Search

Background: Liver disease is an increasing cause of morbidity and mortality in the United Kingdom and can be challenging to live with in the advanced stages. There has been little research exploring the healthcare experiences of UK individuals with decompensated disease when the liver cannot carry out its functions properly. A PhD research project was developed with people who have liver disease to explore care experiences in decompensated advanced liver disease. Public involvement (PI) is an essential aspect of meaningful health research, and this paper reports on the progression of our PI approach in this ongoing study. Objective: To embed PI throughout the research project to ensure that the study is meaningful to individuals with liver disease and the people who support them. Methods: The research adopts a Constructivist Grounded Theory methodology to develop a theory of care experience. Various PI approaches were considered in developing the PI strategy for this qualitative study. Initially, Embedded consultation was the preferred model, which has evolved to include aspects of collaboration and coproduction. A PI group was set up to oversee the project through the national public engagement website VOICE, and reflections on PI from three members of the group are included in this paper to illuminate the PI process. Results: Six individuals with liver disease and three carers from across the United Kingdom are part of an ongoing PI group. Their role includes commenting on the findings of the systematic literature review for this project and contributing to decisions about recruitment, data collection and data analysis. Additionally, they had a direct impact on changing the focus of the research. The PI group will continue involvement until the completion of the project. Conclusion: Successfully embedding PI into doctoral research, as demonstrated in this project, requires commitment, planning and dedication to reciprocal working for the benefit of PI contributors as well as the research. This approach could be adopted by other postgraduate researchers. Patient or Public Contribution: This project is overseen by the PI group, whose contribution is described throughout, including reflections from three PI group members. [ABSTRACT FROM AUTHOR]

The Internet of Multimedia Things (IoMT) refers to a network of interconnected multimedia devices that communicate with each other over the Internet. Recently, smart healthcare has emerged as a significant application of the IoMT, particularly in the context of knowledge‐based learning systems. Smart healthcare systems leverage knowledge‐based learning to become more context‐aware, adaptable, and auditable while maintaining the ability to learn from historical data. In smart healthcare systems, devices capture images, such as X‐rays, Magnetic Resonance Imaging. The security and integrity of these images are crucial for the databases used in knowledge‐based learning systems to foster structured decision‐making and enhance the learning abilities of AI. Moreover, in knowledge‐driven systems, the storage and transmission of HD medical images exert a burden on the limited bandwidth of the communication channel, leading to data transmission delays. To address the security and latency concerns, this paper presents a lightweight medical image encryption scheme utilising bit‐plane decomposition and chaos theory. The results of the experiment yield entropy, energy, and correlation values of 7.999, 0.0156, and 0.0001, respectively. This validates the effectiveness of the encryption system proposed in this paper, which offers high‐quality encryption, a large key space, key sensitivity, and resistance to statistical attacks. [ABSTRACT FROM AUTHOR]

Accurate passenger flow forecasting is crucial in urban areas with growing transit demand. In this paper, we propose a method that combines advanced machine learning with rigorous time series analysis to improve prediction accuracy by integrating different datasets, providing a prescriptive example for passenger flow prediction in urban rail transit systems. The study employs advanced machine learning algorithms and proposes a novel prediction model that combines two-stage decomposition (seasonal and trend decomposition using LOESS–ensemble empirical mode decomposition (STL-EEMD)) and gated recurrent units. First, the STL decomposition algorithm is applied to break down the preprocessed data into trend terms, periodic terms, and irregular fluctuation terms. Then, the EEMD decomposition algorithm is employed to further decompose the irregular fluctuation terms, yielding multiple IMF components and residual residuals. Subsequently, the decomposed data from STL and EEMD are partitioned into training and test sets and normalized. The training set is utilized to train the model for optimal performance in predicting subway short-time passenger flow. The synthesis of these sophisticated methodologies serves to substantially enhance both the predictive precision and the broad applicability of the forecasting models. The efficacy of the proposed approach is rigorously evaluated through its application to empirical metro passenger flow datasets from diverse urban centers, demonstrating marked superiority in predictive performance over traditional forecasting methods. The insights gleaned from this study bear significant ramifications for the strategic planning and administration of public transportation infrastructures, potentially leading to more strategic resource allocation and an enhanced commuter experience. [ABSTRACT FROM AUTHOR]

It is a matter of debate whether the discipline independence in discipline formation narrows its interdisciplinarity. It is also less well understood how disruptive works emerge in investigative practice rather than a theory‐driven approach. Aggregation‐induced emission (AIE) is an atypical photophysical phenomenon, in which the whole (aggregate) is brighter than the sum of its parts (single molecule). Through measuring and computing the cognitive extent and evolution of research on AIE, including topics, epistemic‐social collaborative networks, interdisciplinarity, emergent concepts, core concept networks and knowledge flow, this study shows that a cross‐research scales concept and its practice can establish new bridges in the sciences and promote disruptive work. Focusing on mesoscale entities, scientists from many different branches of science are involved in theoretical research on mechanisms, as well as developing different AIE systems for applications. The data analysis in this study provides details showing how non‐reductionist concepts based on new scientific discoveries cross traditional disciplinary boundaries and aggregate interdisciplinary research. The emergence and evolution of the AIE field implies that scientists may be motivated to embrace nonreductionist ideas at different research scales, leading to a more permeable field boundary. [ABSTRACT FROM AUTHOR]

Suitable and effective matching area selection is crucial for gravity matching‐aided navigation. In this paper, an all‐field extended extremum algorithm based on an adaptive threshold (AT‐AEE) is proposed for matching area selection in the Arctic Sea. The gradient data is obtained by using the convolution of gravity reference graph data and all‐field extended extremum parameters. Then, the adaptive threshold method was employed to determine the optimal gradient threshold based on gravity anomaly data across various test areas. Data points with gradients exceeding the specified threshold are identified as local candidate points for matching areas. The test areas containing a certain proportion of local candidate points are designated as the suitable matching areas. Nine test areas in the Arctic Sea with different gravity change characteristics were chosen for simulation experiments to verify the performance of the proposed algorithm. Simulation experiments showed that superior navigation positioning results could be obtained in the matching areas selected by the AT‐AEE algorithm. Compared to traditional algorithm, the matching areas derived from the AT‐AEE algorithm performed with a better consistency in the gravity matching navigation results. In suitable matching areas with the proportion of local candidate points reaching 70%, the average positioning errors could be reduced to less than 1.5 n miles. [ABSTRACT FROM AUTHOR]

This paper proposes a deep learning method to predict the electromagnetic emission spectrum in the electromagnetic compatibility (EMC) test of aerospace products. A threshold‐based data decomposition method is used to propose the spike signal, reconstruct the original test data, and solve the contradiction between the overfitting and prediction accuracy of the deep learning method to deal with the EMC test spectrum. Using a long short‐term memory neural network architecture for predicting electromagnetic emission spectrum, the Bayesian optimization method is used to optimize the network hyperparameter, and the acquisition function is introduced into the loss function to improve the comprehensive training optimization of deep learning network. Apply the method to three numerical examples: signal line current conduction emission, power line voltage conduction emission, and electric field radiation emission. The analysis results indicate that at a 95% confidence level, the predicted electromagnetic emission spectrum is basically consistent with the test results. The prediction results can be used as the basis for EMC evaluation of aerospace electronic equipment. [ABSTRACT FROM AUTHOR]

Tuberculosis (TB) is an extremely contagious illness caused by Mycobacterium tuberculosis. Chest tuberculosis classification is conducted based on a deep convolutional neural network architecture. In this research, a pre‐trained network is utilized to demonstrate the advantage of using the oversampling technique on the classification of TB and compare results with recent research that used the same dataset. Therefore, the dataset consists of 3500 uninfected TB cases and 700 infected with TB. This paper circumvents the imbalance by using the oversampling technique in X‐ray TB images to be fed into several pre‐trained networks for TB classification. The oversampling technique is crucial in enhancing the performance of TB classification compared with other pre‐trained models reported here. Inceptionv3 shows a promising result compared to other pre‐trained models; it achieves 99.94% accuracy, 99.88% precision, 100% recall, and 99.94% F1‐Score. [ABSTRACT FROM AUTHOR]

The NW‐SE oriented Middle Niger Basin, which is a Campano‐Maastrichtian inland basin has been the subject of debate among geoscientists regarding its origin and development. This paper aims to unravel the basin's origin and evolution by using high‐resolution magnetic, gravity, and topographical data. The integration of aeromagnetic and gravity data provides a means to assess the influence of igneous intrusions during the basin's evolution. The study presents a new perspective on the origin of the basin, suggesting a rift origin where orthogonal extension played a crucial role in its evolution. The research also investigates the relationship between intrusive rocks and the formation of the basin, finding these rocks are solely located within the basement. The magnetic and gravity maps reveal anomalies associated with intrusive structures within the Precambrian crystalline basement. This is a common feature shared with rifted basins on a global scale. Plain Language Summary: The Middle Niger Basin, which spans about 350 km from Dekina in the SE to Shegwa in the NW, is unique among Nigerian basins due to the absence of a universally accepted description of its evolution within the geoscientific community. Magnetic data analysis reveals circular and elliptical anomalies that correspond to intrusive rocks. These anomalies are further enhanced by 2‐D forward modeling and gravity data inversion. Additionally, the SRTM data identifies lineaments that border the rifted basin, supporting the interpretations from the magnetic and gravity data. Our study suggests that fractures facilitated igneous intrusions, shaping the basin's evolution and providing new insights into its tectonic history. The new findings from this research provide valuable insights into the evolution of the Middle Niger Basin and may serve as a useful guide for future exploration initiatives. Key Points: The axis of the Middle Niger Basin is marked by structural discontinuitiesIntrusions in the basement of the basin were constrained by gravity inversionEvolution of the Middle Niger Basin is primarily controlled by crustal extension [ABSTRACT FROM AUTHOR]

Hybridization between organisms from evolutionarily distinct lineages can have profound consequences on organismal ecology, with cascading effects on fitness and evolution. Most studies of hybrid organisms have focused on organismal traits, for example, various aspects of morphology and physiology. However, with the recent emergence of holobiont theory, there has been growing interest in understanding how hybridization impacts and is impacted by host‐associated microbiomes. Better understanding of the interplay between host hybridization and host‐associated microbiomes has the potential to provide insight into both the roles of host‐associated microbiomes as dictators of host performance as well as the fundamental rules governing host‐associated microbiome assembly. Unfortunately, there is a current lack of frameworks for understanding the structure of host‐associated microbiomes of hybrid organisms.In this paper, we develop four conceptual models describing possible relationships between the host‐associated microbiomes of hybrids and their progenitor or 'parent' taxa. We then integrate these models into a quantitative '4H index' and present a new R package for calculation, visualization and analysis of this index.We demonstrate how the 4H index can be used to compare hybrid microbiomes across disparate plant and animal systems. Our analyses of these data sets show variation in the 4H index across systems based on host taxonomy, host site and microbial taxonomic group.Our four conceptual models, paired with our 4H index and associated visualization tools, facilitate comparison across hybrid systems. This, in turn, allows for systematic exploration of how different aspects of host hybridization impact the host‐associated microbiomes of hybrid organisms. [ABSTRACT FROM AUTHOR]

A major advance in global bathymetric observation occurred in 2018 with the launch of NASA's ICESat‐2 satellite, carrying a green‐wavelength, photon‐counting lidar, the Advanced Topographic Laser Altimeter System (ATLAS). Although bathymetric measurement was not initially a design goal for the mission, pre‐ and post‐launch studies revealed ATLAS's notable bathymetric mapping capability. ICESat‐2 bathymetry has been used to support a wide range of coastal and nearshore science objectives. However, analysis of ICESat‐2 bathymetry in numerous locations around the world revealed instances of missing or clipped bathymetry in areas where bathymetric measurement should be feasible. These missing data were due to the ATLAS receiver algorithms not being optimized for bathymetry capture. To address this, two updates have been made to ICESat‐2's receiver algorithm parameters with the goal of increasing the area for which ICESat‐2 can provide bathymetry. This paper details the parameter changes and presents the results of a two‐phased study designed to investigate ICESat‐2's bathymetry enhancements at both local and global scales. The results of both phases confirm that the new parameters achieved the intended goal of increasing the amount of bathymetry provided by ICESat‐2. The site‐specific phase demonstrates the ability to fill critical bathymetric data gaps in open ocean and coastal settings. The global analysis shows that the area of potential bathymetry approximately doubled, with 6.1 million km2 of new area in which bathymetric measurements may be feasible. These enhancements are anticipated to facilitate a range of science objectives and close the gap between ICESat‐2 bathymetry and offshore sonar data. Key Points: Recent updates to ICESat‐2's receiver algorithm parameters have boosted the bathymetric measurement capabilitiesThe new updates allow ICESat‐2 to consistently measure bathymetry up to 41 m deepThe updates allow ICESat‐2 to potentially measure over 6.1 million km2 of new bathymetry in both coastal and ocean settings [ABSTRACT FROM AUTHOR]

Introduction: Telepractice service delivery of allied health interventions to people with disability can potentially reduce access barriers and improve service equity. However, questions remain regarding telepractice functionality for people with disability. This study addressed questions related to how allied health clinicians and managers perceive telepractice as functioning in the provision of therapy services to people with disability. Methods: Thirteen interviews of allied health clinicians and managers from across Australia were conducted between 21 November and 22 February via MS teams. Qualitative methodology and critical realist theoretical paradigm underpin the study. Data analysis was completed using a reflective thematic analysis method and five themes were generated and described utilising an analytic metaphor. Results: The study themes were described in relation to a shopping for shoes analytic metaphor and the five themes included (1) a shoe for every foot, (2) planned purchases, (3) shoe on the other foot, (4) you need both shoes and (5) help choosing their shoes. In summary, the function of telepractice fits differently for each individual, similar to pairs of shoes. Conclusions: Telepractice has its own strengths and weaknesses and isn't a direct substitute for in‐person sessions, much like left and right shoes are similar but not the same. The results support participant perceptions that telepractice functions best as an adjunct to in‐person sessions through a flexible hybrid delivery model in the provision of therapy services to people with a disability. A strategy for improving perceived usefulness may involve positioning telepractice as unique with strengths and weaknesses, not replacing in‐person care. Patient or Public Contribution: The paper forms part of a larger codesign process which included customer and carer participants throughout the design and planning of the project, inclusion of a peer researcher, and the selection of the analytic metaphor including in the findings of this article production. [ABSTRACT FROM AUTHOR]

With the promotion and development of oversized data technology, many data analysis platforms based on super large data storage and computing frameworks have emerged in the industry. While the platforms with oversized economic data analysis combined with machine learning models are still relatively lacking. And oversized data also brings a new problem, that is the security of economic development. It is an important and difficult task to analyse and detect risks from oversized economic data. Based on machine learning, data analysis, economic market and other multidisciplinary fields, this paper proposes a machine learning method, which is a genetic algorithm (GA) based feature selection method: FSGA. This method abstracts every possible feature selection result into an individual in GA, generates a population through genetic operation, and measures the merits of the individual through fitness. In addition, this paper has conducted multitudinous simulation experiments on the GA‐based FSGA method and the traditional LSTM data analysis method respectively. The accuracy rate and other indicators are obtained by comparing the training. The experimental results show that the GA‐based FSGA machine learning method has higher prediction accuracy when analysing oversized economic data. And it is practical to accelerate the development of digital economy. [ABSTRACT FROM AUTHOR]

Aim: The aim of this research is to identify the main approaches and domains of palliative care quality assessment through three questionnaires used for this purpose. Design: Systematic review. Methods: The proposed analysis process consists of three stages from 2000 to 2020: (i) massive literature search, (ii) text mining and (iii) systematic reviews carried out on the QLQ C30, Zarit Burden Interview and FAMCARE questionnaires. The Preferred Reporting Items for Systematic Reviews (PRISMA‐P) have guided our research. Results: Sixteen papers were included in our study. The main findings have been summarised using a descriptive narrative synthesis approach. Systematic reviews evidenced that such tools present variable factor structures or latent domains. The results obtained are generally representative of the evidence supporting the factor structure of the QLQ‐C30 in the general cancer population. The factor structure of the Zarit Burden Interview remains ambiguous, although the idea of a unifactorial structure predominates. In the case of FAMCARE, most of the factor structures differ from the initial proposal of Kristjanson. The categorisation of the main subjective assessment approaches could be useful for the construction of a coherent system of indicators to be used in nursing practice. For its part, the variability in the latent dimensionality of the questionnaires analysed could be due to: (i) the characteristics of the sample, (ii) the population studied, (iii) cross‐cultural variability, (iv) the design of the questionnaire and (v) the analysis techniques employed. [ABSTRACT FROM AUTHOR]

This paper introduces a comprehensive performance evaluation algorithm explicitly designed for secondary equipment in substations, specifically targeting the relay protection system. In contrast to the current evaluation systems, this novel method navigates the complex internal interconnections and mechanisms inherent within secondary system equipment. Such complications have previously impeded the accuracy and breadth of evaluations, thereby limiting the degree of precision and innovation attainable within substations. The proposed approach effectively integrates the improved Analytic Hierarchy Process entropy weight (IAHP‐EW) method with the Learning Vector Quantization (LVQ) neural network. Initially, the IAHP‐EW method identified the comprehensive evaluation indicators and their corresponding weights for relay protection devices. Following weight allocation, these evaluation indicators are scrutinized and computed utilizing the multivariate regression analysis algorithm, resulting in performance evaluation outcomes for the relay protection system. These outcomes are subsequently classified and utilized in training the LVQ neural network, promoting the network's capacity to autonomously evaluate the performance status of the relay protection system. To corroborate the viability and effectiveness of this proposed performance evaluation and prediction algorithm, empirical operating data from a local substation is used. The results suggest a significant improvement in the evaluation accuracy of secondary equipment performance, indicating potential for practical application and a valuable contribution to the field through the introduction of a novel approach to performance assessment of substation relay protection systems. [ABSTRACT FROM AUTHOR]

RETRACTION: G. G. Deme, X. Liang, J. O. Okoro, P. Bhattarai, B. Sun, Y. D. Malann, and R. A. Martin, "Female Lizards (Eremias Argus) Reverse Bergmann's Rule Across Altitude," Ecology and Evolution 13, no. 8 (2023): e10393, https://doi.org/10.1002/ece3.10393. The above article, published online on 07 August 2023 in Wiley Online Library (wileyonlinelibrary.com), has been retracted by agreement between the authors; the journal Editors‐in‐Chief, Allen Moore, Andrew Beckerman, Gareth Jenkins and Marcus Lashley; and John Wiley & Sons Ltd. The authors asked to retract the article as they found significant errors in the dataset used for the analyses presented in the article and publicly archived on Dryad (https://doi.org/10.5061/dryad.41ns1rnkm). The dataset contains the same value of female snout‐vent length (53.21mm) repeated across 138 rows for two populations (Gonghe and Erdos). Following the authors' request to retract, further concerns were raised regarding additional anomalies in the dataset and inconsistencies in the used models and the presented data analysis, therefore the results and the conclusions of the paper are not supported. [ABSTRACT FROM AUTHOR]

Camera traps became the main observational method of a myriad of species over large areas. Data sets from camera traps can be used to describe the patterns and monitor the occupancy, abundance, and richness of wildlife, essential information for conservation in times of rapid climate and land‐cover changes. Habitat loss and poaching are responsible for historical population losses of mammals in the Atlantic Forest biodiversity hotspot, especially for medium to large‐sized species. Here we present a data set from camera trap surveys of medium to large‐sized native mammals (>1 kg) across the Atlantic Forest. We compiled data from 5380 ground‐level camera trap deployments in 3046 locations, from 2004 to 2020, resulting in 43,068 records of 58 species. These data add to existing data sets of mammals in the Atlantic Forest by including dates of camera operation needed for analyses dealing with imperfect detection. We also included, when available, information on important predictors of detection, namely the camera brand and model, use of bait, and obstruction of camera viewshed that can be measured from example pictures at each camera location. Besides its application in studies on the patterns and mechanisms behind occupancy, relative abundance, richness, and detection, the data set presented here can be used to study species' daily activity patterns, activity levels, and spatiotemporal interactions between species. Moreover, data can be used combined with other data sources in the multiple and expanding uses of integrated population modeling. An R script is available to view summaries of the data set. We expect that this data set will be used to advance the knowledge of mammal assemblages and to inform evidence‐based solutions for the conservation of the Atlantic Forest. The data are not copyright restricted; please cite this paper when using the data. [ABSTRACT FROM AUTHOR]

Knowledge and innovation have become a significant source of modern regional development. The paper aims to study the spatial association of innovation and model innovation, besides critiquing regional innovation policy, using the regional knowledge production function approach. At the level of the Egyptian governorates, the number of patent applications has been used as a proxy for innovation. The study uses cross-sectional data for patents in 2018 with lagged time variables for knowledge input factors. The study contributes towards applying the knowledge production function at the level of the Egyptian governorates for the first time. Exploratory spatial data analysis (ESDA) and ordinary least squares (OLS) regression are used, using the knowledge sources and localized characteristics. Our analysis found that research and development activities for academic institutions are associated with innovation outputs. In the context of the Egyptian regional innovation system, the contribution of private research activities is perceived to be negligible, and the innovation outputs are highly associated with the spatial association of the Egyptian governorates. [ABSTRACT FROM AUTHOR]

Superconducting undulators (SCUs) can offer a much higher on-axis undulator field than state-of-the-art cryogenic permanent-magnet undulators with the same period and vacuum gap. The development of shorter-period and high-field SCUs would allow the free-electron laser and synchrotron radiation source community to reduce both the length of undulators and the dimensions of the accelerator. Magnetic measurements are essential for characterizing the magnetic field quality of undulators for operation in a modern light source. Hall probe scanning is so far the most mature technique for local field characterization of undulators. This article focuses on the systematic error caused by thermal contraction that influences Hall probe measurements carried out in a liquid helium cryostat. A novel procedure, based on the redundant measurement of the magnetic field using multiple Hall probes at known relative distance, is introduced for the correction of such systematic error. [ABSTRACT FROM AUTHOR]

This research paper aims at examining the effect of three green capabilities, namely, green entrepreneurial orientation, green learning orientation, and green absorptive capacity, on triple bottom line firm performance under the natural resource‐based view (NRBV) lens. Data were collected through self‐administered surveys distributed among large firms in Egypt that have sustainability initiatives. A total of 211 responses were received, and data analysis was conducted using SMARTPLS 4.0. Results indicated an insignificant direct relationship between green entrepreneurial orientation and triple bottom line firm performance, yet results showed a positive indirect relationship between green entrepreneurial orientation and triple bottom line firm performance in the presence of green learning orientation as a mediator. Moreover, results indicated a positive indirect relationship between green entrepreneurial orientation and triple bottom line firm performance in the presence of green absorptive capacity as a mediator. Last but not the least, the relationship between green entrepreneurial orientation and triple bottom line firm performance was found to be positive when sequentially mediated by green learning orientation and green absorptive capacity. [ABSTRACT FROM AUTHOR]

StreamSAXS is a Python‐based small‐ and wide‐angle X‐ray scattering (SAXS/WAXS) data analysis workflow platform with graphical user interface (GUI). It aims to provide an interactive and user‐friendly tool for analysis of both batch data files and real‐time data streams. Users can easily create customizable workflows through the GUI to meet their specific needs. One characteristic of StreamSAXS is its plug‐in framework, which enables developers to extend the built‐in workflow tasks. Another feature is the support for both already acquired and real‐time data sources, allowing StreamSAXS to function as an offline analysis platform or be integrated into large‐scale acquisition systems for end‐to‐end data management. This paper presents the core design of StreamSAXS and provides user cases demonstrating its utilization for SAXS/WAXS data analysis in offline and online scenarios. [ABSTRACT FROM AUTHOR]

The worldwide occurrence of wind droughts challenges the balance of power systems between energy production and consumption. Expanding inter‐day energy storage serves as a strategic solution, yet optimizing its capacity depends on accurately modeling future renewable energy uncertainties to avoid over‐ or under‐investment. Existing approaches that use the historical extreme scenario set (HESS) to represent future conditions are contentious due to potential inadequacies in forecasting future extreme scenarios (ESs), including those on a decadal or centennial scale. This study addresses the issue by proposing an advanced energy storage expansion framework that leverages Extreme Value Theory (EVT) and a novel Deep Generative Model, namely the Diffusion Model. To model the extremes in a principled way, this work leverages EVT to establish a severity‐probability mapping for wind droughts, guiding the training process of the Diffusion Model. This model excels in generating ESs that accurately reflect the distribution of real‐world extremes, thereby significantly enhancing the predictive capacity of HESS. Case studies on a real‐world power system confirm the method's capacity to generate high‐quality ESs, encompassing the most severe historical wind droughts not included in the training dataset, thereby facilitating resilient energy storage expansion against unforeseen extremes. [ABSTRACT FROM AUTHOR]

Lunar seismic data are essential for understanding the Moon's internal structure and geological history. After five decades, the Apollo data set remains the only available one and continues to offer significant value for current and future lunar seismic data analyses. Recent advances in artificial intelligence for seismology have identified seismic signals that were previously unrecognized. In our study, we utilized deep learning for unsupervised clustering of lunar seismograms, leading to the discovery of a new type of long‐period lunar seismic signal that existed every lunar night from 1969 to 1976. We then conducted a thorough analysis covering the timing, frequency, polarization, and temporal distribution characteristics of this signal to study its properties, occurrence, and probable origins. This signal has a physical cause instead of artificial, such as voltage changes, according to its amplitudes during peaked and flat modes, as well as the digital converter status. Based on its relation to the lunar temperature and documents on Apollo instruments, we conclude that this signal is likely induced by the cyclic heater, with several unresolved questions that might challenge our hypothesis. Excluding interference from this newly identified signal is crucial when analyzing lunar seismic data, particularly in detecting lunar free oscillations. Our research introduced a new method for discovering new types of planetary seismic signals and helped advance our understanding of Apollo seismic data. Furthermore, the discovery of this signal holds valuable implications for the design of future planetary seismometers to avoid encountering similar issues. Plain Language Summary: For over 50 years, our only available lunar seismic data have come from the Apollo missions. These old but valuable data are still valid today, especially with artificial intelligence technology. We applied unsupervised deep learning techniques to study these lunar seismic data. Our study led to an exciting discovery: a new kind of signal in the lunar seismic data that happened every night on the Moon from 1969 to 1976. We examined this signal's timing, frequency, and other features to understand its nature and possible causes. Our studies show that this signal might not be a natural physical phenomenon but related to a cyclic heater on the lunar instrument. This discovery is significant as it helps us to avoid misinterpreting this new signal as other valuable signals, such as free oscillations, which can be used to investigate the Moon's internal structure. Our work sheds new light on the old Apollo lunar seismic data and can advise on the design of future lunar missions to avoid similar issues. Key Points: A new signal is discovered from Apollo seismograms by unsupervised learning, with a period of ∼500 s nightly from 1969 to 1976The signal matches peak/flat mode response shifts and relates to lunar temperature, likely caused by the cyclic operation of the heaterOur study presents a powerful method for discovering new seismic signals, providing insights into the design of future lunar seismometers [ABSTRACT FROM AUTHOR]

Aims: To (a) compare student nurses' health status and levels of ergonomics knowledge and awareness to those of first‐year nursing staff and (b) investigate the association between nurses' ergonomic compliance and health status with their educational preparedness. Design: This cohort study compared ergonomics awareness and knowledge, and health status of nurses when they were students and after their first‐year working in medical facilities. Methods: In total, 133 4th‐year student nurses completed a survey; 45 of them completed a second survey after working 1 year in a medical facility. Their health status was compared over time using repeated‐measures ANOVA. Correlation tests were used to analyse associations between ergonomics knowledge and awareness, health status, demographic variables and educational preparedness. Results: Respondents in both surveys displayed weak knowledge of ergonomic principles significantly associated with increased pain intensity and educational preparedness. Patient contribution: Ergonomics training should be expanded during nursing studies and first‐year training to prevent long‐term health disorders. [ABSTRACT FROM AUTHOR]

Over the last two decades, great efforts and investments have been made in Ethiopia to ensure that all children have equal access to nutrition services in health facilities. While quality health systems are a prerequisite for quality nutrition services, little attention has been given to the evaluation of the supply and delivery services. The purpose of the study was to evaluate the coverage and quality of the nutrition‐specific interventions delivered through the health system. Using an end‐user monitoring (EUM) system, we monitored the delivery of nutrition‐specific interventions in 500 districts, having 2514 health facilities distributed throughout Ethiopia. Data were collected through third‐party monitors between August 2020 and 2021. Roughly 90% of health facilities were performing severe acute malnutrition management in line with the national guideline/protocol, and 2/3 of the assessed facilities were delivering iron and folic acid, vitamin A supplementation and deworming. A third of the messages on AMIYCN were retained by beneficiaries. Warehouse conditions were good in 64.3% of the facilities, but only 22% had good recording practices and about half had problems related to the quality and availability of nutrition supplies. Most beneficiaries were satisfied with the nutrition supplies and service delivered at the health facility level. This study also suggests the relevance of an EUM system to assess the quality of nutrition service delivery and its related supply management, as well as to improve the implementation of nutrition interventions as a decision‐making tool. Key messages: The main bottleneck identified in supply management was the recording system, while roughly 65% of warehouses are in good condition to store nutrition supplies at the health facility level.Roughly 90% of health facilities were performing severe acute malnutrition management in line with the national guideline, while 2/3 of the health facilities are delivering iron and folic acid, vitamin A supplementation and deworming.The knowledge and practices of the HW were above average and only a third of the messages delivered were retained by beneficiariesAlthough messages are being well delivered once counselling is implemented, many opportunities (contact points) are missed to deliver nutrition counselling. [ABSTRACT FROM AUTHOR]

Precisely interpreting frequency response analysis (FRA) curves is crucial when investigating mechanical malfunctions in power transformer windings (TWs). However, many conventional explanations of FRA features cannot be validated by an equivalent circuit (EC) that represents winding structures using resistance, inductance, and capacitance components in a ladder network. Incorrect interpretation can lead to misdiagnosis and confusion in asset management. Previous ECs are often proposed by analysing winding structures without rigorous verification compared to the corresponding FRA data. The specific EC is acquired using the transfer function (TF) derived from the measured FRA data. This allows for the establishment of the relationship between TW FRA curves, TF equations, and EC topologies. The precise interpretation of FRA curves can be achieved by closely observing the FRA curves created from TFs and ECs. The remarkable similarity between the measured and modelled FRA curves verifies the authenticity of the derived ECs. This significant achievement clarifies many misunderstandings regarding FRA and represents a substantial advancement in FRA technology. [ABSTRACT FROM AUTHOR]

Responsive feeding serves as an important protective factor for infant growth and overall health development. This study based on self‐determination theory (SDT) aimed to assess the effects of a responsive breastfeeding (RBF) intervention programme on maternal breastfeeding and infant growth and development. A total of 110 mother–infant pairs were recruited and randomly divided into an intervention group (n = 55) and a control group (n = 55). The primary outcomes were breastfeeding motivation score, breastfeeding self‐efficacy (BSE) and exclusive breastfeeding rate; the secondary outcomes were infant physical development at 6 weeks and 3 months. A repeated measures ANOVA indicated that the intervention group had significantly higher Enjoyment scores compared to the control group at three time points: at discharge (MD: 5.28; 95% CI: 3.68 to 6.89; p < 0.001), 6 weeks post‐partum (MD: 5.06; 95% CI: 3.80 to 6.31; p < 0.001) and 3 months post‐partum (MD: 5.24; 95% CI: 4.12 to 6.35; p < 0.001). Similarly, the intervention group reported significantly higher connection and mother's self‐perception scores at discharge (MD: 4.31; 95% CI: 3.07 to 5.56; p < 0.001), 6 weeks post‐partum (MD: 4.69; 95% CI: 3.71 to 5.68; p < 0.001) and 3 months post‐partum (MD: 4.93; 95% CI: 4.14 to 5.72; p < 0.001), compared to the control group. In contrast, the pressure from significant others scores were higher in the control group relative to the intervention group at discharge (MD: −2.09; 95% CI: −2.88 to −1.31; p < 0.001), 6 weeks post‐partum (MD: −4.35; 95% CI: −5.20 to −3.49; p < 0.001) and 3 months (MD: −4.89; 95% CI: −5.70 to −4.08; p < 0.001). Finally, the intervention group also reported higher Instrumental Needs scores at all three time points: at discharge (MD: 1.96; 95% CI: 1.35 to 2.58; p < 0.001), 6 weeks post‐partum (MD: 3.58; 95% CI: 3.05 to 4.11; p < 0.001) and 3 months post‐partum (MD: 1.18; 95% CI: 0.68 to 1.69; p < 0.001). BSE scores were significantly higher in the intervention group compared to the control group at discharge (MD: 14.29; 95% CI: 10.38 to 18.21; p < 0.001), 6 weeks post‐partum (MD: 14.04; 95% CI: 11.05 to 17.02; p < 0.001) and 3 months post‐partum (MD: 6.80; 95% CI: 4.66 to 8.94; p < 0.001). The rates of exclusive breastfeeding were higher in the intervention group than in the control group at each stage of the intervention (p < 0.01). At 6 weeks post‐partum, the intervention group's infants showed slower weight (t = −0.90, p = 0.371) and length (t = −0.69, p = 0.495) growth compared to the control group, though not significantly. By 3 months post‐partum, there was a significant difference in both weight (t = −3.46, p = 0.001) and length (t = −2.95, p = 0.004) between the groups. The findings in this study suggest that the RBF intervention programme based on SDT may be effective in improving mothers' motivation to breastfeed, building breastfeeding self‐confidence and increasing the rate of exclusive breastfeeding. The effects of the intervention on infant physical development will need to be verified with longer follow‐up in future research. Key messages: This study validated the application of responsive breastfeeding (RBF) intervention during the exclusive breastfeeding phase of Chinese infants.The RBF intervention may help to increase mothers' level of autonomous motivation for breastfeeding, enhance breastfeeding self‐efficacy and increase exclusive breastfeedingrates. The intervention had no significant effect on the weight and length of 6 week old infants but significantly impacted 3monthold infants, with slower growth observed in the intervention group compared to the control group.Future intervention programmes shouldinclude extended follow‐up periods to investigate the long‐term effects of RBF on infant growth and development. [ABSTRACT FROM AUTHOR]

Poor diet quality (diet diversity and animal‐source food [ASF] consumption) during childhood negatively affects growth, development, behaviour and physiologic function in later life. Relatively less is known about the impact of poor diet on the growth of school‐age children compared to children <5 years of age, especially in low/middle‐income countries. A better understanding of delivery strategies for effective interventions to improve diet and hence growth in school‐age children is needed. A 36‐month longitudinal controlled impact evaluation in rural Nepal assessed the nutrition and growth of children <5 years of age in families assigned via community clusters to full package intervention (community development, training in nutrition [during pregnancy and for children <5 years] and livestock husbandry), partial package (training only) or control (no inputs). Concurrent data were collected prospectively (baseline plus additional four rounds) on school‐age children (5–8 years at baseline) in these households; the present study analysed findings in the cohort of school‐age children seen at all five study visits (n = 341). Diet quality improved more in the full package school‐age children compared to those in partial package or control households. full package children consumed more ASF (β +0.40 [CI 0.07,0.73], p < 0.05), more diverse diets (β +0.93 [CI 0.55,1.31], p < 0.001) and had better head circumference z‐scores (β +0.21 [CI 0.07,0.35], p < 0.01) than control children. In conclusion, a multi‐sectoral community development intervention was associated with improvements in diet and growth of school‐age children in rural Nepal even though the intervention focused on the diet of children <5 years of age. The diet and growth of school‐age children can be favourably influenced by community‐level interventions, even indirectly. Key messages: Relatively less is known about the factors that influence diet quality and growth in school‐age children compared to younger children, especially in low‐ and middle‐income countries.Better diet quality and growth were found among school‐age children whose families participated in an impact study multi‐sectoral intervention compared to children in control families or those whose families only received nutrition training.Diet and growth of school‐age children can be favourably influenced by community‐level interventions. [ABSTRACT FROM AUTHOR]

Objective: We aimed to examine the factors associated with treatment outcomes in patients with Alzheimer's disease (AD) after 1 year of acetylcholinesterase inhibitors (AChEI) treatment. Method: We obtained electronic medical records from a medical center in Southern Taiwan between January 2015 and September 2021. Participants aged ≥60 who were newly diagnosed with AD and had been prescribed AChEIs were included. Cognitive assessments were performed before the AChEIs were prescribed and at the 1 year follow‐up. Cognition progressors were defined as a Mini‐Mental State Examination decline of >3 or a Clinical Dementia Rating decline of ≥1 after 1 year of AChEI treatment. The relationship between the baseline characteristics and cognitive status after follow‐up was investigated using logistic regression analysis after adjusting for potential confounders. Results: A total of 1370 patients were included in our study (mean age, 79.86 ± 8.14 years). After adjustment, the body mass index (BMI) was found to be significantly lower in the progressor group [adjusted odds ratio (AOR): 0.970, 95% confidence intervals (95% CIs): 0.943 to 0.997, P = 0.033]. The usage of antipsychotics was significantly higher in the progressor group (AOR: 1.599, 95% CIs: 1.202 to 2.202, P = 0.001). The usage of benzodiazepine receptor agonists also tended to be significantly higher in the progressor group (AOR: 1.290, 95% CIs: 0.996 to 1.697, p = 0.054). Conclusion: These results suggest that patients with AD who receive 1 year of AChEI treatment and have a lower BMI or concurrent treatment with antipsychotics and benzodiazepine receptor agonists are more likely to suffer from cognitive decline. [ABSTRACT FROM AUTHOR]

The epidemic characters of Omicron (e.g. large‐scale transmission) are significantly different from the initial variants of COVID‐19. The data generated by large‐scale transmission is important to predict the trend of epidemic characters. However, the results of current prediction models are inaccurate since they are not closely combined with the actual situation of Omicron transmission. In consequence, these inaccurate results have negative impacts on the process of the manufacturing and the service industry, for example, the production of masks and the recovery of the tourism industry. The authors have studied the epidemic characters in two ways, that is, investigation and prediction. First, a large amount of data is collected by utilising the Baidu index and conduct questionnaire survey concerning epidemic characters. Second, the β‐SEIDR model is established, where the population is classified as Susceptible, Exposed, Infected, Dead and β‐Recovered persons, to intelligently predict the epidemic characters of COVID‐19. Note that β‐Recovered persons denote that the Recovered persons may become Susceptible persons with probability β. The simulation results show that the model can accurately predict the epidemic characters. [ABSTRACT FROM AUTHOR]

A new technique is proposed for loss of field (LOF) detection in synchronous generators (SGs). The proposed scheme employs the variations of SG angular velocity ∆ωφ and load angle ∆δφ to reach this goal, while the mentioned parameters are estimated through air gap flux estimation. The proposed scheme performance is evaluated using realistic simulation case studies based on IEEE C. 37. 102 standard compared to the conventional impedance‐based one. The obtained results show that the presented scheme excels the conventional scheme from speed and security viewpoints. Moreover, the estimation procedure of ∆ωφ and ∆δφ is experimentally validated by using a 10 kVA SG and the results demonstrate that the proposed technique can be easily set and implemented. [ABSTRACT FROM AUTHOR]

Aiming at the problems of large consumption of computational resources and insufficient data feature extraction in the current partial discharge fault identification process of GIS equipment, a partial discharge fault identification method of GIS equipment based on improved deep learning is proposed. Firstly, the audio information of GIS equipment is filtered by a simple power normalised cepstral coefficient (SPNCC). Secondly, the spatial correlation between audio data streams is obtained by a convolutional neural network, the temporal correlation of audio is obtained and the next time slice data stream is predicted by using bi‐directional long short‐term memory (BiLSTM) network, and the attention mechanism is designed to extract deeper data features. Finally, the partial discharge fault identification model of GIS equipment based on improved SPNCC‐CNN‐BiLSTM‐Multi‐att is established, which improves the accuracy of the partial discharge identification method of GIS equipment. Experiments show that when the number of iterations is 100, the accuracy, recall, and F1 value of the proposed GIS equipment partial discharge fault recognition method on the dataset are 0.876, 0.812, and 0.843, respectively. [ABSTRACT FROM AUTHOR]

Codon optimality is a major determinant of mRNA translation and degradation rates. However, whether and through which mechanisms its effects are regulated remains poorly understood. Here we show that codon optimality associates with up to 2-fold change in mRNA stability variations between human tissues, and that its effect is attenuated in tissues with high energy metabolism and amplifies with age. Mathematical modeling and perturbation data through oxygen deprivation and ATP synthesis inhibition reveal that cellular energy variations non-uniformly alter the effect of codon usage. This new mode of codon effect regulation, independent of tRNA regulation, provides a fundamental mechanistic link between cellular energy metabolism and eukaryotic gene expression. Synopsis: Analysis of GTEx data and perturbation experiments in yeast show that cellular energy regulates the effect of optimal codon usage on mRNA stability. Codon optimality matters more in conditions of scarcer energy, such as tissues with low mitochondrial activity, older age, oxygen deprivation, or exposure to specific drugs. This effect can explain up to 2-fold variation in mRNA stability between human tissues and is reflected in the codon usage of tissue-specific cassette exons. ATP-inhibition experiments demonstrate a causal effect. While the underpinning mechanism is unknown, these observations support the independence of this mechanism from tRNA abundance regulation. Analysis of GTEx data and perturbation experiments in yeast show that cellular energy regulates the effect of optimal codon usage on mRNA stability. [ABSTRACT FROM AUTHOR]

Noncompartmental analysis (NCA) is a model‐independent approach for assessing pharmacokinetics (PKs). Although the existing NCA algorithms are very well‐established and widely utilized, they suffer from low accuracies in the setting of sparse PK samples. In response, we developed Deep‐NCA, a deep learning (DL) model to improve the prediction of key noncompartmental PK parameters. Our methodology utilizes synthetic PK data for model training and uses an innovative patient‐specific normalization method for data preprocessing. Deep‐NCA demonstrated adequate performance across six previously unseen simulated drugs under multiple dosing, showcasing effective generalization. Compared to traditional NCA, Deep‐NCA exhibited superior performance for sparse PK data. This study advances the application of DL to PK studies and introduces an effective method for handling sparse PK data. With further validation and refinement, Deep‐NCA could significantly enhance the efficiency of drug development by providing more accurate NCA estimates while requiring fewer PK samples. [ABSTRACT FROM AUTHOR]

Short‐term load forecasting is the basis of power system operation and analysis and is of great significance for the stable operation of power systems. To solve the problems of insufficient mining of the intrinsic information of load data, randomness, and non‐linearity, and to improve the accuracy of load prediction, the authors propose a short‐term power load prediction model based on empirical wavelet transform (EWT) decomposition and the temporal convolutional network (TCN). First, the Pearson coefficient is used to eliminate the less relevant influencing factors to reduce the complexity of the model. Then, the EWT algorithm is used to decompose the original load signal to fully exploit the load data time domain and frequency domain information, while solving the problems of non‐linearity and randomness. Finally, the decomposed subsequences are input to the TCN network for prediction superposition to obtain the final results. The experimental results show that, compared with the single models TCN, gated recurrent units (GRU), and long short‐term memory (LSTM), and the hybrid models EWT‐GRU, EWT‐LSTM, and VMD‐TCN, the R2 of the short‐term power load forecasting model based on EWT‐TCN is improved by 0.2099%, 0.2519%, 0.3453%, 0.0334%, 0.1766%, and 0.1228%, respectively. [ABSTRACT FROM AUTHOR]

Clustering algorithm is the primary technology used in target clustering and group status analysis which are key features of the Unmanned Aerial Vehicles (UAVs) control system. Due to variable application environment, the stability of the algorithm in the UAVs control system needs to be considered. K‐means clustering is a widely used method in intelligent systems. However, K‐means algorithm is susceptible to the local optimum due to the influence of the initial centroid. For this problem, the predecessors have proposed various effective solutions. These algorithms perform better on real and large‐scale datasets, but they are unable to achieve optimum results with unbalanced datasets. Herein, a simpler and more effective algorithm for seed initialization is proposed, it has a better accuracy rate than the alternative algorithms.Moreover, after running tests multiple times with each algorithm independently, it has the highest stability and the lowest overall volatility. With unbalanced datasets, the proposed algorithm performs significantly better than several other algorithms and therefore can solve the problems that other algorithms have with unbalanced datasets. [ABSTRACT FROM AUTHOR]

Aim: The aim of this study was to: (1) use cognitive task analysis to describe final year nursing students situation awareness in recognising, responding and escalating care of deteriorating patients in ward settings; and (2) make recommendations for training and practice. Design: A mixed methods cognitive task analysis with a convergent triangulation design. Method: Data collection involved observations of 33 final year nursing students in simulated deteriorating patient scenarios and retrospective cognitive interviews. A process tracing technique was applied to identify the cues to deterioration participants perceived; how cue perception altered as situational demands increased; the extent that participants made connections between perceived cues and reached a situational understanding; and the factors that influenced and constrained participants situation awareness. Qualitative and quantitative findings are woven together and presented using descriptive statistics, illustrative quotations and timeline extractions. Results: The median cue perception was 65.4% and 57.6% in the medical and surgical scenarios, respectively. Perception was negatively influenced by incomplete vital sign monitoring as situations escalated; limited physical assessments; passive scanning behaviours; poor task automaticity; and excessive cognitive demands. Incomplete perception, poor cue integration and underdeveloped mental models influenced situational understanding. Escalation calls did not always accurately reflect situations and a reporting mindset was evident. Clinical exposure to deteriorating patients was described as variable and opportunistic. Reporting Method: The study is reported in accordance with the Good Reporting of a Mixed Methods Study (GRAMMS) checklist. Patient or Public Contribution: Patients and public were not involved in this research. [ABSTRACT FROM AUTHOR]

Aim: To describe patients' with chronic obstructive pulmonary disease (COPD) experiences of group‐based self‐management education with a digital website. Design: A qualitative approach with a phenomenologicalmethod. Patients participating in an earlier study, with self‐experience of COPD as a special competence, were involved as research partners at the design of this study. Methods: Eleven individual and two group interviews with five participants in each group were conducted. Results: Group‐based self‐management education with a digital website supports learning. Sharing experiences with others in similar situations creates security and reduces the feeling of being alone. Based on questions and discussion in the group, and through self‐reflection, general information is transformed into useful knowledge and understanding of one's own situation. COPD information on the website provides an opportunity to gain knowledge continuously based on needs that contributes to learning. This research has demonstrated that adapting learning activities to individual learning styles increases sustainability of learning. Sharing experiences reduces feelings of loneliness. It is therefore important to create spaces for sharing experiences and in‐depth reflection that support learning over time. [ABSTRACT FROM AUTHOR]

Objectives: The aim of this study was to investigate the retinal morpho‐functional characteristics of patients with neovascular wet age‐related macular degeneration (nAMD) treated with intravitreal injection (IV) of aflibercept (AFL). Methods: The study was conducted on 35 patients previously diagnosed with type 1 nAMD who received a fixed‐dosing regimen of aflibercept injections over 12 months. The goal was to assess trends in visual abilities over time by measuring visual acuity (VA), contrast sensitivity (CS), visual evoked potentials (VEPs), and spectral domain‐optical coherence tomography (SD‐OCT). The same psychophysical, electro‐functional, and morphological tests administered at baseline (T0) were repeated 4 to 8 weeks after the last aflibercept injection (Tn), resulting in a total of six examinations. Results: At Tn, all subjects exhibited improved VA for both far and near distances compared to values detected at T0. Similarly, VEP amplitude and latency values at Tn showed a greater P100 improvement than those observed at T0. Additionally, the CS examination at Tn demonstrated improvement, particularly at high spatial stimulation frequencies. The Tn SD‐OCT results highlighted a reduction in macular thickness compared to T0 values. Conclusions: This exploratory research indicates that intravitreal injections of AFL, following a fixed‐dosing regimen, represent a valuable therapeutic approach for enhancing visual performance. This conclusion is supported by comprehensive statistical analysis of psychophysical, electro‐functional, and morphological examinations within the same group of patients with nAMD, as demonstrated for the first time. [ABSTRACT FROM AUTHOR]

There are few short, validated tools to assess young children's obesity‐related dietary behaviours, limiting the rapid screening of dietary behaviours in research and practice‐based early obesity prevention. This study aimed to develop and assess the reliability and validity of a caregiver‐reported short dietary questionnaire to rapidly assess obesity‐related dietary behaviours in children aged 6 months to 5 years. The Early Prevention of Obesity in Childhood Dietary Questionnaire (EPOCH‐DQ) was developed using a rigorous process to determine content and structural validity. Three age‐appropriate versions were developed for (1) infants, aged 6–12 months, (2) toddlers, aged 1–2.9 years and (3) pre‐schoolers, aged 3–5 years. The questionnaire (7–15 items) measures dietary behaviours, including diet risk from non‐core food and beverage intake, diet quality from vegetable frequency, bread type and infant feeding practices. Test–retest reliability was assessed from repeated administrations 1 week apart (n = 126). Internal consistency, concurrent validity (against a comparison questionnaire, the InFANT Food Frequency Questionnaire), construct validity and interpretability were assessed (n = 209). Most scores were highly correlated and significantly associated (p < 0.05) for validity (rs: 0.45–0.89, percentage agreement 68%–100%) and reliability (intraclass correlation coefficient: 0.61–0.99) for diet risk, diet quality and feeding practice items. The EPOCH‐DQ shows acceptable validity and reliability for screening of obesity‐related behaviours of children under 5 years of age. The short length and, thus, low participant burden of the EPOCH‐DQ allows for potential applications in various settings. Future testing of the EPOCH‐DQ should evaluate culturally and socio‐economically diverse populations and establish the predictive validity and sensitivity to detect change. Key messages: Accurate measurement of obesity‐related dietary behaviours is essential for the evaluation of early obesity prevention programmes and monitoring population trends.The 7–15 item Early Prevention of Obesity in Childhood Dietary Questionnaire (EPOCH‐DQ) was shown to have acceptable validity and reliability for assessing obesity‐related behaviours of children aged 6 months to 5 years of age.The short length and thus low participant burden of the EPOCH‐DQ allows for potential applications in various settings, including research, population health monitoring and use by health professionals.Future testing of the EPOCH‐DQ should include evaluation across culturally and socio‐economically diverse populations and determine the predictive validity and sensitivity to detect change of the EPOCH‐DQ. [ABSTRACT FROM AUTHOR]

Quality complementary feeding (CF) of infants and young children is key to their growth and development. But in Jordan, providing appropriate CF remains a challenge. This study assesses trends in infant and young child feeding (IYCF) practices, and consumption by infants and young children aged 6–23 months of breast milk substitutes (BMSs), sugar‐sweetened beverages (SSBs), and micronutrient‐rich foods in Jordan from 1990 to 2017. We combined dietary data on infants and young children from six Demographic and Health Surveys (DHS) (n = 14,880 children) to compute IYCF indicators. The latter included minimum dietary diversity (MDD), minimum meal frequency (MMF), and minimum acceptable diet (MAD), as well as intake of micronutrient‐rich foods and food groups, specific SSBs, and infant formula. We conducted trend analyses using logistic regression models adjusted for child's age in month, child age squared, governorates, urban/rural residence, mother's educational attainment, and household wealth quintiles. We found that the proportion of consumption of micronutrient‐rich food groups declined significantly between 1990 and 2017, with fewer infants and young children consuming eggs (OR = 0.39, p ≤ 0.001, 2002 reference), meat, poultry, and fish (OR = 0.25, p ≤ 0.001, 2002 reference), dairy (OR = 0.59, p ≤ 0.001, 2002 reference) and Vitamin A‐rich fruits and vegetables (OR = 0.66, p ≤ 0.001, 2002 reference). Conversely, there was increased use of BMSs and sugar‐sweetened juices that paralleled a decline in the share of infants and young children meeting appropriate CF practices and consuming micronutrient‐rich foods and food groups. By 2017, children aged 6–23 months were significantly less likely to meet MDD, MMF, and subsequently MAD; the odds of consuming BMSs were almost three times the reference (OR = 3.8, p ≤ 0.001, 1990 reference), as were the odds of consuming sugar sweetened juices (OR = 3.63, p ≤ 0.001, 1990 reference). Food insecurity and undernutrition are low in Jordan; however, overweight and obesity rates are increasing concurrently as are micronutrient deficiencies. This highlights the need for policymakers to address factors at individual and household levels (behaviours and practices) as well as environmental issues (increasing access to unhealthy and ultraprocessed foods). Key messages: In 2017, Jordanian infants and young children were significantly less likely to meet minimum dietary diversity (MDD), minimum meal frequency (MMF), and minimum acceptable diet (MAD) than in 1990.At the same time, the odds of consuming infant formula were six times higher in 2017, while consuming any breast milk substitute was four time higher (infant formula and/or animal milk).The proportion of infants and young children consuming micronutrient‐rich food groups has declined in Jordan over time, with fewer infants and young children reporting consuming animal‐source foods as well as Vitamin A‐rich fruits and vegetables. [ABSTRACT FROM AUTHOR]

Modern coexistence theory (MCT) offers a conceptually straightforward approach for connecting empirical observations with an elegant theoretical framework, gaining popularity rapidly over the past decade. However, beneath this surface‐level simplicity lie various assumptions and subjective choices made during data analysis. These can lead researchers to draw qualitatively different conclusions from the same set of experiments. As the predictions of MCT studies are often treated as outcomes, and many readers and reviewers may not be familiar with the framework's assumptions, there is a particular risk of 'researcher degrees of freedom' inflating the confidence in results, thereby affecting reproducibility and predictive power.To tackle these concerns, we introduce a checklist consisting of statistical best practices to promote more robust empirical applications of MCT. Our recommendations are organised into four categories: presentation and sharing of raw data, testing model assumptions and fits, managing uncertainty associated with model coefficients and incorporating this uncertainty into coexistence predictions.We surveyed empirical MCT studies published over the past 15 years and discovered a high degree of variation in the level of statistical rigour and adherence to best practices. We present case studies to illustrate the dependence of results on seemingly innocuous choices among competition model structure and error distributions, which in some cases reversed the predicted coexistence outcomes. These results demonstrate how different analytical approaches can profoundly alter the interpretation of experimental results, underscoring the importance of carefully considering and thoroughly justifying each step taken in the analysis pathway.Our checklist serves as a resource for authors and reviewers alike, providing guidance to strengthen the empirical foundation of empirical coexistence analyses. As the field of empirical MCT shifts from a descriptive, trailblazing phase to a stage of consolidation, we emphasise the need for caution when building upon the findings of earlier studies. To ensure that progress made in the field of ecological coexistence is based on robust and reliable evidence, it is crucial to subject our predictions, conclusions and generalisability to a more rigorous assessment than is currently the trend. [ABSTRACT FROM AUTHOR]

Multistate models have been used for decades to analyze the economics of expensive and long‐lasting treatments. More recently they also served to address questions in clinical drug development. It seems timely to introduce the broader pharmacometrics community to the technical aspects and the problem‐solving capabilities of these models. A minimal model is introduced that can answer questions of interest to drug developers, regulatory agencies, and patients (with their carers and payers). A clinical study is simulated where 1000 patients are randomly allocated (1:1) to placebo and active treatment. After a recruitment phase, deaths are counted, and an administrative data cutoff occurs 858 days after the first patient is randomized. The minimal model has one initial state, two transient states, and two absorbing states. Fully parameterized semi‐Markov processes govern the unidirectional transitions between states. Simulations explore the influence of parameter uncertainty and sample size on the validity of statistical inferences. The questions of interest to stakeholders are addressed predominantly with graphic displays. All programming codes are made available. Both drug developers and regulators are invited to re‐evaluate the methods currently in use to assess the benefits and risks of new treatments. [ABSTRACT FROM AUTHOR]

Electronic health record (EHR) data is crucial in providing comprehensive historical disease information for patients and is frequently utilized in health event prediction. However, current deep learning models that rely on EHR data encounter significant challenges. These include inadequate exploration of higher‐order relationships among diseases, a failure to capture dynamic relationships in existing relationship‐based disease prediction models, and insufficient utilization of patient symptom information. To address these limitations, a novel dynamic HyperGraph‐based deep learning model is introduced for disease prediction (DHGL) in this study. Initially, pertinent symptom information is extracted from patients to assign them with an initial embedding. Subsequently, sub‐hypergraphs are constructed to consider distinct patient cohorts rather than treating them as isolated entities. Ultimately, these hypergraphs are dynamized to gain a more nuanced understanding of patient relationships. The evaluation of DHGL on real‐world EHR datasets reveals its superiority over several state‐of‐the‐art baseline methods in terms of predictive accuracy. [ABSTRACT FROM AUTHOR]

Aim: With the outbreak of COVID‐19 and associated challenges such as increased workload for health providers and shortage of equipment, it became more challenging to maintain patients' dignity and satisfaction. This study evaluated the patients' dignity and satisfaction with COVID‐19 in Kashan, Iran, in 2021. Design: A cross‐sectional descriptive study. Methods: In total, 385 patients recovered from COVID‐19 were selected through sequential sampling method. Data were collected using demographic, patient dignity inventory, and patient satisfaction questionnaires. Data were analysed using descriptive analysis, independent t‐test, ANOVA, and Spearman‐Brown coefficient. Results: The mean age of patients was 50.57. The mean scores of patients' dignity and satisfaction were 1.622 ± 0.653 and 3.851 ± 0.548 (out of 5), respectively. The dignity rating was associated with age, gender, education, underlying disease, and length of hospital stay (p < 0.05); but patient satisfaction was only associated with education (p = 0.002). The results indicated that dignity and satisfaction have a significant direct correlation (r = −0.23, p < 0.001). Patient or Public Contribution: This study was designed based on the research priorities and needs in the field of clinical research and patients were involved in conducting the study via participating in data collection. [ABSTRACT FROM AUTHOR]

Aim: To explore nursing students' moral distress (MD) experiences in clinical settings. Design: An interpretative phenomenological analysis (IPA) design was employed. Methods: Purposive sampling was used. In‐depth semi‐structured face‐to‐face interviews were conducted from December 2020 to June 2021 with nursing students who were taking the internship course in clinical settings. Data analysis was conducted following Dickman et al.'s (1989) method. Results: Ten nursing students participated in this study. Three main themes were identified, including (1) negative learning environments, (2) internal disgust and (3) threats to professional identity. Conclusion: Findings showed that value conflict, lack of knowledge of ethical standards and its application, and unprofessional approaches result in negative environmental learning perceptions from the nursing students. Therefore, due to being unable to change the situation, they start to feel guilt and shame and, as a result, decide to escape the problem instead of managing it. These feelings lead to internal disgust. This issue indicates the importance of improving the knowledge and perception of these situations. Thus, nursing students must be prepared for the real world, where their ideals are constantly challenged. MDs were experienced as threats to dignity, inequality, distrust, and change of mentality towards nursing, characterised as threats to professional identity. It is suggested to inquire about the process of nursing students' resiliency in morally disturbing situations to deduce the suitable approach for clinical education. [ABSTRACT FROM AUTHOR]

Space plasma studies frequently use in situ magnetic field measurements taken from many spacecraft simultaneously. A useful data product of these measurements is the reconstructed magnetic field in a volume near the spacecraft observatory. We compare a standard Linear method of computing the magnetic field at arbitrary spatial points to two novel approaches: a Radial Basis Function interpolation and a time‐dependent 2D inverse distance weighted interpolation scheme called Timesync. These three methods, which only require in situ measurements of the magnetic fields and bulk plasma velocities at a sparse set of spatial points, are implemented on synthetic data drawn from a time‐evolving numerical simulation of plasma turbulence. We compare both the topology of the reconstructed field to the ground truth of the simulation and the statistics of the fluctuations found in the reconstructed field to those from the simulated turbulence. We conclude that the Radial Basis Function and Timesync methods outperform the Linear method in both topological and statistical comparisons. Plain Language Summary: Spacecraft are often used to measure things, such as magnetic fields, which can only be measured at a single point, the point where a spacecraft is located. When multiple spacecraft are deployed to measure the magnetic field, we get an understanding of how the magnetic field changes by comparing the measurements made by each of the spacecrafts against one another. When multiple spacecraft are taking measurements, scientists also use mathematical techniques to estimate the magnetic field at points in space where a spacecraft is not located. The process of using a small number of magnetic field measurements, taken from spacecraft, to estimate the value of the magnetic field everywhere is called magnetic field reconstruction. This work has compared the effectiveness of three of these magnetic field reconstruction techniques: one established and two novel. We conclude that the two novel methods are more accurate than the established mathematical tool commonly used to reconstruct magnetic fields. Key Points: We propose two new reconstruction methods that only require measurements of the magnetic field and bulk plasma velocityThe two new methods result in reconstructions with less error than previously employed Linear methodsThe new methods preserve the statistical distribution of scale‐dependent fluctuations from the ground‐truth turbulence simulation [ABSTRACT FROM AUTHOR]

We aimed to assess the quality and reliability of pressure injury‐related videos uploaded on YouTube, analyse the sources and contents, and examine the correlation between video parameters. We searched YouTube using two keywords, "pressure ulcer" and "pressure sore", on August 20, 2022. We sorted the videos according to their number of views and included the top 100 videos for each keyword. The quality of videos was assessed using the Global Quality Scale (GQS), while their reliability was evaluated by the modified DISCERN (mDISCERN) tool. In addition, we evaluated the videos in which content was included, analysed the correlations and differences between GQS, mDISCERN, and video parameters. We initially found a total of 100 videos for each keyword and finally included and analysed 77 videos. The mean scores for the mDISCERN and GQS were 2.35 ± 0.98 and 3.09 ± 0.99, respectively. Both GQS and mDISCERN showed statistically significant correlations with each other (rho = 0.54, p < 0.0001*) and with the length of the videos, respectively (rho = 0.36, p = 0.001*), (rho = 0.29, p = 0.01*). Of the videos created by physicians, 8 (57.1%) included content related to treatment, while of the videos created by nonphysician health personnel, 22 (57.9%) included content related to prevention. Analysing whether there were differences in video parameters based on the sources, we observed significant differences between sources in GQS (p < 0.0001*), mDISCERN (p < 0.0001*), and video length (p = 0.001*). In the post‐hoc analysis, videos uploaded by physicians or nonphysician health personnel showed higher quality and reliability than videos uploaded by other sources. Therefore, the results of this study could be useful for healthcare providers, as well as patients and caregivers, to search for high‐quality and reliable YouTube videos related to pressure injury. [ABSTRACT FROM AUTHOR]

Web‐based studies of human dimensions are increasing across environmental and socio‐ecological disciplines. However, the prevalence of fraud threatens research quality. Increased fraud rates should be expected as surveys move progressively more online, motivated by expanding reach, cost savings and/or in response to COVID‐19. Web‐based research must better account for fraud to maintain confidence in findings. Practical diagnostic tools and data quality protocols are required to detect fraud and ensure results quality.Drawing on our experience using an online participatory mapping case study, we discuss methods to detect potentially fraudulent responses—and identify some limitations. We begin by reviewing the current state of knowledge on fraudulent responses or 'fraudsters' and its relative absence in environmental and socio‐ecological disciplines. We then describe our research approach, the indicators and variables we used to detect and assess fraud and our decision‐making process to eliminate suspicious responses without jeopardizing research integrity.We found that despite several preventative measures, many fraudulent respondents could provide survey responses and effectively mimicked legitimate respondents at first glance. By assuming each response to be 'potentially fraudulent', we determined that the complete screening of each respondent, while time‐consuming, can limit the prevalence of fraud. We also determined that the most common data consistency checks (e.g. duration, trap questions and straight‐liner checks) are unlikely to guarantee valid respondents.If not acknowledged and addressed, fraud has the potential to undermine data integrity, discredit research findings and limit the utility of results for policy. This study contributes to environmental and socio‐ecological research by reviewing existing fraudster literature and using our experience with fraud to provide recommendations for researchers to address this problem. We encourage researchers implementing online qualitative research methods to thoroughly assess and report fraud, when possible, to ensure widespread knowledge of this growing threat. Read the free Plain Language Summary for this article on the Journal blog. Read the free Plain Language Summary for this article on the Journal blog. [ABSTRACT FROM AUTHOR]

Background: Starting in the 1990s in the United States, individuals with lived experience of mental health crises and recovery have been employed as peer support workers (PSWs) internationally. However, the implementation of PSW in clinical contexts remains challenging. Methods: This manuscript presents and discusses the methodological framework of the ImpPeer‐Psy5 study on the PSW implementation in the German mental healthcare sector. This study used a mixed‐methods and collaborative research approach, as well as participatory research strategies. After describing the study design, populations, teamwork and assessments, the epistemic challenges of its methodological framework will be critically discussed and how it has iteratively shaped the object of study. Discussion and Practical Implications: The healthcare, policy and funding context of PSW implementation as well as the study's methodological framework have differently influenced the ways in which the implementation of PSW has been conceived in this study. The choice of a collaborative or participatory methodological framework is advised to better align research questions and procedures to the specific needs and challenges of PSWs and other stakeholders concerned with PSW implementation. Patient and Public Contribution: The research team of the ImpPeer‐Psy5 study was collaboratively staffed by a portion of researchers who also identify as users or survivors of psychiatric services. A nonprofit organization for the training of PSWs served as a practice partner throughout the research process. Different participatory formats involve a significant number of diverse stakeholders relevant to PSW implementation. [ABSTRACT FROM AUTHOR]

Background: Poor quality sleep in hospitals may be problematic for patients, negatively impacting their recovery and wellbeing. This project aimed to investigate the effectiveness of codesign in addressing key issues affecting sleep disruption in the healthcare setting. Methods: Codesign with patients, staff and consumer representatives was conducted in an acute metropolitan tertiary public hospital in Sydney, Australia. Through a four‐stage process, a multimodal intervention to address and reduce the impact of sleep disruptions among hospital inpatients was created. Pre‐ and post‐intervention evaluation was used to determine changes in patient‐reported sleep disruption. Results: 'The HUSH Project' (Help Us Support Healing) intervention resulted from the codesign process, which included the provision of HUSH Sleep Packs (with earplugs, eye masks and herbal tea), patient information resources, and ward‐based Sleep Champions. Survey data from 210 patients revealed a statistically significant decrease in patient‐reported noise disturbances for patients in shared rooms following the 4‐week intervention period of the HUSH program. Conclusion: The HUSH Project demonstrated that a novel multimodal intervention may be valuable in reducing sleep disruption in hospitals. These findings also indicate the benefits of using codesign methodology to support improvement projects that seek to enhance patient experiences of care. Patient or Public Contribution: This project utilised codesign methodology, which involved significant contributions from patients and consumer representatives, from research conceptualisation into intervention design, implementation and project evaluation. [ABSTRACT FROM AUTHOR]