Rate Of Data Research Articles

It's About Time: Parent-Child Turn-Taking in Early Stuttering.

Most common treatments for stuttering offer advice that parents modify temporal features of conversational interaction to assist children who stutter (CWS). Advice includes but is not limited to slowing of adult speech, increasing turn-taking/response-time latencies (RTLs), and reducing interruptions. We looked specifically at RTL and parental speech rate in a longitudinal data set that included baseline behaviors. We used data from baseline recordings (CWNS = 13 CWS-persistent, 28 CWS-recovered, 21 children who did not stutter) of the Illinois International Stuttering Research Project at FluencyBank, using CLAN software with audio linkage to Praat. Group differences in speech rate and RTL at baseline were nonsignificant; parents of CWS-persistent spoke most slowly pre-advisement. No relationships between speech rate or RTL and child fluency were detected. This is a retrospective, observational study, and caution must be used in interpreting our findings. However, current results do not add evidentiary support for common advice to adjust temporal parameters of their interactions made to parents of CWS, in terms of therapeutic outcome or concurrent fluency. We are analyzing subsequent samples, after advisement, to determine potential benefits of such guidance not evident in this analysis. Suggestions for future research and implications for clinical focus and practice are offered.

A novel data fusion method to leverage passively-collected mobility data in generating spatially-heterogeneous synthetic population

Conventional methods to synthesize population use household travel survey (HTS) data. They generate many infeasible attribute values due to sequentially generating sociodemographics and spatial attributes and encounter a low spatial heterogeneity issue due to a low sampling rate of the HTS data. Passively collected mobility (PCM) data (e.g., cellular traces) provides extensive spatial coverage but poses integration challenges with HTS data due to differences in spatial resolution and attributes. This study introduces a novel cluster-based data fusion method to address these limitations and simultaneously generate synthetic populations with accurate sociodemographics and home–work locations at high spatial heterogeneity. Spatial clustering is adopted to align the spatial resolution of HTS and PCM data, facilitating effective data integration. The data fusion process is reformulated into cluster-specific low-dimensional optimization subproblems to ensure computational tractability. Analytical properties are derived to retain essential distributional characteristics from both datasets in the fused distribution. The spatial clustering process is optimized to ensure such distributional consistencies while maintaining a balance between feasibility and heterogeneity of the synthetic population. The data fusion properties are validated using HTS and LTE/5G cellular signaling data from Seoul, South Korea. Validation against census data confirms the method’s efficacy in maintaining distributional consistency while increasing spatial heterogeneity, with 97% of the generated population being unobserved in the HTS data. This research advances methods to synthesize a population by leveraging the complementary strengths of HTS and PCM data, providing a robust framework for generating spatially diverse synthetic populations essential for urban planning.

NetGSR: Towards Efficient and Reliable Network Monitoring with Generative Super Resolution

Network monitoring systems are a key building block in today's networks. They all follow a common framework where measurement data from network elements is aggregated at a central collector for network-wide visibility. When designing network monitoring systems, two key properties have to be taken into account: (1) efficiency, to minimize the communication overhead from network elements to the collector; (2) high-fidelity, to faithfully represent the network status. However, in presence of network dynamics, tracking the right operating point to ensure both high fidelity and efficiency is hard and we observe that prior monitoring approaches trade off one for the other. In this paper, we show that it is possible to satisfy both these properties with NetGSR, a new deep learning based solution we introduce that reconstructs the fine-grained behavior of network status at the collector while requiring low resolution measurement data from network elements. This is achieved through a combination of a new custom-tailored conditional deep generative model (DistilGAN), and a new feedback mechanism (Xaminer) based on model uncertainty estimation and denoising that allows the collector to adjust the sampling rate for measurement data from network elements, at run-time. We extensively evaluate NetGSR using three different network scenarios with corresponding real-world network monitoring datasets as well as two downstream use cases. We show that NetGSR can faithfully reconstruct fine-grained network status with 25x greater measurement efficiency than prior approaches while requiring only few ms of inference time at the collector.

Enhanced In-Network Caching for Deep Learning in Edge Networks

Enhanced In-Network Caching for Deep Learning in Edge Networks

A sEMG-based gesture recognition framework for cross-time tasks

Abstract In the practical application of action pattern recognition based on surface electromyography (sEMG) signals, the displacement of the electrodes and the time-varying characteristics of the signals during signal acquisition cross-time can reduce the classification accuracy. In this paper, a 12-day forearm sEMG signal acquisition experiment was conducted, and a cross-time gesture recognition framework based on deep convolutional neural network (CNN) with sEMG signals and short-time Fourier transform (STFT) images is proposed. In the single-day data cross-validation, the recognition rates using multiple CNN modules exceeded 90%. However, the average recognition rate for cross-day data was only 59.0%. The classification effectiveness of the framework was significantly enhanced in multi-day analysis by gradually increasing the number of training days. In particular, 97.4% accuracy was achieved in the cross-time recognition task by using a specific configuration of DenseNet as the network module and extracting features by using one-dimensional (1-D) convolution on signal fragments. Compared with the way of extracting STFT image features as input using two-dimensional (2-D) convolution, the training method of extracting signal features using 1-D convolution reduces the consumed time to about 1%, which is advantageous in terms of model performance.

Power Load Prediction Algorithm Based on Wavelet Transform

To address the environmental impact, low efficiency, and poor accuracy of existing power load prediction methods, this study innovatively proposes a power load prediction system that combines wavelet transform with digital twin technology. Compared with similar power load prediction methods, the proposed method achieved the highest power load prediction accuracy rate of 97.26%, with the lowest MAPE and RMSE being only 3.96% each. Our proposed method has good noise resistance and overcomes the disadvantage of traditional power load prediction methods that are easily affected by the environment. Moreover, the false detection rate of the load information data obtained from the power system in the Fuxin area from 2022 to 2023 was less than 5%, further verifying the reliability of the proposed method. This achievement is attributed to the powerful signal processing capabilities of the discrete wavelet transform, advanced pattern recognition and prediction capabilities of these three deep learning network algorithms, and the intelligence of digital twin technology. The combination of these three elements has brought new technological breakthroughs to the field of power load prediction.

Management of COVID-19 in the community using virtual care: An Australian perspective.

Virtual care and remote monitoring were widely used during the COVID-19 pandemic. However, early evaluations of effectiveness were often inconclusive due to low rates of enrolment and limited data. The aim of this study was to evaluate the effectiveness of virtual care in managing individuals in community-based self-isolation and quarantine in Australia during this pandemic. A retrospective cross-sectional study was conducted of individuals admitted to a virtual hospital in Sydney who either tested positive for COVID-19 and required self-isolation, or tested negative but still needed to self-isolate, amidst the Delta wave of the COVID-19 pandemic. Outcome measures included health service utilisation, in-hospital mortality, patient-reported experience measures (PREM), and cost savings resulting from avoided emergency department (ED) presentations. Out of 9571 individuals admitted, 8544 (89.3%) had COVID-19. Clinical deterioration or acute illness occurred in 2477 (25.9%) individuals, of whom 890 (9.3%) were referred to ED for further assessment or investigation, and 614 (6.4%) were admitted for inpatient treatment. Overall mortality was 0.2%. Out of 1020 individuals who completed the PREM survey, 846 (82.9%) rated the overall virtual care experience as 'good' or 'very good'. Avoided ED presentations possibly resulted in cost savings estimated between AU$691,214 and AU$2,994,540. Virtual care was successfully used to manage a large number of individuals in community-based self-isolation and quarantine during the COVID-19 pandemic. Scalable pathways for triage, monitoring and clinical escalation via telehealth ensured patient safety and acceptability, and alleviated strain on the broader health system.

An Improved Vital Signal Extraction Method Based on Laser Doppler Effect.

The mixed signal of respiratory waveform and heartbeat waveform detected by the Laser-Doppler system is processed with an intermediate-frequency (IF) interference filtering method, an enhanced extraction method and a waveform-fixing method. To filter the IF interference signals and the noise scatters in the time-frequency graph, the filtering method based on coefficient of variation (CoV) values and the enhanced curve extraction method based on noise-scatter theory are utilized in vital signal analysis. To decouple the respiratory signal and the heartbeat signal in time domain, the waveform-fixing method based on second-order difference theory is utilized in signal decoupling. This method as an algorithm is applied in the computer simulation and laboratory environments. The results show that the above methods can extract the mixed waveforms and identify the respiratory rates and heart rates in real experimental data. The IF interference signal can be filtered adaptively, and the accuracy of the analyzed rates can be improved to about 95%.

Modeling the occupancy rate and nights spent at tourism accommodations of Spain using autoregressive deep learning networks

Purpose The purpose of this paper is to autoregressively model the net occupancy rate of beds and bedrooms in hotels and similar accommodations and the nights spent at these accommodations of Spain for the period of 1990–2023 using monthly data. Design/methodology/approach The monthly occupancy rate of hotels and the total number of hotel nights data of Spain for the 1990M01–2023M09 range is considered. An autoregressive deep learning network is developed for the modeling of both metrics. Moreover, the results of the proposed autoregressive deep learning method are compared to those of a classical artificial neural network. Findings The actual occupancy rate, total night data and the deep learning model results are compared showing the accuracy of the developed model. Moreover, the R2, mean absolute error, root mean square error and mean absolute percentage error of the models are calculated further demonstrating the high performance of the developed model. The R2 values higher than 0.9 are achieved for both occupancy rate and total number of hotel nights data. Practical implications The modeling results given in this paper demonstrate that the previous values of the net occupancy rate and the total number of nights can be used as inputs of a deep learning network model by which accurate forecasts can be made for the future values of the occupancy rate and the total number of hotel nights. This modeling approach possesses importance from the practical viewpoint considering that the accurate planning and forecast of the net occupancy rate and the total number of nights affect the tourism income. Originality/value This study differs from existing literature by attempting to model the occupancy rate and the total number of hotel nights data autoregressively using deep learning networks.

Infectious disease prevention among internally displaced people: Perspectives from Lviv, Ukraine

Abstract Background The ongoing war in Ukraine has increased the risk of infection spikes, especially among internally displaced people. The healthcare challenges in the war context necessitate teaching the population of preventative measures, which have become the primary vector of the activities of the Ukrainian Regional Centers for Disease Control and Prevention. Objectives The research aimed to highlight the activities of the Lviv Regional Center for Disease Control and Prevention (CDC) in relation to internally displaced people. Methods The research project analyzed the incidence rate of infectious diseases and vaccination coverage data among the population and internally displaced people in the region. The data were collected from monthly reports of CDC’s departments and alert messages sent by primary care institutions to the CDC. Results According to the Single register of internally displaced people (IDP), 13% of internally displaced people were permanently staying in the Lviv region (the second place was Zakarpattia region - 9% of IDP, and the third was Kyiv - 7% of IDP). In the Lviv region, 237,000 people registered as forcibly displaced. Total, over 700, 000 people are in the area, including the unregistered and 68,000 children. In 2022-2023, the morbidity rates among IDPs did not exceed the rates among the general population in the area. Each IDP residence place had its physician; 11 thousand IDPs were vaccinated, and 3,235 of those were children. In general, 46 mobile medical brigades worked on-site in the Lviv region. Only one spike of gastrointestinal infection was registered. CDC employees provided lectures and workshops on hygiene and food storage in 56 IDP residence places. Conclusions The review results show that coordination between CDCs, local physicians, and mobile medical brigades in the IDP residence areas prevents outbreaks of infectious diseases and swiftly responds to emerging threats. Key messages • The tragic events in Ukraine resulted in healthcare access issues. Lviv CDC, fighting at its own forefront, quickly assessed the risks and efficiently managed the potential hazards. • The Lviv region remains one of the biggest hubs in Ukraine for the temporary residence of internally displaced people, with CDC employees ensuring that preventive measures are effectively carried out.

Neural networks pipeline for quality management in IVF laboratory

This study introduces a novel neural network-based pipeline for predicting clinical pregnancy rates in IVF treatments, integrating both clinical and laboratory data. We developed a metamodel combining deep neural networks and Kolmogorov-Arnold networks, leveraging their complementary strengths to enhance predictive accuracy and interpretability. The metamodel achieved robust performance metrics after training and fitting on 11500 clinical cases: accuracy = 0.72, AUC = 0.75, F1 score = 0.60, and Matthews Correlation Coefficient of 0.42. According to morpho-kinetical embryo evaluation, our model’s PRC of 0.66 significantly improves over existing time-lapse systems for pregnancy prediction, demonstrating better handling of imbalanced clinical data. The metamodel’s calibration metrics (Brier score = 0.20, expected calibration error = 0.06, maximum calibration error = 0.12, Hosmer-Lemeshow test p-value = 0.06) indicate robust reliability in predicting clinical pregnancy outcomes. We validated the model’s reproducibility using an independent dataset of 665 treatment cycles, showing close alignment between predicted and actual pregnancy rates (58.9% vs. 59.1%). With the Bayesian method, we proposed a robust framework for integrating historical data with real-time predictions from neural networks, enabling a transition from retrospective to prospective analysis. Our approach extends beyond conventional embryo selection, incorporating post-analytical phase evaluation in the IVF laboratory. This comprehensive framework enables detailed analysis across different patient subpopulations and time periods, facilitating the identification of systemic issues and IVF protocol optimization. The model’s ability to track pregnancy probabilities over time and staff members allows for both outcome prediction and retrospective and prospective assessment of IVF treatment efficacy, providing a data-driven strategy for continuous improvement in assisted reproductive technology.

Typical Scenario Load Identification Based on Feature Fusion and Transfer Learning

Background: The electricity demand is continuously increasing. However, various institutions, enterprises, and individuals exhibit many irregularities in their electricity usage, leading to significant wastage of electricity. To achieve effective energy management, researchers are attempting to analyze and regulate users' electricity demands by monitoring their load usage through Non- Intrusive Load Monitoring (NILM) technology. The accuracy of load identification in this technology will greatly impact the results of load monitoring. Although there are currently many articles and patents related to NILM, they utilize a large amount of computational resources and require high sampling rates from devices, yet the results are still unsatisfactory. Therefore, it is necessary to improve the accuracy of load identification in data with relatively low sampling frequencies. Objective: To improve the accuracy of load identification with low sampling frequency data, this paper proposes a typical scenario load identification method based on feature fusion and transfer learning. Methods: This method adopts the fusion of current and power factor angles to provide abundant identification information for NILM, effectively reducing the situation of single-feature overlap of different loads. By inputting the fused feature data into GoogLeNet and utilizing transfer learning for training, not only is the accuracy improved, but also the training time and the requirement for the sampling rate of training data are greatly reduced. In addition, selecting typical scenario loads can monitor loads in a targeted manner, reduce the waste of computing resources caused by irrelevant loads, and more effectively guide electricity usage strategies. Results: The proposed load identification method was tested on the low sampling frequency dataset used in this paper. It achieved an overall load identification accuracy of 94.61% across three scenarios, improving accuracy by 3% to 7% compared to other models. Conclusion: The simulation results indicate that this method achieves high load identification accuracy at low sampling frequencies. It also exhibits good generalization ability. This method not only reduces the performance requirements for monitoring equipment but also enhances monitoring efficiency.

Data-driven system identification and model predictive control of pneumatic conveying using nonlinear dynamics analysis for optimised energy consumption

Pneumatic conveying systems provide secure transportation of particulate material and a dust-free environment. These systems face high energy consumption, material degradation, and pipeline blockages. This research presents an innovative solution by integrating nonlinear dynamics analysis of electrostatic sensor data, including chaos and recurrence quantification analysis, sparse identification of nonlinear dynamics with control (SINDYc) and model predictive control (MPC). The Lyapunov exponent, approximate entropy and recurrence rate of electrostatic sensor data reveal the chaotic nature of gas-solid flows. MPC framework was tailored for real-time optimisation of a pneumatic conveying system. SINDYc system models were developed using data collected from an open-loop control pneumatic conveying process to conduct MPC simulations and select an appropriate model for real-time control. This research illustrates the potential of integrating nonlinear dynamics analysis, SINDYc integrated and MPC for enhanced system performance, showcased a significant reduction in energy consumption without compromising the system's efficiency or reliability.

The state of individual participant data sharing for the highest-revenue medicines.

Amid growing emphasis from pharmaceutical companies, advocacy groups, and regulatory bodies for sharing of individual participant data, recent audits reveal limited sharing, particularly for high-revenue medicines. Therefore, this study aimed to assess the individual participant data-sharing eligibility of clinical trials supporting the Food and Drug Administration approval of the top 30 highest-revenue medicines for 2021. A cross-sectional analysis was conducted on 316 clinical trials supporting approval of the top 30 revenue-generating medicines of 2021. The study assessed whether these trials were eligible for individual participant data sharing, defined as being publicly listed on a data-sharing platform or confirmed by the trial sponsors as in scope for independent researcher individual participant data investigations. Information was gathered from various sources including ClinicalTrials.gov, the European Union Clinical Trials Register, and PubMed. Key factors such as the trial phase, completion dates, and the nature of the data-sharing process were also examined. Of the 316 trials, 201 (64%) were confirmed eligible for sharing, meaning they were either publicly listed on a data-sharing platform or confirmed by the trial sponsors as in scope for independent researcher individual participant data investigations. A total of 102 (32%) were confirmed ineligible, and for 13 (4%), the sponsor indicated that a full research proposal would be required to determine eligibility. The analysis also revealed a higher rate of individual participant data sharing among companies that utilized independent platforms, such as Vivli, for managing their individual participant data-sharing process. Trials not marked as completed had significantly lower eligibility for individual participant data sharing. This study highlights that a substantial portion of trials for top revenue-generating medicines are eligible for individual participant data sharing. However, challenges persist, particularly for trials that are marked as ongoing and for trials where the sharing processes are managed internally by pharmaceutical companies. Data-sharing rates could be improved by adopting open-access individual participant data-sharing models or using independent platforms. Standardizing policies to facilitate immediate individual participant data availability for approved medicines is necessary.

Data‐driven prediction of prolonged air leak after video‐assisted thoracoscopic surgery for lung cancer: Development and validation of machine‐learning‐based models using real‐world data through the ePath system

AbstractIntroductionThe reliability of data‐driven predictions in real‐world scenarios remains uncertain. This study aimed to develop and validate a machine‐learning‐based model for predicting clinical outcomes using real‐world data from an electronic clinical pathway (ePath) system.MethodsAll available data were collected from patients with lung cancer who underwent video‐assisted thoracoscopic surgery at two independent hospitals utilizing the ePath system. The primary clinical outcome of interest was prolonged air leak (PAL), defined as drainage removal more than 2 days post‐surgery. Data‐driven prediction models were developed in a cohort of 314 patients from a university hospital applying sparse linear regression models (least absolute shrinkage and selection operator, ridge, and elastic net) and decision tree ensemble models (random forest and extreme gradient boosting). Model performance was then validated in a cohort of 154 patients from a tertiary hospital using the area under the receiver operating characteristic curve (AUROC) and calibration plots.ResultsTo mitigate bias, variables with missing data related to PAL or those with high rates of missing data were excluded from the dataset. Fivefold cross‐validation indicated improved AUROCs when utilizing key variables, even post‐imputation of missing data. Dichotomizing continuous variables enhanced performance, particularly when fewer variables were employed in the decision tree ensemble models. Consequently, regression models incorporating seven key variables in complete case analysis demonstrated superior discriminatory ability for both internal (AUROCs: 0.77–0.84) and external cohorts (AUROCs: 0.75–0.84). These models exhibited satisfactory calibration in both cohorts.ConclusionsThe data‐driven prediction model implementing the ePath system exhibited adequate performance in predicting PAL post‐video‐assisted thoracoscopic surgery, optimizing variables and considering population characteristics in a real‐world setting.

Performance measurements of Total Body PET scanner simulations using the Geant4 toolkit

Total-Body PET (TB PET) is a novel imaging modality that can image the whole body in a single scan. It has increased sensitivity, improved spatial resolution, reduced imaging times compared to conventional clinical PET scanners, and has the potential to detect smaller lesions and multiorgan diseases. Commercially available TB PET scanners include the Siemens Biograph Vision Quadra [1] and the United Imaging uEXPLORER [2]. This study uses the Geant4 toolkit [3] to create a simulation model of two different TB PET detector geometries; Siemens Biograph Vision Quadra and uEXPLORER. Analysis of simulated data and performance measurements were carried out in Python. The study follows the NEMA NU-2 2018 standard [4]. The simulation was used to explore the effect of different scintillator crystals and evaluates the count rate performance and the sensitivity of the scanner. The simulated data was compared to published experimental data from Siemens and Explorer. Good agreement between the count rates of simulated and experimental data was observed for both geometries. The simulated Siemens Quadra measured a system sensitivity value of 178.6 cps/kBq showing a 2% difference compared to the experimental data of 175.3 cps/kBq for LSO crystals. Axial sensitivity profiles plotted also match well. The comparison of different scintillators showed that BGO performs best as it has the highest peak NECR, thus good signal-to-noise ratio. LSO and LYSO also perform well and their physical properties are similar. NaI and CsI exhibit the worst performance; with low true count rates and the random count rates increased almost linearly with activity concentration. Our simulations compared well against experimental data, allowing us to test further scintillator materials and radioisotopes. After implementing image reconstruction and relevant corrections, this simulation can be used for validation of various research ideas in the future. Please click on the 'PDF' for the full abstract!

NeRSI: Neural Implicit Representations for 5D Seismic Data Interpolation

Due to challenging field operations and resource constraints, seismic data acquisition often requires coping with missing traces. Interpolation algorithms are crucial for reconstructing these missing traces to enable improved subsurface analysis and interpretation. While deep learning has made exciting advances in seismic reconstruction, its focus has predominantly been on 2D and 3D datasets with relatively low rates of missing data. Five-dimensional (5D) seismic data reconstruction entails considering simultaneous sources and receivers deployed in areal arrays to solve the reconstruction problem. The latter offers greater data redundancy, which can be leveraged to enhance interpolation quality. Traditional 5D deep learning interpolation methods rely heavily on synthetic training pairs, posing challenges when applied to real-world data. This necessitates transfer learning techniques, which can be cumbersome. Addressing this, we introduce a self-supervised, coordinate-based deep interpolation algorithm that obviates the need for labeled data. Employing a multi-layer perceptron (MLP) network can effectively encode the continuous seismic wavefield inherent in 5D data. Once trained, the MLP can infer missing trace amplitudes from their coordinates. We contribute to boosting the MLP, enabling it to generate seismic profiles rather than single-point predictions. This enhancement significantly strengthens the model’s performance and efficiency. Moreover, we apply nuclear norm regularization to the output profiles, improving the reconstruction quality. The effectiveness of our algorithm is supported by both synthetic and field data experiments, demonstrating its superior reconstruction capabilities.

QM-ARC: QoS-aware Multi-tier Adaptive Cache Replacement Strategy

Distributed data-centric systems, such as Named Data Networking, utilize in-network caching to reduce application latency by buffering relevant data in high-speed memory. However, the significant increase in data traffic makes expanding memory capacity prohibitively expensive. To address this challenge, integrating technologies like non-volatile memory and high-speed solid-state drives with dynamic random-access memory can form a cost-effective multi-tier cache system. Additionally, most existing caching policies focus on categorizing data based on recency and frequency, overlooking the varying Quality-of-Service (QoS) requirements of applications and customers—a concept supported by Service Level Agreements in various service delivery models, particularly in Cloud computing. One of the most prominent algorithms in caching policy literature is the Adaptive Replacement Cache (ARC), that uses recency and frequency lists but does not account for QoS. In this paper, we propose a QoS-aware Multi-tier Adaptive Replacement Cache (QM-ARC) policy. QM-ARC extends ARC by incorporating QoS-based priorities between data applications and customers using a penalty concept borrowed from service-level management practices. QM-ARC is generic, applicable to any number of cache tiers, and can accommodate various penalty functions. Furthermore, we introduce a complementary feature for QM-ARC that employs Q-learning to dynamically adjust the sizes of the two ARC lists. Our solution, evaluated using both synthetic and real-world traces, demonstrates significant improvements in QoS compared to state-of-the-art methods by better considering priority levels. Results show that QM-ARC reduces penalties by up to 45% and increases the hit rate for high priority data by up to 84%, without negatively impacting the overall hit rate, which also increases by up to 61%.

Specific phobias in children with moderate to severe intellectual disabilities: SPIRIT, an adaptation and feasibility study.

There is a lack of interventions for specific phobia in children and adolescents with moderate to severe intellectual disabilities. The objectives were to: (a) develop an intervention for specific phobia, together with an intervention fidelity checklist and logic model, and evaluate candidate outcome measures, together with parents/carers and clinicians; (b) describe treatment as usual; (c) model the intervention to determine the acceptability and feasibility for all stakeholders, judge the appropriateness of outcome measures, explore recruitment pathways, and examine the feasibility and acceptability of consent and associated processes; and (d) describe factors that facilitate or challenge the intervention. Phase 1a: using consensus methods, an Intervention Development Group was established who met to develop the intervention, review candidate outcome measures and contribute to the development of the intervention fidelity checklists and logic model. Phase 1b: a national online survey was conducted with parents and professionals to describe treatment as usual. Phase 2: a single-group non-randomised feasibility study was designed to model the intervention and to test intervention feasibility and acceptability, outcome measures and aspects of the research process. Phase 2: participants were recruited from National Health Service community child learning disabilities teams and special schools in England. Treatment was delivered in the child learning disabilities teams. Children aged 5-15 years with moderate to severe learning disability and specific phobia, and their parents/carers. The SPIRIT intervention comprised two half-day workshops and eight support sessions plus treatment as usual. The feasibility and acceptability of the intervention and research processes, recruitment, outcome measure completion rates and acceptability, and intervention adherence. Parents completed all of the outcome measures, with very low rates of missing data. The recruitment of sites and participants was impacted by the COVID-19 pandemic. The intervention was successfully developed and modelled with 15 participants with moderate to severe learning disabilities and their parents. The intervention was judged to be feasible and acceptable by parents/carers and therapists. Parents/carers and therapists suggested minor intervention revisions. Randomisation was not modelled within this feasibility study, although the majority of parents and therapists indicated that this would be acceptable. The SPIRIT intervention and associated study processes were judged to be feasible and acceptable. The intervention requires minor revisions. The SPIRIT intervention should be tested further within a clinical trial. Current Controlled Trials ISRCTN34766613. This award was funded by the National Institute for Health and Care Research (NIHR) Health Technology Assessment programme (NIHR award ref: NIHR130177) and is published in full in Health Technology Assessment; Vol. 28, No. 64. See the NIHR Funding and Awards website for further award information.

Design of an FPGA-based short-circuit fault diagnosis algorithm for DC current-limiting fuses

Abstract In recent years, DC current-limiting fuses have played a crucial role in the comprehensive power systems of ships, rail transport, and new energy sectors, ensuring the safe and reliable operation of DC power systems. However, with the increase in the capacity of DC power systems, short-circuit faults can lead to a rise in short-circuit current at rates of up to 40 A/μs, with peak short-circuit currents reaching up to 105KA. This poses higher demands on the accuracy and speed of fuse diagnostics to interrupt short-circuit currents. To address this issue, this paper introduces a fault diagnostic algorithm based on FPGA that can calculate the rise rate of short-circuit currents in real-time: employing FPGA with its high integration, abundant logic resources, and parallel data processing capabilities as the core of the measurement and control system to enhance the speed of short-circuit fault diagnosis from a hardware perspective; replacing the common large current trip protection and DDL protection algorithms with the Newton interpolation algorithm. This improvement calculates the rise rate of each current data point instead of the average rise rate over a period, thus enhancing the speed and accuracy of short-circuit fault diagnosis from a software perspective. The paper also discusses the design of the system’s hardware and software and the construction of the experimental platform. Simulation and experimental results show that the fault diagnosis time of the algorithm has been reduced from 98μs to 36μs, with the numerical error in the short-circuit current rise rate within 2%, and a significant reduction in the peak value of short-circuit current, meeting performance requirements and effectively ensuring the safe and reliable operation of DC power systems.