Sophisticated Feedback Research Articles

Phosphorous metabolism and manipulation in chronic kidney disease.

Chronic kidney disease-mineral bone disorder (CKD-MBD) is a syndrome commonly observed in subjects with impaired renal function. Phosphate metabolism has been implicated in the pathogenesis of CKD-MBD and according to the phosphorocentric hypothesis may be the key player in the pathogenesis of these abnormalities. As phosphorous is an essential component for life, absorption from the bowel, accumulation and release from the bones, and elimination through the kidneys are all homeostatic mechanisms that maintain phosphate balance through very sophisticated feedback mechanisms, which comprise as main actors: vitamin D (VD), parathyroid hormone (PTH), calciproteins particles (CPPs), fibroblast growth factor-23 (FGF-23) and other phosphatonins and klotho. Indeed, as the renal function declines, factors such as FGF-23 and PTH prevent phosphate accumulation and hyperphosphatemia. However, these factors per se may be responsible for the organ damages associated with CKD-MBD, such as bone osteodystrophy and vascular calcification. We herein review the current understanding of the CKD-MBD focusing on phosphorous metabolism and the impact of phosphate manipulation on surrogate and hard outcomes.

Effects of delay and error in the feedback structure of ecological management

Ecosystems face various emergent uncertainties owing to factors such as climate change and accelerating anthropogenic impacts. Uncertainty is a major challenge and a barrier that ecosystem management faces, because it is difficult to precisely predict a priori risks that can have significant impacts on ecosystems. Hence, management with adaptive capacity is recommended to deal with such uncertainties, and feedback structures are central mechanisms for such flexible management. This study used mathematical models to clarify the specific impacts of feedback structures on ecosystem management, such as resource and wildlife management. In particular, the impact of errors in estimating ecosystem status when providing feedback and the impact of the time lag before feedback effects were implemented into management were examined. Overestimation of ecosystem status or a large time lag led to undesirable temporal oscillations in ecosystem status. However, these scenarios can be avoided when combined with management practices that limit the impact of management on the ecosystem, such as input control. Ecosystem management tends to have a large spatiotemporal scale, and implementing highly accurate monitoring and sophisticated feedback structures is difficult. However, the results suggest that effective ecosystem management with a simple feedback structure can be achieved through such complementary institutional design.

A Wearable Personalised Sonification and Biofeedback Device to Enhance Movement Awareness.

Movement sonification has emerged as a promising approach for rehabilitation and motion control. Despite significant advancements in sensor technologies, challenges remain in developing cost-effective, user-friendly, and reliable systems for gait detection and sonification. This study introduces a novel wearable personalised sonification and biofeedback device to enhance movement awareness for individuals with irregular gait and posture. Through the integration of inertial measurement units (IMUs), MATLAB, and sophisticated audio feedback mechanisms, the device offers real-time, intuitive cues to facilitate gait correction and improve functional mobility. Utilising a single wearable sensor attached to the L4 vertebrae, the system captures kinematic parameters to generate auditory feedback through discrete and continuous tones corresponding to heel strike events and sagittal plane rotations. A preliminary test that involved 20 participants under various audio feedback conditions was conducted to assess the system's accuracy, reliability, and user synchronisation. The results indicate a promising improvement in movement awareness facilitated by auditory cues. This suggests a potential for enhancing gait and balance, particularly beneficial for individuals with compromised gait or those undergoing a rehabilitation process. This paper details the development process, experimental setup, and initial findings, discussing the integration challenges and future research directions. It also presents a novel approach to providing real-time feedback to participants about their balance, potentially enabling them to make immediate adjustments to their posture and movement. Future research should evaluate this method in varied real-world settings and populations, including the elderly and individuals with Parkinson's disease.

An adaptive consensus model for multi-criteria sorting under linguistic distribution group decision making considering decision-makers’ attitudes

Group multiple criteria sorting (MCS) has become a trend in dealing with a variety of practical problems. During the process of managing group MCS, it is critical to reduce conflicts among decision-makers (DMs). Given the key role of DMs’ attitudes in affecting consensus level, this study aims to propose a novel consensus-based approach to solve group MCS problems considering DMs’ attitudes with flexible expression linguistic distribution assessments (LDAs) that can capture massive DMs’ qualitative preferences. To achieve this goal, first, a minimum adjustment-based optimization model is built to guide individuals in revising their preferences, and a maximum assignment interval-based optimization model is constructed to derive consistent and possible assignments of each alternative while maintaining the accuracy levels of the original assignments. An attitudinal consensus index is then defined to measure the group consensus level, by which group DMs’ attitudes can be well considered in MCS problems. A sophisticated adaptive feedback adjustment mechanism is also developed and inserted into the consensus model, which provides support for consensus-reaching based on the advantages of both types of adaptive feedback adjustment mechanism strategies. Afterwards, to generate more straightforward and scientific assignment solutions, this study proposes a minimum information loss-based optimization model to identify the final categories of each alternative. Finally, an illustrative example for evaluating livable cities, followed by sensitivity and comparative analyses, is presented to demonstrate the applicability and advantages of the proposed MCS approach.

Proportional control of a parallel robogami delta mechanism with embedded 2D printed angle sensor feedback

Robogami structures are employed for many purposes, including miniature parallel manipulators. Parallel manipulators require accurate joint position feedback, underlining the open problem in robogami structures for providing sophisticated feedback with 2D, fully embeddable, down-scalable, flexible sensors. Existing studies include only on-off control applications, excluding any parallel mechanism implementations and continuous proportional control signals. We implement silver inkjet printed angle sensors for the first time into a robogami structure and use this class of sensors for control. For the first time, a parallel manipulator is controlled with a 2D sensor, and printed sensors are tested for continuous control. Performances are analyzed with set-point and tracking control tasks using a paper Delta mechanism, compared with on-off control and encoder feedback control cases for better evaluation. A detailed comparison with the existing studies both for robogami parallel mechanisms and other sensor-embedded flexible hinges is provided. Encoder-like control performances are achieved in a wide range of the joint space and down to ~1 mm. RMS task space accuracy is recorded. Sources and potential improvements for the less accurate regions are stated. We conclude that printed sensors are promising for parallel mechanisms and continuous signal control applications.

Feedback Analyzer for Employee Progress Graph

In contemporary workplace environments, the assessment and improvement of employee performance are integral components of organizational success. This abstract introduces a sophisticated Feedback Analyzer designed to streamline the process of evaluating and visualizing employee progress through dynamic and interactive graphs. The Feedback Analyzer leverages advanced data analytics and machine learning techniques to analyze feedback data collected from various sources, including performance reviews, peer evaluations, and managerial assessments. The system employs natural language processing algorithms to extract valuable insights feedback, transforming unstructured comments into quantifiable metrics. In this research we focus on the need for effective employee performance evaluation and feedback mechanisms is paramount. Traditional methods of assessing employee progress often fall short in providing real-time insights and actionable data for both employees and managers.

Tillage depth regulation system via depth measurement feedback and composite sliding mode control: A field comparison validation study

The existing methodologies employed for the quantification and regulation of tractor’s tillage depth present considerable shortcomings, primarily characterized by their low accuracy and poor disturbance rejection proficiency in complex agricultural terrains. In this study, we present a sophisticated feedback control strategy designed to mitigate these challenges. Our innovative approach hinges on calculating tillage depth from the alignment of the tractor’s hydraulic lifting arm, achieved by employing a mechanical angle sensor. This sensor adeptly gages the angle of the lifting arm, aligning it with the tillage angle of the pull rod and the implement’s angle, resulting in a robust relational model correlating the lifting arm angle with the tillage depth. This pioneering method amalgamates the accuracy inherent in the static model, derived from the tillage angle-based depth measurement, with the dynamic stability afforded by the mechanical ascertainment of the lifting arm angle. In conjunction, we introduce a Hybrid Extended State Observer-Based Backstepping Sliding Mode Controller (HESO-BacksteppingSMC). The HESO is instrumental in estimating unmeasured state variables and lumped disturbances, utilizing the system’s output feedback signal. Our control frame component capitalizes on the fast power-reaching law to yield a continuously smooth control signal, effectively eradicating the conventional chattering phenomenon inherent in controllers and amplifying its functional applicability. Theoretical evaluations affirm the uniformly and ultimately bounded stability of the errors associated with our proposed observer and controller, underscoring their robustness. The superior performance of our proposed tillage depth measurement and control methodology has been corroborated through a series of comprehensive simulation and field plowing trials, attesting to its precision and reliability in complex agricultural settings.

Automated noise source identification and respective level estimation on mixed-noise construction environments

Noise control on construction sites is essential to manage project risks including safety accidents, worker's health, and civil complaints. This paper presents two advanced Audio Spectrogram Transformer-based models to identify noise source types and estimate their respective noise levels on complex construction environments. The classification model first differentiated the noise sources from mixed noise. Receiving the classification results, the regression model then estimated their noise levels. Nine categories of on-site noise (i.e., six construction and three external noises) were collected, a total of 234.4-min audio files, and mixed randomly with different ratios for the validation purpose. As a result, the models successfully classified the noise sources with F1-score of 0.901 and estimated the respective noise levels with an average absolute error of 0.573 dB. This study is expected to clarify the contributors of the measured noise levels, enabling strategic noise management and sophisticated feedback from on-site noise data.

Medicinal polypharmacology:Exploration and exploitation of the polypharmacolome in modern drug development.

At the core of complex and multifactorial human diseases, such as cancer, metabolic syndrome, or neurodegeneration, are multiple players that cross-talk in robust biological networks which are intrinsically resilient to alterations. These multifactorial diseases are characterized by sophisticated feedback mechanisms which manifest cellular imbalance and resistance to drug therapy. By adhering to the specificity paradigm ("one target-one drug concept"), research focused for many years on drugs with very narrow mechanisms of action. This narrow focus promoted therapy ineffectiveness and resistance. However, modern drug discovery has evolved over the last years, increasingly emphasizing integral strategies for the development of clinically effective drugs. These integral strategies include the controlled engagement of multiple targets to overcome therapy resistance. Apart from the additive or even synergistic effects in therapy, multitarget drugs harbor molecular-structural attributes to explore orphan targets of which intrinsic substrates/physiological role(s) and/or modulators are unknown for future therapy purposes. We designated this multidisciplinary and translational research field between medicinal chemistry, chemical biology, and molecular pharmacology as 'medicinal polypharmacology'. Medicinal polypharmacology emerged as alternative approach to common single-targeted pharmacology stretching from basic drug and target identification processes to clinical evaluation of multitarget drugs, and the exploration and exploitation of the 'polypharmacolome' is at the forefront of modern drug development research.

Design a Robust Proportional-Derivative Gain-Scheduling Control for a Magnetic Levitation System

This study focuses on the design of a robust PD gain-scheduling controller (PD-GS-C) for an unstable SISO (single-input, single-output) magnetic levitation system with two electromagnets (MLS2EM). Magnetic levitation systems offer various advantages, including friction-free, reliable, fast, and cost-effective operations. However, due to their unstable and highly nonlinear nature, these systems require sophisticated feedback control techniques to ensure optimal performance and functionality. To address these challenges, in this study, we derive the nonlinear state-space mathematical model of the MLS2EM and linearize it around five different operating points. The PD-GS-C controller aims to stabilize the system and improve steady-state control error. The strategy for obtaining the PD controller gains involves a parameter space technique, which specifies performance requirements. This technique results in ranges of proportional (KP) and derivative (KD) gains that are used by the PD-GS-C structure. To optimize the controller’s performance further, we utilize the big bang–big crunch optimization technique (BB-BC) to determine the optimal PD gains within the specified ranges. The optimization process focuses on achieving optimal performance in terms of a specific performance index function. This function quantifies the system’s time-domain step response criteria, which include minimizing overshoot percentage, settling time, and rising time. The index function is inversely proportional to the desired performance criteria, meaning that the goal is to maximize the index function to optimize the system’s performance. To validate the effectiveness and viability of the proposed strategy, we conducts MATLAB simulations and real-time experiments. The simulations and experimental findings serve to demonstrate the controller’s performance and verify its capabilities in stabilizing the MLS2EM magnetic levitation system.

Snapshot for Power Grids IoT: Adaptive Measurement for Resilience Intelligent Internet of Things

With the wide application of Internet of Things (IoT) devices in the power grids, the sophisticated feedback on their operating status is of great significance for improving efficiency and reducing accidents. For exquisite management of the resilient intelligent IoT with flexible increase and decrease of devices and heterogeneous operating systems, this paper proposes an adaptive measurement method ’MRAM’, which can snapshot the multidimensional resource view (MRV) of all devices in the jurisdiction. Extensible gateway platform based on CPU, FPGA and cloud computing is applied in MRAM, which liberates the local resources of monitored IoT devices. MRAM improves the LSTM algorithm called ELSTM. ELSTM can accommodate the current IoT devices’ state for detecting the mutation of MRV. The newly collected resources determined by ELSTM whether MRAM enters an abnormal state to drive the adaptive measurement state machine. According to the state machine which endeavors that the MRV is updated timely, MRAM adjusts the measurement granularity in real time. Simulations and experiments have tested the convergence time and occupied bandwidth of MRAM deployed in power grids. These evaluations confirmed MRAM’s practicality and robustness, as well as the MRV is genuine management data for the upper-layer power grids applications. A real environment is built to test the performance of this method as well. MRAM has high measurement accuracy and the precision of mutation detection is 98.41%. It converges the update MRV of second level under the condition of IoT devices and the cloud’s low consumption of memory and CPU utilization.

The licensing and certification roles of the CPA license in the gig economy

U.S. employers, including the Big Four accounting firms, are increasingly using gig economy platforms to hire accounting labor. One factor that employers may consider when hiring accountants in the gig economy is the CPA license. The CPA license has two potential roles: 1) a licensing role, which enables the license holder to perform exclusive jobs; and 2) a certification role, which signals that the license holder is a high-quality accountant. Although prior research finds that CPAs earn a wage premium in the traditional economy, it is unclear whether this relationship will hold in the gig economy. CPA-privileged jobs may not be available in the gig economy, thereby diminishing the importance of the licensing role. Furthermore, unlike the traditional economy, the gig economy has sophisticated reputational feedback systems that provide detailed information about a worker's prior job performance, which could substitute to some extent for the CPA license as a signal of accountant quality. Using a novel dataset, I find that CPAs earn a wage premium in the gig economy when they and their non-CPA counterparts have no reputational feedback. However, the CPA wage premium diminishes in the presence of reputational feedback and disappears in the presence of negative reputational feedback, indicating that reputation can substitute to some extent for the CPA license as a signal of accountant quality. These results have significant implications for accountants, institutions that issue credentials, and academic research related to licensing, certification, and signaling.

Multimodal Optothermal Manipulations along Various Surfaces.

Optical tweezers have provided tremendous opportunities for fundamental studies and applications in the life sciences, chemistry, and physics by offering contact-free manipulation of small objects. However, it requires sophisticated real-time imaging and feedback systems for conventional optical tweezers to achieve controlled motion of micro/nanoparticles along textured surfaces, which are required for such applications as high-resolution near-field characterizations of cell membranes with nanoparticles as probes. In addition, most optical tweezers systems are limited to single manipulation modes, restricting their broader applications. Herein, we develop an optothermal platform that enables the multimodal manipulation of micro/nanoparticles along various surfaces. Specifically, we achieve the manipulation of micro/nanoparticles through the synergy between the optical and thermal forces, which arise due to the temperature gradient self-generated by the particles absorbing the light. With a simple control of the laser beam, we achieve five switchable working modes [i.e., tweezing, rotating, rolling (toward), rolling (away), and shooting] for the versatile manipulation of both synthesized particles and biological cells along various substrates. More interestingly, we realize the manipulation of micro/nanoparticles on rough surfaces of live worms and their embryos for localized control of biological functions. By enabling the three-dimensional control of micro/nano-objects along various surfaces, including topologically uneven biological tissues, our multimodal optothermal platform will become a powerful tool in life sciences, nanotechnology, and colloidal sciences.

Design and implementation of robot assisted arduino based object recognition and sorting

This paper deals with an automatic material handling system that coordinates the movement of a robotic arm to pick up items moving on conveyor belts. The system utilizes advanced sensors and machine learning algorithms to ensure precise and efficient manipulation of objects, enhancing the overall automation and productivity of material handling processes. It aims to organize colored objects approaching on the conveyor by picking and placing them in separate, designated locations. The robotic system employs advanced computer vision algorithms to precisely identify and manipulate the diverse array of colored objects, ensuring efficient and accurate sorting on the conveyor belt. This reduces the tedious work done by humans, ensuring accuracy and rapidity in the process. Additionally, it paves the way for more efficient utilization of human resources, allowing professionals to focus on higher-level tasks that require creativity and critical thinking. The system includes color sensors that detect the items' colors and transmit signals to the controller. Additionally, the controller processes the signals from the color sensors to facilitate accurate identification and sorting of the items. The microcontroller then guides the signal to the motor driving circuit, which operates the different motors of the robotic arm to grasp the object and place it in the correct location. Additionally, the robotic arm's sophisticated sensor feedback system ensures precise positioning and adaptability to varying environmental conditions, enhancing its overall efficiency in object manipulation tasks. Depending on the color sensed, the robotic arm goes to the correct location to release the object and returns to its normal position. Additionally, the robotic arm employs advanced computer vision algorithms to precisely identify and differentiate colors, ensuring accurate execution of tasks in diverse environments.

A multisensory-feedback tactile glove with dense coverage of sensing arrays for object recognition

Somatosensory networks that provide sophisticated sensory feedback and enable the dexterous manipulation of the human grasp remain difficult to replicate in robots, which is attributed to the grand challenge of densely covering the hand with tactile arrays. Here, a multisensory tactile glove is reported that is capable of object recognition with dense coverage of pressure and temperature sensing arrays. The synergistic effect of the multimodal configuration allows the tactile arrays to perceive contact pressure and thermal conductivity of an object involved in grasping motion, thus enhancing the accuracy via the combination of the mechanical features with thermal properties. By leveraging the multiple scanning technology and wireless transmission system, the tactile glove achieves a recognition accuracy of 94.2% in differentiating 20 types of objects with a modified deep learning algorithm. The large-area sensing arrays with high spatiotemporal resolution and multimodal sensing capabilities, which paves the way for the development of robot grasping tools, human–machine interfacing, and advanced prosthetics.

Simultaneous quantification of thyroid hormones using an ultrasensitive single-molecule fourplex nanoimmunosensor in an evanescent field

The thyroid gland, which regulates the metabolism of the human body, has a sophisticated feedback system that induces the secretion of thyroid-stimulating hormone (TSH) to regulate the levels of triiodothyronine (T3) and thyroxine (T4). In this study, a single-molecule fourplex nanoimmunosensor was developed for the simultaneous quantitative analysis of TSH, T3, and T4. The three thyroid hormones were detected with a high signal-to-noise ratio in an evanescent field using laser-induced total internal reflection fluorescence. Additionally, the use of gold nanoislands for the detection of molecular interactions between thyroid hormones and antibodies labeled with quantum dots minimized the background noise from the substrate compared with the use of microislands or microwells. The nanoimmunosensor exhibited excellent detection limits of 114–193 yM (yoctomolar = 10−24 M) for thyroid hormones. The detection sensitivity was approximately 1015-fold higher than that of the conventional enzyme-linked immunosorbent assay. Paired Student's t-test of the human blood samples revealed that the difference between the two methods was insignificant at the 98% confidence level. Therefore, the proposed single-molecule fourplex nanoimmunosensor can be used for early diagnosis and prognosis monitoring at the single-molecule level because it can accurately, rapidly, and simultaneously diagnose various thyroid diseases, such as hyperthyroidism and hypothyroidism.

Flight Control Technology Advancements and Challenges for Future Rotorcraft 40th Alexander A. Nikolsky Honorary Lecture

The goal of my 40th Alexander A. Nikolsky Honorary Lecture and journal paper is to highlight the key flight control technology advances of the past 50 years and demonstrate how these advances are being applied and extended to the new family of rotorcraft: modern high-speed military rotorcraft, eVTOL urban air mobility, and advanced air mobility aircraft. The first part of this journal paper reviews flight control technologies drivers that are unique to rotorcraft and highlights key advances of the past 50 years in the areas of handling-qualities requirements (ADS-33), physics-based models, system identification, and flight control. A central theme is the shift from time-domain to frequency-domain based characterization of the closed-loop response and design methods for rotorcraft that have become increasingly dependent on sophisticated feedback control systems to achieve closed-loop stability, disturbance rejection, and most importantly closed-loop handlingqualities response for all-weather operations. Frequency-domain analysis, design, and test methods of the past 50 years are highlighted relating key advances in each discipline and two integrated example success stories. In the second part of this paper, we consider the key challenges, advancements, and needed future research for four new classes of rotorcraft: the military future vertical lift family of high-speed rotorcraft, unmanned autonomous systems/urban air mobility based on fielded conventional helicopters, small electric VTOL unmanned aerial vehicle rotorcraft, and larger eVTOL urban air mobility rotorcraft. The next sections look across the challenges and solution spaces that are common to these four new classes of rotorcraft as a blueprint for needed research advances. Finally, this paper takes a step back and considers the lessons-learned and key takeaways from the author's perspective as a career-long flight control engineer/researcher, Flight Control Technology Group leader, and senior technologist.

Mechanisms regulating neutrophil responses in immunity, allergy, and autoimmunity.

Neutrophil granulocytes, or neutrophils, are the most abundant circulating leukocytes in humans and indispensable for antimicrobial immunity, as exemplified in patients with inborn and acquired defects of neutrophils. Neutrophils were long regarded as the foot soldiers of the immune system, solely destined to execute a set of effector functions against invading pathogens before undergoing apoptosis, the latter of which was ascribed to their short life span. This simplistic understanding of neutrophils has now been revised on the basis of insights gained from the use of mouse models and single-cell high-throughput techniques, revealing tissue- and context-specific roles of neutrophils in guiding immune responses. These studies also demonstrated that neutrophil responses were controlled by sophisticated feedback mechanisms, including directed chemotaxis of neutrophils to tissue-draining lymph nodes resulting in modulation of antimicrobial immunity and inflammation. Moreover, findings in mice and humans showed that neutrophil responses adapted to different deterministic cytokine signals, which controlled their migration and effector function as well as, notably, their biologic clock by affecting the kinetics of their aging. These mechanistic insights have important implications for health and disease in humans, particularly, in allergic diseases, such as atopic dermatitis and allergic asthma bronchiale, as well as in autoinflammatory and autoimmune diseases. Hence, our improved understanding of neutrophils sheds light on novel therapeutic avenues, focusing on molecularly defined biologic agents.

NF-κB Regulation by Gut Microbiota Decides Homeostasis or Disease Outcome During Ageing.

Human beings and their indigenous microbial communities have coexisted for centuries, which led to the development of co-evolutionary mechanisms of communication and cooperation. Such communication machineries are governed by sophisticated multi-step feedback loops, which typically begin with the recognition of microbes by pattern recognition receptors (PRRs), followed by a host transcriptional response leading to the release of effector molecules. Our gastrointestinal tract being the main platform for this interaction, a variety of host intestinal cells tightly regulate these loops to establish tolerance towards the microbial communities of the gut and maintain homeostasis. The transcription factor, nuclear factor kappa B (NF-κB) is an integral component of such a communication apparatus, which plays a critical role in determining the state of homeostasis or inflammation associated with dysbiosis in the host. Here we outline the crucial role of NF-κB in host response to microbial cues in the context of ageing and associated diseases.

Adaptive feedback from artificial neural networks facilitates pre-service teachers’ diagnostic reasoning in simulation-based learning

In simulations, pre-service teachers need sophisticated feedback to develop complex skills such as diagnostic reasoning. In an experimental study with N = 178 pre-service teachers about simulated pupils with learning difficulties, we investigated the effects of automatic adaptive feedback, which is based on artificial neural networks, on pre-service teachers' diagnostic reasoning. Diagnostic reasoning was operationalised as diagnostic accuracy and the quality of justifications. We compared automatic adaptive feedback with static feedback, which we provided in form of an expert solution. Further, we experimentally manipulated whether the learners worked individually or in dyads on the computer lab-based simulations. Results show that adaptive feedback facilitates pre-service teachers’ quality of justifications in written assignments, but not their diagnostic accuracy. Further, static feedback even had detrimental effects on the learning process in dyads. Automatic adaptive feedback in simulations offers scalable, elaborate, process-oriented feedback in real-time to high numbers of students in higher education.