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This paper analyzes the case study of the optimization of the cold chain distribution path of cold and fresh meat of enterprise H, and obtains the optimized optimal distribution path and the optimal distribution cost by bringing in the case data and using the software running program, and the research conclusion has certain reference value for the planning and development of the related cold chain logistics and transportation industry. [ABSTRACT FROM AUTHOR]

Hydrogen (H 2) is recognized as a viable and environmentally friendly energy source, utilized across various domains, from large-scale chemical energy exports to small-scale power generation in remote areas. However, the storage and distribution costs of H 2 present significant challenges. Ammonia (NH 3) emerges as a carbon-free hydrogen carrier, backed by a robust international transport and storage infrastructure. On-site hydrogen production can be efficiently achieved through NH 3 decomposition, predominantly via thermal catalysis. One innovative approach involves plasma technology, which utilizes NH 3 , alcohols, or hydrocarbons to produce pure hydrogen in plasma reactors. Nonthermal plasma (NTP) in particular, for NH 3 decomposition and H 2 production, has garnered considerable interest owing to its higher energy efficiency than thermal plasma systems. Furthermore, integrating NTP with catalysis, termed plasma-assisted catalysis, creates a synergistic effect, enhancing NH 3 decomposition efficiency for H 2 production through improved plasma-catalyst interactions. Consequently, NTP-catalysis holds the potential to revolutionize NH 3 conversion and utilisation in the future. To date, there have been limited studies on NTP-assisted catalytic NH 3 decomposition. This review article compiles the latest NTP-assisted catalytic NH 3 decomposition methodologies for H 2 production. It delves into the basics of plasma-assisted NH 3 decomposition, including adsorption, desorption, and the synergistic processes during plasma catalysis. Additionally, it examines the impact of NTP on the chemical states and properties of various catalysts and provides a comprehensive analysis of the factors influencing NH 3 -plasma decomposition. • Nonthermal plasma-assisted catalysis NH3 decomposition for COx-free H2 production. • Full NH 3 decomposition achieved at ambient temperature and pressure by applied NTP. • Combining plasma with catalysis has improved the NH3 efficiency decomposition. • NTP catalysis can potentially revolutionize NH3 conversion and utilisation in the future. [ABSTRACT FROM AUTHOR]

This paper examines an understudied way of refusing: counters, i.e. utterances which do not only block one course of action but put forward an alternative. An interactional approach to pragmatic meaning was taken to examine the content, design, and interpersonal implications of counters in (semi-)informal future-action negotiations. Regarding their content, it was found that counters can retain the distribution of cost/benefit of initial proffers (neutral), change them to the refuser's benefit (egoistic), or change them to the profferer's benefit (altruistic). Regarding their design, it was found that counters can be formatted as either interpersonally delicate or non-delicate actions – irrespective of their content. This suggests that specific benefactive chances are not intrinsically associated with specific interpersonal effects, e.g. egoistic and altruistic counters do not necessarily indicate interpersonal trouble or guarantee harmony, respectively. Rather, it is the design which has particular interpersonal implications, with counters formatted as non-delicate actions being hearable as problematising and/or treated as problematic. It is furthermore argued that counters are more constrained in terms of design than initial proffers – which may be formatted as non-delicate without negative interpersonal implications – and that this constraint results from their sequential position and the prior speaker's right to make a proffer. • Counters may change benefactives to the refuser's or the profferer's benefit. • Benefactive changes do not inherently indicate interpersonal conflict or harmony. • All counter types may be designed as interpersonally delicate actions. • This is due to their sequential position and profferers' proximal deontic rights. • Non-delicate counter design is hearable as problematising and can be problematised. [ABSTRACT FROM AUTHOR]

Since cooperation has been identified to be a key factor of future manufacturing firms' success, manufacturing concepts incorporating cooperation gained increasing interest within research and industry. However, the benefits from a financial standpoint have not yet been investigated thoroughly. Therefore, this paper investigates financial aspects of a resource-sharing platform where manufacturing companies can request resources (in case of shortage) or offer them (in case of surplus) through a central platform. In the first step, the additional costs and revenues are worked out in the context of a resource-sharing network. In the second step, a model is developed in order to evaluate and compare the additional costs (manufacturing, management, administrative, penalty, inventory and distribution costs) as well as the additional revenues (incomes) of a resource-sharing platform. Then, platform-based and direct exchange-based resource sharing algorithms are compared from the financial perspective. Finally, the model is validated and tested with experiments conducted using an agent-based simulation model. The findings (1) can be applied in the design and operation of such a platform, and (2) are helpful for companies who are considering this type of collaboration as a way to increase their competitiveness. [ABSTRACT FROM AUTHOR]

• A principal-agent model is proposed for BOT highway contracts under the effects of asymmetric investment cost information. • Properties of these optimal BOT contracts under asymmetric/symmetric cost information are respectively discussed and compared. • The extent to which cost information asymmetries influence these contracts is examined. • Numerical experiments are conducted to demonstrate our results and findings. In the planning stage of build-operate-transfer (BOT) highway projects, private firms usually possess more information about the investment cost than governments. This study considers the problem of how to design project contracts and regulate private firms whose investment costs are unobservable to governments. A principal-agent model is proposed to investigate this type of BOT contracts, in which governments aim to maximize social welfare, while the firms are guaranteed a reserved level of profit and have no incentive to misreport their costs. Besides, the extent to which the information asymmetries influence these contracts is examined, through an analysis of the effects of the distribution of marginal cost parameters and the shadow cost of public funds. Furthermore, properties of these contracts are discussed and compared with the revenue-compensated BOT contracts under symmetric cost information. [ABSTRACT FROM AUTHOR]

The purpose of this study was to estimate the global distribution and financial cost associated with the inequities present in the use of cesarean sections (CS) worldwide. We used the latest estimates on CS rates published by WHO and we adopted 10–15 % as the range of CS rates that are considered optimal for adequate use. We calculated the cost (in USD) to achieve CS rates of 10–15 % for countries that reported rates below 10 %. We also calculated the cost of CS rates in excess (>15 % and > 20 %) by estimating how much it would cost to reduce the rates to 10–15 % for each of those countries. 137 countries are included in this analysis with updated data on CS rates between the years 2010 and 2018. Our analysis found that 36 countries reported CS rates < 10 %, whereas 91 countries reported CS rates > 15 % (a majority of which were > 20 %); only 10 countries reported CS rates between 10 and 15 %. The cost of CS exceeding a rate of 15 % is estimated to be $9,586,952,466 including inflation and exceeding 20 % is $7.169.248.033 (USD). The cost of achieving "needed" CS among countries with CS rates < 10 % is $612,609,418 (USD). The cost of cesarean sections exceeding 15 % has increased by 313 % between 2008 and more recent years, accruing $7 billion (USD) more in surplus since 2008. The reallocation of CS funding would save the global economy $9 billion (USD). Global inequities in CS performed and associated costs have increased since 2008, resulting in a disproportionate number of resources allocated. [ABSTRACT FROM AUTHOR]

In this paper we focus on real situations where certain perfectly divisible estate has to be divided among claimants who can merely indicate the range of their claims, and the available amount is smaller than the aggregated claim. Funds' allocation of a firm among its divisions, taxation problems, priority problems, distribution of costs of a joint project among the agents involved, various disputes including those generated by inheritance, or by cooperation in joint projects based on restricted willingness to pay, fit into this framework. The corresponding claim of each claimant can vary within a closed interval or fuzzy interval. For claims, fuzzy intervals are applied whenever the claimants can distinguish a possibility of attaining the amount of estate, and/or its membership degree of a possibility of attainment. When claims of claimants have fuzzy interval uncertainty, we settle such type of division problems by transforming it into bankruptcy problems under interval uncertainty by interval valued mappings. A similar approach is applied to deal with uncertainty of estate to be divided. Here, a probability interpretation can also be considered e.g. in taxation problems. We consider the division problems under uncertainty of claims and/or estate and present bankruptcy rule, which are consistent with the classical bankruptcy proportional rule. Several examples are presented to illustrate particular problems and solution concepts. [ABSTRACT FROM AUTHOR]

The cold chain logistics (CCL) distribution have been studied by considering customer satisfaction, we propose a multi-objective CCL model with the goals of minimal carbon transaction cost, minimal network cost and maximal customer satisfaction. According to the characteristics of the model, an improved Multi-Objective algorithm was designed, which integrates the dynamic crowding distance and differential mutation operator into non-dominated sorting genetic algorithm II (DDNSGA-II). The DDNSGA-II enhances the diversity of the initial population, the local search ability and search accuracy. Finally, the experimental data show that the proposed approaches effectively increase the customer satisfaction, reduce total distribution costs, and promote energy conservation and emission reduction. [ABSTRACT FROM AUTHOR]

Drug plans take different approaches to determining reimbursement prices for generic drugs. One common approach is to set the maximum reimbursement price as a percentage of the price of the interchangeable branded drug. In many countries this percentage depends on the number of generic entrants, a model we call "tiered pricing." This paper seeks to enhance understanding of how to set the tiers. We construct a simple model of tiered pricing and set parameters to match evidence on generic drug costs and the distribution of revenues. Using simulation methods, we then assess different tier structures in terms of total surplus and average drug cost. We find when tiers are bunched tightly together welfare outcomes are poor. Moreover, there are large welfare gains from increasing the number of tiers from one to two, and only small welfare gains from increasing the number of tiers beyond four. The choice of tiers has substantial welfare and cost implications. While it is possible to refine the simulation analysis based on specific market characteristics, an optimal tier structure, such as the one we propose in the paper, should have at least two tiers. [ABSTRACT FROM AUTHOR]

The use of geothermal energy from tunnels offers an environmentally friendly option for heating and cooling of buildings in the vicinity of the tunnel structure. In the research project "Thermocluster", the geothermal potential of the Brenner Base Tunnel (BBT) was analysed and the utilization of the low-temperature heat to the city of Innsbruck was evaluated. A possible optimization on the geothermal performance was explored by separating colder sectional discharges from the total tunnel water drainage system. Further, the use of decentralized and centralized heat pumps, as well as heat distribution with different district heating networks (local low temperature heating and cooling networks, low-temperature network, classic district heating network), was investigated using process simulation. The results indicate that an optimization of the temperature of a subset of the total tunnel water discharge is possible. Furthermore, it could be shown that wide fluctuations in the efficiency of the heat pump can be expected. In addition, a rough cost estimation regarding the installation and distribution costs of the tunnel heat with different heat network variants is provided. This shows that the variant using decentralized heat pumps in the buildings and distribution via an anergy network is the most favourable in terms of installation. • Geothermal usage of tunnel heat in Austria. • Analysis of the geothermal potential and optimization. • Simply measures to increase the geothermal potential. • Performance estimation of heat pumps using tunnel heat. [ABSTRACT FROM AUTHOR]

Vehicle-based mobile sensing, also known as drive-by sensing, efficiently surveys urban environments at low costs by leveraging the mobility of urban vehicles. While recent studies have focused on drive-by sensing for fleets of a single type, our work explores the sensing power and cost-effectiveness of a mixed fleet that consists of vehicles with distinct and complementary mobility patterns. We formulate the drive-by sensing coverage (DSC) problem, proposing a method to quantify sensing utility and an optimization procedure that determines fleet composition, sensor allocation, and vehicle routing for a given budget. Our air quality sensing case study in Longquanyi District (Chengdu, China) demonstrates that using a mixed fleet enhances sensing utilities and achieves close approximations to the target sensing distribution at a lower cost. Generalizing these insights to two additional real-world networks, our regression analysis uncovers key factors influencing the sensing power of mixed fleets. This research provides quantitative and managerial insights into drive-by sensing, showcasing a positive externality of urban transport activities. • Drive-by sensing has many applications and typically considers single type of vehicles. • We explore the sensing potential of mixed vehicle fleets (taxis, buses, dedicated vehicles). • For a given budget, we determine fleet composition, sensor allocation and vehicle routing. • A case study shows significant sensing improvement and cost saving by using mixed fleet. • The findings and insights are generalized to several real-world networks. [ABSTRACT FROM AUTHOR]

• ACIP prefers enhanced vaccines such as recombinant influenza vaccine (RIV4) and could consider RIV4 for all adults. • This model from the societal perspective suggests that RIV4 is cost-effective compared with SD-IIV4 in adults 18–64 years old. • This model comparing RIV4 and SD-IIV4 in adults 18–64 years is sensitive to vaccine efficacy, vaccine cost and other factors. • Use of RIV4 in 18–64-year-olds would result in fewer influenza cases, outpatient visits, hospitalizations and deaths. The Advisory Committee on Immunization Practices (ACIP) uses the Evidence to Recommendations Framework that includes cost-effectiveness analyses (CEA) for determining vaccine recommendations. ACIP's preference for protecting adults ≥ 65 years is enhanced vaccines, including recombinant influenza vaccine (RIV4), adjuvanted or high dose influenza vaccine. Less is known about the CEA of enhanced vaccines for younger adults. We used decision analysis modeling from a societal perspective to determine the cost-effectiveness, measured in quality adjusted life years (QALYs), of RIV4 compared with standard dose quadrivalent influenza vaccine (SD-IIV4) in adults 18–64 years old. Model inputs included 2018–2020 vaccine effectiveness (VE) estimates based on medical record data from a large local health system, 2019–2020 national vaccination and influenza epidemic parameters, with costs and population distributions fitted to the season. Among adults ages 18–64 years, RIV4 cost $94,186/QALY gained, compared to SD-IIV4. Among those 50–64 years old, RIV4 was relatively more cost-effective ($61,329/QALY gained). Cost-effectiveness estimates for 18–64-year-olds were sensitive to the absolute difference in VE between SD-IIV4 and RIV4, among other parameters. Use of RIV4 in 18–64-year-olds would result in fewer cases (669,984), outpatient visits (261,293), hospitalizations (20,046) and deaths (1,018) annually. The majority (59 %; 597 of 1018) of the decreases in deaths occurred in the 50–64-year-olds. While RIV4 was effective and cost-effective relative to SD-IIV4 for both 50–64-year-old and 18–64-year-old adults, cost-effectiveness was sensitive to small changes in parameters among 18–64-year-olds. Because substantial public health benefits occur with enhanced vaccines, health systems and policy makers may opt for preferential product use in select age/risk groups (e.g., 50–64 year olds) to maximize their cost-benefit ratios. [ABSTRACT FROM AUTHOR]

Shared energy storage has the potential to decrease the expenditure and operational costs of conventional energy storage devices. However, studies on shared energy storage configurations have primarily focused on the peer-to-peer competitive game relation among agents, neglecting the impact of network topology, power loss, and other practical factors on energy storage configuration. Additionally, they do not differentiate between various roles of agents, such as shared energy storage operators, electricity consumers, and distribution network operators. To address the challenges presented by the complex interest structures, diverse usage patterns, and potentially sensitive location associated with shared energy storage, we present a multi-agent model for shared energy storage services that takes into account the perspectives of different actors in distribution networks. We develop a tri-level programming model for the optimal allotment of shared energy storage and employ a combination of analytical and heuristic methods to solve it. A case study demonstrates that our model can attain effective allocation of shared energy storage, take into account the interests of multiple parties, and converge well. We examine the impacts of different energy storage service patterns on distribution network operation modes and compare the benefits of shared and non-shared energy storage patterns. By analyzing data on the cost of operating distribution networks, voltage stability, and distributed power consumption, we investigate the potential advantages of the multi-agent distributed shared energy storage service pattern in distribution networks. Our research provides valuable insights into implementing shared energy storage on a large scale in distribution networks. • A multi-agent model for distributed shared energy storage services is proposed. • A tri-level model is designed for optimizing shared energy storage allocation. • A hybrid solution combining analytical and heuristic methods is developed. • A comparative analysis reveals shared energy storage's features and advantages. [ABSTRACT FROM AUTHOR]

Collaboration is key to addressing operational efficiencies, and in the context of last mile delivery, operational inefficiencies arising from empty trips, low load factor, and long dwell times require collaboration amongst multiple logistics service providers (LSPs). However, existing studies in the last mile delivery adopting collaboration as a means of sharing strategic infrastructure assets, such as distribution centers (DCs), satellites, and driving vehicles, might not be seen as favorable for logistics service providers due to ownership disputes and loss of control over assets. To address this limitation, a new routing method for addressing operational efficiencies in last mile delivery has been proposed in this study, allowing for collaboration across multiple logistics service providers without sharing strategic assets. We have formulated a two-echelon vehicle routing problem with collaboration Points (2E-VRP-CP) where the exchange of goods happens between second-echelon vehicles belonging to different logistics service providers. The method uses a mixed-integer linear programming model (MILP) that minimizes the total distribution cost and has been tested on randomly generated instances. Results suggest that the proposed collaborative approach of exchanging goods between second-echelon vehicles belonging to different logistics service providers can reduce costs by up to 10% relative to the non-collaborative approach and up to 9% compared to the existing collaborative approach that shares strategic assets. A four-phase heuristic algorithm has also been developed to tackle computationally expensive larger instances, which can obtain cost savings of up to 15% compared to a non-collaborative approach. Future work will involve developing a profit-allocation mechanism and will consider additional constraints to make the model more realistic in addressing a real-world problem. Overall, this model can help fleet managers achieve efficient fulfillment in last mile delivery, while the proposed heuristics can enable stakeholders to scale their solutions to real-world scenarios. • A new problem addressing collaboration among multiple logistics providers is studied. • It addresses efficient collaboration among logistics firms without shared facilities. • Synchronization and time constraints are excluded due to computational complexities. • We designed a MILP for small instances and a four-phase heuristic for larger ones. • Our approach cuts costs by 10% on small instances and 15% on large ones. [ABSTRACT FROM AUTHOR]

[Display omitted] • Framework for multiscalar analysis of sanitation systems. • Sanitation systems' structural power in security, production, and finance. • Typology of five ideal-type sanitation bargains. Decentralisation and the flexible combination of infrastructures and technologies are advocated to expand access to safe sanitation amid rapid urbanization, worsening water scarcity, resource depletion, and climate change. Yet, narrowly technical approaches such as the citywide inclusive sanitation approach tend to underestimate social and political bargaining and the role of structural power in shaping access to basic urban services. Building on international political economy, this article develops the territorial political economy framework to explore specific sanitation systems' distribution of structural power in security, production, and finance. Utilizing this framework, the study constructs a typology of five sanitation bargains, derived from theoretical insights, expert interviews, scientific case studies, and key policy documents. Each bargain represents an ideal typical combination of technology, organisation, and finance, with the corresponding spatial and social distribution of costs, benefits, risks, and opportunities. Together, this enables a political economy analysis of sanitation systems in cities that is sensitive to both multiscalar negotiations and distributional outcomes associated with sanitation provision in the city. When expanding sanitation services to so-far unserved areas in the cities, the paper shows how the limitations of the citywide inclusive sanitation approach in its current form can be overcome. It enables to explicitly consider the implications of embedded structural power on households when assessing and selecting technological options and organizational models for providing access to safe sanitation inclusively across the city. [ABSTRACT FROM AUTHOR]

Although surgical conditions account for 32% of the global burden of diseases, approximately 5 billion people worldwide lack access to timely and affordable, surgical and anesthetic services. Disparities in access to surgical care are most evident in low- and middle-income countries, often resulting from a lack of surgical infrastructure. However, the establishment of surgical infrastructure, particularly for specialty surgical services including neurosurgery, is challenging in countries with small populations, irrespective of income classification, due to the distribution of high costs among a lesser number of individuals. One such nation is Iceland. Despite high-income status, high quality of life, literacy, and educational attainment, the population of Iceland has often lacked access to local neurosurgical care, with the establishment of the domestic neurosurgical system in 1971 and continued externalization of complex neurosurgical procedures to neighboring nations and neurosurgeons. A narrative review was conducted. This article provides the first-ever examination of neurosurgery in Iceland. We discussed the history and the social, political, and economical contexts in Iceland. We examined the history of neurosurgery in Iceland, which provided brief biographic sketches of pioneers who have catalyzed the establishment of neurosurgical care and training in Iceland, and characterize the current state of neurosurgery in Iceland. Recommendations derived from the experiences of Icelandic neurosurgeons may guide the international community in future initiatives. [ABSTRACT FROM AUTHOR]

Evaluation of transmission and distribution cost rationality of power grid enterprises enables governments to scientifically approve power transmission and distribution prices. To depict the impact of environmental differences of power grid enterprises on the evaluation of operational efficiency, we measured and calculated meta-frontier efficiency and group-frontier efficiency of 25 provincial-level grid enterprises in China, from 2016 to 2019, by building a meta-frontier-based super-efficiency DEA model, and ranked them based on meta-frontier efficiency, in this paper. The measurement results show that there are pronounced differences in the operational efficiency levels of power grid enterprises, which are not only related to objective differences in economic development level, geographical environment, and historical burden, but also related to technology and management levels of individual enterprises. The cost rationality evaluation method proposed in this paper lays a foundation for introducing "cost benchmarking" in future power transmission and distribution pricing, providing reference for perfecting the transmission and distribution prices regulatory mechanism. [ABSTRACT FROM AUTHOR]

This article provides a critical assessment of H 2 from the standpoint of more widespread use as a sustainable fuel for Indian mobility applications in the global context. The potential techno-economic advantages of utilizing H 2 for automobiles rather than battery electric vehicles or conventional internal combustion engine vehicles are emphasized. The present assessment demonstrates that H 2 production, storage, and distribution costs are the primary challenges, and a significant improvement is still necessary for H 2 to compete either against the internal combustion engine vehicle or the battery electric vehicle to win the race, arguably. The secondary challenges have also been demonstrated, which include the cost of the fuel cell stack and the modifications associated with internal combustion engine vehicles, as well as regulatory and safety concerns, which impede the widespread usage of H 2. It is critical that policy-making for sustainable mobility in India is possible with the aid of a National H 2 Energy Road-Map. This in turn can achieve a cost target of $0.5/kg for H 2. [Display omitted] • Strategies on achieving H 2 production cost of $0.5 per kg is discussed. • Indian H 2 Roadmap targets $1 per kg for H 2 dispensed by 2030. • Projected zero emission vehicle population in India is set to reach 30 M by 2035. [ABSTRACT FROM AUTHOR]

Remaining Useful Life (RUL) prediction and maintenance decision-making are two key tasks within the framework of Prognostics and Health Management (PHM) of system. However, existing works are performing the two tasks separately and hierarchically. Besides, the uncertainty in RUL prediction caused by cognitive level and measurement capabilities has not aroused wide concern and this may reduce the credibility of point prediction. To address these issues and finally ensure the safe and reliable operation of the system, this paper proposes a novel data-driven predictive maintenance strategy. The proposed strategy is a complete process from implementing the RUL prediction with uncertainty to making maintenance decision. Considering the prediction aspect, a Local Uncertainty Estimation (LUE) model with Bidirectional Long-Short Term Memory (Bi-LSTM) is proposed to characterize the uncertainty in RUL prediction. Regarding the post-prediction aspect, the Maintenance Cost Rate (MCR), namely maintenance cost per unit operational time, function is constructed by linking the constructed RUL distribution with maintenance-related costs. Oriented towards the economic requirements of operation management, the time for taking maintenance activities can be determined by optimizing the MCR function. The whole proposition is validated on a case study of the aero-engine health monitoring. The comparison with recent publications and the corresponding analysis results indicate that the proposed method is a promising tool in predictive maintenance applications, which can reduce system maintenance costs. [ABSTRACT FROM AUTHOR]

Objectives: Histology-independent (HI) technologies are authorized for patients with advanced or metastatic cancer if they express a particular biomarker regardless of its position in the body. Although this represents an important advancement in cancer treatment, genomic testing to identify eligible individuals for HI technologies will require substantial investment and impact their cost-effectiveness. Estimating these costs is complicated by several issues, which affect not only the overall cost of testing but also the distribution of testing costs across tumor types.Methods: Key issues that should be considered when evaluating the cost of genomic testing to identify those eligible for HI technologies are discussed. These issues are explored in illustrative analyses where costs of genomic testing for NTRK fusions in England for recently approved HI technologies are estimated.Results: The prevalence of mutation, testing strategy adopted, and current testing provision affect the cost of identifying eligible patients. The illustrative analysis estimated the cost of RNA-based next-generation sequencing to identify 1 individual with an NTRK fusion ranged between £377 and £282 258. To improve cost-effectiveness, testing costs could be shared across multiple technologies. An estimated additional ∼4000 patients would need to be treated with other HI therapies for testing in patients with advanced or metastatic cancer to be cost-effective.Conclusions: The cost of testing to identify individuals eligible for HI technologies affect the drug's cost-effectiveness. The cost of testing across tumor types varies owing to heterogeneity in the mutation's prevalence and current testing provision. The cost-effectiveness of HI technologies may be improved if testing costs could be shared across multiple agents. [ABSTRACT FROM AUTHOR]

Plateau wind power has great potential in reducing carbon emissions; however, compared with other renewable energy, its economics still need to be improved. As an effective approach to enhance its economic feasibility, maintenance strategy optimization aims to reduce maintenance costs per kilowatt-hour and extend equipment lifespan. This paper proposes a multi-objective optimization model for the maintenance decision-making of plateau wind turbines that considers the degradation state. It incorporates: i) modeling the maintenance process of plateau wind turbines by combining time-based and state-based methods; ii) considering the time-varying maintenance costs in complex environments; and iii) employing a multi-objective optimization method to find the optimal strategy that meets maintenance requirements. The complexity considered in the model mainly includes the randomness of the operating duration for each equipment state, the temporal variability of equipment distribution and installation costs, and the uncertainty in maintenance effectiveness. The proposed optimization method is applied to a wind farm in the Yunnan-Guizhou Plateau, China. The results indicate that traditional maintenance strategies underestimate maintenance costs and equipment lifespan losses. Compared with conventional maintenance strategies, this method can reduce equipment maintenance costs by 24.07 % and extend its operating life by 11.58 %. Additionally, this paper has conducted a series of parametric analyses to enhance the generalization performance of the model. The proposed method effectively addresses the economic issues of plateau wind turbine maintenance and provides a valuable decision-making tool for guiding the long-term maintenance of wind turbines in complex environments. • A multi-objective optimization model for maintenance decision of plateau wind turbine. • The time-varying properties of downtime, distribution costs are modeled. • The constant cost assumption is changed by calculating the time for device degradation. • The run time of each degraded state of the wind turbine is calculated. • The maintenance intensity and frequency are determined according to the maintenance target. [ABSTRACT FROM AUTHOR]

Renewable hydrogen is a flexible and versatile energy vector that can facilitate the decarbonization of several sectors and simultaneously ease the stress on the electricity grids that are currently being saturated with intermittent renewable power. But hydrogen technologies, are currently facing limitations related to existing infrastructure limitations, available markets as well as production, storage and distribution costs. These challenges will be gradually addressed through the establishment, operation and scaling-up of hydrogen valleys. Hydrogen valleys are an important stepping stone towards the full-scale implementation of the hydrogen economy with the target to foster sustainability, lower carbon emissions, and derisk the associated hydrogen technologies. These hydrogen ecosystems integrate renewable energy sources, efficient hydrogen production, storage, transportation technologies as well as diverse end-users within a defined geographical region. This study offers an overview of the hydrogen valleys concept analyzing the critical aspects of their design and the key segments that constitute the framework of a hydrogen valley. Α holistic overview of the key characteristics of a hydrogen valley is provided whereas an overview of key, on-going hydrogen valley projects is presented. This work underscores the importance of addressing challenges related to the integration of renewable energy sources into electricity grids as well as scale-up challenges associated with economic and market conditions, society awareness and political decision-making. • Hydrogen valleys are gathering hydrogen production, storage and end-use technologies within a defined geographical region. • Hydrogen valleys are expected to integrate various renewable energy sources and ease stress on electricity grids. • A current status of hydrogen valleys is provided along with critical challenges and the path forward. [ABSTRACT FROM AUTHOR]

This study aimed to determine the relationship between the overall fat percentage of pork belly and the visual fat level of pork belly, and to determine whether these factors influence the distributor's preference. Fat percentages in LYD pigs' pork bellies were measured using VCS2000, and visual fat levels were assessed through cross-sectional photographs. Additionally, a survey was conducted across four distributor types (direct managed stores, supermarkets, National Agricultural Cooperative Federations (NACFs), and butcher shops) to understand distributor preferences in the pork market. The results demonstrated a moderate correlation between pork belly fat percentage and appearance-based belly fat level. The regression equations between distributors' pork belly preferences and pork belly fat percentage showed that all regression equations exhibited coefficient of determination (R2) values below 0.2, indicating that pork belly fat percentage could not be explained by distributors' pork belly preferences. The regression equations between appearance-based belly fat level and distributors' pork belly preferences demonstrated that all regression equations exhibited an R2 value of 0.75 or higher, indicating that appearance-based belly fat level can effectively explain distributors' pork belly preferences. Furthermore, it was observed that there were variations in distributors' preferences contingent on the appearance-based belly fat level. Appearance-based belly fat level was found to be reliable indicators for predicting pork belly preferences. The findings of this study are expected to assist in the reduction of unnecessary distribution costs when planning the delivery of pork belly. [ABSTRACT FROM AUTHOR]

Peer-to-Peer (P2P) energy trading between Interconnected Micro-Grids (IMGs) presents a promising approach for enhancing the economic advantages for prosumers while mitigating supply–demand imbalances within individual Micro-Grids (MGs). This paper proposes a trustful double auction mechanism for P2P energy trading among prosumers in IMGs, structured into two distinct stages. Initially, concurrent auctions are conducted within each MG to facilitate intra-grid trading, followed by subsequent auctions facilitating inter-grid trading across IMGs. We figure out the allocation and pricing rules that satisfy the required properties in mechanism design even when the auction consists of two stages. Given that energy transactions across IMGs entail non-negligible power losses, equitable allocation of these losses among prosumers is paramount. To address this, we integrate a fair cost distribution methodology into our auction mechanism, implemented by an iterative algorithm. Rigorous analysis substantiates our proposed auction mechanism's incentive compatibility, individual rationality, and budget balance, thereby fostering truthful and voluntary prosumer participation while averting potential market deficits. Numerical analysis underscores the efficacy of our approach, showcasing a significant improvement in P2P transactions and supply–demand balance enhancement compared to trading solely within individual MGs. • A double auction is designed for Peer-to-Peer energy trading between interconnected micro-grids. • A two-stage auction is developed to increase the engagement of prosumers. • A fair and feasible cost distribution method is developed to fully recover the budget deficit. • The proposed model mitigates supply–demand imbalance and improves economic benefits. [ABSTRACT FROM AUTHOR]

The economics of decentralized gasification and centralized steam turbine systems with optimized harvesting, chipping, and transportation costs of unutilized forest biomass were analyzed within the context of the spatial distribution of forest biomass resources using a geographic information system in the Fuji area of Shizuoka Prefecture, Japan. The optimized harvesting, chipping, and transportation costs were 62.50 USD/dry-t for the decentralized system and 65.86 USD/dry-t for the centralized system, confirming that the decentralized system was less expensive than the centralized system. However, electricity production costs for centralized systems, ranging from 0.137 USD/kWh (capacity: 5,000 kW), were found to be more economical than that for decentralized systems, at 0.151–0.165 USD/kWh (capacity: 482 kW), resulting in the higher costs of decentralized systems because of expensive initial investment than that of the centralized system. The rate of heat sold, which lowers the electricity production costs of gasification below those of centralized systems, was 20–40 % for all such plants. To lower the electricity production cost of gasification plants below that of steam-turbine systems, a heat sales rate of approximately 10% and a minimum staff of 0.2 persons for remote operation of the gasification system. Higher heat sales rates further decrease the cost of gasification, making it cheaper than steam-turbine systems even at lower operating rates. To achieve this cost advantage at current low utilization rates, a heat sales rate of 50% and a utilization rate of 90%, based on a unit price of 0.05 USD/kWh, are required. [ABSTRACT FROM AUTHOR]

Ensuring equitable decision-making and distribution of costs and benefits in conservation and natural resource management is morally right and instrumental to achieving positive social and ecological outcomes. Understanding perceived equity is key; equity is subjective, context-dependent and has implications for legitimacy, cooperation and wellbeing. Since gender, in combination with other social characteristics, influences how people benefit or participate in management, examining perceived fairness from an intersectional perspective is crucial. However, few studies have examined people's perceptions of equity and how those perceptions are related to intersecting identities. Using data from ten villages in Fiji, we assess how perceptions of distributional and procedural equity differ by gender and the intersection between gender and other social identity characteristics (migrant status, age, education, marital status and wealth). We found that the majority of respondents identified the broader community as benefiting the most from management, while women were the most negatively affected. Overall, respondents' perceptions of distributional and procedural fairness were high regardless of gender. The intersection between gender and other social identity characteristics was not significantly related to perceived fairness, except in relation to migrant status; migrant men were less likely to perceive distributional fairness. Our study provides new insights into patterns of perceived (un)fairness in marine management and conservation. It reveals a discrepancy between conservation costs (women are seen as more negatively affected by conservation) and fairness perceptions (women are not more likely to perceive unfairness). Our findings can inform conservation theory and practice aimed at fostering equity in conservation and management. • People generally perceived fair distribution of management costs and benefits and fair decision-making. • High levels of perceived distributional and procedural fairness were related to the distributional criteria equality and public good. • Perceived distributional and procedural fairness did not differ by gender, even though women were more negatively impacted by management. • Little evidence of a relationship between intersectionality and fairness perceptions, except for migrant status and gender. • Migrant men were less likely to perceive distributional fairness. [ABSTRACT FROM AUTHOR]

Traditional semi-global matching (SGM) lacks interaction between scanlines and struggles to deal with the ambiguity of pixels in homogenous areas. In this paper, we propose a novel path-centering graph to perform weighted omnidirectional SGM (WOdSGM), in which the input image is divided into eight sub-trees, corresponding to eight optimization directions. In each pass, the outputs of pixels are recursively computed from leaf nodes to the root node along the tree structure. As the results of SGM from multiple scanlines often show different biases owing to respective message propagation directions, one solution is accurate in certain areas while others are not. By delving into the distribution of aggregated costs for pixels from various structures, we find that the minimum cost of reliable output is heavily biased from others. Therefore, we perform line fitting to approximate the distribution of aggregated costs and use the distance between the fitted line and the point corresponding to the minimum cost to evaluate the reliability of aggregated costs. Furthermore, we present a weighted fusion strategy to incorporate outputs from multiple directions, enabling our method to provide directionally biased constraints. Extensive experiments and analyses on widely used stereo datasets show that our approach outperforms typical traditional SGM-based algorithms. • A novel path-centering graph to perform 1D optimization along all directions. • A recursive algorithm to compute the output of each pixel along the tree structure. • A novel weighted scheme to fuse aggregated costs from multiple directions. • Extensive experiments and analyses on the Middlebury and KITTI datasets validate the effectiveness of our approach, and it turns out our framework outperforms the traditional SGM and its variants. [ABSTRACT FROM AUTHOR]

Stereo matching acts a crucial role in computer vision and robotics applications. An accurate cost volume and robust disparity regression method are essential for stereo matching of high accuracy. Following GCNet and PSMNet, constructing 4D cost volume and then using the soft argmin method to regress has been dominated. However, it will encounter many difficulties due to the multi-modal distribution of cost volume. One of the reasons for this multi-modal distribution is the occlusion area which not be possible to find a matching region on the reference image and rarely discussed. In this paper, we propose to use global context information could improve the performance of model in occluded regions. Recently, novel recurrent neural network regression methods are proposed, but most of them regress disparity maps from 3D cost volume. In this paper, we propose the new combinatorial paradigm that combine stacked hourglass modules and recurrent neural networks to further aggregate 4D cost volume and regress disparity respectively. The proposed method can be seamlessly integrated into most stereo matching networks, we improved the accuracy by 45% for PSMNet and 38% for GwcNet in our experiment. Experimental results on Scene Flow, KITTI2012, KITTI2015, and ETH3D datasets show our method is competitive. The code is available at: https://github.com/truman1211/HCRnet. • Replacing the soft argmin method with GRU modules. • Different context information is embedded in different stages of the GRU. • The GRU model is regressed on the cost volume at different scales. • Attention mechanism is used to provide global information for the occlusion region. [ABSTRACT FROM AUTHOR]

The rapid uptake of renewable energy technologies in recent decades has increased the demand of energy researchers, policymakers and energy planners for reliable data on the spatial distribution of their costs and potentials. For onshore wind energy this has resulted in an active research field devoted to analysing these resources for regions, countries or globally. A particular thread of this research attempts to go beyond purely technical or spatial restrictions and determine the realistic, feasible or actual potential for wind energy. Motivated by these developments, this paper reviews methods and assumptions for analysing geographical, technical, economic and, finally, feasible onshore wind potentials. We address each of these potentials in turn, including aspects related to land eligibility criteria, energy meteorology, and technical developments of wind turbine characteristics such as power density, specific rotor power and spacing aspects. Economic aspects of potential assessments are central to future deployment and are discussed on a turbine and system level covering levelized costs depending on locations, and the system integration costs which are often overlooked in such analyses. Non-technical approaches include scenicness assessments of the landscape, constraints due to regulation or public opposition, expert and stakeholder workshops, willingness to pay/accept elicitations and socioeconomic cost-benefit studies. For each of these different potential estimations, the state of the art is critically discussed, with an attempt to derive best practice recommendations and highlight avenues for future research. • Review of over 300 studies on large-scale onshore wind potential assessments. • Analysis of geographic, technical, economic and 'feasible' potentials. • Identify weaknesses in methods and best practice examples. • Methods could improve transparency, validation, and sensitivities. • Further interdisciplinary research required on 'feasible' potentials. [ABSTRACT FROM AUTHOR]

Public investments in research, development, demonstration, and deployment in energy systems are crucial for accelerating the adoption of new energy technologies that can support a low-carbon just energy transition and promote the fair and equitable distribution of benefits and costs, improving social equity. However, the impact of energy innovation funding on social equity through progressing the just energy transition has not been explored in prior research for advanced economies. Thus, this research quantitively examines how energy innovation funding affects social equity in 23 developed economies for the period of 1995–2020. Employing a fixed effect, augmented mean group, and a system-generalized method of moments technique along with a mediating model, this study finds that increasing energy innovation funding significantly improves social equity directly as well as indirectly via accelerating energy justice in advanced economies. Therefore, proper allocation and utilization of public energy innovation budgets are necessary to promote clean energy technologies, advance the just energy transition, and improve social equity, inclusion, and community engagement. This study emphasizes the importance of policy interventions that promote a just energy transition and energy innovation funding to build resilient and inclusive energy systems that improve social equity while achieving Sustainable Development Goal 7, affordable and clean energy. • Energy innovation funding is crucial for sustainable, equitable energy systems. • Just energy transition and social equity are positively linked. • Energy innovation funding impacts social equity via just energy transition. • Combining just energy transition and energy innovation funding can promote social equity. • Policy interventions promoting just energy transition are crucial for social equity. [ABSTRACT FROM AUTHOR]

In recent decades, the increasing demand for water and resource limitations have posed challenges to water resources management. This study proposes a comprehensive model for redesigning urban water distribution network under disruptions, focusing on household consumption. The proposed model integrates strategic decisions for network, and addresses disruption risks. To ensure the resilience of the network against disruptions caused by earthquakes, we developed a regret-based robust optimization model. The model minimizes redesign and operational costs by optimizing water flow, construction locations, and pipeline connections. Numerical results demonstrate the efficacy of the proposed model, revealing a reduction in average costs—approximately 0.6 %—compared to the deterministic model. This reduction is noteworthy given the scale of municipal water distribution configuration costs. Also, resiliency analysis demonstrates the superiority of the robust solution compared to the deterministic structure. The model supports policymakers for operational and long-term decisions, considering complex hydraulic constraints and horizontal transfers between reservoirs. [ABSTRACT FROM AUTHOR]

Biochar is made from organic materials and plays an important role in greenhouse gas removal (GGR) and achieving net-zero target. However, economic feasibility has become a primary constraint hindering the large-scale production of biochar. Existing research lacks consideration of practical factors such as feedstock supply, pricing, and factory scale, and cannot accurately evaluate the greenhouse gas (GHG) reduction effect and corresponding costs at scale. We develop a space-based environmental economic model to quantify the impact of feedstock supply and plant strategies on costs and benefits. The results show that biochar production in the East of England and the East Midlands could achieve significant net GHG reduction and GGR benefits. Environmental benefits are not related to factory strategy but are positively correlated with feedstock supply strategy. Biochar production imposes additional financial burdens that are affected by feedstock supply and factory strategy. The main factors influencing biochar scalability are the quantity and pricing of feedstock and the price of byproducts. Spatial heterogeneity significantly influences the unit cost of GGR benefits. Compared to previous studies, spatial analysis provides a more detailed understanding of the costs associated with scaling up biochar production and the spatial distribution of production costs. This has crucial implications for biochar promotion and the implementation of effective policies. • Biochar production can bring significant greenhouse gas removal benefits. • Additional economic support is still required for the biochar application. • Biochar scalability is primarily affected by the price of feedstock and byproduct. • Spatial variability has a significant impact on the unit cost of biochar production. [ABSTRACT FROM AUTHOR]

Land use is a crucial symbol of sustainable development. Rural homestead idleness is problem that is caused by market failure and related to the national economy and people's livelihood. (1) Background: Developing countries should use idle land to develop their economies. The government has introduced various land policies for the reuse of idle residential land to improve the land utilization rate. (2) Methods: This paper investigates players' behavior in reusing rural homesteads based on an improved dynamic game model. An improved tripartite evolutionary game model is developed by introducing principal-agent theory, which is the major contributor to behavioral investigation. MATLAB2022a is used to simulate the analysis to propose countermeasures and suggestions for optimizing homestead reuse. (3) Results: In this paper, it was found that disputes among participants about the sharing of reuse costs and the distribution of potential benefits constituted a potent system of explanatory coordinates that effectively revealed the hidden logic of homestead reuse. The government can strengthen incentives by providing dynamic subsidies linked with individual behavior. (4) Conclusions: Regardless of the initial probability, players will jointly reuse homesteads in the end. Local governments and rural collective economic organizations play an important role in reusing homesteads, so the carrot-and-stick approach supervised by local government is the key to maintaining the stability of cooperation. Moreover, under the concept of sustainable development, governments of all countries actively release the policy of reusing idle land to promote the utilization of resources. • The key of idle land reuse is to form economic contract between multiple parties. • Idle land reuse decision is a complex multi-party dynamic evolutionary game. • Government's participation can mediate conflicts between enterprises and farmers. • Government subsidy and regulation can motivate participants to cooperate. • Evidence from China provides new research path for land reuse. [ABSTRACT FROM AUTHOR]

The study of vehicle stops in last-mile delivery has gained ground in the specialized logistics literature. An efficient last-mile delivery reduces distribution costs and mitigates negative externalities such as pollution and congestion. This paper estimates the stops of last-mile trucks that deliver food products in Santiago, Chile. The aim is to study last-mile delivery operations using a non-intrusive, low-cost method. Particularly, we devise a novel methodology that employs multiple data sources to detect the primary stops of cargo vehicles. The proposed methodology involves the following two steps. First, we use GPS data to identify all the candidates for stops, that is, clusters of points close to each other in terms of distance and time, and then, these stops are classified as primary according to the proximity to planned visits or check-out markings. Finally, we conduct a case study involving food distribution, deriving managerial and public policy insights. We find that the most adequate time threshold to detect stops in our context is 4 min, which is considerably lower than previous studies. This last may be explained by the last-mile nature of our study. Our results show that primary (i.e., delivery) stops are concentrated mainly in the center of Santiago, with a duration that decreases as the hours go by. This last means that some of the externalities caused by truck stops (e.g., road capacity reduction) are exacerbated during the morning rush hour. We also find that the average duration of the primary stops is 12.5 min, while the mean distance traveled between two consecutive stops is 4.68 km. [ABSTRACT FROM AUTHOR]

In 2022, distribution utility costs per customer rose an average of nearly 7% among a sample of 75 U.S. distribution utilities. Although this growth rate substantially exceeds the historical average growth rate over fifteen years (3.85%), it is substantially lower than the growth in input prices faced by distribution utilities. The price of distribution utility plant, for example, rose at a rate above 17% in some regions of the country during the same time period. We compare these findings with recent trends of other factors that drive the cost of retail electricity prices, noting that distribution operating costs represent only 2.0% of total electric utility cost to serve, while a much larger portion of costs pertain to the cost of power production. • Measurement of distribution utility industry total costs per customer in the United States. • Discussion of factors underlying retail electricity cost increases. • Financial context of utility cost recovery. [ABSTRACT FROM AUTHOR]

• We estimate the marginal external cost distribution of parking over time and space. • This allows cities to evaluate their parking policy from a welfare perspective. • Given an optimal parking policy, the cost of the parking supply is self-financing. Practitioners need to know the level of cruising for parking when designing parking policies. Existing methodologies, such as counting, experiments, and survey, are either too expensive or infeasible to be undertaken on a large scale. Inci et al. (2017) introduce an instrumental-variables-based econometric methodology using administrative data to estimate the average level of cruising when parking is close to full occupancy. This paper introduces a novel methodology to estimate the marginal external cruising time (and thus cost) across time and space. Our methodology is easier to implement, requires even less data, estimates the whole distribution rather than the average, and does not require parking to be near full occupancy. It also allows for welfare evaluations of parking fees and supply. To illustrate all these, we apply our methodology to Melbourne, which generates rich policy insights. We also apply it to the same dataset that Inci et al. (2017) use for Istanbul and find consistent results, rendering confidence to both methodologies. [ABSTRACT FROM AUTHOR]

Quantitative approaches for the integration of production and distribution planning are attracting the interest of scholars and companies in recent years. They can significantly improve supply chain performance and sustainability. In this paper, we propose an optimization model for the integrated scheduling of production and distribution activities, with reference to a real-life company in the food sector. The model takes into consideration changeover times and perishability, and aims to jointly minimize energy, storage and distribution costs. Its applicability is shown through a set of computational experiments, carried out on instances generated from historical data. Two different rescheduling strategies, where the first one reproduces the current behaviour of the firm, are compared. The results show that the current practices of the company can be improved and the model is a valid tool for supporting operational business decisions. [ABSTRACT FROM AUTHOR]

The goal of this research was to optimize the cost of goods distribution to some locations. The problem can be considered as Vehicle Routing Problem (VRP). The main characteristic of this problem is that the solution space expands exponentially. In our case at hand, the goods distribution that have been done, mostly done manually. Therefore, it may not optimize the costs of distribution. Manual optimization cannot be used if the number is locations is more than five because the solution space is too big to solve by hand. Pigeon Inspired Optimization (PIO) is proposed as a heuristic method for optimizing the VRP to optimize the cost and then it will be compared with Particle Swarm Optimization (PSO) method as comparison algorithm. Evaluation was conducted by comparing their performance in optimizing cost for several solution spaces. The achieved result is the shortest distribution path according to the constraints given and has lowest total cost of distribution. It can be concluded that PIO is better for optimizing the goods distribution path so that the distribution cost becomes minimal. [ABSTRACT FROM AUTHOR]

• An advanced social force model is proposed for pedestrian evacuation. • Dynamic navigation field is introduced to describe pedestrians' desired walking direction. • The new model can describe typical stages of the pedestrian evacuation dynamics. • The new model can describe the route/exit choice behaviors of pedestrians. • The new model can reduce the total evacuation time and the required CPU time. This paper presents a generalized walking cost distribution to determine a dynamic navigation field in the social force model for pedestrian evacuation. The local walking cost per unit distance of movement includes the cost associated with travel time and other additional costs incurred by pedestrians to avoid colliding with obstacles in a dynamic environment. In the dynamic navigation field, pedestrians expect to choose an optimal path with the lowest walking cost to reach their target destination reactively based on available instantaneous information. The social force model with the dynamic navigation field is validated by comparing the simulation results with empirical observations. The fundamental diagrams for observations and simulation data agree well, which indicates the effectiveness of the model. Numerical results show that the model with the dynamic navigation field can reproduce typical stages of the dynamics of pedestrian evacuation, such as self-organized arching and queuing phenomena, and can capture the route choice and exit choice behaviors of pedestrians during the evacuation process. Compared to the model with the static navigation field, the model with the dynamic navigation field can reduce the total evacuation time of the room and save the required CPU time for a large group of pedestrians. Furthermore, the strong tendency to avoid local high-density regions (i.e., minimizing collisions) can also reduce the total evacuation time under the same conditions. [ABSTRACT FROM AUTHOR]

This paper presents a magnetically coupled hybrid renewable energy system (RES) for residential applications. The proposed system integrates the energies of a set of PV panels, a fuel cell stack, and a battery using a multi-winding magnetic link to supply a residential load. It can operate in multiple grid-connected and off-grid operation modes. An energy management unit including an off-line dynamic programming-based optimization stage and a real-time rule-based controller is designed to optimally control the power flow in the system according to the provided energy plan. The system is designed according to the required standards of the grid-connected residential RES. Different sections of the proposed system including steady-state operation, control techniques, energy management method and hardware design are studied in brief. A prototype of the proposed system is developed and experimentally tested for an energy management scenario considering both sunny and cloudy profiles of the PV generation. The energy distribution and cost analysis approved the benefits of the proposed system for residential consumers. • Combining dynamic programming based optimization and real-time control techniques. • A multi-port multi-operation mode magnetically coupled renewable energy system. • An optimized energy management technique using real-time and forecasted data. • Design of residential renewable energy system according to the recent standards. • Providing energy distribution and energy cost analysis of the system. [ABSTRACT FROM AUTHOR]

• This paper introduces an operating problem in a bimodal transport system. • In-vehicle congestion cost functions are assumed to be unknown. • The distribution of perceived travel cost errors is also assumed to be unknown. • The paper proposes two convergent trial-and-error operation schemes for the problem. • The first scheme determines bus fares to minimize total system cost. • The second one determines both the bus fares and frequencies to maximize the operating profit. We concern the modal choice of commuters in a transport system comprising a highway, which is only used by autos, in parallel to a transit line, which is only used by buses. In the transport system, the in-vehicle congestion of passengers in bus carriages is treated as a negative externality cost of affecting the modal choice of commuters and commuters choose their travel modes according to the perceived travel costs of transport modes. We propose two trial-and-error operation schemes for the transport system without resorting to both the function of in-vehicle congestion costs and the distribution of perceived travel cost errors. In the first operation scheme, the manager (or the government) determines the transit fare charged from (or financial subsidy to) bus users from period to period so as to minimize the system time cost of the transport system. The second operation scheme is established from the viewpoint of a private firm that operates the public transit line. The operator determines the transit fare and bus run frequency from period to period in order to maximize its operating profit. Moreover, we demonstrate the effectiveness of the two operation schemes for optimizing the system time cost and the operating profit by both theoretical analyses and numerical examples. [ABSTRACT FROM AUTHOR]

This paper addresses integrated production and distribution scheduling problem in which orders/jobs are undergone a single operation on any one of the identical machines in parallel and upon the completion of the production they are distributed to destined customers by limited number of vehicles. Customers located in dispersed regions place their orders with predetermined demand size and time windows. On production side, machines operate under discrete speed modes, low of which requires less energy cost or vice versa. On distribution side, energy consumed by a vehicle varies depending on the size of load on it. Therefore, objective is minimizing the sum of the weighted cost emanating from early and tardy deliveries plus production and distribution costs. Operational decisions for (i) production are to determine the allocation of jobs to the machines and sequence of jobs on any machine as well as speed mode of each machine for a particular job. As for (ii) distribution: We need to decide vehicle assignment to specific subset of consolidated jobs and the sequence of customer visitation for each vehicle. We develop a formulation for the problem at hand involving parallel machine scheduling and vehicle routing to obtain solutions to optimality. Not surprisingly, however, CPLEX only provides optimum solution for all instances with customer number up to and 6, for which reason we present two metaheuristics, namely Memetic Algorithm (MA) and Iterated Local Search (ILS) for practical sized instances. Computational results indicate that ILS yields better solutions in shorter times as compared to its counterpart. [ABSTRACT FROM AUTHOR]

Decarbonizing electric systems through the integration of large amounts of intermittent renewable energy complicates the balancing of demand and supply, increasing the value of storage. With the rapid expansion of short-term storage options such as batteries, flexible resources such as existing hydroelectric reservoir storage are often overlooked. Using a capacity expansion and dispatch model of the Northeastern North American power system, we draw lessons from the interconnection potential of Quebec's large existing hydropower capacities. We find that reservoir-transmission coupling significantly decreases the cost of decarbonization, even when short-term storage costs are low, because it is used for both daily and seasonal balancing. Conversely, the value of short-term storage is compromised by the availability of reservoir-transmission coupling, even assuming high investment costs for transmission. We also describe the regional distribution of costs and benefits of storage and transmission and discuss implications for regional cooperation. [ABSTRACT FROM AUTHOR]

Generative adversarial network (GAN) is a framework for generating fake data using a set of real examples. However, GAN is unstable in the training stage. In order to stabilize GANs, the noise injection has been used to enlarge the overlap of the real and fake distributions at the cost of increasing variance. The diffusion process (or data smoothing in its spatial domain) removes fine details in order to capture the structure and important patterns in data but it suppresses the capability of GANs to learn high-frequency information in the training procedure. Based on these observations, we propose a data representation for the GAN training, called noisy scale-space (NSS), that recursively applies the smoothing with a balanced noise to data in order to replace the high-frequency information by random data, leading to a coarse-to-fine training of GANs. We experiment with NSS using DCGAN and StyleGAN2 based on benchmark datasets in which the NSS-based GANs outperforms the state-of-the-arts in most cases. • Optimization method of generative adversarial networks using a balanced composition of noise with diffusion process. • Generator is trained using simplified data first while keeping its learning capability for high frequency information. • Synthetic dataset demonstrates the side-effect of the conventional diffusion. • Experimental results using the state-of-arts with major benchmarks support the effectiveness of the presented method. • Code is presented in Supplemental File. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach to modeling the gradual reduction in heat demand and the evolving expansion of district heating (DH) grids for assessing the DH potential in EU member states (MS). It introduces new methodological elements for modeling the impact of connection rates below 100% on heat distribution costs in both dense and sparse areas. The projected heat demand in 2050 is derived from a decarbonization scenario published by the EU, which would lead to a reduction in demand from 3128 TWh in 2020 to 1709 TWh by 2050. The proposed approach yields information on economic DH areas, DH potential, and average heat distribution costs. The results confirm the need to expand DH grids to maintain supply levels in view of decreasing heat demand. The proportion of DH potential from the total demand in the EU-27 rises from 15% in 2020 to 31% in 2050. The analysis of DH areas shows that 39% of the DH potential is in areas with heat distribution costs above 35 EUR/MWh, but most MS have average heat distribution costs between 28 and 32 EUR/MWh. The study reveals that over 40% of the EU's heat demand is in regions with high potential for implementing DH. • A novel method for analyzing district heating potential in the EU-27 is presented. • District heating can cover 31% of heat demand (non-industry) in EU-27 up to 2050. • Grid expansion is needed in economically favorable areas to reduce specific costs. • A yearly investment of €11.7B at the EU level is required for the grid expansion. [ABSTRACT FROM AUTHOR]

Does the degree of finance dependence, defined as firms' reliance on external finance for regular production activities, determine exporters' heterogeneous responses to real exchange rate shocks? This paper develops a stylized model to illustrate the role of finance dependence in shaping exporters' pricing decisions when the bilateral real exchange rate fluctuates. The model features distribution costs, endogenous markup, and firm heterogeneity. In the model, finance dependence can impact firms' export pricing decisions in a way isomorphic to firm productivity but in the opposite direction: firms with high finance dependence have a high demand elasticity and a low price elasticity to exchange rate fluctuations. Therefore, in response to real exchange rate shocks, financially more dependent firms exhibit lower exchange rate pass-through into export prices denominated in home country currency (RMB). To assess the model prediction, I develop a firm-destination-year-specific measure of finance dependence based on the basket of goods that firms export and employ the Chinese manufacturing firm export data from 2000 to 2006 for analysis. The empirical test is carried out at the most disaggregated firm-product-destination level and confirms the model prediction. • A stylized model is developed to feature distribution costs, endogenous markup, and firm heterogeneity. • The model explains how financial dependence can affect firms' choice of export price in response to exchange rate shocks. • Financially more dependent firms should exhibit lower ERPT into export unit price denominated in home currency. • The model prediction is confirmed using the Chinese manufacturing data from 2000-2006 at firm-destination-product level. [ABSTRACT FROM AUTHOR]

• Two different practices in the analysis of binary classifiers contrasted. • A machine learning binary classifier is analysed with detection theory methods. • Behaviour categorised into a small number of classes; extremal properties analysed. • Novel insights into classifier behaviour; but more user choice reduces objectivity. Most binary classifiers work by processing the input to produce a scalar response and comparing it to a threshold value. The various measures of classifier performance assume, explicitly or implicitly, probability distributions P s and P n of the response belonging to either class, probability distributions for the cost of each type of misclassification, and compute a performance score from the expected cost. In machine learning, classifier responses are obtained experimentally and performance scores are computed directly from them, without any assumptions on P s and P n. Here, we argue that the omitted step of estimating theoretical distributions for P s and P n can be useful. In a biometric security example, we fit beta distributions to the responses of two classifiers, one based on logistic regression and one on ANNs, and use them to establish a categorisation into a small number of classes with different extremal behaviours at the ends of the ROC curves. [ABSTRACT FROM AUTHOR]

• Three variants of AdaBoost adaptation to example-dependent cost-sensitive classification problem. • Impact of posterior probability calibration on model performance. • Impact of cost distribution on the model performance. • Achieving superior classification results comparing to state-of-art methods. Intelligent computer systems aim to help humans in making decisions. Many practical decision-making problems are classification problems in their nature, but standard classification algorithms often not applicable since they assume balanced distribution of classes and constant misclassification costs. From this point of view, algorithms that consider the cost of decisions are essential since they are more consistent with the requirements of real life. These algorithms generate decisions that directly optimize parameters valuable for business, for example, the costs savings. But despite on practical value of cost-sensitive algorithms, the little number of works study this problem concentrating mainly on the case when the cost of a classifier error is constant and does not depend on a specific example. However, many real-world classification tasks are example-dependent cost-sensitive (ECS), where the costs of misclassification vary between examples and not only within classes. Existing methods of ECS learning include just modifications of the simplest models of machine learning (naive Bayes, logistic regression, decision tree). These models produce promising results, but there is a need for further improvement in performance that can be achieved by using gradient-based ensemble methods. To break this gap, we present the ECS generalization of AdaBoost. We study three models which differ by the ways to introduce cost into the loss function: inside the exponent, outside the exponent, and both inside and outside the exponent. The results of the experiments on three synthetic and two real datasets (bank marketing and insurance fraud) show that example-dependent cost-sensitive modifications of AdaBoost outperform other known models. Empirical results also show that critical factors influencing the choice of the model are not only the distribution of features, which is typical for cost-insensitive and class-dependent cost-sensitive problems but also the distribution of costs. Next, since the outputs of AdaBoost are not well calibrated posterior probabilities, we check three approaches to calibration of classifier scores: Platt scaling, isotonic regression, and ROC modification. The results show that calibration not only significantly improves the performance of specific ECS models but allows making better capabilities of original AdaBoost. Obtained results provide new insight regarding the behavior of the cost-sensitive model from a theoretical point of view and prove that the presented approach can significantly improve the practical design of intelligent systems. [ABSTRACT FROM AUTHOR]