Future Interactions Research Articles

Smile mimicry smoothens human-virtual human interactions

During social interactions, people often unintentionally mimic each other's behaviors, which can occur at the motor level (e.g. smiles or head movements), and the autonomic level (e.g. pupil size). Mimicry has often been linked with increases in liking and trust and tends to smoothen the bond between interaction partners. However, people with social anxiety are generally less expressive and they may also have altered mimicry behavior. The current study aims to explore the relationship between mimicry, person-perception, and social anxiety levels by having participants interact with virtual humans. Compared to real interaction partners or photographed or filmed actors, virtual humans can be controlled and behaviors very precisely manipulated. Participants observed ten virtual humans telling a neutral story on a computer monitor. At predetermined time points, the virtual humans showed four types of expressions: smiles, head movements, pupil dilation, and blushing. After each story, we measured to what extent participants liked and trusted the virtual humans via the avatar evaluation questionnaire, the desire for future interaction scale, and an investment game. In addition, we assessed participants’ social anxiety levels. The results show that participants, regardless of anxiety levels, mimicked the virtual humans’ smiles and that smile mimicry is associated with an increased liking and trust toward the virtual humans. These results build on previous research showing that the mimicry of smiles acts as a social glue and also benefits virtual social interactions.

Characteristics and risk assessment of microplastic mediated heavy metal transport in black clam Villorita cyprinoides from a Ramsar wetland of South India

The synergetic and interactive effects of microplastics (MPs) as potential carriers of pollutants and pathogens have a manifold impact on human health and aquatic biota. The present study delineates the role of MPs in transporting heavy metals to the black clam, Villorita cyrpinoides of Vembanad Lake in South India. MPs in water (3.41 ± 1.87 items/L), sediment (154.6 ± 45.4 items/kg) and within clams (0.87 ± 0.34 items/g tissue) showed considerable variation based on their physical and chemical characteristics, which in turn reflected its differential potential to carry heavy metals. Though the levels of heavy metals (Al, Fe, Mn, Zn, Cr, Pb) within the clam and those carried by MPs were less than the hazardous limits, the concentration of Cd within the clam and MP was found to have a positive correlation. Though the present levels of heavy metal pollution in the lake do not pose a direct threat to humans, anthropogenic interventions and reduced water flow have turned the lower reaches of the lake into a sink of heavy metals. Hence with MP concentration increasing it is worth investigating how their future interactions with heavy metals and other pollutants would pose a risk to living organisms.

Utilizing wearable technology to characterize and facilitate occupant collaborations in flexible workspaces

Hybrid working strategies have become, and will continue to be, the norm for many offices. This raises two considerations: newly unoccupied spaces needlessly consume energy, and the occupied spaces need to be effectively used to facilitate meaningful interactions and create a positive, sustainable work culture. This work aims to determine when spontaneous, collaborative interactions occur within the building and the environmental factors that facilitate such interactions. This study uses smartwatch-based micro-surveys using the Cozie platform to identify the occurrence of and spatially place interactions while categorizing them as a collaboration or distraction. This method uniquely circumvents pitfalls associated with surveying and qualitative data collection: occupant behaviors are identified in real-time in a non-intrusive manner, and survey data is corroborated with quantitative sensor data. A proof-of-concept study was deployed with nine hybrid-working participants providing 100 micro-survey cluster responses over approximately two weeks. The results show the spontaneous interactions occurring in hybrid mode are split evenly among the categories of collaboration, wanted socialization, and distraction and primarily occur with coworkers at one’s desk. From these data, we can establish various correlations between the occurrence of positive spontaneous interactions and different factors, such as the time of day and the locations in the building. This framework and first deployment provide the foundation for future large-scale data collection experiments and human interaction modeling.

You versus we: How pronoun use shapes perceptions of receptiveness

In response to increasing societal divisions, an extensive literature has emerged examining the construct of receptiveness. This literature suggests that signaling receptiveness to others confers a variety of interpersonal benefits, such as increased persuasiveness. How do people signal their receptiveness to others? The current research investigates whether one of the most fundamental aspects of language—pronoun use—could shape perceptions of receptiveness. We find that in adversarial contexts, messages containing second-person pronouns (“you” pronouns) are perceived as less receptive than messages containing first-person plural pronouns (“we” pronouns). We demonstrate that “you” pronouns signal aggressiveness, which reduces perceived receptiveness. Moreover, we document that perceived receptiveness influences important downstream consequences such as persuasion, interest in future interaction, sharing intentions, and censorship likelihood. These findings contribute to a fast-growing literature on perceived receptiveness, uncover novel consequences of signaling receptiveness, and contribute to our understanding of how pronouns shape social perception.

Proposal of an active senior travel service scenario through the future PBV platform interaction

Proposal of an active senior travel service scenario through the future PBV platform interaction

Recent advances on microplastics/nanoplastics interaction with plant species: A concise review

The transmission, toxicity, and removal of micro/nanoplastics (MPs/NPs) have been the subject of extensive attention and have impacted concerns globally. The inclusion of microplastic pollution can have a plethora of effects on plant growth, depending on the composition of the planting media. Following a recent year of research focusing mostly on aquatic systems, attention has begun shifted to the consequences of microplastic on plant cells. Therefore, there is a significant knowledge gap regarding the extent to which MPs have an impact on terrestrial environments, particularly agroecosystems, and the risks that this has for human health. Within this review, the interaction of MPs/NPs to plant species is due to the abundance of microplastics in soil following human activity. This review also summarised the routes of MPs/NPs to the plant through the root and shoot of the plant. Subsequently, the emergence of MPs/NPs influences and brings implications on plant growth, growth, and crop production according to each plant species. Besides, the recommendations for further research on the phytotoxic effects of MPs on plants, the method of uptake and translocation in plant tissues, detection techniques for MPs in plants, and, most importantly, the potential for future interactions and accumulation of MPs in plants have also been discussed thoroughly in this paper. The most recent developments in this area are summarised at the end, with an emphasis on the future directions for studying microplastics in terrestrial systems.

A wearable device based on the ionic liquid decorated sponge-like ultraviolet-curable resin for recognizing human mental health conditions

Developing a wearable device that can reliably and continuously detect human pulse signals and assess heart rate variability (HRV) parameters is essential for discriminating healthy subjects from patients with cardiovascular diseases and mental health disorders. Here, by combining a template-assisted ultraviolet-curing method with a subsequent ionic liquid modification, we propose a sponge-like dielectric layer and adopt it to construct a wearable device for decoding human mental health conditions. Benefiting from the coexisting sponge-like structures and an ionic liquid, the proposed wearable device presents a robust capacitance response, and could continuously monitor human pulse signals as well as analyze inter-beat intervals (IBIs) and HRV parameters of volunteers under different emotional states. Lastly, IBIs distributions and heart rates extracted from the pulse signals of the volunteers with different emotional conditions are employed as input features to train a machine-learning model, which is performed to further demonstrate the possibility of recognizing human emotions via pulse signal analysis. In conclusion, by incorporating the proposed wearable device with a machine-learning model, it is expected to achieve the emotional recognition of the volunteers with a piece of the pulse signal, showing promising potential in future human-machine interaction personalized with medical services, communications, and entertainment.

Pointing Tasks Using Spatial Audio on Smartphones for People With Vision Impairments

Abstract We present an experimental investigation of spatial audio feedback using smartphones to support direction localization in pointing tasks for people with visual impairments (PVIs). We do this using a mobile game based on a bow-and-arrow metaphor. Our game provides a combination of spatial and non-spatial (sound beacon) audio to help the user locate the direction of the target. Our experiments with sighted, sighted-blindfolded, and visually impaired users show that (a) the efficacy of spatial audio is relatively higher for PVIs than for blindfolded sighted users during the initial reaction time for direction localization, (b) the general behavior between PVIs and blindfolded individuals is statistically similar, and (c) the lack of spatial audio significantly reduces the localization performance even in sighted-blindfolded users. Based on our findings, we discuss the system and interaction design implications for making future mobile-based spatial interactions accessible to PVIs.

Exploring the historical and future spatial interaction relationship between urbanization and ecosystem services in the Yangtze River Basin, China

Diagnosing the spatial interaction between urbanization and ecosystem services (ES) is of guiding significance for regional sustainable development. The complex spatial interactions mechanism between different types of urbanization and ES during historical and future periods were rarely explored at the whole watershed scale. To fill this gap, we systematically evaluated, predicted and compared the complex spatial interaction relationship between different types of urbanization and ES in the Yangtze River Basin (YRB) during 1990–2019 and in 2050 under different Shared Socioeconomic Pathways (SSPs) and Representative Concentration Pathways (RCPs) scenarios. Results showed that economic and land urbanization in the YRB will increase sharply during 1990–2050, while the population urbanization will drop slightly during 2019–2050. More than 59% of the YRB will experience an increase in total ecosystem services (TES) in SSP1-2.6 and SSP2-4.5 scenarios during 2019–2050. The average coupling coordination degree (CCD) between urbanization and TES in the YRB ranged 0.16–0.61 during 1990–2050, with a gradual increase from severe uncoordinated to basic coordination, and experienced a transition from lagging urbanization to lagging TES. More than 64% and 38% of the YRB will achieve strong decoupling between economic and population urbanization and TES during 1990–2050, respectively, which largely occur in the upper reaches, while over 34% of the YRB belong to weak decoupling between land urbanization and TES except for SSP1-2.6, which cluster in the middle and lower reaches. Moreover, we found that SSP1-2.6 will be the most promising scenario to achieve a win-win situation between different types of urbanization and TES, while prominent conflict between them will likely emerge in SSP5-8.5 scenario. The findings can contribute to further understanding the spatial interaction relationship between urbanization and ES, and provide support for ecosystem protection and sustainable urban planning.

Do Imagined Intergroup Contact Interventions and Metastereotyping Processes Interact to Predict Expectations for Future Inter-Age Interactions?

ABSTRACT We examined whether imagining intergenerational contact and activating positive aging metastereotypes could enhance the quality of contact young adult participants anticipated having with older persons and attenuate expectations of communicative nonaccommodation by older adults. We also investigated the role of intergenerational trust in these processes. By increasing perceived trustworthiness, imagining intergenerational contact made young adults less prone to expect older people to nonaccommodate (i.e. communicate in a problematic way), compared to a control group. Relative to those in the metastereotype control condition, participants in the positive metastereotype manipulation anticipated less nonaccommodation from older adults.

Discrete element contact model and parameter calibration for clayey soil particles in the Southwest hill and mountain region

Distinct physical properties of red clay soil in hilly and mountainous regions of southwest China, including high adhesiveness and density, challenge the operation of agricultural machinery. A scarcity of accurate discrete element simulation parameters for this soil type restricts computational modeling. The study was focused on red clay soil with a moisture content of 12.50% ± 1% and a measured repose angle of 35.54°. The soil's inherent physical properties were identified through experimental assessments. Soil contact mechanical parameters were obtained from the GEMM database, and optimal contact parameter ranges were determined using Steepest Ascent Experiments, with the simulated soil particle repose angle serving as the response value. A second-order regression model was developed using a quadratic regression rotation orthogonal combination test. By taking the actual repose angle as the optimization criterion, parameters were optimized. The optimal contact mechanical parameters in EDEM simulations were identified as: JKR surface energy at 8.981 J/m2, recovery coefficient at 0.474, dynamic friction coefficient at 0.196, and static friction coefficient at 0.45. The model yielded a repose angle of 36.21°, closely corresponding with the observed value, with a relative error of 1.80%. The parameters calibrated in this study offer a valuable reference for future soil-tool interaction studies and tillage implement optimization in these regions.

Simulated Participants' Experiences and Challenges With Online and Face-to-Face Interactions During COVID-19: A Case Study in UAEU.

Direct patient contact is essential when training health professionals for future patient-centered interactions. Simulated participants (SPs) play a critical role during training; however, limited information is available about their personal perspectives. This study explored SPs at the College of Medicine and Health Sciences experiences, adaptations, and preferences regarding online and face-to-face encounters. After ethical approval, a qualitative research study using structured in-depth interviews was conducted online with 16 SPs, applying critical incident narratives and storytelling. Interview data were transcribed and encoded using thematic framework analysis, after which member checking was conducted to increase credibility. All SPs acknowledged the benefits of online delivery models during the pandemic, including the convenience of working from home, continued clinical skills training, and personal protection from COVID infection. Participants preferred face-to-face delivery over online encounters, except for one SP, who saw no difference. Challenges included technical issues, communication problems, and lack of realism. Although these results cannot be generalized, all participants acknowledged the relevance of online portrayals during the pandemic. Most preferred face-to-face delivery models for improving role-playing and enhancing personal communication to achieve better patient outcomes, while one SP had no preference. The use of concurrent online and face-to-face methods may more effectively engage SPs in simulation-based education. Before restructuring programs, further research is needed, including a deeper exploration of students' and educators' perspectives.

A Roadmap for Future Interactions Between Research on Personality and Learning

In this introduction to the special collection of papers on the relation between learning and personality research, we provide a functional-cognitive framework that can guide interactions between learning and personality researchers. It highlights that learning researchers can treat personality variables as potential (first or second order) moderators of learning effects. They can also examine whether the effects of personality on learning itself depend on other moderators. Personality researchers can benefit from looking for stable individual differences in known learning effects. Together with learning researchers, they can analyze personality as learned and thus malleable behavior. We end by summarizing the papers of the special collection and by situating them within our framework.

A virtual peer tutoring project to improve communication skills

This article exposes an educational proposal of virtual reciprocal peer tutoring that has been devised to improve communication skills between primary school students from Catalonia and Scotland by each developing the role of tutor in their language, and that of tutee in the foreign language based on a virtual exchange. The results of this experience show that offering communication tools and strategies to students during initial training in their role as a tutor can be beneficial to satisfactorily fulfil their role, as well as anticipate future synchronous interaction in the foreign language. For later implementations, it is determined that the degree of participation and involvement of the student tutors in the preparation of the materials for their respective tutoring could increase the learning opportunities of both sides. This experience is easily applicable to different languages and countries.

Arf6 as a therapeutic target: Structure, mechanism, and inhibitors.

ADP-ribosylation factor 6 (Arf6), a small G-protein of the Ras superfamily, plays pivotal roles in multiple cellular events, including exocytosis, endocytosis, actin remodeling, plasma membrane reorganization and vesicular transport. Arf6 regulates the progression of cancer through the activation of cell motility and invasion. Aberrant Arf6 activation is a potential therapeutic target. This review aims to understand the comprehensive function of Arf6 for future cancer therapy. The Arf6 GEFs, protein structure, and roles in cancer have been summarized. Comprehending the mechanism underlying Arf6-mediated cancer cell growth and survival is essential. The structural features of Arf6 and its efforts are discussed and may be contributed to the discovery of future novel protein-protein interaction inhibitors. In addition, Arf6 inhibitors and mechanism of action are listed in the table. This review further emphasizes the crucial roles in drug resistance and attempts to offer an outlook of Arf6 in cancer therapy.

Tracking and predicting technological knowledge interactions between artificial intelligence and wind power: Multimethod patent analysis

To track the dynamics of AI and wind power technology knowledge interaction and predict future interaction directions, this study proposes a multiview and multilayer patent analysis framework based on three data-driven methods: DMC co-occurrence networks, LDA, and link prediction. The framework is applied to collate and analyse patents related to wind power technologies using artificial intelligence from 2010 to 2021. We find that the number of AI and wind power technology knowledge interactions increases significantly over time, but the network is sparse overall and still has much room for improvement. Second, the AI and wind power technology knowledge interaction patterns show a shift from machine learning models (generation-side wind power technology) to deep learning models (generation-side and transmission- and distribution-side wind power technology) to hybrid AI models (generation, transmission, distribution, and power consumption in the whole process of wind power technology). Finally, possible future directions of interaction between AI and wind power are predicted. The proposed framework is expected to yield a new empirical perspective on green energy technology development. Additionally, the obtained results provide a comprehensive understanding of AI application research in wind power generation.

A meta-learning based stacked regression approach for customer lifetime value prediction

Companies across the globe are keen on targeting potential high-value customers in an attempt to expand revenue, and this could be achieved only by understanding the customers more. Customer lifetime value (CLV) is the total monetary value of transactions or purchases made by a customer with the business over an intended period of time and is used as a means to estimate future customer interactions. CLV finds application in a number of distinct business domains, such as banking, insurance, online entertainment, gaming, and e-commerce. The existing distribution-based and basic (recency, frequency, and monetary)-based models face limitations in terms of handling a wide variety of input features. Moreover, the more advanced deep learning approaches could be superfluous and add an undesirable element of complexity in certain application areas. We, therefore, propose a system that is able to qualify as both effective and comprehensive, yet simple and interpretable. With that in mind, we develop a meta-learning-based stacked regression model that combines the predictions from bagging and boosting models that are found to perform well individually. Empirical tests have been carried out on an openly available online retail dataset to evaluate various models and show the efficacy of the proposed approach.

Move First to Avoid the Worst: Leadership Turnover and the Targeting of New Leaders

Abstract Are leaders more likely to face militarized challenges earlier in their tenure? Existing studies posit contradictory hypotheses: new leaders can both invite challengers to take advantage of their inexperience and deter challengers by their strong incentive to establish a reputation for resolve. This paper seeks to reconcile these competing propositions by developing an argument that centers on the direction of foreign policy preference change associated with leadership turnover. I argue that foreign adversaries are likely to challenge a new leader in their rival state only when the newcomer is perceived to be more hawkish than the predecessor. The perception of a heightened risk of conflict accompanied with the emergence of a more hawkish leader in the rival state gives foreign adversaries a stronger incentive to seek for an early confrontation in which they can (re)demonstrate their own position to the new hawk. In contrast, when the newcomer is perceived to be more dovish than the predecessor, optimistic expectations of future interactions tend to restrain foreign adversaries from provoking the new dove, whose reputation concerns are high. A series of statistical analyses on post-WWII dyadic rivalries with democratically elected leaders on the target side yield strong evidence that supports this conditional hypothesis.

Abstracting road traffic via topological braids: Applications to traffic flow analysis and distributed control

Despite the structure of road environments, imposed via geometry and rules, traffic flows exhibit complex multiagent dynamics. Reasoning about such dynamics is challenging due to the high dimensionality of possible behavior, the heterogeneity of agents, and the stochasticity of their decision-making. Modeling approaches learning associations in Euclidean spaces are often limited by their high sample complexity and the sparseness of available datasets. Our key insight is that the structure of traffic behavior could be effectively captured by lower-dimensional abstractions that emphasize critical interaction relationships. In this article, we abstract the space of behavior in traffic scenes into a discrete set of interaction modes, described in interpretable, symbolic form using topological braids. First, through a case study across real-world datasets, we show that braids can describe a wide range of complex behavior and uncover insights about the interactivity of vehicles. For instance, we find that high vehicle density does not always map to rich mixing patterns among them. Further, we show that our representation can effectively guide decision-making in traffic scenes. We describe a mechanism that probabilistically maps vehicles’ past behavior to modes of future interaction. We integrate this mechanism into a control algorithm that treats navigation as minimization of uncertainty over interaction modes, and investigate its performance on the task of traversing uncontrolled intersections in simulation. We show that our algorithm enables agents to coordinate significantly safer traversals for similar efficiency compared to baselines explicitly reasoning in the space of trajectories across a series of challenging scenarios.

Human Brain Project Partnering Projects Meeting: Status Quo and Outlook.

As the European Flagship Human Brain Project (HBP) ends in September 2023, a meeting dedicated to the Partnering Projects (PPs), a collective of independent research groups that partnered with the HBP, was held on September 4-7, 2022. The purpose of this meeting was to allow these groups to present their results, reflect on their collaboration with the HBP and discuss future interactions with the European Research Infrastructure (RI) EBRAINS that has emerged from the HBP. In this report, we share the tour-de-force that the Partnering Projects that were present in the meeting have made in furthering knowledge concerning various aspects of Brain Research with the HBP. We describe briefly major achievements of the HBP Partnering Projects in terms of a systems-level understanding of the functional architecture of the brain and its possible emulation in artificial systems. We then recapitulate open discussions with EBRAINS representatives about the evolution of EBRAINS as a sustainable Research Infrastructure for the Partnering Projects after the HBP, and also for the wider scientific community.