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The feeling of awe is a complicated, mixed emotion that integrates confusion, astonishment, admiration and submission. Researchers have found beneficial effects of awe on individuals' social lives. This work aims to explore the impeding effect of awe on the relationship between narcissism and irrational consumption by performing 4 studies. After study 1 verified that narcissism was positively related to impulsive consumption, study 2 activated participants' feelings of awe through video clips and proved that awe reduced the effect of narcissism on impulsive consumption. Study 3 showed that the reduction in impulsive tendency was caused by awe rather than priming materials, and eliciting awe did not change an individual's sense of control. Study 4 involved an online experiment among office workers and revealed that the moderating effect of awe was fully mediated by self-focused attention. Overall, the mediated moderation model is verified. This study enriches the theoretical research of awe in the Chinese cultural context and provides practical meanings., Author(s): Mengwei Zhang [sup.1] [sup.2] , Li Li [sup.3] , Yujian Ye [sup.4] , Shiyi Yu [sup.1] , Jianan Zhong [sup.1] Author Affiliations: (1) Law and Politics School, Zhejiang University [...]

The development of renewable‐dominated power systems (RDPS) in the context of pursuing deep decarbonisation targets necessitates suitable exploitation of the peak shaving capacity (PSC) of central air conditioning loads (CACL) in multiple time scales. Based on the evaluation framework of CACL participating in the RDPS PSC and the CAC apparent temperature model and its operation mode, the CACL PSC evaluation model related to the resource allocation in multiple time scales is proposed. For the time scale of online dispatching, the capacity evaluation model for rapid CACL response to power system emergency peak shaving command and its continuous response potential is constructed. The method of optimising CACL PSC based on the load aggregator model is developed. For the intraday pre‐scheduling time scale, the evaluation model of CACL's remaining capacity after undertaking the peak shaving task and the ability to respond continuously to the peak shaving command is constructed. For the day‐ahead dispatching time scale, the PSC of CACL under different weather conditions, temperatures, holidays, and other typical scenarios is evaluated. Finally, case studies carried out on 14 central air‐conditioned buildings in Shanghai city comprehensively validate the effectiveness of the proposed CACL PSC evaluation model. [ABSTRACT FROM AUTHOR]

With the expansion of system scale and data size in power systems, data-driven methods are gradually becoming widely used. However, compared with traditional methods, data-driven methods face more threats in data security and algorithm security. This paper proposes a black box data injection attack method against data-driven strategies in power systems. To obtain a stealthy attack scheme with the greatest impact, an attack vector generator based on deep convolutional neural networks (DCNN) is designed. The generator includes a node selector, a filter, an encoder, and a decoder. The node selector is used to select the most likely successful attack scheme for the filter, the encoder is used to extract sample features, and the decoder is used to generate disturbances based on the features. The proposed generator is trained with an improved generative adversarial network (GAN) and can generate minimal disturbances in real time based on measurement data from the power grid. The effectiveness of the proposed method is demonstrated with an attack experiment on an online transient stability application. [ABSTRACT FROM AUTHOR]

Peer-to-peer (P2P) transactive energy trading has emerged as a promising paradigm towards maximizing the flexibility value of prosumers’ distributed energy resources (DERs). Despite reinforcement learning constitutes a well-suited model-free and data-driven methodological framework to optimize prosumers’ energy management decisions, its application to the large-scale coordinated management and P2P trading among multiple prosumers within an energy community is still challenging, due to the scalability, non-stationarity and privacy limitations of state-of-the-art multi-agent deep reinforcement learning (MADRL) approaches. This paper proposes a novel P2P transactive trading scheme based on the multi-actor-attention-critic (MAAC) algorithm, which addresses the above challenges individually. This method is complemented by a P2P trading platform that incentivizes prosumers to engage in local energy trading while also penalizes each prosumer’s addition to rebound peaks. Case studies involving a real-world, large-scale scenario with 300 residential prosumers demonstrate that the proposed method significantly outperforms the state-of-the-art MADRL methods in reducing the community’s cost and peak demand. [ABSTRACT FROM AUTHOR]

Distributed, consensus-based algorithms have emerged as a promising approach for the coordination of distributed energy resources (DER) due to their communication, computation, privacy and reliability advantages over centralized approaches. However, state-of-the-art consensus-based algorithms address the DER coordination problem in independent time periods and therefore are inherently unable to capture the time-coupling operating characteristics of flexible demand (FD) and energy storage (ES) resources. This paper demonstrates that state-of-the-art algorithms fail to converge when these time-coupling characteristics are considered. In order to address this fundamental limitation, a novel consensus-based algorithm is proposed which includes additional consensus variables. These variables express relative maximum power limits imposed on the FD and ES resources which effectively mitigate the concentration of the FD and ES responses at the same time periods and steer the consensual outcome to a feasible and optimal solution. The convergence and optimality of the proposed algorithm are theoretically proven while case studies numerically demonstrate its convergence, optimality, robustness to initialization and information loss, and plug-and-play adaptability. [ABSTRACT FROM AUTHOR]

The effective pricing of electric vehicle (EV) charging by aggregators constitutes a key problem toward the realization of the significant EV flexibility potential in deregulated electricity systems and has been addressed by previous work through bi-level optimization formulations. However, the solution approach adopted in previous work cannot capture the discrete nature of the EV charging/discharging levels. Although reinforcement learning (RL) can tackle this challenge, state-of-the-art RL methods require discretization of state and/or action spaces and thus exhibit limitations in terms of solution optimality and computational requirements. This article proposes a novel deep reinforcement learning (DRL) method to solve the examined EV pricing problem, combining deep deterministic policy gradient (DDPG) principles with a prioritized experience replay (PER) strategy and setting up the problem in multi-dimensional continuous state and action spaces. Case studies demonstrate that the proposed method outperforms state-of-the-art RL methods in terms of both solution optimality and computational requirements and comprehensively analyze the economic impacts of smart-charging and vehicle-to-grid (V2G) flexibility on both aggregators and EV owners. [ABSTRACT FROM AUTHOR]

Brand anthropomorphism is one of the most widely used marketing strategies, and numerous studies have confirmed the positive effect of anthropomorphism on consumers' brand attitude. However, anthropomorphism does not always produce positive effects in particular conditions. This study focuses on the interaction effect of brand anthropomorphism and brand distinctiveness on brand attitude and tests the mediating effect of warmth and competence using the Stereotype Content Model. The results of two experiments show that brand anthropomorphism positively predicts consumers' brand attitude, and brand position (distinctiveness vs. popularity) moderates this relationship. Anthropomorphism may improve consumers' attitudes when the brand is positioned to be popular but has no effect on consumers' attitudes when the brand is positioned to be distinctive. Additionally, warmth (not competence) mediates the interaction effect of anthropomorphism and brand position on brand attitude. This study expands the extant knowledge on anthropomorphism and stereotypes in the field of consumption psychology and provides marketers with more rational strategies when applying anthropomorphism in marketing campaigns. [ABSTRACT FROM AUTHOR]

Multi-energy systems (MES) are attracting increasing attention driven by its potential to offer significant flexibility in future smart grids. At the residential level, the roll-out of smart meters and rapid deployment of smart energy devices call for autonomous multi-energy management systems which can exploit real-time information to optimally schedule the usage of different devices with the aim of minimizing end-users’ energy costs. This paper proposes a novel real-time autonomous energy management strategy for a residential MES using a model-free deep reinforcement learning (DRL) based approach, combining state-of-the-art deep deterministic policy gradient (DDPG) method with an innovative prioritized experience replay strategy. This approach is tailored to align with the nature of the problem by posing it in multi-dimensional continuous state and action spaces, facilitating more cost-effective control strategies to be devised. The superior performance of the proposed approach in reducing end-user’s energy cost while coping with the MES uncertainties is demonstrated by comparing it against state-of-the-art DRL methods as well as conventional stochastic programming and robust optimization methods in numerous case studies in a real-world scenario. [ABSTRACT FROM AUTHOR]

The investigation of the effects of demand flexibility on the pricing strategies and the profits of electricity retailers has recently emerged as a highly interesting research area. However, the state‐of‐the‐art, bi‐level optimisation modelling approach makes the unrealistic assumption that retailers treat wholesale market prices as exogenous, fixed parameters. This study proposes a tri‐level optimisation model, which drops this assumption and represents the wholesale market clearing process endogenously, thus capturing the realistic implications of a retailer's pricing strategies and the resulting demand response on the wholesale market prices. The scope of the examined case studies is three‐fold. First of all, they demonstrate the interactions between the retailer, the flexible consumers and the wholesale market and analyse the fundamental effects of the consumers' time‐shifting flexibility on the retailer's revenue from the consumers, its cost in the wholesale market and its overall profit. Furthermore, they analyse how these effects of demand flexibility depend on the retailer's relative size in the market and the strictness of the regulatory framework. Finally, they highlight the added value of the proposed tri‐level model by comparing its outcomes against the state‐of‐the‐art bi‐level modelling approach. [ABSTRACT FROM AUTHOR]

Bi-level optimization and reinforcement learning (RL) constitute the state-of-the-art frameworks for modeling strategic bidding decisions in deregulated electricity markets. However, the former neglects the market participants’ physical non-convex operating characteristics, while conventional RL methods require discretization of state and/or action spaces and thus suffer from the curse of dimensionality. This paper proposes a novel deep reinforcement learning (DRL) based methodology, combining a deep deterministic policy gradient (DDPG) method with a prioritized experience replay (PER) strategy. This approach sets up the problem in multi-dimensional continuous state and action spaces, enabling market participants to receive accurate feedback regarding the impact of their bidding decisions on the market clearing outcome, and devise more profitable bidding decisions by exploiting the entire action domain, also accounting for the effect of non-convex operating characteristics. Case studies demonstrate that the proposed methodology achieves a significantly higher profit than the alternative state-of-the-art methods, and exhibits a more favourable computational performance than benchmark RL methods due to the employment of the PER strategy. [ABSTRACT FROM AUTHOR]

Bi-level optimization constitutes the most popular mathematical methodology for modeling the deregulated electricity market. However, state-of-the-art models neglect the physical non-convex operating characteristics of market participants, due to their inherent inability to capture binary decision variables in their representation of the market clearing process, rendering them problematic in modeling markets with complex bidding and unit commitment (UC) clearing mechanisms. This paper addresses this fundamental limitation by proposing a novel modeling approach enabling incorporation of these non-convexities into bi-level optimization market models, which is based on the relaxation and primal-dual reformulation of the original, non-convex lower level problem and the penalization of the associated duality gap. Case studies demonstrate the ability of the proposed approach to closely approximate the market clearing solution of the actual UC clearing algorithm and devise more profitable bidding decisions for strategic producers than the state-of-the-art bi-level optimization approach, and reveal the potential of strategic behavior in terms of misreporting non-convex operating characteristics. [ABSTRACT FROM AUTHOR]

Decarbonization of the electricity system will require significant and continued investment in low-carbon energy sources and electrification of the heat and transport sectors. With diminishing output and shorter operating hours of conventional large-scale fossil-fueled generators, there is a growing need and opportunity for distributed energy resources (DERs) to contribute to the provision of system balancing and security services and support a cost-effective transition to a lower-carbon energy system. Emerging smart technologies will open new opportunities for millions of users to participate actively in the trading of electricity and various ancillary services through alternative mechanisms, such as dynamic pricing, local energy markets, security markets, and peer-to-peer (P2P) trading. [ABSTRACT FROM AUTHOR]

The envisaged decarbonisation of electricity systems has attracted significant interest around the role and value of energy storage systems (ESSs). In the deregulated electricity market, there is a need to investigate the complex impacts of ESSs, considering the potential exercise of market power by strategic players. This study aims at comprehensively analysing the impacts of both price‐taking and price‐making storage behaviours on energy market efficiency, corresponding to potential settings with small and large storage players, respectively. In order to achieve this and in contrast to previous papers, this work develops a multi‐period equilibrium programming market model to determine market equilibrium stemming from the interactions of independent strategic producers and ESSs, while capturing the time‐coupling operational constraints of ESSs as well as network constraints. The results of case studies on a test market capturing the general conditions of the GB electricity system demonstrate that the introduction of ESSs mitigates market power exercise and improves market efficiency, with this beneficial impact being higher when ESSs act as price takers. When the electricity network is congested, the location of ESSs also affects the market outcome, with their beneficial impact on market efficiency being higher when they are located in higher‐priced areas. [ABSTRACT FROM AUTHOR]

Previous work on the role of the demand side in imperfect electricity markets has demonstrated that its self-price elasticity reduces electricity producers’ ability to exercise market power. However, the concept of self-price elasticity cannot accurately capture consumers’ flexibility, as the latter mainly involves shifting of loads’ operation in time. This paper provides for the first time theoretical and quantitative analysis of the beneficial impact of demand shifting (DS) in mitigating market power by the generation side. Quantitative analysis is supported by a multiperiod equilibrium programming model of the imperfect electricity market, accounting for the time-coupling operational constraints of DS as well as network constraints. The decision making process of each strategic producer is modeled through a bi-level optimization problem, which is solved after converting it to a Mathematical Program with Equilibrium Constraints (MPEC) and linearizing the latter through suitable techniques. The oligopolistic market equilibria resulting from the interaction of multiple independent producers are determined by employing an iterative diagonalization method. Case studies on a test market reflecting the general generation and demand characteristics of the GB system quantitatively demonstrate the benefits of DS in mitigating producers’ market power, by employing relevant indexes from the literature. [ABSTRACT FROM AUTHOR]

In this paper, Painlevé integrability of a generalized Hirota-Satsuma (gHS) equation is confirmed by using the Weiss-Tabor-Carnevale (WTC) test. Then, a new exact solution with two arbitrary functions is constructed. Some new soliton structures are illustrated analytically by selecting appropriate functions. [ABSTRACT FROM AUTHOR]

Based on a matrix Lie algebra consisting of block matrices, new tri-integrable coupling of the Kaup-Newell soliton hierarchy is constructed. Then, the bi-Hamiltonian structure which leads to Liouville integrability of this coupling is furnished by the variational identity. [ABSTRACT FROM AUTHOR]

In this article, Painlevé integrability of the multi-component Sasa-Satsuma equation is confirmed by using the standard WTC approach and the Kruskal simplification. Then, by means of the multi-linear variable separation approach, a new exact solution with lower-dimensional arbitrary functions is constructed. For the physical quantity new coherent structure which possesses peakons at x-axis and compactons at y-axis is illustrated both analytically and graphically. [ABSTRACT FROM AUTHOR]

Uniform marginal pricing cannot generally support competitive equilibrium solutions in markets with non-convexities, yielding surplus sub-optimality effects. Previous work has identified non-convexities associated with the generation side of electricity markets and proposed different approaches to address surplus sub-optimality. This paper extends this concept to incorporate the demand side. Non-convexities of flexible demand (FD) are identified, including options to forgo demand activities as well as discrete and minimum power levels, and resulting surplus sub-optimality effects are demonstrated through simple examples and a larger case study. Generalized uplift and convex hull pricing approaches addressing these effects are extended to account for FD non-convexities. Concerning the former, generalized uplift functions for FD participants are proposed, and a new rule is introduced for equitable distribution of the total surplus loss compensation among market participants. Regarding the latter, it is demonstrated that convex hull prices are flattened at periods when FD is scheduled to eliminate surplus sub-optimality associated with the FD ability to redistribute energy requirements across time. [ABSTRACT FROM PUBLISHER]

Psoriasis is a chronic inflammatory skin disease whose etiology has not yet been determined. MicroRNAs (miRs) regulate the early stages of psoriasis and are targets for therapeutic intervention. The present study aimed to investigate the functional role of miR-489-3p in psoriasis. The present study first assessed the expression levels of miR-489-3p and Toll-like receptor (TLR)4 mRNA using reverse transcription-quantitative PCR, and also detected the protein expression levels of TLR4 and NF-κB via western blot analysis. TargetScan and miRDB target gene prediction tools were used to confirm the regulation of Toll-like receptor (TLR)4 by miR-489-3p. Moreover, a Cell Counting Kit (CCK)-8 assay was conducted to evaluate cell viability, while cell cycle and colony formation assays were performed to evaluate cell proliferation. Human keratinocytes (HaCaT) were co-transfected with TLR4-small interfering RNA and miR-489-3p-inhibitor plasmids, and analysis of cell proliferation and inflammatory cytokine secretion was performed using CCK-8 assay and ELISA. It was found that miR-489-3p expression was downregulated in patients with psoriasis. Bioinformatics analysis identified that TLR4 was a direct target of miR-489-3p. This was confirmed via luciferase reporter assays in HaCaT cells. The overexpression of miR-489-3p inhibited the TLR4/NF-κB signaling pathway and reduced cell proliferation. TLR4 silencing alleviated the effects of miR-489-3p, and enhanced cell proliferation and inflammatory cytokine secretion. Taken together, these data suggested that miR-489-3p may be a key effector of psoriasis, which promotes inflammatory responses by direct targeting of TLR4. miR-489-3p therefore represents a promising prognostic biomarker and therapeutic target for psoriasis treatment. [ABSTRACT FROM AUTHOR]

With the roll-out of smart meters and the increasing prevalence of distributed energy resources (DERs) at the residential level, end-users rely on home energy management systems (HEMSs) that can harness real-time data and employ artificial intelligence techniques to optimally manage the operation of different DERs, which are targeted toward minimizing the end-user's energy bill. In this respect, the performance of the conventional model-based demand response (DR) management approach may deteriorate due to the inaccuracy of the employed DER operating models and the probabilistic modeling of uncertain parameters. To overcome the above drawbacks, this paper develops a novel real-time DR management strategy for a residential household based on the twin delayed deep deterministic policy gradient (TD3) learning approach. This approach is model-free, and thus does not rely on knowledge of the distribution of uncertainties or the operating models and parameters of the DERs. It also enables learning of neural-network-based and fine-grained DR management policies in a multi-dimensional action space by exploiting high-dimensional sensory data that encapsulate the uncertainties associated with the renewable generation, appliances' operating states, utility prices, and outdoor temperature. The proposed method is applied to the energy management problem for a household with a portfolio of the most prominent types of DERs. Case studies involving a real-world scenario are used to validate the superior performance of the proposed method in reducing the household's energy costs while coping with the multi-source uncertainties through comprehensive comparisons with the state-of-the-art deep reinforcement learning (DRL) methods. [ABSTRACT FROM AUTHOR]

The use of hydropower has expanded in recent years in southwest China, playing a key role in the success of China's West-to-East energy strategy. Although installed hydropower capacity has reached a record high, the problem of abandoned water has intensified and has been gradually highlighted by researchers. Abandoned water has resulted in a significant waste of carbon-free and inexpensive renewable energy, which is not conducive to China's goals to reduce emissions to save energy. This waste has seriously affected the development of China's hydropower industry. Therefore, now is the time to analyze the main reasons for the abandoned water problem and to identify the crux of the problem. In this paper, we collected the actual operation data of all the power stations in the case area and established the mathematical model according to the quantity of abandoned water as well as the actual and the theoretical output curve of the power station to quantify the abandoned water into energy loss. We then classified the abandoned water according to three groups of causes, namely, generation-side, line-side, and market-side causes. In 2016, the total energy loss of abandoned water of Sichuan Province was 28.73 × 109 kW h, of which the generation-, line-, and market-side causes account for 7.76%, 11.68%, and 80.56% of the energy loss, respectively. Therefore, the market-side causes are the main cause for the abandoned water problem. Such market-side causes primarily include the imbalance between electricity supply and demand, the provincial barriers of the local government system and its mechanisms, and the hysteresis congestion of the transmission lines. Seizing the favorable opportunity presented by new power system reforms in China, this study proposes several market-oriented measures to improve hydropower consumption capacity. [ABSTRACT FROM AUTHOR]

Introduction: Benzodiazepine receptor agonists (BZRAs), which include benzodiazepines and Z-drugs, are the most commonly prescribed psychotropic drugs worldwide, and their inappropriate use places a significant burden on public health. Given the widespread use of BZRAs in psychiatric settings, this condition may result from doctors' improper prescribing. Researchers have developed an electronic intervention system to assist psychiatrists in prescribing BZRAs appropriately. This study aims to determine the efficacy and utility of electronic intervention in reducing improper BZRAs prescriptions in real-world psychiatric outpatient settings., Methods and Analysis: A multicentre randomised controlled research study will be conducted in real-world settings with licensed psychiatrists with prescription qualifications from five of Chinese most significant regional hospitals that provide high-quality mental healthcare. Participants will be 1:1 randomly assigned to receive a 3-month electronic intervention (11 related information pushing and 3 online lectures) or be placed on a waiting list. The primary outcome is the change in the proportion of inappropriate BZRAs prescriptions between the baseline period (3 months before the intervention) and 3 months after the intervention. Secondary outcomes will be examined at baseline, the third month and the sixth month. The secondary outcomes include psychiatrists' knowledge and attitudes about appropriate BZRAs prescription, the associated side effects of BZRAs among patients and self-efficacy. To measure the utility, intervention assessment and system utilisation data from the intervention group were collected., Ethics and Dissemination: The institutional review board and ethics committees of Shanghai Mental Health Center, Second Xiangya Hospital, West China Hospital, Guangji Hospital and Wuhan Mental Health Center approved the study. After the study is completed, the results will be published in peer-reviewed journals or presented at conferences. If the educational materials are effective, they are available to the general public., Trial Registration Number: NCT03724669; Pre-results., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)