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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]

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]

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]

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]

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]

The goal of this paper is to propose a method to achieve a higher classification rate in Polarimetric Synthetic Aperture Radar (PolSAR) image classification. In our work, PolSAR features are extracted from Convolutional Neural Networks (CNNs) and also Graph Convolutional Networks (GCNs). Due to the construction of the adjacency matrix on all the data, traditional GCNs usually suffer from a substantial computational cost, particularly in large‐scale remote sensing (RS) problems. To this end, first of all, we propose a new mini‐batch GCN (called Mini‐GCN), which allows to train large‐scale GCNs in a mini‐batch fashion. More significantly, our designed mini‐GCN is capable of inferring out‐of‐sample data without re‐training networks and improving classification performance. Furthermore, as CNNs and GCNs can extract different types of PolSAR features, an intuitive solution to break the performance bottleneck of a single model is to fuse them. Since mini‐GCNs can perform batch‐wise network training (enabling the combination of CNNs and GCNs), we can use the feature fusion strategy. Therefore, in this paper, a local graph‐based fusion method is proposed to couple dimension reduction and feature fusion of the information extracted from the designed CNN and Mini‐GCN. Experimental results on real PolSAR data are very encouraging. Compared to the methods using only single feature and stacking all the features together, the proposed feature fusion method improves the overall classification accuracy on the real PolSAR data sets for more than 5%. Moreover, the experiments conducted on PolSAR datasets, demonstrate the advantages of the used mini‐GCNs over the traditional GCNs and the superiority of the proposed feature fusion method with regards to the single CNN or GCN models. [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]

Photoemission spectroscopy, an experimental method based on the photoelectric effect, is now an indispensable technique used in various fields such as materials science, life science, medicine and nanotechnology. However, part of the experimental process of photoemission spectroscopy relies on experience and intuition, which is obviously a problem for novice users. In particular, photoemission spectroscopy experiments using high-brilliance synchrotron radiation as a light source are not easy for novice users because measurements must be performed quickly and accurately as scheduled within a limited experimental period. In addition, research on the application of information science methods to quantum data measurement, such as photoemission spectroscopy, is mainly aimed at the development of analysis methods, and few attempts have been made to clarify the problems faced by users who lack experience. In this study, the problems faced by novice users of photoemission spectroscopy are identified, and a native application named synapse with functions to solve these problems is implemented and evaluated qualitatively and quantitatively. This paper describes the contents of an interview survey, the functional design and the implementation of the application synapse based on the interview survey, and results and discussion of the evaluation experiment. [ABSTRACT FROM AUTHOR]

This paper presents tools that help researchers implement the processes of data-led studies of upper-atmospheric phenomena. These tools were developed as a part of the activities of the Inter-university Upper atmosphere Global Observation NETwork (IUGONET) of Japan, which is a project to develop infrastructure for upper-atmospheric research data. This paper focuses on the data service named IUGONET Type-A, which was launched in October 2016 and has since evolved. In addition to being a conventional metadata catalogue, it has many other useful functions: an easy cross-searching system, a quick-look data-plotting procedure, an interactive data visualization system named UDAS web, and strong linkage with analysis software. Users can pick up relevant data from a huge number of data sets using either lists categorized by instruments/projects, observed regions and special campaigns or a world map of observatories. Users can quickly find the time, location and nature of phenomena that occurred by comparing the quick-look plots of various data displayed by the browser. UDAS web allows researchers to interactively create stacked plots of various data types that can facilitate the understanding of the relationships among phenomena observed in different regions. Furthermore, it presents a command list for software dedicated to data analysis that can smoothly lead users to perform detailed analyses. IUGONET Type-A provides a one-stop data service that can assist users in searching, examining and comprehending data for advanced analysis. It is also capable of handling old data, including analogue data and written paper documents. Thus, it will provide useful support for innovative interdisciplinary scientific research on solar-terrestrial phenomena. [ABSTRACT FROM AUTHOR]

Numerical models have been developed to investigate and understand responses of biogeochemical cycle to global changes. Steady state, when a system is in dynamic equilibrium, is generally required to initialize these model simulations. However, the spin‐up process that is used to achieve steady state pose a great burden to computational resources, limiting the efficiency of global modeling analysis on biogeochemical cycles. This study introduces a new Semi‐Analytical Spin‐Up (SASU) to tackle this grand challenge. We applied SASU to Community Land Model version 5 and examined its computational efficiency and accuracy. At the Brazil site, SASU is computationally 7 times more efficient than (or saved up to 86% computational cost in comparison with) the traditional native dynamics (ND) spin‐up to reach the same steady state. Globally, SASU is computationally 8 times more efficient than the accelerated decomposition spin‐up and 50 times more efficient than ND. In summary, SASU achieves the highest computational efficiency for spin‐up on site and globally in comparison with other spin‐up methods. It is generalizable to wide biogeochemical models and thus makes computationally costly studies (e.g., parameter perturbation ensemble analysis and data assimilation) possible for a better understanding of biogeochemical cycle under climate change. Plain Language Summary: Land carbon cycle models, a critical component of earth system models, have been developed to investigate and understand ecological responses to global changes. However, the model spin‐up process to achieve steady state are usually time‐consuming and pose great computational burden. The spin‐up problem hinders our ability to study some key issues in land carbon cycle modeling, such as sensitivity analysis. This paper applies a new SASU framework to accelerate spin‐up of Community Land Model matrix version 5. Compared with traditional native dynamic approach, SASU speeds up the spin‐up by approximately 50 times, achieving the fastest spin‐up speed than all the previously recorded methods. Our SASU method is applicable to biogeochemical models and, therefore, liberates the models for computationally costly studies, such as parameter perturbation ensemble analysis and data assimilation. Key Points: A new semi‐analytical spin‐up (SASU) framework combines the default accelerated spin‐up method and matrix analytical algorithmSASU accelerates CLIM5 spin‐up by tens of times, becoming the fastest method to our knowledgeSASU is applicable to most biogeochemical models and enables computationally costly study, for example, sensitivity analysis [ABSTRACT FROM AUTHOR]

Grassland is an important resource for China's economic development and the main economic source of animal husbandry. The identification and classification of grassland forage is an important part of the improvement of forage varieties and the monitoring of germplasm resources, which can fundamentally solve the problems of poor forage quality and low reproduction rate. For the problem of low accuracy of forage identification and classification, the authors put forward a new 3DSECNN model to remove the preprocessing operation and directly study the images. The authors took forage hyperspectral image (HSI) images on the field and built dataset, used 3DSECNN to train the images to improve the classification effect. The outstanding contributions of this paper are: (1) The authors took high‐precision forage HSI images in the field, established a dedicated database of forage HSIs, and expanded the datasets; (2) the process of integrating preprocessing ideas into the network and replacing the traditional method of preprocessing the data and then extracting features; (3) proposing the 3DSECNN model, which adds SENet on the basis of the traditional 3DCNN, strengthens the correlation of the spatial dimension, selects the key features for the classification by calculating the channel weight, inhibits the unimportant information, and achieves the purpose of integrating the preprocessing ideas into the network. The experimental results show the overall accuracy (OA) of 3DSECNN is 94.36%, Precision, Recall, F1‐score, Kappa, and Time also showed good levels. The experimental results prove that the 3DSECNN strengthens the correlation between image channels, enhances the performance ability of features, and provides a new method for the identification and classification research of forage. [ABSTRACT FROM AUTHOR]

The switching power supply is widely used in the secondary equipment of the power grid. The failure of the switching power supply will cause the protection device to malfunction, leading to a risk of large‐scale power failure. In this paper, a real‐time monitoring method based on only output voltage information is proposed. The experiment shows that this parameter‐reduced approach, relying only on voltage information, outperforms the existing method in detection accuracy. [ABSTRACT FROM AUTHOR]

As a special case of common object removal, image person removal is playing an increasingly important role in social media and criminal investigation domains. Due to the integrity of person area and the complexity of human posture, person removal has its own dilemmas. In this paper, a novel idea is proposed to tackle these problems from the perspective of data synthesis. Concerning the lack of a dedicated dataset for image person removal, two dataset production methods are proposed to automatically generate images, masks and ground truths, respectively. Then, a learning framework similar to local image degradation is proposed so that the masks can be used to guide the feature extraction process and more texture information can be gathered for final prediction. A coarse‐to‐fine training strategy is further applied to refine the details. The data synthesis and learning framework combine well with each other. Experimental results verify the effectiveness of the method quantitatively and qualitatively, and the trained network proves to have good generalization ability either on real or synthetic images. [ABSTRACT FROM AUTHOR]

This paper determines the optimal capacity of solar photovoltaic (PV) and battery energy storage (BES) for a grid‐connected house based on an energy‐sharing mechanism. The grid‐connected house, also mentioned as house 1 where it is relevant, shares electricity with house 2 under a mutually agreed fixed energy price. The objective is to minimize the cost of electricity (COE) for house 1 while decreasing the electricity cost of house 2. Practical factors such as real data for solar insolation, electricity consumption, grid constraint, ambient temperature, electricity rate, and battery degradation are considered based on actual data. The developed methodology is examined by taking the actual load data of two houses in South Australia. Different scenarios of contract years between the houses are investigated to make it more practical in real life. Sensitivity analyses are conducted for the sharing of energy between the houses and by changing parameters like export power limitation, load of houses, and costs of PV and BES. Likewise, operational analysis is done for two days of summer and winter. It is found that when energy sharing is applied, the optimal design of the PV‐BES system will achieve lower COE for both houses. [ABSTRACT FROM AUTHOR]

Anomaly detection aims at locating the spectral different objects of a specific scene without any prior information, and has gained increasing attention. By decomposing the input hyperspectral image (HSI) into a background tensor and an anomaly tensor, the tensor approximation is an efficient tool for detecting the anomalies. Low rankness is usually utilized as the regularizer during the background reconstruction process. Different from most existing hyperspectral anomaly detection methods which compute the truncated nuclear norm of the third folding of the original HSI, a novel weighted‐sparsity‐regularized tensor linear representation (WsrTLR) method is proposed for hyperspectral anomaly detection in this paper. Tensor linear representation is utilized to formulate the background HSI by a three‐dimensional (3D) representation base and the corresponding 3D representation coefficient. Low rankness is applied to constrict the representation coefficient, an operation which avoids destroying the multi‐way structure and losing information during the matrixing process, and ensures a satisfactory detection accuracy. Meanwhile, by incorporating the weighted‐sparsity‐regularized tensor linear representation to reconstruct the background tensor, the anomalies can be easily detected by eliminating the background tensor from the original scene. In addition, to avoid negative influence caused by the redundant bands and noisy bands in the representation process, informative bands have been first selected via an optimal neighborhood reconstruction strategy. Experimental results and data analysis on four real hyperspectral datasets, which contain anomalies with different sizes, have demonstrated the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

One of the problems in implementing DRPs is the lack of sufficient understanding of consumers' behaviour when implementing DRPs. This paper compares consumers' load patterns annually by the improved Weighted Fuzzy Average (WFA) K‐means clustering method. According to the collected data, DRPs are discussed annually using a combined Convolutional Neural Network (CNN) and Long Short‐Term Memory (LSTM). To make CNN‐LSTM a practical algorithm for executing DRPs, Time Series Prediction (TSP) operations must be performed, and input data must be memorized. Finally, according to the TSP done for the obtained data, the implementation of DRPs in practice is facilitated. Then, the DRPs executed with the Deep Learning (DL) model and the power model will be prioritized by Technique for Order of Preference by Similarity to the Ideal Solution (TOPSIS) method, and decision indicators will be determined and weighted using the Shannon entropy method. The numerical studies section shows that the CNN‐LSTM algorithm is able to simulate DRPs with the Mean Absolute Percentage Error (MAPE) almost below 1% in the residential cluster and the highest MAPE in the commercial cluster of 15%. Nevertheless, this algorithm could easily give the same answer as the DRPs power model in the optimal selection of programs. [ABSTRACT FROM AUTHOR]

This paper introduces a novel, temporally sensitive analytical method for qualitative researchers, which is simultaneously timely and necessary given increasing recognition of the fundamental role that time plays in organizational life and scholarship. As a result of this recognition, research designs considering temporality have substantially increased over the past decade. However, while methods for qualitative data collection using longitudinal and 'shortitudinal' designs, in particular qualitative diary methods, have become increasingly common, analytical methods capable of fully exploiting the temporal nature of such data have lagged behind their quantitative counter‐parts, where we see marked progression in analytical methods and procedures. In this paper, we argue that this lack of progression in approaches for analysing qualitative diary data hinders our knowledge and theoretical development when it comes to incorporating temporality, particularly in the exploration of phenomena at individual‐/micro‐levels, arguably most salient to organizational psychology researchers. We respond to these challenges by introducing a novel, step‐by‐step analytical approach that facilitates rigorous incorporation of temporality into the analysis and theorization of micro‐level, qualitative diary data, termed Thematic Trajectory Analysis (TTA). Practitioner points: Existing qualitative analytical methods have limitations when applied to qualitative diary data and have thereby limited the questions that may be explored, and understood, through qualitative data.Offers an alternative, step‐by‐step, analytical approach for researchers and practitioners seeking to understand within‐person changes and dynamism in organizations.Enables the benefits of qualitative diaries to be better exploited by both researchers and practitioners and thereby lead to better understanding of how organizational processes unfold, and in turn, lead to stronger intervention mechanisms.Demonstrates the utility of combing textual and visualized data outputs in understanding complex and dynamic phenomena in organizations. [ABSTRACT FROM AUTHOR]

The recognition of malware in network traffic is an important research problem. However, existing solutions addressing this problem rely heavily on the source code and misrecognise vulnerabilities (i.e. incur a high false positive rate (FPR)) in some cases. In this paper, we initially use the K‐means clustering algorithm to extract malware patterns under user to root attacks in network traffic. Since the traditional K‐means algorithm needs to determine the number of clusters in advance and it is easily affected by the initial cluster centres, we propose an improved K‐means clustering algorithm (NIKClustering algorithm) for cluster analysis. Furthermore, we propose the use of self‐similarity and our improved clustering algorithm to recognise buffer overflow vulnerabilities for malware in network traffic. This motivates us to design and implement a recognition approach for buffer overflow vulnerabilities based on self‐similarity and our improved clustering algorithm, called Reliable Self‐Similarity with Improved K‐means Clustering (RSS‐IKClustering). Extensive experiments conducted on two different datasets demonstrate that the RSS‐IKClustering can achieve much fewer false positives than other notable approaches while increasing accuracy. We further apply our RSS‐IKClustering approach on a public dataset (Center for Applied Internet Data Analysis), which also exhibited a high accuracy and low FPR of 96% and 1.5%, respectively. [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]

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]

Real‐world data (RWD) reflecting patient treatment in routine clinical practice can be used to develop external control groups for single‐arm trials. External controls can provide valuable benchmark results on potential comparator drug effectiveness, particularly in rare indications when randomized controlled trials are either infeasible or unethical. This paper describes lessons learned from a descriptive real‐world external control cohort study conducted to provide benchmark data for a single‐arm clinical trial in a rare oncology biomarker driven disease. Conducting external control cohort studies to evaluate treatment effectiveness in rare indications likely will present data and analysis challenges as seen in the example study. However, there are mitigating measures that can be applied in the study design, identification of RWD sources, and data analysis. The lessons learned and reported here with a proposal of an external control study framework can provide guidance for future research in this area, and may be applicable as well in other rare indications. Taking these learnings into consideration, the use of real‐world external controls to contextualize treatment effectiveness in rare indications is a valuable approach and warrants further application in the future. [ABSTRACT FROM AUTHOR]

Background: This paper is a follow‐up study continuing the COVISTRESS network previous research regarding health‐related determinants. Objective: The aim was to identify the main consequences of COVID‐19 lockdown on Body MassIndex and Perceived Fragility, related to Physical Activity (PA), for different categories of populations, worldwide. Design: The study design included an online survey, during the first wave of COVID‐19 lockdown, across different world regions. Setting and participants: The research was carried out on 10 121 participants from 67 countries. The recruitment of participants was achieved using snowball sampling techniques via social networks, with no exclusion criteria other than social media access. Main outcome measures: Body MassIndex, Physical Activity, Perceived Fragility and risk of getting infected items were analysed. SPSS software, v20, was used. Significance was set at P <.05. Results: Body MassIndex significantly increased during lockdown. For youth and young adults (18‐35 years), PA decreased by 31.25%, for adults (36‐65 years) by 26.05% and for the elderly (over 65 years) by 30.27%. There was a high level of Perceived Fragility and risk of getting infected for female participants and the elderly. Correlations between BMI, Perceived Fragility and PA were identified. Discussion and Conclusions: The research results extend and confirm evidence that the elderly are more likely to be at risk, by experiencing weight gain, physical inactivity and enhanced Perceived Fragility. As a consequence, populations need to counteract the constraints imposed by the lockdown by being physically active. [ABSTRACT FROM AUTHOR]

Cross modal retrieval can retrieve images through a text query and vice versa. In recent years, cross modal retrieval has attracted extensive attention. The purpose of most now available cross modal retrieval methods is to find a common subspace and maximize the different modal correlation. To generate specific representations consistent with cross modal tasks, this paper proposes a novel cross modal retrieval framework, which integrates feature learning and latent space embedding. In detail, we proposed a deep CNN and a shallow CNN to extract the feature of the samples. The deep CNN is used to extract the representation of images, and the shallow CNN uses a multi‐dimensional kernel to extract multi‐level semantic representation of text. Meanwhile, we enhance the semantic manifold by constructing cross modal ranking and within‐modal discriminant loss to improve the division of semantic representation. Moreover, the most representative samples are selected by using online sampling strategy, so that the approach can be implemented on a large‐scale data. This approach not only increases the discriminative ability among different categories, but also maximizes the relativity between different modalities. Experiments on three real word datasets show that the proposed method is superior to the popular methods. [ABSTRACT FROM AUTHOR]

Self‐employment is a geographical phenomenon influenced by national and regional contexts. However, the study of both contexts combined is scarce in the literature on the formation of regional clusters. Using panel data from the USA for 1998‐2018, we perform different techniques to study both contexts combined, including exploratory spatial data analysis and dynamic spatial estimations. We find evidence of spatial dependence of selfemployment rates, although it has decreased over time. Results also suggest that most of the spatial dependence is explained by the clusters of regions with low entrepreneurship activity, and that clusters formed by highly entrepreneurial regions are the most sensitive to fluctuations in the national selfemployment rate. [ABSTRACT FROM AUTHOR]