The Effect of Fairness Concern on Carbon Emission Reduction and Revenue Distribution in Construction Supply Chain: Power Structure Perspective

This paper invested a two-echelon construction supply chain that consists of a general contractor and a subcontractor. This paper constructs the centralized model and the decentralized models, respectively, and studies the emission reduction and revenue distribution strategies of construction supply chain considering fairness concern and cap-and-trade. Numerical analysis is carried out to analyze the influence of cap-and-trade and fairness concern on the optimal decision and the maximum profit of construction supply chain. This paper shows that, under cap-and-trade policy, the centralized model has the best emission reduction effect and the highest supply chain profit without fairness concern, while the general contractor’s Stackelberg model has the best emission reduction effect and the highest supply chain profit with fairness concern. In the two scenarios, the Vertical Nash model is the most unfavorable to emission reduction, and it will also seriously damage the interests of enterprises. In practice, supply chain should choose the general contractor’s Stackelberg model and avoid the Vertical Nash model. Because fairness concern of the subcontractor will damage the supply chain profits and emission reduction performance, the general contractor shall try to select the subcontractor with lower fairness concern to avoid the loss of profit. Besides, enterprises should actively take measures to reduce fairness concern, such as enterprises signing the contract price confidentiality clause, which aims to reduce fairness concern of the subcontractor. The results of this paper can not only enrich the research content of construction supply chain management, but also provide references for the government to formulate emission reduction policies.

Federated learning over WiFi: Should we use TCP or UDP?

Federated learning is a distributed learning paradigm where a centralized model is trained on data distributed over a large number of clients, each with unreliable and relatively slow network connections. The client connections typically have limited bandwidth available to them when using networks such as 2G, 3G, or WiFi. As a result, communication often becomes a bottleneck. Currently, the communication between the clients and server is mostly based on TCP protocol. In this paper, we explore using the UDP protocol for the communication between the clients and server. In particular, we develop UDP-based algorithms for gradient aggregation-based federated learning and model aggregation-based federated learning. We propose methods to construct model updates in case of packet loss with the UDP protocol. We present a scalable framework for practical federated learning. We conduct experiments over WiFi and observe that the UDP-based protocols can lead to faster convergence than the TCP-based protocol -- especially in bad networks. Code available at the repository: \url{https://github.com/vineeths96/Federated-Learning}.

U.S. & China Approaches to Global Internet Governance: "New Bipolarity" in Terms of "the Network Society"

From the perspective of power transition theory, international relations system is gradually entering the phase of "power transition" where the United States, as a global hegemon, seeks to maintain the existing world order, and China establishes alternative international formats to reorganize the system of international relations and strengthen its structural power. Cyberspace and technological sphere are becoming the field of non-violent competition between states, which makes the study of global governance of cyberspace critical for the understanding of the outlines of the "new bipolarity". The analysis in the paper is focused on U.S. & China approaches to global governance of cyberspace through the prism of Manuel Castells' theory of "network society". The authors aimed to determine the directions of the U.S. and China policy in the course of four types of "power" in cyberspace: networking power, network power, networked power and network-making power. Present analysis concludes that the United States play crucial role in the course of all four types of "power" at the expense of decentralized model of Internet governance which is based on the idea of "multistakeholderism". NGO and other entities play a decisive role in such a model. Nonetheless, China has already developed necessary tools for reforming the present system of global governance of cyberspace based on centralized model with the leading role of United Nations as an international governance organization and state as a basic actor. The main beneficiaries of the centralized model are developing countries, which are unable to influence the global governance of cyberspace under the dominance of private companies from developed countries.

A Centralized Model Enabling Channel Reuse for Spectrum Allocation in Cognitive Radio Networks

Cognitive Radio (CR) is an advanced technology, which intends to boost the radio spectrum utilization. On perceiving the spectrum holes, next there is a need to provide a fair distribution of the vacant licensed channels amongst Secondary Users (SUs) during the spectrum allocation process. In this context, our paper introduces two allocation models to resolve the spectrum allocation problem. Initially, we design a simple centralized model to assign the channels. Then, we extend it to a centralized fair allocation model that aims to impart a better utilization of the free channels. Both approaches assign a common channel to a group of non-interfering SUs simultaneously. This facilitates spectrum reuse. The constraint related to dynamics in spectrum opportunities in CR is handled during channel allocation. Simulation study analyzes the proposed approaches with an existing allocation mechanism and reveals the performance improvement of centralized fair allocation model in terms of spectrum utilization.

A Lightweight Blockchain Framework for IoT Integration in Smart Cities

Smart cities heavily rely on technological enablers for their success, and more specifically on the IoT. This network, which will reach billions of components, now suffers from several constraints. In recent research, the blockchain has been proposed to provide answers on the limits of the centralized model, and on security. The integration of blockchain and IoT, however, still has issues that are being resolved, which are energy consumption, computing capacity, and storage capacity, due to the low capabilities of connected objects. In this article, we present our lightweight framework that we implemented for the integration of blockchain and IoT, and simulating on machines close to the configuration of the majority of current connected objects.

E-Commerce Supply Chain Models under Altruistic Preference

This paper reviews the decisions, coordination contract, and altruistic preference of an e-commerce supply chain (eSC) composed of a manufacturer and a third-party e-commerce platform (platform). The research derives optimal decisions in a decentralized model with altruistic preference; it is indicated that altruistic preference can help increase the sales price and sales service level. Although the platform’s altruistic preference is not beneficial to its own profits, it can help increase the manufacturer’s profits. Moreover, when the degree of altruistic preference is kept within a limit, the profit of the decentralized system is higher in a model with altruistic preferences, which indicates that altruistic preference can contribute toward maintaining a friendly, harmonious, and cooperative a relationship between the platform and manufacturers. Contrary to a traditional offline supply chain, where the sales price is the lowest in the centralized model, the sales price is highest in a centralized eSC model. In the proposed coordination contract, the platform with a certain degree of altruistic preference offers zero-commission sales service and charges a certain amount for a fixed professional service fee. The proposed contract is more applicable to products whose market demands are less affected by sales prices and more affected by the sales service level.

Digital Contact Tracing Based on a Graph Database Algorithm for Emergency Management During the COVID-19 Epidemic: Case Study

Background The COVID-19 epidemic is still spreading globally. Contact tracing is a vital strategy in epidemic emergency management; however, traditional contact tracing faces many limitations in practice. The application of digital technology provides an opportunity for local governments to trace the contacts of individuals with COVID-19 more comprehensively, efficiently, and precisely. Objective Our research aimed to provide new solutions to overcome the limitations of traditional contact tracing by introducing the organizational process, technical process, and main achievements of digital contact tracing in Hainan Province. Methods A graph database algorithm, which can efficiently process complex relational networks, was applied in Hainan Province; this algorithm relies on a governmental big data platform to analyze multisource COVID-19 epidemic data and build networks of relationships among high-risk infected individuals, the general population, vehicles, and public places to identify and trace contacts. We summarized the organizational and technical process of digital contact tracing in Hainan Province based on interviews and data analyses. Results An integrated emergency management command system and a multi-agency coordination mechanism were formed during the emergency management of the COVID-19 epidemic in Hainan Province. The collection, storage, analysis, and application of multisource epidemic data were realized based on the government’s big data platform using a centralized model. The graph database algorithm is compatible with this platform and can analyze multisource and heterogeneous big data related to the epidemic. These practices were used to quickly and accurately identify and trace 10,871 contacts among hundreds of thousands of epidemic data records; 378 closest contacts and a number of public places with high risk of infection were identified. A confirmed patient was found after quarantine measures were implemented by all contacts. Conclusions During the emergency management of the COVID-19 epidemic, Hainan Province used a graph database algorithm to trace contacts in a centralized model, which can identify infected individuals and high-risk public places more quickly and accurately. This practice can provide support to government agencies to implement precise, agile, and evidence-based emergency management measures and improve the responsiveness of the public health emergency response system. Strengthening data security, improving tracing accuracy, enabling intelligent data collection, and improving data-sharing mechanisms and technologies are directions for optimizing digital contact tracing.