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This paper proposes diary studies as a methodological choice for addressing crucial questions in qualitative process research. Diary studies unearth within-individual relationships that capture individual experiences and sense-making of these experiences in a processual manner. Therefore, using diary studies not only addresses the need for more dynamic methodological designs in qualitative research, but is advantageous for providing a process view of how the individual engages with the constructs studied. While process-studies, studied longitudinally, are often fraught with challenges of attrition or disengagement, this paper discusses how a methodological choice which is perceived as being beneficial by the participant can sustain engagement and consequently, research rigour. On one hand, a comparison of interview and diary data from the same participants show evidence of memory decay in interviews and reinforce the rigour of diary studies in the study of processual phenomenon. On the other hand, this study also showcases how researcher know-how, unintentionally imparted through reflective and processual questions asked in the study positively impacts the participants, and when reflected on, enhanced learning. This paper therefore demonstrates the ability of a research method to benefit both the research and the researched, and suggests a step-by-step design to implement a rigorous and impactful diary study using user-friendly forms and reflective questioning. [ABSTRACT FROM AUTHOR]

Background : The current UK standard for major trauma patients is to record notes in a paper trauma booklet. Through an innovative collaboration between a major trauma centre and a digital transformation industry partner, a TraumaApp was developed. Electronic notes have been shown to have fewer errors, granular data collection and enable time stamped contemporaneous record keeping. Implementation of digital clinical records presents a challenge within the context of trauma multidisciplinary trauma resuscitation. Data can be easily accessible and shared for quality improvement, audit and research purposes. This study compared paper and electronic notes for completeness and for acceptability data following the implementation of the TraumaApp. Methods: Trauma team members who performed scribe function attended training for the newly launched TraumaApp. Two staff members acted as scribe, using either the paper trauma booklet or TraumaApp, and attended major trauma calls. A framework for comparison of paper and electronic notes was created and used for a retrospective review of major trauma patients' notes. Statistical analysis was performed using a two-tailed t-test. Staff using the TraumaApp completed a System Usability Score questionnaire. Results: There was a total of 37 data points for collection per case. The mean numbers collected were paper notes 24.1 of 37 (65.1%) and electronic notes, 25.7 of 37 (69.5%). There was no statistical significance between the completeness of paper and electronic notes. The mean System Usability Score was 68.4. Discussion: Recording accurate patient information during a major trauma call can be challenging and the role of the scribe to accurately record events is critical for immediate and future care. There was no statistically significant difference in completeness of paper and electronic notes, however the mean System Usability Score was 68.4, which is greater than the internationally validated standard of acceptable usability. Conclusion: It is feasible to introduce digital data collection tools enabling accurate record keeping during trauma resuscitation and improve information sharing between clinicians. [ABSTRACT FROM AUTHOR]

Applying the concept of diaspora, this paper examines an organization in Kampala, Uganda that utilizes sport to foster cultural belonging and increase livelihood opportunities for refugees. A participatory action research approach was implemented with multiple forms of data collection including semistructured interviews, photovoice, and photocollaging. Findings highlight how sport and livelihoods are used by refugees to resist exclusion and other inequalities in Kampala and to express diaspora space and consciousness. Discussion highlights the usefulness of the concept of diaspora for understanding the intersections of sport, refugees, livelihoods, and, more importantly, to stimulate a homing desire for refugees forcibly displaced. [ABSTRACT FROM AUTHOR]

Objective To assess the association between use of an oxytocin decision support checklist with oxytocin usage and clinical outcomes. Study Design We conducted a retrospective cohort study of patients with singleton gestations at 37 0/7 weeks or greater who received oxytocin during labor from October 2012 to February 2017 at an integrated community health care system during three exposure periods: (1) prechecklist; (2) after paper checklist implementation; and (3) after checklist integration into the electronic medical record (EMR). The checklist was a clinical decision support tool to standardize the dosing and management of oxytocin. Thus, our primary outcomes included oxytocin infusion rates and cumulative dose. Secondary outcomes included maternal and neonatal outcomes. We controlled for maternal risk factors with multivariable regression analysis and stratified by mode of delivery. Results A total of 34,269 deliveries were included. Unadjusted analyses showed that compared with prechecklist, deliveries during the paper and EMR-integrated periods had a lower cumulative dose (4,670 ± 6,174 vs. 4,318 ± 5,719 and 4,286 ± 5,579 mU, p < 0.001 for both), lower maximal infusion rate (9.9 ± 6.8 vs. 8.7 ± 5.8 and 8.4 ± 5.6 mU/min, p < 0.001 for both), and longer duration of oxytocin use (576 ± 442 vs. 609 ± 476 and 627 ± 488 minutes, p < 0.001 and p = 0.01, respectively). The unadjusted rates of cesarean, 5-minute Apgar <7, mechanical ventilation, and neonatal hospital length of stay were similar between periods. The adjusted mean difference in time from admission to delivery was longer during the EMR-integrated period compared with prechecklist (3.0 [95% confidence interval: 2.7–3.3] hours, p < 0.001). Conclusion Oxytocin checklist use was associated with decreased oxytocin use patterns at the expense of longer labor times. Findings were more pronounced with EMR integration. Key Points An oxytocin decision support checklist is associated with reduced amounts of oxytocin used. However, checklists were associated with longer duration of oxytocin use and of labor. Results were more pronounced in the EMR-integrated checklist compared with paper checklist. [ABSTRACT FROM AUTHOR]

In this paper, we propose a method of generating the elemental image array (EIA) for the integral imaging (InIm) based light field display (LFD). Firstly, we reveal the intrinsic voxel spatial distributions of the InIm‐based LFD and analyze how the voxel space affects the display performances such as spatial resolution, depth of field, and smoothness of parallax. Then, we present an algorithm to generate an EIA from a pair of RGB and depth (RGBD) images and an optimally selected voxel space. In the experiments, we tested the display performances of the voxels on different depth planes and obtained results consistent with the theoretical analyses. We also experimented with a computer 3D model and a real‐world scene on two InIm‐based LFD prototypes working in different modes, one in real mode and the other in virtual mode, and obtained favorable 3D display effects. The proposed method simplifies the light field data acquisition and image synthesis process, and supports handling both the virtual and the real‐world 3D scenes. We expect the proposed system to help the practical application of the InIm‐based LFD technology. [ABSTRACT FROM AUTHOR]

In the academic world, the number of scientists grows every year and so does the number of authors sharing the same names. Consequently, it is challenging to assign newly published papers to their respective authors. Therefore, author name ambiguity is considered a critical open problem in digital libraries. This paper proposes an author name disambiguation approach that links author names to their real-world entities by leveraging their co-authors and domain of research. To this end, we use data collected from the DBLP repository that contains more than 5 million bibliographic records authored by around 2.6 million co-authors. Our approach first groups authors who share the same last names and same first name initials. The author within each group is identified by capturing the relation with his/her co-authors and area of research, represented by the titles of the validated publications of the corresponding author. To this end, we train a neural network model that learns from the representations of the co-authors and titles. We validated the effectiveness of our approach by conducting extensive experiments on a large dataset. [ABSTRACT FROM AUTHOR]

Background Research theses can have a significant impact on the development of applied sciences, especially when their results are published. Objective This study aims to determine publication rate of papers extracted from the medical residency and master’s degree theses of Guilan University of Medical Sciences (GUMS) during 2008-2018. Methods This is a descriptive cross-sectional study. The data were collected from the medical residency and master’s degree theses of GUMS from 2008 to 2018. Then, they were searched in international and national databases according to the title, keywords, and names of supervisors, advisors, and students. Results Of 774 theses, 449 (58 %) papers had been extracted and published in journals indexed by ISI/ PubMed (41.2%), Scopus (11.8%), BIOSIS, Chemical Abstract, and Embase (0.44%) and others (46.56%). Most of them (98.66%) were original research. Moreover, in 383(85.3%) of the theses, there was no statistician or data advisor. The median time from defending theses to the publication of a paper from them was two years. Conclusion The publication of papers extracted from the theses of GUMS has increased, and more than 50% of them have been published in journals indexed by ISI, PubMed, and Scopus. However, the factors associated with the publication of papers need to be surveyed. [ABSTRACT FROM AUTHOR]

In clinical nutrition research, the medical industry chain generates a wealth of multidimensional spatial data across various formats, including text, images, and semi-structured tables. This data's inherent heterogeneity and diversity present significant challenges for processing and mining, which are further compounded by the data's diverse features, which are difficult to extract. To address these challenges, we propose an innovative integration of artificial intelligence (AI) with the medical industry chain data, focusing on constructing semantic knowledge graphs and extracting core features. These knowledge graphs are pivotal for efficiently acquiring insights from the vast and granular big data within the medical industry chain. Our study introduces the Clinical Feature Extraction Knowledge Mapping (CFEKM) model, designed to augment the attributes of medical industry chain knowledge graphs through an entity extraction method grounded in syntactic dependency rules. The CFEKM model is applied to real and large-scale datasets within the medical industry chain, demonstrating robust performance in relation extraction, data complementation, and feature extraction. It achieves superior results to several competitive baseline methods, highlighting its effectiveness in handling medical industry chain data complexities. By representing compact semantic knowledge in a structured knowledge graph, our model identifies knowledge gaps and enhances the decision-making process in clinical nutrition research. [ABSTRACT FROM AUTHOR]

The growing popularity of JSON as the data storage and interchange format increases the availability of massive multi-structured data collections. Clustering JSON documents has become a significant issue in organising large data collections. Existing research uses various structural similarity measures to perform clustering. However, differently annotated JSON structures may also encode semantic relatedness, necessitating the use of both syntactic and semantic properties of heterogeneous JSON schemas. Using the SchemaEmbed model, this paper proposes an embedding-based clustering approach for grouping contextually similar JSON documents. The SchemaEmbed model is designed using the pre-trained Word2Vec model and a deep autoencoder that considers both syntactic and semantic information of JSON schemas for clustering the documents. The Word2Vec model learns the attribute embeddings, and a deep autoencoder is designed to generate context-aware schema embeddings. Finally, the context-based similar JSON documents are grouped using a clustering algorithm. The effectiveness of the proposed work is evaluated using both real and synthetic datasets. The results and findings show that the proposed approach improves clustering quality significantly, with a high NMI score of 75%. In addition, we demonstrate that clustering results obtained by contextual similarity are superior to those obtained by traditional semantic similarity models. [ABSTRACT FROM AUTHOR]

This paper is a retrospective look at the last ten years of development in online third-party arrangements within the Australian higher education sector. A total of 42 higher education providers are reviewed. The analysis initially focuses on the state of online third-party arrangements in Australian higher education. It then investigates the current scale of partnership arrangements these universities have with third-party companies that offer education technology platforms to deliver online courses. The analysis indicates that online third-party arrangements are likely to grow in post COVID-19 context. However, despite the scale of the activities, this paper reveals that better data collection and reporting are needed to inform third-party education delivery at the national level. The paper highlights the need for universities to set and monitor standards for the courses delivered in an online third-party arrangement. [ABSTRACT FROM AUTHOR]

Purpose: The aim of this paper is to enhance understanding of the use of sourcing teams (STs) by organisations in their procurement and supply chain management. The paper achieves this by exploring, within the context of the supply chain directorate of a global aerospace manufacturing company (GAMC), both the relationship between sourcing teamwork effectiveness (TE) and sourcing task-work effectiveness (TA) and the relationship between individual team member knowledge, skills and abilities (KSAs) and TE. Design/methodology/approach: The authors develop a theoretical model positing positive links between both KSAs and TE and TE and TA. The model is empirically validated using partial least squares structural equation modelling in a survey of 108 ST members from a GAMC. Findings: The authors identify that, within GAMC, four of five KSAs drive TE and further discover the direct effects of TE on improved TA. Additionally, the authors observe within GAMC the indirect effects of KSAs on TA cascading through TE. Research limitations/implications: Limitations include the use of a single firm and self-report measures for data collection. In spite of this, the paper has numerous research implications. Previous research on STs has combined factors related to TE and TA. In this paper, TE and TA were disaggregated, and the relationships between them were explored. The relationships were found to be positive within GAMC, a finding that strengthens the evidence base supporting the use of STs by organisations in their procurement and supply chain management. In addition, the paper also strengthened the evidence base regarding the importance of KSAs to TE, which complements existing research highlighting the importance of team-level factors and individual technical attributes. Practical implications: The findings from GAMC suggest that executives/managers should take an individual as well as a team-level perspective when developing STs and should consider KSAs as well as technical knowledge when judging individuals' suitability for inclusion within an ST. There are established KSA tests in the literature that could be used by managers for this task. The findings also inform executives/managers that TE matters for TA and needs attention and investment, especially where sourcing tasks concern high-value areas and/or critical incidents within supply chains. Originality/value: To the best of the authors' knowledge, this is the first paper to explore the relationship between TE and TA. Establishing that this relationship is a positive one provides critically important evidence regarding the efficacy of STs, which are widely used within procurement and supply chain management. It is also a rare study looking at TE from the perspective of individual team member KSAs, with further positive relationships revealed. Both findings enhance what is a very limited literature on a widely used practice within procurement and supply chain management. [ABSTRACT FROM AUTHOR]

mHealth applications (apps) are tools that can enhance research by efficiently collecting and storing large amounts of data. However, data collection alone does not lead to change. Innovation and practice change occur through utilisation of evidence. The volume of data collected raises questions regarding utilisation of data by nurses and midwives, and how data from mHealth apps can be used to improve person-centred practice. There is limited empirical evidence and a lack of direction from global health authorities to guide nurses and midwives in this area. To describe strategies for nurses and midwives that could enhance the effective use of data generated by mHealth apps to inform person-centred practice. The purpose of this paper is to stimulate reflection and generate actions for data utilisation when using mHealth apps in nursing research and practice. This discussion paper has been informed by current evidence, the integrated-Promoting Action on Research Implementation in Health Services (i-PARIHS) framework, and research experience as part of doctoral study. Before engaging in data collection using mHealth apps, nurses and midwives would benefit from considering the nature of the evidence collected, available technological infrastructure, and staff skill levels. When collecting data and interpreting results, use of a team approach supported by engaged leadership and external facilitation is invaluable. This provides support to operate apps, and more importantly use the data collected to inform person-centred practice. This paper addresses the limited available evidence to guide nurses and midwives when using mHealth apps to collect and use data to inform practice change. It highlights the need for appropriate technology, external facilitative support, engaged leadership, and a team approach to collect meaningful evidence using mHealth apps. Clinicians, leaders, and researchers can apply the strategies provided to enhance the use of mHealth apps and ensure translation of evidence into practice. [ABSTRACT FROM AUTHOR]

The vibration of the rolling mill has been a persistent issue affecting its safe and stable operation. To address the vibration problem in the F2 stand of a continuous rolling mill, this paper establishes a digital twin model of the rolling mill. Based on the digital twin model, a rolling mill virtual commissioning framework has been proposed to enhance the accuracy of real-time data and improve the utilization of production data, laying the foundation for reducing vibration during the operation of the rolling mill. In this process, we first construct a digital twin framework for the rolling mill, consisting of four levels: physical rolling mill, virtual rolling mill, twin data, and services. Next, an online data acquisition and virtual commissioning framework is established to achieve a state mapping from the physical rolling mill to the virtual rolling mill. Finally, using the digital twin model, the effects of real-time process parameter optimization are analyzed. The impact of process parameter optimization on the dynamic response of the rolling mill system is examined. The optimized rolling process parameters effectively reduce the vertical vibration of the rolling mill system. [ABSTRACT FROM AUTHOR]

This study conducts a systematic review of salesperson sampling within the pages of the Journal of Personal Selling and Sales Management from 2013 to 2022. A total of 284 papers (all inclusive) were identified in the first step of the review. Next articles were qualified based on article types, methodology (research approach), and respondent type before additional analysis was conducted. This resulted in 109 published manuscripts with 147 unique salesperson samples for further review. Specifically, sample scope and participant profiles were examined based on the 109 articles. The sample scope included response metrics, study deployment, and contextual aspects. Participant profile includes the examination of demographics, experience, work details, and performance. Ensuing the systematic review, managerial importance and implications, best practices, and research insights and future research directions are provided. This includes guidance on the sampling frame overview, data collection, and sample contexts of participants for a better understanding of what information should be included in research studies. In turn, this study puts forth evidence that one type of salesperson sample does not fit all sales research, and the generalizability of the sample should be carefully considered. [ABSTRACT FROM AUTHOR]

Copyright of Environmental & Social Management Journal / Revista de Gestão Social e Ambiental is the property of Environmental & Social Management Journal and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper presents TimelinePTC, a web-based tool developed to improve the collection and analysis of Pathways to Care (PTC) data in first episode psychosis (FEP) research. Accurately measuring the duration of untreated psychosis (DUP) is essential for effective FEP treatment, requiring detailed understanding of the patient's journey to care. However, traditional PTC data collection methods, mainly manual and paper-based, are time-consuming and often fail to capture the full complexity of care pathways. TimelinePTC addresses these limitations by providing a digital platform for collaborative, real-time data entry and visualization, thereby enhancing data accuracy and collection efficiency. Initially created for the Specialized Treatment Early in Psychosis (STEP) program in New Haven, Connecticut, its design allows for straightforward adaptation to other healthcare contexts, facilitated by its open-source codebase. The tool significantly simplifies the data collection process, making it more efficient and user-friendly. It automates the conversion of collected data into a format ready for analysis, reducing manual transcription errors and saving time. By enabling more detailed and consistent data collection, TimelinePTC has the potential to improve healthcare access research, supporting the development of targeted interventions to reduce DUP and improve patient outcomes. [ABSTRACT FROM AUTHOR]

Spatial interaction models with spatial origin-destination (OD) filters are powerful tools to characterize trip flows in space, which is a classic and important problem in regional science. To the authors' knowledge, existing studies adopting OD filters mostly specify the spatial dependence as an autoregressive process, which may not be the full picture of spatial effects. To examine the problem, this paper proposes the hypotheses that 1) spatial OD dependences can take place in both the spatial autoregressive term and the spatial error term in a spatial interaction model. 2) Estimating a spatial autoregressive model with spatial autoregressive disturbances (SARAR) model with OD filters would disentangle where the spatial dependence exists and by how much. 3) The marginal effects obtained from SARAR models would be preferred to analysts when SARAR models outperform spatial autoregressive (SAR) models and spatial error models (SEM) from the statistical point of view. To assess these hypotheses, this paper specifies, estimates, and applies SARAR models with OD filters to investigate trip distributions. By comparing against alternative models, this paper investigates the estimation results in SAR, SEM and SARAR models using an empirical data collected from Hangzhou, China. The contribution of this paper is to be the first in developing an SARAR model with OD filters for trip distribution analyses and examining its performance. [ABSTRACT FROM AUTHOR]

The global rise of data volume from Zettabyte to Yottabyte raises concerns about effectively managing its use and storage. One concern is dark data, which needs to be tapped and used for critical decision-making and insights generation. This phenomenon is a reality in the data economy that requires a solution through productization. The term data economy refers to the expanding economic activity centered around data collection, analysis, and sale. With the digitization of society and the exponential growth of the internet and social media, individuals, organizations, and society are continuously generating and collecting massive amounts of data. This data can be invaluable for companies and institutions to gain insights into customer behavior, market trends, and the performance of their businesses. Despite the scholarly literature that provides some insights, there remains a need for further exploration and clarification of the intersection between the data economy and productization. This study addresses these unresolved issues by thoroughly analyzing academic and industrial literature. The study contributes to the existing body of knowledge by extending the TTF (Task-Technology Fit) theory to conceptualize the relationship between data economy and productization. The study also proposes a comprehensive framework for countries, organizations, and institutions and offers theoretical and managerial implications. It is important to note that this study has its limitations, and future research should address these limitations and further advance the understanding in this research domain. [ABSTRACT FROM AUTHOR]

Global science is set to experience different times in the 2020s. China surpasses the USA in terms of the number of scientific papers in 2020 in most scientific databases. This scenario is expected to have implications not only in East Asia but also beyond the region. Against this backdrop, this paper investigates the scientific influence of the US-China competition on six major Muslim-majority science systems. Multiple data sources are used to collect data for the bibliometric analyses, which include the trend analysis of collaboration patterns, discipline-based collaborations, authorship patterns for responsibility in collaborations, and citation premium of collaborations with the USA versus China. All the analyses consistently demonstrate that the USA is losing its scientific influence on the selected Muslim-majority science systems to China. Analysis results indicate an astonishing increase in collaborations with China-based scientists in the last decade. Half of the selected Muslim-majority science systems collaborate more with China-based scientists, whereas the other half collaborate more with the USA in 2021, indicating a fifty-fifty split. The collaborations with China-based authors garner higher citation premiums for the selected Muslim-majority science systems than the collaborations with the USA. [ABSTRACT FROM AUTHOR]

This paper critically evaluates the conventional insistence on establishing measurement invariance (MI) in cross-cultural psychology. We argue that complex and seemingly arbitrary benchmarks for assessing MI can be unrealistic and effectively prohibit meaningful research. The widespread use of various MI criteria creates unnecessary and often unattainable hurdles for cross-cultural researchers who have made the effort to collect data in multiple cultural contexts. Additionally, the prohibitionist tone of discussions surrounding MI is unhelpful, unscientific, and discouraging. We argue that emerging findings that cultural differences might not be as widespread or profound as once assumed imply that significant cross-cultural differences in measurement should not be the default assumption. Additionally, we advocate a shift towards external validity as a more useful metric of measurement quality. Our overall message is that researchers who go to the considerable trouble of gathering data in more than one country should not be disadvantaged compared to researchers who avoid cross-cultural complications by gathering data only at their home campus. [ABSTRACT FROM AUTHOR]

Copyright of Automobile Technology & Material is the property of Automobile Technology & Material Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The proliferation of GPS-enabled devices and advances in positioning technologies have greatly facilitated the collection of user location data, making them valuable across various domains. One of the most common and practical uses of these location datasets is to recommend the most probable route between two locations to users. Traditional algorithms for route recommendation rely on true trajectory data collected from users, which raises significant privacy concerns due to the personal information often contained in location data. Therefore, in this paper, we propose a novel framework for computing optimal routes using location data collected through differential privacy (DP)-based privacy-preserving methods. The proposed framework introduces a method for accurately extracting transitional probabilities from perturbed trajectory datasets, addressing the challenge of low data utility caused by DP-based methods. Specifically, to effectively compute transitional probabilities, we present a density-adjusted sampling method that enables the collection of representative data across all areas. In addition, we introduce an effective scheme to approximately estimate transitional probabilities based on sampled datasets. Experimental results on real-world data demonstrate the practical applicability and effectiveness of our framework in computing optimal routes while preserving user privacy. [ABSTRACT FROM AUTHOR]

Due to the high detection efficiency of the airborne time-domain electromagnetic method, it can quickly collect electromagnetic response data for large area-wide regions, but it also brings great challenges to the inversion interpretation of the data because there are numerous survey data that need to be inverted. Conventional optimal inversion and fast imaging methods still take a long time to obtain conductivity and depth information, which affect the efficiency of real-time data interpretation. In this paper, we present a deep learning inversion method that can be used to solve the fast inversion problem of airborne time-domain electromagnetic data; the method uses a one-dimensional convolutional neural network. The network structure consists of two parts containing different numbers of convolutional and pooling layers. The training sample dataset was generated via two ways of constructing geoelectric models through forward modelling. To check the effectiveness of our deep learning inversion strategy, we tested it on synthetic data and two types of survey data. The experimental results show that this inversion method is effective and that it can be applied to airborne time-domain electromagnetic data collected using different observation systems. The proposed inversion method can obtain better inversion results for both simple and complex stratigraphic structures and requires significantly less computation time compared to conventional optimal inversion methods. [ABSTRACT FROM AUTHOR]

Robotic grasping is a common operation scenario in industry and agriculture, in which the force sensing function is a significant factor to achieve reliable grasping. Existing force sensing methods of flexible grippers require intelligent materials or force sensors embedded in the flexible gripper, which causes such problems of higher manufacturing requirements and contact surface properties changing. In this paper, a novel rope-driven force sensing flexible gripper is designed based on the fin-shaped gripper structure, which can realize the grasping sensing functions of contact nodes and contact forces without the need for force sensors. Firstly, the rope-driven force sensing flexible gripper is designed, including the driving unit, the transmission part, the gripper unit, and the force sensing unit. The force sensing unit and the gripper unit are connected by rope, and the prototype of the rope-driven force sensing flexible gripper is completed. Secondly, a force sensing algorithm and control system based on finite element method and grasping geometric relationship are designed to realize the rope-driven force sensing flexible gripper grasping control and sensor data acquisition and processing. Finally, the experimental system of the rope-driven force sensing flexible gripper is built, and the grasping experimental tests of objects with different diameters and different contact nodes are carried out to verify the force sensing function of the rope-driven force sensing flexible gripper. The force sensing flexible gripper designed in this paper can provide a new idea for the design and force sensing method of intelligent robotic grasping system in robotic teaching, scientific research, and industrial applications. [ABSTRACT FROM AUTHOR]

Detrital zircon U–Pb chronology database of Precambrian deposits provides a context for the interpretation of the origin and evolution of ancient crust. Here, we tried to summarize the published literature containing detrital zircon U–Pb ages and Hf isotope data of Precambrian deposits in South China and then established a database, which contains details of information, such as reference, sample ID, locality, rock type, research institution, GPS coordinates, U–Pb ages and εHf(t) values. The data statistics of this paper rely on the OneSediment Working Group of The Deep‐time Digital Earth program (DDE). By November 2022, 610 samples with 38,278 U–Pb ages and 8,798 Lu‐Hf isotope data were collected from 136 papers, and these data can be downloaded from DDE Data Publish & Repository website, https://repository.deep‐time.org/. The purpose of the establishment of the dataset is to provide guidance and convenience for the research direction of future generations in South China and to improve the previous studies through the integrated data to avoid the waste of resources caused by a large number of repeated studies. [ABSTRACT FROM AUTHOR]

Objective: This study aimed to examine changes in the demographics and regions of work of Australian doctors over a period of 9 years. Methods: A retrospective study of Australian Health Practitioner Regulation Agency (AHPRA) registration data was performed. Data were sourced from the Health Workforce Dataset Online Data Tool which was derived from annual registration data from AHPRA for 2013–2022. The demographic factors analysed were gender, age, and origin of qualification. Regions of work were defined using the Modified Monash (MM) model. Results: The number of registered doctors increased from 82,408 in 2013 to 111,908 in 2022 but mean hours worked per week decreased from 41 to 39 leading up to 2020. Trends in age and origin of qualification remained stable, but the proportion of female workers increased from 38.5% in 2013 to 44.5% in 2022. While female hours per week (~37) were consistent from 2013 to 2020, male hours per week decreased from 43 to 41. The number of registrations and total full-time equivalent (FTE) outcomes increased consistently in metropolitan (MM1) and rural (MM2–5) regions but did not increase for remote and very remote (MM6–7) regions. Conclusion: The Australian medical workforce both grew and changed between 2013 and 2022. An overall decrease in mean hours worked appears to be driven by a decline in hours worked by male workers and the increased proportion of female workers. While increased gender parity and a decrease in hours worked per week were positive outcomes, a lack of growth in the medical workforce was noted in remote and very remote regions of Australia. What is known about the topic? Changes in the medical workforce over time are likely to impact health service delivery. What does this paper add? This paper examined changes in the gender, age, origin of qualification, and the region of work of Australia's medical workforce from 2013 to 2022. What are the implications for practitioners? An increase in gender parity and a decrease in the mean hours worked suggests improvements in workforce participation and sustainability, but lack of growth in medical workforce was noted in remote and very remote regions of Australia. [ABSTRACT FROM AUTHOR]

In this paper, we describe the challenges of collecting data in the Māori population for automatic detection of schizophrenia using natural language processing (NLP). Existing psychometric tools for detecting are wide ranging and do not meet the health needs of indigenous persons considered at risk of developing psychosis and/or schizophrenia. Automated methods using NLP have been developed to detect psychosis and schizophrenia but lack cultural nuance in their designs. Research incorporating the cultural aspects relevant to indigenous communities is lacking in the design of existing automatic prediction tools and one of the main reasons is the scarcity of data from indigenous populations. This paper explores the current design of the New Zealand health care system and its potential impacts on access and inequities in the Māori population and details the methodology used to collect speech samples of Māori at risk of developing psychosis and schizophrenia. The paper also describes the major obstacles faced during speech data collection, key findings, and probable solutions. [ABSTRACT FROM AUTHOR]

As the Internet of Vehicles advances, the demand for timely data acquisition by vehicle users continues to escalate, albeit confronted with the challenge of excessive data retrieval latency. The emergence of edge computing provides technical support for the development of vehicular networks by caching data in advance to reduce data acquisition latency. Therefore, how to effectively cache and query data becomes a key issue in addressing the timeliness of data acquisition in vehicular networks. In this paper, we investigate an efficient query optimization model to minimize data acquisition latency. Firstly, based on the distribution of data query frequencies across different servers, we propose an edge collaborative caching strategy using a tabu search algorithm. This strategy prioritizes high-traffic data by finding two optimal storage nodes for each high-traffic data in descending order of data popularity, ensuring a backup within the collaborative domain for each data segment. This not only reduces data transmission latency between nodes during task execution but also prevents single-point failures. Secondly, we deploy cuckoo filters on edge nodes to enable rapid localization of cached data nodes when users query data, thus reducing data processing latency. Finally, simulation results demonstrate that the proposed query optimization model outperforms other schemes in terms of average data query latency. [ABSTRACT FROM AUTHOR]

As a critical component of substations, the substation frames are characterized by significant height and span, which presents substantial challenges and risks in conducting dynamic response tests using traditional sensors. To simplify these difficulties, this paper introduces an experimental method utilizing MEMS sensor-based vibration acquisition. In this approach, smartphones equipped with MEMS sensors are deployed on the target structure to collect vibration data under environmental excitation. This method was applied in a dynamic field test of a novel composite substation frame. During the test, the proposed MEMS-based vibration acquisition method was conducted in parallel with traditional ultra-low-frequency vibration acquisition methods to validate the accuracy of the MEMS data. The results demonstrated that the MEMS sensors not only simplified the testing process but also provided reliable data, offering greater advantages in testing convenience compared with traditional contact methods. The modal parameters of the substation frame, including modal frequencies, damping ratios, and mode shapes, were subsequently identified using the covariance-driven stochastic subspace identification method. The experimental methodology and findings presented in this paper offer valuable insights for structural dynamic response testing and the wind-resistant design of substation frames. [ABSTRACT FROM AUTHOR]

This paper describes a measuring unit for synchronously collecting the air dose rate and measurement position for efficient dosimetry surveying and data logging. The developed prototype comprises a three-dimensional light detection and ranging-based mapping part and dosimetry part, which are integrated into a single measuring unit through an embedded computer that installs a ROS (robot operating system) framework. The unit can function as a standalone system with embedded batteries. Since it is portable, on-line data gathering in the workspace can be realized, thereby maintaining consistency between the air dose rate and measurement position. In this paper, we describe the functional requirements for the measuring unit, the prototype system configuration, and the experimental results obtained in the mockup environment and nuclear facility to discuss its performance. [ABSTRACT FROM AUTHOR]

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Digital life is an integral part of our life these days. Intelligently crafted front ends of platforms often make us addicted to it. In this scenario, the research concerning digital nudging requires our attention. This paper attempts to mine insights from the research done so far using bibliometric analysis. Data was collected from the Scopus database based on the publications from 2005 to 2024. The study's objective is to perform performance analysis and co-occurrence analysis of obtained data to discover the themes and emerging research areas concerning digital nudging. VOSviewer software is used for analysing the data. Four themes relating to digital nudging could be identified through the study. Interestingly, the study also uncovered the increase of study among student groups. [ABSTRACT FROM AUTHOR]

Zadeh introduced fuzzy sets to study imprecision in real life after which many variants of fuzzy sets have been developed in literature. In this paper, new variants of fuzzy sets named Hybrid fuzzy sets and Hybrid intuitionistic fuzzy sets have been defined based on some real life scenarios where conventional fuzzy sets are not applicable for dealing Multi-Criteria Decision Making (MCDM) problems. Further, total ordering algorithms on those proposed sets based on scoring functions are defined and applied in MCDM problems where decision matrix is incomplete with respect to some criteria. The major drawback of MCDM problems is non-availability / missing of data values for some alternatives with respect to some criteria while collecting data from the experts. To overcome this, researchers infuse the missing data for one expert from the corresponding data collected from the other expert or deleting that criteria to form a single complete MCDM table. But there are situations in which decisions should be taken with single MCDM matrix with missing data points. In this paper, we have proposed a new method in which we find the unknown data from known values / other entries of the table using relative performances of the alternatives with respect to other criteria through which we attain a ranking of the alternatives of MCDM using total ordering defined on the hybrid intuitionistic fuzzy sets. Further, we proposed another method in which we obtain the score of missing values based on the scores defined on the hybrid intuitionistic fuzzy sets through which final decision / ranking is made. Finally, the applicabilities of new types of fuzzy sets and total ordering methods are established in the field of Fuzzy decision making by comparing with existing imputation methods in the literature. [ABSTRACT FROM AUTHOR]

Highlights Mosquito adulticiding over operationally relevant areas is feasible using UASS. Wind speed plays an important role in the spray landing position, as expected based on previous studies using CASA. Results are based on both passive and active collectors, including flat cards, rotary impingers, and bottle brushes. Over 1900 total samples were collected in 8 trials, and elevated samplers allowed analysis of the descent of the droplet cloud. A spatially extensive experiment was designed to evaluate the effectiveness of Uncrewed Aerial Spray Systems (UASS) in mosquito adulticiding. The practice of adulticiding uses very fine droplets (DV0.5 <50µm). The objective is to fill a volume of air with fine droplets that a flying adult mosquito will encounter in flight. A field program was designed with samplers as far as 500 m downwind from the UASS flight line. A total of over 1900 samples were collected over the course of eight trials. Both passive and active collectors were deployed. Tests were conducted in the early morning and were generally successful in terms of meteorological conditions and data collection. Elevated samplers successfully tracked the descent of the spray cloud, and spray was collected from the most distant samplers. The known dependence of spray movement on wind speed that has been widely observed and codified into label language for Crewed Aerial Spray Aircraft (CASA) was observed. Very low wind speed conditions are problematic for adulticiding in general, and undesirable near-field deposition of adulticiding spray was observed in the data presented here under low wind speed conditions. It needs to be emphasized that much more data needs to be collected before statistically robust conclusions can be drawn. Generally, these data show that UASS can be used to disperse very fine spray droplets over areas representative of operational adulticiding. This paper presents and discusses spray movement and deposition, a second paper is in preparation comparing these data to existing models of spray dispersion and deposition from UASS spraying. A third paper examining mortality from UASS adulticiding has been published. [ABSTRACT FROM AUTHOR]

Purpose: The rapid growth of the mobile money industry has been matched by a rise in mobile money fraud. The technology required to apprehend perpetrators of such fraud is nonexistent in most developing countries. Hence, the need for individuals to be willing to pay for insurance against such frauds is crucial. This paper aims to examine individuals' willingness to pay for insurance against mobile money fraud in Ghana. Design/methodology/approach: The paper uses nationally representative data collected from 4,266 adults (persons 18 years and above) in Ghana. Individuals' willingness to pay premiums for protection against mobile money fraud was elicited by a single-bound dichotomous choice and open-ended contingent valuation designs. Findings: On average, 24.34% of Ghanaians are willing to pay premiums for insurance against mobile money frauds, with more men (26.37%) being willing than women (22.56%). Similarly, the average monthly premium that men are willing to pay for protection against mobile money fraud is GH¢32.16 (US$8.16), while that of women is GH¢22.5 (US$5.62). Furthermore, the results show that years of schooling, income, previous fraud experience, and using the accounts for saving are all positively associated with willingness to pay. However, using other networks apart from MTN has a negative association with willingness to pay. Originality/value: To the best of our knowledge, this is the first study that examines willingness to pay for insurance against mobile money fraud. Thus, this is the first that estimate quantitatively how much mobile account holders will pay as premiums for insurance against mobile money fraud. [ABSTRACT FROM AUTHOR]

Grand challenges such as achieving sustainability goals and managing aging infrastructure are creating an unprecedented demand for deconstruction. However, deconstructing aging infrastructure is inherently risky, repetitive, and costly, necessitating effective project planning. Virtual reality (VR) technology offers the potential for planners to improve deconstruction project risk, time, safety, and cost. We propose that planners leverage VR early in the planning phase to compare alternative feasible tools and processes. Planners can create low-fidelity models of various deconstruction process alternatives and collect metrics on their suitability with VR-capable three-dimensional (3D) game engines. We present and formalize a methodology for conducting comparisons of candidate deconstruction processes by modeling candidates in VR, conducting trials, and collecting analytics data from the VR engine. We present a case study that demonstrates our approach to a cutting and waste packing process for nuclear power plant (NPP) decommissioning. As a novel contribution in this paper, we show that VR simulations can efficiently produce detailed insights useful for critically analyzing and comparing deconstruction process alternatives. [ABSTRACT FROM AUTHOR]

In this paper, we study the Greek wiretappings scandal, which was revealed in 2022 and attracted significant attention from the press and citizens. Specifically, we propose a methodology for collecting data and analyzing patterns of online public discussions on Twitter. We apply our methodology to the Greek wiretappings use case and present findings related to the evolution of the discussion over time, its polarization, and the role of the Media. The methodology can be of wider use and replicated to other topics. Finally, we publicly provide an open dataset and online resources with the results. [ABSTRACT FROM AUTHOR]

The increasing frequency and sophistication of cyber-attacks pose significant threats to organizational entities and critical national infrastructure, leading to substantial financial and operational consequences. Detecting such attacks early and accurately remains a complex endeavour, compounded by challenges in intrusion detection system (IDS) design, the exploitation of zero-day attacks, and issues of reliability and resiliency in physical systems. This research addresses these challenges through a two-fold approach: firstly, implementing input data fusion from diverse and heterogeneous sources, and secondly, fusing classifiers from multiple deep learning (DL)-based algorithms. The success of machine learning (ML) and DL models for IDS relies on meticulous data collection and classifier selection. The paper underscores the limitations of relying on single datasets and ML/DL algorithms, emphasizing potential biases and training restrictions. Rigorous experiments were conducted to identify optimal DL architectures, ensuring the creation of models that exhibit robust generalization on new traffic instances, leading to trusted and unbiased results. The study demonstrates the efficacy of the proposed models through comprehensive evaluations and metrics. Results indicate that the fusion of data and classifiers significantly improves model generalization. The paper also outlines key challenges and future trends in data fusion, emphasizing its role in enhancing IDS performance for securing critical infrastructure. [ABSTRACT FROM AUTHOR]

Urban transformation in the post-socialist city is connected to comprehensive debates. This paper steers the reader through these, emphasising three contentions that serve to clarify the approach to two interconnected questions: How do people give meaning to urban transformation? In which ways does an analysis of societal change in a peripheral city in pre-war Russia contribute to debates on global urbanism? The paper is structured in three main parts, beginning with the review of the post-socialist city literature. The focus in this study is on experiences, bringing a more human perspective on urban transformation in combination with analysis of structural dimensions impeding on the everyday. In the second part, Togliatti is introduced as a research site and details on data collection are provided. The paper contributes empirically with a study of urban transformation in the auto-town Togliatti in the Samara region southeast of Moscow, Russia. The third part is devoted to interlocutors' perspectives and the emergence of the narrative of 'becoming marginal'. In conclusion, becoming marginal is a narrative that gives meaning to the historical conjuncture and the grievances people reflect on as they reason about individual life trajectories. While place-specific, these experiences link to global (urban) discussions on marginality and precarity. The paper underscores that making sense of experiences means to reflect on these in context, and in a final discussion, it reflects how much more difficult such research has become after Russia's military attack on Ukraine on 24 February 2022. [ABSTRACT FROM AUTHOR]

Aims: To describe a reflexive account of a mental health nurse as researcher undertaking a mixed methods study as part of a doctoral degree, with women in a secure forensic hospital. Design: A discursive paper that draws upon the primary author's experience of dual roles as a mental health nurse and a novice researcher, and relevant literature pertaining to positionality and reflexivity in the context of conducting mixed methods research. Methods: A mixed methods study was conducted within a secure forensic hospital in Australia, to identify factors associated with the frequency and duration of seclusion for women and, to explore their experiences of seclusion. Notes and a reflective diary were used to demonstrate the reflexive approach and strategies used throughout the study design and data collection stages of the research. Results: Women in the secure forensic hospital setting often have complex histories and experiences and are considered a vulnerable group, which can add additional challenges when undertaking research with this population. As a doctoral student with clinical experience in mental health undertaking research in the forensic hospital setting with women, positionality and the connection with reflexivity requires exploration to understand self and the influence on research. This understanding along with identified strategies to enhance reflexivity, supports the management of the dual nurse as researcher roles to enhance all stages of the research process. Conclusion: Doctoral nursing candidates undertaking clinical research in their area of clinical practice and at the location of previous employment, may experience challenges in relation to identity and the dichotomy of roles. Further challenges can be posed when research involves complex populations and/or controversial practices. Support to balance role conflict and maintain reflexivity is critical to understanding the role of self in research and to enhance credibility. Implications for the Profession and/or Clinical Practice: Women are a complex, yet minority population in forensic mental health settings, however the numbers of women being admitted to services is increasing. Future research may consider the use of the strategies explored here to enhance the reflexive process, and support future researchers in this field to conduct research that aims to support an often‐misheard population to better support health outcomes. Patient or Public Involvement: There was no patient or public contribution to this paper, however the study in which this paper is based on, is grounded in the experience of patients (women). [ABSTRACT FROM AUTHOR]

Regulatory failure caused by overlapping regulations is ubiquitous, with examples in all jurisdictions across a range of disciplines. Overlapping regulation can be problematic. It obscures policy objectives and hinders the development of effective and clear regulation. In addition, regulatory overlap can inflict real costs on businesses through repetitive inspections and data collection efforts. It is particularly burdensome when agencies issue conflicting rules with inconsistent standards. Recognizing that regulatory overlap exists and is a problem provides the context to this program of research. Our research project was an exploration using a systematic quantitative literature review (SQLR) method to better understand the way regulatory failure, caused by overlapping regulations, has featured in academic literature. The SQLR method was chosen as it employs a systematic process to consolidate a sample of literature, and quantitative measures to draw connections between different academic sources. Ultimately, our research concluded that the literature does not provide clear prescriptive principles for reducing unnecessary regulatory overlap. This begs a question as to whether more research is needed in this area, or alternatively whether the complexities raised by regulatory overlap are not reducible to general principles. [ABSTRACT FROM AUTHOR]

Over the past few years, the emergence of electrochemical wearable sensors has attracted considerable attention because of their promising application in point‐of‐care testing due to some features such as high sensitivity, simplicity, miniaturization, and low fabrication cost. Recent developments in new fabrication approaches and innovative substrates have resulted in sensors able to real‐time and on‐body measurements. Wearable electrochemical sensors have also been combined with paper‐based substrates and directly used on human skin for different applications for non‐invasive analyses. Furthermore, wearable electrochemical sensors enable monitoring analytes in different biofluids without complex procedures, such as pre‐treatment or sample manipulation. The coupling of IoT to various wearable sensors has also attracted attention due to real‐time data collection and handling in remote and resource‐limited conditions. This mini‐review presents the significant advances in developing wearable electrochemical devices, such as sampling, data collection, connection protocols, and power sources, and discusses some critical challenges for higher performance in this field. We also present an overview of the application of paper as an intelligent substrate for electrochemical wearable sensors and discuss their advantages and drawbacks. Lastly, conclude by highlighting the future advances in wearable sensors and diagnostics by coupling real‐time and on‐body measurements to multiplexed detection of different biomarkers simultaneously, reducing the cost and time of classical analysis to provide fast and complete overall physiological conditions to the wearer. [ABSTRACT FROM AUTHOR]

The data paper is an emerging academic genre that focuses on the description of research data objects. However, there is a lack of empirical knowledge about this rising genre in quantitative science studies, particularly from the perspective of its linguistic features. To fill this gap, this research aims to offer a first quantitative examination of which rhetorical moves—rhetorical units performing a coherent narrative function—are used in data paper abstracts, as well as how these moves are used. To this end, we developed a new classification scheme for rhetorical moves in data paper abstracts by expanding a well‐received system that focuses on English‐language research article abstracts. We used this expanded scheme to classify and analyze rhetorical moves used in two flagship data journals, Scientific Data and Data in Brief. We found that data papers exhibit a combination of introduction, method, results, and discussion‐ and data‐oriented moves and that the usage differences between the journals can be largely explained by journal policies concerning abstract and paper structure. This research offers a novel examination of how the data paper, a data‐oriented knowledge representation, is composed, which greatly contributes to a deeper understanding of research data and its publication in the scholarly communication system. [ABSTRACT FROM AUTHOR]

With the advancement of mobile technology, mobile-assisted language learning (MALL) has significant potential regarding its practical applications and benefits in foreign language learning. Nevertheless, little research was conducted to examine factors regarding the facilitation of oral communication through MALL based on established theories or models. The purpose of this review is to investigate the main constituents conducive to the intended outcomes based on an adapted model from Beatty (2010) and further provide guidelines for enthusiastic stakeholders in the field. Twenty-eight empirical studies were collected and categorized according to four key variables of the design model and analyzed qualitatively, with the key findings identified as follows. Due to the technical affordance of mobile technology, it is found that a student-centered self-regulated learning context is created, where students construct knowledge through self-instruction, self-evaluation, and self-correction. Furthermore, a speaking strategy-driven collaborative-based learning design enhances students' oral proficiency through strong social connections, interactions, and communication. Finally, given the pedagogical design and practices, high-level cognitive thinking is thereby promoted, with promising affective learning outcomes. In light of the findings, guidelines for educational practitioners, learners, and system designers are provided for pedagogical and practical application in the future. [ABSTRACT FROM AUTHOR]

Collision accidents involving construction vehicles and workers frequently occur at construction sites. Computer vision (CV) technology presents an efficient solution for collision-risk pre-warning. However, CV-based methods are still relatively rare and need an enhancement of their performance. Therefore, a novel three-stage collision-risk pre-warning model for construction vehicles and workers is proposed in this paper. This model consists of an object-sensing module (OSM), a trajectory prediction module (TPM), and a collision-risk assessment module (CRAM). In the OSM, the YOLOv5 algorithm is applied to identify and locate construction vehicles and workers; meanwhile, the DeepSORT algorithm is applied to the real-time tracking of the construction vehicles and workers. As a result, the historical trajectories of vehicles and workers are sensed. The original coordinates of the data are transformed to common real-world coordinate systems for convenient subsequent data acquisition, comparison, and analysis. Subsequently, the data are provided to a second stage (TPM). In the TPM, the optimized transformer algorithm is used for a real-time trajectory prediction of the construction vehicles and workers. In this paper, we enhance the reliability of the general object detection and trajectory prediction methods in the construction environments. With the assistance afforded by the optimization of the model's hyperparameters, the prediction horizon is extended, and this gives the workers more time to take preventive measures. Finally, the prediction module indicates the possible trajectories of the vehicles and workers in the future and provides these trajectories to the CRAM. In the CRAM, the worker's collision-risk level is assessed by a multi-factor-based collision-risk assessment rule, which is innovatively proposed in the present work. The multi-factor-based assessment rule is quantitatively involved in three critical risk factors, i.e., velocity, hazardous zones, and proximity. Experiments are performed within two different construction site scenarios to evaluate the effectiveness of the collision-risk pre-warning model. The research results show that the proposed collision pre-warning model can accurately predict the collision-risk level of workers at construction sites, with good tracking and predicting effect and an efficient collision-risk pre-warning strategy. Compared to the classical models, such as social-GAN and social-LSTM, the transformer-based trajectory prediction model demonstrates a superior accuracy, with an average displacement error of 0.53 m on the construction sites. Additionally, the optimized transformer model is capable of predicting six additional time steps, which equates to approximately 1.8 s. The collision pre-warning model proposed in this paper can help improve the safety of construction vehicles and workers. [ABSTRACT FROM AUTHOR]

Aiming at the received signal strength index (RSSI) in wireless positioning system, an improved weighted KNN fingerprint positioning algorithm is proposed in this paper. The algorithm pre-processes fingerprint data in offline stage that including eliminating outliers and Kalman filtering first, in order to improve the accuracy of data acquisition. Secondly, the fingerprint data is partitioned by using RSSI to attenuate obstacles such as walls. Then, points with significant RSSI differences in each region are selected as regional feature points, and the distance between RSSI of test points and feature points in each region is calculated respectively to determine the region in which the test points are located. Geometric method is used to analyse and define the correlation degree, and KNN is re-weighted to achieve accurate positioning in the region. Finally, experiments were carried out in the indoor environment to complete the establishment of the fingerprint database. Compared with the existing NN, KNN and WKNN, the experimental analysis results show that the accumulated error and average error are better than the traditional algorithm with the increase of measurement points, which has reference value for the complex environment positioning technology. [ABSTRACT FROM AUTHOR]

For the estimation of global karst carbon sink, a few conventional methods usually require the parameters that are difficult to measure, resulting in the big cost. Moreover, under the constraints of incomplete and timeliness issues in the collection of data over a large region, it has remained a challenge for these methods to study global karst carbon sink. Therefore, this paper proposes estimating the global karst carbon sink, and analyzing the suitability of the response surface methodology and the fluctuating variation of karst carbon sink in global karst regions from 1951 to 2050. This paper shows that the proposed method can reduce the time of numerical calculation and is suitable for application in global weathering models; The global karst carbon sink in the future changes not only displays an upward trend but also exposures its fluctuating trend largely. This fluctuation is probably due to global warming. [ABSTRACT FROM AUTHOR]

Recently, deep learning (DL) based dynamic security assessment (DSA) methods have been very successful. However, although a DSA model can be trained well for a specific topology, it often does not perform well for other topologies. Since the topology in real‐world power systems is frequently changing, the performance reduction of DL‐based DSA methods is very serious, which is a challenging and urgent problem. This paper proposes a novel DSA method based on a convolutional neural network (CNN) to solve this problem. In the proposed method, a strong yet simple domain adaptation approach named adaptive batch normalization (AdaBN) is used, which significantly enhances the extensibility and generalizability of the DSA model when the topology changes and eliminates the need to train a large number of models. This approach achieves a deep adaptation effect by modulating the statistics from the source domain to the target domain in all batch normalization layers across the model. Unlike other domain adaptation methods, this method is parameter‐free, requires no additional components, and has advanced performance despite its simplicity. In addition, this paper introduces TGAN‐based data augmentation to deal with the difficulty of costly data collection and labelling. This data augmentation makes the proposed model applicable to small databases. The test results of the proposed method on IEEE 39‐bus and IEEE 118‐bus systems show that this method can evaluate system dynamic security during topology changes and in the face of data noise with high accuracy. [ABSTRACT FROM AUTHOR]