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Autonomous vehicle (AV) companies are rolling out driverless taxi-type services in cities around the world, promising to enhance road safety, promote equity, and foster environmental sustainability while simultaneously threatening the jobs of current taxi and ride-hailing drivers. The potential impacts of these "robotaxi" services hinge upon not only whether AV technology becomes sufficiently performant, but also whether AV services become economically competitive. In this study, we develop ground-up cost models for a traditional taxi and a robotaxi service to compare their relative competitiveness. We draw on direct observations of commercially available robotaxi services, semi-structured interviews with AV operational and regulatory experts (N = 27), and archival documents to include the most detailed accounting of frontline robotaxi labor roles to date. We find that labor remains a significant cost for existing robotaxi services but that robotaxi operating costs are still lower than those of traditional taxi services. Ultimately, utilization rates and annual mileage will serve as the most influential factors for robotaxi competitiveness. Finally, if jobs shift from traditional taxi to robotaxi services, the total number of frontline jobs could decrease by between 57% and 76%, but the distribution of worker wages would shift higher. Transportation planners, researchers, and policymakers should continue accounting for labor costs in an AV future, as these costs will influence where AV deployment and job losses are (and are not) likely to occur, and should proactively investigate alternative career paths for potentially displaced frontline workers. Robotaxi costs are highly sensitive to lower labor ratios (i.e., fewer vehicles per worker), but firms reach economies of scale relatively quickly. Robotaxi firms should weigh marginal labor cost reductions against impacts to passengers and workers. • Fieldwork and semi-structured interviews identify additional labor roles for robotaxis. • Labor remains a significant operational cost for existing robotaxi services. • Robotaxi operating costs are still lower than those of traditional taxi services. • Utilization rates and annual mileage will be limiting factors for robotaxi competitiveness. • Shift from taxi to robotaxi services could decrease the total number of frontline jobs. [ABSTRACT FROM AUTHOR]

Survey response rates have declined dramatically in recent years, increasing the costs of data collection. Despite this, there is little existing research on how to most efficiently allocate samples in a manner that incorporates response rate information. Existing mathematical theory on allocation for single-stage stratified sample designs generally assumes complete response. A common practice is to allocate sample under complete response, then to inflate the sample sizes by the inverse of the anticipated response rates. However, we show that this method can fail to improve upon an unadjusted allocation, due to ignoring the associated increase in the cost per interview. We provide mathematical theory on how to allocate single-stage designs in a manner that incorporates the effects of nonresponse on cost efficiency. We derive the optimal allocation for the poststratified estimator under nonresponse, which minimizes either the unconditional variance of our estimator or the expected costs, holding the other constant, and taking into account uncertainty in the number of respondents. We assume a cost model that incorporates effects of nonresponse. We provide theoretical comparisons between our allocation and common alternatives, which illustrate how response rates, population characteristics, and cost structure can affect the methods' relative efficiency. In an application to a self-administered survey of US military personnel, the proposed allocation increases the effective sample size by 25 percent, compared with common practice. [ABSTRACT FROM AUTHOR]

In this paper, we derive a condition-based maintenance strategy for a degradation-shock dependence system subject to two causes of failure, degradation and random shocks. We develop a reliability model for a system under warranty for failure in which we consider the dependence between the degradation process and random shocks and do a warranty cost analysis based on the suggested model. We consider a degradation-shock dependence system that is under warranty with three separate services: repair, replacement, and preventive maintenance. These are triggered when a product degradation crosses one of three predetermined degradation thresholds. We also investigate the dependence between random shocks and degradation modelled by a time-scaled covariate factor. We study the random shock considering fatal shocks that require replacement and nonfatal shocks that require only repair and the degradation caused by nonfatal shocks. The condition-based maintenance strategy for a degradation-shock dependence system is a novel approach to analysing system degradation, with a goal of optimising warranty costs; we also minimise the expected total system cost to find an optimal warranty length. We present several numeric examples and real-world applications to illustrate our proposed approach. [ABSTRACT FROM AUTHOR]

With customers looking for the highest level of services and reduction of cost on international trade of goods, shippers have recently begun to focus on the inland leg of containerized import/export, making the competition advance between 'seaport against seaport' to 'seaport-hinterland against seaport-hinterland'. Bearing this in mind, this paper seeks to assess the logistic costs and the import process time in order to identify how these factors influence seaport-hinterland customer's choice and what is the effect of these factors on the main actors' competitiveness. In terms of time, the findings indicate that dry ports have been operationally more efficient, with customs and delivery process faster than in seaports and extended gates. In terms of cost we have applied Monte-Carlo simulation in a case study in Brazil to assess stochastically the import cost considering multiple actors as options. The study makes practical contributions, showing the cost-efficient zone for each macro region in the studied area and simulating a hypothetical scenario, where multimodal transportation is a delivery option. Lastly, the study makes theoretical contributions, discussing the competitiveness environment in light of the literature, bringing relevant insights to aid customers' and practitioners decision-making. [ABSTRACT FROM AUTHOR]

The importance and complexity of spatial join operation resulted in the availability of many join algorithms, some of which are tailored for big-data platforms like Hadoop and Spark. The choice among them is not trivial and depends on different factors. This paper proposes the first machine-learning-based framework for spatial join query optimization which can accommodate both the characteristics of spatial datasets and the complexity of the different algorithms. The main challenge is how to develop portable cost models that once trained can be applied to any pair of input datasets, because they are able to extract the important input characteristics, such as data distribution and spatial partitioning, the logic of spatial join algorithms, and the relationship between the two input datasets. The proposed system defines a set of features that can be computed efficiently for the data to catch the intricate aspects of spatial join. Then, it uses these features to train five machine learning models that are used to identify the best spatial join algorithm. The first two are regression models that estimate two important measures of the spatial join performance and they act as the cost model. The third model chooses the best partitioning strategy to use with spatial join. The fourth and fifth models further tune two important parameters, number of partitions and plane-sweep direction, to get the best performance. Experiments on large-scale synthetic and real data show the efficiency of the proposed models over baseline methods. [ABSTRACT FROM AUTHOR]

The budgeted influence maximization (BIM) problem aims to identify a set of seed nodes that adhere to predefined budget constraints within a specified network structure and cost model. However, it is difficult for the existing algorithms to achieve a balance between timeliness and effectiveness. To address this challenge, our study initially proposes a refined cost model through empirical scrutiny of Weibo's quote data. Subsequently, we introduce a proxy-based algorithm, i.e., the budget-aware local influence iterative (BLII) algorithm tailored for the BIM problem, aimed at expediently identifying seed nodes. The algorithm approximates the global influence by leveraging the user's one-hop influence and circumvents influence overlap among seed nodes via iterative influence updates. Comparative experiments involving eight algorithms across four real networks demonstrate the effectiveness, efficiency, and robustness of the BLII algorithm. In terms of influence spread, the proposed algorithm outperforms other proxy-based algorithms by 20%–255 % and reaches the state-of-the-art simulation-based approach by 96 %. In addition, the running time of the BLII algorithm is reasonable. Generally, the proposed cost model and BLII algorithm provide novel insights and potent tools for studying BIM problems.

The purpose of this study is to provide an overview of a methodology, which will enable the industrial end-users to identify the potential parts to be manufactured by freeform injection moulding (FIM). Our proposed approach can be used 'with' or 'without' FIM reference parts. In the reference part-based screening method, the process of part selection starts by identifying the most similar parts (from the end-user part libraries) to some reference parts, which can be produced by FIM, followed by part size classification and cluster analysis based on the importance factors. A multi-criteria decision-making approach is then used to rank the clusters and the parts within each cluster using a user-defined criteria. The identification process without using FIM reference part does not include comparison with the reference parts. In both methods, once the first set of potential parts have been identified, the cost of using FIM is calculated using the developed FIM cost model and compared with the production cost using a conventional manufacturing. The novelty of this research lies in using a comprehensive methodology to compare the suitability of a reference part-based approach with the non-reference part-based approach. The results show that using a reference part-based approach is more suitable for the parts with bigger size. [ABSTRACT FROM AUTHOR]

Blockchain infrastructures have emerged as a disruptive technology and have led to the realization of cryptocurrencies (peer‐to‐peer payment systems) and smart contracts. They can have a wide range of application areas in e‐Science due to their open, public nature and global accessability in a trustless manner. We propose and implement a smart contract called eBlocBroker, which is an autonomous blockchain‐based middleware system for volunteer computing and providing data resources for e‐Science. The eBlocBroker infrastructure connects requesters who need to combine applications (jobs) with datasets and run them via an Ethereum‐based private blockchain network (Bloxberg) on providers that utilize computational and data resources on clouds or home servers. It uses cloud storage, such as B2DROP, IPFS, or Google Drive, to store and transfer data between requesters and providers. Each provider utilizes the Slurm workload manager to execute jobs submitted through eBlocBroker. In this paper, we demonstrate how an autonomous organization programmed as a smart contract can be used to deploy a marketplace that supports data and computation‐intensive research projects. We propose a cost model implemented as a function in the smart contract which calculates and records computation and dataset usage costs. We develop a Python‐based system to communicate with eBlocBroker and orchestrate jobs' execution on the provider's end. We present eBlocBroker's features, infrastructure, implementation, algorithms and experimental results. [ABSTRACT FROM AUTHOR]

We present a cost model for implementing a deployment scale effort for conducting ocean iron fertilization (OIF) for marine‐based carbon dioxide removal (CDR). The model incorporates basic oceanographic parameters critical for estimating the effective export of newly fixed CO2 into biomass that is stimulated by Fe addition to an Fe‐limited region of the Southern Ocean. Estimated costs can vary by nearly 100‐fold between best‐case and worst‐case scenarios, with best‐case values of $7/net tonne C captured versus worst‐case $1,500/net tonne C captured, without accounting for verification costs. Primary oceanographic factors that influence cost are the net primary productivity increases achieved via OIF, the amount of C exported into the deep ocean, and the amount of CO2 ventilated back to the atmosphere. The model compares ship‐based versus aerial delivery of Fe to the ocean, and estimates aerial delivery can be 30%–40% more cost effective; however, the specific requirements for aerial delivery require additional research and development. The model also estimates costs associated with verification and environmental monitoring of OIF. These costs increase $/net tonne C captured by 3–4‐fold. Best, intermediate, and worst cases for aerial delivery and ship delivery are $21, $83, $2,033, and $24, $94, $4,691, respectively, inclusive of verification costs. The primary goal of this model is to demonstrate the variability in cost of OIF as a CDR method, and to better understand where additional research is needed to determine the major factors that may make OIF a tractable, nature‐based CDR method. Plain Language Summary: This paper presents a basic cost model for undertaking ocean iron fertilization (OIF) as a means of removing carbon dioxide from the atmosphere. It presents a simple application scenario that compares two different modes for delivering iron to the ocean, plane‐based and ship‐based, and estimates costs associated with verification of carbon export into the deep ocean, as well as assessing the environmental changes that may occur as result. Key findings are that, in terms of USD/tonnes carbon exported, OIF can be at the lower cost end (

Workforce well-being and associated factors such as burnout, depression and documentation burden, have been identified as the highest concerns to be addressed. In academia, the new essentials of nursing practice including domain 8 for informatics and healthcare technology have become a focus for curricular revisions/enhancements. Our study focused on technology skills by using the technology of an academic EHR to measure baselines and progression of EHR use, sense of confidence, documentation competency, and post-graduation employer-based performance assessment. We provide results of an ongoing 1.5-year study and overarching strategy for university-wide deployment and financing. [ABSTRACT FROM AUTHOR]

The UK government plans to phase out pure diesel trains by 2040 and fully decarbonize railways by 2050. Hydrogen fuel cell (HFC) trains, electrified trains using pantographs (Electrified Trains), and battery electric multiple unit (BEMU) trains are considered the main solutions for decarbonizing railways. However, the range of these decarbonization options’ line upgrade cost advantages is unclear. This paper analyzes the upgrade costs of three types of trains on different lines by constructing a cost model and using particle swarm optimization (PSO), including operating costs and fixed investment costs. For the case of decarbonization of the London St. Pancras to Leicester line, the electrified train option is more cost-effective than the other two options under the condition that the service period is 30 years. Then the traffic density range in which three new energy trains have cost advantages on different line lengths is calculated. For route distances under 100 km and with a traffic density of less than 52 trips/day, BEMU trains have the lowest average cost, while electrified trains are the most cost-effective in other ranges. For route distances over 100 km, the average cost of HFC trains is lower than that of electrified trains at traffic densities below about 45 trips/day. In addition, if hydrogen prices fall by 26 %, the cost advantage range of HFC trains will increase to 70 trips per day. For route distances under 100 km, BEMU trains still maintain their advantages in terms of lower traffic density.

This paper deals with incentivizing public transit systems to exert effort to improve their performance to meet a programmatic goal of a funding agency. It shows the relationship between organisational and employee effort, formulates a net benefit equation with and without user cost savings and derives equations for effort. From these equations it surmises that (a) the larger the incentive the larger the effort transit systems and employees will exert to improve organisational performance, (b) effort is large when user costs are considered and (c) the larger the wage rate the smaller is the effort. In addition, it specifies a labour compensation equation that includes effort and labour intensity as some of its arguments and estimates it with a derived cost function and share equations as a system. The coefficients from them are used to show that when employees are incentivized to exert additional effort to improve organisational performance it increases the incentives, revenue and user cost savings by large proportions and cost by a very small proportion. [ABSTRACT FROM AUTHOR]

In this paper, we analyze an automated manufacturing system modeled as a queueing system with multiple servers, differentiated vacation (DV) policy, customer impatience, and server failures. We consider the waiting servers policy, where the servers wait for a random duration before leaving for a vacation once the system gets empty. Upon their return, if no customers are waiting, the servers may leave for a second type-2 vacation of shorter duration. The system may experience breakdowns and require immediate repair. During the repair period, customers are served at a lower rate, and they become impatient during busy, vacation, and repair periods, abandoning the queue if their timer expires. Further, based on the queue length, arriving customers may either join the system or balk with some probability. We use the matrix analytic method to derive the steady-state probability of the system and several performance measures. Moreover, we illustrate the effect of different system parameters on various performance measures. Additionally, we develop a cost model and optimize the system capacity, number of servers, and service rates such that cost per unit time is minimized. Finally, we analyze the impact of system parameters on the cost through numerical examples.

This paper analyses the efficiency and carbon footprint of different last-mile delivery solutions, including parcel lockers, pick-up points, and home deliveries. A Decision Support Tool (DST) is developed, utilizing real data on parcel deliveries and time allocation. The DST distinguishes between fixed, variable, and salary costs, revealing that time spent on delivery tasks and associated salary costs are the primary cost drivers. Deliveries to pick-up points are more efficient than deliveries to parcel lockers, but this efficiency depends on the number of parcels delivered. The environmental footprint of the solutions is influenced by how recipients collect their parcels.

Force Account Mechanism (FAM) is the predominant road maintenance system in Uganda’s local government setup and a similar, though slightly different approach, is used in some large private sector agriculture plantations. With the Uganda Road Fund (URF) 2021/2022 annual report and previous research citing challenges in cost management and efficiency of the FAM method of road maintenance, it becomes paramount to analyse how FAM is implemented in government-led operations, in comparison to similar private sector approaches, while proposing possible solutions to these challenges. This research offered to analyse unpaved road maintenance cost drivers alongside providing a cost model solution to improve on cost prediction of the FAM system. Gulu District Local Government (DLG) and Kakira Sugar Limited (KSL) were selected as case study areas. Two descriptive research methods were used: observations and case study approach. The selected case study areas were accessible and reachable in terms of data. Control parameters affecting unpaved mechanized road maintenance were identified as machine repair costs, tool costs, labour costs, material costs, fuel costs and machine fuel costs. Unpaved mechanized road maintenance costs at KSL and Gulu DLG were computed as a cost/km ratio of 26,442,032Ugx/km (6,958.4USD/km) and 32,674,895Ugx/km (8,598.65USD/km) respectively. The Uganda National Roads Authority (UNRA) unpaved road maintenance costs were calculated as an average of 34,987,122.9Ugx/km (9,165USD/km) while the World Bank ROCKS database provided a comparable figure of 7,971USD/km (30,553,440.83Ugx/km). A USD to Ugx conversion rate of 3,800 was used. Two linear regression cost models with a 0.679 and 0.687 R2 value were computed, and these can be used in preliminary road maintenance cost prediction. The study recommends the need for an effective, digital road maintenance cost database system for mechanized unpaved road maintenance works, cost driver analytics and management, alongside improvement in aspects of maintenance processes at both the DLG and KSL. Further research can be conducted on equipment condition level prediction and analytics in the private sector and at the DLG.

Abstract We present a cost model for implementing a deployment scale effort for conducting ocean iron fertilization (OIF) for marine‐based carbon dioxide removal (CDR). The model incorporates basic oceanographic parameters critical for estimating the effective export of newly fixed CO2 into biomass that is stimulated by Fe addition to an Fe‐limited region of the Southern Ocean. Estimated costs can vary by nearly 100‐fold between best‐case and worst‐case scenarios, with best‐case values of $7/net tonne C captured versus worst‐case $1,500/net tonne C captured, without accounting for verification costs. Primary oceanographic factors that influence cost are the net primary productivity increases achieved via OIF, the amount of C exported into the deep ocean, and the amount of CO2 ventilated back to the atmosphere. The model compares ship‐based versus aerial delivery of Fe to the ocean, and estimates aerial delivery can be 30%–40% more cost effective; however, the specific requirements for aerial delivery require additional research and development. The model also estimates costs associated with verification and environmental monitoring of OIF. These costs increase $/net tonne C captured by 3–4‐fold. Best, intermediate, and worst cases for aerial delivery and ship delivery are $21, $83, $2,033, and $24, $94, $4,691, respectively, inclusive of verification costs. The primary goal of this model is to demonstrate the variability in cost of OIF as a CDR method, and to better understand where additional research is needed to determine the major factors that may make OIF a tractable, nature‐based CDR method.

The analysis of an M/M/1/N feedback working vacation queueing system with reneging is presented in this paper. Customers may become impatient and even disappointed when they see a long line. In the literature on queueing, customer dissatisfaction caused on by unsatisfactory service is referred as feedback. In the case of feedback, customers retry services after receiving unsatisfactory or incomplete. First, we create the equations for the steady-state probabilities using the Markov process method. The steady-state probabilities are then solved by the matrix method. We then provide some system performance measures. We create a cost model using performance analysis. Finally, we give some numerical examples to show how the various model parameters affect the system’s behaviour. [ABSTRACT FROM AUTHOR]

The failure of computer software system is inevitable and it is important to analyze the performance of the system. It is equally important to consider the cost factor that occurs during repair. This paper models malware infected system and analyzes its behavior using a hybrid technique. A likelihood estimation is used for filtering malwares and improving the performance of the system. A discrete-time single server retrial queue with two types of arrivals is modeled to address infected system characteristics. A request which is originally legitimate known as the positive arrival, who finds the host free gets instant service; otherwise it joins the buffer. The malware (request) which is not detected by the filter known as the negative arrival will lead to the breakdown of the server, irrespective of the fact that the server is idle or busy. Further, it is assumed that suspicious request has no impact on the system when the server is under repair. Once the server is affected by suspicious request, it is sent for repair immediately. The retrial, service, and repair times are arbitrarily distributed. Performance characteristics such as mean buffer size, mean waiting time have been obtained. The reliability measures such as server availability, failure frequency, probability of packet lost, reliability of the underlying queueing system have also been derived. A cost model has been developed. The efficacy of filtering method and its level of accuracy have been demonstrated experimentally. The infected system has been detected from the performance measures and reliability metrics. A comparative study with and without filtering has been discussed numerically. The optimum cost analysis has also been presented. [ABSTRACT FROM AUTHOR]

The aim of this work was to determine a cost‐optimal design of the distillation unit of the Andrussow process. For this purpose, a feed with a mass flow rate of 121 t h−1 and a concentration of ca. 2 wt % hydrogen cyanide (HCN) was considered. An approach for a cost‐optimal process intensification was developed with the goal to achieve the desired product qualities, while minimizing the organonitrile accumulation in the column. For this purpose, the simple distillation column of the established cost‐optimal design of the base case was extended to a configuration with a side stripper with taking into consideration heat integration in the process. It was found that this new configuration allows a much smaller accumulation of organonitriles in the main column; reducing thereby the operation issues of the process while decreasing considerably the total annual cost of the distillation unit by 61 % as compared to that of the base case design. [ABSTRACT FROM AUTHOR]

Multistores are data management systems that enable query processing across different and heterogeneous databases; besides the distribution of data, complexity factors like schema heterogeneity and data replication must be resolved through integration and data fusion activities. Our multistore solution relies on a dataspace to provide the user with an integrated view of the available data and enables the formulation and execution of GPSJ queries. In this paper, we propose a technique to optimize the execution of GPSJ queries by formulating and evaluating different execution plans on the multistore. In particular, we outline different strategies to carry out joins and data fusion by relying on different schema representations; then, a self-learning black-box cost model is used to estimate execution times and select the most efficient plan. The experiments assess the effectiveness of the cost model in choosing the best execution plan for the given queries and exploit multiple multistore benchmarks to investigate the factors that influence the performance of different plans. [ABSTRACT FROM AUTHOR]

Carbon Integration methods help identify the appropriate allocation of captured carbon dioxide (CO2) streams into CO2-using sinks, and are especially useful when a number of CO2 sink options are present simultaneously. The method helps identify CO2 allocation scenarios when subjected to an emission target on the CO2 overall network. Many carbon dioxide sink options are costly, and more often than not, require a high purity carbon dioxide source to satisfy the sink demand. Hence, it is imperative to effectively incorporate treatment units in such networks, to obtain high-purity CO2 streams. In fact, it has been previously reported in many studies that the most expensive step in Carbon Capture, Utilization and Sequestration (CCUS) is the treatment system. As a result, this paper focuses on reassessing the performance of carbon integration networks using a more rigorous cost model for the treatment design stage. The effect of utilizing different treatment operating conditions on the overall cost of the treatment stage of CO2 (before allocation) is first captured using a detailed cost model. Subsequently, this information is then fed into a network design problem that involves a CO2 source-sink allocation network problem, and different CO2 net capture targets within the network. For this, an enhanced treatment model that captures all necessary treatment design parameters has been utilized alongside the original model. The original carbon integration formulation has been adopted from previous work. Many of the cost items have been lumped into single parameters in the original formulation, and lack the necessary depth required to carry out the necessary investigations for this work. Hence, the treatment model introduced in this paper is more rigorous, as it accounts for important technical performance constraints on the system to be assessed. Utilizing a more detailed cost model was found to be very helpful in understanding several effects of varying parameters on the overall source-sink allocations, when subjected to different CO2 net emission reduction targets. The cost of the carbon network increases when the solvent temperatures are increased. However, there was a noticeable linear trend at lower temperatures compared to higher temperatures, where the increase became non-linear. Furthermore, it was discovered that for net capture targets of 20% and 25%, no revenue from carbon storage could be generated beyond a solvent temperature of 25 °C. Additionally, the optimal diameter of the treatment column was more responsive to changes in solvent temperature for cases with low net capture targets (below 10%), while its sensitivity decreased for higher capture targets (above 10%). [ABSTRACT FROM AUTHOR]

Open burning of agricultural residues as a means of disposal causes massive atmospheric air pollution in northern Thailand. Converting this agricultural residual biomass to clean energy can help reduce this problem. They may be utilized as solid fuel for electrical power generation, where cost is one of the major factors affecting long term success in the use of biomass and bioenergy. In this work, a cost optimization model was developed for Thailand scenario by considering two parts of total cost: biomass and transportation costs. Ten potential agricultural residual biomass materials which were the most abundant in Thailand were taken into account. For transportation cost, K-means clustering was applied to group a number of supplies and demands, and to identify centroids of each cluster to use them as representatives in calculating the transportation cost from a supply to a demand point. Each kind of biomass was gathered into six different types of power plant. It was transported by 10-wheel trucks. A wide range of input data (biomass and transportation costs) from a supply to demand was also evaluated. From a case study considered, it was demonstrated that reduction of the total costs by more than 50% could be achieved. These results were from only biomass and transportation costs without taking other factors such as seasonal conditions or process limitations into consideration. Different input parameters can change the finalized total cost.

Cooperative, connected and automated mobility (CCAM) has a great potential to change drastically the mobility landscape and improve safety on the roads by assisting drivers to take the best decisions in given circumstances or by supporting autonomous driving features aboard the car, among other expected benefits. Yet, providing CCAM services poses not only technical but also business challenges, especially in cross-border environments. In this paper, we present a general techno-economic methodology intended first, to identify the cross-border ecosystem and the potential business models to enable CCAM services provision and second, to derive the Total Cost of Ownership (TCO) of deploying the required Vehicle to Infrastructure (V2I) and Vehicle to Network (V2N) infrastructure. As an illustration, we then apply this methodology to a canonical Cooperative Lane Merging (CLM) scenario in a representative border environment (i.e., the Brenner pass between Italy and Austria). On this occasion, the impact of Multi-access Edge Computing (MEC) placement on the deployment cost is also discussed and finally, a cost allocation model is developed to link these deployment costs to the key stakeholders within the identified business models. Results aim to give insights into the different deployment strategies in typical cross-border environments and settings. • This paper presents a comprehensive techno-economic analysis of providing CCAM services in cross-border environment considering both technical and business challenges. • Proposed business models based on the adoption of new 5G technologies namely network slicing and MEC to ensure seamless service provision. • Collaborative business model is the most promising but needs review of regulations. • The collaborative business model is particularly attractive when considering the implementation of passive and active network sharing strategies. • Cost modelling results provide TCO insights for 5G network deployment scenarios. [ABSTRACT FROM AUTHOR]

Hybrid transactional/analytical processing (HTAP) workloads on graph data can significantly benefit from GPU accelerators. However, to exploit the full potential of GPU processing, dedicated graph representations are necessary, which mostly make in-place updates difficult. In this paper, we discuss an adaptive update handling approach in a graph database system for HTAP workloads. We discuss and evaluate strategies for propagating transactional updates from an update-friendly table storage to a GPU-optimized sparse matrix format for analytics. [ABSTRACT FROM AUTHOR]

The gradual commercialization of entirely autonomous vehicles is expected to bring numerous benefits, such as structural transformation in the industry. Specifically, in maritime transportation, automobile terminals that import and export finished autos are seen to transform their current loading system into a CAV (connected automated vehicle)-loading system to accommodate autonomous vehicles. In this study, the impact of introducing a CAV-loading system to a roll-on, roll-off (RORO) ports was investigated. Simulation models were developed to test the performance of the terminal with the CAV-loading system. Then, a cost model was developed to determine the economic benefits of the CAV-loading system. The results in this study revealed that operating costs were reduced by 90%, while terminal operations were significantly improved. In addition, the study revealed that using the CAV-loading system resulted in a 12% reduction in CO2 emissions compared to that using the current loading system. The originality of this study lies in its transformative potential for an industry that heavily relies on human labor and has limited mechanization and automation. This study provides significant implications for incorporating autonomous vehicles in planned automobile terminal operations. [ABSTRACT FROM AUTHOR]

Urban traffic congestion is a critical problem for port-city areas. The truck restriction time window (TRTW) aims to curb congestion by restricting truck use on city roads during peak hours. But TRTW can cause congestion at ports after the restriction ends, leading to economic losses and higher emissions. This paper proposed a buffer stack model to reduce truck delays and emissions from TRTW at port-city areas. The model was implemented for Port of Izmir with using Discrete Event SimulationF (DES). A cost model was used for evaluating DES results. The results showed that buffer stack reduced truck delays by 28.1–43.9% and gate queue lengths by 31.2–43.2% in high and low truck traffic scenarios. The congestion costs (NOX, CO2 PM2.5, emission, and truck delay costs) were also reduced by 35.8–43.9% and the total cost including the buffer stack operation was reduced by 25.6–36.3% for high and low truck traffic. The comparison between buffer stack and chassis exchange terminal (CET) system showed that both systems had comparable emission reduction values, however, CET had a higher reduction in truck delays (83.8% compared to 28.0%). Results showed buffer stacks are effective and affordable solution for ports to mitigate the impact of peak truck traffic resulted from TRTW, lower truck emissions, and reduce the logistics cost. [ABSTRACT FROM AUTHOR]

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This paper deals with a M/M/c queueing system with waiting servers, balking, reneging, and K-variant working vacations subjected to Bernoulli schedule vacation interruption. Whenever the system is emptied, the servers wait for a while before synchronously going on vacation during which services are offered with a lower rate. We obtain the steady-state probabilities of the system using the matrix-geometric method. In addition, we derive important performance measures of the queueing model. Moreover, we construct a cost model and apply a direct search method to get the optimum service rates during both working vacation and regular working periods at lowest cost. Finally, numerical results are provided. [ABSTRACT FROM AUTHOR]

This paper is concerned with the analysis of an infinite-capacity batch arrival multi-server queueing system with Bernoulli feedback, synchronous multiple and single working vacation policies, waiting servers, reneging and retention of reneged customers. The steady-state solution of the queueing system is obtained by using probability generating function (PGF). In addition, important performance measures of the queueing system are derived. Then, a cost model is formulated in order to carry out the parameter optimization using genetic algorithm (GA). Finally, numerical study is presented in which various system performance measures are evaluated based on supposed numerical values given to the system parameters.

It is always foreseen to upturn the efficiency of an acceptance sampling plan for lot sentencing. In this research, variable multiple dependent state sampling plan considering the quality cost with rectifying inspection is proposed for normally distributed quality characteristics. The staple objective is to curb the total cost of the lot under inspection and improve the quality of outgoing lots. Tables are constructed for optimal plan parameters by increasing forgoing lots. The behavior of cost parameters on total cost has been investigated. Protection and cost curves are portrayed to show the efficacy of the proposed plan with rectifying single acceptance sampling plan using the cost model. Using the cost model, the proposed plan turns out to be more effectual than the single acceptance sampling plan in terms of sample size, average outgoing quality, and total cost. c i has emerged having a substantial effect on total cost trailed by c o and c f with the tiniest effect. Finally, an example is given to demonstrate the proposed plan. [ABSTRACT FROM AUTHOR]

This research paper describes the accounting practices of Jordanian companies engaged in agricultural activities, and identifies the influence of company characteristics on measurement practices related to asset pricing and level of disclosure required by IAS 41. Company characteristics were considered as: size, intensity of biological assets (BA), level of international activities, and audit for the Big Four. Dependent variables were considered measurement practices related to valuing BA as well as resultant harvest and disclosure level, the latter being measured by mandatory and voluntary disclosures. The entire population of companies that include one or more agricultural activities in their purposes and are considered reporting companies formed the research sample, giving a total of 259 companies. The findings revealed that both intensity of BA and level of international activities have a positive impact on measurement practices. Audit for the Big Four was the strongest variable influence, the overall disclosure level prescribed by IAS 41, followed by the level of international activities variable. However, the intensity of the BA variable affects only the overall disclosure level for companies that measure their BA based on the cost method. Firm size was found to have no influence on either measurement practices or disclosure level. The key value of this paper is its examination of the role of company characteristics on measurement practices and level of disclosure required by IAS 41 in the context of Jordanian companies. Through this examination, this study is helpful to standards setters and regulators who obligate and issue the financial regulation and reporting standards at a national or international level, supporting their understanding of measurement and disclosure practices adopted in agricultural companies in the developing country context of Jordan. [ABSTRACT FROM AUTHOR]

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An economic-statistical design is developed for the variable sampling interval (VSI) Poisson exponentially weighted moving average chart, namely the VSI PE chart, where the cost function is minimized by incorporating statistical constraints. The statistical performance measures of the VSI PE chart are derived using the Markov chain approach. A sensitivity analysis based on the design of the experiment is conducted to study the effects of the cost model parameters on the optimal solution. The results show that the VSI PE chart performs better than the fixed sampling interval PE chart, in terms of the expected cost and the statistical performances. [ABSTRACT FROM AUTHOR]

In assembly processes, collaborative robots (cobots) can provide valuable support to improve production performance (assembly time, product quality, worker wellbeing). However, there is a lack of models capable of evaluating cobot deployment and driving decision-makers to choose the most cost-effective assembly configuration. This paper tries to address this gap by proposing a novel cost model to evaluate and predict assembly costs. The model allows a practical and straightforward comparison of different potential assembly configurations in order to guide the selection towards the most effective one. The proposed cost model considers several cost dimensions, including manufacturing, setup, prospective, retrospective, product quality and wellbeing costs. The cost estimation also considers learning effects on assembly time and quality, particularly relevant in low-volume and mass customised productions. Three real manufacturing case studies accompany the description of the model. [ABSTRACT FROM AUTHOR]