Dynamic Feedback Research Articles

Towards dynamic multiscale feedback during the injection moulding cycle of plastics

Injection moulding is a very popular technology for shaping plastics. Its history stretches back to the nineteenth century, and, as a consequence, it has developed outside the framework of digitisation. In order to fully implement the concepts of Industry 4.0, we need to update these legacy technologies so that they can fully benefit from the developments inherent in the “Internet of Things” and allow the process of injection moulding to take full advantage of digital optimisation so that it can fit effectively in the digital factory. In this work, we explore the quantitative use of X-ray scattering as a technology that can provide dynamic and multiscale feedback during the injection moulding cycle to be able to exploit digital twin technology as a means to optimise the operational parameters involved in injection moulding and to enable digital design of moulds in the fullest sense. This manuscript provides a way to mark future work and draw these possibilities to a wider audience.

Clean Cooking Programs’ Long-Term Success Emerges from Feedback Dynamics

Clean Cooking Programs’ Long-Term Success Emerges from Feedback Dynamics

Application of torque observer based on belief rule base to power redistribution process

When navigating in ice zones, broken ice can be drawn into the propeller by the wake current, significantly altering the propeller load and causing severe fluctuations in the power grid of electrical propulsion ship. To address this issue, this paper presents two kinds of power redistribution control (PRC) strategies, which utilises the dynamic feedback from the ship's generator sets to the electric propulsion system to improve the frequency control. The PRC strategy #2, which receives feedback from generator's torque deviation, has better control effect than PRC strategy #1 with feedback from speed deviation. Since the accuracy of the estimated mechanical torque directly affects the control effect of strategy #2, this paper designs a torque observer based on the Belief Rule Base (BRB) by matching the PRC strategy to regulate the grid frequency in real-time. Given the power grid's fluctuations primarily caused by aft propellers, the power will be partly redistributed to the bow thruster to stabilise the ship's grid. Experiments show that this method can maintain the stability of the grid frequency by quickly correcting the power distribution during the process of sudden shedding propeller load.

Continuous Growth Monitoring and Prediction with 1D Convolutional Neural Network Using Generated Data with Vision Transformer

Crop growth information is collected through destructive investigation, which inevitably causes discontinuity of the target. Real-time monitoring and estimation of the same target crops can lead to dynamic feedback control, considering immediate crop growth. Images are high-dimensional data containing crop growth and developmental stages and image collection is non-destructive. We propose a non-destructive growth prediction method that uses low-cost RGB images and computer vision. In this study, two methodologies were selected and verified: an image-to-growth model with crop images and a growth simulation model with estimated crop growth. The best models for each case were the vision transformer (ViT) and one-dimensional convolutional neural network (1D ConvNet). For shoot fresh weight, shoot dry weight, and leaf area of lettuce, ViT showed R2 values of 0.89, 0.93, and 0.78, respectively, whereas 1D ConvNet showed 0.96, 0.94, and 0.95, respectively. These accuracies indicated that RGB images and deep neural networks can non-destructively interpret the interaction between crops and the environment. Ultimately, growers can enhance resource use efficiency by adapting real-time monitoring and prediction to feedback environmental controls to yield high-quality crops.

A bio-inspired visual collision detection network integrated with dynamic temporal variance feedback regulated by scalable functional countering jitter streaming

In pursuing artificial intelligence for efficient collision avoidance in robots, researchers draw inspiration from the locust’s visual looming-sensitive neural circuit to establish an efficient neural network for collision detection. However, existing bio-inspired collision detection neural networks encounter challenges posed by jitter streaming, a phenomenon commonly experienced, for example, when a ground robot moves across uneven terrain. Visual inputs from jitter streaming induce significant fluctuations in grey values, distracting existing bio-inspired networks from extracting visually looming features. To overcome this limitation, we derive inspiration from the potential of feedback loops to enable the brain to generate a coherent visual perception. We introduce a novel dynamic temporal variance feedback loop regulated by scalable functional into the traditional bio-inspired collision detection neural network. This feedback mechanism extracts dynamic temporal variance information from the output of higher-order neurons in the conventional network to assess the fluctuation level of local neural responses and regulate it by a scalable functional to differentiate variance induced by incoherent visual input. Then the regulated signal is reintegrated into the input through negative feedback loop to reduce the incoherence of the signal within the network. Numerical experiments substantiate the effectiveness of the proposed feedback loop in promoting collision detection against jitter streaming. This study extends the capabilities of bio-inspired collision detection neural networks to address jitter streaming challenges, offering a novel insight into the potential of feedback mechanisms in enhancing visual neural abilities.

Artificial synthetic gene circuits for dynamic monitoring and feedback intervention of chemotherapy resistance in bladder cancer

Artificial synthetic gene circuits for dynamic monitoring and feedback intervention of chemotherapy resistance in bladder cancer

Changes in the SST Seasonal Cycle in a Warmer North Pacific without Ocean Dynamical Feedbacks

Abstract Climate models project a significant intensification of the sea surface temperature (SST) seasonal cycle over the subpolar North Pacific due to global warming, with the shallower mixed layer widely recognized as the dominant factor. However, employing slab ocean experiments with only ocean–atmosphere thermal coupling, we find a substantial contribution from changes in surface heat flux to this seasonal cycle intensification. In particular, the stronger Newtonian cooling effect in winter acts as a more potent damping than in summer. This differential damping inhibits the warming in colder seasons, significantly contributing to the intensified SST seasonal cycle in the subpolar North Pacific. In addition, consistent phase shifts in the North Pacific are identified across CMIP6 models. In the northwest North Pacific, a phase advance is associated with anomalous heating in early spring, driven by enhanced warm atmospheric advection from lower latitudes and sea ice melting in marginal seas. In contrast, the southeast North Pacific exhibits a phase delay attributed to the anomalous cooling in spring relative to autumn. This cooling is due to weakened trade winds and increased presence of high clouds. The former leads to stronger evaporative cooling in spring, while the latter impedes shortwave radiation from reaching the ocean.

Disruption of a massive molecular cloud by a supernova in the Galactic Centre

The Milky Way’s Central Molecular Zone (CMZ) differs dramatically from our local solar neighbourhood, both in the extreme interstellar medium conditions it exhibits (e.g. high gas, stellar, and feedback density) and in the strong dynamics at play (e.g. due to shear and gas influx along the bar). Consequently, it is likely that there are large-scale physical structures within the CMZ that cannot form elsewhere in the Milky Way. In this paper, we present new results from the Atacama Large Millimeter/submillimeter Array (ALMA) large programme ACES (ALMA CMZ Exploration Survey) and conduct a multi-wavelength and kinematic analysis to determine the origin of the M0.8–0.2 ring, a molecular cloud with a distinct ring-like morphology. We estimate the projected inner and outer radii of the M0.8–0.2 ring to be 79″ and 154″, respectively (3.1 pc and 6.1 pc at an assumed Galactic Centre distance of 8.2 kpc) and calculate a mean gas density >104 cm−3, a mass of ~106 M⊙, and an expansion speed of ~20 km s−1, resulting in a high estimated kinetic energy (>1051 erg) and momentum (>107 M⊙ km s−1). We discuss several possible causes for the existence and expansion of the structure, including stellar feedback and large-scale dynamics. We propose that the most likely cause of the M0.8–0.2 ring is a single high-energy hypernova explosion. To viably explain the observed morphology and kinematics, such an explosion would need to have taken place inside a dense, very massive molecular cloud, the remnants of which we now see as the M0.8–0.2 ring. In this case, the structure provides an extreme example of how supernovae can affect molecular clouds.

CityLearn v2: energy-flexible, resilient, occupant-centric, and carbon-aware management of grid-interactive communities

As more distributed energy resources become part of the demand-side infrastructure, quantifying their energy flexibility on a community scale is crucial. CityLearn v1 provided an environment for benchmarking control algorithms. However, there is no standardized environment utilizing realistic building-stock datasets for distributed energy resource control benchmarking without co-simulation or third-party frameworks. CityLearn v2 extends CityLearn v1 by providing a stand-alone simulation environment that leverages the End-Use Load Profiles for the U.S. Building Stock dataset to create grid-interactive communities for resilient, multi-agent, and objective control of distributed energy resources with dynamic occupant feedback. While the v1 environment used pre-simulated building thermal loads, the v2 environment uses data-driven thermal dynamics and eliminates the need for co-simulation with building energy performance software. This work details the v2 environment and provides application examples that use reinforcement learning control to manage battery energy storage system, vehicle-to-grid control, and thermal comfort during heat pump power modulation.

An Energy-Efficient Dynamic Feedback Image Signal Processor for Three-Dimensional Time-of-Flight Sensors

With the recent prominence of artificial intelligence (AI) technology, various research outcomes and applications in the field of image recognition and processing utilizing AI have been continuously emerging. In particular, the domain of object recognition using 3D time-of-flight (ToF) sensors has been actively researched, often in conjunction with augmented reality (AR) and virtual reality (VR). However, for more precise analysis, high-quality images are required, necessitating significantly larger parameters and computations. These requirements can pose challenges, especially in developing AR and VR technologies for low-power portable devices. Therefore, we propose a dynamic feedback configuration image signal processor (ISP) for 3D ToF sensors. The ISP achieves both accuracy and energy efficiency through dynamic feedback. The proposed ISP employs dynamic area extraction to perform computations and post-processing only for pixels within the valid area used by the application in each frame. Additionally, it uses dynamic resolution to determine and apply the appropriate resolution for each frame. This approach enhances energy efficiency by avoiding the processing of all sensor data while maintaining or surpassing accuracy levels. Furthermore, These functionalities are designed for hardware-efficient implementation, improving processing speed and minimizing power consumption. The results show a maximum performance of 178 fps and a high energy efficiency of up to 123.15 fps/W. When connected to the hand pose estimation (HPE) accelerator, it demonstrates an average mean squared error (MSE) of 10.03 mm, surpassing the baseline ISP value of 20.25 mm. Therefore, the proposed ISP can be effectively utilized in low-power, small form-factor devices.

Unveiling User Sentiment: Aspect-Based Analysis and Topic Modeling of Ride-Hailing and Google Play App Reviews

Background: Mobile app usage is increasing in the digital age, with Ride-Hailing app becoming the primary example of this trend. To obtain valuable understanding of how people perceive and interact with mobile app, user reviews on platforms such as Google Play are usually analyzed. This analysis can assist developers to identify areas for improvement in both Ride-hailing and Google Play App. A promising method that can be used to analyze user perception in this instance is Aspect-Based Sentiment Analysis (ABSA). Objective: This research aimed to apply ABSA to user reviews using Bidirectional Encoder Representations from Transformers (BERT) models. In this context, aspect identification and topic modeling were performed by using Latent Dirichlet Allocation (LDA). The model extracted topics from the reviews and used Generative Artificial Intelligence (GenAI) to define the aspects of the topics to further enhance the analysis. For consistency and accuracy, the method included sentiment annotation by a human annotator. Methods: A total of two datasets were used in this research, with the first collected by scraping user reviews of Ride-Hailing App while the second was obtained from Kaggle, and to identify relevant topics, modeling was performed using LDA. These topics were then categorized into aspects using GenAI, covering areas, such as customer experience, service, payment, app features, task management, and event management. Subsequently, sentiment labeling was conducted using human annotators to provide a reliable baseline. BERT model was then used to classify sentiment with aspect hints, and the evaluation included calculations of accuracy, precision, recall, and F1-score. Results: The results showed that BERT model achieved the highest accuracy of 97% in sentiment analysis across all datasets. Conclusion: This research provided valuable understanding of user experience and established a strong ABSA framework for analyzing user reviews using LDA, Aspect Annotation, GenAI, and BERT sentiment models. Future research could expand this method to other app categories and incorporate real-time ABSA for continuous monitoring and dynamic feedback. Keywords: User Reviews, Aspect-Based Sentiment Analysis (ABSA), Sentiment Analysis, Topic Modeling, Generative Artificial Intelligence (GenAI)

A system dynamics model based on ISM for risk perception in emergency by employees in chemical industrial parks

Risk perception is one of the factors that guide human behavior in the workplace. In accident prevention and emergency response, the risk perception of employees affects the safety behaviour and efficiency of emergency disposal. Besides, risk perception is a complicated process restricted by many influencing factors. The study aims to identify the most important factors affecting risk perception. This study had three phases. In the first, factors affecting the risk perception were extracted based on the questionnaire survey and the expert scoring. Then, the Interpretive Structure Model (ISM) was used to stratify the 14 influencing factors of risk perception and obtain a hierarchical structure chart. Finally, by analyzing the influence paths in the ISM, the system dynamics feedback loop diagram was constructed. The model took the state variable “risk perception→ risk response→ risk identification→ risk communication→ risk perception” as the main circular loop, and was supplemented and perfected by multiple feeder loop circuits. Research indicates risk experience is the most fundamental factor affecting risk perception. In the aspect of sensitivity analysis, the study shows that the risk perception of employees is related to the distance of the risk source. Its effectiveness in quantifying qualitative data, identifying influential factors, and establishing cause-effect relationships make it a valuable tool for enhancing risk perception in chemical industry park.

Can dynamic normative feedback enhance household kitchen waste separation? evidence from two field intervention studies

Imagine if more and more households in your community started separating waste, would this trend inspire you to do the same? Previous research on social norms has been predominantly static, focusing on individual behavior and neglecting the potential of leveraging group dynamics. To address this gap, we conducted two field studies using group feedback to activate dynamic norms. Using an A-B-A time-series design in two communities, we collected 21,555 observations of waste disposal behavior. During the intervention period, we publicly displayed the weekly changing participation rates of household kitchen waste separation within the community, a strategy referred to as dynamic normative feedback. Both communities experienced a significant increase in participation in waste separation. Furthermore, the feedback effect persisted for up to three months after the intervention was removed. Our findings demonstrate that group dynamic normative feedback could be an effective strategy for promoting long-term separation behavior and have implications for waste management.

A fully integrated breathable haptic textile.

Wearable haptics serve as an enhanced media to connect humans and VR/robots. The inevitable sweating issue in all wearables creates a bottleneck for wearable haptics, as the sweat/moisture accumulated in the skin/device interface can substantially affect feedback accuracy, comfortability, and create hygienic problems. Nowadays, wearable haptics typically gain performance at the cost of sacrificing the breathability, comfort, and biocompatibility. Here, we developed a fully integrated breathable haptic textile (FIBHT) to solve these trade-off issues, where the FIBHT exhibits high-level integration of 128 pixels over the palm, great stretchability of 400%, and superior permeability of over 657 g/m2/day (moisture) and 40 mm/s (air). It is a stand-alone haptic system totally composed of stretchable, breathable, and bioadhesive materials, which empowers it with precise, sweating/movement-insensitive and dynamic feedback, and makes FIBHT powerful for virtual touching in broad scenarios.

Evaluating Driver Response to Bridge Deck Warning Systems During Winter Weather Conditions

Warning signs are often deployed at bridge overpasses during winter to warn motorists of potentially icy surface conditions on the bridge, although the effectiveness of these signs is questionable. One potential improvement to the standard signage methods is the bridge deck warning system (BDWS), which activates a flashing warning sign border or beacon when conditions warrant based on real-time weather and bridge surface data. However, BDWSs have not been broadly implemented in the United States, and consequently, their effectiveness as a safety countermeasure is not well established. To address this knowledge gap, a series of winter field evaluations were performed along three freeway bridge overpasses in Michigan to assess the effectiveness of various BDWS strategies as a speed reduction countermeasure for motorists approaching a potentially icy bridge. The results showed that a BDWS with a flashing light emitting diode (LED) border reduced motorist speeds when encountering a bridge during winter weather conditions compared with the standard “Bridge Ices Before Road” warning sign. The speed reduction effect was consistent between passenger cars and heavy trucks, but was somewhat stronger at night compared with daytime. Greater speed reductions were observed when the BDWS sign was combined with a dynamic speed feedback sign (DSFS) that displayed a “Slow Down” or “Icy Road” message to approaching motorists, with the strongest effects observed when the DSFS message was pulsed between high and low brightness intensities. BDWSs that included only a flashing overhead beacon did not have a significant effect on speeds compared with the standard warning sign.

Development of an improved mass transfer model for condensation with dynamic feedback regulation

In the field of condensation research, accurate and efficient numerical simulation models are highly desired. One of the widely used phase change mass transfer models is known as the Lee model. However, this model has limitations due to uncertainties in the mass transfer intensity factor, which can lead to simulation failures and impede large-scale engineering applications. This study presents an improved mass transfer model which employs a tuning function to dynamically and accurately modify the mass transfer intensity factor in each two-phase cell via feedback adjustment. This approach enables immediate and customized adjustment of the factor for each grid within the limit of the tuning function, significantly reducing the errors caused by repeated artificial adjustments and related uncertainties. The adjustment capabilities of the hyperbolic tangent and softsign functions are evaluated. The softsign function is superior in both regulation efficiency and accuracy. The model's accuracy is verified by comparing with previous experiments and simulations, as well as by the one-dimensional Stefan problem. The proposed model achieves high prediction accuracy, reduces the risk of simulation dispersion, and significantly enhances computational efficiency with the iteration number reduced by nearly half under certain conditions compared to the Lee model. Furthermore, the new model shows robustness as the prediction accuracy is insensitive to variations in critical parameters. The improved model is expected to provide valuable assistance for future research and applications in flow condensation with outstanding computational accuracy, efficiency, and stability.

Coevolution of non-pharmaceutical interventions and infectious disease spreading in age-structured populations

In the face of emerging major infectious diseases, non-pharmaceutical interventions (NPIs) such as mask-wearing are the primary means of disease control in the early stages due to the lack of effective pharmaceutical interventions. Yet the current understanding of the dynamic feedback loop between NPIs and infectious disease spreading remains limited. In this study, we proposed an asymmetrically coupled dynamic model of the coevolution of mask-wearing behaviour and infectious disease spreading in an age-structured population to describe the bidirectional relationship between NPIs such as mask-wearing and infectious disease spreading. A theoretical analysis framework was developed by extending the microscopic Markov chain method. To reflect the spreading process more realistically, we conducted numerical simulations from a mesoscopic perspective through an age-contact data-driven approach. Critical transition, multi-wave epidemics, multi-transitions, and mixed-phase transitions resulting from the coevolution of mask and disease are observed. The results highlight the critical role of NPIs in controlling disease spreading. The mask-wearing can effectively reduce the daily peak infection density and prevent large-scale outbreaks within a short period. However, it will also prolong the duration of the epidemic and cause the disease to exist in the population for a long time. There is a trade-off between the two. For certain diseases with specific spreading capabilities, pharmaceutical interventions such as vaccination, which can directly reduce the disease’s infectiousness, may result in more people being infected if NPIs are not implemented as early as possible. In addition, results from Anhui indicate that people aged 42 years are at high risk of being infected because of their large population base, which suggests that priority should be given to the prevention and control of this group.

Four-stage cascaded adaptive sliding mode control for automatic carrier landing with airwake disturbances and uncertainties

ObjectiveThis paper addresses the carrier landing affected by airwake, parametric uncertainties, and carrier deck motion, its main target being the design of a novel sliding mode based automatic carrier landing system to obtain accurate tracking of the reference trajectory, robustness in terms of disturbances and uncertainties, as well as excellent touchdown accuracy. ApproachFor an aircraft nonlinear dynamics, written under a four-stage cascaded strict feedback form, the design of the novel landing control architecture involves the design of a guidance subsystem, robust sliding mode controllers (for the control of the heading angle, attitude angles, and angular rates), an approach power compensation system, adaptive control laws suppressing the uncertainties and disturbances, a Kalman filter for deck motion prediction, a block computing the reference trajectory, a tracking differentiator block for deck motion compensation, and first-order command filters. Main resultsThe software validation process proves the effectiveness of the sliding mode based control scheme and the suppression of the uncertainties and disturbances. Also, the comparison between the performances of the sliding mode control based carrier landing system and the ones associated to other automatic carrier landing systems shows the superiority of the sliding mode based control scheme, as well as its better touchdown accuracy and landing success rate. SignificanceThis study innovatively transforms the general carrier landing problem into a time-varying tracking control problem for cascaded strict feedback dynamics with disturbances and uncertainties. The new designed automatic carrier landing system is the first control architecture in the literature employing the sliding mode control augmented by adaptive control laws for carrier landing, subjected to airwake, deck motion, and uncertainties.

A simple model and rules for the evolution of microbial mutualistic symbiosis with positive fitness feedbacks

The evolution of microbe-microbe mutualistic symbiosis is considered to be promoted by repeated exchanges of fitness benefits, which can generate positive fitness feedbacks ('partner fidelity feedback') between species. However, previous evolutionary models for mutualism have not captured feedback dynamics or coupling of fitness between species. Here, a simple population model is developed to understand the evolution of mutualistic symbiosis in which two microbial species (host and symbiont) continuously grow and exchange fitness benefits to generate feedback dynamics but do not strictly control each other. The assumption that individual microbes provide constant amounts of resources, which are equally divided among interacting partner individual, enables us to reveal a simple rule for the evolution of costly mutualism with positive fitness feedbacks: the product of the benefit-to-cost ratios for each species exceeds one. When this condition holds, high cooperative investment levels are favored in both species regardless of the amount invested by each partner. The model is then extended to examine how symbiont mutation, immigration, or switching affects the spread of selfish or cooperative symbionts, which decrease and increase their investment levels, respectively. In particular, when a host associates with numerous symbionts without enforcement, neither mutation nor immigration but rather random switching would allow the spread of cooperative symbionts. Examples using symbiont switching for evolution would include large ciliates hosting numerous intracellular endosymbionts. The simple model and rules would provide a basis for understanding the evolution of microbe-microbe mutualistic symbiosis with positive fitness feedbacks and without enforcement mechanisms.

Emphasizing the role of dynamic synoptic eddy feedback to the interdecadal change in the influence of the Siberian high on subsequent ENSO development

Emphasizing the role of dynamic synoptic eddy feedback to the interdecadal change in the influence of the Siberian high on subsequent ENSO development