Frequent Updates Research Articles

Translating Research Evidence Into Marketplace Application: Cohort Study of Internet-Based Intervention Platforms for Perinatal Depression.

Internet-based intervention platforms may improve access to mental health care for women with perinatal depression (PND). Though the majority of platforms in the market lack an evidence base, a small number of them are supported by research evidence. This study aims to assess the current status of internet-based PND intervention platforms supported by published evidence, understand the reasons behind the disappearance of any of these previously accessible platforms, examine adjustments made by those active platforms between research trials and market implementation, and evaluate their current quality. A cohort of internet-based PND intervention platforms was first identified by systematic searches in multiple academic databases from database inception until March 26, 2021. We searched on the World Wide Web and the iOS and Android app stores to assess which of these were available in the marketplace between April and May 2021. The basic characteristics of all platforms were collected. For inaccessible platforms, inquiries were made via email to the authors of publications to determine the reasons for their unavailability. We compared the intervention-related information of accessible platforms in the marketplace with that reported in original publications and conducted quality assessments using the App Evaluation Model of the American Psychiatric Association. Fisher exact tests were used to compare the functional characteristics in publications of available and unavailable platforms and to investigate potential associations between functional adjustments or quality indices and platform survival time. Out of 35 platforms supported by research evidence, only 19 (54%) were still accessible in the marketplace. The main reason for platforms disappearing was the termination of research projects. No statistically significant differences were found in functional characteristics between available and unavailable platforms. A total of 18 (95%) platforms adapted their core functions from what was reported in related publications. The adjustments included changes to intervention methods (11/19, 58%), target population (10/19, 53%), human resources for intervention support (9/19, 47%), mood assessment and monitoring (8/19, 42%), communication modality (4/19, 21%), and platform type (2/19, 11%). Quality issues across platforms included low frequency of update, lack of crisis management mechanism, poor user interactivity, and weak evidence base or absence of citation of supporting evidence. Platforms that survived longer than 10 years had a higher tendency to use external resources from third parties compared to those that survived less than 10 years (P=.04). No significant differences were found for functional adjustments or other quality indices. Internet-based platforms supported by evidence were not effectively translated into real-world practice. It is unclear if adjustments to accessible platforms made during actual operation may undermine the proven validity of the original research. Future research to explore the reasons behind the success of the implementation of evidence-based platforms in the marketplace is warranted.

Exploring the Boundaries of Reality: Investigating the Phenomenon of Artificial Intelligence Hallucination in Scientific Writing Through ChatGPT References.

Background Chatbots are computer programs that use artificial intelligence (AI) and natural language processing (NLP) to simulate conversations with humans. One such chatbot is ChatGPT, which uses the third-generation generative pre-trained transformer (GPT-3) developed by OpenAI. ChatGPT has been praised for its ability to generate text, but concerns have been raised about its accuracy and precision in generating data, as well as legal issues related to references. This study aims to investigate the frequency of AI hallucination in research proposals entirely drafted by ChatGPT. Methodology An analytical design was employed to investigate AI hallucination by ChatGPT. A total of 178 references listed by ChatGPT were verified for inclusion in the study. Statistical analysis was performed by five researchers who entered their data into a Google Form, and the final results were represented using pie charts and tables. Results Out of the 178 references analyzed, 69 references did not have a Digital Object Identifier (DOI), and 28 references neither turned up on Google search nor had an existing DOI. Three references were listed from books and not research articles. These observations suggest that ChatGPT's ability to generate reliable references for research topics may be limited by the availability of DOI and the accessibility of online articles. Conclusions The study highlights the potential limitations of ChatGPT's ability to generate reliable references for research proposals. AI hallucination is a problem that may negatively impact decision-making and may give rise to ethical and legal problems. Improving the training inputs by including diverse, accurate, and contextually relevant data sets along with frequent updates to the training models could potentially help address these issues. However, until these issues are addressed, researchers using ChatGPT should exercise caution in relying solely on the references generated by the AI chatbot.

An approach to rapid distributed manufacturing of broad spectrum anti-viral griffithsin using cell-free systems to mitigate pandemics

This study describes the cell-free biomanufacturing of a broad-spectrum antiviral protein, griffithsin (GRFT) such that it can be produced in microgram quantities with consistent purity and potency in less than 24 h. We demonstrate GRFT production using two independent cell-free systems, one plant and one microbial. Griffithsin purity and quality were verified using standard regulatory metrics. Efficacy was demonstrated in vitro against SARS-CoV-2 and HIV-1 and was nearly identical to that of GRFT expressed in vivo. The proposed production process is efficient and can be readily scaled up and deployed wherever a viral pathogen might emerge. The current emergence of viral variants of SARS-CoV-2 has resulted in frequent updating of existing vaccines and loss of efficacy for front-line monoclonal antibody therapies. Proteins such as GRFT with its efficacious and broad virus neutralizing capability provide a compelling pandemic mitigation strategy to promptly suppress viral emergence at the source of an outbreak.

A robust adaptive linear parameter-varying gain-scheduling controller for aeroengines

This paper presents an on-board model-based adaptive linear parameter-varying (LPV) gain-scheduling controller for aeroengines exhibiting strong nonlinearity, uncertainty, and component performance degradation. A novel hybrid on-board adaptive model is proposed using a dual-loop (fast-slow) structure, which consists of an adaptive LPV model as the fast loop to guarantee the on-board performance, and a component-level model as the slow loop to provide accurate model information at a slow update frequency, without violating the real-time requirement of the fast loop. An innovative mixed adaptive extended Kalman filter is designed to estimate and update the model, by introducing an extended-Kalman-filter-based covariance scaling mechanism and updating the system noise covariance matrix by employing full covariance estimation. The component-level adaptive nonlinear model and mixed adaptive extended Kalman filter increase the accuracy of the on-board model, while the adaptive LPV model enhances its real-time performance. Finally, a robust polytopic adaptive LPV gain-scheduling controller is designed based on the adaptive LPV model by introducing a new scheduling variable, i.e., health parameters estimated by the on-board adaptive model. The proposed adaptive model and controller are implemented to a high fidelity nonlinear aeroengine model. Simulation results demonstrate that the estimation error of health parameters and component degradation level of the proposed method is 0.0763%, which gives an order of magnitude higher health parameter and component degradation level estimation accuracy compared with the other commonly applied models. The proposed model-based adaptive LPV gain scheduling control scheme has a steady-state error of 0.0028%, gives one order of magnitude higher control accuracy than that of three state-of-the-art baseline controllers while demonstrating significantly reduced overshoot and adjustment time. This verifies the robustness and effectiveness of the proposed controller, and verifies that the model can meet the technical requirements of the engine control system and significantly improve the engine control performance.

Time Series Prediction on Settlement of Metro Tunnels Adjacent to Deep Foundation Pit by Clustering Monitoring Data

High requirements are put forward for the settlement control of metro tunnel to ensure the normal and safe operation of adjacent metro line during the process of deep foundation pit construction. Monitoring and predicting could constantly monitor the settlement of the tunnel and make safety early-warning, and massive data to be processed is collected by sensors in this process. In the study, an improved clustering method based on Gaussian mixture model (GMM) is proposed to deal with a large amount of monitoring data. Four initial eigenvalues are defined and the initial core points of clustering are selected by grouping monitoring sensors based on the characteristics of the project site and sensors. An improved method is utilized to the metro tunnel of Metro Line 9 near Xujiahui station. Compared with the traditional clustering method, the improved method has more reliable results, and reduces the operation time by 57.9%. Representative monitoring sensors are selected from each cluster to predict based on Long Short-Term Memory (LSTM) neural network. The prediction results well agree with the measured value and the prediction accuracy is reaching to 99.3%. Compared with other sensor selection ways, the data of representative sensors exhibits good representativeness and effectiveness. Finally, the prediction result after data update is more consistent with the monitoring data than the prediction result without data update. Increasing the data update frequency improves the accuracy of the prediction results in practical engineering application.

MP34-01 UTI KNOWLEDGE ASSESSMENT AND ACQUISITION FOR URINARY TRACT INFECTIONS AMONG ADULT WOMEN

You have accessJournal of UrologyCME1 Apr 2023MP34-01 UTI KNOWLEDGE ASSESSMENT AND ACQUISITION FOR URINARY TRACT INFECTIONS AMONG ADULT WOMEN Stephanie Gleicher, Elisabeth Sebesta, Melissa Kaufman, Roger Dmochowski, and W. Stuart Reynolds Stephanie GleicherStephanie Gleicher More articles by this author , Elisabeth SebestaElisabeth Sebesta More articles by this author , Melissa KaufmanMelissa Kaufman More articles by this author , Roger DmochowskiRoger Dmochowski More articles by this author , and W. Stuart ReynoldsW. Stuart Reynolds More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000003268.01AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Substantial information and misinformation exists about urinary tract infection (UTI) management, and there is significant variability in provider expertise. While evidence-based UTI guidelines exist for providers, many women are not able to access this information. The aim of this study was to assess current UTI knowledge and preferences for information dissemination in a population-based cohort of adult women. METHODS: Subjects were recruited from Researchmatch database to participate in a web-based study. Inclusion criteria included non-pregnant females aged 18 or older who had a UTI in the last 12 months. Subjects were provided various statements to assess UTI knowledge/perceptions, and asked about preferences for health-information communication. SPSS was used for the statistical analysis. RESULTS: Among the 755 subjects, >50% believe most women suffer from UTI, women get more UTIs as they get older, and supplements are available to prevent UTI (Table 1). 46.2% of women believe UTIs are caused by tight fitting clothing and 44.6% believe UTIs are caused by lack of cleanliness. Among subjects, 89.8% have looked up information regarding UTI, and 81.2% endorsed that health information is more meaningful from their health care provider (HCP). 88.5% of women normally obtain health information from their HCP, and 79.7% obtain information from online websites. When asked about educational preferences, 53.8% strongly preferred office-based HCP visits, followed by virtual HCP visits (25.3%) and online websites (25.2%). More than 30% of subjects would be willing to spend 15+ minutes reviewing information about UTIs, and 33.5% would like to receive UTI information weekly or monthly. Barriers to obtaining information included that information is too complex (26.6%), they did not know where to find information (26.%) and having too much written information (24.6%). CONCLUSIONS: Women have misperceptions about UTIs. Despite most subjects looking up UTI health information, it is often too complex, hard to find, or cumbersome. Most women find health information more meaningful from their HCP and prefer office-based HCP educational platforms. Almost half of participants are interested in frequent UTI information updates. Future research is needed to better design educational campaigns regarding UTI. Source of Funding: VICTR Award ID VR63981 © 2023 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 209Issue Supplement 4April 2023Page: e459 Advertisement Copyright & Permissions© 2023 by American Urological Association Education and Research, Inc.MetricsAuthor Information Stephanie Gleicher More articles by this author Elisabeth Sebesta More articles by this author Melissa Kaufman More articles by this author Roger Dmochowski More articles by this author W. Stuart Reynolds More articles by this author Expand All Advertisement PDF downloadLoading ...

Event-triggered near-optimal tracking control based on adaptive dynamic programming for discrete-time systems

Frequent state monitoring and controller updates can enhance the precision of tracking control, while simultaneously overburdening the communication network transmission. In this paper, For the purpose of saving communication costs, we propose event-triggered control algorithms for the optimal tracking control problem. First, we reconstruct the discrete-time nonlinear system into a converted system. Then, the adaptive dynamic programming algorithm is employed to find the optimal controller off-line, and the event-triggered scheme is used to reduce the communication costs online. Novel triggering conditions with fewer assumptions are designed to implement the event-triggered scheme. Different from existing works, the event-triggered scheme can be introduced not only into the converted system but also into the actual system, which is more practical because the actual controller is what one can only access in practice. In addition, with the developed algorithms, the tracking error can be proved to be stable at the origin, in other words, the actual system can be guaranteed to track the desired trajectory. Algorithms developed in this paper are implemented by three neural networks, the model network, the action network and the critic network. Finally, examples are presented to verify the effectiveness and rationality of the algorithms.

Assessment of E-Procurement of Construction Products and Services

According to Presidential Regulation No. 54 of 2010 governing Procurement of Government goods/services and its revisions, this research seeks to assess the efficacy of e-Procurement using e-Tendering procedures. Qualitative methods were used in this investigation. The information was gathered through conducting in-depth interviews with members of the procurement committee, as well as providers, companies, and relevant documents. Descriptive case and context analysis is employed for this analysis. The review of the e-procurement implementation at the North Maluku PUPR Construction Services selection implementation center revealed that the process had gone smoothly. This is evident from the study of e-several procurement's dimensions and the various means through which its goals might be attained. Presidential Regulation No. 4 of 2015 governs the use of e-Procurement. Human resources in the Working Group were found to be a positive factor in terms of e-viability Procurement's in this study's findings, while a lack of socialization, a dearth of LPSE human resources, an absence of training, inadequate infrastructure, frequent updates to the SPSE application's version, a lack of regional regulations governing the electronic procurement of goods and services, and a lack of funding were found to be negative factors.

Changes in Glacial Area Extent in Auyuittuq National Park, Pangnirtung

With global temperatures rising four times faster in the Canadian Arctic compared to global averages, glaciers in the Canadian Arctic Archipelago (CAA) are starting to shrink at an alarming rate (Paul et al., 2020). To quantify rates of change, frequent updates of glacial outlines to provide an accurate database for monitoring are needed (Schaffer et al., 2017). Rundle, Nerutusoq, and Fort Beard Glaciers are all located on the Penny Ice Cap within Auyuittuk National Park, Baffin Island, Canada. Using a combination of aerial photography and ArcGIS Pro, I examined the historical and recent changes in glacial area extents. High resolution historical aerial photographs from the National Air Photo Library dating to September 1959 were manually georeferenced onto ArcGIS Pro. Photogrammetry techniques were then used to combine the aerial photographs into one combined orthographic image. The historic glaciers were then outlined using a manual technique based on the pixel size (8 μm) of the images using ArcGIS Pro. This allows me to compare glacial outlines from 2010 using the Randolph glacier inventory and 2022 using manual outline techniques described, respectively. Because using a manual technique can create accuracy limitation, specifically during the georeferencing process, I compared results for both time periods using an automatic method. Results from my work will provide estimates of changes in glacier area over time, the relative precision of different methods, and weather rates of shrinkage have increased over time. ReferencesPaul, F., Rastner, P., Azzoni, R. S., Diolaiuti, G., Fugazza, D., Le Bris, R., Nemec, J., Rabatel, A., Ramusovic, M., Schwaizer, G., & Smiraglia, C. (2020). Glacier shrinkage in the Alps continues unabated as revealed by a New Glacier Inventory from sentinel-2. Earth System Science Data, 12(3), 1805–1821. https://doi.org/10.5194/essd-12-1805-2020 Schaffer, N., Copland, L., & Zdanowicz , C. (2017). Ice velocity changes on Penny Ice Cap, Baffin Island, since the 1950s. Journal of Glaciology, 63(240), 716–730. https://doi.org/10.1017/jog.2017.40

Event-Trigger-Based Finite-Time Privacy-Preserving Formation Control for Multi-UAV System

Privacy-preserving has been crucial technique of multi-UAV systems, including cooperative detection, cooperative penetration and strike. Unprocessed interactive information poses a serious privacy threat to UAV swarm collaborative tasks. Considering not only privacy-preserving but also bandwidth constraints and the convergence performance of multi-UAV systems, this paper comprehensively proposes an original event-triggered-based finite-time privacy-preserving formation control scheme to resolve these three factors. Firstly, this paper adopted a local, deterministic, time-varying output mapping function for a privacy mask, which encodes the internal states of the UAV prior to its public transmission, and the initial true value of each UAV’s states is kept indecipherable for honest-but-curious UAVs and other malicious eavesdropping attackers. Then, considering the limited communication bandwidth and channels, we employed a distributed event-triggered strategy and deduced the triggering condition for consensus-based formation control, which effectively reduces the excessive consumption of communication and computational resources in contrast to time-triggered strategy. In terms of the convergence performance of the UAVs, finite-time stability theory was introduced to make the system reach the desired formation in finite time and obtain a settling time related to the initial state. Compared with the existing literature, this paper systematically took into account the above three factors for multi-UAV systems and provides a convergence analysis and a privacy analysis in detail. Finally, the effectiveness of the finite-time privacy-preserving protocol based on an event-triggered strategy was demonstrated by numerical simulation examples and comparative experiments. The proposed method achieves the formation control under privacy-preserving, improves the convergence rate and reduces the frequency of controller updates and information transmission.

Event-Triggered Sliding Mode Neural Network Controller Design for Heterogeneous Multi-Agent Systems

A class of heterogeneous second-order multi-agent consensus problems is studied, in which an event-triggered method is used to improve the feasibility of the control protocol. The sliding mode control method is used to achieve the robustness of the system. A special type of general radial basis function neural network is applied to estimate the uncertainties. The event-triggered mechanism is introduced to reduce the update frequency of the controller and the communication frequency among the agents. Zeno behavior is avoided by ensuring a lower bound between two adjacent trigger instants. Finally, the simulation results are provided to demonstrate that the time evolution of consensus errors eventually approaches zero. The consensus of multi-agent systems is achieved.

Enhancing data protection with a distributed storage system based on the redundant residue number system

Big data becomes the key for ubiquitous computing and intelligence, and Distributed Storage Systems (DSS) are widely used in large-scale data centers or in the cloud for efficient data management. However, the data on stored are likely to be unavailable due to hardware failures and cyberattacks, e.g. DDoS. Maximum Distance Separable (MDS) codes are commonly used for the recovery of faulty storage nodes or unavailable data. However, the recovery of data nodes usually involves access to multiple nodes, which introduces significant communication overheads to the DSS. In this paper, a new DSS based on the Redundant Residue Number System (RRNS) is proposed, where efficient recovery is enabled by applying the second version of Chinese Remainder Theorem (CRT-II). The complexity and network traffic of the proposed data protection scheme is analyzed theoretically and compared with that of traditional MDS based DSSs. Experimental results show that the proposed DSS achieves lower encoding complexity, lower recovery complexity and lower network traffic than the MDS based schemes. Although the proposed data protection scheme introduces computation overheads for the case on which there are no failing nodes, its complexity is still lower for scenarios with frequent data updates. In addition, the proposed scheme introduces additional advantages in terms of security and storage flexibility.

Research on the Efficient Space Debris Observation Method Based on Optical Satellite Constellations

The increasing amount of space debris poses a major threat to the security of space assets. The timely acquisition of space debris orbital data through observations is essential. We established a mathematical model of optical satellite constellations for space debris observation, designed a high-quality constellation configuration, and designed a space debris tracking observation scheduling algorithm. These tools can realize the efficient networking of space debris from a large number of optical satellite observation facilities. We designed a constellation consisting of more than 20 low-Earth orbit (LEO) satellites, mainly dedicated to the observation of LEO space objects. According to the observation scheduling method, the satellite constellation can track and observe more than 93% of the targets every day, increase the frequency of orbital data updates, and provide support for the realization of orbital space debris cataloguing. Designing optical satellite constellations to observe space debris can help realize the advance perception of dangerous collisions, timely detect dangerous space events, make key observations about high-risk targets, greatly reduce the false alarm rate of collisions, and provide observational data support for space collisions.

MalBERTv2: Code Aware BERT-Based Model for Malware Identification

To proactively mitigate malware threats, cybersecurity tools, such as anti-virus and anti-malware software, as well as firewalls, require frequent updates and proactive implementation. However, processing the vast amounts of dataset examples can be overwhelming when relying solely on traditional methods. In cybersecurity workflows, recent advances in natural language processing (NLP) models can aid in proactively detecting various threats. In this paper, we present a novel approach for representing the relevance and significance of the Malware/Goodware (MG) datasets, through the use of a pre-trained language model called MalBERTv2. Our model is trained on publicly available datasets, with a focus on the source code of the apps by extracting the top-ranked files that present the most relevant information. These files are then passed through a pre-tokenization feature generator, and the resulting keywords are used to train the tokenizer from scratch. Finally, we apply a classifier using bidirectional encoder representations from transformers (BERT) as a layer within the model pipeline. The performance of our model is evaluated on different datasets, achieving a weighted f1 score ranging from 82% to 99%. Our results demonstrate the effectiveness of our approach for proactively detecting malware threats using NLP techniques.

Two-stage multi-sensor fusion positioning system with seamless switching for cooperative mobile robot and manipulator system

The stoppage of a mobile platform is generally scheduled to feed parts for machines on production lines, such as fenced industrial robotic manipulators. A non-stop mobile robotic part feeding system can contribute to production efficiency and flexibility but contains several challenging tasks. For example, the industrial robotic manipulator needs to perceive the positions of the mobile robot accurately and robustly before grasping the supplies when the mobile robot moves around. Thus, based on the relative distance between the two robots, an interaction mode of the integrated robotic system consisting of a fixed robotic manipulator and a mobile robot is developed for robotic interaction. In order to accurately and robustly perceive the positions of a mobile robot, two different positioning approaches for the robotic manipulator positioning mobile robot in an indoor environment are utilised. One approach is ultrasonic sensors fused with inertia measurement units (IMU) by extended Kalman filter (EKF). Furthermore, an outlier rejection mechanism is implemented to escape outliers from ultrasonic measurement. Another positioning approach is achieved by detecting an ArUco marker with visual sensor. Lastly, a positioning switching strategy according to the visual sensor state allows the robotic manipulator to reposition the mobile robot seamlessly. According to the static experiments, EKF-based positioning approach fusing IMU with ultrasonic sensor can export high-accuracy (the root mean square error is 0.04 m) and high-precision (the standard deviation is 0.0033 m) in positioning while keeping a high update frequency of 181.9 HZ in static positioning. Evaluations through dynamic experiments demonstrate that the proposed positioning system can suppress the positioning drifts over time in comparison with wheel encoder-based positioning method. The two-stage repositioning strategy can support the robotic manipulator to identify the positions of the mobile robot robustly, even in the case when the visual sensor is occluded.

A cross-sectional study assessing family satisfaction in the intensive care environment in Bahrain: opportunities for improvement

Background: Assessment of patients’ family experience and levels of satisfaction have become a measure of intensive care. The aim of this study was to assess the needs of patient’s family members in an Intensive Care Unit (ICU) and to identify areas for improvement in quality care. Methods: A prospective, cross-sectional, a questionnaire survey study was carried out with 100 randomly selected family members of 77 critically ill patients cared for in surgical and medical ICUs. The family satisfaction (FS-ICU-24) questionnaire was given to family members, and the responses were used to assess their experience of ICU care. Results: The responses of patients’ relatives exposed an overall level of satisfaction of 5.04 ± 1.104 on a scale of 1-6. Satisfaction with care was scored higher than satisfaction with decision-making. Family members reported low satisfaction with communication with physicians, the ICU atmosphere, and the waiting room atmosphere. Factors such as the completeness of the information received by relatives (p=0.007) and the ease of obtaining information (p=0.007) demonstrated a significant association with family satisfaction. Suggestions received from families with the high frequency where they need lengthier visiting hours, regular updates on medical reports, better care, and communication with ICU staff. Conclusions: Although overall family satisfaction was high, some areas emerged for improvement. The family members want to increase the length of visiting hours, frequency of patient status updates, communication with physicians, involvement in decision-making, and presence during medical visits. Considering the opportunity to improve ICU care, we propose periodic assessments of family satisfaction with ICU experiences.

Event-triggered bipartite consensus for nonlinear multi-agent systems under switching topologies: A time-varying gain method

In this paper, the event-triggered bipartite consensus problem is investigated for nonlinear multi-agent systems under switching topologies, only part of topologies contain directed spanning tree rooted at the leader. First, a dynamic bipartite compensator is constructed based on relative output information to provide control signal. Then, the time-varying gain method is adopted to propose a compensator-based event-triggered control protocol without Zeno behavior. Notably, the control protocol proposed achieves the bipartite consensus while reducing update frequency effectively. Moreover, a low conservative switching law is designed by the topology-dependent average dwell time strategy, which fully considers the differences among topologies and provides an independent average dwell time for each topology. As an extension, the nonlinear multi-agent systems with non-zero input of leader are further studied. Finally, a practical example is presented to demonstrate the feasibility of proposed control protocol.

Quantized control for networked switched systems under denial-of-service attacks via a barrier event-triggered mechanism

This paper studies the quantized control problem for networked switched systems (NSSs) under denial-of-service (DoS) attacks. The quantized state information, together with the switching signal, is transmitted to the controller through a network. In order to reduce communication consumption and controller update frequency, a barrier event-triggered mechanism is utilized to monitor the state at discrete time. Because of the event-triggered mechanism and the DoS attacks on the network, the mismatch between the system mode and the controller mode is thus frequently encountered, which may lead to quantization saturation and system instability. To solve the problem, an update rule is presented for the dynamic quantizer by switching between zooming in and zooming out of the zooming variable, and a feedback controller is proposed with a jointly designed event-triggered mechanism and a dynamic quantizer. Sufficient conditions on the constraints of DoS frequency and duration are obtained to ensure the exponential stability of the switched system. The effectiveness of the obtained results is illustrated by simulation examples and comparative studies.

Staying Informed of Best Evidence to Guide Practice

Staying Informed of Best Evidence to Guide Practice

Hypergraph and cross-attention-based unsupervised domain adaptation framework for cross-domain myocardial infarction localization

Solving individual differences between subjects is critical for the promotion of electrocardiogram (ECG) classification algorithms in the intelligent health monitoring industry. Popular inter-subject-based solutions usually require the manual labeling of heartbeats and frequent updating of the model for new subjects. To track these problems, we propose a hypergraph and cross-attention-based unsupervised domain adaptation (HGCA-UDA) framework for the myocardial infarction localization. Specifically, we first build a hypergraph-based dual-channel network, that can simultaneously learn specific feature representations from an ECG lead and disease category levels for samples from different domains. We then design a cross-attention module to align cross-domain locally similar samples. Subsequently, a domain alignment strategy based on the Wasserstein distance is proposed to align the global edge feature distribution. Finally, a pseudo-label generation scheme is proposed to further align fine-grained category information. We conduct extensive experiments on two public benchmark datasets (the Physikalisch-Technische Bundesanstalt (PTB) and PTB_XL database), and the results show that the proposed HGCR-UDA (with unlabeled patients) achieves comparable results compared with state-of-the-art inter-patient-based methods (with labeled patients) and has excellent applications prospects in the field of intelligent health monitoring.