Logic Mapping Research Articles

Chaotic laser time series prediction based on an improved logistic mapping algorithm echo state network

Chaos synchronization plays vital functions in the fields of optical chaos secure communication. The synchronization performance can be significantly degraded by parameter mismatches between the chaotic transmitter and receiver. In this paper, the Deep-Logistical Mapping Echo State Network (D-LMESN) is proposed to enhance the performance of chaos synchronization. The network is upgraded by using an improved logical mapping algorithm and a deep reserve pool structure with phase space reconstruction. Results show that D-LMESN exhibits better performance in the prediction of chaotic time series, thanks to the adaptive parameter adjustment, which increases the ability to capture the dynamic characteristics of complex systems. Compared with ESN, the mean square error of this model is reduced by 55% and 72%, respectively, in chaotic laser simulation and actual data experiments. This provides a new possibility, to our knowledge, for the development of chaotic secure communication.

Enhancing public opinion monitoring for social hot events with a time series neural network-based logic map

A method for constructing a commercial map of social hotspots based on time-series neural networks is proposed. It explores map construction through three stages: atomic event graph building, fusion, and public opinion analysis. The neural network captures event dynamics and public opinion evolution. An unsupervised learning approach trains the model to explore deep event connections. A time map reflects event trends and opinion changes. A sentence-level event extraction model provides rich information. An empirical mode decomposition method analyzes IMF data to understand intrinsic patterns. Time-series neural networks classify and reconstruct event graphs, constructing a knowledge graph. Experiments show this method reduces storage costs, simplifies prediction, and exhibits robustness. It offers new insights for commercial mapping and contributes to public opinion monitoring.

Enhancing River Flood Prediction in Early Warning Systems Using Fuzzy Logic-Based Learning

Previous studies show that the fuzzy-based approach predicts incoming floods better than machine learning (ML). However, with numerous observation points, difficulties in manually determining fuzzy rules and membership values increase. This research proposes a novel fuzzy logic-based learning (FLBL) that embeds missing data imputations and a fuzzy rule optimization strategy to enhance ML performance while still benefiting from fuzzy theory. The simple moving average handles sensors’ missing data. The logical mapping is used for fuzzification automation and fuzzy rule generation. The join function between the Szymkiewicz–Simpson coefficient similarity and max function is applied to optimize a fuzzy rules model. The case study uses observation data from three rivers traversing three districts in Semarang City. As a result, FLBL achieves 97.87% accuracy in predicting flood, outperforming the decision tree (96%) and the neural network (73.07%). This work is significant as a part of preventive flood-related disaster plans.

Data-driven inverse design of the perforated auxetic phononic crystals for elastic wave manipulation

Abstract In addition to the distinctive features of tunable Poisson’s ratio from positive to negative and low stress concentration, the perforated auxetic metamaterials by peanut-shaped cuts have exhibited excellent phononic crystal (PNC) behavior as well for elastic wave manipulation. Thus they have attracted much attention in vibration suppression for dynamic applications. However, traditional structural designs of the auxetic PNCs considerably depend on designers’ experience or inspiration to fulfill the desired multi-objective bandgap properties through extensive trial and error. Hence, developing a more efficient and robust inverse design method remains challenging to accelerate the creation of auxetic PNCs and improve their performance. To shorten this gap, a new machine learning (ML) framework consisting of double back propagation neural network (BPNN) modules is developed in this work to produce desired configurations of the auxetic PNCs matching the customized bandgap. The first inverse BPNN module is trained to establish a logical mapping from the bandgap properties to the structural parameters, and then the second forward BPNN module is introduced to give the new property prediction by using the design configurations generated from the former. The error between the new predictions and the desired target properties is minimized through a limited number of iterations to produce the final optimal objective configurations. The results indicate that the perforated auxetic metamaterials behave relatively wide complete bandgap and the present ML model is effective in designing them with specific bandgaps within or beyond the given dataset. The study provides a powerful tool for designing and optimizing the perforated auxetic metamaterials in dynamic environment.

Machine learning-accelerated inverse design of programmable bi-functional metamaterials

Bi-functional metamaterials with programmable coefficients of thermal expansion (CTEs) and Poisson’s ratios (PRs) have garnered significant attention among researchers due to the ability to manifest desired deformations under thermal and mechanical loads. Nevertheless, a current challenge lies in efficiently achieving the inverse design of these metamaterials to meet diverse application requirements. This paper presents a machine learning (ML) model that can establish a logical mapping relationship between geometric/material parameters and mechanical properties, and it is applied to the inverse design of bi-functional metamaterials with desired CTEs and PRs. Furthermore, the inverse design capability of the ML model was validated by the finite element analysis and experimental test. The results demonstrate that the geometric models obtained from the inverse prediction can effectively exhibit the desired deformation behavior under thermal and mechanical loads. And the ML model proves to be a valuable tool, offering effective guidance for the design of bi-functional metamaterials with specific CTEs and PRs.

Research Plan Entrepreneurial Logical Mapping: Production and Re-engineering of a Pragmatic, Fluid and Tantalizing Academic Research Plan: A Practical Example (Extract from My 2024-2029 Research Plan)

For the young, ambitious academics and upcoming Researchers placed at any tertiary institution of higher learning, or research organizations, this position paper (research plan) is a must read master piece. Mature researchers and potential candidatures in research are also invited to consume this information. I acknowledge that while it may not be 100% tantalizing, but it is 100% worth of going through. Completing the reading and mastering some key techniques will produce researchers who work smart, organize their research and independent in thinking. This plan is a real abstract of my comprehensive plan for period 2024 through 2029. The document is my strategic research plan projected for [2024 to 2029]. The plan provides a major and rigorous diagnosis regarding the opportunity to talk about my proposed research plan-covering [Entrepreneurship, Business leadership and Corporate governance, Human and business epistemology, Project Management, logistics and Business Communication plus general management areas] within the domain of Private and Public sector sectors. The plan further describes benefits, and how it will be implemented and conducted. This publication strategy is a thought tool for the future and is in a road map delineating what, when, and how my research will be conducted and in some cases published. It does include the type of article and journal, based on the target audience, the environment in which research will be done. For my strategy to work, I have established it as early as possible, and will revise it throughout a research project’s life cycle which aligns to the five (5) year period. The form of research is multi-faceted in outlook. As both part of raising BA ISAGO university as a knowledge producer and also part of my personal profile enhancement, I have lined up a number a of structures within my strategic research paradigms-following a very strong and waterproof personal research model [The Gwakwa Knee jack Research framework ( GNJRF)] This model postulates a bi-partisan and bi-projectory approach where the research is biased towards promoting institutional values and place the university at the center of knowledge production within the global arena. To achieve this ,three key approaches (to be elucidated in the main document) will be adopted namely organizing and conducting international research conferences/symposiums and or workshops; holding classical and internal capped public lectures with a global touch; rigorous search and conducting intensive community based research; conducting intensive business based research; aggressive publications mainly touching on the business landscape with emphasis on entrepreneurship research, leadership and business management disciplinarian researches and lastly but not list embark on a series of participating n international research conferences where I will make active presentations in line with conference thematic areas

Evaluation of groundwater quality indices using multi-criteria decision-making techniques and a fuzzy logic model in an irrigated area

The quality of water for irrigation presents a fundamental challenge to the sustainability of agricultural practices. This study, conducted in the Ras Jbel (RJ) aquifer of Tunisia, aims to evaluate the suitability of groundwater for both drinking and irrigation purposes and to identify regions suitable for agriculture. In this research, various indices were employed, including water quality index (WQI), entropy water quality index (EWQI), and Fuzzy logic for irrigation water quality index (FLIWQ). The conventional irrigation indices, such as sodium adsorption ratio (SAR), magnesium hazard (MH), sodium percentage (Na%), Kelly's ratio (KR), permeability index (PI), residual sodium carbonate (RSC), and potential salinity (PS) were converted into fuzzy membership and produced dry and wet seasons maps. The dry and wet season fuzzy logic maps were used as input for final FLIWQI. Eighty-nine groundwater samples were collected from shallow wells and analyzed for major cations and anions. Most parameters exceeded the recommended values for drinking, according to both WHO and Tunisian standards, across all samples. The results of the WQI and EWQI indicated that, during the wet season, over 90% and 86% of the water samples were classified as poor and very poor quality, respectively, compared to 93.33% and 49% during the dry season. For IWQI, the wet season fuzzy logic map suggests 55.57% groundwater suitability, mainly in the central area. In the dry season, the map indicates 41.11% suitability for irrigation. The FLIWQI map confirms that the groundwater in the central part of the study area is suitable for agriculture. This study can help decision-makers initiate efficient water quality management plans in similar water-scarce environments worldwide.

A New Teaching Mode: Logic Map

A New Teaching Mode: Logic Map

New Patterns for Low-income Families - A model for comprehensive and coordinated efforts.

Background: In Norway, more than one out of ten children grow up in poverty. Poverty and its consequences are often transferred across generations. Services to these families are seldom integrated and coordinated. We lack knowledge about the complexity they face and how measures should be organized to reach all family members. “New patterns” is a model for comprehensive and coordinated efforts for families with children who struggle with persistent low income and where the family experiences complex challenges.
 Aims: The aim was to develop a cross-sectoral service model to coordinate services for integrated health and social carefor poor families, to prevent transfer of low income and the accompanying social, economic, and health-related consequences across generations. 
 Methods: In New Patterns a multidisciplinary team of researchers and practitioners/social workers has searched for best practice and included findings from a pilot study where families expressed frustration and powerlessness because no one addressed the “whole picture” of their situation. Based on these findings the New patterns-model was developed. 
 We have employed a project design where evaluation and implementation function as simultaneous and reinforcing processes, inspired by the framework Effectiveness-implementation Hybrid Designs (1). The model has continuously been developed based on the experiences and feedbacks from participating families and practitioners. Dialogue with local and national policymakers and professionals has been an important part of the implementation process. 
 Results: In New Patterns a family coordinator is responsible for close follow-up of children and adults in 10 families over a period of 5 years. Follow-up includes services from different sectors, i.e., culture, education, health- and social services, and volunteer organizations. The family coordinator preforms a detailed mapping of the family`s needs, and together with the family develop a family plan enabling appropriate help and needed services. Through systematic mapping, close follow-up over time, practical and social support, and coordinator skills, the family coordinator develops trust-based relationships that provide new insight into the challenges and optimize the use of instruments in the traditional support system. 
 We have identified 10 effective elements in the family coordinator's follow-up of the participating families. These elements are illustrated in a Logic map, which shows the connection between follow-up and goal achievement, in short and long term, and the elements that must be present to succeed. The model enables integrated health care and social care to families with complex challenges. New Patterns is implemented in 10 municipalities in Norway, including 200 families.
 Conclusion: The family coordinator works with the family in the family’s everyday life, including home visits and accompanying the family members to meetings with relevant collaborating services. The intervention does not introduce new services but coordinates optimal use of existing municipal welfare services. The New Patterns model is relevant for other target groups with complex challenges and were the health and welfare systems struggle to provide integrated care to all family members.
 1.Curran GM,Bauer M,Mittman B,Pyne JM,Stetler C. Effectiveness-implementation Hybrid Designs: Combining Elements of Clinical Effectiveness and Implementation Research to Enhance Public Health Impact. Med Care. 2012;50(3):217-26. DOI: 10.1097/MLR.0b013e3182408812 

Quality Control of Mass-Encoded Nanodevices by Compartmented DNA Origami Frames for Precision Information Coding and Logic Mapping.

Encoded nanostructures afford an ideal platform carrying multi-channel signal components for multiplexed assay and information security. However, with the demand on exclusivity and reproducibility of coding signals, precise control on the structure and composition of nanomaterials featuring fully distinguishable signals remains challenging. By using the multiplexing capability of mass spectrometry (MS) and spatial addressability of DNA origami nanostructures, we herein propose a quality control methodology for constructing mass-encoded nanodevices (namely MNTs-TDOFs) in the scaffold of compartmented tetrahedral DNA origami frames (TDOFs), in which the arrangement and stoichiometry of four types of mass nanotags (MNTs) can be finely regulated and customized to generate characteristic MS patterns. The programmability of combinatorial MNTs and orthogonality of individual compartments allows further evolution of MNTs-TDOFs to static tagging agents and dynamic nanoprobes for labeling and sensing of multiple targets. More importantly, structure control at single TDOF level ensures the constancy of prescribed MS outputs, by which a high-capacity coding system was established for secure information encryption and decryption. In addition to the multiplexed outputs in parallel, the nanodevices could also map logic circuits with interconnected complexity and logic events of c-Met recognition and dimerization on cell surface for signaling regulation by MS interrogation.

Quality Control of Mass‐Encoded Nanodevices by Compartmented DNA Origami Frames for Precision Information Coding and Logic Mapping

AbstractEncoded nanostructures afford an ideal platform carrying multi‐channel signal components for multiplexed assay and information security. However, with the demand on exclusivity and reproducibility of coding signals, precise control on the structure and composition of nanomaterials featuring fully distinguishable signals remains challenging. By using the multiplexing capability of mass spectrometry (MS) and spatial addressability of DNA origami nanostructures, we herein propose a quality control methodology for constructing mass‐encoded nanodevices (namely MNTs‐TDOFs) in the scaffold of compartmented tetrahedral DNA origami frames (TDOFs), in which the arrangement and stoichiometry of four types of mass nanotags (MNTs) can be finely regulated and customized to generate characteristic MS patterns. The programmability of combinatorial MNTs and orthogonality of individual compartments allows further evolution of MNTs‐TDOFs to static tagging agents and dynamic nanoprobes for labeling and sensing of multiple targets. More importantly, structure control at single TDOF level ensures the constancy of prescribed MS outputs, by which a high‐capacity coding system was established for secure information encryption and decryption. In addition to the multiplexed outputs in parallel, the nanodevices could also map logic circuits with interconnected complexity and logic events of c‐Met recognition and dimerization on cell surface for signaling regulation by MS interrogation.

The past is prologue part II: a study of PDS comparison dissertations

PurposeThe study aims to present a logic map linking the Professional Development School (PDS) Nine Essentials as a PDS theory of action and offer an analysis of dissertations that compare outcomes of learning in PDS and non-PDS contexts.Design/methodology/approachFor this current study, the authors identified 25 of the 210 dissertations from a larger study that used a comparison methodology to provide a window into how learning in PDS and non-PDS settings may differ. In reviewing these comparison studies, the authors identified a set of clustered themes, as well as a variety of comparison constructs and measurements researchers used to determine the impact of PDS.FindingsFive themes emerged including (1) the experience of learning to teach in a PDS setting vs. a non-PDS; (2) the experience of teaching in a PDS vs. non-PDS; (3) teacher candidate quality in a PDS vs. non-PDS; (4) teacher quality in a PDS vs. non-PDS; (5) school leader quality in a PDS vs. non-PDS; and (6) K-12 student learning in PDS vs. non-PDS.Research limitations/implicationsLimitations of this study include the complications related to comparison, logic-related fallacies and the complexity of capturing simultaneous renewal.Originality/valueIn the 30th year of PDS work, the study utilizes a theory of action comprised of linking the PDS Nine Essentials to situate the comparison dissertation analysis of outcomes in PDS and non-PDS contexts suggesting challenges and possibilities and perhaps a direction for new research questions.

Exploring the potential of offline cryptography techniques for securing ECG signals in healthcare

In the research, a software for ECG signal based on Chaos encryption based on C#-programmed and Kit of Microsoft Visual Studio Development was implemented. A chaos logic map (ChLMp ) and its initial value are utilized to create Level-1 ECG signal based on Chaos encryption bit streams. A ChLMp, an initial value, a ChLMp bifurcation parameter, and two encryption level parameters are utilized to create level-2 ECG signal based on Chaos encryption bit streams. The level-3 ECG signal based on Chaos encryption software utilizes two parameters for the level of encryption, a permutation mechanism, an initial value, a bifurcation parameter of the level of encryption, and a ChLMp. We assess 16-channel ECG signals with great resolution utilizing encryption software. The level-3 ECG signal based on Chaos encryption program has the slowest and most reliable encryption speed. The encryption effect is superior, according to test findings, and when the right decoding parameter is utilized, the ECG signals may be completely recovered. The high resolution 16-channel ECG signals (HRMCECG) won't be recovered if an invalid input parameter occurred, such as a 0.00001% initial point error, which will result in chaotic encryption bit streams.

Approaching the mapping limit with closed-loop mapping strategy for deploying neural networks on neuromorphic hardware.

The decentralized manycore architecture is broadly adopted by neuromorphic chips for its high computing parallelism and memory locality. However, the fragmented memories and decentralized execution make it hard to deploy neural network models onto neuromorphic hardware with high resource utilization and processing efficiency. There are usually two stages during the model deployment: one is the logical mapping that partitions parameters and computations into small slices and allocate each slice into a single core with limited resources; the other is the physical mapping that places each logical core to a physical location in the chip. In this work, we propose the mapping limit concept for the first time that points out the resource saving upper limit in logical and physical mapping. Furthermore, we propose a closed-loop mapping strategy with an asynchronous 4D model partition for logical mapping and a Hamilton loop algorithm (HLA) for physical mapping. We implement the mapping methods on our state-of-the-art neuromorphic chip, TianjicX. Extensive experiments demonstrate the superior performance of our mapping methods, which can not only outperform existing methods but also approach the mapping limit. We believe the mapping limit concept and the closed-loop mapping strategy can help build a general and efficient mapping framework for neuromorphic hardware.

A Review of Knowledge Graph-Based Reasoning Technology in the Operation of Power Systems

Knowledge graph (KG) technology is a newly emerged knowledge representation method in the field of artificial intelligence. Knowledge graphs can form logical mappings from cluttered data and establish triadic relationships between entities. Accurate derivation and reasoning of knowledge graphs play an important role in guiding power equipment operation and decision-making. Due to the complex and weak relations from multi-source heterogeneous data, the use of KGs has become popular in research to represent potential information in power knowledge reasoning. In this review, we first summarize the key technologies of knowledge graph representation and learning. Then, based on the complexity and real-time changes of power system operation and maintenance, we present multiple data processing, knowledge representation learning, and the graph construction process. In three typical power operation and fault decision application scenarios, we investigate current algorithms in power KG acquisition, representation embedding, and knowledge completion to illustrate accurate and exhaustive recommendations. Thus, using KGs to provide reference solutions and decision guidance has a significant role in improving the efficiency of power system operations. Finally, we summarize the achievements and difficulties of current research and give an outlook for future, promising roles of KG in power systems.

Parallel-Prefix Adder in Spin-Orbit Torque Magnetic RAM for High Bit-Width Non-Volatile Computation

Recently, many computing-in-memory (CIM) systems based on non-volatile devices have been implemented well. However, they perform poorly in high bit-width processes due to device access latency and energy cost. In this brief, we present an in memory fully non-volatile parallel-prefix adder(PPA) based on CIM system for 2T1MTJ spin-orbit torque magnetic random access memory(SOT MRAM). Unlike traditional CMOS circuits, CIM prefers uniform logic gates(only AND or OR gates at each logic level). Thus, to calculate the output of each logic level, traditional AND/OR logic is substituted with MAJORITY/NOT logic. We also propose an optimized logical mapping strategy that requires one write operation instead of continuous write operations at each write-back stage, which maximizes the performance benefits of the PPA.

Ultra-small superparamagnetic iron oxide nanoparticles for intra-articular targeting of cartilage in early osteoarthritis.

Early diagnosis of osteoarthritis (OA) is critical for effective cartilage repair. However, lack of blood vessels in articular cartilage poses a barrier to contrast agent delivery and subsequent diagnostic imaging. To address this challenge, we proposed to develop ultra-small superparamagnetic iron oxide nanoparticles (SPIONs, 4 nm) that can penetrate into the matrix of articular cartilage, and further modified with the peptide ligand WYRGRL (particle size, 5.9 nm), which allows SPIONs to bind to type II collagen in the cartilage matrix and increase the retention of probes. Type II collagen in the cartilage matrix is gradually lost with the progression of OA, consequently, the binding of peptide-modified ultra-small SPIONs to type II collagen in the OA cartilage matrix is less, thus presenting different magnetic resonance (MR) signals in OA group from the normal ones. By introducing the AND logical operation, damaged cartilage can be differentiated from the surrounding normal tissue on T1 and T2 AND logical map of MR images, and this was also verified in histology studies. Overall, this work provides an effective strategy for delivering nanosized imaging agents to articular cartilage, which could potentially be used to diagnosis joint-related diseases such as osteoarthritis.

Providing evidence-based knowledge on nursing interventions at the point of care: findings from a mapping project

BackgroundIn healthcare there is a call to provide cost-efficient and safe care. This can be achieved through evidence-based practice (EBP), defined as the use of evidence from research, context, patient preferences, and clinical expertise. However, the contemporary and process-integrated supply of evidence-based knowledge at the point of care is a major challenge. An integrative knowledge management system supporting practicing clinical nurses in their daily work providing evidence-based knowledge at the point of care is required. The aim of this study was (1) to map standardized and structured nursing interventions classification and evidence on a knowledge platform to support evidence-based knowledge at the point of care, and (2) to explore the challenge of achieving interoperability between the source terminology of the nursing interventions classification (LEP Nursing 3) and the target format of the evidence provided on the knowledge platform (FIT-Nursing Care).MethodsIn an iterative three-round mapping process, three raters, nurses with clinical and nursing informatics or EBP experience, matched nursing interventions from the LEP Nursing 3 classification and evidence provided from Cochrane Reviews summarized on FIT-Nursing Care as so-called study synopses. We used a logical mapping method. We analysed the feasibility using thematic analysis.ResultsIn the third and final mapping round, a total of 47.01% (252 of 536) of nursing interventions from LEP Nursing 3 were mapped to 92.31% (300 of 325) of synopses from FIT-Nursing Care. The interrater reliability of 77.52% suggests good agreement. The experience from the whole mapping process provides important findings: (1) different content orientations—because both systems pursue different purposes (content validity), (2) content granularity—differences regarding the structure and the level of detail in both systems, and (3) operationalization of knowledge.ConclusionMapping of research evidence to nursing classification seems feasible; however, three specific challenges were identified: different content orientation; content granularity; and operationalization of knowledge. The next step for this integrative knowledge management system will now be testing at the point of care.

Artificial intelligence framework‐based ultra‐lightweight communication protocol for prediction of attacks in Internet of Things environment

AbstractThe Internet of Things is an enhanced intelligent infrastructure that is created through the use of a number of different devices that are capable of self‐organization. Data privacy and protection are the primary problems brought up by this enormous network. In addition, the devices in the network with constrained resources are battery‐powered, thus they only have a limited capacity to store resources internally. Because of this, it is vital to discover solutions that are more resource‐optimized and connected to security in order to solve the issues that were caused by the network. The devices are being forced to maintain more complicated cryptographic algorithms for higher security. Therefore, this article proposes an integrated communication protocol that requires only symmetric key‐based cryptography with a deep learning convolutional neural network, which is employed for predicting the normal and attacked data from the input requested data. Here, the logical map is used to produce the symmetric keys due to its resistance against the attacks of key reset and device capture. This integrated model provides an ultra‐lightweight communication protocol with reduced attack detection time while enhancing the attack detection parameters such as precision, accuracy, F1‐score, and recall.

Digital Peer-Supported Self-Management Intervention Codesigned by People With Long COVID: Mixed Methods Proof-of-Concept Study.

BackgroundThere are around 1.3 million people in the United Kingdom with the devastating psychological, physical, and cognitive consequences of long COVID (LC). UK guidelines recommend that LC symptoms be managed pragmatically with holistic support for patients’ biopsychosocial needs, including psychological, emotional, and physical health. Self-management strategies, such as pacing, prioritization, and goal setting, are vital for the self-management of many LC symptoms.ObjectiveThis paper describes the codevelopment and initial testing of a digital intervention combining peer support with positive psychology approaches for self-managing the physical, emotional, psychological, and cognitive challenges associated with LC. The objectives of this study were to (1) codesign an intervention with and for people with LC; (2) test the intervention and study methods; (3) measure changes in participant well-being, self-efficacy, fatigue, and loneliness; and (4) understand the types of self-management goals and strategies used by people with LC.MethodsThe study used a pre-post, mixed methods, pragmatic, uncontrolled design. Digital intervention content was codeveloped with a lived-experience group to meet the needs uncovered during the intervention development and logic mapping phase. The resulting 8-week digital intervention, Hope Programme for Long COVID, was attended by 47 participants, who completed pre- and postprogram measures of well-being, self-efficacy, fatigue, and loneliness. Goal-setting data were extracted from the digital platform at the end of the intervention.ResultsThe recruitment rate (n=47, 83.9%) and follow-up rate (n=28, 59.6%) were encouraging. Positive mental well-being (mean difference 6.5, P<.001) and self-efficacy (mean difference 1.1, P=.009) improved from baseline to postcourse. All goals set by participants mapped onto the 5 goal-oriented domains in the taxonomy of everyday self-management strategies (TEDSS). The most frequent type of goals was related to activity strategies, followed by health behavior and internal strategies.ConclusionsThe bespoke self-management intervention, Hope Programme for Long COVID, was well attended, and follow-up was encouraging. The sample characteristics largely mirrored those of the wider UK population with LC. Although not powered to detect statistically significant changes, the preliminary data show improvements in self-efficacy and positive mental well-being. Our next trial (ISRCTN: 11868601) will use a nonrandomized waitlist control design to further examine intervention efficacy.