Search Results (266)

The large-scale integration of renewable energy and the use of high-energy-consuming equipment in agricultural parks have a great influence on the security of rural distribution networks. To ensure reliable power delivery for residential and agricultural activities and sustainable management of distributed energy resources, this paper develops a distributed generation-supported interactive optimization framework coordinating distribution networks and agricultural parks. Specifically, a wind–photovoltaic scenario generation method based on Copula functions is first proposed to characterize the uncertainties of renewable generation. Based on the generated scenario, a bi-level interactive optimization framework consisting of a distribution network and agricultural park is constructed. At the upper level, the distribution network operators ensure the security of the distribution network by reconfiguration, coordinated distributed resource dispatch, and dynamic price compensation mechanisms to guide the agricultural park’s electricity consumption strategy. At the lower level, the agricultural park users maximize their economic benefits by adjusting controllable loads in response to price compensation incentives. Additionally, an improved particle swarm optimization combined with a Gurobi solver is proposed to obtain equilibrium by iterative solving. The simulation analysis demonstrates that the proposed method can reduce the operation costs of the distribution network and improve the satisfaction of users in agricultural parks. Full article

Empirical studies have suggested that travelers’ risk attitudes affect their choice behavior when travel conditions are stochastic. By considering the travelers’ risk attitudes, we extend the classical two-route model, in which road capacities vary due to such shocks as bad weather, accidents, and special events. Two information regimes have been investigated. In the zero-information regime, we postulate that travelers acquire the variability in route travel time based on past experiences and choose the route to minimize the travel time budget. In the full-information regime, travelers have pre-trip information of the road capacities and thus choose the route to minimize the travel time. User equilibrium states of the two regimes have been analyzed, based on the canonical BPR travel time function with power coefficient $p$. In the special case $p=1$, the closed form solutions have been derived. Three cases and eleven subcases have been classified concerning the dependence of expected total travel times on the risk attitude in the zero-information regime. In the general condition $p>0$, although we are not able to derive the closed form solutions, we proved that the results are qualitatively unchanged. We have studied the benefit gains/losses by shifting from the zero-information to the full-information regime. The circumstance under which pre-trip information is beneficial has been identified. A numerical analysis is conducted to further illustrate the theoretical findings. Full article

Edge computing refers to provision storage and computation resources at the network edge, closer to end users than the remote cloud. In such edge–cloud computing environments, many cloud providers intend to offload cloud services to the edge nodes to offer high-quality services for data-intensive and latency-sensitive applications. The major obstacle is that edge nodes are rarely willing to offer resources voluntarily without any rewards. To this end, this paper proposes an efficient incentive mechanism for edge–cloud computing environments using Stackelberg game theory to motivate more edge nodes to host offloaded cloud services. We analyze the properties of the game model and present a solution to compute the unique Stackelberg Equilibrium (SE) of the nonlinear model. On this basis, we propose an efficient polynomial-time algorithm to find the SE. Moreover, we discuss the adaptation of our incentive mechanism to dynamic node joining or departing. Performance evaluations compare our incentive mechanism with three benchmarks and a state-of-the-art mechanism. The results indicate that our incentive mechanism can effectively motivate both the edge nodes and the remote cloud to participate in the edge–cloud environment, achieving maximum resource utilization with minimal rewards while remaining robust in dynamic situations. Full article

This paper investigates the competition between two types of bike-sharing services, particularly at bus stops, subway stations, and residential areas. Two types of shared bicycle travel choice models are constructed. A shared bicycle operator attracts users by implementing discount incentives, and the comfort levels of riding the two types of shared bicycles are different. The equilibrium fares, potential user demand, and operator profits under joint profit maximization, price competition, and potential user demand competition scenarios are derived, and the competitive results under the three scenarios are compared. The results show that, in the potential user demand competition, the difference in potential demand between the two operators is largest; in the joint profit maximization scenario involving shared bicycle operators, the difference in potential user demand is smallest. In all competitive scenarios, higher operating costs and costs in lowering comfort loss for the shared bicycle operators will increase fares; the substitution level between the two types of shared bicycles has a positive impact on potential user demand, and the higher the substitution level, the better the effect of discounts in attracting users. Full article

This research investigates the strategic decision related to original content provision and business model selection within the rapidly evolving online video industry. We develop a two-sided market model involving a video platform, users, and advertisers to analyze how the platform balances subscription and advertising revenue while offering original content. This study finds that the amount of original content provided directly influences market equilibrium, affecting the platform’s choice between a purely subscription-based model or a mixed model. The cost of original content production plays a critical role in this decision-making process. High production costs may lead the platform to favor a mixed model, offering less original content to generate both subscription and ad revenue. Lower costs, however, encourage a subscription model with more original content to boost subscribing revenue. Additionally, factors such as network externalities between users and advertisers also impact the business model choice. Importantly, the model shows that allowing free users limited access to original content can expand both subscriber and advertiser engagement, enabling platforms to enhance dual revenue streams without having to sacrifice one for the other. Full article

Clock synchronization, as the fundamental service in the power grid, provides a unified time standard for all kinds of power services, such as metering, controlling, and scheduling. Since massive new energy devices are connected to the power grid, they bring new demands for differentiated synchronization accuracy and dynamic access synchronization. Considering the limited communication resources of the power grid, we propose a master–slave clock synchronization method based on the two-layer Stackedberg game to satisfy the synchronization demand by diverse devices as much as possible. First, the master station dynamically prices users based on the required synchronization accuracy and the number of synchronous devices, creating a game process between users. Secondly, an iterative algorithm for solving game equilibrium is designed. Finally, the simulation results are given in MATLAB to prove that the proposed method can converge quickly and dynamically adjust allocation according to user demand. Furthermore, the network simulator Mininet and the open-source software-defined network framework Ryu are used to evaluate the performance of the proposed method. Compared with the bandwidth reservation scheme, our proposed method is more suitable for differentiated clock synchronization of resource-limited new energy power networks. This paper investigates the impact of network capacity changes on the clock synchronization accuracy of user equipment in power networks for the first time. Then, a two-layer Stackedberg game model has emerged to achieve optimal bandwidth allocation in the network and improve clock synchronization accuracy. Simulation results show that our method can not only meet the differentiated clock synchronization requirements but also achieve the optimal allocation in dynamic access scenarios. Full article

Fog computing introduces a new dimension to the network edge by pooling diverse resources (e.g., processing power, memory, and bandwidth). However, allocating resources from heterogeneous fog nodes often faces limited capacity. To overcome these limitations, integrating fog nodes with cloud resources is crucial, ensuring that Service Providers (SPs) have adequate resources to deliver their services efficiently. In this paper, we propose a game-theoretic model to describe the competition among non-cooperative SPs as they bid for resources from both fog and cloud environments, managed by an Infrastructure Provider (InP), to offer paid services to their end-users. In our game model, each SP bids for the resources it requires, determining its willingness to pay based on its specific service demands and quality requirements. Resource allocation prioritizes the fog environment, which offers local access with lower latency but limited capacity. When fog resources are insufficient, the remaining demand is fulfilled by cloud resources, which provide virtually unlimited capacity. However, this approach has a weakness in that some SPs may struggle to fully utilize the resources allocated in the Nash equilibrium-balanced cloud solution. Specifically, under a nondiscriminatory pricing scheme, the Nash equilibrium may enable certain SPs to acquire more resources, granting them a significant advantage in utilizing fog resources. This leads to unfairness among SPs competing for fog resources. To address this issue, we propose a price differentiation mechanism among SPs to ensure a fair allocation of resources at the Nash equilibrium in the fog environment. We establish the existence and uniqueness of the Nash equilibrium and analyze its key properties. The effectiveness of the proposed model is validated through simulations using Amazon EC2 instances, where we investigate the impact of various parameters on market equilibrium. The results show that SPs may experience profit reductions as they invest to attract end-users and enhance their quality of service QoS. Furthermore, unequal access to resources can lead to an imbalance in competition, negatively affecting the fairness of resource distribution. The results demonstrate that the proposed model is coherent and that it offers valuable information on the allocation of resources, pricing strategies, and QoS management in cloud- and fog-based environments. Full article

Regional planning agencies increasingly rely on Spatial Economic Models (SEMs) to evaluate the impact of various policies. However, traditional SEMs often employ homogeneous technical coefficients (TCs) to represent technology patterns used by activities located in very different areas of a region, leading to misrepresentations of production and consumption behaviors, and consequently, inaccurate modeling results. To this end, we propose a Differential Spatial Economic Modeling (DSEM) framework that incorporates region-specific TCs for activities within Independent Planning Units (IPUs), such as provinces or cities, each characterized by unique economic, demographic, and technological features. The DSEM framework comprises three core components: (1) a regional economy model that forecasts activity totals for each IPU using economic and demographic forecasting model, supplemented by statistical analyses like the Gini index and K-means clustering to group activities from different IPUs into homogeneous ‘technology’ clusters based on their TCs; (2) a land use model that allocates IPU activity totals to corresponding traffic analysis zones (e.g., counties or districts) using the Differential Spatial Activity Allocation (DSAA) method. This determines the spatial distribution of commodities (such as goods, services, floor space, and labor) across exchange zones, balancing supply and demand to achieve spatial equilibrium in both quantity and price; and (3) a transport model that performs modal split and network assignment, distributing commodity trip origin–destination matrices across a multimodal transportation supernetwork (highways, railways, and waterways) using a probit-based stochastic user equilibrium assignment model. The proposed method is applied to a case study of the Yangtze River Economic Belt, China. The results demonstrate that the proposed DSEM yields better goodness-of-fit (R²) values between observed and estimated flows compared to the traditional aggregate SEM. This indicates a more precise and objective representation of spatial economic activities and technological patterns, thus resulting in improved estimates of freight flows for individual transportation modes and specific links. Full article

To investigate the pneumatic characteristics of a piston-type air compressor during the rapid transient processes of intake and compression, this study establishes a computational model incorporating the tank, valves, cylinder, intake and discharge pipe, etc. Utilizing the dynamic mesh method combined with user-defined functions, numerical calculations were performed to analyze the compression process, focusing on pressure variation patterns at various positions inside the cylinder and their impact on compressor performance. The purpose is to enhance understanding of these dynamics. Key findings reveal that during the intake phase, pressure at all monitored points rapidly decreases, with the most significant pressure changes occurring directly below the intake valve. Pressure variations on the surfaces of the intake and discharge valves exhibit high consistency. However, during compression, negative pressure changes become more pronounced. The pressures on the top, side walls, and bottom of the cylinder rapidly decrease as the compression ends. Furthermore, as air flows into the storage tank, its pressure decreases but remains mostly stable until equilibrium is reached, causing the tank pressure to rise. Finally, significant low-pressure areas were observed in small corners below the pipe, while higher pressure values were found in larger corners above the side, demonstrating flow characteristics and energy loss under different geometric conditions. Full article

Dynamic mechanical analysis (DMA) is routinely practiced in the semiconductor industry to measure the viscoelastic properties of various thermosetting polymers. Modern commercial DMA test machines are highly-advanced systems which enable users to perform automatic testing and post-processing of the experimental data. When highly filled thermosets like epoxy-based molding compound (EMC) are tested, unique challenges are encountered during measurements due to the extremely large change in modulus over the testing temperature range. An advanced procedure is proposed to cope with these problems. The first part is the use of different oscillation strain amplitudes so that the variations in stress amplitudes across the testing domain remain consistent. The second part is the conducting of two monotonic tests at the lowest and highest temperatures to obtain the glassy modulus and equilibrium modulus, which can guide the master curve construction accurately. The results of the proposed procedure are presented. The relaxation modulus master curve is used to conduct a virtual testing to verify the accuracy of the generalized Maxwell model constants determined from the frequency data using the proposed procedure. Full article

Despite calls to counter unsustainable urbanization, dense, concrete, and overheated urban spaces remain a reality. However, ideological assumptions and priorities related to creating livable urban spaces are only a part of a broader chain of actions that is the construction investment process (CIP), and its participants (Theoreticians, Investors, Designers, Contractors, Controllers, and Users) can play a significant role in addressing negative urban changes through their attitudes. The starting point for analyzing attitudes was Jung’s theory, which led to identifying key attitudes: Thinking, Feeling, Perception, Intuition, Creativity, and Equilibrium. This study aimed to determine the values reflected in CIP participants’ attitudes, identified through individual in-depth interviews with selected experts. Statements were divided into phrases referring to specific features or values and were subjected to qualitative (and limited quantitative) content analyses, supplemented by thematic and sentiment analyses. Feeling emerged as the most frequently mentioned value, followed by Thinking. Designers and Investors attracted the most attention, while Designers and Users were seen as the most controversial groups due to the values and stances they represented. Contractors (especially general and specialized) received the most favorable evaluations. This study concludes with a detailed characterization of the attitudes and values of the CIP participants, highlighting their contributions to the CIP. Full article

A numerical simulation was carried out to study the transient snow drifting phenomena around buildings. The method employs the commercial CFD software Ansys Fluent with additional user-defined functions to model snow transport. Drifting snow analysis around buildings has been extensively studied, but there have been few validation examples in non-equilibrium flow fields around buildings using the saltation fetch distance to account for the snow transport rate. Therefore, in this study, we conducted snowdrift analysis in three types of non-equilibrium flow fields and compared the results with actual measurements. For cube-shaped buildings and two-level flat roofs, the simulation results captured the trends observed in the actual measurements. However, in the case of snow fence analysis, an underestimation of the accumulation amount was observed downstream of the snowdrifts. Full article

Currently, some platform-based enterprises are selling users’ private information to capture high revenue, which poses a great threat to users’ privacy and security and also poses a challenge to the work of regulators. This paper constructs a tripartite evolutionary game model among platform-based enterprises, users, and regulators by combining theories of daily activities. This paper explores the equilibrium strategy for the leakage of users’ privacy information on online platforms under the concept of multiparty governance and designs four simulation experiments based on the revenue intervals of platform-based enterprises’ decisions. Finally, the model is validated by means of simulation. The results show that when platform-based enterprises make less profit from compromising users’ privacy, users are motivated to participate in shared governance with minimal incentives from the regulator. Regulators can effectively deter privacy leakage by fining platform-based enterprises for leaking user privacy information and requiring them to compensate users for their losses. At this point, they can achieve effective control over privacy leakage by using traditional regulation. When platform-based enterprises make high profits from leaking users’ privacy, they will adhere to their privacy leaking strategy. At this point, privacy security is at its most serious, and users’ privacy rights are not effectively protected. Users and regulators will tend to cooperate to form a multiparty regulatory system, but this does not influence the strategy choice of platform-based enterprises. Full article

The adaptive reuse of historic buildings into contemporary office spaces prompts intriguing inquiries regarding its impact on employee satisfaction and workplace culture. This study explores the potential of adaptive reuse to transform historic buildings into functional, sustainable offices, using Erbil Citadel houses as a base for the study. Through this research study, user preferences and perceptions of the integration of historical features into the modern work environment were examined. Quantitative data were extracted from 60 survey respondents and analyzed in terms of medians, modes and the analysis of key themes, such as historical aesthetics, employee creativity, work satisfaction and environmental factors, including natural light and airflow. The findings emphasize the equilibrium between safeguarding the cultural heritage of the historical structure and the requirements of contemporary office environments. The findings underscore the need for sustainable practices and technological integration to enhance workplace functionality and team well-being, particularly in shared spaces. This paper highlights the importance of decision makers’ perspectives on heritage conservation, stressing the necessity for a culturally attuned and sustainable reuse strategy that addresses community requirements. This study offers a methodological framework for reconciling historical narratives with modern office requirements while also addressing broader discussions on adaptive reuse and the potential for enhancing workplace quality. Full article

Overloaded trucks directly contribute to road infrastructure deterioration and undermine safety, posing significant challenges to sustainability. This makes enforcement to reduce their numbers and impacts essential. Weigh-in-motion (WIM) systems use road-embedded sensors to measure truck weights and enforce regulations. However, WIM cannot be installed on all routes, and some overloaded truck drivers can detour to avoid them instead of giving up overloading if the detour penalty is still lower than the extra profit from overloading. This paper focuses on optimal WIM location planning for overloaded truck management, incorporating a demand shift and user equilibrium model based on the utility functions of overloaded and non-overloaded trucks. The presented framework includes an upper-level problem for WIM placement and a lower-level problem for demand shifts and traffic assignments among overloaded trucks, non-overloaded trucks, and light-duty vehicles for a given WIM placement. Particularly, at the upper level, the primary objective is to minimize the traffic loadings, i.e., the expected equivalent single-axle load–kilometers per unit time, with the secondary objective of minimizing the total traffic disruptions over the target network. Simulations and sensitivity analyses are conducted through a numerical example. Consequently, this study proposes an optimal WIM placement framework that considers drivers’ utility-based route choice and social costs such as ESAL and traffic congestion. Full article