, and disclosure). The descriptive-analytical approach was adopted by using a questionnaire. This questionnaire sheds light on the variables and dimensions of the present study. The population consists of all the auditors practicing the profession (407 auditors). A random sample was chosen from the population. It consists of 196 auditors. It was found that meeting the international financial reporting standards (IFRS) No. 15 has a significant impact on the quality of financial reports in Jordanian construction companies. The researcher of the present study recommends providing more attention to the disclosure of information about the revenues derived from the contracts in a manner separate from the other revenues. He recommends providing more attention to the disclosure of information about the derived assets, such as the contract costs. He recommends increasing the procedures taken for disclosing the data related to guidelines and the provisions applicable to customers and companies. He recommends showing more attention to meeting the international financial reporting standards (IFRS) No. 15 due to their positive impacts on the Jordanian construction companies.

Performance of Risk-Based Estimating for Capital Projects

Risk-based approaches to capital project cost and schedule estimation have been adopted by many public agencies in recent years. Such techniques are intended to produce more reliable cost and schedule forecasts, enhance risk management, and improve project management and project delivery outcomes. However, limited research has been conducted in relation to the true effectiveness of such risk assessment programs. The impact of process change must be measured to appraise the value differential. Where a high-value proposition exists, it is the duty of public servants to disseminate the opportunity to the industry for the good of the public. This paper measures and compares the cost and schedule performance of 28 highway projects completed in Washington state between 2016 and 2019. Results from a group of projects that deployed a formal cost/schedule risk assessment with risk management (referred here as “CRA”) are compared with a second group that did not. The data reveal that projects that completed a CRA were more likely to be completed with a total contract cost at or below the engineer’s estimate, experience lower post-award cost and schedule growth, and experience more predictable outcomes than those projects that did not deploy a CRA. Additional observed benefits of a CRA process are also discussed.

Smart contracts vs incomplete contracts: A transaction cost economics viewpoint

Smart contracts (i.e., agreements enforced by a blockchain) are supposed to work at lower transaction costs than traditional (and incomplete) contracts that instead exploit a costly legal enforcement. This paper challenges that claim. I argue that because of the need for adaptation to mutable and unpredictable occurrences (a chief challenge of transaction cost economics à la Oliver Williamson), smart contracts may incur higher transaction costs than traditional contracts. This paper focuses on two problems related to the adaptation: first, smart contracts are constructed to limit and potentially avoid any ex-post legal intervention, including efficiency-enhancing adaptation by courts. Second, the consensus mechanism on which every smart contract depends may lead to additional transaction costs due to a majority-driven adaptation of the blockchain that follows Mancur Olson's Logic of groups. The paper further proposes several institutional expedients that may reduce these transaction costs of smart contracts.

The Application of Earned Value Analysis of The Progress of Construction Work in The Development of The Porch or Vestibule of The Basement Building Area Youth Surabaya

Performance requirements project is the good of the products, who reached the cost and time in the possible lower of the plan.An approach to condition of ideal performance evaluation done as in the construction of the building type b1 ((basement area youth hall)). With one is a methods used in the management of the cost and time intregity. Methods to research is earned value analysis with data collection in field in building construction project type b1 ((basement area youth hall)). The data used in this method is, time schedule the budget costs and. weekly progress reportsFrom the data and produce values bcwp bcws and predict the time the end of the project. Building construction project type b1 (( basement area youth hall )) having evaluation. later when the end of the projectIn evaluating the performance of the cost of this project had surpluses the cost of the value of the contract (cost overrun) while in evaluating the performance of the project accelerated schedule where implementation schedule faster of the project schedule ( schedule underrun ) estimated to be completed during the day calendar 539.

Extreme Gradient Boosting-Based Machine Learning Approach for Green Building Cost Prediction

Accurate building construction cost prediction is critical, especially for sustainable projects (i.e., green buildings). Green building construction contracts are relatively new to the construction industry, where stakeholders have limited experience in contract cost estimation. Unlike conventional building construction, green buildings are designed to utilize new technologies to reduce their operations’ environmental and societal impacts. Consequently, green buildings’ construction bidding and awarding processes have become more complicated due to difficulties forecasting the initial construction costs and setting integrated selection criteria for the winning bidders. Thus, robust green building cost prediction modeling is essential to provide stakeholders with an initial construction cost benchmark to enhance decision-making. The current study presents machine learning-based algorithms, including extreme gradient boosting (XGBOOST), deep neural network (DNN), and random forest (RF), to predict green building costs. The proposed models are designed to consider the influence of soft and hard cost-related attributes. Evaluation metrics (i.e., MAE, MSE, MAPE, and R2) are applied to evaluate and compare the developed algorithms’ accuracy. XGBOOST provided the highest accuracy of 0.96 compared to 0.91 for the DNN, followed by RF with an accuracy of 0.87. The proposed machine learning models can be utilized as a decision support tool for construction project managers and practitioners to advance automation as a coherent field of research within the green construction industry.

Enduring corporate political connections and loan contracting: evidence from the GCC countries

PurposeAn important question is whether lenders perceive politically connected firms as having less or higher default risk, and thus provide them with more or less preferential loan terms compared with non-connected firms. This paper aims to examine the relationship between political connections of corporate board members and cost of debt and loan contracting in the Gulf Cooperation Council (GCC) countries.Design/methodology/approachThe initial sample comprises 288 GCC firm-year observations from 227 publicly listed firms in Oman, Qatar, Saudi Arabia and United Arab Emirates for the period from 2011 to 2015. It includes all the GCC publicly listed firms, excluding those in the financial, insurance and banking sectors because these entities are subject to different regulations. The ordinary least squares, logit regression and other sensitivity tests have been used to analyse the data and enhance reliability of the results.FindingsThis study finds that politically connected firms, particularly those connected through ruling royal family members, are associated with lower cost of debt, greater amounts of loans and longer-term government loans. Therefore, these findings support the prediction that political connections benefit GCC firms in the form of access to favourable terms from both government and commercial banks.Originality/valueThis study contributes to the extant literature by providing insightful analysis using unique political features of the GCC, integrated with agency and resource dependency theories. In particular, this study fills the gap in understanding the nature of loan contracting offered by government and commercial banks in the presence of politically connected boards within GCC setting.

Empirical risk assessment of maintenance costs under full-service contracts

We provide a data-driven framework to conduct a risk assessment, including data pre-processing, exploration, and statistical modeling, on a portfolio of full-service maintenance contracts. These contracts cover all maintenance-related costs for a fixed, upfront fee during a predetermined horizon. Charging each contract a price proportional to its risk prevents adverse selection by incentivizing low risk (i.e., maintenance-light) profiles to not renege on their agreements. We borrow techniques from non-life insurance pricing and tailor them to the setting of maintenance contracts to assess the risk and estimate the expected maintenance costs under a full-service contract. We apply the framework on a portfolio of about 5000 full-service contracts of industrial equipment and show how a data-driven analysis based on contract and machine characteristics, or risk factors, supports a differentiated, risk-based break-even tariff plan. We employ generalized additive models (GAMs) to predict the risk factors’ impact on the frequency (number of) and severity (cost) of maintenance interventions. GAMs are interpretable yet flexible statistical models that capture the effect of both continuous and categorical risk factors. Our predictive models quantify the impact of the contract and machine type, service history, and machine running hours on the contract cost. We additionally utilize the predictive cost distributions of our models to augment the break-even price with the appropriate risk margins to further protect against the inherently stochastic nature of the maintenance costs. The framework shows how maintenance intervention data can set up a differentiated tariff plan.

Stated preferences for family doctor contract services: a survey of the rural elderly in Anhui Province, China

ObjectiveA number of factors contribute to the utilisation of family doctor contract services (FDCS) in China. This study aims to measure the preferences of the elderly for the FDCS and...

Time–Cost Schedules and Project–Threats Indication

One of the most common disciplines in a business or economic project is timing and resource review. Despite the frequency of use, the level of sophistication is not high enough to maintain its level of importance. Exceeding deadlines and non-compliance with contractual costs is more than common. Moreover, there are projects where uncertainties are a naturally accompanying phenomenon. Research projects, implementation of solutions in a time-limited situation, or in an environment of limited knowledge creates risk. Any project proposal faces future realization risks when its planning management does not know with certainty where the current risks and uncertainties may come from. Decision-making, risk management dynamics, and simulations have developed in recent decades into an erudite and useful discipline. The aim is to indicate how much of the time–cost schedule proposal is stable, controllable, and economically feasible. The approach is based on the idea that modern resource scheduling requires nonlinear dynamic calculating models and simulations. The methodology presented is based on the dynamics of underlying physical and economic processes that form a spatial pattern of a time series. The article’s objective is devoted to the early indication of a dynamic project schedule’s instability and predisposition to bifurcation and chaos. In other words, the aim is to show not only what will happen but how diverse and damaging the project may become in the future.

A Multi-Criteria Model to Evaluate Public Services Contracts

A major trend to improve Public Administration has been the increase of contracting out services hoping to achieve better levels of performance. However, the effectiveness and efficiency of this approach implies the application of appropriate models to evaluate such performance&#x0D; &#x0D; This is why a multi-criteria model was developed by the authors to evaluate and to improve the performance of public services contracts focusing on four key dimensions: the process of contract formation, the contract costs, the benefits achieved by the contract execution and its impacts. The proposed model provides a stable, consistent and integrated framework allowing not just the evaluation of contracts but also the identification of the priorities for improvement.&#x0D; &#x0D; A successful application to the evaluation of Home Respiratory Services is also presented.

Influence of muscle-belly and tendon gearing on the energy cost of human walking.

This study combines metabolic and kinematic measurements at the whole‐body level, with EMG and ultrasound measurements to investigate the influence of muscle‐tendon mechanical behavior on the energy cost (Cnet) of walking (from 2 to 8 km·h−1). Belly gearing (Gb = Δmuscle‐belly length/Δfascicles length) and tendon gearing (Gt = ∆muscle‐tendon unit length/∆muscle‐belly length) of vastus lateralis (VL) and gastrocnemius medialis (GM) were calculated based on ultrasound data. Pendular energy recovery (%R) was calculated based on kinematic data, whereas the cumulative activity per distance travelled (CMAPD) was calculated for the VL, GM, tibialis anterior, and biceps femoris as the ratio between their EMG activity and walking speed. Finally, total CAMPD (CMAPDTOT) was calculated as the sum of the CMAPD of all the investigate muscles. Cnet and CMAPDTOT showed a U‐shaped behavior with a minimum at 4.2 and 4.1 km·h−1, respectively; while %R, VL, and GM belly gearing showed an opposite trend, reaching a maximum (60% ± 5%, 1.1 ± 0.1 and 1.5 ± 0.1, respectively), between 4.7 and 5 km·h−1. Gt was unaffected by speed in GM (3.5 ± 0.1) and decreased as a function of it in VL. A multiple stepwise linear regression indicated that %R has the greatest influence on Cnet, followed by CMAPDTOT and GM belly gearing. The role of Gb on Cnet could be attributed to its role in determining muscle work: when Gb increases, fascicles shortening decreases compared with that of the muscle‐belly, thereby reducing the energy cost of contraction.

Risk Assessment Model for Optimal Gain–Pain Share Ratio in Target Cost Contract for Construction Projects

Recently various contracting methods have been introduced for construction projects. Target cost contracts have been used widely where risk can be shared between contract parties. This gain–pain sharing method has proven its efficiency as a fair contracting method, but it depends on the adopted gain–pain share ratio. Minimal research efforts have quantified the optimal gain–pain share using game theory simulation. This research closes the gap by developing a mathematical formulation of the utility functions for both contract parties (i.e., the owner and the contractor) in target cost contracts. The effect of the risk-sharing rate on the behavior of the contact parties and risk direction was modeled. The developed mathematical model was analyzed to consider all possible scenarios. A simulation analysis that considered project characteristics and construction cost variations was performed to provide a robust valuation of potential risk allocation in determining the optimal share ratio using real-world construction data. The results showed that the optimal gain–pain share ratio varies according to the difference between the construction project cost variation and the contractor’s efforts fees. It is practical to set different gain–pain share ratios based on the construction project characteristics and the contractor’s efforts fees to satisfy contract parties in this complicated and interconnected relationship. Quantifying the optimal gain–pain share ratio helps to produce more-reliable target cost contracts.