Feedback Mechanism Research Articles

Development of an algorithm impacting COPD care through personalized nutrition and IoT-based monitoring

BackgroundChronic obstructive pulmonary disease (COPD) is a chronic respiratory condition characterized by high morbidity and mortality rates. This study aims to assess the clinical outcomes of COPD patients after implementing an algorithm within the MyTatva app.MethodsThe study involved a sample of 10 COPD patients, evaluating key parameters such as Forced Expiratory Volume in 1 s (FEV1), Forced Vital Capacity, Weight, Body Mass Index (BMI), Fat-Free Mass Index, and Distance Covered during the 6-Minute Walk Test (6MWT) before and after the algorithm’s implementation in the MyTatva app. Patient satisfaction was assessed through a CSAT survey.ResultsFollowing the implementation of the MyTatva care plan, significant improvements were observed in several key clinical outcomes for COPD patients. FEV1 increased from a median of 3.24–2.0 L (p = 0.0379), while weight and BMI decreased significantly, with a reduction in weight from a median of 86–70 kg (p = 0.0007) and a corresponding decrease in BMI from 28.43 to 24 kg/m2 (p = 0.0031). The distance covered during the 6MWT also improved from 420 to 568 m (p = 0.0019). The participation of 10 COPD patients in surveys yielded an overall CSAT score of 85%, indicating a high level of satisfaction with the MyTatva app.ConclusionThe comprehensive features and functionalities of the MyTatva app, combined with the personalized care plan and real-time feedback mechanisms, have led to substantial clinical improvements in COPD management. These findings highlight the promise of this innovative digital therapeutic approach in addressing chronic respiratory conditions.

Multi-stage degradation feature with dynamic feedback mechanism for remaining useful life prediction

Multi-stage degradation feature with dynamic feedback mechanism for remaining useful life prediction

Differential absorption laser spectroscopy at 8 kHz using precompensated current modulation

We present a differential laser absorption spectroscopy (DLAS) system operating at 1550 nm for rapid and sensitive gas concentration measurements. A dual-wavelength toggling mechanism is presented, which significantly reduces data processing, hence supporting a high update rate and data robustness against fast-changing environmental conditions. We showcase the ability to toggle between two wavelengths separated by 90 pm in 14 μs and with minimal chirp (∼1 pm), facilitating sensitive DLAS measurements at 8 kHz update rate. This performance is achieved by driving a 1550 nm diode laser with a modified square-wave current pulse, overcoming the thermal time constant limited wavelength-modulation response of the diode laser. A sensitive feedback mechanism ensures excellent long-term wavelength stability better than 1.4 pm peak-to-peak at 8 kHz toggling over 20 h. As a performance test, we measured the volumetric ratio (VMR) of hydrogen cyanide (HCN) gas in a fiber-coupled gas cell with less than 0.2% peak-to-peak variation over 20 h at 40 Hz. A best sensitivity in VMR of 8×10−6 was achieved at 25 ms integration time. The simplicity and high update rate of our system make it well-suited for gas monitoring in dynamic atmospheric and industrial environments. Further, it offers potential utility in applications requiring precise wavelength control, such as injection seeding of pulsed lasers. A simple analytical model is derived, which, in detail, supports the experimental results, hence offering a tool for future design optimization.

A roadmap to sustainable management of commercial medicinal and aromatic plants, fungi, and lichens in Nepal.

Thousands of plants, fungi, and lichen species are traded every year. Although sustainable use is critical for livelihoods and biodiversity conservation, insufficient data prevent detailed sustainability assessments for most species. How can the sustainability of trade in such data-deficient species be enhanced? We considered a country-level example of 300 medicinal and aromatic plant, fungus, and lichen species traded in tens of thousands of tons worth tens of millions of US dollars in and from Nepal annually. Past interventions have not ensured sustainable trade, leaving species vulnerable to commercial harvesting and threatening rural household incomes, the processing industry, and government revenues. Building on documented evidence and stakeholder involvement, we used a theory of change approach to develop a sustainable management approach. We produced a draft plan (roadmap) by combining interventions proposed at annual key stakeholder dialogue meetings with recommendations extracted from a literature review on the trade and conservation of commercial medicinal and aromatic plants, fungi, and lichens in Nepal. The draft roadmap was discussed at a national workshop with sector-wide stakeholder representation to derive the final roadmap. The literature review showed the 41 causal assumptions and theoretical explanations for actions and outcomes. Feedback mechanisms included 6 bundles of mutually reinforcing actions, such as the relationship between increased cultivation and decreased wild harvesting. The roadmap has 5 pathways: increase cultivation, strengthen local management, support domestic businesses, improve sector governance, and increase international collaboration. Each pathway is associated with 2-5 actions (e.g., hand over high-elevation areas to local communities) that lead to outputs (2-5) (e.g., an increased area under local management) and outcomes (2-6) (e.g., less overharvesting). Accordingly, the roadmap offers stakeholders a structured approach to implement future activities and investments to enhance sustainable trade. The approach can be replicated for other countries and products.

Artificial Intelligence-Driven High School English Reading Instruction

This study examines the integration of Artificial Intelligence (AI) technology in high school English reading instruction, analyzing its advantages over traditional teaching methods. A midst the test-oriented education system, high school English reading instruction encounters numerous challenges, including a narrow reading scope for students and a lack of diversity in teaching approaches by educators. The incorporation of AI, particularly Natural Language Processing (NLP), enables in-depth analysis of English reading comprehension and enhances teaching efficiency and student motivation through personalized recommendations, intelligent tutoring, and assessment. AI technology offers tailored learning paths based on specific student needs, achieving personalized instruction, and improves the accuracy and efficiency of assessment through intelligent evaluation and feedback mechanisms. Furthermore, AI enriches teaching resources and interaction modes, increasing student interest and engagement. The study posits that the application of AI technology in high school English reading instruction has broad prospects and is expected to significantly improve educational quality and talent cultivation.

Balancing innovation: the role of paradoxical leadership and ambidexterity in fostering team creativity

Purpose This study, grounded in ambidexterity theory, aims to investigate how paradoxical leadership (PXL) fosters team creativity in high-tech manufacturing companies, with team ambidexterity as a mediator and leader vision as a moderator. Given the fast-paced, innovation-driven demands of the high-tech sector, this research provides insights into how managers can effectively balance contradictory behaviors to drive team innovation. Design/methodology/approach Data was collected through a two-wave survey from team leaders and members across 169 teams in high-tech manufacturing companies. The time interval between data collection waves was two weeks. The data was aggregated at the team level using estimates for interrater reliability and interrater agreement. The structural equation modeling in R was used to test hypotheses. Findings The findings indicate that PXL positively impacts team creativity directly and through its influence on team ambidexterity. Team ambidexterity mediates the relationship between PXL and team creativity, while leader vision moderates the effects of PXL on team ambidexterity. Practical implications PXL benefits high-performance teams in technology sectors, such as software development, where rapid innovation and iterative processes are essential. In health care, PXL can optimize team dynamics, enabling medical research and patient care delivery breakthroughs. Regular training in ambidextrous thinking and feedback mechanisms makes this approach actionable for managers striving to cultivate creativity in high-stakes environments. Originality/value This study extends ambidexterity theory by linking PXL and team ambidexterity to team creativity. It provides high-tech manufacturing managers with concrete strategies to balance exploration and exploitation. These findings highlight actionable pathways for companies to sustain innovation in competitive, technology-driven industries.

Personalized learning path based on graph attention mechanism deep reinforcement learning research on recommender systems

In e-learning, the rapid expansion of learning resources poses challenges for learners in finding suitable materials due to their diverse preferences and cognitive abilities. Consequently, personalized learning path recommendation has emerged as a pivotal research area, especially for advancing e-learning systems. This paper introduces an algorithmic framework that integrates deep reinforcement learning with a graph attention mechanism to tailor learning paths to individual learners. The online course dataset is selected and a series of controlled experiments are conducted on the common recommendation models proposed in the past, and the experimental results are analyzed using a combination of two evaluation indices, such as data evaluation results and model variance. The experimental results show that adding the attention mechanism can significantly improve the accuracy of the model recommendation, compared with the deep reinforcement learning model without adding the graph attention mechanism, the comprehensive scores of the students in the test set were improved by 5.8 and 12.8 points, respectively, and the accuracy was improved by 5.3% compared with the previous deep learning model; the deep reinforcement model used in this paper with the addition of the labeling feedback mechanism was improved by 5.3% compared with the deep learning with feedback mechanism. In the recommendation model, the final scores of the students were improved by 3.7 and 8.2 points, respectively. In addition, the Advanced test set in the recommendation model of the learning path recommended by the student score improvement is more than two times of the Middle test set’s scores improvement, indicating that more learning recommended object knowledge points the richer, the model recommendation accuracy rate is higher. By merging graph attention mechanisms with deep reinforcement learning, our system provides precise recommendations, offering insights into the development of efficient personalized learning path systems and accelerating their educational applications.

Mechanisms and Modeling of Sea Level Rise in the Context of Global Warming

Sea level rise due to global warming is an important topic in current climate change research. In this study, we explore how complex natural processes triggered by global warming drive sea level change by analyzing climate models. We focus on the main drivers of sea level rise, including ice sheet melting and ocean thermal expansion. In addition, the article discusses in detail how positive feedback mechanisms and negative feedback mechanisms work together to influence the climate change process. In order to predict the future trend of sea level rise, this article models and simulates the global temperature, glacier changes and ocean dynamics based on the General Circulation Model (GCM). Although the model has some uncertainties, especially in cloud feedback and data completeness, it still provides a valuable predictive tool for understanding future sea level changes. The conclusions of this study point to the possibility of accelerated sea level rise as global temperatures continue to rise, with wide-ranging and far-reaching impacts on coastal ecosystems and human societies around the globe.

A novel Bayesian approach for decomposing the radio emission of quasars: II. Link between quasar radio emission and black hole mass

Abstract Whether the mass of supermassive black hole (MBH) is directly linked to the quasar radio luminosity remains a long-debated issue, and understanding the role of MBH in the evolution of quasars is pivotal to unveiling the mechanism of AGN feedback. In this work, based on a two-component Bayesian model, we examine how MBH affects the radio emission from quasars, separating the contributions from host galaxy star formation (SF) and AGN activity. By modelling the radio flux density distribution of Sloan Digital Sky Survey (SDSS) quasars from the LOFAR Two-metre Sky Survey Data Release 2, we find no correlation between MBH and SF rate (SFR) at any mass for quasars at a given redshift and bolometric luminosity. The same holds for AGN activity across most MBH values; however, quasars with the top 20 % most massive SMBHs are 2 to 3 times more likely to host strong radio jets than those with lower-mass SMBHs at similar redshift and luminosity. We suggest defining radio quasar populations by their AGN and SF contributions instead of radio loudness; our new definition unifies previously divergent observational results on the role of MBH in quasar radio emissions. We further demonstrate that this radio enhancement in quasars with the 20 % most massive SMBHs affects only the $\sim 5~{{\%}}$ most radio bright quasars at a given redshift and bolometric luminosity. We discuss possible physical origins of this radio excess in the most massive and radio-bright quasar population, which remains an interest for future study.

A Review Study on the Construction of Medical Institution Service Quality Performance Evaluation System under the Background of Internet Plus

With the advent of the "Internet Plus" era, the medical service industry is undergoing a significant transformation. This paper systematically reviews and analyzes the construction process of the medical institution service quality performance evaluation system, aiming to provide theoretical support and practical guidance for the improvement of service quality in internet hospitals. The article first introduces the challenges and opportunities faced by the medical service industry under the "Internet Plus" background, then discusses the definition, dimensions, and theoretical framework of medical service quality evaluation. Through a comparative analysis of the service quality evaluation systems of Chinese and American medical institutions, the similarities and differences in the construction of medical service evaluation systems between the two countries are revealed. Finally, based on the "Internet Plus" background, the paper proposes principles, objectives, indicator system design, and implementation and feedback mechanisms for constructing a service quality performance evaluation system. The research results indicate that constructing a comprehensive and objective service quality evaluation system is of great significance for optimizing resource allocation, innovating service models, improving service efficiency, and reducing service costs, which helps to promote the implementation of the Healthy China strategy.

The TRP Channels Serving as Chemical-to-Electrical Signal Converter.

Biology uses many signaling mechanisms. Among them, calcium and membrane potential are two prominent mediators for cellular signaling. TRPM4 and TRPM5, two calcium-activated monovalent cation-conducting ion channels, offer a direct linkage between these two signals. Their activities convert a rise in the intracellular calcium level-a chemical signal-into depolarization of membrane potential-an electrical signal. Interestingly, membrane depolarization can in turn alter the electrical driving force or membrane permeability for calcium entry, hence offers feedback mechanisms for regulating calcium signaling. By converging two powerful cellular signals, TRPM4 and TRPM5 are capable of contributing to many fundamental biological processes including cardiovascular biology, immunology, insulin release, chemo-sensation, and others. Numerous mutations in TRPM4 are linked to human hereditary cardiac and skin diseases, whereas knocking out TRPM5 in mice abolishes perception of sweet, umami and bitter tastes. This review summarizes what are currently known about the signaling roles of these unique TRP channels, and what remain mysterious.

Modeling the Impact of Greenland Ice Sheet Melting on Global Sea Level Elevation and Climate Change Feedback Mechanisms

The rapid acceleration of global warming is leading to an increased rate of glacier melt at the Earth's poles, which exacerbates the threats posed by the climate crisis and contributes to rising ocean levels. The Greenland Ice Sheet, recognized as the second-largest ice sheet globally, has housed its extensive glaciers and ice caps for at least 18 million years [1]. The melting and potential collapse of this ice sheet could significantly affect Worldwide climate and sea height. This research aims to develop a coupled model to assess rising sea levels caused by the thawing of the Greenland Ice Sheet, incorporating factors such as global warming, glacier volume, hydrothermal expansion, isostatic rebound, ice dynamics, changes in the gravitational field, and the absorption and release of methane gas. Additionally, the paper investigates the hazards connected to melting glaciers exacerbating global warming. This research provides a crucial foundation for forecasting the long-term impacts of the Greenland Ice Sheet's melting on global climate change.

The application of artificial intelligence in biomechanical feedback and learning effectiveness enhancement in ideological and political education

The application of artificial intelligence (AI) in ideological and political education (IPE) optimizes the learning effect with the help of biomechanical feedback mechanism, and provides technical support for deepening the reform of IPE in colleges and universities. With the help of AI technology, especially its advantages in data analysis and deep learning (DL), accurate matching and personalized recommendation of learning resources can be realized. In this study, an AI-assisted IPE system with biomechanical feedback was constructed. By analyzing students’ physiological reactions and behavior patterns in the learning process, the system can evaluate the learning state and adjust teaching strategies. The key findings show that this learning method combined with biomechanical feedback can significantly improve students’ learning participation, understanding depth and satisfaction, and make IPE more in line with students’ needs.

Technologies for enhancing tactile perception in prosthetic devices

Tactile perception is a vital sensory function, and its role has been widely researched in the development of sensor technologies. The role of tactile feedback in augmenting the functional attributes of prosthetic devices is well-documented, as it facilitates a more seamless and natural engagement between the user and the surrounding environment. This paper explores advancements in tactile sensor technologies used to enhance sensory feedback in prosthetics, focusing on piezoresistive, piezoelectric, and capacitive sensors. The integration of these sensors allows for precise control, improving object manipulation and overall user experience. By reviewing recent innovations such as multimodal sensors, artificial skin technologies, and nerve interfacing systems, this research highlights how these advancements have contributed to enhanced motor control and real-time feedback mechanisms in prosthetics. Despite significant progress, challenges such as high costs and limited accessibility remain. However, potentially leading to the widespread adoption of more effective and equitable prosthetic solutions.

Enhancing Learning Efficiency and Critical Thinking Skills of Vocational Nursing Students: A Hybrid Instructional Approach Based on Cooperative Learning and Project-Based Learning

The purpose of this study is to examine the design and implementation process of Project-Based Cooperative Learning (PBCL) teaching models. The research initially introduces the theoretical framework, encompassing cooperative learning theory, project-based learning theory, and the theory of PBCL. Subsequent sections elaborate on the construction of the PBCL teaching model, focusing on the design of teaching objectives, integrating cooperative and project-based learning, the provisioning of learning resources and support, and the establishment of evaluation and feedback mechanisms. Building upon this foundation, the study delineates the steps to implement PBCL teaching methods, including pre-class preparations, project initiation, collaborative learning and project implementation, project summarization and evaluation, and post-class extension. Finally, the study analyzes the strengths and limitations of this teaching approach. The strengths include deep learning experiences, enhanced collaborative abilities, and the cultivation of critical thinking skills. However, limitations are evident in the greater practical input and resource demands, inadequacies in the capabilities of some students, and deficiencies in assessment effectiveness.

A Bio-Inspired Visual Neural Model for Robustly and Steadily Detecting Motion Directions of Translating Objects Against Variable Contrast in the Figure-Ground and Noise Interference

(1) Background: At present, the bio-inspired visual neural models have made significant achievements in detecting the motion direction of the translating object. Variable contrast in the figure-ground and environmental noise interference, however, have a strong influence on the existing model. The responses of the lobula plate tangential cell (LPTC) neurons of Drosophila are robust and stable in the face of variable contrast in the figure-ground and environmental noise interference, which provides an excellent paradigm for addressing these challenges. (2) Methods: To resolve these challenges, we propose a bio-inspired visual neural model, which consists of four stages. Firstly, the photoreceptors (R1–R6) are utilized to perceive the change in luminance. Secondly, the change in luminance is divided into parallel ON and OFF pathways based on the lamina monopolar cell (LMC), and the spatial denoising and the spatio-temporal lateral inhibition (LI) mechanisms can suppress environmental noise and improve motion boundaries, respectively. Thirdly, the non-linear instantaneous feedback mechanism in divisive contrast normalization is adopted to reduce local contrast sensitivity; further, the parallel ON and OFF contrast pathways are activated. Finally, the parallel motion and contrast pathways converge on the LPTC in the lobula complex. (3) Results: By comparing numerous experimental simulations with state-of-the-art (SotA) bio-inspired models, we can draw four conclusions. Firstly, the effectiveness of the contrast neural computation and the spatial denoising mechanism is verified by the ablation study. Secondly, this model can robustly detect the motion direction of the translating object against variable contrast in the figure-ground and environmental noise interference. Specifically, the average detection success rate of the proposed bio-inspired model under the pure and real-world complex noise datasets was increased by 5.38% and 5.30%. Thirdly, this model can effectively reduce the fluctuation in this model response against variable contrast in the figure-ground and environmental noise interference, which shows the stability of this model; specifically, the average inter-quartile range of the coefficient of variation in the proposed bio-inspired model under the pure and real-world complex noise datasets was reduced by 38.77% and 47.84%, respectively. The average decline ratio of the sum of the coefficient of variation in the proposed bio-inspired model under the pure and real-world complex noise datasets was 57.03% and 67.47%, respectively. Finally, the robustness and stability of this model are further verified by comparing other early visual pre-processing mechanisms and engineering denoising methods. (4) Conclusions: This model can robustly and steadily detect the motion direction of the translating object under variable contrast in the figure-ground and environmental noise interference.

Sustainability of regional Antarctic ice sheets under late Eocene seasonal atmospheric conditions

Abstract. The Eocene–Oligocene transition (EOT) is marked by a sudden δ18O excursion occurring in two distinct phases approximately 500 kyr apart. These phases signal a shift from the warm middle to late Eocene greenhouse climate to cooler conditions, with global surface air temperatures decreasing by 3–5 °C and the emergence of the first continent-wide Antarctic ice sheet (AIS). While ice sheet modelling suggests that ice sheet growth can be triggered by declining pCO2, it remains unclear how this transition was initiated, particularly the first growth phase that appears to be related to oceanic and atmospheric cooling rather than ice sheet growth. Recent climate model simulations of the late Eocene show improved accuracy but depict climatic conditions that are not conducive to the survival of incipient ice sheets throughout the summer season. This study therefore examines whether it is plausible to develop ice sheets of sufficient scale to trigger the feedback mechanisms required to disrupt the atmospheric regime above the Antarctic continent during warm and moist late Eocene summers and establish more favourable conditions for ice expansion. We aim to assess the sustainability of an incipient AIS under varying radiative, orbital and cryospheric forcing. To do so, we evaluate Community Earth System Model 1.0.5 simulations, using a 38 Ma geographical and topographical reconstruction, considering different radiative and orbital forcings. The climatic conditions prevailing during (and leading up to) the EOT can be characterised as extremely seasonal and monsoon-like, featuring a short yet intense summer period and contrasting cold winters. A narrow convergence zone with moist convection around the region with high sub-cloud equivalent potential temperature exhibits a ring-like structure, advecting moist surface air from the Southern Ocean in both summer and winter. This advection leads to high values of moist static energy and subsequent precipitation in coastal regions. Paradoxically, this atmospheric regime – particularly its coastal precipitation in winter – appears to be necessary for the sustenance of the moderately sized regional ice sheets we imposed on the continent, contrary to our assumption that these ice sheets would disrupt the atmospheric regime. This underscores a hysteresis effect for regional ice sheets on the Antarctic continent, suggesting the potential for a significant volume of ice on the continent without imminent full glaciation prior to the EOT.

Coherently averaged optical frequency comb spectroscopy with a single electro-optic modulator.

We present a new, to the best of our knowledge, approach for self-heterodyne optical frequency comb (OFC) spectroscopy in which a single Mach-Zehnder modulator is utilized to generate both an optical frequency comb and a frequency-shifted local oscillator. This method allows for coherent, time-domain averaging to be performed without the need for feedback mechanisms or software corrections. As an initial demonstration, we have measured acetylene rovibrational transition frequencies with coherently averaged comb spectra. Spectra averaged for less than 10 s yielded standard fit uncertainties for transition frequencies of less than 150 kHz, with deviations from literature values under 1.1 MHz, limited by pressure uncertainty. This approach has important implications for high-resolution spectroscopy, trace gas detection, and on-chip integration of electro-optic frequency combs.

The Role of Learning Analytics in Evaluating Course Effectiveness

This study aims to examine the use of learning analytics in course evaluation within higher education institutions, in order to identify effective methodologies and best practices for leveraging data to improve educational effectiveness. Following the PRISMA guidelines, a systematic literature search was conducted in Scopus, yielding 34 relevant studies published between 2015 and 2024 for analysis. The results reveal six key categories of learning analytics applications: sentiment analysis, questionnaire analysis, engagement analysis, topic classification, predictive modelling, and performance analysis. The data sources for learning analytics applications primarily include questionnaires and learning management systems. While descriptive analysis was found to be the most commonly employed analytical technique, advanced techniques such as machine learning, artificial intelligence, and social network analysis are becoming more prominent. The studies addressed a wide range of elements associated with course evaluation, including course design, content quality, assignments, instructional strategies, workload, feedback mechanisms, and the integration of technology. These findings highlight the importance of adopting holistic approaches to capture the multifaceted nature of student experiences. This study also uncovers major limitations in the existing research, such as small sample sizes, potential biases due to the use of survey-based methods, and challenges in generalising findings across disciplines. These insights underscore the need for further research to enhance the methodologies used in course evaluations. This study contributes to advancing learning analytics practices and emphasises the importance of innovative approaches for evaluating and improving course effectiveness.

Positive Feedback Mechanism of Stability Degradation of the IrO2/TiO2 Electrocatalyst Induced by Inferior Catalytic Activity

Positive Feedback Mechanism of Stability Degradation of the IrO₂/TiO₂ Electrocatalyst Induced by Inferior Catalytic Activity