Feedback Channel Research Articles

Analysis of appeals of the population of the Nizhny Novgorod region to the territorial health management authority during the COVID-19 pandemic

Introduction. Appeals of the population are important indicators of the realization of patient rights. Therefore, the Ministry of Health of the Nizhny Novgorod region pays great attention to the analysis of their causes. The purpose of this study was to study the impact of the COVID-19 pandemic on the number and reasons for written appeals from the population to the territorial health authority of the Nizhny Novgorod region regarding the medical care provided. The materials were 37,898 citizens’ appeals over 2019–2021. Results. The number of written appeals in 2109 was 4916, in 2020 — 14832, in 2021 — 18150. The share of complaints in the structure of appeals decreased from 10,5 ± 0,4% for 2019 to 6,0 ± 0,2% in 2020 and 4,6 ± 0,2% for 2021. Reasons for writing complaints included the quality of medical care (32,9 ± 2,1% during 2019, 34,2 ± 1,6% during 2020, 22,6 ± 1,4% during 2021); refusal to provide medical care (20,5 ± 1,8% in 2019, up to 15,7% ± 1,2% over 2020 and 11,5 ± 1,1% over 2021); violations of ethics and deontology (18,8 ± 1,7% for 2019, 15,8 ± 1,6% for 2020 and 15,3 ± 1,2% for 2021); problems with access to medical care (12,0 ± 1,4% during 2019, 15,2 ± 1,2% during 2020, 19,2 ± 1,4% duirng 2021 year). Research limitations. Repeated requests were excluded. Conclusion. The example of the Nizhny Novgorod region demonstrates the openness of the Ministry of Health, the formation of feedback channels with patients to help to effectively mobilize resources to promptly address issues of providing medical care to the population in an emergency situation.

Channel reconstruction through improvised deep learning architecture for high-speed networks

Efficient acquisition of channel state information (CSI) is quite complicated process but immensely essential to exploit probable benefits of massive multiple input multiple output (MIMO) systems. Therefore, a deep learningbased model is utilized to estimate channel feedback in a massive MIMO system. The proposed improvised deep learning-based channel estimation (IDLCE) model enhances channel reconstruction efficiency by using multiple convolutional layers and residual blocks. The proposed IDLCE model utilizes encoder network to compress CSI matrices where decoder network is used to downlink reconstruct CSI matrices. Here, an additional quantization block is incorporated to improve feedback reconstruction accuracy by reducing channel errors. A COST 2,100 model is adopted to analyse performance efficiency for both indoor and outdoor scenarios. Further, deep learning-based model is used to train thousands of parameter and correlation coefficients much faster and to minimize computational complexity. The proposed IDLCE model evaluate performance in terms of normalized mean square error (NMSE), correlation efficiency and reconstruction accuracy and compared against varied state-of-art-channel estimation techniques. Excellent performance results are obtained with large improvement in channel reconstruction accuracy

Novel Fluidic Oscillator Evaluation Considering Dimensional Modifications

Although flow mixing and cooling can be greatly enhanced when considering the use of fluidic oscillators (FOs), they are more commonly employed in active flow control (AFC) applications where the injected pulsating flow interacts with the boundary layer, usually in order to delay its separation. In fact, prior to any FO implementation in a given application, it is essential to study the range of frequencies and amplitudes it can generate as a function of the incoming mass flow and its dimensions. This is what is being performed in the present manuscript for a rather novel FO configuration. A numerical study of a standard three-dimensional (3D) FO configuration, and also using a two-dimensional (2D) approach, is initially presented. After comparing the 3D and the 2D results and analyzing the main differences, we modified some of the internal dimensions of the FO in order to evaluate the variation in its dynamic performance. The present results clarify which internal dimensional modifications are more effective in generating larger output frequencies and velocity field variations. Care is taken to analyze the origin of self-sustained oscillations. This paper links, for the first time, the origin of the pressure force oscillations at the feedback channel’s outlet, with the interaction of the mixing chamber central jet and the reverse feedback channel flow at the mixing chamber’s converging walls. A novel equation relating the FO outlet mass flow frequency with the time-averaged FC reverse flow is presented and discussed. In fact, the present study needs to be seen as the continuation of a former one, recently published by authors, where the effects of several Reynolds numbers as well as some different internal dimensions were considered.

Maximum Doppler Shift Identification Using Decision Feedback Channel Estimation

This paper introduces a new method for estimating the maximum Doppler shift using decision feedback channel estimation (DFCE). In highly mobile environments, which are expected to be covered beyond 5G and 6G systems, the relative movement between the transmitter and receiver causes Doppler shifts. This leads to inter-carrier interference (ICI), significantly degrading communication quality. To mitigate this effect, systems that estimate the maximum Doppler shift and adaptively adjust communication parameters have been extensively studied. One of the most promising methods for maximum Doppler shift estimation involves inserting pilot signals at both the beginning and end of the packet. Although this method achieves high estimation accuracy, it introduces significant latency due to the insertion of the pilot signal at the packet’s end. To address this issue, this paper proposes a new method for rapid estimation using DFCE. The proposed approach compensates for faded signals using channel state information obtained from decision feedback. By treating the compensated signal as a reference, the Doppler shift can be accurately estimated without the need for pilot signals at the end of the packet. This method not only maintains high estimation accuracy but also significantly reduces the latency associated with conventional techniques, making it well-suited for the requirements of next-generation communication systems.

Participatory Renewal of Historic Districts Based on Bayesian Network

Public participation is a requirement for contemporary urban governance in China, especially in the protection planning of historic districts. In the rapid development stage, many historic districts have lost their functional positioning in cities and are gradually disappearing due to outdated facilities and services. Previous studies have pointed out that protecting historic and cultural districts requires the joint efforts of multiple stakeholders. Integrating multiple stakeholders’ interests and needs is a complex problem in related research fields and planning practices. The traditional forms of public participation, such as questionnaire surveys, interviews, and symposiums, have the problem of high time costs for collection and analysis. At the same time, the information has been translated multiple times, making it challenging to avoid misunderstandings and errors. Therefore, there is an urgent need for an alternative platform for public participation in the renewal of China’s historic districts. Taking Houzaimen Street in Jinan City as an example, this paper uses the Bayesian network to establish a spatial evaluation model of historic districts. Our aims are to translate natural semantics into the design semantics of the architectural field, accurately locate the spatial problems of the historic district, and provide targeted improvement measures. In this paper, a public feedback channel for Houzaimen Street is established to provide a reference for the renewal of the historic district and the advancement of public participation.

Development of an integrated model for SME marketing and CRM optimization

The development of an integrated model for optimizing marketing and customer relationship management (CRM) in small and medium-sized enterprises (SMEs) presents a crucial advancement in enhancing business competitiveness and customer engagement. This paper proposes a unified model that merges scalable software solutions for social media analytics with AI-driven CRM to provide a comprehensive framework for SMEs to enhance their marketing strategies and customer interactions. Social media platforms have become vital channels for customer outreach, engagement, and feedback, yet SMEs often face challenges in leveraging these platforms due to limited resources and expertise. Integrating social media analytics into CRM systems offers businesses the ability to analyze customer behaviors, preferences, and sentiment on a large scale. The proposed model utilizes artificial intelligence (AI) to streamline customer interactions, enabling personalized communication and improving customer retention. By incorporating AI into CRM, SMEs can automate routine tasks, such as responding to customer inquiries and segmenting audiences based on their social media activity. This allows for more efficient marketing strategies and enables businesses to proactively address customer needs, ultimately driving customer loyalty and long-term growth. Furthermore, the unified model focuses on scalability, ensuring that SMEs can adopt and adapt these technologies according to their evolving needs and resources. This paper also explores the benefits of integrating these technologies, including the potential for improved data-driven decision-making, enhanced customer experience, and increased operational efficiency. In conclusion, this integrated model presents a strategic pathway for SMEs to optimize their marketing and CRM efforts through the adoption of scalable, AI-driven tools that enhance customer engagement and boost business performance. Keywords: SME Marketing, CRM Optimization, Social Media Analytics, AI-Driven CRM, Customer Engagement, Scalable Software Solutions, Business Performance, Personalized Communication, Customer Retention, Data-Driven Decision-Making.

ПРОБЛЕМЫ РЕАЛИЗАЦИИ СИСТЕМ ПОЛУАВТОМАТИЧЕСКОГО УПРАВЛЕНИЯ, ВКЛЮЧАЮЩИХ КОНТУР ОБРАТНОЙ СВЯЗИ ПО УСИЛИЮ

The issues of force-torque sensing in semi-automatic systems are considered. The paper describes the main control principles in ergatic systems from the viewpoint of automatic control theory, considers a method for improving the control quality by introducing additional feedback for the operator. The work proposes force-torque sensing as the most promising feedback channel for the implementation, describes the relevance of this channel in the fulfilled automatic control systems, gives examples. A description of the feedback loop for the efforts on the manipulator’s working element is given. The work highlights the following as the main problems in implementing the considered semi-automatic systems: the kinematic chain influence, filtering and delay, gravity compensation, as well as the problem of the natural feedback loop.

Dynamic event‐triggered model‐free adaptive control for networked control systems with random packet loss

AbstractA novel dual‐channel dynamic event‐triggered model‐free adaptive control method with compensation is proposed for nonlinear networked control systems (NCSs) subjected to random packet loss. In order to tackle the issue of redundant data transmission, a dual‐channel triggering control framework is designed, where data transmission only occurs upon meeting the triggering conditions. Compared with the traditional static event‐triggered schemes with fixed triggering thresholds, the proposed method allows for dynamically adjustable triggering thresholds. This means that the number of data transmissions in the forward and feedback channels can be further reduced. In addition, considering the detrimental impact of random packet loss and output event‐triggered on the system, compensation is applied to the system's output data using the linear data model of the nonlinear system, which is directly derived from I/O data without incorporating any other mechanistic model information, thereby ensuring optimal control performance. In contrast to existing results, the proposed control strategy can enhance system performance while conserving network communication resources. The effectiveness and superiority of the proposed scheme have been validated through two simulations.

Persistent post-flood hillslope activity posing a potential landslide dam hazard in the Ahr valley, Germany

BackgroundExtreme floods are known to severely reorganise inhabited landscapes by inundation, clogging, scouring and damaging infrastructure and lives. However, their post-event impacts are poorly understood, especially concerning coupled hillslope channel feedbacks such as the reactivation of slope instabilities connected to the river and that may be able to block it upon sudden failure. The July 2021 Ahr valley flood exemplified this ability of concurrent and sustained landscape reorganisation. Here, we study a retrogressive slope instability near the town of Müsch, in the upper Ahr valley using field mapping, repeat airborne laser scanning, electrical resistivity tomography and passive seismic monitoring to reveal the failure geometry, its mechanisms and transient activity.ResultsThe old landslide developed in lower Devonian rocks. It is 100 m wide, 200 m long and approximately 15–20 m deep, which leads to a total volume of about 430,000 m3. This landslide was severely undercut by the 2021 flood with 7000 m3 of material eroded at the landslide toe. The landslide has started to react. Given the narrow section of the river at this location, there is a potential landslide dam hazard. We modelled the inundation volumes and back fill times for different failure scenarios, ranging between 20,000 m3 and 330,000 m3 accumulating within 5 min and 20 h.ConclusionsOur results imply a need to systematically screen flood impacted landscapes for sustained post-event hillslope activity that governs hillslope-channel coupling, driving both persistent sediment injection into the stream and sudden river blocking and subsequent damming.

Implementing social and affective touch to enhance user experience in human-robot interaction.

In this paper, we discuss the potential contribution of affective touch to the user experience and robot performance in human-robot interaction, with an in-depth look into upper-limb prosthesis use as a well-suited example. Research on providing haptic feedback in human-robot interaction has worked to relay discriminative information during functional activities of daily living, like grasping a cup of tea. However, this approach neglects to recognize the affective information our bodies give and receive during social activities of daily living, like shaking hands. The discussion covers the emotional dimensions of affective touch and its role in conveying distinct emotions. In this work, we provide a human needs-centered approach to human-robot interaction design and argue for an equal emphasis to be placed on providing affective haptic feedback channels to meet the social tactile needs and interactions of human agents. We suggest incorporating affective touch to enhance user experience when interacting with and through semi-autonomous systems such as prosthetic limbs, particularly in fostering trust. Real-time analysis of trust as a dynamic phenomenon can pave the way towards adaptive shared autonomy strategies and consequently enhance the acceptance of prosthetic limbs. Here we highlight certain feasibility considerations, emphasizing practical designs and multi-sensory approaches for the effective implementation of affective touch interfaces.

Adaptive Frame Structure Design for Sensing-Assisted Downlink Communication in the Vehicle-to-Infrastructure Scenario.

Vehicle-to-everything (V2X) is considered a key factor in driving the future development of intelligent transport, which requires high-quality communication and fast sensing of vehicle information in high-speed mobile scenarios. However, high-speed mobility makes the wireless channel change rapidly, which requires frequent channel estimation and channel feedback between a vehicle and the roadside unit (RSU), resulting in an increase in communication overhead. At the same time, the high maneuverability of vehicles leads to frequent switching and misalignment of communication beams, so the RSU must have better beam prediction and tracking capabilities. To address this problem, this paper proposes an adaptive frame structure design scheme for sensing-assisted downlink (DL) communication. The basic idea of the scheme involves analyzing the communication model during the vehicle's movement. This analysis aims to establish a theoretical relationship between the Symbol Error Rate (SER) and the following two key factors: the vehicle's starting position and the distance it travels across. Subsequently, the scheme leverages the vehicle's position data, as detected by the RSU, to calculate the real-time SER for DL communication. The SER threshold is set based on the requirements of DL communication. If the real-time SER is below this threshold, channel estimation becomes unnecessary. This decreases the frequency of channel estimation and frees up time and frequency resources that would otherwise be occupied by channel estimation processes within the frame structure. The design of an adaptive frame structure, as detailed in the above scheme, is presented. Furthermore, the performance of the proposed method is analyzed and compared with that of the traditional communication protocol frame structure and the beam prediction-based frame structure. The simulation results indicate that the communication throughput of the proposed method can be improved by up to 6% compared with the traditional communication protocol frame structure while maintaining SER performance.

Crippled Giant: Institution Crisis of Monarchy since

All human institutions are dynamic. Traditional institutions emerged because of man's need for them, and they change because of various factors. However, monarchical institutions are known to be conservative, culture bound and consistently consistent. Notwithstanding, this giant had faced a lot of crisis and had been crippled over the years in Nigeria through the imposition of the British Colonial rule and post –Independence political developments. In contemporary times, they have been battered, hurt and brutalized by modern political leaders who have seen the monarchical institution as a rival and as an obstacle to their hopes or as instrument to be used and chosen to accomplish their aims. To this extent, little is known historically on the event of change and continuity in the traditional powers and functions of the monarchical institutions. The paper highlights the functions and powers of the monarchy in the society before and during the colonial rule. It also examines their place during the military interregnum and in the current political dispensation. The authors insist with all sense of responsibility, that, monarchical institution has a pivotal role as a link between modern government and local communities, thereby providing a feedback channel through which community needs and aspirations are felt, assessed and addressed. However, what should be done constitutionally is to accept the traditional rulers as purveyors and upholders of traditions, while curtailing and limiting their powers so that they do not clash with executive authorities.

Feedback Approach for the Relay Channel with Noisy Feedback and Its Security Analysis.

Relay channels capture the essence of several important communication scenarios such as sensor network and satellite communication. In this paper, first, we propose an efficient coding scheme for an additive white Gaussian noise (AWGN) relay channel in the presence of AWGN feedback, which generalizes the conventional scheme for the AWGN relay channel with noiseless feedback by introducing a lattice-based strategy to eliminate the impact of the feedback channel noise on the performance of the original scheme. Next, we further extend the proposed scheme to the multi-input single-output (MISO) fading relay channel (FRC) with noisy feedback. The key to this extension is to use a pre-coding strategy to transform the MISO channel into a single-input single-output (SISO) channel and applying a two-dimensional lattice coding strategy to deal with the feedback fading channel noise. Finally, we analyze the security performance of our proposed scheme in several cases, and the results of this paper are further illustrated by numerical examples.

Design of multi-modal feedback channel of human–robot cognitive interface for teleoperation in manufacturing

AbstractTeleoperation, which is a specific mode of human–robot collaboration enabling a human operator to provide instructions and monitor the actions of the robot remotely, has proved beneficial for application to hazardous and unstructured manufacturing environments. Despite the design of a command channel from human operators to robots, most existing studies on teleoperation fail to focus on the design of the feedback channel from the robot to the human operator, which plays a crucial role in reducing the cognitive load, particularly in precise and concentrated manufacturing tasks. This paper focuses on designing a feedback channel for the cognitive interface between a human operator and a robot considering human cognition. Current studies on human–robot cognitive interfaces in robot teleoperation are extensively surveyed. Further, the modalities of human cognition that foster understanding and transparency during teleoperation are identified. In addition, the human–robot cognitive interface, which utilizes the proposed multi-modal feedback channel, is developed on a teleoperated robotic grasping system as a case study. Finally, a series of experiments based on different modal feedback channels are conducted to demonstrate the effectiveness of enhancing the performance of the teleoperated grasping of fragile products and reducing the cognitive load via the objective aspects of experimental results and the subjective aspects of operator feedback.

An Experimental Investigation Of Sweeping Jets Impinging Flow Field: Effects Of Nozzle-To-Plate Distance And Feedback Channel Minimum Cross-Sectional Area

Without requiring any moving part or piezoelectrical elements, fluidic oscillators are actuators able to convert a steady jet into an oscillating one, only due to internal fluid dynamic mechanisms. Since the oscillations of such jets are self-induced and self-sustained, the interest in fluidic oscillators has grown over the last years, especially for film cooling, flow control and heat transfer applications. The oscillation frequency, the sweeping angle and the flow field characteristics of sweeping jets greatly depend on various geometric and fluid dynamic parameters. Therefore, in the current work, through the application of the planar PIV technique, the effects of nozzle-to-plate distance and feedback channel minimum cross-sectional area on the external flow field of impinging sweeping jets are investigated. Non-dimensional nozzle-to-plate distances H/w equal to 2, 4, 6, 8 and 10 are taken into account, being w the exit nozzle throat section width. To analyze the effects of the feedback channel minimum cross-sectional area, a fluidic oscillator having a mixing chamber length Lf/w = 4.5 is provided with threaded holes in correspondence of the feedback channels, in which a micrometric screw is fastened to vary the parameter g/w, being g the feedback channel minimum passage width; values of g/w equal to 0.17, 0.33, 0.50, 0.67 and 1 are considered. Time-averaged and phase-averaged analyses are performed; indeed, the pressure signal measured between the inlet and outlet sections of one feedback channel is used to identify the phase of each velocity snapshot, despite the velocity measurements are not time-resolved. Firstly, the configuration with g/w=1 is taken as reference case, thus analyzing the nozzle-to-plate distance effect. The phase-averaged velocity and fluctuating kinetic energy fields show that the jet sweeps in the external flow field, with a sweeping angle approximately equal to 25° and a frequency of 50 Hz. Due to this behavior, the time-averaged velocity field exhibits a twin-jet pattern, which can also be identified from the time-averaged Phase-correlated Kinetic Energy (PKE) distributions (see Figure 1, left). Indeed, the extreme positions of the oscillating motion are the zones in which the coherent oscillation of the velocity values is greater and, therefore, the PKE values are the greatest. Furthermore, while at H/w=2 the jet sweeps in an almost rigid-like way between the two extreme positions reached during its oscillating motion, at greater nozzle-to-plate distances a phase delay between the motion of the jet near the impinging plate and near the exit nozzle section can be observed. Considering the effects of the feedback channel minimum cross-sectional area, by reducing g/w the sweeping angle of the jet decreases, while the oscillation frequency increases. Still, a twin-jet pattern can be identified for g/w≥0.33, as it can be seen from the crosswise profiles of the dimensionless time-averaged axial velocity (see Figure 1, right), while for g/w=0.17 a single-jet velocity structure can be identified. Nevertheless, as g/w decreases the maximum values of the velocity increase while the oscillation intensity decreases, resulting in a decrease of the PKE values.

Research on load-resistant enhancement control optimization of composite energy power supply system based on deep reinforcement learning

Abstract To solve the problem of insufficient inertia in the power electronics components under the development trend of power electronics in the transmission system of heavy-duty electric vehicles, combined with the composite energy power supply mode and control method, voltage-based feedback channels and virtual capacitor branches are designed for both engine-generator sets and DCDC control loops. These innovations compensate for system inertia and introduce a TD3 deep reinforcement learning-based adaptive regulation component to manage electrical systems in response to DC voltage fluctuations. This approach enhances the stability of the entire system in the face of load disturbances. Strategy validations demonstrate that this method ensures stable system operation, significantly mitigates DC voltage fluctuations during sudden load changes and is highly adaptable without relying on system models.

Deep learning-based channel estimation with application to 5G and beyond networks

Channel state information (CSI) feedback estimation for a downlink medium in a massive multiple input multiple output (MIMO) system is an essential and critical task to improve channel capacity and performance yield, especially in a frequency division duplex (FDD) multiplexing system. However, spectral efficiency degradation is a massive issue due to high channel feedback overhead. This work proposes a deep learning-based channel estimation (DLCE) model to improve channel reconstruction efficiency and channel overhead reduction accuracy. The proposed deep learning (DL) mechanism consists of encoder and decoder network where encoder network is utilized to compress CSI matrices whereas decoder network is used to decompress obtained CSI matrices. Here, inverse discrete Fourier transform (IDFT) method is utilized to convert CSI matrices of frequency domain into CSI matrices of delay domain. Simulation results are evaluated between uplink and downlink medium in the massive MIMO system considering a co-operation in science and technology (COST) 2,100 model. Here, a significant improvement in correlation and normalized mean square error (NMSE) results is observed. The proposed DLCE model shows superior performance against varied channel estimation techniques in terms of NMSE and correlation efficiency.

Multichannel Sensorimotor Integration with a Dexterous Artificial Hand

People use their hands for intricate tasks like playing musical instruments, employing myriad touch sensations to inform motor control. In contrast, current prosthetic hands lack comprehensive haptic feedback and exhibit rudimentary multitasking functionality. Limited research has explored the potential of upper limb amputees to feel, perceive, and respond to multiple channels of simultaneously activated haptic feedback to concurrently control the individual fingers of dexterous prosthetic hands. This study introduces a novel control architecture for three amputees and nine additional subjects to concurrently control individual fingers of an artificial hand using two channels of context-specific haptic feedback. Artificial neural networks (ANNs) recognize subjects’ electromyogram (EMG) patterns governing the artificial hand controller. ANNs also classify the directions objects slip across tactile sensors on the robotic fingertips, which are encoded via the vibration frequency of wearable vibrotactile actuators. Subjects implement control strategies with each finger simultaneously to prevent or permit slip as desired, achieving a 94.49% ± 8.79% overall success rate. Although no statistically significant difference exists between amputees’ and non-amputees’ success rates, amputees require more time to respond to simultaneous haptic feedback signals, suggesting a higher cognitive load. Nevertheless, amputees can accurately interpret multiple channels of nuanced haptic feedback to concurrently control individual robotic fingers, addressing the challenge of multitasking with dexterous prosthetic hands.

Cloud-based time-varying formation active fault-tolerant predictive control of networked multi-agent systems with actuator and sensor faults

This paper addresses the time-varying formation control problem of second-order networked multi-agent systems consisting of a virtual leader and multiple followers, where random communication constraints in the forward and feedback channels as well as actuator and sensor faults are considered. A state observer is designed to estimate the system state and the potential faults, based on which a time-varying formation active fault-tolerant predictive control scheme is proposed. Then, a necessary and sufficient condition is derived to guarantee the stability of the closed-loop system and to achieve the time-varying formation, which is independent of random communication constraints as well as actuator and sensor faults. Finally, simulation results of three contrastive cases are presented to verify the effectiveness of the proposed control scheme.

Emotion-Aware In-Car Feedback: A Comparative Study

We investigate personalised feedback mechanisms to help drivers regulate their emotions, aiming to improve road safety. We systematically evaluate driver-preferred feedback modalities and their impact on emotional states. Using unobtrusive vision-based emotion detection and self-labeling, we captured the emotional states and feedback preferences of 21 participants in a simulated driving environment. Results show that in-car feedback systems effectively influence drivers’ emotional states, with participants reporting positive experiences and varying preferences based on their emotions. We also developed a machine learning classification system using facial marker data to demonstrate the feasibility of our approach for classifying emotional states. Our contributions include design guidelines for tailored feedback systems, a systematic analysis of user reactions across three feedback channels with variations, an emotion classification system, and a dataset with labeled face landmark annotations for future research.