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Smart tourism destinations (STDs), driven by technological advancements, aim to increase travellers' experiences and residents' quality of life. However, the existing research primarily focuses on the perspectives of tourists while neglecting the attitudes of residents. Residents' attitudes will impact their support of local smart tourism development. Without residents' support, STD-related projects may face failure, as exemplified by the case of the Google smart city in Toronto. The present study fills this gap by exploring how residents perceive the impact of STD in their communities through the development and validation of an impact scale. Said scale is rigorously conceptualized as a formative multidimensional construct with 19 items across four dimensions: smart economy, smart travel/tourism, smart environment, and smart governance. This study offers a valuable measurement tool to recognize the perceptual determinants of STD on residents' overall life satisfaction and also provides a novel approach to evaluate the effectiveness of STDs. [ABSTRACT FROM AUTHOR]

Aiming at the current lithium-ion battery storage power station model, which cannot effectively reflect the battery characteristics, a proposed electro-thermal coupling modeling method for storage power stations considers the characteristics of the battery body by combining the equivalent circuit model and accounting for the effect of temperature on the battery. Based on the modeling of a single lithium-ion battery, the equivalent circuit model and thermal model are integrated to create the battery's electro-thermal coupling model. The parameters of this coupling model are determined using the particle swarm algorithm. On this basis, the battery compartment model of the energy storage station is analyzed and verified by utilizing the circuit series-parallel connection characteristics. Subsequently, the electro-thermal coupling model of the energy storage station is established. The dual Kalman filter algorithm is utilized to simulate and validate the electric-thermal coupling model of the energy storage power station, considering ontological factors such as battery voltage, current, and temperature. The results demonstrate that the established coupling model can accurately determine the SOC and temperature of the power station. This ability allows for a more precise reflection of the battery characteristics of the energy storage station. It also validates the accuracy and effectiveness of the electric-thermal coupling model of the energy storage station. This finding is crucial for assessing the state and ensuring the safe operation of the battery system in the energy storage station. [ABSTRACT FROM AUTHOR]

Monolayer and few-layer CoPS3 have been successfully synthesized via an Au-assisted exfoliation technique, with their transport properties subsequently investigated by conductive atomic force microscopy. Rectification effects can be observed in CoPS3 nanosheets due to the Schottky junction formation at the Au/CoPS3 interface, especially in configurations comprising two or more layer flakes. The distinct transport properties between monolayer and few-layer samples demonstrate that the charge transport behavior in the vertical direction is associated with the van der Waals gap. The calculated electron affinity for CoPS3 is about 4.84 eV, as revealed by the thermionic emission model. The results obtained contribute to the knowledge base concerning the transport characteristics of ultrathin MPX3, facilitating further exploration of the transport behavior of these materials and their potential applications in novel electronic devices. [ABSTRACT FROM AUTHOR]

The direct growth of III-V quantum dot (QD) lasers on silicon substrate has been rapidly developing over the past decade and has been recognized as a promising method for achieving on-chip light sources in photonic integrated circuits (PICs). Up to date, O- and C/L-bands InAs QD lasers on Si have been extensively investigated, but as an extended telecommunication wavelength, the E-band QD lasers directly grown on Si substrates are not available yet. Here, we demonstrate the first E-band (1365 nm) InAs QD micro-disk lasers epitaxially grown on Si (001) substrates by using a III-V/IV hybrid dual-chamber molecular beam epitaxy (MBE) system. The micro-disk laser device on Si was characterized with an optical threshold power of 0.424 mW and quality factor (Q) of 1727.2 at 200 K. The results presented here indicate a path to on-chip silicon photonic telecom-transmitters. [ABSTRACT FROM AUTHOR]

Silicon photonics is becoming a competitive technology to settle the issues of power and speed in data centers and computing systems. However, the absence of an on‐chip light source restricts the wide and deep application of silicon photonics. Essentially, efficient edge coupling from a Si‐based III‐V laser to silicon photonic components remains the major obstacle for on‐chip integration. Here, two compact edge couplers with ultra‐low coupling loss and eased fabrication for the light transmission from a Si‐based III‐V laser to Si3N4 waveguide are realized. An ultra‐low laser‐to‐chip coupling loss of only 1.175 dB for the butt coupling of a Si‐based InAs QD laser is experimentally demonstrated with high alignment tolerance. The strategies presented here provide a competitive solution for realizing ultra‐efficient integration of Si‐based light sources and silicon photonic components. [ABSTRACT FROM AUTHOR]

Benefitting from low propagation loss and complementary metal–oxide–semiconductor compatibility, Si 3 N 4 is heavily explored for the applications of nonlinear optical signal processing. However, the Si 3 N 4 waveguide is limited by its low optical Kerr nonlinearity. In this work, we introduce highly nonlinear ultra-Si-rich-nitride (USRN) deposited by the plasma enhanced chemical vapor deposition system. The measured linear refractive index of USRN is 3.09 at a wavelength of 1550 nm, while the Kerr nonlinearity of the USRN waveguide is experimentally extracted with a value of 2.25 × 10 − 17 m 2 /W. Moreover, a broadband wavelength conversion ranging from S-band to L-band by a four-wave-mixing experiment is realized via designed USRN waveguide with a relatively short length of 3 mm. The measured bandwidth is 190 nm with a continuous-wave pump laser located at 1530 nm. The conversion efficiency is measured approximately − 48 dB under a relatively low pump power of 7 dBm. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to use mixed methods to create a new conceptual framework to understand the unique characteristics of virtual tourism experiences (VTE), which has not been systemically examined. Design/methodology/approach: Study 1 uses topic modeling with Latent Dirichlet Allocation to analyze 91,609 online reviews from the Airbnb Experience platform. Study 2 uses content analysis of open-ended qualitative responses from VTE customers. The two studies together are used to build a new conceptual model. Findings: Building upon the Stimulus–Organism–Response (S-O-R) model and the experience economy, results present a new conceptual model and identify VTE as unique in terms of Stimulus (education, entertainment, esthetics, escapism and connection), Organism (experiencing synchronicity, telepresence, participation and customization, emotion) and Response (evaluation and behavioral responses). Given the uniqueness of VTE, the new construct of the virtual servicescape is incorporated, recognizing the host, the focal customer and other customers, and the technology as the four main components. Practical implications: The proposed framework can be used to guide the design, development, and evaluation of VTE, including identifying the key considerations, engagement within the ecosystem and providing guidance to hosts and operators. Originality/value: To the best of the authors' knowledge, this is the first study that systematically explores VTE and proposes the theoretical framework to comprehensively understand this new form of experience in sharing economy by combining the unique aspects of the stimulus, organism, response and virtual servicescape. [ABSTRACT FROM AUTHOR]

The development of the low dislocation density of the Si-based GaAs buffer is considered the key technical route for realizing InAs/GaAs quantum dot lasers for photonic integrated circuits. To prepare the high-quality GaAs layer on the Si substrate, we employed an engineered Ge-buffer on Si, used thermal cycle annealing, and introduced filtering layers, e.g., strained-layer superlattices, to control/reduce the threading dislocation density in the active part of the laser. In this way, a low defect density of 2.9 × 107 cm−2 could be achieved in the GaAs layer with a surface roughness of 1.01 nm. Transmission electron microscopy has been applied to study the effect of cycling, annealing, and filtering layers for blocking or bending threading-dislocation into the InAs QDs active region of the laser. In addition, the dependence of optical properties of InAs QDs on the growth temperature was also investigated. The results show that a density of 3.4 × 1010 InAs quantum dots could be grown at 450 °C, and the photoluminescence exhibits emission wavelengths of 1274 nm with a fullwidth at half-maximum (FWHM) equal to 32 nm at room temperature. The laser structure demonstrates a peak at 1.27 μm with an FWHM equal to 2.6 nm under a continuous-wave operation with a threshold current density of ∼158 A/cm2 for a 4-μm narrow-ridge width InAs QD device. This work, therefore, paves the path for a monolithic solution for photonic integrated circuits when III−V light sources (which is required for Si photonics) are grown on a Ge-platform (engineered Ge-buffer on Si) for the integration of the CMOS part with other photonic devices on the same chip in near future. [ABSTRACT FROM AUTHOR]

To improve the search efficiency of Rao algorithm, a behavior-selection based Rao Algorithm is proposed in this paper. Our proposed algorithm is a parameter-less and metaphor-less algorithm, which has three major improvements. (i) Three perturbation operators for offspring population are proposed to effectively balance algorithm exploitation and exploration capability. (ii) A behavior selection strategy is proposed for evaluating the three new perturbation operators and the original operators of Rao Algorithms. The upper confidence bound algorithm is modified and used for calculating the future value of the operators. (iii) A new mapping strategy is designed to increase the diversity of the solutions. The behavior-selection based Rao algorithm is tested based on some benchmark functions and the power system economic load dispatch problems, compared to the other well-known algorithms, the proposed algorithm has a competitive superiority in terms of convergence performance and global search capability. [ABSTRACT FROM AUTHOR]

Investigation of the non-ohmic transport behaviors under high magnetic fields can provide a new way to explore novel field-induced phenomena. We present the current–voltage measurements under high magnetic fields based on the flat-top pulsed magnetic field system. Two different measurement strategies were compared, given that the excitation current swept continuously or increased by a series of pulses. For the short duration of the flat-top pulsed field, the continuous current method was adopted and well optimized to reduce the Joule heating and achieve the quasi-static measurements. Finally, the non-ohmic behaviors of a quasi-one-dimensional charge density wave Li0.9Mo6O17 were successfully studied under the magnetic field up to 30 T at 4.2 K, which was the first current–voltage measurements carried out in pulsed magnetic fields. [ABSTRACT FROM AUTHOR]

Local Differential Privacy (LDP) is a popular standard for privacy-preserving data collection. Numerous LDP protocols have been proposed in the literature which differ in how they provide higher utility in different settings. Yet, few have engaged in analyzing the privacy relationships of these protocols under varying settings, and consequently, it is non-trivial to select which LDP protocol is best to use in a newly emerging application. In this paper, we present an adversarial approach to protocol analysis and selection and make three original contributions. First, we introduce a Bayesian adversary to analyze the privacy relationships of LDP protocols under varying settings. We show that different protocols have substantially different responses to the attack effectiveness of the Bayesian adversary, measured in terms of Adversarial Success Rate (ASR). Second, we provide a formal and empirical analysis on a set of privacy and utility-critical factors, including encoding parameters, privacy budget, data domain, adversarial knowledge, and statistical distribution. We show that different settings of these factors have significant effects on the ASRs of LDP protocols, and no protocol provides consistently low ASR across all settings. Third, we design and develop LDPLens, a prototype implementation of our proposed framework. Given a data collection scenario with various factors and constraints, LDPLens enables optimized selection of a desirable LDP protocol for the given scenario. We evaluate the effectiveness of LDPLens using three case studies with real-world datasets. Results show that LDPLens recommends a different protocol in each case study, and the protocol recommended by LDPLens can yield up to 1.5–2 fold reduction in utility loss, ASR or privacy budget compared to a randomly selected protocol. [ABSTRACT FROM AUTHOR]

Gradient leakage attacks are considered one of the wickedest privacy threats in deep learning as attackers covertly spy gradient updates during iterative training without compromising model training quality, and yet secretly reconstruct sensitive training data using leaked gradients with high attack success rate. Although deep learning with differential privacy is a defacto standard for publishing deep learning models with differential privacy guarantee, we show that differentially private algorithms with fixed privacy parameters are vulnerable against gradient leakage attacks. This paper investigates alternative approaches to gradient leakage resilient deep learning with differential privacy (DP). First, we analyze existing implementation of deep learning with differential privacy, which use fixed noise variance to injects constant noise to the gradients in all layers using fixed privacy parameters. Despite the DP guarantee provided, the method suffers from low accuracy and is vulnerable to gradient leakage attacks. Second, we present a gradient leakage resilient deep learning approach with differential privacy guarantee by using dynamic privacy parameters. Unlike fixed-parameter strategies that result in constant noise variance, different dynamic parameter strategies present alternative techniques to introduce adaptive noise variance and adaptive noise injection which are closely aligned to the trend of gradient updates during differentially private model training. Finally, we describe four complementary metrics to evaluate and compare alternative approaches. Extensive experiments on six benchmark datasets show that differentially private deep learning with dynamic privacy parameters outperforms the deep learning using fixed DP parameters, and existing adaptive clipping approaches in all aspects: compelling accuracy performance, strong differential privacy guarantee, and high attack resilience. [ABSTRACT FROM AUTHOR]

Big data powered Deep Learning (DL) and its applications have blossomed in recent years, fueled by three technological trends: a large amount of digitized data openly accessible, a growing number of DL software frameworks in open source and commercial markets, and a selection of affordable parallel computing hardware devices. However, no single DL framework, to date, dominates in terms of performance and accuracy even for baseline classification tasks on standard datasets, making the selection of a DL framework an overwhelming task. This paper takes a holistic approach to conduct empirical comparison and analysis of four representative DL frameworks with three unique contributions. First, given a selection of CPU-GPU configurations, we show that for a specific DL framework, different configurations of its hyper-parameters may have a significant impact on both performance and accuracy of DL applications. Second, to the best of our knowledge, this study is the first to identify the opportunities for improving the training time performance and the accuracy of DL frameworks by configuring parallel computing libraries and tuning individual and multiple hyper-parameters. Third, we also conduct a comparative measurement study on the resource consumption patterns of four DL frameworks and their performance and accuracy implications, including CPU and memory usage, and their correlations to varying settings of hyper-parameters under different configuration combinations of hardware, parallel computing libraries. We argue that this measurement study provides in-depth empirical comparison and analysis of four representative DL frameworks, and offers practical guidance for service providers to deploying and delivering DL as a Service (DLaaS) and for application developers and DLaaS consumers to select the right DL frameworks for the right DL workloads. [ABSTRACT FROM AUTHOR]

Membership inference attacks seek to infer membership of individual training instances of a model to which an adversary has black-box access through a machine learning-as-a-service API. In providing an in-depth characterization of membership privacy risks against machine learning models, this paper presents a comprehensive study towards demystifying membership inference attacks from two complimentary perspectives. First, we provide a generalized formulation of the development of a black-box membership inference attack model. Second, we characterize the importance of model choice on model vulnerability through a systematic evaluation of a variety of machine learning models and model combinations using multiple datasets. Through formal analysis and empirical evidence from extensive experimentation, we characterize under what conditions a model may be vulnerable to such black-box membership inference attacks. We show that membership inference vulnerability is data-driven and corresponding attack models are largely transferable. Though different model types display different vulnerabilities to membership inference, so do different datasets. Our empirical results additionally show that (1) using the type of target model under attack within the attack model may not increase attack effectiveness and (2) collaborative learning exposes vulnerabilities to membership inference risks when the adversary is a participant. We also discuss countermeasure and mitigation strategies. [ABSTRACT FROM AUTHOR]

AlxGa1-xAs compound is one of the promising platforms to realize high performance nonlinear optical devices, which provide ultra-high third order nonlinearity and negligible two-photon absorption in the range of telecom wavelength. To achieve highly efficient optical confinement, the conventional AlGaAs waveguide cladding layer is achieved by using SiO2 via the wafer bonding process or AlGaAs with higher Al concentration, which requires a complex fabrication process. In this work, we demonstrate a suspended Al0.5Ga0.5As waveguide structure directly grown on the GaAs substrate by using the molecular beam epitaxy system. Both self-phase modulation and four-wave-mixing experiments are performed. By solving the nonlinear Schrödinger equations and the degenerated parametric amplification process, the n2 value is calculated to be 1.6 × 10−17 m2/W, and the nonlinear parameter is determined to be 155 W−1 m−1. As the AlGaAs thin film can be directly grown on the Si based substrate, this suspended waveguide platform could potentially be developed on a large scale silicon wafer for integrated nonlinear photonic devices. [ABSTRACT FROM AUTHOR]

Chiral quantum optics has attracted considerable interest in the field of quantum information science. Exploiting the spin‐polarization properties of quantum emitters and engineering rational photonic nanostructures has made it possible to transform information from spin to path encoding. Here, compact chiral photonic circuits with deterministic circularly polarized chiral routing and beamsplitting are demonstrated using two laterally adjacent waveguides coupled with quantum dots. Chiral routing arises from the electromagnetic field chirality in waveguide, and beamsplitting is obtained via the evanescent field coupling. The spin‐ and position‐dependent directional spontaneous emission are achieved by spatially selective micro‐photoluminescence measurements, with a chiral contrast of up to 0.84 in the chiral photonic circuits. This makes a significant advancement for broadening the application scenarios of chiral quantum optics and developing scalable quantum photonic networks. [ABSTRACT FROM AUTHOR]

Chiral light–matter interaction between photonic nanostructures with quantum emitters shows great potential to implement spin–photon interfaces for quantum information processing. Position-dependent spin momentum locking of the quantum emitter is important for these chiral coupled nanostructures. Here, we report the position-dependent chiral coupling between quantum dots (QDs) and cross waveguides both numerically and experimentally. Four quantum dots distributed at different positions in the cross section are selected to characterize the chiral properties of the device. Directional emission is achieved in a single waveguide and in both two waveguides simultaneously. In addition, the QD position can be determined with the chiral contrasts from four outputs. Therefore, the cross waveguide can function as a one-way unidirectional waveguide and a circularly polarized beam splitter by placing the QD at a rational position, which has potential applications in spin-to-path encoding for complex quantum optical networks at the single-photon level. [ABSTRACT FROM AUTHOR]

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In this work, we experimentally study the optical Kerr nonlinearities of graphene/Si hybrid waveguides with enhanced self-phase modulation. In the case of CMOS compatible materials for nonlinear optical signal processing, Si and silicon nitride waveguides have been extensively investigated over the past decade. However, Si waveguides exhibit strong two-photon absorption (TPA) at telecommunication wavelengths, which leads to a significant reduction of the nonlinear figure-of-merit (FOM). In contrast, a silicon nitride based material system usually suppresses the TPA but simultaneously leads to the reduction of Kerr nonlinearity by one order of magnitude. Here, we introduce a graphene/Si hybrid waveguide, which maintains the optical properties and CMOS compatibility of Si waveguides, while enhancing the Kerr nonlinearity, by transferring over to the top of the waveguides. The graphene/Si waveguides are measured to have an enhanced nonlinear parameter of 510 W−1 m−1, compared with that of the Si waveguide of 150 W−1 m−1. An enhanced nonlinear FOM of 2.48 ± 0.25 has been achieved, which is four times larger than that of the Si waveguide of 0.6 ± 0.1. This work reveals the potential application of graphene/Si hybrid waveguides with high Kerr nonlinearity and FOM for nonlinear all-optical signal processing. [ABSTRACT FROM AUTHOR]

Featured Application: optical communication, silicon photonics. Direct epitaxial growth of III-V heterostructure on CMOS-compatible silicon wafer offers substantial manufacturing cost and scalability advantages. Quantum dot (QD) devices are less sensitive to defect and temperature, which makes epitaxially grown III-V QD lasers on Si one of the most promising technologies for achieving low-cost, scalable integration with silicon photonics. The major challenges are that heteroepitaxial growth of III-V materials on Si normally encounters high densities of mismatch dislocations, antiphase boundaries and thermal cracks, which limit the device performance and lifetime. This paper reviews some of the recent developments on hybrid InAs/GaAs QD growth on Ge substrates and highly uniform (111)-faceted hollow Si (001) substrates by molecular beam epitaxy (MBE). By implementing step-graded epitaxial growth techniques, the emission wavelength can be tuned into either an O band or C/L band. Furthermore, microcavity QD laser devices are fabricated and characterized. The epitaxially grown III-V/IV hybrid platform paves the way to provide a promising approach for future on-chip silicon photonic integration. [ABSTRACT FROM AUTHOR]

In the present study, we developed a novel ratiometric fluorescent sensor for the detection of cysteine (Cys) by linking the recognition indicators glutathione-capped gold nanoclusters (GSH–AuNCs) onto the surface of amino-modified SiO2NPs@SiO2 spheres. The as-prepared nanohybrid (SiO2NPs/AuNCs) exhibited dual-emission fluorescence peaking at 425 and 605 nm under single excitation of 360 nm. Furthermore, its morphology, composition, surface chemical features and optical properties have been fully characterized through transmission electron microscopy, X-ray photoelectron spectroscopy, Fourier transform infrared spectroscopy, UV-vis absorption spectroscopy, and fluorescence spectroscopy. For Cys detection, the red fluorescence intensity of AuNCs of the nanohybrid could be selectively and effectively enhanced by Cys due to specific interaction between the indicator and the target, whereas the reference signal of blue-emitting SiO2NPs@SiO2 spheres remained constant as SiO2NPs were entirely encapsulated by the silica shell. Thus, the variation in the intensity ratio (I605/I425) of the nanohybrid could be used to determine Cys based on the ratiometric method. The I605/I425 against the concentration of Cys demonstrated a good linear relationship from 0 to 20 μM, and the detection limit reached as low as 0.35 μM. Moreover, this ratiometric system has been applied to determine Cys in serum samples with satisfactory results. [ABSTRACT FROM AUTHOR]

Thanks to the rapid development of information technology, the size of a wireless network is becoming larger and larger, which makes spectrum resources more precious than ever before. To improve the efficiency of spectrum utilization, game theory has been applied to study efficient spectrum sharing for a long time. However, the scale of wireless networks in existing studies is relatively small. In this paper, we introduce a novel game called aggregative game and use it to model spectrum sharing in large-scale, heterogeneous, and dynamic networks. Meanwhile, the massive usage of the spectrum leads to easier divulgence of privacy of spectrum users, which calls for privacy and truthfulness guarantees. In a large decentralized scenario, each user has no priori about other users’ channel access decisions, which forms an incomplete information game. A “weak mediator,” e.g., the base station or licensed spectrum regulator, is introduced and it turns the incomplete spectrum sharing game into a complete one. This is essential in reaching a Nash equilibrium (NE). By utilizing past channel access experience, we propose an online learning algorithm to improve the utility of each user. We show that the learning algorithm achieves an NE over time and provides no regret guarantee for each user. Specifically, our mechanism admits an approximate ex-post NE, and is joint differentially private and incentive-compatible. Efficiency of the approximate NE is evaluated, and innovative scaling law results are disclosed. We also provide simulation results to verify our analysis. [ABSTRACT FROM AUTHOR]

Drawing upon the identity-based perspective, the current research focused on the role of affective commitment and future work self salience ( FWSS) in the relationship between abusive supervision and job performance. We expected that affective commitment, which represents the organization-based identities of employees, would mediate the relationship between abusive supervision and job performance. Furthermore, we predicted that employees' FWSS, which represents the ease of construction and clarity of an individual's hoped-for work-based identity, would amplify the indirect effect of abusive supervision on job performance via affective commitment. Specifically, FWSS was expected to play an amplifying role in the abusive supervision-affective commitment path. Based on a sample of 480 salespersons, the results of a 3-wave study revealed that affective commitment mediated the abusive supervision-sales performance relationship. Moreover, the indirect effect of abusive supervision on sales performance via affective commitment was stronger for employees with higher FWSS. Specifically, the deleterious effect of abusive supervision on affective commitment was amplified by FWSS. This was the case even when emotional exhaustion and leader-member exchange were incorporated as competing mediators. Implications of our findings and future directions are discussed. Practitioner points Organizations should pay greater attention to employees with higher FWSS because they are more vulnerable to abusive supervision in terms of their decreased affective commitment and performance., To maintain affective commitment and job performance of employees with higher FWSS, organizations should train leaders to reduce or eliminate abusive behaviours, especially for those leading teams with a majority of high- FWSS employees. [ABSTRACT FROM AUTHOR]

The concrete experience of physical warmth has been demonstrated to promote interpersonal warmth. This well-documented link, however, tells only half of the story. In the current study, we thus examined whether physical coldness can also increase interpersonal warmth under certain circumstances.Weconducted three experiments to demonstrate that the relationship between the experience of physical temperature and interpersonal outcomes is context dependent. Experiment 1 showed that participants touching cold (vs.warm)objectsweremorewilling to forgive a peer's dishonest behaviour. Experiment 2 demonstrated the fully interactive effect of temperature and context on interpersonal warmth: Participants touching cold (vs. warm) objects were less likely to assist an individual who had provided them with good service (positive social context), but morelikely to assist an individualwhohad providedthem with poor service (negative social context). Experiment 3 replicated the results of Experiment 2 using the likelihood to complain, a hostility-related indicator, as the dependent variable: In a pleasant queue (positive social context), participants touching cold objects were more likely to complain and those touching warm objects were less likely to complain compared with the control group. This pattern was reversed in an annoying queue (negative social context). [ABSTRACT FROM AUTHOR]

Previous neuroeconomic studies have observed that people display sympathetic neural responses toward others’ misfortunes. We argue that the reverse emotions, such as gloating orschadenfreude, may also emerge in certain circumstances. To examine this theory, we recorded feedback-related negativity (FRN) toward others’ large or small gains or losses in a stock market context. We adopted the framework of social distance, according to which we hypothesized that because others in the stock market are far away, unidentified, and indistinct, people would show less sympathy or evenschadenfreudetoward others’ large losses. The results indicated that FRN at Fz was significantly less negative when observing larger decreases in others’ stock, indicating that others’ large losses are not unexpected negative events in the stock market and suggesting the existence ofschadenfreude.Our research contributes to the understanding of social neurofinance by demonstrating theschadenfreudeeffect in relation to the stock market. This study also provides new information regarding the relationship between FRN and the social emotions that form the expectations of gain and loss. [ABSTRACT FROM PUBLISHER]