Internal Feedback Loop Research Articles

Biomimetic array actuators for multisituation low-grade energy harvesting

Low-grade energy resources are abundant and widely available, yet efficiently harnessing and continuously collecting this energy presents a considerable challenge, leading to significant energy losses. In this work, we introduce a biomimetic array actuator constructed with a sodium alginate (SA)-tellurium nanowires (Te NWs)/polyvinylidene fluoride (PVDF) bilayer structure that responds to various stimuli such as low temperature, natural light, and humidity. Significantly, this actuator generates autonomous self-oscillatory motion driven by an internal mechanical feedback loop. Utilizing its high-frequency self-oscillating motion, this actuator efficiently harnesses low-grade energy sources, including waste heat, non-concentrated sunlight, and exhaust steam. Under various driving conditions, including a 60 °C heater, natural light, and 70 °C steam, the 3 × 3 array actuators can generate peak voltages of 1.5 V, 2 V, and 4 V, respectively. This research has the potential to unlock opportunities for a range of low-grade energy harvesting technologies utilizing flexible actuators.

Intrinsic resonance in gyrotron using non-resonant RF structure

Gyromonotrons are typically believed to rely on the convective interaction between the co-propagating beam and wave, with the extended energy-exchanging process stemming from the external feedback. However, numerous studies focusing on both transverse electric (TE) mode and transverse magnetic (TM) mode gyrotrons have consistently shown that beam–wave interactions in weak-feedback systems or even in uniform tubes without any structural feedback can yield a theoretical beam efficiency of more than 30% with major forward-wave output during near-cutoff operation, which is the typical operating condition for gyromonotrons. These intriguing findings raise questions about the actual feedback mechanism of gyromonotrons. In this article, comparative studies on the linear and nonlinear behaviors of uniform-tube gyrotron are investigated. The forward and backward waves are observed to co-generate and exhibit similar characteristics of ultra-slow group velocity under near-cutoff operation. This situation allows the as-generated forward wave to modulate the fresh beam, establishing a new backward-wave-like internal feedback loop. Additionally, the quasi-degenerate nature of the bi-directional propagating waves ensures their intrinsic in-phase relationship. The consequent constructive interference enables the uniform tube to function as a high-Q resonator. These findings are found to be independent of the choices of TE or TM modes, providing valuable insights into the underlying interaction mechanism of gyrotron devices.

Historical and contemporary drivers of knickpoint retreat and morphological evolution along Bayou Pierre, Mississippi

AbstractChannel incision in rivers can cause marked ecological and economic damages. This phenomenon is abundant and generally well documented along impacted streams in the loess hills adjacent to the Lower Mississippi River valley. Bayou Pierre, an ecologically important small tributary of the Mississippi River, is currently incising but causes are not well understood. In this study, we examine diverse data sources to: (1) reconstruct a history of erosional stimuli and possible origins and (2) examine effects of contemporary controls. Review of long‐term planform and land use data for the Mississippi River revealed episodic foreshortening events followed by episodic deforestation and reforestation. Hydrologic data suggest an increase in rainfall over the last few decades. Estimates of knickpoint retreat place origins prior to Mississippi River channel straightening (1929–1944). Planform analysis in three focal reaches of Bayou Pierre demonstrates slow change prior to 1982, but accelerated changes after those periods. Mean rainfall and 3‐day storm intensity correlate to some planform changes; however, the storm of 1983 may be a better explanation of sudden planform change. We found some evidence of potential internal feedback loops in patterns of bar growth. Together, our analyses provide a synthesis of stimuli experienced by Bayou Pierre over the last ~200 years and suggest both channel migration events before straightening of the Mississippi River and more recent hydrologic events have influenced patterns of geomorphic change in Bayou Pierre.

Multi-bit per cycle true random number generator based on XOR-XNOR ring oscillator unit

To achieve a fast and resource-efficient entropy source, we present a novel oscillator unit, called XOR-XNOR ring oscillator (XXRO) which consists of an XOR gate and an XNOR gate. The XXRO unit has an internal feedback loop that allows the accumulation of jitter within the unit. Connecting multiple XXRO units forms a new entropy source. We utilize the differences between the XXRO units in the entropy sources to output two bits pre cycle, increasing the throughput of the True Random Number Generator (TRNG) while optimizing the use of hardware resources. In this paper, we use four proposed entropy source to form a TRNG, by sampling the two outputs of each entropy source individually and performing an XOR computation based on the corresponding positions to obtain random numbers. Experimental results indicate that when working at a throughput of 250 Mbps under a 125 MHz sampling clock, both outputs of the proposed TRNG pass the randomness tests and other performance tests.

Mathematical models and structures of the vehicle lateral stability stabilization system

Currently, intensive research is being carried out to improve the operational characteristics of the car: vibration protection, smoothness, stability, controllability. These properties are largely determined by the characteristics of the vehicle suspension, which provides a connection between the carrier system and the wheels of the vehicle. Significant attention is paid to the development of active suspensions, in which additional actuators are used to form the necessary characteristics, in particular, linear DC motors. The use of active actuators allows you to control the position of the car body, including its lateral roll. In the article, relations are obtained that establish the dependence of additional elastic deformations in the suspension and the car's roll angle on the centrifugal force in a stationary mode. When developing a linearized mathematical model of the control object for the study of non-stationary modes, a two-mass design scheme was used and operator equations were obtained that take into account the elastic-dissipative properties of the sprung and unsprung parts of the car, as well as an additional control action created by the actuator. It is shown that the dynamic properties of the studied control object can be approximately described by the transfer functions of a second-order aperiodic link or an oscillatory link. For the first case, a single-loop system was developed, closed in terms of the angle of heel with a PID controller. In the second situation, it is advisable to use a two-loop system with an internal flexible feedback loop for suspension deformation and an external loop closed for the roll angle using a PID controller. The possibility of forming a feedback signal on the strain rate of the suspension in the wind circuit with the help of an EMF sensor of a linear DC motor is shown.
 On the basis of the block diagram, a computer model of the system was developed, and for typical parameters of the control object, a study was made of transient processes of working off a disturbance in the form of a change in centrifugal force. Based on the simulation results, it was found that the use of the developed ACS provides high accuracy in stabilizing the vehicle roll angle.

A new PID controller design using differential operator for the integrating process

In the present study, a new control structure is proposed to control the integrating process. The proposed control structure is composed of both inner and outer controllers. The internal controller is integrated with the differential operator to overcome the structural limitations of the PID controller to control the integrating process. The proposed internal feedback loop converts the integrating process into an open-loop stable process with a leaded phase. Moreover, it converts the process with higher ultimate frequency allowing the outer controller to be more strongly tuned. From the result of the simulation study, the proposed control structure guarantees better control performance than previous control methods. Furthermore, the control performance of the proposed method shows similar or better results than the previously proposed control methods (ITAE improved by 40.6% from the previous method in the water level control example).

The 4R Model of Mood and Emotion for Sustainable Mental Health in Organisational Settings

Organisations are aware of the need to maintain the mental health of their employees. People’s capacity to recognise and manage their moods and emotions is critical to sustainable mental health, performance, and quality of life, while failure to do so can result in underperformance, disengagement, and in some cases, mental illness. Employees of organisations that provide an appropriate strategy and support are likely to experience sustained psychological and mental health benefits. In this paper, we synthesise previous research into a theoretical framework distinguishing mood from emotion via both top-down (cognitive) and bottom-up (biological) factors. We propose a 4R model to help individuals Recognise a mood as distinct from an emotion, or vice-versa, and respond in one of three ways; Restore, Resolve, or Regulate. The model posits mood as an interoceptive signal of internal biological homeostasis, and emotion as a signal of external, often social, events that disrupt homeostasis; mood and emotion serve as internal and external bio-affective feedback loops, respectively. We propose that mood is modified positively by the restoration of homeostasis, whereas emotion is modified positively by behavioural resolution of the emotion-eliciting event. The 4R model is low-cost, preventative, and can be applied peer-to-peer in organisations without expert supervision.

Aïda Muluneh's Homebound: A Journey in Photography & Addis Foto Fest: Nine Years Survey

Aïda Muluneh's Homebound: A Journey in Photography & Addis Foto Fest: Nine Years Survey

Adaptive active noise feedforward compensation for exhaust ducts using a FIR Youla parametrization

The adaptive feedforward control strategy can effectively attenuate the low-frequency broad-band noise with unknown and variable characteristics. However, most feedforward active noise control systems have the internal acoustic feedback coupling between the secondary noise source and the measurement of the image of the reference noise, which may lead to instability of the system. In addition, there are potential fields of applications in which the broad-band noise source may be mixed with the narrow-band noise components and the delay of the secondary path may be larger than the delay of the primary path. In order to deal with these limitations, in this paper an adaptive Youla(Q) parameterized feedforward active noise control system is introduced. Firstly, a central controller is designed to stabilize the internal acoustic feedback loop. Then the central controller will be augmented into a Youla(Q) parameterized FIR-type adaptive controller with the recursive least squares (RLS) adaptive algorithm, and the Q parameter can be adjusted online to the desired value to attenuate the unknown and variable noise. Finally, the proposed adaptive Youla(Q) parameterized feedforward active noise control approach is successfully applied to the noise attenuation of a “U”-shaped industrial exhaust duct.

Models of pursuit.

This chapter deals with mathematical models for smooth-pursuit eye movements, starting with simple negative-feedback schemes. After pointing out their deficiencies, Robinson developed models that account for specific dynamic properties of pursuit behavior, such as the transient ocular oscillations that may occur at onset, and the adaptive properties of pursuit. The challenges posed by the inherent latency of visual responses to target motion-specifically the instability of a negative feedback model-are resolved by including an efference copy internal positive feedback loop, and distributing system delays throughout the model's pathways. A model for smooth combined eye-head tracking is presented in which the brain sends an efference copy of the planned head movement to null out the vestibular signal expected.

Physical reservoir computing with FORCE learning in a living neuronal culture

Rich dynamics in a living neuronal system can be considered as a computational resource for physical reservoir computing (PRC). However, PRC that generates a coherent signal output from a spontaneously active neuronal system is still challenging. To overcome this difficulty, we here constructed a closed-loop experimental setup for PRC of a living neuronal culture, where neural activities were recorded with a microelectrode array and stimulated optically using caged compounds. The system was equipped with first-order reduced and controlled error learning to generate a coherent signal output from a living neuronal culture. Our embodiment experiments with a vehicle robot demonstrated that the coherent output served as a homeostasis-like property of the embodied system from which a maze-solving ability could be generated. Such a homeostatic property generated from the internal feedback loop in a system can play an important role in task solving in biological systems and enable the use of computational resources without any additional learning.

Mapping farmer perceptions, Conservation Agriculture practices and on-farm measurements: The role of systems thinking in the process of adoption

CONTEXTConservation Agriculture (CA) usage, particularly in Southern Africa, has remained low with lower yield, higher weed pressure and lower soil quality cited as reasons for ‘disadoption’. OBJECTIVEUsing a detailed case study of 50 farmers in two villages in Cabo Delgado (Northern Mozambique), this study seeks to test the hypothesis that farmers' perceptions of CA are associated with distinctly different ‘mental models’ and if these "ways of thinking" overlap with farmers' identified/self-identified groupings (e.g. CA users, ‘disadopters’ and conventional tillage users). Secondly, we examine whether these different mental models (perceptions) are associated with actual differences in on-farm measurements. Finally, we explore the hypothesis that ‘systems thinking’ (i.e., understanding nonlinear causal relationships and internal feedback loops that drive a complex system) and CA usage are positively associated. METHODSFuzzy Cognitive Mapping (FCM) was used to elicit representations of farmers' mental models. To explore the association between farmers' mental models of CA/conventional practices and on-farm measurements we evaluated cowpea aboveground biomass, yield, weed cover, and soil quality parameters from the farmer's main plot. We drew on network analysis to measure structural metrics of cognitive maps that provide important information about a person's mental model (perceptions) of causal interdependencies of farming dynamics. RESULTS AND CONCLUSIONSWe find evidence of two data-driven distinct clusters of farmers' mental models that are in relative alignment with farmers' identified/self-identified groupings. Cluster 1 mainly consists of conventional users and cluster 2 mainly consists of CA users/disadopters. While no significant differences in socio-demographic variables were observed, clusters of mental models were associated with key differences in on-farm measurements. Importantly, cluster 1, who tended to be conventional users, had lower yields, lower soil cover, significantly lower carbon stock and higher weed coverage than cluster 2. Soil quality indicators were higher in cluster 2 as were farmers' overall revenue per hectare. Moreover, cluster 2 had significantly higher degrees of ‘systems thinking’ (measured through complex network analysis of graphical mental models) than cluster 1 which had higher forms of linear thinking. We argue that higher forms of experiential learning and practice of CA relate to higher degrees of systems thinking and stronger positive perceptions of CA, even among the CA ‘disadopters’. SIGNIFICANCEOur findings highlight the importance of systems thinking abilities and inclusion of biophysical, socio-economic and mental modelling variables rather than simple binary measurements which may have led to erroneous conclusions on CA and thus has implications for how CA is understood and promoted in future.

Trend correlations for coastal eutrophication and its main local and whole-sea drivers – Application to the Baltic Sea

Coastal eutrophication is a major environmental issue worldwide. In the Baltic Sea, eutrophication affects both the coastal waters and the open sea. Various policy frameworks aim to hinder its progress but eutrophication-relevant water quality variables, such as chlorophyll-a concentrations, still exhibit opposite temporal trends in various Baltic Sea marine and coastal waters. In this study, we investigate the temporal-trend linkages of measured water quality variables and their various anthropogenic, climatic and hydrospheric drivers over the period 1990–2020 with focus on the Swedish coastal waters and related marine basins in the Baltic Sea.We find that it is necessary to distinguish more and less isolated coastal waters, based on their water exchanges with the open sea, to capture different coastal eutrophication dynamics. In less isolated coastal waters, eutrophication is primarily related to nitrogen concentrations, while it is more related to phosphorus concentrations in more isolated coastal waters. In the open sea, trends in eutrophication conditions correlate best with trends in climatic and hydrospheric drivers, like wind speed and water salinity, respectively. In the coastal waters, driver signals are more mixed, with considerable influences from anthropogenic land-based nutrient loads and sea-ice cover duration. Summer chlorophyll-a concentration in the open sea stands out as a main change driver of summer chlorophyll-a concentration in less isolated coastal waters. Overall, coastal waters are a melting pot of driver influences over various scales, from local land-based drivers to large-scale total catchment and open sea conditions. The latter in turn depend on long-term integration of pathway-dependent influences from the various coastal parts of the Baltic Sea and their land-based nutrient load drivers, combined with overarching climate conditions and internal feedback loops. As such, our results challenge any unidirectional local source-to-sea paradigm and emphasize a need for concerted local land-catchment and whole-sea measures for robust coastal eutrophication management.

Fractional Sliding-Mode Control for Microgyroscope Based on Multilayer Recurrent Fuzzy Neural Network

In this article, an approximation-based adaptive fractional sliding-mode control (SMC) scheme is proposed for a microgyroscope, where a double-loop recurrent fuzzy neural network (DLRFNN) is employed to approximate system uncertainties and disturbance. A fractional-order term is incorporated into the sliding surface that could add an extra degree of freedom and combine the advantages of fractional calculus and SMC. A new four-layer fuzzy neural network (FNN) is studied, which has two feedback loops (internal feedback loop and external feedback loop) to capture the weights and output signal calculated in the previous step and use it as a feedback signal for the next step. On the one hand, the proposed DLRFNN structure combines a fuzzy system to process uncertain information with a neural network to learn from the process. On the other hand, both the internal state information and the output signal are acquired and stored so that better approximation performance is obtained compared with a regular FNN system. Furthermore, the adaptive laws of DLRFNN parameters are derived, which can automatically update free parameters with a bound. Finally, the effectiveness of the proposed adaptive fractional SMC using the DLRFNN strategy is identified by the simulations’ analysis with different fractional orders, whereby tracking errors are uniformly ultimately bounded. The proposed adaptive fractional SMC using the DLRFNN strategy can achieve remarkably superior tracking performance in terms of high precision and fast response by comprehensive comparisons.

Integrating Non-spiking Interneurons in Spiking Neural Networks.

Researchers working with neural networks have historically focused on either non-spiking neurons tractable for running on computers or more biologically plausible spiking neurons typically requiring special hardware. However, in nature homogeneous networks of neurons do not exist. Instead, spiking and non-spiking neurons cooperate, each bringing a different set of advantages. A well-researched biological example of such a mixed network is a sensorimotor pathway, responsible for mapping sensory inputs to behavioral changes. This type of pathway is also well-researched in robotics where it is applied to achieve closed-loop operation of legged robots by adapting amplitude, frequency, and phase of the motor output. In this paper we investigate how spiking and non-spiking neurons can be combined to create a sensorimotor neuron pathway capable of shaping network output based on analog input. We propose sub-threshold operation of an existing spiking neuron model to create a non-spiking neuron able to interpret analog information and communicate with spiking neurons. The validity of this methodology is confirmed through a simulation of a closed-loop amplitude regulating network inspired by the internal feedback loops found in insects for posturing. Additionally, we show that non-spiking neurons can effectively manipulate post-synaptic spiking neurons in an event-based architecture. The ability to work with mixed networks provides an opportunity for researchers to investigate new network architectures for adaptive controllers, potentially improving locomotion strategies of legged robots.

Racial Disparities in Neurological Care in the United States: An Internal Mechanism

The COVID-19 pandemic highlights the need for equitable access to healthcare across all ethnic groups in the United States. Given that COVID-19 patients often experience neurological symptoms, there is a need to reevaluate the accessibility of neurological care in the United States. This paper explores the reasons behind the lack of neurological care in Black, Asian, and minority ethnic groups (BAME). After exploring existing literature and case studies, this paper ultimately concludes that the systemic referral process and lack of advocacy in BAME communities act as an internal feedback loop, heightening racial inequality within the neurological healthcare system. The paper will then discuss the implications of this internal feedback loop, addressing the causal mechanism for racial disparities specific to healthcare providers.

Algorithms for Adaptive Feedforward Noise Attenuation—A Unified Approach and Experimental Evaluation

Adaptive feedforward broadband noise compensation is currently used when a correlated measurement with the disturbance (an image of the disturbance) is available. Most of the active feedforward noise control systems feature an internal “positive” acoustical feedback between the compensation system and the disturbance measurement, which has to be taken into account. Adaptive algorithms for active feedforward noise attenuation have been developed since 1985 from a local optimization point of view. This article presents two classes of adaptive configurations for active feedforward noise compensation developed from a stability point of view. The first class uses a standard infinite impulse response (IIR) [finite impulse response (FIR)] compensator structure, while the second class uses the Youla-Kučera parameterization of the feedforward noise compensator. This allows separating the problem of stabilizing the internal positive feedback loop from the minimization of the residual noise. These adaptive schemes have been developed in the context of active vibration control, but, as will be shown, they can be used also in active noise control. This article presents, in a unified manner, the available algorithms and compensator structures for adaptive feedforward noise attenuation and provides a comparative experimental evaluation on a relevant experimental test bench (a duct silencer).

Epigenetic Regulation of Circadian Rhythm and Its Possible Role in Diabetes Mellitus.

This review aims to summarize the knowledge about the relationship between circadian rhythms and their influence on the development of type 2 diabetes mellitus (T2DM) and metabolic syndrome. Circadian rhythms are controlled by internal molecular feedback loops that synchronize the organism with the external environment. These loops are affected by genetic and epigenetic factors. Genetic factors include polymorphisms and mutations of circadian genes. The expression of circadian genes is regulated by epigenetic mechanisms that change from prenatal development to old age. Epigenetic modifications are influenced by the external environment. Most of these modifications are affected by our own life style. Irregular circadian rhythm and low quality of sleep have been shown to increase the risk of developing T2DM and other metabolic disorders. Here, we attempt to provide a wide description of mutual relationships between epigenetic regulation, circadian rhythm, aging process and highlight new evidences that show possible therapeutic advance in the field of chrono-medicine which will be more important in the upcoming years.

Modeling and simulation of a passive variable inertia flywheel for diesel generator

The power supply system of diesel generators is isolated. Sudden loading and unloading will lead to fluctuation of the engine speed and exert an impact on the power supply quality. In order to improve the speed stability of diesel generators against the loading impact, this paper proposes a flywheel with variable moment of inertia, through the adjustment of which, the speed stability can be improved. Imbalance between the output torque and the load torque of the diesel engine can cause changes in the moment of inertia of variable inertia flywheel. The variable inertia flywheel forms the internal feedback loop of the speed control system, it takes the Angular acceleration of engine as the input signal and its output reduces the pulse impact of loading. SIMULINK simulation results under sudden loading and unloading condition have verified that, the variable inertia flywheel makes the diesel generator less sensitive to the loading impact. Meanwhile, variable inertia flywheel changes the maximum magnitude and bandwidth of closed-loop system, and endows the speed control system with a smoother response and better anti-disturbance ability.

Variance based weighting of multisensory head rotation signals for verticality perception.

We tested the hypothesis that the brain uses a variance-based weighting of multisensory cues to estimate head rotation to perceive which way is up. The hypothesis predicts that the known bias in perceived vertical, which occurs when the visual environment is rotated in a vertical-plane, will be reduced by the addition of visual noise. Ten healthy participants sat head-fixed in front of a vertical screen presenting an annulus filled with coloured dots, which could rotate clockwise or counter-clockwise at six angular velocities (1, 2, 4, 6, 8, 16°/s) and with six levels of noise (0, 25, 50, 60, 75, 80%). Participants were required to keep a central bar vertical by rotating a hand-held dial. Continuous adjustments of the bar were required to counteract low-amplitude low-frequency noise that was added to the bar's angular position. During visual rotation, the bias in verticality perception increased over time to reach an asymptotic value. Increases in visual rotation velocity significantly increased this bias, while the addition of visual noise significantly reduced it, but did not affect perception of visual rotation velocity. The biasing phenomena were reproduced by a model that uses a multisensory variance-weighted estimate of head rotation velocity combined with a gravito-inertial acceleration signal (GIA) from the vestibular otoliths. The time-dependent asymptotic behaviour depends on internal feedback loops that act to pull the brain's estimate of gravity direction towards the GIA signal. The model's prediction of our experimental data furthers our understanding of the neural processes underlying human verticality perception.