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To address the accuracy degradation as well as prolonged convergence time due to the inherent data heterogeneity among end-devices in federated learning (FL), we introduce the joint batch size and weighted aggregation adjustment problem, which is non-convex problem. To adjust optimal hyperparameters, we develop deep reinforcement learning (DRL) to empower a mechanism known as Batch size and Weighted aggregation Adjustment (BWA). Experimental evaluation demonstrates that BWA not only outperforms methods optimized solely from either a local training or server perspective but also achieves higher accuracy, with an increase of up to 5.53% compared to FedAvg, and additionally accelerates convergence speeds.

This paper designs a novel Hierarchical Federated Learning (HFL) management scheme, enabled by deep reinforcement learning (DRL), for multi-access edge computing (MEC) environments to accelerate convergence. To do this, the proposed scheme controls the number of local updates performed by mobile devices (MDs) and the number of intermediate aggregations at the MEC server before data is transmitted to the cloud for global aggregation. This optimization aims to i) mitigate the straggler effect by balancing training times between MEC and cloud servers, and ii) reduce the risk of overfitting by avoiding excessive reliance on faster MDs. Additionally, to improve the efficiency of DRL, Bayesian optimization is employed to initialize action values, thereby avoiding inefficient exploration of actions. Extensive simulations demonstrate that our proposed scheme outperforms various benchmarks in terms of test accuracy and training time.

Edge computing has emerged as a crucial technology for addressing the increasing demand for low-latency and high-speed services in the era of 5G and beyond. However, efficient resource allocation and pricing in edge computing environments remain significant challenges. This paper adopts an economic approach to optimizing the operation and placement of edge computing systems from a macroscopic perspective, addressing a critical gap in the current literature. While previous studies have primarily focused on individual user pricing or static models, this research presents a comprehensive, dynamic pricing strategy that considers the entire market ecosystem. The study considers user demand, operating costs, and resource availability to investigate a dynamic pricing policy that enhances the efficiency and profitability of edge computing operations. The existence of equilibrium price and quantity under dynamic conditions is analyzed, and a novel pricing strategy that adapts to real-time changes in server load and market demand is proposed. The approach integrates both operational expenditures (OPEX) and capital expenditures (CAPEX) to determine optimal resource allocation, a crucial aspect often overlooked in existing research. Employing a linear demand and supply model, a case study is conducted to derive the closed-form solution for the optimal operating quantity, corresponding price, and the optimal amount of edge computing resource installation. The proposed method is then applied to a dynamic market simulation model, demonstrating its economic effectiveness. Results show significant improvements in resource utilization and profitability compared to static pricing models. This research contributes to the field by providing a robust framework for dynamic pricing in edge computing, offering valuable insights for both academic researchers and industry practitioners.

This paper designs a novel energy-efficient hybrid federated and centralized learning (HFCL) framework for training wireless traffic prediction models in aerial networks over distributed multi-access edge computing (MEC) servers where multiple network data analytics functions (NWDAFs) are embedded in the unmanned aerial vehicle (UAV)-aided MEC servers according to the framework standardized by the 3rd Generation Partnership Project (3GPP) specification. Here, UAVs can use federated learning (FL) with their part of local datasets, while the remaining local datasets can be offloaded to the centralized server for centralized learning (CL). To achieve such HFCL energy-efficiently for battery-constrained UAVs, we propose an energy-efficient computation offloading (ECO) scheme for the HFCL. We rigorously formulate analytical models for the overall energy consumption at MEC servers on UAVs and total latency for HFCL process based on the amount of offloaded datasets at each UAV. Here the energy consumption includes i) the transmission energy consumption for offloading the local dataset and ii) the energy consumption for FL processing, which includes local training and local model uploading. To balance between the overall energy consumption and total latency during HFCL process while preventing overload at UAVs, we propose a theoretical framework for the ECO problem that optimizes the amount of offloaded local datasets over multiple UAVs on aerial networks. Here, the ECO problem is formulated as the convex optimization problem under the continuous domain of the amount of offloaded datasets. Our numerical and simulation results show that our proposed framework can construct wireless traffic prediction models with an acceptable training accuracy in an energy-efficient manner over various benchmarks by striking a balance between energy consumption and overall training latency.

To alleviate the substantial local training burden on clients in the federated learning (FL) process, this paper proposes a more efficient approach based on hybrid federated and centralized learning (HFCL), leveraging the Mobile Edge Computing (MEC) environment within wireless communication networks. Considering the existence of heterogeneous data types with different privacy levels -such as 1) sensitive data, which can not be exposed, and 2) less-sensitive data, which can be exposed for centralized learning (CL)-we formulate an optimization problem aimed at achieving a balance between 1) total latency, including computation and communication, and 2) the training burden on the MEC server. This balance is achieved by adjusting the set of participants in FL, taking into account client selection under different privacy levels. A multi-objective optimization problem is designed using mixed-integer nonlinear programming, which is generally recognized as NP-hard. We employ relaxation techniques in combination with the Mutas & Simulated Annealing Heuristic algorithm to develop a near-optimal yet practical algorithm. Our numerical and simulation results reveal that the proposed scheme effectively achieves a global model by striking a balance between the total time required for model convergence and the computational load on the MEC server. Furthermore, experimental results on three well-known real-world datasets demonstrate that the proposed scheme maintains an acceptable level of accuracy and loss.

To support dramatically increasing services from internet of thing (IoT) devices with the sporadic and fluctuated generation of short packet traffic, this paper investigates joint dynamic time division duplexing (TDD) and radio resource control (RRC) connection management in a single-cell massive IoT network. Specifically, under the grant-free transmission incurring packet collision, this study models the factors affecting the time resource utilization (TRU) and energy consumption of IoT devices as a comprehensive system utility and further formulates the problem as a decision-making process aiming for balancing the long-term average TRU and energy consumption. To address the formulated problem, based on the deep reinforcement learning framework, this paper designs an experience-driven joint dynamic TDD and RRC connection management scheme that intelligently i) determines the TDD configuration based on the most recent downlink (DL)/uplink (UL) traffic demands and ii) adjusts the RRC state of each IoT device to control the maximum number of transmitting IoT devices. Finally, trace-driven simulation results demonstrate that the proposed scheme outperforms existing benchmarks, such as Static TDD and Dynamic TDD, in terms of transmission success ratio difference (TSRD) (up to 89% reduction), time resource utilization (TRU) (up to $17\times $ increase), and energy consumption (up to 70% reduction) of IoT devices.

This paper proposes a novel decentralized federated reinforcement learning (DFRL) framework that integrates deep reinforcement learning (DRL) with decentralized federated learning (DFL). The DFRL framework boosts efficient virtual instance scaling in Mobile Edge Computing (MEC) environments for 5G core network automation. It enables multiple MECs to collaboratively optimize resource allocation without centralized data sharing. In this framework, DRL agents in each MEC make local scaling decisions and exchange model parameters with other MECs, rather than sharing raw data. To enhance robustness against malicious server attacks, we employ a committee mechanism that monitors the DFL process and ensures reliable aggregation of local gradients. Extensive simulations were conducted to evaluate the proposed framework, demonstrating its ability to maintain cost-effective resource usage while significantly reducing blocking rates across diverse traffic conditions. Furthermore, the framework demonstrated strong resilience against adversarial MEC nodes, ensuring reliable operation and efficient resource management. These results validate the framework’s effectiveness in adaptive and efficient resource management, particularly in dynamic and varied network scenarios.

This paper proposes a novel offloading and super-resolution (SR) control scheme for energy-efficient mobile augmented reality (MAR) in multi-access edge computing (MEC) using SR as a promising generative artificial intelligence (GAI) technology. Specifically, SR can enhance low-resolution images into high-resolution versions using GAI technologies. This capability is particularly advantageous in MAR by lowering the bitrate required for network transmission. However, this SR process requires considerable computational resources and can introduce latency, potentially overloading the MEC server if there are numerous offload requests for MAR services. In this context, we conduct an empirical study to verify that the computational latency of SR increases with the upscaling level. Therefore, we demonstrate a trade-off between computational latency and improved service satisfaction when upscaling images for object detection, as it enhances the detection accuracy. From this perspective, determining whether to apply SR for MAR, while jointly controlling offloading decisions, is challenging. Consequently, to design energy-efficient MAR, we rigorously formulate analytical models for the energy consumption of a MAR device, the overall latency and the MAR satisfaction of service quality from the enforcement of the service accuracy, taking into account the SR process at the MEC server. Finally, we develop a theoretical framework that optimizes the computation offloading and SR control problem for MAR clients by jointly optimizing the offloading and SR decisions, considering their trade-off in MAR with MEC. Finally, the performance evaluation indicates that our proposed framework effectively supports MAR services by efficiently managing offloading and SR decisions, balancing trade-offs between energy consumption, latency, and service satisfaction compared to benchmarks.

Federated Learning (FL) is a decentralized machine learning method in which individual devices compute local models based on their data. In FL, devices periodically share newly trained updates with the central server, rather than submitting their raw data. The key characteristics of FL, including on-device training and aggregation, make it interesting for many communication domains. Moreover, the potential of new systems facilitating FL in sixth generation (6G) enabled Passive Optical Networks (PON), presents a promising opportunity for integration within this domain. This article focuses on the interaction between FL and PON, exploring approaches for effective bandwidth management, particularly in addressing the complexity introduced by FL traffic. In the PON standard, advanced bandwidth management is proposed by allocating multiple upstream grants utilizing the Dynamic Bandwidth Allocation (DBA) algorithm to be allocated for an Optical Network Unit (ONU). However, there is a lack of research on studying the utilization of multiple grant allocation. In this paper, we address this limitation by introducing a novel DBA approach that efficiently allocates PON bandwidth for FL traffic generation and demonstrates how multiple grants can benefit from the enhanced capacity of implementing PON in carrying out FL flows. Simulations conducted in this study show that the proposed solution outperforms state-of-the-art solutions in several network performance metrics, particularly in reducing upstream delay. This improvement holds great promise for enabling real-time data-intensive services that will be key components of 6G environments. Furthermore, our discussion outlines the potential for the integration of FL and PON as an operational reality capable of supporting 6G networking.

Abstract Thermal pastes, thermally conductive fillers dispersed in liquid matrices, are widely used as thermal interface materials (TIMs). TIMs transfer heat generated from electronics to the surroundings, ensuring optimal operating temperatures. Thus, it is crucial to obtain high thermal conductivity (TC) by forming a continuous heat‐conduction pathway through interconnected filler‐networks within the TIM. Therefore, for paste‐type TIMs with spherical fillers, high TC can only be realized at sufficiently high filler loadings (>60 vol%). However, the pastes bearing such high filler loadings are thick, stiff, and less applicable. To these ends, particle‐stabilized emulsions composed of immiscible liquids (silicone oil and glycerol) and spherical alumina are utilized as thermal pastes. Owing to this structure, the resulting form‐factor‐free thermal paste exhibits higher TC and stability than a simple mixture consisting of alumina and a single‐liquid‐matrix (either silicone oil or glycerol). Furthermore, the high applicability of the emulsion‐type pastes enables syringe extrusion, 3D printing, multiple cycles of reprocessing/molding, and eco‐friendly recycling.

Abstract The realization of hybrid optics could be one of the best ways to fulfill the technological requirements of compact, light-weight, and multi-functional optical systems for modern industries. Planar diffractive lens (PDL) such as diffractive lenses, photonsieves, and metasurfaces can be patterned on ultra-thin flexible and stretchable substrates and be conformally attached on top of arbitrarily shaped surfaces. In this review, we introduce recent research works addressed to the design and manufacturing of ultra-thin graphene optics, which will open new markets in compact and light-weight optics for next-generation endoscopic brain imaging, space internet, real-time surface profilometry, and multi-functional mobile phones. To provide higher design flexibility, lower process complexity, and chemical-free process with reasonable investment cost, direct laser writing (DLW) of laser-induced-graphene (LIG) is actively being applied to the patterning of PDL. For realizing the best optical performances in DLW, photon-material interactions have been studied in detail with respect to different laser parameters; the resulting optical characteristics have been evaluated in terms of amplitude and phase. A series of exemplary laser-written 1D and 2D PDL structures have been actively demonstrated with different base materials, and then, the cases are being expanded to plasmonic and holographic structures. The combination of these ultra-thin and light-weight PDL with conventional bulk refractive or reflective optical elements could bring together the advantages of each optical element. By integrating these suggestions, we suggest a way to realize the hybrid PDL to be used in the future micro-electronics surface inspection, biomedical, outer space, and extended reality (XR) industries.

Pyroptosis represents a type of cell death mechanism notable for its cell membrane disruption and the subsequent release of proinflammatory cytokines. The Nod-like receptor family pyrin domain containing inflammasome 3 (NLRP3) plays a critical role in the pyroptosis mechanism associated with various diseases resulting from particulate matter (PM) exposure. Tert-butylhydroquinone (tBHQ) is a synthetic antioxidant commonly used in a variety of foods and products. The aim of this study is to examine the potential of tBHQ as a therapeutic agent for managing sinonasal diseases induced by PM exposure. The occurrence of NLRP3 inflammasome-dependent pyroptosis in RPMI 2650 cells treated with PM < 4 µm in size was confirmed using Western blot analysis and enzyme-linked immunosorbent assay results for the pyroptosis metabolites IL-1β and IL-18. In addition, the inhibitory effect of tBHQ on PM-induced pyroptosis was confirmed using Western blot and immunofluorescence techniques. The inhibition of tBHQ-mediated pyroptosis was abolished upon nuclear factor erythroid 2-related factor 2 (Nrf2) knockdown, indicating its involvement in the antioxidant mechanism. tBHQ showed potential as a therapeutic agent for sinonasal diseases induced by PM because NLRP3 inflammasome activation was effectively suppressed via the Nrf2 pathway.

Various website fingerprinting attacks (WF) have been developed to detect anonymous users accessing illegal websites in Tor networks by analyzing Tor traffic. These attacks consider several traffic features, such as packet length, number of packets, and time, to identify users who attempt to access prohibited content. Due to the advance of artificial intelligence (AI) technologies, machine learning or deep learning techniques have been widely adopted for WF to generate an accurate model to break the privacy of illegal users. Nevertheless, such state-of-the-art approaches to WF assumed that entire data from various Tor nodes are collected and trained in a centralized way to generate the model: However, training data sets from Tor nodes may contain sensitive information that the Tor nodes may not want to share. In addition, significant computing and network bottleneck at the centralized server is inevitable in collecting and training various data in a centralized manner. Correspondingly, this paper proposes a novel framework using federated learning (FL) for WF in the Tor network (denoted as FedFingerprinting), enabling Tor nodes to generate the global model collaboratively without exposing their local data sets. Specifically, to alleviate the burden for local training of selected Tor nodes in the FL process, the importance of various handcrafting features used for WF is firstly evaluated through the analysis of the accuracy of features under the ensemble of tree machine learning methods. Then, to balance the accuracy and training time, the combination of selected top-ranked features is trained using FL approaches rather than raw data in the model. Moreover, considering the local model accuracy of each Tor node, effective Tor node selection for the FL process is also designed. Finally, under closed-world settings with the real-world Tor data sets, we empirically demonstrate the comparisons of the proposed FedFingerprinting with raw data and feature selection compared to various benchmarks in terms of the training time and accuracy. Then, the superior performance of the FedFingerprinting with Tor node selection is evaluated in terms of convergence speed.

Federated learning (FL) in mobile edge computing has emerged as a promising machine-learning paradigm in the Internet of Things, enabling distributed training without exposing private data. It allows multiple mobile devices (MDs) to collaboratively create a global model. FL not only addresses the issue of private data exposure but also alleviates the burden on a centralized server, which is common in conventional centralized learning. However, a critical issue in FL is the imposed computing for local training on multiple MDs, which often have limited computing capabilities. This limitation poses a challenge for MDs to actively contribute to the training process. To tackle this problem, this paper proposes an adaptive dataset management (ADM) scheme, aiming to reduce the burden of local training on MDs. Through an empirical study on the influence of dataset size on accuracy improvement over communication rounds, we confirm that the amount of dataset has a reduced impact on accuracy gain. Based on this finding, we introduce a discount factor that represents the reduced impact of the size of the dataset on the accuracy gain over communication rounds. To address the ADM problem, which involves determining how much the dataset should be reduced over classes while considering both the proposed discounting factor and Kullback–Leibler divergence (KLD), a theoretical framework is presented. The ADM problem is a non-convex optimization problem. To solve it, we propose a greedy-based heuristic algorithm that determines a suboptimal solution with low complexity. Simulation results demonstrate that our proposed scheme effectively alleviates the training burden on MDs while maintaining acceptable training accuracy.

Video surveillance systems are integral to bolstering safety and security across multiple settings. With the advent of deep learning (DL), a specialization within machine learning (ML), these systems have been significantly augmented to facilitate DL-based video surveillance services with notable precision. Nevertheless, DL-based video surveillance services, which necessitate the tracking of object movement and motion tracking (e.g., to identify unusual object behaviors), can demand a significant portion of computational and memory resources. This includes utilizing GPU computing power for model inference and allocating GPU memory for model loading. To tackle the computational demands inherent in DL-based video surveillance, this study introduces a novel video surveillance management system designed to optimize operational efficiency. At its core, the system is built on a two-tiered edge computing architecture (i.e., client and server through socket transmission). In this architecture, the primary edge (i.e., client side) handles the initial processing tasks, such as object detection, and is connected via a Universal Serial Bus (USB) cable to the Closed-Circuit Television (CCTV) camera, directly at the source of the video feed. This immediate processing reduces the latency of data transfer by detecting objects in real time. Meanwhile, the secondary edge (i.e., server side) plays a vital role by hosting a dynamically controlling threshold module targeted at releasing DL-based models, reducing needless GPU usage. This module is a novel addition that dynamically adjusts the threshold time value required to release DL models. By dynamically optimizing this threshold, the system can effectively manage GPU usage, ensuring resources are allocated efficiently. Moreover, we utilize federated learning (FL) to streamline the training of a Long Short-Term Memory (LSTM) network for predicting imminent object appearances by amalgamating data from diverse camera sources while ensuring data privacy and optimized resource allocation. Furthermore, in contrast to the static threshold values or moving average techniques used in previous approaches for the controlling threshold module, we employ a Deep Q-Network (DQN) methodology to manage threshold values dynamically. This approach efficiently balances the trade-off between GPU memory conservation and the reloading latency of the DL model, which is enabled by incorporating LSTM-derived predictions as inputs to determine the optimal timing for releasing the DL model. The results highlight the potential of our approach to significantly improve the efficiency and effective usage of computational resources in video surveillance systems, opening the door to enhanced security in various domains.

Abstract Ga and Ga‐based alloys have recently received significant attention as “liquid metals (LMs)” with the combined advantages of a low toxicity, low melting point, high fluidity, and high conductivity. An important method for modifying LMs for enhanced processabilities and new applications is to tailor them into colloidal microdroplets suspended in a liquid medium. In this study, the unique vitrification behavior of oil‐based colloidal systems is shown with suspended LM microdroplets induced by various mineral oxide (MO) nanoparticles that are added as solid rheology modifiers. MOs exhibit a high affinity for the surfaces of suspended LM droplets in an apolar oil medium due to the polar interaction between the MO surface and the oxide skin of the LM. Thus, even minute amounts of added MOs (ΦMO < 0.01) transform a free‐flowing LM suspension (ΦMO ≈ 0.55) into a highly viscoelastic fluid that enables advanced processing (e.g., 3D printing). At high MO loadings (ΦMO ≥ 0.1), an emulsion with unprecedentedly high rheological strength is obtained, characterized by a yield stress of ≈104 Pa. In highly vitrified emulsions, partial sintering effects are induced by high internal sample stress, which improves the thermophysical properties of emulsions that may be useful for several practical applications.

This paper proposes a novel user-preference-aware power scheduling scheme for application at an electric vehicle (EV) charging facility. Here, the preference of the EV user is characterized as a utility function by considering two different factors: 1) a satisfaction factor according to the charged energy, and 2) payment for the received charging service. As a key component of the power scheduling method, this paper proposes a two-stage power charging method and analyzes the advantage of the proposed method from a monetary perspective. In addition, this paper analyzes the economic benefits of a charging facility and EVs using the single-leader, multi-follower Stackelkberg game. By showing the existence of a unique best response for each participant, this paper presents the improvement of financial profit of EV users and charging facility through comparison with the case where the charging facility does not apply a proper power management scheme. Based on the actual world datasets, this paper shows that the proposed power scheduling scheme is feasible for actual environment. With satisfying the constraints, it is possible to reduce overall electricity cost up to 8.59% compared to the case without considering the peak power in EV charging facility.

In this study, to reduce the energy consumption for the federated learning (FL) participation of mobile devices (MDs), we design a novel joint dataset and incentive management mechanism for FL over mobile edge computing (MEC) systems. We formulate a Stackelberg game to model and analyze the behaviors of FL participants, referred to as MDs, and FL service providers, referred to as MECs. In the proposed game, each MEC is the leader, whereas the MDs are followers. As the leader, to maximize its own revenue by considering the trade-off between the cost of providing incentives and the estimated accuracy attained from an FL operation, each MEC provides full incentives to the MDs for the participation of each FL task, as well as the target accuracy level for each MD. The suggested total incentives are allocated over MDs’ proportion to the amount of dataset applied for local training, which indirectly affects the global accuracy of the FL. Based on the suggested incentives, the MDs determine the amount of dataset used for the local training of each FL task to maximize their own payoffs, which is defined as the energy consumed from FL participation and the expected incentives. We study the economic benefits of the joint dataset and incentive management mechanism by analyzing its hierarchical decision-making scheme as a multi-leader multi-follower Stackelberg game. Using backward induction, we prove the existence and uniqueness of the Nash equilibrium among MDs, and then examine the Stackelberg equilibrium by analyzing the leader game. We also discuss extensions of the proposed mechanism where the MDs are unaware of explicit information of other MD profiles, such as the weights of the revenue as a practical concern, which can be redesigned into the Stackelberg Bayesian game. Finally, we reveal that the Stackelberg equilibrium solution maximizes the utility of all MDs and the MECs.

Abstract Preclinical and clinical studies have shown that sex is a significant risk factor for perinatal morbidity and mortality, with males being more susceptible to neonatal hypoxic ischemic (HI) brain injury. No study has investigated sexual dimorphism in the efficacy of umbilical cord blood (UCB) cell therapy. HI injury was induced in postnatal day 10 (PND10) rat pups using the Rice-Vannucci method of carotid artery ligation. Pups received 3 doses of UCB cells (PND11, 13, 20) and underwent behavioural testing. On PND50, brains were collected for immunohistochemical analysis. Behavioural and neuropathological outcomes were assessed for sex differences. HI brain injury resulted in a significant decrease in brain weight and increase in tissue loss in females and males. Females and males also exhibited significant cell death, region-specific neuron loss and long-term behavioural deficits. Females had significantly smaller brains overall compared to males and males had significantly reduced neuron numbers in the cortex compared to females. UCB administration improved multiple aspects of neuropathology and functional outcomes in males and females. Females and males both exhibited injury following HI. This is the first preclinical evidence that UCB is an appropriate treatment for neonatal brain injury in both female and male neonates.

We present a phase-locked synthetic wavelength interferometer that enables a complete elimination of cyclic errors in absolute distance measurements. With this method, the phase difference between the reference and measurement paths is fed back into a phase lock-in system, which is then used to control the synthetic wavelength and set the phase difference to zero using an external cavity acousto-optic modulator. We validated the cyclic error removal of the proposed phase-locked method by comparing it with the conventional phase-measuring method of the synthetic wavelength interferometer. By analyzing the locked error signal, we achieved a precision of 0.6 mrad in phase without any observed cyclic errors.

Abstract Background Particulate matter (PM) can cause various negative acute and chronic diseases of the respiratory system, including the upper airways. Curcumin has been reported to have anti-inflammatory and anti-oxidative effects; therefore, we investigated the effects of curcumin on nasal fibroblasts exposed to urban PM (UPM). Methods Samples of inferior turbinate tissue were obtained from six patients. Flow cytometry was used to assess the levels of reactive oxygen species (ROS) following the treatment of nasal fibroblasts with UPM and/or curcumin. We evaluated the effects of UPM and/or curcumin on the expression of phosphorylated ERK, Nrf2, HO-1, and SOD2 in fibroblasts by Western blotting. Results When UPM was applied to nasal fibroblasts, ROS production was significantly increased in a dose-dependent manner. UPM-exposed fibroblasts caused the activation of ERK to increase HO-1 expression and decrease SOD2 expression. Treatment with curcumin reduced the UPM-mediated increase in ROS; this decrease in ROS occurred in a dose-dependent manner. The UPM-induced activation of ERK was inhibited by curcumin. Nrf2 production was also promoted to increase the expression of HO-1 and SOD2 by curcumin. Conclusion Curcumin reduced ROS production caused by UPM in human nasal fibroblasts in a dose-dependent manner, suggesting that curcumin has anti-oxidative effects and may be useful in the treatment of nasal diseases caused by UPM, such as allergic and chronic rhinitis.

Nowadays, deep learning (DL)-based video surveillance services are widely used in smart cities because of their ability to accurately identify and track objects, such as vehicles and pedestrians, in real time. This allows a more efficient traffic management and improved public safety. However, DL-based video surveillance services that require object movement and motion tracking (e.g., for detecting abnormal object behaviors) can consume a substantial amount of computing and memory capacity, such as (i) GPU computing resources for model inference and (ii) GPU memory resources for model loading. This paper presents a novel cognitive video surveillance management with long short-term memory (LSTM) model, denoted as the CogVSM framework. We consider DL-based video surveillance services in a hierarchical edge computing system. The proposed CogVSM forecasts object appearance patterns and smooths out the forecast results needed for an adaptive model release. Here, we aim to reduce standby GPU memory by model release while avoiding unnecessary model reloads for a sudden object appearance. CogVSM hinges on an LSTM-based deep learning architecture explicitly designed for future object appearance pattern prediction by training previous time-series patterns to achieve these objectives. By referring to the result of the LSTM-based prediction, the proposed framework controls the threshold time value in a dynamic manner by using an exponential weighted moving average (EWMA) technique. Comparative evaluations on both simulated and real-world measurement data on the commercial edge devices prove that the LSTM-based model in the CogVSM can achieve a high predictive accuracy, i.e., a root-mean-square error metric of 0.795. In addition, the suggested framework utilizes up to 32.1% less GPU memory than the baseline and 8.9% less than previous work.

The Internet of things (IoT) combines different sources of collected data which are processed and analyzed to support smart city applications. Machine learning and deep learning algorithms play a vital role in edge intelligence by minimizing the amount of irrelevant data collected from multiple sources to facilitate these smart city applications. However, the data collected by IoT sensors can often be noisy, redundant, and even empty, which can negatively impact the performance of these algorithms. To address this issue, it is essential to develop effective methods for detecting and eliminating irrelevant data to improve the performance of intelligent IoT applications. One approach to achieving this goal is using data cleaning techniques, which can help identify and remove noisy, redundant, or empty data from the collected sensor data. This paper proposes a deep reinforcement learning (deep RL) framework for IoT sensor data cleaning. The proposed system utilizes a deep Q-network (DQN) agent to classify sensor data into three categories: empty, garbage, and normal. The DQN agent receives input from three received signal strength (RSS) values, indicating the current and two previous sensor data points, and receives reward feedback based on its predicted actions. Our experiments demonstrate that the proposed system outperforms a common time-series-based fully connected neural network (FCDQN) solution, with an accuracy of around 96% after the exploration mode. The use of deep RL for IoT sensor data cleaning is significant because it has the potential to improve the performance of intelligent IoT applications by eliminating irrelevant and harmful data.

This paper proposes a joint demand response and energy trading for electric vehicles in an off-grid system. We consider isolated microgrid in a region where, at a given time, some renewable energy generators have superflous energy for sale or to keep in storage facilities, whereas some electric vehicles wish to buy additional energy to meet their deficiency. In our system model, broker lead the market by determining the optimal transaction price by considering a trade-off between commission revenue and power reliability. Buyers and sellers follow the broker's decision by independently submitting a transaction price to the broker. Correspondingly transaction energy is allocated to the buyers in the proportion to their payment, whereas the revenue is allocated to the sellers in proportion to their sales. We investigated the economic benefits of such a joint demand response and energy trading by analyzing its hierarchical decision-making scheme as a single-leader-heterogeneous multi-follower Stackelberg game. With demonstrating an existence of a unique Stackelberg equilibrium, we show that the transaction price in the proposed market model is up to 25.8% cheaper than the existing power market. In addition, we compare the power reliability results with other algorithm to show the suitability of proposed algorithm in the isolated microgrid environment.

LPMLN is a probabilistic extension of answer set programs with the weight scheme adapted from Markov Logic. We study the concept of strong equivalence in LPMLN, which is a useful mathematical tool for simplifying a part of an LPMLN program without looking at the rest of it. We show that the verification of strong equivalence in LPMLN can be reduced to equivalence checking in classical logic via a reduct and choice rules as well as to equivalence checking under the "soft'' logic of here-and-there. The result allows us to leverage an answer set solver for LPMLN strong equivalence checking. The study also suggests us a few reformulations of the LPMLN semantics using choice rules, the logic of here-and-there, and classical logic.

Creating voids between thin films is a very effective method to improve thin film crystal quality. However, for AlN material systems, the AlN layer growth, including voids, is challenging because of the very high Al atom sticking coefficient. In this study, we demonstrated an AlN template with many voids grown on AlN nanorods made by polarity selective epitaxy and etching methods. We introduced a low V/III ratio and NH3 pulsed growth method to demonstrate high-quality coalesced AlN templates grown on AlN nanorods in a metal organic chemical vapor deposition reactor. The crystal quality and residual strain of AlN were enhanced by the void formations. It is expected that this growth method can contribute to the demonstration of high-performance deep UV LEDs and transistors.

With the deployment of the fifth generation (5G) mobile network systems and the envisioned heterogeneous ultra-dense networks (UDNs), both small cell (SmC) and distributed antenna system (DAS) technologies are required by mobile network operators (MNOs) and venue owners to support multiple spectrum bands, multiple radio access technologies (RATs), multiple optical central offices (COs), and multiple MNOs. As a result, the neutral host business model representing a third party responsible for managing the network enterprise on behalf of multiple MNOs has emerged as a potential solution, mainly influenced by the desire to provide a high user experience without significantly increasing the total cost of ownership (TCO). However, designing a sustainable business model for a neutral host is a nontrivial task, especially when considered in the context of 5G and beyond (5GB) UDNs. In this paper, under an integrated optical wireless network infrastructure, we review how SmC and DAS technologies are evolving towards the adoption of the neutral host business model and identify key challenges and requirements for 5GB support. Thus, we explore recent candidate advancements in heterogeneous network integration technologies for the realization of an efficient 5GB neutral host business model design capable of accommodating both SmC and DAS. Furthermore, we propose a novel design architecture that relies on virtual radio access network (vRAN) to enable real-time dynamic resource allocation and radio over Ethernet (RoE) for flexible and reconfigurable fronthaul. The results from our simulations using MATLAB over two real-life deployment scenarios validate the feasibility of utilizing switched RoE considering end-to-end delay requirements of 5GB under different switching schemes, as long as the queuing delay is kept to a minimum. Finally, the results show that incorporating RoE and vRAN technologies into the neutral host design results in substantial TCO reduction by about 81% in an indoor scenario and 73% in an outdoor scenario.

In preoperative imaging, the demarcation of rectal cancer with magnetic resonance images provides an important basis for cancer staging and treatment planning. Recently, deep learning has greatly improved the state-of-the-art method in automatic segmentation. However, limitations in data availability in the medical field can cause large variance and consequent overfitting to medical image segmentation networks. In this study, we propose methods to reduce the model variance of a rectal cancer segmentation network by adding a rectum segmentation task and performing data augmentation; the geometric correlation between the rectum and rectal cancer motivated the former approach. Moreover, we propose a method to perform a bias-variance analysis within an arbitrary region-of-interest (ROI) of a segmentation network, which we applied to assess the efficacy of our approaches in reducing model variance. As a result, adding a rectum segmentation task reduced the model variance of the rectal cancer segmentation network within tumor regions by a factor of 0.90; data augmentation further reduced the variance by a factor of 0.89. These approaches also reduced the training duration by a factor of 0.96 and a further factor of 0.78, respectively. Our approaches will improve the quality of rectal cancer staging by increasing the accuracy of its automatic demarcation and by providing rectum boundary information since rectal cancer staging requires the demarcation of both rectum and rectal cancer. Besides such clinical benefits, our method also enables segmentation networks to be assessed with bias-variance analysis within an arbitrary ROI, such as a cancerous region.

In this paper, to balance power supplement from the solar energy’s intermittent and unpredictable generation, we design a solar energy generation and trading platform (EggBlock) using Internet of Things (IoT) systems and blockchain technique. Without a centralized broker, the proposed EggBlock platform can promote energy trading between users equipped with solar panels, and balance demand and generation. By applying the second price sealed-bid auction, which is one of the suitable pricing mechanisms in the blockchain technique, it is possible to derive truthful bidding of market participants according to their utility function and induce the proceed transaction. Furthermore, for efficient generation of solar energy, EggBlock proposes a Q-learning-based dynamic panel control mechanism. Specifically, we set the instantaneous direction of the solar panel and the amount of power generation as the state and reward, respectively. The angle of the panel to be moved becomes an action at the next time step. Then, we continuously update the Q-table using transfer learning, which can cope with recent changes in the surrounding environment or weather. We implement the proposed EggBlock platform using Ethereum’s smart contract for reliable transactions. At the end of the paper, measurement-based experiments show that the proposed EggBlock achieves reliable and transparent energy trading on the blockchain and converges to the optimal direction with short iterations. Finally, the results of the study show that an average energy generation gain of 35% is obtained.

In this paper, we newly model computation offloading competition when multiple clients compete with each other so as to reduce energy cost and improve computational performance. We consider two types of destination of offloading request, such as a cloudlet and a remote cloud. Here, the cloudlet consists of locally connected mobile terminals with low-latency and high bandwidth but suffering from task overload due to its limited computational capacity. On the other hand, the remote cloud has a high and stable capacity but the high latency. To facilitate the competition model, on the destination sides, we have designed an energy-oriented task scheduling scheme, which aims to maximize the welfare of clients in terms of energy efficiency. Under this proposed job scheduling, as a joint consideration of the destination and client sides, competition behavior among multiple clients for optimal computation offloading is modeled and analyzed as a non-cooperative game by considering a trade-off between different types of destinations. Based on this game-theoretical analysis, we propose a novel energy-oriented weight assignment scheme in the mobile terminal side to maximize mobile terminal energy efficiency. Finally, we show that the proposed scheme converges well to a unique equilibrium and it maximizes the payoff of all participating clients.

This paper proposes three hierarchical levels of a competitive big-data market model. We consider that a service provider gathers data from multiple data sources and provides valuable information from refined data as a service to its customers. Under our approach, a service provider determines optimal data procurement from multiple data sources within its budget constraint. The multiple data sources follow the service provider's action by independently submitting bidding prices to the service provider. Further, customers decide whether to subscribe or not based on the subscription fee, their willingness-to-pay, and the quality of the refined data. We study the economic benefits of such a market model by analyzing the hierarchical decision making procedures as a Stackelberg game. We show the existence and the uniqueness of the Nash equilibrium (NE), and the NE solution is given as a closed form. Finally, we reveal that the obtained unique equilibrium solution maximizes the payoff of all market participants.

In this paper, we propose a periodic energy trading system model in microgrids with future forecasting and forecasting errors. In the proposed model, retailers in a microgrid can purchase (sell) energy periodically from (to) other retailers in the same microgrid. In this regard, our proposed model uses stochastic processes that capture the series of forecasted energy generation/consumption over time and the forecasting errors as transitions among states. We show with the proposed model that it is enough to consider only one time period to maximize revenues of retailers over the whole time period. We further design a hierarchical algorithm that provides an equilibrium price of energy among retailers to balance between energy demand and supply in the microgrid. Some numerical examples show that our proposed model and algorithm outperform traditional ones by efficiently managing forecasting errors.

Background and Objective The instrument-assisted soft tissue mobilization (IASTM) is a form of mechanical stimulation. This treatment is known to provide an effective way to improve muscle function and attenuate muscle pain. However, limited study showed the effect of the IASTM on acute condition such as exercise-induced muscle damage. This study aimed to examine the effects of IASTM on exercise-induced muscle dam-age and fibrotic factor. Material and Methods Sixteen healthy male college students were randomly assigned to IASTM (n=8) and control (n=8). After performing two sets of 25 eccentric contractions of elbow flexors, IASTM was applied for 8 min immediately and 48 h after exercise. Maximal isometric strength (MIS), muscle soreness, and creatine kinase (CK) activity were measured as indicators of muscle damage. Transforming growth factor-β1 (TGF-β1) levels were assessed as a fibrotic factor. Results The recovery of MIS was faster (control vs. IASTM: 96 h: 60.7% ± 7.9% vs. 89.1% ± 10.4%, P0.05). Conclusion IASTM may be an effective method for reducing scar tissue and restoring muscle function quickly after exercise-induced muscle damage.

Background: Hypoxic ischemic (HI) insult in term babies at labor or birth can cause long-term neurodevelopmental disorders, including cerebral palsy (CP). The current standard treatment for term infants with hypoxic ischemic encephalopathy (HIE) is hypothermia. Because hypothermia is only partially effective, novel therapies are required to improve outcomes further. Human umbilical cord blood cells (UCB) are a rich source of stem and progenitor cells making them a potential treatment for neonatal HI brain injury. Recent clinical trials have shown that UCB therapy is a safe and efficacious treatment for confirmed cerebral palsy. In this study, we assessed whether early administration of UCB to the neonate could improve long-term behavioral outcomes and promote brain repair following neonatal HI brain injury.Methods: HI brain injury was induced in postnatal day (PND) 7 rat pups via permanent ligation of the left carotid artery, followed by a 90 min hypoxic challenge. UCB was administered intraperitoneally on PND 8. Behavioral tests, including negative geotaxis, forelimb preference and open field test, were performed on PND 14, 30, and 50, following brains were collected for assessment of neuropathology.Results: Neonatal HI resulted in decreased brain weight, cerebral tissue loss and apoptosis in the somatosensory cortex, as well as compromised behavioral outcomes. UCB administration following HI improved short and long-term behavioral outcomes but did not reduce long-term histological evidence of brain injury compared to HI alone. In addition, UCB following HI increased microglia activation in the somatosensory cortex compared to HI alone.Conclusion: Administration of a single dose of UCB cells 24 h after HI injury improves behavior, however, a single dose of cells does not modulate pathological evidence of long-term brain injury.

This paper presents a novel adaptive object movement and motion tracking (AdaMM) framework in a hierarchical edge computing system for achieving GPU memory footprint reduction of deep learning (DL)-based video surveillance services. DL-based object movement and motion tracking requires a significant amount of resources, such as (1) GPU processing power for the inference phase and (2) GPU memory for model loading. Despite the absence of an object in the video, if the DL model is loaded, the GPU memory must be kept allocated for the loaded model. Moreover, in several cases, video surveillance tries to capture events that rarely occur (e.g., abnormal object behaviors); therefore, such standby GPU memory might be easily wasted. To alleviate this problem, the proposed AdaMM framework categorizes the tasks used for the object movement and motion tracking procedure in an increasing order of the required processing and memory resources as task (1) frame difference calculation, task (2) object detection, and task (3) object motion and movement tracking. The proposed framework aims to adaptively release the unnecessary standby object motion and movement tracking model to save GPU memory by utilizing light tasks, such as frame difference calculation and object detection in a hierarchical manner. Consequently, object movement and motion tracking are adaptively triggered if the object is detected within the specified threshold time; otherwise, the GPU memory for the model of task (3) can be released. Moreover, object detection is also adaptively performed if the frame difference over time is greater than the specified threshold. We implemented the proposed AdaMM framework using commercial edge devices by considering a three-tier system, such as the 1st edge node for both tasks (1) and (2), the 2nd edge node for task (3), and the cloud for sending a push alarm. A measurement-based experiment reveals that the proposed framework achieves a maximum GPU memory reduction of 76.8% compared to the baseline system, while requiring a 2680 ms delay for loading the model for object movement and motion tracking.

Background: Eupatilin is an active flavon extracted from the Artemisia species and has properties such as antioxidant, anti-inflammatory, and anti-cancer. We examined the effect of eupatilin using fine particulate matter (FPM) and human bronchial epithelial cell line (BEAS-2B) to confirm the potential of eupatilin as a therapeutic agent for respiratory diseases caused by FPM. Methods: Reactive oxygen species (ROS) levels were checked by flow cytometry to identify if FPM and eupatilin affect ROS production. Western blotting was performed to identify the mechanism of action of eupatilin in FPM-exposed BEAS-2B cells. Results: When cells were exposed to FPM above 12.5 μg/mL concentration for 24 h, ROS production increased significantly compared to the control. When eupatilin was added to cells exposed to FPM, the ROS level decreased proportionally with the eupatilin dose. The phosphorylation of Akt, NF-κB p65, and p38 MAPK induced by FPM was significantly reduced by eupatilin, respectively. Conclusion: FPM cause respiratory disease by producing ROS in bronchial epithelial cells. Eupatilin has been shown to inhibit ROS production through altering signaling pathways. The ROS inhibiting property of eupatilin can be exploited in FPM induced respiratory disorders.

This paper presents the design of an event-driven energy trading system among microgrids. Each microgrid can be either a provider or a consumer depending on the status of its energy generation and local demands. Under this approach, an aperiodic market model is newly proposed such that trading occurs when one of the consumers requests energy from the trading market. To promote the trading system, a consumer-side reward concept is introduced. The consumer makes a decision on the size of the posted reward to procure energy depending on its required energy level. Providers then react to this posted reward by submitting their energy bid. Accordingly, the posted reward is allocated to the providers in proportion to their energy bids. Moreover, for practical concerns, a transmission and distribution loss factor is considered as a heterogeneous energy trading system. The problem is then formulated as a non-cooperative Stackelberg game model. The existence and uniqueness of Stackelberg equilibrium (SE) are shown and the closed form of the SE is derived. Using the SE, an optimal trading algorithm for microgrids is provided. The stability of the energy trading system is verified due to the unique SE. In this approach, no expected waiting time for trading is required for sustaining an energy trading market.

Exercise-induced rhabdomyolysis (exRML), a pathophysiological condition of skeletal muscle cell damage that may cause acute renal failure and in some cases death. Increased Ca2+ level in cells along with functional degradation of cell signaling system and cell matrix have been suggested as the major pathological mechanisms associated with exRML. The onset of exRML may be exhibited in athletes as well as in general population. Previous studies have reported that possible causes of exRML were associated with excessive eccentric contractions in high temperature, abnormal electrolytes balance, and nutritional deficiencies possible genetic defects. However, the underlying mechanisms of exRML have not been clearly established among health professionals or sports medicine personnel. Therefore, we reviewed the possible mechanisms and correlated prevention of exRML, while providing useful and practical information for the athlete and general exercising population.

Malignant melanoma rarely develops in the paranasal sinuses, and generally has a poor prognosis. However, mucosal melanoma can masquerade both clinically and histopathologically as a benign lesion, rendering accurate early diagnosis difficult. On the other hand, angiofibroma, a benign tumor, is more easily diagnosed than a mucosal melanoma, because the former exhibits specific histopathological features. No cases of concurrent angiofibroma and mucosal melanoma have been reported to date. We describe such a case below.

The production of reactive oxygen species (ROS) by cisplatin is one of the major mechanisms of cisplatin-induced cytotoxicity. We examined the preventive effect of α-lipoic acid (LA) on cisplatin-induced toxicity via its antioxidant effects on in vitro and ex vivo culture systems. To elucidate the mechanism of the antioxidant activity of LA, NRF2 was inhibited using NRF2 siRNA, and the change in antioxidant activity of LA was characterized. MTT assays showed that LA was safe at concentrations up to 0.5 mM in HEI-OC1 cells and had a protective effect against cisplatin-induced cytotoxicity. Intracellular ROS production in HEI-OC1 cells was rapidly increased by cisplatin for up to 48 h. However, treatment with LA significantly reduced the production of ROS and increased the expression of the antioxidant proteins HO-1 and SOD1. Ex vivo, the organs of Corti of the group pretreated with LA exhibited better preservation than the group that received cisplatin alone. We also confirmed the nuclear translocation of NRF2 after LA administration, and that NRF2 inhibition decreased the antioxidant activity of LA. Together, these results indicate that the antioxidant activity of LA was through the activation of the NRF2/HO-1 antioxidant pathway.

A prototype of the GMT FSM has been developed to acquire and to enhance the key technology – mirror fabrication and tiptilt actuation. The ellipsoidal off-axis mirror has been designed, analyzed, and fabricated from light-weighting to grinding, polishing, and figuring of the mirror surface. The mirror was tested by using an interferometer together with CGHs, which revealed the surface error of 13.7 nm rms in the diameter of 1030 mm. The SCOTS test was employed to independently validate the test results. It measured the surface error to be 17.4 nm rms in the diameter of 1010 mm. Both tests show the optical surface of the FSMP mirror within the required value of 20 nm rms surface error.

In this paper, a clustering method with proposed distance measurement to extract base load profiles from arbitrary data sets is studied. Recently, smart energy load metering devices are broadly deployed, and an immense volume of data is now collected. However, as this large amount of data has been explosively generated over such a short period of time, the collected data is hardly organized to be employed for study, applications, services, and systems. This paper provides a foundation method to extract base load profiles that can be utilized by power engineers, energy system operators, and researchers for deeper analysis and more advanced technologies. The base load profiles allow them to understand the patterns residing in the load data to discover the greater value. Up to this day, experts with domain knowledge often have done the base load profile realization manually. However, the volume of the data is growing too fast to handle it with the conventional approach. Accordingly, an automated yet precise method to recognize and extract the base power load profiles is studied in this paper. For base load profile extraction, this paper proposes Sample Pearson Correlation Coefficient (SPCC) distance measurement and applies it to Mean-Shift algorithm based nonparametric mode-seeking clustering. The superiority of SPCC distance over traditional Euclidean distance is validated by mathematical and numerical analysis.

Males and females sometimes significantly differ in their propensity to develop neurological disorders. Females suffer more from mood disorders such as depression and anxiety, whereas males are more susceptible to deficits in the dopamine system including Parkinson’s disease (PD), attention-deficit hyperactivity disorder (ADHD) and autism. Despite this, biological sex is rarely considered when making treatment decisions in neurological disorders. A better understanding of the molecular mechanism(s) underlying sex differences in the healthy and diseased brain will help to devise diagnostic and therapeutic strategies optimal for each sex. Thus, the aim of this review is to discuss the available evidence on sex differences in neuropsychiatric and neurodegenerative disorders regarding prevalence, progression, symptoms and response to therapy. We also discuss the sex-related factors such as gonadal sex hormones and sex chromosome genes and how these might help to explain some of the clinically observed sex differences in these disorders. In particular, we highlight the emerging role of the Y-chromosome gene, SRY, in the male brain and its potential role as a male-specific risk factor for disorders such as PD, autism, and ADHD in many individuals.

This paper investigates the problem of power distribution for an off-grid base station (BS) that operates sustainably without an electrical grid. We consider that multiple retailers with heterogeneous renewable energy sources (RESs) compete to maximize their revenues by individually setting the unit power price. Energy outages (EOs), which cause the power supply to fall below that which is sufficient for ensuring the traffic arrival rate required for the off-grid BS, critically affect the users’ service quality. To minimize EOs and operational expenditure (OPEX), the off-grid BS manages the power supply by reacting to the retailers’ pricing decisions. We analyze the economic benefits of power distribution to the off-grid BS from the perspective of the retailers’ pricing competition, by designing a hierarchical decision-making scheme as a multi-leader single-follower Stackelberg game. We derive a closed form expression for the optimal behavior of the off-grid BS and retailers, based on well-designed utility functions. Finally, numerical results demonstrate the proposed solution with its practical convergence time.

This paper proposes a learning-based adaptive imputation method (LAI) for imputing missing power data in an energy system. This method estimates the missing power data by using the pattern that appears in the collected data. Here, in order to capture the patterns from past power data, we newly model a feature vector by using past data and its variations. The proposed LAI then learns the optimal length of the feature vector and the optimal historical length, which are significant hyper parameters of the proposed method, by utilizing intentional missing data. Based on a weighted distance between feature vectors representing a missing situation and past situation, missing power data are estimated by referring to the k most similar past situations in the optimal historical length. We further extend the proposed LAI to alleviate the effect of unexpected variation in power data and refer to this new approach as the extended LAI method (eLAI). The eLAI selects a method between linear interpolation (LI) and the proposed LAI to improve accuracy under unexpected variations. Finally, from a simulation under various energy consumption profiles, we verify that the proposed eLAI achieves about a 74% reduction of the average imputation error in an energy system, compared to the existing imputation methods.

This study investigates a demand-side management problem in which multiple suppliers compete with each other to maximize their own revenue. We consider that suppliers have heterogeneous energy sources and individually set the unit price of each energy source. Then, consumers that share a net utility react to the suppliers’ decisions on prices by deciding the amount of energy to request, or how to split the consumers’ aggregated demand over multiple suppliers. In this case, the consumers need to consider the power loss and the price to pay for procuring electricity. We analyze the economic benefits of such a pricing competition among suppliers (e.g., a demand-side management that considers consumers’ reaction). This is achieved by designing a hierarchical decision-making scheme as a multileader–multifollower Stackelberg game. We show that the behaviors of both consumers and suppliers based on well-designed utility functions converge to a unique equilibrium solution. This allows them to maximize the payoff from all participating consumers and suppliers. Accordingly, closed-form expressions are provided for the corresponding strategies of the consumers and the suppliers. Finally, we provide numerical examples to illustrate the effectiveness of the solutions. This game-theoretic study provides an example of incentives and insight for demand-side management in future power grids.