Interaction Scenarios Research Articles

Implementing Adaptive Neuro-Fuzzy Inference Systems (ANFIS) for Risk Assessment of Drug Interactions

Novel drug development is time-consuming, difficult, and often unsuccessful. This has made combining therapies increasingly common and profitable in recent years. Healthcare professionals in the pharmaceutical sector are interested in the combination, but we must instantly solve drug-drug interactions. In few cases, single-perspective DDI evaluations are insufficient. Pharmacological therapy and patient safety depend on accurate drug interaction risk assessments. This work uses an Adaptive Neuro-Fuzzy Inference System (ANFIS) to assess and predict medication interaction hazards. The proposed method uses neural networks' generative skills and fuzzy logic to construct a robust decision-making framework that can handle drug interaction scenarios and their unpredictability. Fuzzy rules provide all the necessary risk assessment components. This includes dose, patient health issues, drug interaction profiles, and pharmacological properties. Accuracy, specificity, and Mean Squared Error evaluate the model after training with known drug interactions. These metrics evaluate model performance. These measures are essential for machine learning evaluation. The ANFIS-based model outperforms previous risk assessment methods in risk classification and prediction. This study found that the ANFIS architecture may improve medication management security and prevent dangerous drug interactions.

Identification of muscle-activation-dependent human-exoskeleton coupling parameters

This paper proposed a muscle-activation-dependent human-exoskeleton model for predicting human-exoskeleton coupling parameters to improve the studies of coupling dynamics. With a newly designed platform and the help of 20 volunteers (10 males and 10 females, age: 24.45 ± 2.31 years old, height: 167.70 ± 8.35 cm, weight: 66.50 ± 18.74 kg), coupling parameters were identified with surface electromyographic (EMG) signals monitored to represent muscle activation. Then convolutional neural network (CNN) was used to predict coupling parameters with six EMG features as inputs:mean absolute value (MAV), mean absolute value slope (MAVSLP), waveform length (WL), Willison Amplitude (WAMP), variance (VAR), and auto regressive (AR) coefficients. Finally, sensitivity analysis of the CNN’s performance identified AR, MAV, and VAR as the key determinants of the coupling parameters. Further analysis unveiled strong correlation between coupling stiffness and both MAV and VAR. The novelty and contribution are the design of coupling experimental platform and the establishment of muscle-activation-dependent human-exoskeleton coupling model which provides a possibility to obtain coupling parameter identification form complex human-exoskeleton interaction scenarios.

Research on Network Security Protection Technology Based on P2AEDR in New Low-Voltage Control Scenarios for Power IoT and Other Blockchain-Based IoT Architectures.

In the construction of new power systems, the traditional network security protection mainly based on boundary protection belongs to static defense and still relies mainly on manual processing in vulnerability repair, threat response, etc. It is difficult to adapt to the security protection needs in large-scale distributed new energy, third-party aggregation platforms, and flexible interaction scenarios with power grid enterprise systems. It is necessary to conduct research on dynamic security protection models for IoT and other Blockchain-based IoT architectures. This article proposes a network security comprehensive protection model P2AEDR based on different interaction modes of cloud-edge interaction and cloud-cloud interaction. Through continuous trust evaluation, dynamic access control, and other technologies, it strengthens the internal defense capabilities of power grid business, shifting from static protection as the core mode to a real-time intelligent perception and automated response mode, and ultimately achieving the goal of dynamic defense, meeting the security protection needs of large-scale controlled terminal access and third-party aggregation platforms. Meanwhile, this article proposes a dynamic trust evaluation algorithm based on deep learning, which protects the secure access and use of various resources in a more refined learning approach based on the interaction information monitored in the system. Through experimental verification of the dynamic trust evaluation algorithm, it is shown that the proposed model has good trust evaluation performance. Therefore, this research is beneficial for trustworthy Power IoT and other Blockchain-based IoT architectures.

THÖR-MAGNI: A large-scale indoor motion capture recording of human movement and robot interaction

We present a new large dataset of indoor human and robot navigation and interaction, called THÖR-MAGNI, that is designed to facilitate research on social human navigation: for example, modeling and predicting human motion, analyzing goal-oriented interactions between humans and robots, and investigating visual attention in a social interaction context. THÖR-MAGNI was created to fill a gap in available datasets for human motion analysis and HRI. This gap is characterized by a lack of comprehensive inclusion of exogenous factors and essential target agent cues, which hinders the development of robust models capable of capturing the relationship between contextual cues and human behavior in different scenarios. Unlike existing datasets, THÖR-MAGNI includes a broader set of contextual features and offers multiple scenario variations to facilitate factor isolation. The dataset includes many social human–human and human–robot interaction scenarios, rich context annotations, and multi-modal data, such as walking trajectories, gaze-tracking data, and lidar and camera streams recorded from a mobile robot. We also provide a set of tools for visualization and processing of the recorded data. THÖR-MAGNI is, to the best of our knowledge, unique in the amount and diversity of sensor data collected in a contextualized and socially dynamic environment, capturing natural human–robot interactions.

Potential risk and efficiency analysis of decision-making dilemmas in connected dual-vehicle interactions at uncontrolled intersections

In dual-vehicle interactive scenarios, drivers often encounter decision-making dilemmas, even with connected information, thus posing significant risk challenges to traffic operations. This study explored the impact of such dilemmas on decision-making and traffic operations for drivers involved in straight-going (SG) and left-turning (LT) vehicles at uncontrolled intersections. Employing driving simulation technology, a decision-making experiment incorporating preemptive (P) and yielding (Y) strategies was conducted with 72 S G and 72 L T drivers under connected information provision. By analyzing joint decisions (P-P, Y-P, and Y-Y) made by drivers on both sides, we investigated implications for traffic operational safety and efficiency. Furthermore, recognizing the diversity in individual characteristics and time pressure, we developed a random parameters multinomial logit model to examine the relationship between potential traffic operational performance (both safety and efficiency) and various contributing factors. Moreover, we calculated the marginal effects of these contributing factors. By assessing the probability change of Y-P joint decisions balancing safety and efficiency, we identified reference values for each contributing factor that promote effective interactive operations. The findings suggest that: (1) Regarding interactive dilemmas conditions, Conditions where the SG vehicle had higher speed and farther distance than the LT vehicle were more likely to result in heterogeneous Y-P joint decisions, in which one driver chooses to yield and the other party passes quickly, thereby improving traffic performance. (2) Time pressure increased the likelihood of heterogeneous Y-P joint decisions but also increased P-P joint decisions. (3) heterogeneous individual attributes did not always lead to higher heterogeneous decisions. Overall, this study provides insights into factors influencing joint decisions and traffic operational performance under connected information provision, considering individual heterogeneity and time pressure, aiming to the advancement of more efficient and safer vehicle-connected transportation systems.

Interactive strategy of shared control vehicle and automated vehicles based on adaptive non-cooperative game

Considering the complex traffic environment in the future, an interactive strategy between human-machine shared control vehicle (HSCV) and automated vehicles (AVs) based on the adaptive non-cooperative game (NCG) theory is proposed to ensure the safety of vehicles interaction control involving drivers with different characteristics. In HSCV, first, considering the environmental risk potential field (ERPF) and the human-machine conflict coefficient, an adaptive weight distribution method is designed to realize the weight distribution between a driver and an automation system. Subsequently, the steering angle command imposed by a driver is introduced into the game solving process between HSCV and AVs, enabling adaptive dynamic game through weight adjustment. During V2V interaction, based on NCG and distributed model predictive control, a motion planning controller (MOPC) is designed to integrate the optimal solution problem of path planning and tracking control and solve the interaction problem with constraints. Two typical interactive scenarios are used to verify the feasibility of the strategy. The results demonstrate that the interaction strategy considering the different drivers’ operating commands not only realizes the interaction between a driver and an ego vehicle but also realizes the interaction between an ego vehicle and nearby vehicles to ensure the safety of vehicles.

Biomimetic multiscale structure with hierarchically entangled topologies of cellulose-based hydrogel sensors for human-computer interaction

The development of high-performance cellulose-based sensors with superior interfacial compatibility, flexibility, and strength has always been challenging. Drawing inspiration from the intricate multiscale hierarchy found in resilient natural materials, the incorporation of this structure into cellulose-based hydrogels using biomimetic strategies is anticipated to enhance their properties. Therefore, the cellulose/polyacrylamide (PAM) hybrid hydrogels are fabricated using the aqueous AlCl3/ZnCl2 system through an all-green one-pot method at room temperature, achieving efficient dissolution of cellulose at multiple scales and in-situ polymerization of polyacrylamide. The multiscale cellulose/PAM hydrogels achieve good interfacial compatibility, with the reinforcing mechanism being confirmed through a combination of experimental validation and atomic simulations. The findings underscore the key elements that drive the multiscale behavior of hydrogels, encompassing a range of factors such as multiple interfacial interactions, hierarchical molecular entanglements, and microskeleton reinforcement. The hydrogels exhibit satisfactory stretchability (412 %), compressive strength (1.2 MPa), conductivity (1.65 S m−1), and ultralong circulability (> 12,000 cycles), rendering them suitable for various scenarios of human-computer interaction. This work establishes a comprehensive interpretation of the strengthening mechanism in cellulose-based multiscale hierarchical structures, thereby providing novel insights for the advanced design strategies of other promising hierarchical materials.

Multimodal adversarial informer for highway vehicle lane-changing trajectory prediction

To enhance the perception of vehicle trajectory information and lane-changing decision-making capabilities in intelligent connected vehicles during multivehicle interaction scenarios, we propose a novel method based on a Multimodal Adversarial Informer (MAI) for highway multivehicle lane-changingrajectory prediction. This method achieves spatiotemporal features of target and surrounding vehicles through graph learning of temporal features and spatial adjacency matrices. Considering the heading angle and vehicle local X-axis displacement, the vehicle trajectory samples are categorized for training and validation of the multimodal Informer. A multi-criterion discriminator is utilized to judge whether the generated trajectory fits the requirements of accuracy and rationality. After adversarial learning, the optimal vehicle lane-changing trajectory prediction is obtained using the proposed MAI. Experiments conducted with the NGSIM dataset demonstrate the comparative performance of baseline models on three different noise-added testing datasets using MAE, RMSE, and R² metrics. The MAI model consistently outperforms the others, achieving the lowest MAE and RMSE and the highest R² values across all datasets, indicating superior predictive accuracy and fit. Furthermore, the results show that the proposed MAI framework maintains a relatively low prediction error over both short-term and long-term horizons compared to baseline models.

Primary Education’s Stakeholders Behavioural Intention to use Prospective Game-based Learning for Waste Awareness

Despite some efforts to design games to raise primary school students’ awareness of the waste problems, there is a lack of research studying the directly involved stakeholder’s behavioural intention to use the games. Prior studies have explored the users’ games acceptance of the games that have already been developed. However, we believe that understanding the user’s acceptance from the beginning of the game design process is also crucial. This research investigates the factors that influence the behavioural intention to use prospective game-based learning (GBL) for waste awareness among primary school parents, teachers, and students. An online survey based on the UTAUT 2 was distributed to each stakeholder, supplemented by a video demonstrating an Augmented Reality-based mobile game interaction scenario. The sample of participants included 135 parents, 47 teachers, and 129 primary school students from Malang, Indonesia. The internal consistency of game acceptance from the surveys was evaluated through Kruskall Wallis. Results show that Effort Expectancy is the factor with the highest acceptance value while Facilitating Condition has the lowest value. The three stakeholders argued that the prospective GBL seems easy to play and learn, but in contradiction, the adults are concerned that they cannot assist in teaching how to use the game and cannot provide the necessary technology. The cultural background seems to influence user game acceptance. Indonesians are known to be quite open to new technologies like GBL, although the facilitation condition might be low. Furthermore, there are some significant differences between the student-parent and student-teacher groups, but not in the parent-teacher group. Findings indicated that Indonesian teachers are more ready to adopt GBL than students and their parents. By understanding the perceptions and attitudes of these groups, educators and researchers can develop strategies to address any concerns or doubts they may have and promote the benefits of GBL.

Enhancing behavior classification of children in dynamic interaction scenes through improved DCNN model

The rapid development of society makes people pay more attention to the quality of the environment for children’s growth. However, due to the differences of young children, different environments are often needed for cultivation in dynamic interaction scenarios. Therefore, the authors propose an environment creation method for children’s behavior classification to improve the quality of children’s growth environment. Taking the video data of children for a period of time as input, the encoder and decoder are designed to classify children’s behavior and obtain behavior characteristics. After the input image is processed by the backbone network DCNN, two outputs are obtained, which are four times of shallow features and 16 times of high-level features. Aiming at the semantic gap between environmental features and children’s behavior features, the DenseNet model is used to remove the semantic difference between children’s behavior features and environmental features, and the similarity between the two features is fitted as much as possible. The dense blocks obtained by different expansion factors of the network are used for feature connection, so that the model is suitable for feature similarity calculation of different modes. The experimental results show that this method can accurately classify children’s behavior, and the F value is more than 70%, which can provide prerequisites for children’s environment creation. This environment creation model can clearly point out the suitable environment for children and provide a guarantee for children’s growth.

Two novel pure-state coherence measures in quantifying coherence

Quantification is one of the primary goals of quantum coherence resource theory. Here, we put forward two coherence measures, analytically prove their validity in the pure-state regime following the axiomatic definition of a legitimate pure-state coherence measure (PSCM), and provide their general normalized forms. We further discuss their extensions in the domain of mixed states with the assistance of the convex roof of coherence, and top coherence (quantification in terms of pure state coherence) and define their classes (coherence \emph{monotone} or \emph{measure}); in this regard, we thoroughly investigate the top coherence class. For the quantitative demonstration, we pick up different laser pulse-two-qubit interaction scenarios and compare the evolutions of these two coherence measures along with \(l_{1}\)-norm of coherence and relative entropy of coherence; this study also signifies the importance of coherence in probing an atomic or molecular system.

Resolving the Intricate Effects of Multiple Global Change Drivers on Root Litter Decomposition.

Plant roots represent about a quarter of global plant biomass and constitute a primary source of soil organic carbon (C). Yet, considerable uncertainty persists regarding root litter decomposition and their responses to global change factors (GCFs). Much of this uncertainty stems from a limited understanding of the multifactorial effects of GCFs and it remains unclear how these effects are mediated by litter quality, soil conditions and microbial functionality. Using complementary field decomposition and laboratory incubation approaches, we assessed the relative controls of GCF-mediated changes in root litter traits and soil and microbial properties on fine-root decomposition under warming, nitrogen (N) enrichment, and precipitation alteration. We found that warming and N enrichment accelerated fine-root decomposition by over 10%, and their combination showed an additive effect, while precipitation reduction suppressed decomposition overall by 12%, with the suppressive effect being most significant under warming-alone and N enrichment-alone conditions. Significantly, changes in litter quality played a dominant role and accelerated fine-root decomposition by 15% ~ 18% under warming and N enrichment, while changes in soil and microbial properties were predominant and reduced decomposition by 7% ~ 10% under precipitation reduction and the combined warming and N enrichment. Examining only the decomposition environment or litter properties in isolation can distort global change effects on root decomposition, underestimating precipitation reduction impacts by 38% and overstating warming and N effects by up to 73%. These findings highlight that the net impact of GCFs on root litter decomposition hinges on the interplay between GCF-modulated root decomposability and decomposition environment, as well as on the synergistic or antagonistic relationships among GCFs themselves. Our study emphasizes that integrating the legacy effects of multiple GCFs on root traits, soil conditions and microbial functionality would improve our prediction of C and nutrient cycling under interactive global change scenarios.

Soundscape Composition in the Framework of Cognitive Systems

After more than fifty years of development, soundscape composition has evolved from its initial purposes of education and archival work into a significant branch of contemporary music creation. With the advancement of recording technology, digital audio processing tools, and computer music software, the creative means of soundscape composition have been greatly expanded. At the same time, soundscape composition requires composers to make comprehensive use of multiple fields such as technology, art, science, society and culture. However, this also introduces more complex creative challenges, as composers may need to efficiently coordinate the use of various tools in multitasking environments and manage multidimensional interactive scenarios, which demands higher levels of cognitive ability and clearer workflow. This paper aims to discuss my own soundscape composition project within the framework of cognitive theories (extended mind and distributed cognition), in order to explore underdeveloped perspectives on the practice and learning of soundscape composition. This perspective may provide soundscape composers with a richer cognitive theoretical foundation and potential methodological support in the creative process, helping composers or researchers understand the dynamic cognitive processes of soundscape composition based on an analysis of the entire environment.

Are lithium-rich giants binaries? A radial velocity variability analysis of 1400 giants

Context. The existence of low-mass giants with large amounts of lithium (Li) in their surfaces has challenged stellar evolution for decades. One of the possibilities usually discussed in the literature to explain these Li-rich giants involves the interaction with a close binary companion, a scenario that predicts that, when compared against their non-enriched counterparts, Li-rich giants should preferentially be found as part of binary systems. Aims. We aim to assemble the largest possible sample of low-mass giants with well-measured Li abundances, to determine with high statistical significance the close binary fractions of Li-rich and Li-normal giants, and thus test the binary interaction scenario for the emergence of Li-rich giants. Methods. We developed a method that uses radial velocities (RVs) at three different epochs to quantify the degree of RV variability, which we used as a proxy for the presence of a close binary companion. The method was tested and calibrated against samples of known RV standard stars and known spectroscopic binaries. We then assembled a sample of 1418 giants with available RVs from RAVE, GALAH, and Gaia, as well as stellar parameters and Li abundances from GALAH, to which we applied our variability classification. We could determine an evolutionary state for 1030 of these giants. We also compared the results of our RV variability analysis with binarity indicators from the Gaia mission. Results. When applying our methodology to the control samples, we found that the accuracy of the classification is controlled by the precision of the RVs used in the analysis. For the set of RVs available for the giants, this accuracy is 80–85%. Consistent with seismic studies, the resulting sample of giants contains a fraction of Li-rich objects in the red clump (RC) that is twice as large as that in the first ascent red giant branch (RGB). Among RC giants, the fractions of Li-rich objects with a high RV variability and with no RV variability are the same as those for Li-normal objects, but we find some evidence that these fractions may be different for giants in the first-ascent RGB. Analysis of binary indicators in Gaia DR3 shows a smaller fraction of binary giants than our criteria, but no relation can be seen between Li enrichment and binarity either. Conclusions. Our RV variability analysis indicates that there is no preference for Li-rich giants in the RC to be part of binary systems, thus arguing against a binary interaction scenario for the genesis of the bulk of Li-rich giants at that evolutionary stage. On the other hand, Li-rich giants in the RGB appear to have a small but measurable preference for having close companions, something that deserves further scrutiny with more and better data. Additional measurements of the RVs of these giants at a higher RV precision would greatly help in confirming and more robustly quantifying these results.

Bridging vision and touch: advancing robotic interaction prediction with self-supervised multimodal learning.

Predicting the consequences of the agent's actions on its environment is a pivotal challenge in robotic learning, which plays a key role in developing higher cognitive skills for intelligent robots. While current methods have predominantly relied on vision and motion data to generate the predicted videos, more comprehensive sensory perception is required for complex physical interactions such as contact-rich manipulation or highly dynamic tasks. In this work, we investigate the interdependence between vision and tactile sensation in the scenario of dynamic robotic interaction. A multi-modal fusion mechanism is introduced to the action-conditioned video prediction model to forecast future scenes, which enriches the single-modality prototype with a compressed latent representation of multiple sensory inputs. Additionally, to accomplish the interactive setting, we built a robotic interaction system that is equipped with both web cameras and vision-based tactile sensors to collect the dataset of vision-tactile sequences and the corresponding robot action data. Finally, through a series of qualitative and quantitative comparative study of different prediction architecture and tasks, we present insightful analysis of the cross-modality influence between vision, tactile and action, revealing the asymmetrical impact that exists between the sensations when contributing to interpreting the environment information. This opens possibilities for more adaptive and efficient robotic control in complex environments, with implications for dexterous manipulation and human-robot interaction.

A Precessing Stellar Disk Model for Superorbital Modulations of the Gamma-Ray Binary LS I+61° 303

Gamma-ray binary LS I+61° 303 consists of a neutron star orbiting around a Be star with a period of P orb ≃ 26.5 days. Apart from orbital modulations, the binary shows long-term flux variations with a superorbital period of Psup≃4.6yr as seen in nearly all wavelengths. The origin of this superorbital modulation is still not well understood. Under the pulsar wind–stellar outflow interaction scenario, we propose that the superorbital modulations of LS I+61° 303 could be caused by the precession of the Be disk. Assuming X-rays arise from synchrotron radiation of the intrabinary shock, we develop an analytical model to calculate expected flux modulations over the orbital and superorbital phases. The asymmetric two-peak profiles in orbital light curves and sinusoidal-like long-term modulations are reproduced under the precessing disk scenario. The observed orbital phase drifting of the X-ray peak and our fitting of long-term X-ray data indicate that the neutron star is likely orbiting around the star with a small eccentricity and periastron phase around Φp ∼ 0.6. We compare the Corbet diagrams of LS I+61° 303 with other Be/X-ray binaries, and the linear correlation in the Psup–Porb diagram suggests that the precession of the Be disk in LS I+61° 303 is induced by the tidal torque of its neutron star companion.

EmoiPlanner: Human emotion and intention aware socially acceptable robot navigation in human‐centric environments

AbstractThe deployment of robots in human‐centric environments has significantly increased in recent years. It is crucial for robots to navigate human environments while understanding social norms and personal boundaries to ensure a harmonious coexistence between humans and robots. A socially aware robot should be capable of interpreting and responding appropriately to human cues, expressions, and intentions, thereby fostering trust and confidence among humans. However, prior studies were insufficient or unable to address the navigation challenges in human‐populated environments, as they perceive people as obstacles rather than social agents. Recent studies have utilized proxemic areas that are present in interpersonal interactions for human‐robot interaction scenarios, but they have enforced consistent proxemic areas for social robot navigation. This approach fails to fully capture the highly sophisticated behaviour and preferences of humans. Therefore, we propose a psychologically‐based adaptive proxemic area that fluctuates based on the human's emotional state. Furthermore, we integrate this feature into a reinforcement learning‐based social navigation framework, making our navigation framework robust to the unpredictable affections of humans. Additionally, our navigation framework includes human intention prediction to approximate the future proxemic area, thereby avoiding interference with the path to be taken by individuals. We have named our framework the Human Emotion and Intention Aware Path Planner (EmoiPlanner). Our framework has been subjected to case studies involving realistic crowd navigation scenarios, and the results indicate that it enables robots to navigate through crowds without causing discomfort to pedestrians who exhibit stochastic behaviours and emotional states, while also ensuring efficient path planning.

Evaluating the Effects of Epichloë Fungal Endophytes of Perennial Ryegrass on the Feeding Behaviour and Life History of Rhopalosiphum padi.

The bird cherry-oat aphid, Rhopalosiphum padi (L.), is an economically significant pest of pasture grasses, the latter being capable of hosting several fungal endophyte-perennial ryegrass symbiota rich in alkaloids and toxic to vertebrates and invertebrates. Measuring aphid feeding behaviour can provide insights into the effectiveness and mode of action of different fungal endophytes. This study investigated the effects of different Epichloë-perennial ryegrass symbiota on the feeding behaviour of R. padi using the electrical penetration graph technique while also assessing the aphid life history. In most cases, endophytes had significant feeding deterrence and paired fecundity and mortality effects. But, in some instances, endophytes with the highest aphid mortality did not significantly deter feeding, suggesting a more complicated scenario of interactions between the relative concentration of metabolites, e.g., host plant defence response metabolites and alkaloids, and/or physical changes to leaf morphology. Overall, this study sheds light on the mode of action of Epichloë endophytes against aphids and highlights the importance of Epichloë-perennial ryegrass symbiota in the management of insect pests such as aphids in pasture-based grazing systems.

A Combined CNN Architecture for Speech Emotion Recognition.

Emotion recognition through speech is a technique employed in various scenarios of Human-Computer Interaction (HCI). Existing approaches have achieved significant results; however, limitations persist, with the quantity and diversity of data being more notable when deep learning techniques are used. The lack of a standard in feature selection leads to continuous development and experimentation. Choosing and designing the appropriate network architecture constitutes another challenge. This study addresses the challenge of recognizing emotions in the human voice using deep learning techniques, proposing a comprehensive approach, and developing preprocessing and feature selection stages while constructing a dataset called EmoDSc as a result of combining several available databases. The synergy between spectral features and spectrogram images is investigated. Independently, the weighted accuracy obtained using only spectral features was 89%, while using only spectrogram images, the weighted accuracy reached 90%. These results, although surpassing previous research, highlight the strengths and limitations when operating in isolation. Based on this exploration, a neural network architecture composed of a CNN1D, a CNN2D, and an MLP that fuses spectral features and spectogram images is proposed. The model, supported by the unified dataset EmoDSc, demonstrates a remarkable accuracy of 96%.

Nonlinear dynamical social and political prediction algorithm for city planning and public participation using the impulse pattern formulation

A nonlinear-dynamical algorithm for city planning is proposed as an impulse pattern formulation (IPF) for predicting relevant parameters such as health, artistic freedom, or financial developments of different social or political stakeholders over the cause of a planning process. The IPF has already shown high predictive precision at low computational cost in musical instrument simulations, brain dynamics, and human–human interactions. The social and political IPF consists of three basic equations of system state developments, self-adaptation of stakeholders, two adaptive interactions, and external impact terms suitable for respective planning situations. Typical scenarios of stakeholder interactions and developments are modeled by adjusting a set of system parameters. These include stakeholder reaction to external input, enhanced system stability through self-adaptation, stakeholder convergence due to adaptive interaction, as well as complex dynamics in terms of fixed stakeholder impacts. A workflow for implementing the algorithm in real city planning scenarios is outlined. This workflow includes machine learning of a suitable set of parameters suggesting best-practice planning to aim at the desired development of the planning process and its output.