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Data collection and data preprocessing are crucial stages in web usage mining, mainly because of the unstructured, diverse, and noisy nature of log data. During data collection, log file datasets are loaded and merged. Effective and comprehensive data preprocessing plays a vital role in ensuring the efficiency and scalability of algorithms used in the pattern discovery phase of web usage mining. This work aims to address these phases by introducing two innovative approaches. The first approach focuses on determining the device used for accessing the web, distinguishing between computers and mobile devices. The second approach aims to determine user sessions and complete paths by utilizing the referrer URL. The entire preprocessing pipeline has been implemented using the C# programming language, and the source code is available on GitHub at the following link: https://github.com/Mohammed91/Web-Usage-Mining.

The increasing of complexity of urban traffic call for innovative approaches in monitoring and management. While legacy, ground-based methods struggle with problems like a lack of complete sensing as well as latency, preventing real-time insights and response ability. This article investigates how cooperative Device-to-Device (D2D) communications with aid of Unmanned Aerial Vehicle (UAV) could potentially disrupt the conventional drone-assisted traffic monitoring in 5G networks. In this work, we introduce a new system to utilize the data collection possibility of drones in a distributed way, which can facilitate more effective urban data acquisition and assimilation. The article conducted extensive simulations in various traffic conditions to demonstrate the effectiveness of our system. The results were compared with traditional ground-based monitoring metrics, while against a drone-based system that without cooperative D2D communications. The proposed approach was evaluated through a number of wide-range simulations in diverse traffic scenarios. Simulation results indicate significant improvements in data collection and distribution over conventional strategies. Our proposed system on average improved the throughput by 35%, and latency by 45% over traditional ground-based monitoring methods. The energy efficiency of the system was also investigated, considering the UAVs' short flight durations. Results also showed the D2D communication technique, which increased through boosting by using drones, helps reduce energy consumption by 25% relative to traditional methods, maximizing drone uptime. This study indicates that cooperative D2D communications of drones can be useful for traffic monitoring. This technique helps to reduce the latency and atrocity with which data are collected for intelligent transport systems, thereby enhances the traffic management and road safety.

Previous articles in this series have looked at various aspects of planning, designing, conducting and interpreting biomedical research. In this article, we offer an overview of some tools and resources available to health and biomedical researchers, to help them with their research.

Abstract Background A decade ago paper questionnaires were more common in epidemiology than those administered online, but increasing Internet access may have changed this. Researchers planning to use a self-administered questionnaire should know whether response rates to questionnaires administered electronically differ to those of questionnaires administered by post. We analysed trials included in a recently updated Cochrane Review to answer this question. Methods We exported data of randomised controlled trials included in three comparisons in the Cochrane Review that had evaluated hypotheses relevant to our research objective and imported them into Stata for a series of meta-analyses not conducted in the Cochrane review. We pooled odds ratios for response using random effects meta-analyses. We explored causes of heterogeneity among study results using subgroups. We assessed evidence for reporting bias using Harbord’s modified test for small-study effects. Results Twenty-seven trials (66,118 participants) evaluated the effect on response of an electronic questionnaire compared with postal. Results were heterogeneous (I-squared = 98%). There was evidence for biased (greater) effect estimates in studies at high risk of bias; A synthesis of studies at low risk of bias indicates that response was increased (OR = 1.43; 95% CI 1.08–1.89) using postal questionnaires. Ten trials (39,523 participants) evaluated the effect of providing a choice of mode (postal or electronic) compared to an electronic questionnaire only. Response was increased with a choice of mode (OR = 1.63; 95% CI 1.18–2.26). Eight trials (20,909 participants) evaluated the effect of a choice of mode (electronic or postal) compared to a postal questionnaire only. There was no evidence for an effect on response of a choice of mode compared with postal only (OR = 0.94; 95% CI 0.86–1.02). Conclusions Postal questionnaires should be used in preference to, or offered in addition to, electronic modes.

Standardised forms for capturing clinical data promote consistency in data collection and analysis across research sites, enabling faster, higher-quality evidence generation. ISARIC and the World Health Organization have developed case report forms (CRFs) for the clinical characterisation of several infectious disease outbreaks. To improve the design and quality of future forms, we analysed the inclusion and completion rates of the 243 fields on the ISARIC-WHO COVID-19 CRF. Data from 42 diverse collaborations, covering 1886 hospitals and 950,064 patients, were analysed. A mean of 129.6 fields (53%) were included in the adapted CRFs implemented across the sites. Consistent patterns of field inclusion and completion aligned with globally recognised research priorities in outbreaks of novel infectious diseases. Outcome status was the most highly included (95.2%) and completed (89.8%) field, followed by admission demographics (79.1% and 91.6%), comorbidities (77.9% and 79.0%), signs and symptoms (68.9% and 78.4%), and vitals (70.3% and 69.1%). Mean field completion was higher in severe patients (70.2%) than in all patients (61.6%). The results reveal how clinical characterisation CRFs can be streamlined to reduce data collection time, including the modularisation of CRFs, to offer a choice of data volume collection and the separation of critical care interventions. This data-driven approach to designing CRFs enhances the efficiency of data collection to inform patient care and public health response.

The data used during training in any given application space are directly tied to the performance of the system once deployed. While there are many other factors that are attributed to producing high-performance models based on the Neural Scaling Law within Machine Learning, there is no doubt that the data used to train a system provide the foundation from which to build. One of the underlying heuristics used within the Machine Learning space is that having more data leads to better models, but there is no easy answer to the question, “How much data is needed to achieve the desired level of performance?” This work examines a modulation classification problem in the Radio Frequency domain space, attempting to answer the question of how many training data are required to achieve a desired level of performance, but the procedure readily applies to classification problems across modalities. The ultimate goal is to determine an approach that requires the lowest amount of data collection to better inform a more thorough collection effort to achieve the desired performance metric. By focusing on forecasting the performance of the model rather than the loss value, this approach allows for a greater intuitive understanding of data volume requirements. While this approach will require an initial dataset, the goal is to allow for the initial data collection to be orders of magnitude smaller than what is required for delivering a system that achieves the desired performance. An additional benefit of the techniques presented here is that the quality of different datasets can be numerically evaluated and tied together with the quantity of data, and ultimately, the performance of the architecture in the problem domain.

The article presents the results of a study on methods for obtaining labour market data from web sources. The relevance of the research stems from the need to explore innovative approaches to collecting and analyzing labour market data, ensuring comprehensive, reliable, and timely information in the context of the digital transformation of society. The purpose of the study is to compare the methods of collecting and analyzing information, to identify their advantages and disadvantages. Methodologically, an interdisciplinary approach combines the methods of data science, machine learning, natural language processing and human resource management. Empirically, the study draws from expert surveys conducted with heads of Belarusian IT companies and data sourced from the Artificial Analysis platform. Key findings reveal that large language models (LLM) outperform other methods in terms of speed, quality and ease of use. Among the top 10 models, GPT-4o is recognized as the most effective, prioritized for business tasks and economic analysis. The advantages and disadvantages of data collection and analysis methods are revealed. The scientific novelty of the research consists in obtaining new theoretical and empirical results that reveal the potential of using LLM to obtain data on the labour market, as well as in identifying priority areas for the development of methodology and tools in this area. The practical significance lies in the development of a methodology for comparative analysis of the effectiveness of methods for collecting and analyzing information about the labour market from web sources; substantiation of promising directions for the development of information collection methods that open up opportunities for creating innovative solutions in the field of human resource management based on artificial intelligence and big data processing. Financial support for the research was provided by the Belarusian Republican Foundation for Basic Research within the framework of the scientific project No Г24-013 “Theoretical approaches and methodological support for labour market analysis in the Republic of Belarus using big data”.

Lynx have been captured for radio-collaring as part of three research projects in Norway between 1987 and 1996. Box traps, padded leghold traps, foot snares, combinations of foot snares and leghold traps, chasing animals up trees, catching neonates at lairs, and darting individuals while crossing roads have all been used to capture individuals. Mortality was particularly high (2 of 3 died) in the first project in Aust-Agder and Nord-Trøndelag (1987 - 88). Methodological improvements reduced this mortality rate to one lynx of 17 (6%) in Nord-Trøndelag (1994 - 96) and two of 17 (12%) in Hedmark (1995 - 96). Lessons from these accidents should reduce future mortality even further. Leghold traps and foot snares placed around a carcass of a prey animal killed by the lynx are far more effective than box traps, but maintain a higher risk of injuries. Lynx appeared more likely to return to roe deer carcasses than those of semi-domestic reindeer. There was no selection for lighter lynx. It is important to reduce the time-span between capture and immobilisation to lower the risk of injuries. It is recommended to avoid capturing and immobilising lynx at extremely low or high temperatures. Non-target animals getting caught can pose a minor problem when leghold traps stay open for longer periods.

Disbursements for bounties were made locally in Norway from 1733 to 1845 for the killing of brown bears (Ursus arctos) and wolves (Canis lupus). More than 3000 bounty records of these two species from 19 parishes in central south Norway were analysed. The greatest number of bounties paid per unit of area was found in forested areas in the lowland. The number of bounties paid was positively correlated with the size of forested areas and negatively with the density of people and their wealth, represented by the number of cows per inhabitant. The gradient of the regression lines for bounties paid in relation to time in the period 1738 - 1815 was negative, even after correcting for inflation, suggesting that the bounty system caused a slow decrease in the populations of both brown bears and wolves during the 18th century. The introduction of better firearms after about 1850 led to an abrupt decline in the wolf population, which was nearly exterminated around 1870. The brown bear survived a few decades longer, but was also gradually reduced to very small numbers in the beginning of the 20th century.

The breeding populations of Atlantic Puffins Fratercula arctica, Common Guillemots Uria aalge and Black-legged Kittiwakes Rissa tridactyla have been monitored on Hornøya, East Finnmark since 1980 as part of the Norwegian seabird monitoring programme. Whereas numbers of Puffin burrows in plots monitored in 1981 - 1993 increased at a rate of 2.6% per year, there was no subsequent trend in counts made in a new scheme of circular plots started in 1990. Numbers of Common Guillemots collapsed between 1985 - 1987 but have since increased in all plots at a rate of 11.6% per year. Kittiwake numbers dropped significantly (1.5% per year) between 1980 - 1994, but stabilised between 1994 - 2000. Suggestions are made for improvements to the monitoring scheme to overcome inconsistencies in changes in numbers between plots (e.g. of Kittiwakes), to increase the sensitivity of the counts and for additions to help explain why any changes in the populations occur.

In sexually monomorphic animal species it is difficult to sex unknown individuals. The most widely used method of sexing in those cases is discriminant analysis. In species that are socially monogamous and have biparental care, however, there exists another method which utilises the intra-pair difference in the trait used in sexing. Equations for calculating the fraction of wrongly sexed individuals using both methods are given and used to show that the latter method is more exact than discriminant analysis. The advantages and limits of both methods are compared. Field data from the kittiwake (Rissa tridactyla) are used to exemplify the intra-pair comparison method, and to test the assumptions underlying this method for this species. Among other things, it is shown that kittiwakes display random mating with respect to head+bill length. Sexing kittiwakes at Hornøya, northern Norway, by intra-pair differences in head+bill length gives correct results in 96% of the cases.

Report from the Research Council of Norway’s conference on “The changing landscape”, Oslo, 27-28 March 2001.

This paper reviews how assessment has evolved in the past ten years at Carleton University in Ottawa, Ontario, Canada. From having 1.5 people devoted to assessment to one person in charge of assessment while also being Head of Collections, this paper will examine how it became imperative to determine how assessment could be integrated into the day to day workings of the library. By examining the library’s deployment of the Insync survey, reliance on external and internal university reports, assessment for strategic planning activities, and progression of how data is gathered over time, this paper will reveal how Carleton Library has reviewed the services, collection, and space in a time where assessment is important but the time to devote to it is lacking. Partnering with other departments on campus and within the library is key.

In autumn 1999, large, compact flocks of foraging diving ducks, particularly Common Eiders Somateria mollissima, were observed within and at the mouth of Trondheimsfjord in Central Norway. Some of these aggregations were at places previously not known as particularly rich feeding grounds. This situation continued through the winter, and still existed in March 2000. Investigations on these feeding grounds showed that the only possible prey species occurring in significant quantities was Sandeel (only Ammodytes tobianus was verified). Although most of the fish were swallowed before the birds reached the surface, some ducks (including Velvet Scoter Melanitta fusca and Goldeneye Bucephala clangula) were observed eating Sandeels. Sandeel populations fluctuate in size annually and shoals are very patchily distributed. Consequently, the very variable occurrence in time and space of this food resource may occasionally bring about unusually dense and patchy aggregations of various species of diving ducks. Due to the specific habitat requirements of the Sandeel, these aggregations may also occur at localities that are not generally preferred feeding grounds for diving ducks, as was the case here. These findings may be relevant for some monitoring projects involving populations of diving ducks in marine environments.

Summary: Background: Clinical trials and registry studies are essential for advancing research and developing novel treatments. However, these studies rely on manual entry of thousands of variables for each patient. Repurposing real-world data can significantly simplify the data collection, reduce transcription errors, and make the data entry process more efficient, consistent, and cost-effective. Methods: We developed an open-source computational pipeline to collect laboratory and medication information from the electronic health record (EHR) data and populate case report forms. The pipeline was developed and validated with data from two independent pediatric hospitals in the US as part of the Long-terM OUtcomes after Multisystem Inflammatory Syndrome In Children (MUSIC) study. Our pipeline allowed the completion of two of the most time-consuming forms. We compared automatically extracted results with manually entered values in one hospital and applied the pipeline to a second hospital, where the output served as the primary data source for case report forms. Findings: We extracted and populated 51,845 laboratory and 4913 medication values for 159 patients in two hospitals participating in a prospective pediatric study. We evaluated pipeline performance against data for 104 patients manually entered by clinicians in one of the hospitals. The highest concordance was found during patient hospitalization, with 91.59% of the automatically extracted laboratory and medication values corresponding with the manually entered values. In addition to the successfully populated values, we identified an additional 13,396 laboratory and 567 medication values of interest for the study. Interpretation: The automatic data entry of laboratory and medication values during admission is feasible and has a high concordance with the manually entered data. By implementing this proof of concept, we demonstrate the quality of automatic data extraction and highlight the potential of secondary use of EHR data to advance medical science by improving data entry efficiency and expediting clinical research. Funding: NIH Grant 1OT3HL147154-01, U24HL135691, UG1HL135685.

Abstract To achieve the goals of the 2030 Global Biodiversity Framework, the European Biodiversity Strategy, and the EU Green Deal, biodiversity monitoring is critical. Monitoring efforts in Europe, however, suffer from gaps and biases in taxonomy, spatial coverage, and temporal resolution, resulting in fragmented and disconnected data. To assess user and policy needs in biodiversity monitoring, we employed a four‐step user‐centered stakeholder engagement process with over 300 stakeholders including a public stakeholder workshop, online survey, interviews, and a meeting with experts from 18 EU member states, the European Commission, and the European Environment Agency. The stakeholders identified policy needs, current challenges, and potential solutions. Based on the policy and stakeholder assessment, we recommend establishing a European Biodiversity Observation Coordinating Centre to optimize existing observation efforts, harmonize data, and enhance our ability to predict and respond to key challenges related to biodiversity loss in Europe.

This article addresses the pervasive issue of missing data in biomedical research and the potential solutions to prevent it. The focus is on proactive strategies for prevention at various stages of the research process. Advocating for a comprehensive study design, clear communication through information sheets during consent, and the use of well-designed data collection tools are key to preventing missing data issues in health research. In addition, the training and supervision for data collectors, especially in the context of potential biases arising from sensitive questions, are critical. Electronic data collection has the advantages of validation and checks to prevent missing data. Hence, adequate planning is a must to reduce missing values and improve the validity of the study outcome.

Visual elements play an important role within the multimodal nature of social media (Pearce et al., 2020). A growing body of research has focused on the analysis of still and moving images from different social media platforms from various perspectives of communication and media studies (Hautea, Parks, Takahashi, & Zeng, 2021; Li & Xie, 2020; Veum & Undrum, 2018). Although the aforementioned studies describe visual data collection, their principal focus does not rely on this collection, but on data analysis. Little attention has been paid to the challenges of collecting visual datasets (Highfield & Leaver, 2016). In this paper, I propose a methodological overview of several strategies for collecting large corpora of visual data from image-based social media platforms. Provided with exemplary publications, I review five strategies for collecting visual corpora: hashtag-based, account-based, metadata-based, random sampling, and mixed approach. Lastly, I present a case study with my own mixed approach to the collection of visual data from Instagram. Considering the usage, advantages and limitations of each strategy, the article will contribute to the developing science of social media research. I believe that a literature analysis of visual data collection strategies and a provided case study can help researchers optimize visual data collection from image-based social media.

Objective. The purpose of the article is to present the main capabilities and advantages of implementing and using SIEM systems.Method. System analysis method was used.Result. The main systems of the SIEM class are described, their main capabilities, advantages and disadvantages are listed, and various options for constructing such systems and principles of data collection are considered.Conclusion. Studying the functioning of systems of this type allows us to assess the possibility of their use in the construction of security systems of various scales and architectures. To make maximum use of the capabilities of SIEM systems, it is necessary to adapt and configure it to specific information security requirements. The prospect for further research will be the use of hybrid approaches based on intermediate storage using data streaming.

Rare diseases are predominantly genetic or inherited, and patients with these conditions frequently exhibit neurological symptoms. Diagnosing and treating many rare diseases is a complex challenge, and their low prevalence complicates the performance of research, which in turn hinders the advancement of therapeutic options. One strategy to address this issue is the creation of national or international registries for rare diseases, which can help researchers monitor and investigate their natural progression. In the Republic of Korea, we established a registry across 5 centers that focuses on 3 rare diseases, all of which are characterized by gait disturbances resulting from motor system dysfunction. The registry will collect clinical information and human bioresources from patients with amyotrophic lateral sclerosis, spinocerebellar ataxia, and hereditary spastic paraplegia. These resources will be stored at ICreaT and the National Biobank of Korea. Once the registry is complete, the data will be made publicly available for further research. Through this registry, our research team is dedicated to identifying genetic variants that are specific to Korean patients, uncovering biomarkers that show a strong correlation with clinical symptoms, and leveraging this information for early diagnosis and the development of treatments.

Abstract Background The challenges to conducting oral health studies involving older people in long-term care facilities (LTCFs) must be debated. Objective This study aimed to investigate researchers’ perceptions and experiences while conducting an epidemiological survey on oral health among older individuals residing in LTCFs. Methods A qualitative study was conducted involving six researchers who utilized field diaries to record their impressions during data collection through interviews (older individuals (or their proxies), caregivers, and LTCF coordinators) and oral examinations of the older people participants. Additionally, researchers responded to open-ended questions about their experiences. The collected material was subjected to content analysis by two researchers. Results The themes that emerged from the analysis were institutional context, aspects affecting the operationalization of the study, and data collection oriented by the clinical-functional profile of the older people. According to the researchers’ perceptions, LTCF coordinators demonstrated concern for the study’s benefits for older adults and the preservation of institutional routines during the research process. Caregivers emerged as vital sources of information, guiding researchers in navigating the challenges posed by the physical and mental complexities of the older people participants, necessitating empathy, sensitivity, and attentive listening from the researchers. The organization of materials and a streamlined data collection process proved essential for optimizing time efficiency and reducing stress for participants and researchers. Conclusion The researchers recognized the important role played by LTCF coordinators and formal caregivers, underscoring the significance of empathetic methodologies and streamlined data collection processes in mitigating the challenges inherent to research conducted within LTCFs.

Abstract Quantifying the intensity of leaf herbivory pressure is crucial for understanding the interaction between plants and herbivores in both applied and basic science. Visual estimates and digital analysis have been commonly used to estimate leaf herbivore damage but are time‐consuming which limits the amount of data that can be collected and prevent answering big picture questions that require large‐scale sampling of herbivory pressure. Recent developments in deep learning have provided a potential tool for automatic collection of ecological data from various sources. However, most applications have focused on identification and counting, and there is a lack of deep learning tools for quantitative estimation of leaf herbivore damage. Here, we trained generative adversarial networks (GANs) to predict the intact status of damaged leaves and applied image processing technique to estimate the area and percentage of leaf damage. We first described procedures for collecting leaf images, training GAN models, predicting intact leaves and calculating leaf area, with a Python package provided to enable hands‐on application of these procedures. Then, we collected a large leaf data set to train a universal deep learning model and developed an online app HerbiEstim to allow direct use of pretrained models to estimate herbivory damage of leaves. We tested these methods using both simulated and real leaf damage data. The procedures provided in our study greatly improved the efficiency of leaf herbivore damage estimation. Our test demonstrated that the reconstruction of damaged leaf image resembled the ground‐truth image with a similarity of 98.8%. The estimation of leaf herbivore damage exhibited a high accuracy with an averaged root mean square error of 1.6% and had a general applicability to different plant taxa and leaf shapes. Overall, our work demonstrated the feasibility of applying deep learning techniques to quantify leaf herbivory intensity. The use of GANs allows automatic estimation of leaf damage, representing a major advantage of the method. The Python package and the online app with pre‐trained models will facilitate the use of our method for the analysis of large data sets of plant–herbivore interactions.

An integrated computer software system for macromolecular crystallography (MX) data collection at the BL02U1 and BL10U2 beamlines of the Shanghai Synchrotron Radiation Facility is described. The system, Finback, implements a set of features designed for the automated MX beamlines, and is marked with a user-friendly web-based graphical user interface (GUI) for interactive data collection. The Finback client GUI can run on modern browsers and has been developed using several modern web technologies including WebSocket, WebGL, WebWorker and WebAssembly. Finback supports multiple concurrent sessions, so on-site and remote users can access the beamline simultaneously. Finback also cooperates with the deployed experimental data and information management system, the relevant experimental parameters and results are automatically deposited to a database.

Abstract Traditional survey methods can provide noisy data arising from recall, memory and other biases. Technological advances (particularly in neuroscience) are opening new ways of monitoring physiological processes through non-intrusive means. Such dense continuous data provide new and fruitful avenues for complementing self-reported data with a better understanding of human dynamics and human interactions. In this study, we use a survey to collect positive affect (feelings) data from more than 300 individuals over a period of 24 h, and at the same time, map their core activities (5000 recorded activities in total) with measurements of their heart rate variability (HRV). Our results indicate a robust correlation between the HRV measurements and self-reported affect. By drawing on the neuroscience and wellbeing literature we show that dynamic HRV results are what we expect for positive affect, particularly when performing activities like sleep, travel, work, exercise and eating. This research provides new insights into how to collect HRV data, model and interpret it.

Ruichen Fang,1,2 Yihong Cheng,1,2 Fan Li,3 Yan Xu,1,2 Yuanhui Li,4 Xiang Liu,4 Simin Guo,4 Yuling Wang,1,2 Jinnong Jiang,1,2 Dan Zhou,1,2 Bin Zhang1,2 1Department of Psychiatry, Sleep Medicine Center, Nanfang Hospital, Southern Medical University, Guangzhou, People’s Republic of China; 2Key Laboratory of Mental Health of the Ministry of Education, Guangzhou, People’s Republic of China; 3School of Biomedical Engineering, Faculty of Medicine, Dalian University of Technology, Dalian, 116024, People’s Republic of China; 4Adai Technology (Beijing) Co., Ltd, Beijing, People’s Republic of ChinaCorrespondence: Bin Zhang, Department of Psychiatry, Sleep Medicine Center, Nanfang Hospital, No. 1838 North, Guangzhou Avenue, Guangzhou, 510515, People’s Republic of China, Email zhang73bin@hotmail.comPurpose: In this study, we established the Chinese Clinical Sleep Database (CCSD), aiming to provide a safe, scalable, and user-friendly database that includes high-quality clinical data from Chinese population to facilitate sleep research.Material and Methods: We collect individual’s demographic data, scales, anthropometric measurements, clinical diagnosis, and polysomnography (PSG) recordings from the routine medical process of sleep medicine centers using standardized procedures. The distributed cluster storage technology are utilized to store these data. The structured data are stored in a high-performance MySQL database, while the unstructured data are stored in an object storage service. And we have developed an online data platform to share and manage our data.Results: The data collection has been conducted in three hospitals. In the preliminary stage of data collection (from October 18, 2022 to September 4, 2023), our database included a total of 1183 patients. Among them, 56.8% were male and their ages ranged from 3 to 88 years. These patients were diagnosed with various types of sleep disorders.Conclusion: Since the CCSD’s inception, it has demonstrated good stability, security, and scalability. As an public database, the CCSD also exhibits user-friendliness. The CCSD contains comprehensive clinical data, which can contribute to the advancement of the diagnosis and treatment strategies for sleep disorders, ultimately promoting sleep health.Keywords: sleep medicine, methodology, database, data collection, collaboration tool

This article points out deficiencies in present-day definitions of public health surveillance, which include data collection, analysis, interpretation and dissemination, but not public health action. Controlling a public health problem of concern requires a public health response that goes beyond information dissemination. It is undesirable to have public health divided into data generation processes (public health surveillance) and data use processes (public health response), managed by two separate groups (surveillance experts and policy-makers). It is time to rethink the need to modernize the definition of public health surveillance, inspired by the authors’ enhanced Data, Information, Knowledge, Intelligence and Wisdom model. Our recommendations include expanding the scope of public health surveillance beyond information dissemination to comprise actionable knowledge (intelligence); mandating surveillance experts to assist policy-makers in making evidence-informed decisions; encouraging surveillance experts to become policy-makers; and incorporating public health literacy training – from data to knowledge to wisdom – into the curricula for all public health professionals. Work on modernizing the scope and definition of public health surveillance will be a good starting point.

Background: The digital age has not just changed, but transformed the field of communication. It has revolutionized how research is conducted and how research results are communicated to the general public. Therefore, up-to-date empirical information is crucial to grasp the current state of research communication as well as communication research. Objective: This study aims to understand how digital media platforms have affected communication research and research communication. Methodology: The researchers used a qualitative approach with a structured interview as the data collection instrument. Overall, 32 communication researchers were interviewed through Google Meet. The results were analysed using thematic analysis and presented qualitatively. Results: The result of the study showed that digital media platforms have changed the face of communication research in four ways. First, research conducting is now significantly influenced by digital media. Digital media now influence stages like data gathering, analysis and reporting. Secondly, manuscript submission processes have also changed from what they used to be with the use of more interactive platforms that allow researchers to track the progress of their submissions to journals. Thirdly, communication research output has also been influenced by digital media. In the fourth place, people can track the impact of their research efforts in the digital era more than ever. Contribution: This study has not only provided empirical evidence regarding the current state of research communication and communication research in the digital era, but also shed light on the profound impact of digital media on these fields. Conclusion: The influence of digital media platforms on communication research and research communication is not limited to developed countries. This study has shown that digital media has significantly affected these fields, even in developing countries like Nigeria, highlighting its global impact. Recommendation: The study recommends, among other things, that researchers should improve their skills to leverage the opportunities available in digital tools for research purposes.

ABSTRACT Objectives: to evaluate software technical quality for collecting data from patients under palliative care. Methods: this is methodological technology evaluation research, according to the technical standard International Organization for Standardization/International Electrotechnical Commission 25040-2011, developed from August 2021 to August 2023. Eight nurses and eight information technology professionals participated as judges, who evaluated six quality characteristics and 23 subcharacteristics. Items that reached a percentage of agreement greater than 70% were considered suitable. Results: the characteristics evaluated by nurses/information technology professionals received the following percentages of agreement, respectively: functional suitability (94%-84%); reliability (100-70%); usability (89.9-66.8%); performance efficiency (95.8%-86.1%); compatibility (95.8-79.6%); and safety (96%-83.4%). Conclusions: the software was considered suitable in quality evaluation to offer support to nurses in collecting patient data under palliative care, with the potential to operationalize the first Nursing Process stage.

EU Member States should ensure that they implement adequate health surveillance schemes in all aquaculture farming areas, as appropriate for the type of production. This study presents the results of applying the FAO’s Surveillance Evaluation Tool (SET) to assess the Spanish disease surveillance system for farmed fish species, which although applied previously in livestock production, is applied here to aquaculture for the first time. Overall, there were important score differences between trout and marine fish (seabass and seabream) surveillance, which were higher for trout in the following areas: Institutional (70.8% versus 50.0%), Laboratory (91.7% versus 47.2%), and Surveillance activities (75.3% versus 61.3%). For other categories, the values were lower and no significant differences were found. However, most surveillance efforts focused only on trout, for which there are EU and WOAH listed (notifiable) diseases. In contrast, for seabream and seabass, for which there are no listed diseases, it was considered that surveillance efforts should, nevertheless, be in place and should focus on the identification of abnormal mortalities and emerging diseases, for which there are as yet no standardized harmonised methodologies.

Qualitative research has traditionally relied on specific methodologies for data collection, with interviews being the predominant and preferred approach. Vast quantities of information have been published regarding traditional mechanisms for collecting qualitative data, but few studies have addressed more unconventional mechanisms such as body mapping. This is a distinctive technique commonly used in health sciences. It offers a versatile alternative that encourages participant creativity, reflexivity, and protagonism. It was adapted for use in an educational study with the aim of eliciting participants' constructivist experiences and emotions related to a particular social phenomenon. It was used as part of a descriptive case study which employed two other triangulation techniques, namely semi-structured virtual interviews, and document collection. After the researchers’ reflective processes, it was concluded that body mapping represented an ideal means to facilitate the data collection process. The primary objective of the study, grounded in qualitative research, was to explore the perceptions of five primary English teachers in the Sarapiquí Regional Directorate of Education in Costa Rica regarding the return to in-person education amid the COVID-19 public health crisis. For the body mapping technique, three participants engaged in a face-to-face session, structured into seven phases: map outlining, reflections on distance education, reflections on combined education, reflections on in-person education, finalization of body maps, testimony giving, and concluding reflections on the technique. Although the focus was on in-person education (2022), the distance and combined modalities (2020-2021) were used to activate participants’ prior knowledge and to provide context for the study. This paper demonstrates the procedure followed in applying the body mapping technique for qualitative data collection in a study on the perspective and experiences of teachers, and highlights the valuable insights gained through its use.

Abstract Recent studies have revealed that numerous lncRNAs can translate proteins under specific conditions, performing diverse biological functions, thus termed coding lncRNAs. Their comprehensive landscape, however, remains elusive due to this field's preliminary and dispersed nature. This study introduces codLncScape, a framework for coding lncRNA exploration consisting of codLncDB, codLncFlow, codLncWeb, and codLncNLP. Specifically, it contains a manually compiled knowledge base, codLncDB, encompassing 353 coding lncRNA entries validated by experiments. Building upon codLncDB, codLncFlow investigates the expression characteristics of these lncRNAs and their diagnostic potential in the pan‐cancer context, alongside their association with spermatogenesis. Furthermore, codLncWeb emerges as a platform for storing, browsing, and accessing knowledge concerning coding lncRNAs within various programming environments. Finally, codLncNLP serves as a knowledge‐mining tool to enhance the timely content inclusion and updates within codLncDB. In summary, this study offers a well‐functioning, content‐rich ecosystem for coding lncRNA research, aiming to accelerate systematic studies in this field.

Efficient data collection and sharing play a crucial role in power infrastructure construction. However, in an outdoor remote area, the data collection efficiency is reduced because of the sparse distribution of base stations (BSs). Unmanned aerial vehicles (UAVs) can perform as flying BSs for mobility and line-of-sight transmission features. In this paper, we propose a multiple temporary UAV-assisted data collection system in the power infrastructure scenario, where multiple temporary UAVs are employed to perform as relay or edge computing nodes. To improve the system performance, the task processing model selection, communication resource allocation, UAV selection, and task migration are jointly optimized. We designed a QMIX-based multi-agent deep reinforcement learning algorithm to find the final optimal solutions. The simulation results show that the proposed algorithm has better convergence and lower system costs than the current existing algorithms.

According to the World Health Organisation (WHO), brain stroke is the second leading cause of death and disability worldwide in terms of key ICD (International Cause of Death) categories. Every 4 min, on average, a stroke-related mortality occurs in India alone.Machine learning (ML) has developed into a potent tool in healthcare settings, providing individualised therapeutic treatment for stroke patients. ML gives an accurate and speedy prediction conclusion. However, it is clear from the research that better training quality is required, along with the identification of the best classifiers for combining to improve prediction accuracy. In this article, we have suggested a framework to aid diagnosis and Clinical treatment for patients with stroke.In this study, we introduced a system called Enhanced Stroke Prediction Ensemble using Stacking Methodology (ESPESM) in Intelligent Sensors, which expands on the previously successful SPE using Stacking and leverages a hybrid method considering feature engineering and ensemble classification.The fundamental classifiers for the proposed stacking prediction model were Random Forest (RF), K-Nearest Neighbours (KNN), Logistic Regression (LR), Support Vector Machine (SVM), Naive Bayes (NB), Gradient Boosting Classifier (GBC), Decision Tree Classifier, Stochastic Gradient Descent(SGD), and Bernoulli NB(BNB),while Random Forest was selected as the meta learner. Accuracy, Precision, Recall, and F1 score have been used to assess the suggested stacking model's performance.The most accurate models from a pool of potential brain stroke prediction models are selected, and these models are then layered to create an ensemble model. According to prior established literature, stacked ensembles often give a more accurate prediction performance than simple individual models or average ensemble addition. The framework is accomplished by utilising and adapting previously existing methods. They are referred to as “Hybrid Ensemble and Feature Engineering for Stroke Prediction (HEFE-SP)'' and “Hybrid Measures Approach for Feature Engineering (HMA-FE)''.Additionally, when a dataset is imbalanced, as it is in most medical datasets, the Matthews Correlation Coefficient (MCC) has also been utilised for more accurate evaluation. The accuracy and MCC values of the proposed stacking model, which are 98 % and 94 % respectively, show that it outperforms solo classifiers.

Unmanned Aerial Vehicle (UAV) can be used as wireless aerial mobile base station for collecting data from sensors in UAV-based Wireless Sensor Networks (WSNs), which is crucial for providing seamless services and improving the performance in the next generation wireless networks. However, since the UAV are powered by batteries with limited energy capacity, the UAV cannot complete data collection tasks of all sensors without energy replenishment when a large number of sensors are deployed over large monitoring areas. To overcome this problem, we study the Real-time Data Collection with Laser-charging UAV (RDCL) problem, where the UAV is utilized to collect data from a specified WSN and is recharged using Laser Beam Directors (LBDs). This problem aims to collect all sensory data from the WSN and transport it to the base station by optimizing the flight trajectory of UAV such that real-time data performance is ensured It has been proven that the RDCL problem is NP-hard. To address this, we initially focus on studying two sub-problems, the Trajectory Optimization of UAV for Data Collection (TODC) problem and the Charging Trajectory Optimization of UAV (CTO) problem, whose objectives are to find the optimal flight plans of UAV in the data collection areas and charging areas, respectively. Then we propose an approximation algorithm to solve each of them with the constant factor. Subsequently, we present an approximation algorithm that utilizes the solutions obtained from TODC and CTO problems to address the RDCL problem. Finally, the proposed algorithm is verified by extensive simulations.

Background and Aim: Qualitative research plays an important role in improving nursing knowledge. Understanding the concept of saturation is essential to conducting rigorous qualitative research that contributes to evidence-based practice. The purpose of this study is to clarify the concept of saturation in qualitative research. Method: Evolutionary concept analysis was performed. A literature search was conducted using a variety of online databases for the years 2005- 2023. In total, 33 articles and books were analyzed using thematic analysis to identify the attributes, antecedents and consequences of saturation. The validity of the data was obtained by examining the analysis process by two independent researchers. Results: Saturation in qualitative research is a context-dependent, subjective process that requires detailed systematic analysis. Saturation is used in four ways in qualitative research: theoretical saturation, data saturation, code or thematic saturation, and meaning saturation. The antecedents of saturation were classified into two categories: study related factors and researcher related factors. The consequences of saturation were identified as: ensuring credibility and quality in qualitative research and time, energy and budget saving. Conclusion: This concept analysis serves to enhance the understanding of the concept of saturation, thereby offering valuable resources for qualitative researchers. By gaining a profound comprehension of saturation and its various types, researchers can ensure the validity of their studies while also optimizing time and resource allocation by avoiding redundant data collection. Future investigation warranted to elucidate how factors associated with reaching saturation impact estimations sample size.

Missing data are an inevitable part of research and lead to a decrease in the size of the analyzable population, and biased and imprecise estimates. In this article, we discuss the types of missing data, methods to handle missing data and suggest ways in which missing data can be minimized.

This article discusses the modeling and detection of attacks in self-organizing decentralized wireless sensor networks (WSNs) that can be applied to various critical scenarios in practice. Security issues in this type of network have previously been studied to a rather poor extent. In particular, existing attack detection approaches and algorithms do not rely on the properties of self-organization and decentralization, which an attacker is able to exploit to compromise the network and its services. We propose, first, a model of a self-organizing decentralized wireless sensor network; second, a model of the attacks on such networks; third, algorithms for data collection and attack detection; and, finally, a technique for their application. The WSN model represents a formal specification of this type of network, defining the conditions and limitations of network self-organization and decentralization. The model is characterized by a proposed underlying role-based operation of network nodes and a set of their functional states. The proposed attack model covers the possible types of attacks that are relevant to a given type of WSN and are based on the exploitation of the self-organization and decentralization of the network. The developed algorithm for collecting data for attack detection presents specific types of data and their sources. The developed combined attack detection algorithm is formed of actions that detect relevant attacks on self-organizing decentralized WSNs using machine learning methods. The distinctive element of this algorithm is a set of highly specific features that are obtained by analyzing the data collected in the WSN and used to detect attacks. The proposed technique combines the constructed models and algorithms for the sake of tuning and deploying the attack detection module and the effective detection of attacks in practice. This technique specifies the main steps for the joint use of the models and algorithms and the assignment of data collection and detection parameters. The results of the experiments confirm the correctness of the constructed models, algorithms and technique due to the high values of the attack detection quality indicators. Therefore, the practical application of the proposed apparatus will facilitate improvements in the security of self-organizing decentralized WSNs. Experimental research has confirmed the practical applicability of our proposed solutions. In particular, it has shown that the proposed algorithms and the detection technique can detect both attacks implemented through the exploitation of the network’s properties of decentralization/self-organization and common variations in these attacks (i.e., without exploiting the decentralization property). In general, the experimental results expose a high quality of detection, with an f1-score equal to 0.99.

Abstract Background People with dementia are routinely included as research participants in trials and other quantitative studies in which they are invited to respond to standardised measures. This paper reviews the reporting of standardised data collection from people with dementia in reports published in the National Institute for Health and Care Research (NIHR) Journals Library. The aim was to understand how the administration of standardised, self-report measures with people with dementia is reported in NIHR monographs and what could be learnt from this about the feasibility and acceptability of data collection approaches for future studies. Methods This was a systematic review with narrative synthesis. Broad search terms (Dementia OR Alzheimer*) were used to search the NIHR Journals Library website in December 2021. All studies that used (or intended to use) standardised measures to collect research data directly from people with dementia were eligible for inclusion. Information was extracted (where reported) on the process of data collection, dementia severity, levels of missing data and the experiences and reflections of those involved. Results Searches returned 42 records, from which 17 reports were assessed as eligible for inclusion, containing 22 studies. Response rates from participants with dementia in these studies varied considerably and appeared to be related to dementia severity and place of residence. Little information was reported on the process of data collection or the reasons for missing data, and most studies did not report the experiences of participants or those administering the measures. However, there was an indication from two studies that standardised data collection could provoke emotional distress in some participants with dementia. Conclusions Through this review we identified both variation in levels of missing data and gaps in reporting which make it difficult to ascertain the reasons for this variation. We also identified potential risks to the well-being of participants with dementia which may be associated with the content of standardised measures and the context of data collection. Open reporting of and reflection upon data collection processes and the experiences of people involved is essential to ensure both the success of future data collection and the wellbeing of study participants. Trial registration Registered with Research on Research https://ror-hub.org/study/2905/ .

Aiming at the current problem of insufficient security in clustering data collection and publication, in order to protect user privacy and improve data quality in clustering data, a privacy protection method for K-Modes clustering data collection and publication was proposed without trusted third parties based on the shuffled differential privacy model.K-Modes clustering data collection algorithm was used to sample the user data and add noise, and then the initial order of the sampled data was disturbed by filling in the value domain random arrangement publishing algorithm.The malicious attacker couldn’t identify the target user according to the relationship between the user and the data, and then to reduce the interference of noise as much as possible a new centroid was calculated by cyclic iteration to complete the clustering.Finally, the privacy, feasibility and complexity of the above three methods were analyzed from the theoretical level, and the accuracy and entropy of the three real data sets were compared with the authoritative similar algorithms KM, DPLM and LDPKM in recent years to verify the effectiveness of the proposed model.The experimental results show that the privacy protection and data quality of the proposed method are superior to the current similar algorithms.

To improve the efficiency of large-scale Internet of things (IoT) data collection, a coordinated trajectory planning algorithm for multiple aerial and ground vehicles based on load balancing region partitioning was proposed, where unmanned aerial vehicles (UAVs) acting as aerial base stations were dispatched to gather data from IoT devices and unmanned ground vehicles (UGVs) acting as mobile battery swap stations were used to compensate for the shortage of UAV’s energy.Aiming at shortening the mission completion time, the optimization task was to minimize the longest mission time among a fleet of UAV-UGVs, which was formulated as a variant of min-max multi-depot vehicle routing problem and solved from the load-balancing perspective.Specifically, the IoT devices were assigned to the UAV-UGVs’ service zones by a load-balancing region partition algorithm, based on which the trajectory planning problem of multiple UAV and UGV was reduced to several independent route planning problems for each UAV-UGV pair.Then, a cooperative trajectory planning strategy was developed to optimize the route in each service zone.Numerical results validate that the proposed algorithm outperforms the compared algorithms in terms of mission completion time and balancing degree.

In the field of nondestructive evaluation (NDE), we sometimes need an intricate system of multiple actuators and sensors to measure and assess the material condition or structural integrity of a specimen. Complicated systems are especially necessary for more advanced techniques that involve multiple phenomena or modeling in a geometric context. In the research laboratory, we rarely understand the intricacies of the measurement up front, and we need the agility to reconfigure our measurement system as needs evolve. Software is the glue that ties our measurement systems together. The traditional approach of ad hoc software quickly becomes unsustainable in the modern environment. We propose an alternative approach that addresses the need for agility in the modern NDE laboratory: a reconfigurable, modular software architecture that is built from the ground up to accommodate conflicting requirements in the areas of data management, automation, parallelism, geometry and robotics, and version control. We describe a new pair of open-source tools, Dataguzzler-Python and SpatialNDE2, that facilitate instrumentation control, data acquisition, and processing for the NDE laboratory. The tools make up a framework that provides the following: multiplexed automatic and manual control of instrumentation, a versioned database to store the acquired data, parallel acquisition and live high performance/GPU computation, the ability to acquire and store data in geometric context, and the ability to visualize and interact with the acquired data. This article discusses their design, implementation, and initial experiences in using them in the NDE laboratory.

Autism is a developmental condition that affects motor skill development. There is a lack of comprehensive research exploring the potential benefits of extended reality (XR) technologies, including virtual reality (VR) and augmented reality (AR), for improving motor skills in autistic children. This systematic literature review (SLR) addresses this research gap by investigating XR-based interventions targeting motor skill development in autistic children. The SLR analysed 15 primary studies published between 2012 and 2024, examining the use of VR and AR to improve motor skill development in autistic children. The interventions ranged from one session to 12 weeks, lasting 12 to 45 minutes, and targeted various body movements. XR platforms and devices used included VR headsets, motion capture systems, and exercise bikes. Recurrent neural networks, dynamic difficulty adjustment, and behaviour trees were employed to enhance intervention dynamics and extract valuable insights from collected data. The results suggest that using XR interventions has significant potential to improve physical activity levels and motor skills development among autistic children. However, the research designs varied, with only one study including their intervention framework’s generalisation and maintenance phases. This study offers an encouraging avenue for future research and intervention design in this field.

The goal of this research is to identify, scientifically describe, classify and partly explain the weaknesses of data collection as an executive function in law enforcement intelligence operations in the Republic of Serbia using methods of scientific research. The conducted theoretical research identified problems of a functional nature using the executive function of data collection, which can be classified as following problems: 1) theoretical differences in the conceptual determination of data and criminal-intelligence, 2) traditional (reactive) data collection in relation to modern proactive methods, 3) coordination and motivation of police officers, 4) decentralization of the database. Executive functions in criminal-intelligence operations are performed through the criminal-intelligence process, which represents an evidentiary tool for analyzing the actions pertaining to all forms of crime, misdemeanors and other security-related acts, and enables decision-making process based on the collection, processing and analysis of data, as well as undertaking preventive measures. The general purpose of criminal-intelligence operation is to collect data on individuals or organized criminal groups that are involved in criminal activities, in order to gain insight into their functioning. The research was carried out as a theoretical-empirical one, using combination of both theoretical and empirical methods of research (attitudes and opinions of police officers in the police of the Republic of Serbia), that is: general scientific, logical and empirical methods. In this research, the theoretically-empirically confirmed position is that the functioning of the police organization in the Republic of Serbia can be improved, which implies appropriate (functional) changes in its executive functions, including especially changes in the performance of criminal-intelligence operations, i.e. in the collection of data that could contribute to better development and use of criminal-intelligence outcomes (strategic assessment of public security, operational assessments of public security, profile of a security related problem, profile of a person/group of security interest and other analytical products), which would have impact on the more efficient, effective and economical performance of police work and tasks. The scientific justification of the research derives from its expected results, which can: 1) contribute to the deepening and expansion of scientifically verified knowledge in the field of criminalistics, security and organizational scientific disciplines, 2) indicate the directions, areas and topics of future scientific research and 3) enrich the methodological practice of scientific research on the organization and proactive functioning of the police in contemporary conditions

In recent years, machine learning (ML) and deep learning (DL) have been the leading approaches to solving various challenges, such as disease predictions, drug discovery, medical image analysis, etc., in intelligent healthcare applications. Further, given the current progress in the fields of ML and DL, there exists the promising potential for both to provide support in the realm of healthcare. This study offered an exhaustive survey on ML and DL for the healthcare system, concentrating on vital state of the art features, integration benefits, applications, prospects and future guidelines. To conduct the research, we found the most prominent journal and conference databases using distinct keywords to discover scholarly consequences. First, we furnished the most current along with cutting-edge progress in ML-DL-based analysis in smart healthcare in a compendious manner. Next, we integrated the advancement of various services for ML and DL, including ML-healthcare, DL-healthcare, and ML-DL-healthcare. We then offered ML and DL-based applications in the healthcare industry. Eventually, we emphasized the research disputes and recommendations for further studies based on our

In this paper, we describe data preparation for our proposed chatbot PerInfEx (Persian Information Extraction chatbot). It aims to interactively chit-chat with users in Persian and by asking the least number of direct questions, extract as much personal information as possible such as user’s age or occupation. Collecting data in considerable size and aligned with our system’s specifics is a crucial step to train data-hungry modules of Natural Language Understating (NLU) and Natural Language Generating (NLG). Initially, for NLU module, we collect 99 free-discussion dialogues and crawl 74 English training conversations as more-general datasets while also manually translate 72 dialogues of ConvAI2 corpus. Moreover, we gamify collection by implementing a chatting website results in 94 dialogues. It detects direct questions and assigns random profiles to participants. They should guess the opponents profile. Also, we propose two augmentation methods: a semi-automatic and a novel fully automatic method, comprehensively evaluated on NLU benchmarks and applied on our datasets. Also, by prompting OpenAI’s GPT-3.5 model, we automatically generate 304 dialogues. The first part of these datasets is manually annotated while we use an active learning method for annotating rest of them. Next, to evaluate data quality, we assess them extrinsically using NLU baseline which results in intent-accuracy = 88.64, slot-F1 = 83.68 and exact-match = 78.22. Also, for NLG module, we automatically translate almost the rest of ConvAI2 corpus (16,217 dialogues) and paraphrase previously sets for its fine-tuning using GPT-3.5 model. Their assessment using our NLG baseline results in perplexity of 15.74 on train and 52.17 on test set.

Wireless sensor networks (WSNs) deployed in remote areas face a challenge in uploading the collected data to data centers due to limited network coverage. Unmanned aerial vehicles (UAVs) can extend network coverage to remote WSNs by flying and communicating with WSN aggregator nodes to collect data. UAV-assisted data collection systems need to be carefully developed to collect all data efficiently while considering the UAV and WSN constraints. This paper provides an energy-efficient multi-UAV data collection framework for WSNs. We formulate the data collection system as a problem that jointly optimizes the system cost and energy consumption constrained by the communication power, UAV mission time, and memory size. The problem is resolved over two steps: First, the location and number of aggregators needed are determined using a triangulation-based K-means clustering that minimizes the number of aggregators used and the system cost. Second, the dockstation position that minimizes the energy consumption is obtained using the gaining-sharing knowledge (GSK) optimization algorithm. The optimum UAV trajectory for each GSK candidate solution is designed by solving a capacitated vehicle routing problem (CVRP) that combines heuristic and metaheuristic solving techniques. Simulations show that our framework outperforms other recent techniques by minimizing the overall system cost and energy consumption.