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In recent years, blockchain technology has developed rapidly and has been widely used in medical, financial, energy and other fields. However, in the process of practical application, each blockchain is a small independent ecosystem, with all transactions and operations limited to the chain, resulting in a large number of mutually heterogeneous to independent blockchains. It presents challenges for cross-chain interactions, cross-organization data sharing, and cross-blockchain expansion, and hinders the wider application of blockchain technology. In addition, the traditional digital signature method based on elliptic curve cipher faces the threat of being cracked by quantum computing attacks. To solve the aforementioned problems, this paper proposed a blockchain smart contract technique based on quantum computing attack resistance(BSCTQCAT). The technique first introduces the digital signature of the lattice cipher into the blockchain to resist the quantum search algorithm attack. Then, based on the smart contract authentication scheme, the nodes on multiple heterogeneous chains are organized into an identity agent layer P2P network, through which transactions on the chain will establish a credible identity management and message authentication mechanism between different chains, solving the current problem that each chain is difficult to communicate with each other. In this paper, the performance of the algorithm is evaluated by simulating the Bitcoin transaction scenario and analyzing the experimental data. [ABSTRACT FROM AUTHOR]

This paper presents a detailed review of the existing literature on peer-to-peer (P2P) energy trading considering market architectures, trading strategies, and enabling technologies. P2P energy trading enables individual users in the electricity network to act as sellers or buyers and trade energy among each other. To facilitate the discussion on different aspects of P2P energy trading, this paper focuses on P2P market mechanisms, relevant bidding strategies, and auction models. In addition, to solve the energy management problems associated with P2P energy trading, this paper investigates widely used solution methods such as game-theoretic models, mathematical optimisation, as well as more recent machine learning techniques and evaluates them in a critical manner. The outcomes of this investigation along with the identification of the challenges and limitations will allow researchers to find suitable P2P energy trading mechanisms based on different market contexts. Moreover, the discussions on potential future research directions are expected to improve the effectiveness of P2P energy trading technologies. [ABSTRACT FROM AUTHOR]

Purpose: The persistent gap between population indicators of poor mental health and the uptake of services raises questions about similarities and differences between social and medical/psychiatric constructions. Rarely do studies have assessments from different perspectives to examine whether and how lay individuals and professionals diverge. Methods: Data from the Person-to-Person Health Interview Study (P2P), a representative U.S. state sample (N ~ 2700) are used to examine the overlap and correlates of three diverse perspectives—self-reported mental health, a self/other problem recognition, and the CAT-MH™ a validated, computer adaptive test for psychopathology screening. Descriptive and multinominal logit analyses compare the presence of mental health problems across stakeholders and their association with respondents' sociodemographic characteristics. Results: Analyses reveal a set of socially constructed patterns. Two convergent patterns indicate whether there is (6.9%, The "Sick") or is not (64.6%, The "Well") a problem. The "Unmet Needers" (8.7%) indicates that neither respondents nor those around them recognize a problem identified by the screener. Two patterns indicate clinical need where either respondents (The "Self Deniers", 2.9%) or others (The "Network Deniers", 6.0%) do not. Patterns where the diagnostic indicator does not suggest a problem include The "Worried Well" (4.9%) where only the respondent does, The "Network Coerced" (4.6%) where only others do, and The "Prodromal" (1.4%) where both self and others do. Education, gender, race, and age are associated with social constructions of mental health problems. Conclusions: The implications of these results hold the potential to improve our understanding of unmet need, mental health literacy, stigma, and treatment resistance. [ABSTRACT FROM AUTHOR]

The fast development of the sensors in the wireless sensor networks (WSN) brings a big challenge of low energy consumption requirements, and Peer-to-peer (P2P) communication becomes the important way to break this bottleneck. However, the interference caused by different sensors sharing the spectrum and the power limitations seriously constrains the improvement of WSN. Therefore, in this paper, we proposed a deep reinforcement learning-based energy consumption optimization for P2P communication in WSN. Specifically, P2P sensors (PUs) are considered agents to share the spectrum of authorized sensors (AUs). An authorized sensor has permission to access specific data or systems, while a P2P sensor directly communicates with other sensors without needing a central server. One involves permission, the other is direct communication between sensors. Each agent can control the power and select the resources to avoid interference. Moreover, we use a double deep Q network (DDQN) algorithm to help the agent learn more detailed features of the interference. Simulation results show that the proposed algorithm can obtain a higher performance than the deep Q network scheme and the traditional algorithm, which can effectively lower the energy consumption for P2P communication in WSN. [ABSTRACT FROM AUTHOR]

Engagement in citizen science can result in participant outcomes including increased science and environmental literacy and civic action. One factor which may increase the likelihood of these outcomes is facilitation by groups such as employers, schools, or other organizations. We examined how a partnership between SciStarter and Girl Scouts of the USA facilitated participation in citizen science to shape participants' learning and civic engagement. Between July 2017 and February 2020, participants from over 200 Girl Scout troops completed the Think Like a Citizen Scientist Journey, consisting of science learning activities, participation in an environmental citizen science project on SciStarter, and a Take Action Project (TAP). Troop leaders provided open-ended descriptions of TAPs which we analyzed qualitatively. Responses provided evidence of learning outcomes spanning informal science learning goals, Girl Scout Leadership Experience outcomes, and Girl Scout STEM outcomes. Participants' TAPs overwhelmingly related to science and environmental topics (81%) and the majority sought to educate and inspire others (66%), reaching audiences of peers, adults, the general public, and civic leaders. This program demonstrates the potential for facilitator organizations to leverage existing citizen science projects to promote learning outcomes, civic science education, and community action with participants as young as 4–5 years old. [ABSTRACT FROM AUTHOR]

Bitcoin was launched over a decade ago and has made an increasing impact on the world's financial order, which has attracted the attention of researchers all over the world. The Bitcoin system runs on a dynamic P2P network, containing tens of thousands of nodes, including reachable nodes and unreachable nodes. In this article, a detection system, BNS (Bitcoin Network Sniffer), which could collect as many Bitcoin nodes as possible is proposed. For reachable nodes, the authors designed an algorithm, BRF (Bitcoin Reachable-Nodes Finding), based on node activity evaluation which reduces the nodes to be detected and greatly shortens the detection time. For unreachable nodes, the authors trained a decision tree model, BUF (Bitcoin Unreachable-Nodes Finding), to identify unreachable nodes based on attribute features from a large number of node addresses. Experiments showed that BNS discovered an average of 1093 more reachable nodes (6.4%) and 662 more unreachable nodes (2.3%) than the well-known website "Bitnodes" per day. It showed better performance in total nodes and efficiency. Based on the experimental results, the authors analyzed the real network size, node "churn", and geographical distribution. [ABSTRACT FROM AUTHOR]

In the evolving landscape of Industry 4.0, the convergence of peer-to-peer (P2P) systems, LoRa-enabled wireless sensor networks (WSNs), and distributed hash tables (DHTs) represents a major advancement that enhances sustainability in the modern agriculture framework and its applications. In this study, we propose a P2P Chord-based ecosystem for sustainable and smart agriculture applications, inspired by the inner workings of the Chord protocol. The node-centric approach of WiCHORD+ is a standout feature, streamlining operations in WSNs and leading to more energy-efficient and straightforward system interactions. Instead of traditional key-centric methods, WiCHORD+ is a node-centric protocol that is compatible with the inherent characteristics of WSNs. This unique design integrates seamlessly with distributed hash tables (DHTs), providing an efficient mechanism to locate nodes and ensure robust data retrieval while reducing energy consumption. Additionally, by utilizing the MAC address of each node in data routing, WiCHORD+ offers a more direct and efficient data lookup mechanism, essential for the timely and energy-efficient operation of WSNs. While the increasing dependence of smart agriculture on cloud computing environments for data storage and machine learning techniques for real-time prediction and analytics continues, frameworks like the proposed WiCHORD+ appear promising for future IoT applications due to their compatibility with modern devices and peripherals. Ultimately, the proposed approach aims to effectively incorporate LoRa, WSNs, DHTs, cloud computing, and machine learning, by providing practical solutions to the ongoing challenges in the current smart agriculture landscape and IoT applications. [ABSTRACT FROM AUTHOR]

With the widespread use of P2P network technology, the problem of its system energy consumption has become a topic of general interest. This paper studies how to lower the energy consumption of P2P networks, using queueing theory to analyze the problem of high system energy consumption in P2P networks. On the basis of the classical queueing model, an M/Md /c queue is established by introducing strategies such as asynchronous single vacation and bulk service. The steady-state distribution of the queueing model is solved using the matrix-geometric solution method to derive performance metrics. Finally, the problem of energy consumption in different states are studied, and the optimal parameters of the system are determined by constructing the benefit function, which provides a thought for the optimization of the hybrid P2P network. [ABSTRACT FROM AUTHOR]

Bitcoin is a decentralized digital cryptocurrency. Its network is a Peer-to-peer(P2P) network consisting of distributed nodes. Some of these nodes are always online and in this article are called Bitcoin backbone nodes. They have a significant impact on the stability and security of the Bitcoin network, so it is meaningful to analyze and discuss them. In this paper, we first continuously collect information about Bitcoin nodes from July 2021 through June 2022 (which is the longest duration of data collection to date). In total, we collect information on 127,613 Bitcoin nodes. At the same time, we conclude that the fluctuation of Bitcoin nodes is directly related to the fluctuation of onion network nodes. Further, we filtered 2694 Bitcoin backbone nodes based on our algorithm. By analyzing the backbone nodes' attributes such as geographic distribution, client version, operator, node function, and abnormal port number, it is demonstrated that these nodes are centralized and play an important role in the Bitcoin network. Based on this, three unsupervised machine learning algorithms are selected to cluster multiple attributes of backbone nodes in a more scientific way. In this paper, the whole process from data collection to cluster analysis is completed and the best results are obtained by comparison. The experiments proved the existence of centralization of Bitcoin backbone nodes and obtained the number of nodes within each cluster. Finally, cluster nodes are de-anonymized based on the optimal results. Through our experiments, we obtain organizational information about the deployers of 103 nodes, linking the Bitcoin backbone nodes to the real world, thus accurately demonstrating the existence of Bitcoin centrality. [ABSTRACT FROM AUTHOR]

With the massive access to distributed energy resources, an increasing number of users have transformed into prosumers with the functions of producing, storing, and consuming electric energy. Peer-to-peer (P2P) energy trading, as a new way to allow direct energy transactions between prosumers, is becoming increasingly widespread. How to determine the trading strategy of prosumers participating in P2P energy trading while the strategy can satisfy multiple optimization objectives simultaneously is a crucial problem to be solved. To this end, this paper introduces the demand response mechanism and applies the dissatisfaction function to represent the electricity consumption of prosumers. The mid-market rate price is adopted to attract more prosumers to participate in P2P energy trading. The P2P energy trading process among multiple prosumers in the community is constructed as a Markov decision process. We design the method of deep reinforcement learning (DRL) to solve the optimal trading policy of prosumers. DRL, by engaging in continual interactions with the environment, autonomously learns the optimal strategies. Additionally, the deep deterministic policy gradient algorithm is well-suited for handling the continuous and intricate decision problems that arise in the P2P energy trading market. Through the judicious construction of a reinforcement learning environment, this paper achieves multi-objective collaborative optimization. Simulation results show that our proposed algorithm and model reduce costs by 16.5%, compared to the transaction between prosumers and grid, and can effectively decrease the dependence of prosumers on the main grid. [ABSTRACT FROM AUTHOR]

Nowadays, the integration of home automation systems with smart thermostats is a common trend, designed to enhance resident comfort and conserve energy. The introduction of smart thermostats that can run machine learning algorithms has opened the door for on-device training, enabling customized thermal experiences in homes. However, leveraging the flexibility offered by on-device learning has been hindered by the absence of a tailored learning scheme that allows for accurate on-device training of thermal models. Traditional centralized learning (CL) and federated learning (FL) schemes rely on a central server that controls the learning experience, compromising the home's privacy and requiring significant energy to operate. To address these challenges, we propose PePTM, a personalized peer-to-peer thermal modeling algorithm that generates tailored thermal models for each home, offering a controlled learning experience with a minimal training energy footprint while preserving the home's privacy, an aspect difficult to achieve in both CL and FL. PePTM consists of local and collaborative learning phases that enable each home to train its thermal model and collaboratively improve it with a set of similar homes in a peer-to-peer fashion. To showcase the effectiveness of PePTM, we use a year's worth of data from US homes to train thermal models using the RNN time-series model and compare the data across three learning schemes: CL, FL, and PePTM, in terms of model performance and the training energy footprint. Our experimental results show that PePTM is significantly energy-efficient, requiring 695 and 40 times less training energy than CL and FL, respectively, while maintaining comparable performance. We believe that PePTM sets the stage for new avenues for on-device thermal model training, providing a personalized thermal experience with reduced energy consumption and enhanced privacy. [ABSTRACT FROM AUTHOR]

The purpose of this study is to show how machine learning can be leveraged as a tool to govern social impact and drive fair and equitable investments. Many organizations today are establishing financial inclusion goals to promote social impact and have been increasing their investments in this space. Financial inclusion is the opportunity for individuals and businesses to have access to affordable financial products including loans, credit, and insurance that they may otherwise not have access to with traditional financial institutions. Peer-to-peer (P2P) lending serves as a platform that can support and foster financial inclusion and influence social impact and is becoming more popular today as a resource to underserved communities. Loans issued through P2P lending can fund projects and initiatives focused on climate change, workforce diversity, women's rights, equity, labor practices, natural resource management, accounting standards, carbon emissions, and several other areas. With this in mind, AI can be a powerful governance tool to help manage risks and promote opportunities for an organization's financial inclusion goals. In this paper, we explore how AI, specifically machine learning, can help manage the P2P platform Kiva's investment risks and deliver impact, emphasizing the importance of prediction model retraining to account for regulatory and other changes across the P2P landscape to drive better decision-making. As part of this research, we also explore how changes in important model variables affect aggregate model predictions. [ABSTRACT FROM AUTHOR]

In recent years, peer-to-peer (P2P) as a means of content distribution and sharing service are receiving extensive attention. In this paper, the hybrid P2P network structure model is combined with the knowledge of queueing theory. In order to address the problem generated by unnecessary online behavior of nodes, the working sleep mechanism for the serving nodes is introduced, and the synchronous multiple working vacation strategy is also adopted to decrease the system energy consumption. Then a queueing model with working vacation and repairable fault is built, which used to simulate the resource transmission process of P2P network and to study the performance of P2P sharing system. The steady-state probability vector is derived by employing quasi-birth-and-death process and matrix-geometric solution method. And the expressions for system performance indicators, such as the average delay, are obtained by employing Gauss–Seidel iteration method. Through numerical experiments, the performance indicators of the P2P resource sharing system are studied, which provides a theoretical basis to decrease online energy consumption of nodes in the hybrid P2P network. And through employing Nash equilibrium and social optimal strategy, the value of the social maximum benefit under the social optimization is obtained. [ABSTRACT FROM AUTHOR]

Peer-to-peer (P2P) energy sharing can complement other energy management strategies needed in the energy transition to clean energy such as renewables. The recent advances in artificial intelligence, machine learning and internet of things can provide cutting-edge solutions to dynamic information interaction and power exchange, enabling efficient P2P energy sharing and trading schemes. However, the current electricity market fails to comprehensively consider devices degradation costs and transmission losses, fairness distribution and allocation on cost-benefit model, synergistic collaborations and operations for different stakeholders. Here, a comprehensive review on P2P energy sharing and trading is presented covering novel system configurations, energy sharing and marginal/trading price mechanisms, and decision-making in dynamic P2P energy trading combined with synergistic collaboration and operation among key stakeholders. Significances of this review include comprehensive consideration on internal trading pricing, fairness distribution and allocation with active participation in P2P energy sharing, synergistic collaborations and operations with mutual economic benefits. Modelling tools for P2P energy trading platforms for real applications are also reviewed. The review shows that P2P when optimally designed can provide win-win economic benefits to all related stakeholders such as prosumers, consumers, retailers, and aggregators with fair access to distributed energy resources. The P2P energy sharing can improve the system efficiency, reduce energy storage capacity and primary energy consumption, improve renewable penetration, avoid energy quality devaluation and mitigate stressed grid power together with voltage support and congestion management for the local power grid. Employing blockchain technology improves the automation level in P2P thus decreasing human interactions improving the security with transparent, tamper-proof and secure systems, and accelerating real-time settlements with smart contracts. Synergistic collaboration and operation among stakeholders can ensure economic benefits for each participant, fair energy distribution and cost allocation, and decrease reliance on retailers or aggregators. The results presented provide cutting-edge guidelines for increasing the use and acceptance of smart and fair P2P energy sharing and trading frameworks. • P2P energy sharing among prosumers, consumers, retailers, and aggregators. • System configurations, sharing mechanism, marginal pricing, and decision-making. • Dynamic P2P energy trading with synergistic collaboration and operation. • Blockchain for trading automation with transparency, tamper-proof and security. • Interdisciplinary study with dynamic pricing, machine learning and decision-making. [ABSTRACT FROM AUTHOR]

This study delves into the application of min-max fuzzy relation systems, specifically focusing on equality and inequality, in the examination of educational institution networks and their resource-sharing dynamics, akin to a P2P network structure. Expanding upon this framework, this article intricately explores a mathematical system featuring three or more terminals within an education resource system, each comprising distinct sharing capabilities. The terminals exchange information on various educational facets, encompassing variations and expenditures per share data, within the framework of a programming-type fuzzy objective function, constrained by the parameters of an educational information system. In these scenarios, the transfer of resources from one terminal to another is interlinked, introducing complexities that warrant a comprehensive examination. Notably, the consideration of greater downloading resources in specific cases is proposed for enhanced practicality. The primary objective of this article is to minimize network congestion within different educational networks, given fixed priority grades assigned to the terminals. To achieve this, the concept of a Lexicographic minimum solution is introduced to address max-min fuzzy relation inequalities, aligning with the defined objectives. The article presents a detailed and effective scenario for implementing the Lexicographic minimal solution. For validation purposes, the proposed scenario is supported by illustrative examples, offering tangible insights into its applicability and efficacy [ABSTRACT FROM AUTHOR]

Blockchain introduces challenges related to the reliability of user identity and identity management systems; this includes detecting unfalsified identities linked to IoT applications. This study focuses on optimizing user identity verification time by employing an efficient encryption algorithm for the user signature in a peer-to-peer decentralized IoT blockchain network. To achieve this, a user signature-based identity management framework is examined by using various encryption techniques and contrasting various hash functions built on top of the Modified Merkle Hash Tree (MMHT) data structure algorithm. The paper presents the execution of varying dataset sizes based on transactions between nodes to test the scalability of the proposed design for secure blockchain communication. The results show that the MMHT data structure algorithm using SHA3 and AES-128 encryption algorithm gives the lowest execution time, offering a minimum of 36% gain in time optimization compared to other algorithms. This work shows that using the AES-128 encryption algorithm with the MMHT algorithm and SHA3 hash function not only identifies malicious codes but also improves user integrity check performance in a blockchain network, while ensuring network scalability. Therefore, this study presents the performance evaluation of a blockchain network considering its distinct types, properties, components, and algorithms' taxonomy. [ABSTRACT FROM AUTHOR]

Payment apps and digital wallets are powerful tools used to exchange e-money via the internet. However, with the progressive disappearance of cash, there is a need for the digital equivalent of physical banknotes to guarantee the same level of anonymity of private payments. Few efforts to solve the double-spending problem exist in P2P payments (i.e., in avoiding the possibility of a payer retaining copies of digital coins in absence of a trusted third party (TTP)), and further research efforts are needed to explore options to preserve the privacy of payments, as per the mandates of numerous central bank digital currency (CBDC) exploratory initiatives, such as the digital euro. Moreover, generic programmability requirements and energetic impacts should be considered. In this paper, we present a sustainable offline P2P payment scheme to face the double-spending problem by means of a one-time program (OTP) approach. The approach consists of wiping the business logic out of a client's app and allowing financial intermediaries to inject a certified payment code into the user's device, which will execute (asynchronously and offline) at the time of payment. To do so, we wrap each coin in a program at the time of withdrawal. Then the program exploits the trusted execution environment (TEE) of modern smartphones to transfer itself from the payer to the payee via a direct IoT link. To confirm the validity of the approach, we performed qualitative and quantitative evaluations, specifically focusing on the energetic sustainability of the proposed scheme. Results show that our payment scheme is energetically sustainable as the current absorbed for sending one coin is, at most, ~1.8 mAh on an Apple smartphone. We advance the state-of-the-art because the scheme meets the programmability, anonymity, and sustainability requirements (at the same time). [ABSTRACT FROM AUTHOR]

With the electric power grid experiencing a rapid shift to the smart grid paradigm over a deregulated energy market, Internet of Things (IoT)-based solutions are gaining prominence, and innovative peer-to-peer (P2P) energy trading at a micro level is being deployed. Such advancement, however, leaves traditional security models vulnerable and paves the path for blockchain, a distributed ledger technology (DLT), with its decentralized, open, and transparency characteristics as a viable alternative. However, due to deregulation in energy trading markets, most of the prototype resilience regarding cybersecurity attack, performance and scalability of transaction broadcasting, and its direct impact on overall performances and attacks are required to be supported, which becomes a performance bottleneck with existing blockchain solutions such as Hyperledger, Ethereum, and so on. In this paper, we design a novel permissioned Corda framework for P2P energy trading peers that not only mitigates a new class of cyberattacks, i.e., delay trading (or discard), but also disseminates the transactions in a optimized propagation time, resulting in a fair transaction distribution. Sharing transactions in a permissioned R3 Corda blockchain framework is handled by the Advanced Message Queuing Protocol (AMQP) and transport layer security (TLS). The unique contribution of this paper lies in the use of an optimized CPU and JVM heap memory scenario analysis with P2P metric in addition to a far more realistic multihosted testbed for the performance analysis. The average latencies measured are 22 ms and 51 ms for sending and receiving messages. We compare the throughput by varying different types of flow such as energy request, request + pay, transfer, multiple notary, sender, receiver, and single notary. In the proposed framework, request is an energy asset that is based on payment state and contract in the P2P energy trading module, so in request flow, only one node with no notary appears on the vault of the node.Energy request + pay flow interaction deals with two nodes, such as producer and consumer, to deal with request and transfer of asset ownership with the help of a notary. Request + repeated pay flow request, on node A and repeatedly transfers a fraction of energy asset state to another node, B, through a notary. [ABSTRACT FROM AUTHOR]

Networked control systems (NCSs) are widely used in practical applications because of their flexibility in deployment. However, due to the dependence on the communication network, NCSs could be vulnerable to malicious cyberattacks. To address this problem, a novel blockchain technology-assisted networked predictive secure control approach is presented for the first time in this article. First, the introduction of blockchain technology brings a significant boost to the inherent resilience of the NCS in an active manner without relying on any prior knowledge of the system or potential attacks. However, blockchain technology would induce time delays unfavorable to the NCS, which could result in the low real-time performance of the control system. Subsequently, a networked Kalman filter-based predictive control is specially designed to compensate for the low real-time property of blockchain technology. A detailed analysis of the security and stability of the closed-loop NCS with the developed networked predictive secure controller is also presented, while sufficient conditions for the closed-loop system to be simultaneously stable and safe in a probabilistic sense are given. Finally, to verify the performance of the proposed approach in terms of practicality, an experimental prototype of a photovoltaic (PV)-based power generation system subjected to random cyber-attacks is built for voltage regulation. [ABSTRACT FROM AUTHOR]

This paper proposes a new peer-to-peer (P2P) energy trading method between energy sellers and consumers in a community based on multi-agent reinforcement learning (MARL). Each user of the community is treated as a smart agent who can choose the amount and the price of the electric energy to sell/buy. There are two aspects we need to examine: the profits for the individual user and the utility for the community. For a single user, we consider that they want to realise both a comfortable living environment to enhance happiness and satisfaction by adjusting usage loads and certain economic benefits by selling the surplus electric energy. Taking the whole community into account, we care about the balance between energy sellers and consumers so that the surplus electric energy can be locally absorbed and consumed within the community. To this end, MARL is applied to solve the problem, where the decision making of each user in the community not only focuses on their own interests but also takes into account the entire community's welfare. The experimental results prove that our method is profitable both both the sellers and buyers in the community. [ABSTRACT FROM AUTHOR]

Peer-to-Peer (P2P) networks is a distributed structure, which is widely used in file sharing, distributed computing, deep search engines. Each node plays the dual role of client and server, which makes the P2P network further exacerbates the insecurity of P2P systems. For the behavior of nodes in P2P systems, requesting nodes are abstracted into customers and service nodes are abstracted into servers. An M/M/c queueing model is developed with dynamically changing servers in almost unobservable cases, strategies are introduced, including start-up period, working sleep of service nodes and negative customers, etc. Using the matrix-geometric solution and Gauss-Seidel iteration method, average waiting length, average sojourn time, total energy consumption and throughput are derived. Through numerical experiments, the process of P2P file sharing is simulated, the optimal value of social utility is obtained using Nash equilibrium and social optimal strategy, and a theoretical basis is provided for node scheduling in P2P systems. [ABSTRACT FROM AUTHOR]

In recent years, cloud computing concepts have been extended towards the network edge, leading to paradigms like fog and edge computing. As a result, applications can be placed on a variety of resources, including fog nodes and cloud data centers. Application placement has significant impact on important metrics like latency. Finding an optimal application placement is computationally challenging, particularly because of the potentially huge number of infrastructure nodes and application components. To overcome the limited scalability of application placement algorithms, optimization can be decentralized, i.e., performed separately for different parts of the infrastructure. The infrastructure can be split into fog colonies, where a fog colony consists of the computational resources in a given geographical region. Application placement can then be performed for the individual fog colonies, thus mitigating the scalability problem. However, independent optimization of application placement in different fog colonies may lead to missed synergies and thus to sub-optimal overall results. Hence, some kind of coordination between fog colonies may be beneficial. In this article, we analyze the effects of decentralization and coordination on the optimization results. In particular, we compare empirically four different approaches: (i) centralized decision-making, where decisions are made in one go for the entire infrastructure, (ii) independent fog colonies, where optimization is carried out in each fog colony independently from each other, (iii) fog colonies with communication, where excess application components in one fog colony can be sent to a neighboring fog colony, and (iv) fog colonies with overlaps, where shared resources may be dynamically distributed between neighboring fog colonies. Our experiments show that, for large problem instances, decentralization combined with coordination leads to the best results. [ABSTRACT FROM AUTHOR]

Distributed energy resources (DERs), such as small-scale renewable energy generators, storage systems, and controllable loads, have been attracting great attention. Accordingly, interest in peer-to-peer (P2P) energy trading between prosumers with DERs is growing. The prosumers may perform the P2P electricity trading within the loss-guided framework, where network losses are primarily considered during the peer matching process. However, the loss-guided framework has limitations in that prosumer welfare is neglected in favor of prioritizing the network losses caused by the P2P transactions. Thus, in this study, a price-based framework for P2P electricity trading is suggested, where the prosumer welfare is considered by including not only network loss costs but also energy costs in the matching procedure. The effects of the suggested price-based framework on network efficiency, prosumer welfare, and social welfare are examined by comparing simulation results with the loss-guided framework and the random transactions. Further, how those three properties are affected by the change in loss price is analyzed and a guideline for the suitable choice of the loss price is suggested. [ABSTRACT FROM AUTHOR]

With COVID‐19 compelling some countries to close their schools, e‐learning has now become the primary mode of learning. Researchers have renewed their interest in users' acceptance of e‐learning via different platforms, given the possibility of different results relative to what was known pre‐pandemic. However, e‐learning still poses issues such as isolation, demotivation and learning attrition, which may be counterbalanced by peer‐to‐peer (P2P) learning. On this basis, this study surveyed 417 Indian students on their acceptance of P2P e‐learning with the extended Technology Acceptance Model. The data analysis confirmed that perceived ease of use was positively associated with the perceived usefulness of and students' attitude towards P2P e‐learning. Credibility was also found to be positively associated with perceived ease of use, perceived usefulness, and attitude towards P2P e‐learning. However, the study could not confirm the association between perceived usefulness and attitude towards P2P e‐learning. Additionally, self‐efficacy displayed a weak, but significant association with perceived ease of use and perceived usefulness of P2P e‐learning. Internet self‐efficacy was found to have a positive association on the intention to use P2P e‐learning, but not its perceived ease of use. Practitioner notesWhat is already known about this topic E‐learning which poses issues such as isolation, demotivation and learning attrition became the primary means of education due to the COVID‐19 pandemic.Issues with e‐learning can be counterbalanced by peer‐to‐peer (P2P) learning.There is a renewed interest in user acceptance of e‐learning via different platforms, as the sudden and prolonged shift to e‐learning is revealing different results relative to what was known pre‐pandemic.What this article adds Usage of the extended TAM model to predict users' acceptance of P2P e‐learning platforms during a pandemic.Extending the TAM model with a renewed focus on the importance of self‐efficacy and Internet self‐efficacy in accepting P2P e‐learning during a period of complete remote learning.Extending the TAM model with credibility as a factor: because P2P platforms allow any user to provide answers, students may struggle with choosing which answer to accept.Credibility was discovered to have a positive association with perceived ease of use, perceived usefulness and attitude towards P2P platforms.A positive association between perceived usefulness and attitude could not be confirmed—a discovery we consider related to the supplementary way the P2P platform was utilised.Perceived ease of use had a larger association with the intention to use P2P e‐learning than perceived usefulness, thus suggesting that students are more concerned with the ease of finding an answer than the usefulness obtained from it.Implications for practice and/or policy P2P e‐learning platforms should consider means through which an answer's credibility can be increased in order to bolster the ATT towards them.At a macro level, schools should consider the self‐efficacy and Internet self‐efficacy of students to ensure they are able to partake in e‐learning and provide training and resources to those who are lacking.P2P e‐learning platforms need to improve their user interface design and user experience to increase the Perceived ease of use for their users. [ABSTRACT FROM AUTHOR]

In this work, the performance of a peer-to-peer (P2P) network has been discussed. As being one of the most promising technology in today's world, P2P systems exploit computation, bandwidth and other resources at the edge of the Internet, with the lower cost of building and maintenance. In the past, a lot of research has been done to improve the reliability of P2P systems focusing on paradigms like- routing strategies, reliable file exchange, resource location and placement, security. However, not much effort has been focused on studying their overall reliability incorporating essential failure causes altogether. In this research work, the authors have designed a mathematical model for a P2P network using Markov Process to holistically determine the availability, reliability and MTTF (Mean time to failure) and examined the most vulnerable components in the system whose failure rates can be controlled to achieve a more reliable network. In such networks, performance is affected to some degree due to failure or disturbance in any component. [ABSTRACT FROM AUTHOR]

The vital dependence of peer to peer (P2P) energy trading frameworks on creative Internet of Things (IoT) has been making it more vulnerable against a wide scope of attacks and performance bottlenecks like low throughput, high latency, high CPU, memory use, etc. This hence compromises the energy exchanging information to store, share, oversee, and access. Blockchain innovation as a feasible solution, works with the rule of untrusted members. To alleviate this threat and performance issues, this paper presents a Blockchain based Confidential Consortium (CoCo) P2P energy trading system that works on the trust issues among the energy exchanging networks and limits performance parameters. It reduces the duplicate validation by creating a trusted network on nodes, where participants identities are known and controlled. A Java-script-based smart contract is sent over the Microsoft CoCo system with Proof of Elapsed Time (PoET) consensus protocol. Also, a functional model is designed for the proposed framework and the performance bench-marking has been done considering about latency, throughput, transaction rate control, success and fail transaction, CPU and memory usage, network traffic. Additionally, it is shown that PoET's performance is superior to proof of work (PoW) for multi-hosting conditions. The measured throughput and latency moving toward database speeds with more flexible, business-specific confidentiality models, network policy management through distributed governance, support for non-deterministic transactions, and reduced energy consumption. [ABSTRACT FROM AUTHOR]

Blockchain protocols require nodes to verify all received transactions before forwarding them. However, massive spam transactions cause the participants in blockchain systems to consume many resources in verifying and propagating transactions. This paper proposes a reputation-based mechanism to increase the efficiency of processing transactions by considering the reputations of the sending nodes. Reputations are in turn adjusted based on the quality of transaction processing. Our proposed reputation-based mechanism offers three main contributions. First, we modify the verification strategy so that nodes set a probability of verifying a received transaction considering the likelihood of it being spam: transactions from a node with a low reputation have a high probability of being verified. Second, we optimize the transaction forwarding protocol to reduce propagation delay by prioritizing forwarding transactions to reputable receivers. Third, we design a data request protocol that provides alternative data exchange methods for nodes with different reputations. A series of simulations demonstrate the performance of our reputation-based mechanism. [ABSTRACT FROM AUTHOR]

Graph matching involves combinatorial optimization based on edge-to-edge affinity matrix, which can be generally formulated as Lawler's quadratic assignment problem (QAP). This paper presents a QAP network directly learning with the affinity matrix (equivalently the association graph) whereby the matching problem is translated into a constrained vertex classification task. The association graph is learned by an embedding network for vertex classification, followed by Sinkhorn normalization and a cross-entropy loss for end-to-end learning. We further improve the embedding model on association graph by introducing Sinkhorn based matching-aware constraint, as well as dummy nodes to deal with unequal sizes of graphs. To our best knowledge, this is one of the first network to directly learn with the general Lawler's QAP. In contrast, recent deep matching methods focus on the learning of node/edge features in two graphs respectively. We also show how to extend our network to hypergraph matching, and matching of multiple graphs. Experimental results on both synthetic graphs and real-world images show its effectiveness. For pure QAP tasks on synthetic data and QAPLIB benchmark, our method can perform competitively and even surpass state-of-the-art graph matching and QAP solvers with notable less time cost. We provide a project homepage at http://thinklab.sjtu.edu.cn/project/NGM/index.html. [ABSTRACT FROM AUTHOR]

In this article, we propose a mobile crowdsourced guiding and finding (MCGF) framework using smartphones to guide indoor people and find missing targets through Internet-of-Things (IoT) localization. The MCGF framework can cooperatively find lost/stolen targets equipped with mobile iBeacon nodes through participatory sensing networks formed by mobile users using smartphones in places with static iBeacon nodes. To precisely localize the missing target, fundamental target localization cases in distinct crowdsourced environments are formally classified and efficiently addressed to reduce the positioning errors with different numbers of smartphones detecting the missing target and different numbers of fixed iBeacon nodes nearby these target-detecting smartphones. According to our review of relevant research, this is the first solution that can provide the crowdsourced guiding path to a missing target with high localization accuracy for all densities of participating smartphones and iBeacon nodes. In particular, an Android-based prototype with static and mobile iBeacon nodes is implemented to verify the feasibility and superiority of our framework. Experimental results show that MCGF outperforms the existing methods and can significantly reduce the localization errors of mobile users and missing targets. [ABSTRACT FROM AUTHOR]

Traffic congestion during peak periods has become a serious issue around the globe, partly due to the high number of single-occupancy commuter trips. Peer-to-peer (P2P) ridesharing platforms can present a suitable alternative for serving commuter trips. However, they face a major obstacle that prevents them from being a viable mode of transportation in practice: ridesharing users often provide tight time windows, which ultimately leads to a low matching rate. This study addresses this issue by introducing a subsidy scheme that allocates incentives to encourage a few carefully selected set of travelers to change their desired departure or arrival times, and thereby form successful matches. In order to implement this scheme for a ridesharing platform in the existence of private information, we propose an auction-based mechanism that guarantees truthfulness, individual rationality, budget-balance, and computational efficiency. Using numerical experiments, we show the merits of the proposed subsidy scheme when compared to its no-deficit variant as well as the conventional VCG mechanism. • Proposed a subsidy scheme for a ridesharing market with incomplete information. • Designed a mechanism with bounded deficit for a two-sided market. • The proposed mechanism is IR, weakly BB, DSIC, and CE. • The proposed mechanism shows good performance compared with VCG. [ABSTRACT FROM AUTHOR]

This paper proposes a novel reactive power-based control strategy for single-phase PV inverters (PVIs) to simultaneously improve voltage unbalance (VU) and voltage regulation (VR) in low-voltage distribution networks. The proposed strategy relies on communication links between neighboring PVIs to exchange limited data. In this strategy, each PVI finds communication paths between itself and the closest neighboring ones connected to other phases. Then, using the obtained paths and the maximum and the minimum voltage magnitude of the grid, PVIs improve both VU and VR at the same time. The performance of the proposed control strategy is evaluated by various simulation studies using the IEEE European low-voltage test feeder and considering different operational conditions. In addition, the impacts of moving clouds and a failure in the communication links have been assessed. The simulation results exhibit that using the proposed control strategy, the voltage magnitude of all the nodes will remain within the allowed limits and at the same time, the phase voltage unbalance factor will be also significantly improved. [ABSTRACT FROM AUTHOR]

Blockchains are currently gaining attention as a newly emerging technology in both academia and industry, capable of impacting a variety of domains beyond cryptocurrencies. Performance modeling can be used to provide us with a deeper understanding of the behavior and dynamics within blockchain peer-to-peer networks. Blockchain system architects can leverage network models to properly tune their system and to reduce design costs significantly. In this article, we focus on the original and well-established Bitcoin blockchain network. In particular, we propose a random graph model for performance modeling and analysis of the inventory-based protocol for block dissemination. This model addresses the impact of key blockchain parameters on the overall performance of Bitcoin. We derive some explicit and closed-form equations for block propagation delay and traffic overhead in the Bitcoin network. We also adapt our model to study the impact of deploying a relay network and investigate the effect of the relay network size on the network performance and decentralization. We implement our model using the popular network simulator OMNet++. We validate the accuracy of our theoretical model and its implementation with our dataset mined from the Bitcoin network. Our results show the tradeoff between the default number of connections per node, network bandwidth, and block size in order to compute the optimal block propagation delay over the network. Additionally, we found that bigger relay networks can jeopardize the decentralization of the Bitcoin network. [ABSTRACT FROM AUTHOR]

In Smart Grid (SG), Transactive Energy Management (TEM) is one of the most promising approaches to boost consumer participation in energy generation, energy management, and establishing decentralized energy market models using Peer-to-Peer (P2P). In P2P, a prosumer produces electric energy at their place using Renewable Energy Resources (RES) such as solar energy, wind energy, etc. Then, this generated energy is traded with consumers (who need the energy) in a nearby locality. P2P facilitates energy exchange in localized micro-energy markets of the TEM system. Such decentralized P2P energy management could cater to diverse prosumers and utility business models. However, the existing P2P approaches suffer from several issues such as single-point-of-failure, network bandwidth, scalability, trust, and security issues. To handle the aforementioned issues, this paper proposes a Decentralized and Transparent P2P Energy Trading (DT-P2PET) scheme using blockchain. The proposed DT-P2PET scheme aims to reduce the grid's energy generation and management burden while also increasing profit for both consumers and prosumers through a dynamic pricing mechanism. The DT-P2PET scheme uses Ethereum-blockchain-based Smart Contracts (SCs) and InterPlanetary File System (IPFS) for the P2P energy trading. Furthermore, a recommender mechanism is also introduced in this study to increase the number of prosumers. The Ethereum SCs are designed and deployed to perform P2P in real time in the proposed DT-P2PET scheme. The DT-P2PET scheme is evaluated based on the various parameters such as profit generation (for prosumer and consumer both), data storage cost, network bandwidth, and data transfer rate in contrast to the existing approaches. [ABSTRACT FROM AUTHOR]

Power systems are facing the challenges of integrating massive electric vehicles (EVs) and lacking operational flexibility. An optimal and efficient coordination strategy is needed to coordinate the operations among EV charging stations (CSs) and the distribution system operator (DSO) and to make full use of the EVs’ charging flexibility for improving the operational flexibility of power systems. To this end, this paper proposes a CSs-DSO distributed coordination strategy. The proposed strategy integrates a supply-curve-based quantification method to quantify the EVs’ flexibility contribution and a Nash bargaining game-theory-based peer-to-peer (P2P) transactions model for maximizing the profits of entities participating in the coordination. To tackle the CSs-DSO coordination problem that has a bi-level-parallel coupling structure, an analytical target cascading (ATC) and alternating direction method of multipliers (ADMM) jointly-based distributed solution approach is proposed. First, the CSs-DSO bi-level model is decoupled by ATC with tie-line power being the only exchanged information; then, the model is further decomposed for each CS by ADMM for protecting the CSs’ privacy and autonomy. The effect of coordination and the computational efficiency of the proposed strategy is verified by numerical simulations on a modified IEEE 33-bus distribution system connecting multiple CSs. [ABSTRACT FROM AUTHOR]

A great challenge for credit-scoring models in online peer-to-peer (P2P) lending platforms is that credit-scoring models simply discard rejected applicants. This selective discard can lead to an inability to increase the number of potentially qualified applicants, ultimately affecting the revenue of the lending platform. One way to deal with this is to employ reject inference, a technique that infers the state of a rejected sample and incorporates the results into a credit-scoring model. The most popular approach to reject inference is to use a credit-scoring model built only on accepted samples to directly predict the status of rejected samples. However, the distribution of accepted samples in online P2P lending is different from the distribution of rejected samples, and the credit-scoring model on the original accepted sample may no longer apply. In addition, the acceptance sample may also include applicants who cannot repay the loan. If these applicants can be filtered out, the losses to the lending platform can also be reduced. Therefore, we propose a global credit-scoring model framework that combines multiple feature selection methods and classifiers to better evaluate the model after adding rejected samples. In addition, this study uses outlier detection methods to explore the internal relationships of all samples, which can delete outlier applicants in accepted samples or increase outlier applicants in rejected samples. Finally, this study uses four data samples and reject inference to construct four different credit-scoring models. The experimental results show that the credit-scoring model combining Pearson and random forest proposed in this study has significantly better accuracy and AUC than other scholars. Compared with previous studies, using outlier detection to remove outliers in loan acceptance samples and identify potentially creditworthy loan applicants from loan rejection samples is a good strategy. Furthermore, this study not only improves the accuracy of the credit-scoring model but also increases the number of lenders, which in turn increases the profitability of the lending platform. [ABSTRACT FROM AUTHOR]

In order to increase the economic benefits of residential photovoltaic (PV), many countries have considered diverse compensation policies for surplus energy, such as fixed feed-in tariff, time-varying feed-in tariff, and net metering, where the surplus energy is offset against consumption. More recently, there has been a growing interest in schemes that allow end-users to trade surplus energy via peer-to-peer (P2P) electricity trading, rather than relying on government incentives. This study conducts a quantitative analysis of on how the introduction of P2P trading affects the benefits of each stakeholder, offering a guide for policy adoption. Firstly, this study devises a P2P trading mechanism based on credit auctions tailored for residential, while considering the application of the increasing block rate tariff often applied to residential customers in the real world, and assesses its benefits in comparison to the current net metering scheme. Multiple case studies were undertaken, encompassing various scenarios, including the level of purchase price of utility for uncleared auction offers, the proportion of prosumers participating in the trading network, and the level of capacity of PV. This study shows that the P2P trading does not always yield positive benefits for prosumers compared to the prior policy. Nevertheless, this study identifies specific conditions under which the P2P trading can stimulate residential PV installations. • Credit P2P trading is modeled under the practical tariff system. • It is analyzed whether the P2P trading is a more efficient compared to the net metering. • Case studies are conducted to identify the conditions where prosumers obtain positive benefits. • The P2P trading does not always beneficial for prosumers over the net metering scheme. [ABSTRACT FROM AUTHOR]

Demand-side energy management programs, such as Demand Response (DR) and Peer-to-Peer (P2P) energy sharing, have recently begun to be implemented to optimize the integration of distributed clean energy resources and to leverage generation and demand-side flexibility in the distribution system. In general, if not optimally planned, P2P energy sharing may have a negative impact on power distribution utilities' primary objectives, such as minimizing energy losses, flattening the load profile, and improving voltage regulation. The DR program is considered as one of the potential mechanisms to alleviate the possible adverse impact of P2P energy sharing while maintaining the interest of peers participating in decentralized energy sharing. The analysis of the impact of P2P energy sharing with varying peers' physical locations involved utilizing the Voltage Sensitivity Factor (VSF). The influence on the distribution network is contingent upon the difference in VSF values among the individual peers engaged in energy sharing. This study introduced the k-Means algorithm for VSF-based peer clustering, thereby generating the merit order and realizing the network-aware P2P energy transactions for the selected scenarios. A multi-objective optimization model-based real-time pricing-based DR scheme has been formulated to address the adverse impact of P2P energy sharing. Furthermore, this study quantifies the impact of DR potentials, which vary from 5% to 45% in increments of 10%, on the Peak-to-Average Ratio (PAR), overall energy loss, and total energy cost reduction. The modified IEEE 33 bus system incorporating industrial, commercial, and residential consumers' load profile of 24 h at selected buses has been considered to test and validate the proposition. • VSF-based clustering of peers using the k-Means model. • Network aware P2P energy sharing impact analysis. • RTP-based DR program implementation. • DR potential impact analysis. [ABSTRACT FROM AUTHOR]

Energy networks around the world have experienced a significant increase in the amount of distributed generation. This decentralization of energy markets has led to a surge of interest in Peer-to-Peer energy trading between individuals. There are many challenges associated with Peer-to-Peer energy trading, e.g. behaviour modelling, decision making and optimization. Multi-agent systems is a prominent subfield of Artificial Intelligence research that is very effective for these types of problems. This research aims to provide a detailed survey of recent advances in the application of multi-agent systems in Peer-to-Peer energy trading. The challenges encountered in implementing these systems are discussed, e.g. agent learning, privacy, and computational power. The primary advantages of multi-agent systems for Peer-to-Peer energy trading reported in the literature are: improved efficiency of renewable utilization, reduced transaction costs, and scalability. Current challenges and future directions of research are also outlined. [ABSTRACT FROM AUTHOR]

This paper considers community inference methods for finding communities on a graph. We treat the setting where the edges are not fully observed. Instead, inference is based on partially observed filtered graph signals where observations from some nodes are missing. Under this setup, we treat two related tasks: $\mathsf{A}$) blind inference which recovers the inherited communities on the sub-graph; $\mathsf{B}$) semi-blind inference which recovers communities on the full graph with additional partial topology information. For task $\mathsf{A}$ , we suggest a spectral method which analyzes the principal components of the data covariance matrix. We prove that it succeeds in finding the ‘true’ communities if the graph filter is low-pass and the nodes are uniformly sampled. For task $\mathsf{B}$ , we propose a method using spectral interpolation with a Nyström extension. The latter approach is proven to succeed in finding the ‘true’ communities for modular graphs and low-pass graph filters. Numerical experiments on synthetic and real data corroborate our results. [ABSTRACT FROM AUTHOR]

For distributed estimation arising in the nonlinear least squares (NLLSs) problems over adaptive networks, where every node has the abilities of data processing and learning, only the incomplete local data are exploited by the traditional noncooperative method, thereby resulting in the degradation on estimation performance. In this article, a cooperative diffusion strategy is proposed by using a Gauss–Newton (GN) method in order to fully utilize the diversity of temporal–spatial data on local updates. The proposed algorithm includes two steps, i.e., aggregate then update (ATU), where the aggregating step collects in real time the global information instead of local information due to the diffusion strategy, and the updating step implements the local GN iteration. The resulting ATU diffusion algorithm is a distributed and cooperative system without any increase on communication cost, as compared with the noncooperative version. Based on the detailed convergence analysis for ATU, which is fundamental to the promotion of this algorithm, the sufficient conditions for convergence are derived and the evidences of faster convergence than the noncooperative version are provided. The simulation results confirm the obtained theoretical derivations by applying the ATU algorithm to an NLLS-based target localization problem and show the cooperation gains in many aspects, such as the convergence rate, steady-state accuracy, and robustness to noisy range, step size, node, and link failures. [ABSTRACT FROM AUTHOR]

Many signal processing and machine learning problems can be formulated as consensus optimization problems which can be solved efficiently via a cooperative multi-agent system. However, the agents in the system can be unreliable due to a variety of reasons: noise, faults and attacks. Providing erroneous updates leads the optimization process in a wrong direction, and degrades the performance of distributed machine learning algorithms. This paper considers the problem of decentralized learning using ADMM in the presence of unreliable agents. First, we rigorously analyze the effect of erroneous updates (in ADMM learning iterations) on the convergence behavior of the multi-agent system. We show that the algorithm linearly converges to a neighborhood of the optimal solution under certain conditions and characterize the neighborhood size analytically. Next, we provide guidelines for network design to achieve a faster convergence to the neighborhood. We also provide conditions on the erroneous updates for exact convergence to the optimal solution. Finally, to mitigate the influence of unreliable agents, we propose ROAD, a robust variant of ADMM, and show its resilience to unreliable agents with an exact convergence to the optimum. [ABSTRACT FROM AUTHOR]

With the rapid development of large-scale enterprise informatization construction, the network scale has become huge and complex, and the data traffic carried by the network is increasing. Accurate network traffic identification is the basis of network management and is of great significance to enterprise informatization construction and operation and maintenance. In response to the network operation and maintenance requirements of large enterprises, this paper analyzes the network architecture and network traffic distribution of large enterprise groups from the perspective of enterprise network operators and introduces the current operation and maintenance process of enterprise network performance failures. Maintenance process optimization and reengineering are carried out to plan and find out the shortcomings of the current process links and put forward corresponding solutions. Based on the research of traffic identification in recent years, a fast P2P network traffic anomaly identification algorithm based on decision tree model is proposed, which improves the efficiency and accuracy of network traffic application identification and network traffic anomaly data identification. [ABSTRACT FROM AUTHOR]