Network Energy Research Articles

Designing a Multivariate Belt Conveyor Idler Stall Detection and Identification System with Scalability Analysis

Belt conveyor idlers are freely rotating idlers supporting the belt of a conveyor, and can induce severe frictional damage to the belt as they fail. Therefore, fast and accurate detection of idler faults is crucial for the effective maintenance of belt conveyor systems. In this article, we implement and evaluate the performance of an idler stall detection system based on a multivariate deep learning model using accelerometers and microphone sensor data. Emphasis is place on the scalability of the system, as large belt conveyor installations can span multiple kilometers, potentially requiring hundreds or even thousands of sensor units to monitor. The accuracy of the proposed system are analyzed and reported, along with its network bandwidth and energy requirements. The results suggest that while implementing accurate large-scale idler stall detection is feasible, careful consideration must be paid to observing the available network bandwidth and energy budget in order to avoid prolonged downtimes.

ANTMAC: Addressing Novel Congestion Technique Hybrid Model for Collision Control in IoT-based Environments using Contention-based MAC Protocol

In the communication model of the OSI layer, the Media access control (MAC) layer has been given higher priority than other layers. It is a sub-layer of the data link layer, mainly controlling the physical equipment and interacting with the channels over the Internet of Things (IoT) sensor nodes. Mac layers have used two protocol types: contention-based and contention-free during transmission. These two protocols have controlled the physical equipment and data transmission for the last decade. Yet in the MAC layers transmission, some challenging issues are complicated to resolve. Data collisions are the significant changing issues at the MAC layer. As per the survey of researchers, the contention-based protocol is more affected by collision due to allowing the sharing of channels to all nodes over networks. As a result, it has got message delay, demanding more energy, data loss, and retransmission. The researcher always focuses on reducing collision during transmission to overcome these issues. They mainly evaluate the priority-based collision control using the contention-based protocol. In this ANTMAC model, we have considered the lower energy nodes’ priority to enhance the likelihood that a node will gain access to the transmission channel before its power and batteries run out. Our recommended method ANTMAC outperforms ECM-MAC in terms of content retrieval time (CRT), total no of retransmission (TNR), total energy consumption (TEcm), throughput and network lifetime (NLT).

Node-Alive Index Driven Redundancy Elimination for Energy-Efficient Wireless Sensor Networks

Wireless Sensor Networks (WSNs) generate correlated and redundant data. This redundancy increases energy consumption during transmission and aggregation, which reduces the network lifespan. Eliminating data redundancy using appropriate data aggregation mechanisms in the dynamic environment is challenging. To address these issues, we designed the Data Aggregation with Redundancy Removal (DARR) protocol and implemented it in two phases. In Phase I, the DARR protocol identifies redundant nodes by calculating the spatial distance between the adjacent nodes. Over time, nodes may run out of energy and stop working after continuously sensing, aggregating, and transmitting the data. The dead nodes can obstruct data forwarding to intermediate nodes, so it is important to check periodically whether the nodes are alive or dead. The periodic time check identifies the status of each node, allowing the protocol to focus only on active nodes. It sets redundant nodes to sleep, which conserves network energy. In Phase II, the protocol reduces data redundancy at the source nodes using temporal correlation between data measurements. We enhanced the DARR protocol by incorporating a High Compression Temporal (HCT) mechanism, which further reduces data redundancy. Simulations show that the DARR protocol reduces data transmissions by 24% and lowers network energy consumption by up to 31% by eliminating redundant data at both the network and node levels.

A Green Multi-Attribute Client Selection for Over-The-Air Federated Learning: A Grey-Wolf-Optimizer Approach

Federated Learning (FL) has gained attention across various industries for its capability to train machine learning models without centralizing sensitive data. While this approach offers significant benefits such as privacy preservation and decreased communication overhead, it presents several challenges, including deployment complexity and interoperability issues, particularly in heterogeneous scenarios or resource-constrained environments. Over-the-air (OTA) FL was introduced to tackle these challenges by disseminating model updates without necessitating direct device-to-device connections or centralized servers. However, OTA-FL brought forth limitations associated with heightened energy consumption and network latency. In this paper, we propose a multi-attribute client selection framework employing the grey wolf optimizer (GWO) to strategically control the number of participants in each round and optimize the OTA-FL process while considering accuracy, energy, delay, reliability, and fairness constraints of participating devices. We evaluate the performance of our multi-attribute client selection approach in terms of model loss minimization, convergence time reduction, and energy efficiency. In our experimental evaluation, we assessed and compared the performance of our approach against the existing state-of-the-art methods. Our results demonstrate that the proposed GWO-based client selection outperforms these baselines across various metrics. Specifically, our approach achieves a notable reduction in model loss, accelerates convergence time, and enhances energy efficiency while maintaining high fairness and reliability indicators.

Криза прав власності у розподільчому сегменті ринку природного газу України

During the time of independent Ukraine's existence, the authorized state bodies have not developed effective mechanisms for the use of state-owned gas distribution networks by private operators. Despite the state being the de facto owner of these networks, it was unable to receive appropriate compensation for their operation, and no incentives were created for the modernization of the networks. Meanwhile, private operators were motivated to consistently report a decrease in the value of such assets. Another complex issue, in terms of the efficiency and fairness of managing the gas distribution system, is the ownership of networks built with consumer funds and transferred to gas distribution network operators for operation. The lack of clear understanding regarding the ownership of gas transportation networks results in significant wear and tear of the networks and substantial production and technical losses. Gas transmission operators are not interested in making significant investments in the renewal and development of networks, as they do not own them, and there are no other obligations that could incentivize such activities. The state does not receive any income from the use of state property by economic entities. In the context of the Russian-Ukrainian war, the issue of property rights and further investments in the development of gas distribution networks has once again come to the forefront. The need to adapt the energy system to the new challenges of the war necessitates the formation of a decentralized model of the national energy system. In the gas sector, this means that at the local level, local self-government bodies will need to take the initiative in implementing small energy projects to meet the energy supply demands of the local population, such as the construction of various types of gas cogeneration plants. The construction of such projects will require the adaptation and development of the existing gas distribution networks to meet new requirements. The formation of a new decentralized model of the national energy network is impossible without resolving the old problems related to the ownership rights of gas distribution networks and establishing a market-based tariff setting for the operation of these networks. The article analyzes the theoretical prerequisites for the emergence of the property rights crisis in the distribution segment of the natural gas market based on the theory of property rights. Through retrospective analysis, the main flaws in the state energy policy in the management of state-owned gas distribution networks are identified, which led to the current property rights crisis and increased transaction costs among entities in Ukraine’s natural gas market. Based on the obtained results and taking into account foreign experience in the privatization and regulation of the economic activities of gas distribution operators, the author proposes various ways to address the problems of managing property rights for the distribution gas networks.

Cooperative Active Sites on Ag2Pt3TiS6 for Enhanced Low‐Temperature Ammonia Fuel Cell Electrocatalysis

AbstractAmmonia has attracted considerable interest as a hydrogen carrier that can help decarbonize global energy networks. Key to realizing this is the development of low temperature ammonia fuel cells for the on‐demand generation of electricity. However, the efficiency of such systems is significantly impaired by the sluggish ammonia oxidation reaction (AOR) and oxygen reduction reaction (ORR). Here, we report the design of a bifunctional Ag2Pt3TiS6 electrocatalyst that facilitates both reactions at mass activities exceeding that of commercial Pt/C. Through comprehensive density functional theory calculations, we identify that active site motifs composed of Pt and Ti atoms work cooperatively to catalyze ORR and AOR. Notably, in situ shell‐isolated nanoparticle‐enhanced Raman spectroscopy (SHINERS) experiments indicate a decreased propensity for *NOx formation and hence an increased resistance toward catalyst poisoning for AOR. Employing Ag2Pt3TiS6 as both the cathode and anode, we constructed a low temperature ammonia fuel cell with a high peak power density of 8.71 mW cm−2 and low Pt loading of 0.45 mg cm−2. Our findings demonstrate a pathway towards the rational design of effective electrocatalysts with multi‐element active sites that work cooperatively.

Energy management in smart distribution networks: Synergizing network reconfiguration, energy storage, and electric vehicles with disjunctive convex hull relaxation

Efficient energy management is critical for modern distribution networks integrating renewable energy, storage systems, and electric vehicles. This paper introduces a novel approach combining network reconfiguration with disjunctive convex hull relaxation to optimize power flow, minimize energy losses, and enhance grid stability. Case studies on 33-node and 84-node networks showcase the model’s effectiveness, reducing energy losses to 0.939 MWh and 7.214 MWh, respectively, outperforming Big-M and McCormick methods. Additionally, the approach achieved a 78 % efficiency improvement, enhanced emissions control, and improved voltage regulation, demonstrating superior renewable energy integration. These results highlight the model's potential to provide a more efficient and sustainable energy management solution, addressing the challenges of modern power grids.

Optimal operation of hydrogen-based multi-energy microgrid integrating water network and transportation sector

To address the rising demand for hydrogen energy and its reliance with the water sector, this study presents an optimal scheduling framework for a multi-energy microgrid (MEMG) that integrates electric, thermal, water, and hydrogen energy networks. To this end, a mixed-integer linear programming (MILP) model is formulated to minimize both operational costs and emissions. A bi-variate piecewise McCormick envelope technique is utilized to manage the non-linear constraints associated with the water network. The model also incorporates the transportation sector, including electric and hydrogen vehicles (EVs, HVs), with vehicle-to-grid (V2G) technology, and models their associated uncertainties using Monte Carlo simulation (MCS). Additionally, the sale of oxygen as a by-product of the hydrogenation process is also considered. The case study shows significant economic and environmental benefits, with a 29.66% cost reduction and 22.26% emissions decrease from water network integration. Oxygen sales further reduce costs by 14.19%, and V2G technology contributes an additional 2.35% cost and 6.01% emissions reduction. The proposed linear approximation method achieved superior performance, with a root mean square error (RMSE) of 0.72 and a relative error of 2.132%.

Distributed decision making for unmanned aerial vehicle inspection with limited energy constraint

The unsatisfactory energy density of the state-of-art batteries imposes constraints on the practical application of unmanned aerial vehicles (UAVs). Establishing a UAV airport network that integrates energy supply and information exchange functionalities represents an ideal solution for enabling synergistic UAV operations. However, devising efficient distribution protocols for these airports remains a challenge. By leveraging modeling and analysis of the energy density of existing UAV batteries, we can forecast the flight range and distances achievable by UAVs. Here, we propose a distribution protocol for UAV airport platforms aimed at enhancing distribution accuracy by the use of AI principles. Furthermore, considering the possibility of emergency UAV stop, we introduce an emergency stop system in conjunction with standard stopping procedures to optimize distribution efficiency and enhance UAV inspection safety. Moreover, existing UAV airports usually provide energy to UAVs without harnessing UAVs to facilitate interconnection and interoperability among different airports. This inefficiency leads to significant resource wastage in energy distribution. To address this, we introduce a shared energy network that allows different companies to operate according to energy distribution needs. This network not only supplies energy to UAVs but also employs UAVs for energy collection and transportation, facilitating energy trading, business collaboration, and data transmission among diverse organizations. By enabling ubiquitous energy trading, this study provides us an ideal strategy for the future construction of energy network with interconnection and interoperability, which can be extended to other applications calling for energy distribution.

A novel energy efficient data gathering algorithm for wireless sensor networks using artificial intelligence

Energy efficiency is challenging task in wireless sensor network (WSN), it is the main barrier in extending network lifespan. In WSN, maximum energy is wasted during data gathering, hence energy efficient algorithms using artificial intelligence can be designed, that preserves energy while data gathering. Thus, our proposed methodology, A novel energy efficient data gathering algorithm using artificial intelligence for wireless sensor networks (NDGAI), uses novel artificial intelligence algorithms and addresses issue of energy consumption while gathering data. In our proposed work, mobile element is utilized to gather information from sensor nodes in the clusters, formed using amended-expectation-maximization. Each cluster should have a cluster leader and a virtual-point. These cluster leaders are formed utilizing fuzzy logic technique. Virtual-points are formed in the range of cluster leader, only when cluster leader has data. The mobile element reaches virtual point by taking the optimal path, that determined by the hybrid artificial intelligence algorithms, such as artificial-bee-colony (ABC) technique and particle swarm optimization (PSO) algorithms. Thus, by properly performing clustering, cluster leader selection, virtual-point selection and optimal path determination, lead to improved network lifetime and energy saving while gathering the data. Results are simulated and compared with scalable gridbased data gathering algorithm for environmental monitoring wireless sensor networks (SGBDN) and proposed algorithm performs better.

Deep Reinforcement Learning and Enhanced Optimization for Real-Time Energy Management in Wireless Sensor Networks

Constraints are a major issue in radio-based communication in Wireless Sensor Networks, where each sensor node has a limited amount of power. Conventional clustering and optimization methods have been inappropriate for dynamic conditions which lead to timely energy drainage and reduce the network lifetime. In this research, the novel Deep Reinforcement Learning-Enhanced Hybrid African Vulture and Aquila Optimizer has been proposed that optimizes the dynamic clustering and energy-based parameters in real time. The proposed model is designed for optimizing the Wireless Sensor Networks, by including Deep Reinforcement Learning to adjust the dynamic formation of the base of the cluster on real-time data which leads to efficient energy utilization among all the sensor nodes. It combines the best properties of the Aquila and African Vulture Optimizer to optimize the network lifetime and energy consumption. The network lifetime, which is one of the most crucial characteristics, is optimized by using the global search algorithm of African Vulture Optimiser. In contrast, it is optimized by the localized search of Aquila optimizer to reduce energy consumption. The presented novel African Vulture and Aquila model outperforms the existing methods used convention-based optimization methods. It shows a 20% improvement in energy efficiency and faster convergence with better robustness while keeping the network scalability. The proposed approach is perfectly suited for the scalable WSNs which are mainly used in the environment such as smart cities and IoT systems where a timely adaptation process is inevitable.

Coordinated Optimization Method for Distributed Energy Storage and Dynamic Reconfiguration to Enhance the Economy and Reliability of Distribution Network

To fully leverage the application potential of distributed energy storage systems (DESS) and network reconfiguration, a coordinated optimization method is proposed to enhance the economic efficiency of distribution networks under normal conditions and the reliability of a power supply during fault conditions. First, a scenario-generation method is developed based on Latin hypercube sampling and Kantorovich distance synchronous back-substitution reduction is used to obtain the typical scenario of wind and solar output. Next, a planning operation coordinated optimization framework and model are established, considering both normal and fault states of the distribution network. In the planning layer, the objective is to minimize the annual comprehensive capital expenditures for the distribution network to improve the economic efficiency of the distribution network. The operation layer includes both normal operation and fault operation states, with the optimization goal of minimizing the sum of normal operation costs and the fault costs associated with load shedding. Subsequently, a hybrid optimization algorithm combining an improved Aquila Optimizer-Second-Order Cone Programming (IAO-SOCP) is proposed to solve the coordinated optimization model. Finally, the proposed coordinated optimization method is validated using an enhanced IEEE 33-bus distribution network case study. The results demonstrate that the method effectively reduces network losses and minimizes load shedding costs during fault conditions, thereby ensuring a balance between the economic efficiency and reliability of the distribution network.

Cooperative resource sharing and cost minimization in energy hub systems using an improved grasshopper optimization algorithm approach

This study presents a cooperative paradigm for energy hub systems (EHSs) where a network of interconnected hubs cooperates in exploiting the resources with the purpose of economic saving. In such an architecture, each hub provided with various sources of energy, such as combined heat and power (CHP), hot water tanks, renewable sources, electric chillers, and absorption chillers, will integrate all these sources for more adaptability and efficiency to the system. Moreover, the integration of energy storage systems (ESSs) is considered to enhance the flexibility of the energy hub concerning power, heating, and cooling. Recognizing the complexity associated with incorporating multiple constraints, the improved grasshopper optimization algorithm (IGOA) is introduced to effectively address this challenge. By leveraging this algorithm, the study aims to overcome the intricacies involved in considering various constraints and achieve an optimal outcome. The IGOA improves the efficiency and effectiveness of local and national searches in solving complex energy hub optimization problems. Reducing the likelihood of getting stuck in suboptimal solutions, enhances the algorithm's ability to find optimal solutions considering multiple constraints, thereby enhancing the overall performance and cost-effectiveness of EHSs. The issue is defined as a planning challenge, and by collaborative efforts, the expenses associated with the network energy hubs are reduced, illustrating the efficacy of this concept. The findings indicate the influence of the suggested cooperative technique, with operating cost reductions of 19.09 %, 13.27 %, and 8.75 % for Hub 1, Hub 2, and Hub 3, respectively. Furthermore, the cooperative framework eradicates energy deficits and disruptions, in contrast to 1,198.21 kWh of unfulfilled demand and 22 interruptions in the non-cooperative scenario. These results underscore the significant advantages of the collaborative technique in improving cost-efficiency, reliability, and resource utilization.

Mitigating blackhole attacks in wireless body area network

<p>In this paper, we aimed to develop a trusted secured routing Ad-hoc on-demand distance vector (AODV) protocol to fight against blackhole attacks within the wireless body area network (WBAN). The trusted secure routing protocol incorporates a routing strategy based on trust value to detect malicious nodes based on their trust value, a routing technique based on node residual energy to select the node with the highest residual energy during the communication process, and a hybrid cryptography algorithm that merges the Affine cipher with the modified RSA cipher algorithm to secure communication against malevolent biomedical sensor attacks. Simulation outcomes demonstrate that the suggested protocol outperforms the traditional AODV routing protocol in all evaluation metrics, including data rate, energy consumption, and packet delivery ratio. Its main strength is that it considers several factors, like illegitimate medical sensor detection, efficient network energy use, and secure data transmission, unlike similar secured routing protocols. Furthermore, the hybrid cipher algorithm improves the effectiveness and increases the security level of sensitive data compared to traditional cipher algorithms such as the Affine cipher and the RSA cipher.</p>

Temporal asynchrony analysis for dynamic operation of hydraulic-thermal-electricity multiple energy networks based on holomorphic embedding method

Analyzing the operational states of multiple energy networks (MEN) in multi-energy systems is crucial for ensuring system stability. The dynamic operational characteristics of different energy flows pose challenges for computational analysis. Traditional steady-state methods are inadequate for addressing the dynamics of MEN, especially when dealing with temporal discrepancies between hydraulic and thermal flows in thermal networks (TN) and the heterogeneity between TN and electrical networks. Therefore, this paper proposes a novel holomorphic embedding method (HEM) based on multi-stage decomposition method. The developed HEM constructs a time coefficient matrix and utilize inner-outer loop recursion to handle the time lag between thermal flow and hydraulic flow in the TN. Additionally, we reconstruct a holomorphic matrix, integrating hydraulic flow to bridge thermal and electric power flows, thereby improving the operational heterogeneity among different networks. Real-case simulations show that when the Taylor expansion order in HEM is equal to 4, the proposed method achieves a mere 1% discrepancy from actual operational data, enhancing computational efficiency by 60% compared to the Newton-Raphson method. Moreover, in this real-case, the TN exhibits a maximum delay response time of 180s compared to electrical networks. Exploiting this delay time effectively increases renewable energy generation within multi-energy systems by 961.58kW per day.

Blockchain-Enabled Transformation: Decentralized Planning and Secure Peer-to-Peer Trading in Local Energy Networks

This paper introduces a novel blockchain-based automatic load response architecture for local energy networks, focusing on secure peer-to-peer (P2P) energy trading and decentralized planning. Departing from traditional centralized methods, the proposed system leverages non-cooperative game theory for pricing-based decentralized planning, enabling efficient resource distribution without a central authority. A key contribution is the integration of a machine-governed smart contract mechanism, which ensures secure, transparent, and consistent transactions in P2P energy trading. Additionally, an adaptive evaluation system for transaction nodes enhances the system’s responsiveness to dynamic energy demands. A distributed algorithm is developed to optimize the implementation of this architecture, ensuring practical efficiency. Case studies confirm significant improvements in operational efficiency, security, and economic outcomes, marking a substantial advancement in decentralized energy management. Key findings demonstrate that the proposed automatic load response strategy significantly enhances load curve stability, achieving a 99.16% reduction in net load fluctuations and an 8.24% reduction in operational costs compared to traditional methods. Additionally, the framework improves the self-consumption rate of renewable energy by up to 14.62% and reduces the average cost for electric vehicle (EV) users by 26.12%. These results highlight the framework's effectiveness in fostering a more balanced supply-demand relationship within local energy networks while ensuring economic and computational efficiency. The study underscores the potential to revolutionize decentralized energy management, offering a sustainable and cost-effective solution for future energy systems.

Complex network analysis of transmission networks preparing for the energy transition: application to the current French power grid

The worldwide process of replacing fossil fuels with low-carbon energy sources is underway. Existing energy networks are expected to be deeply modified in nature and structure during this transition. This work uses graph-theoretical statistical physics tools to analyze topology and structural changes of power grids, with the French grid as a case study. We discuss the small-world model to define an optimality criterion, the construction of a graph model for the French high-voltage transmission grid, and the development of a growth model to study the dynamics of such networks. The main result of our project suggests that the high efficiency level in the current French network is due to a high-voltage mesh interconnecting thermal power plants. Since implementing low-power-density renewable energy sources would imply non-trivial adjustments to maintain features, such as efficiency and robustness, these considerations must be added to economic and energetic assessments of transition scenarios.Graphic

Altered brain network stability in OCD following rTMS intervention: Insights from structural balance theory

Repetitive Transcranial Magnetic Stimulation (rTMS) is a promising intervention for Obsessive-Compulsive Disorder (OCD). However, understanding brain network changes following rTMS remains limited, despite its potential to enhance treatment efficacy. In this retrospective study, we investigated brain network reorganization in OCD patients after rTMS, using structural balance theory as a framework. We hypothesized that rTMS-induced functional plasticity would alter brain network topology, particularly affecting triadic associations, and leading to increased balance energy levels, indicative of a less stable network state. Brain functional networks were constructed from resting-state EEGs of OCD patients, with phase lag indexes calculated both before and after rTMS treatment. These networks were analyzed by comparing global parameters, including positive and negative links, triadic interactions (balanced/unbalanced), hub formation tendencies, and balance energy levels. We observed a significant decrease in weak-balanced triads and an increase in strong-unbalanced triads within the Beta І frequency band (12–15 Hz). Additionally, there was a notable reduction in the tendency of negative links to form hubs across certain frequency bands. These changes led to an increase in the network's balanced energy level, pushing it toward a less stable state. We hope these findings will refine rTMS strategies by facilitating brain network reorganization.

Research on High-Efficiency Routing Protocols for HWSNs Based on Deep Reinforcement Learning

In heterogeneous wireless sensor networks (HWSNs), optimizing energy efficiency presents significant challenges due to variations in node energy levels and the complexity of the network environment. This paper introduces an energy efficiency optimization algorithm for HWSNs based on the Deep Q-Network (HDQN). The algorithm aims to address these challenges by selecting the optimal information transmission path. The HDQN leverages energy differences between nodes and real-time environmental data to enhance network efficiency. Its reward function takes into account node distance, remaining energy, and relay node count to balance node participation and minimize overall energy consumption. The Deep Q-Network (DQN) uses the mean squared error for precise reward estimation, and an improved packet header structure supports effective routing decisions. Simulation results show that the HDQN significantly outperforms existing algorithms—EEHCHR, 2L-HMGEAR, NCOGA, DEEC, and SEP—in terms of energy efficiency, network lifetime, and robustness, demonstrating its potential to advance the performance of HWSNs. The research results of the paper provide a theoretical basis for future energy efficiency research in wireless communication and contribute to the study of the new generation of wireless networks.

Performance Analysis of Reconfigurable Intelligent Surface (RIS)-Assisted Satellite Communications: Passive Beamforming and Outage Probability

Reconfigurable intelligent surfaces (RISs), which consist of numerous passive reflecting elements, have emerged as a prominent technology to enhance energy and spectral efficiency for future wireless networks. RISs have the capability to intelligently reconfigure the incident wave, reflecting it towards the intended target without requiring energy for signal processing. Consequently, they have become a promising solution to support the demand for high-throughput satellite communication (SatCom) and enhanced coverage for areas inaccessible to terrestrial networks. This paper presents an asymptotic analysis of an RIS-assisted SatCom system. In this system, an unmanned aerial vehicle equipped with an RIS operates as a mobile reflector between a satellite and users. In particular, a passive beamformer is designed with the aim of asymptotically attaining optimal performance, considering the limitations imposed by practical SatCom systems. Moreover, the closed-form expressions for the ergodic achievable rate and outage probability are derived considering the proposed passive beamforming technique. Furthermore, we extend the system model to a multicast system and asymptotically analyze the optimality of the proposed scheme, leveraging the derived asymptotic results in the unicast system. The results of the simulations confirm that our analyses can precisely and analytically assess the performance of the RIS-assisted SatCom system, confirming the asymptotic optimality of the proposed scheme.