Data Sources Research Articles

Seeding innovation: the role of internal and external digital data in agri-food product innovation

PurposeSince previous literature provides fragmented and conflicting results about the use of digital data for product innovation, the article aims to comprehensively explore and shed light on how agri-food firms utilise external and internal digital data sources when dealing with different product innovations, such as incremental, architecture and radical innovation.Design/methodology/approachThis paper adopts an exploratory multiple-case study and a theory-building process, focussing on the agri-food industry. We collected primary and secondary data from eight manufacturing companies.FindingsThe findings of this research show an empirical framework of six agri-food firms’ digital data utilisation behaviours: the supervisor, the passive supervisor, the developer, the passive developer, the pathfinder and the conjunction behaviour. These digital data utilisation behaviours vary according to a combination of data sources, such as internal data related to inside phenomenon measures (e.g. data generated by sensors installed in the production plan) or external data (e.g., market trends, overall sector sales), and innovation purposes.Practical implicationsThis article offers guiding principles that assist agri-food companies when utilising internal and external digital data sources for specific product innovation outcomes such as incremental, architectural and radical innovation.Originality/valueThe significance of external and internal data sources in stimulating product innovation has garnered substantial attention within academic discussions, highlighting the critical importance of analysing digital data for driving such innovation. Nonetheless, the predominant approach is to study a single innovation outcome through the lens of digital technology. In contrast, our study stands out by adopting a fundamental perspective on data sources, enabling a more nuanced explanation of the overall product innovation outcomes within the agri-food sector.

Incremental schema integration for data wrangling via knowledge graphs

Virtual data integration is the current approach to go for data wrangling in data-driven decision-making. In this paper, we focus on automating schema integration, which extracts a homogenised representation of the data source schemata and integrates them into a global schema to enable virtual data integration. Schema integration requires a set of well-known constructs: the data source schemata and wrappers, a global integrated schema and the mappings between them. Based on them, virtual data integration systems enable fast and on-demand data exploration via query rewriting. Unfortunately, the generation of such constructs is currently performed in a largely manual manner, hindering its feasibility in real scenarios. This becomes aggravated when dealing with heterogeneous and evolving data sources. To overcome these issues, we propose a fully-fledged semi-automatic and incremental approach grounded on knowledge graphs to generate the required schema integration constructs in four main steps: bootstrapping, schema matching, schema integration, and generation of system-specific constructs. We also present Nextia DI , a tool implementing our approach. Finally, a comprehensive evaluation is presented to scrutinize our approach.

Skating into the Unknown: Scoping the Physical, Technical, and Tactical Demands of Competitive Skateboarding.

The inclusion of skateboarding in the Olympics suggests that athletes and coaches are seeking ways to enhance their chances of succeeding on the world stage. Understanding what constitutes performance, and what physical, neuromuscular, and biomechanical capacities underlie it, is likely critical to success. The aim was to overview the current literature and identify knowledge gaps related to competitive skateboarding performance and associated physical, technical, and tactical demands of Olympic skateboarding disciplines. A systematic scoping review was performed considering the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (Extension for Scoping Reviews) guidelines. Data sources were MEDLINE (Ovid), Scopus, SPORTDiscus, and PubMed. We included all peer-reviewed literature after 1970 describing the physiological, neuromuscular, biomechanical, and/or tactical aspects of skateboarding. Nineteen original articles explored the physiological (n = 9), biomechanical (n = 8), and technical (n = 10) demandsof skateboarding. No research explored the tactical demands of competition. Moreover, although competitive males (n = 2 studies) and females (n = 1 study) were recruited as participants, no research directly related skateboarding demands to performance success in competitive environments. Ultimately, what constitutes and distinguishes competitive skateboarding is unexplored. There is some evidence indicating aspects of the sport require flexibility and elevated and fast force output of the lower limbs, which may be valuable when attempting to maximise ollie height. Nonetheless, a lack of ecological validity, such as using static ollie tests as opposed to rolling, restricted our ability to provide practical recommendations, and inconsistency of terminology complicated delineating discipline-specific outcomes. Future researchers should first look to objectively identify what skaters do in competition before assessing what qualities enable their performance.

Revenue concentration and capital structure

PurposeWe conducted this study to examine the relationship between revenue concentration and a firm's financial leverage. We aimed to analyze whether revenue concentration influences a firm's capital structure decisions and whether this relationship is driven by customer-specific investments or the direct effect of revenue concentration itself. Additionally, we investigated the role of asset redeployability in mediating or moderating the relationship between revenue concentration and financial leverage.Design/methodology/approachThe paper investigates the relationship between revenue concentration and a firm's financial leverage. The results indicate a negative association between revenue concentration and financial leverage. This finding holds across various regression models and is statistically significant. Furthermore, the paper explores the potential role of asset redeployability in explaining the relationship between revenue concentration and financial leverage. The results indicate that even after controlling for asset redeployability, the negative relationship between revenue concentration and leverage remains significant, suggesting that revenue concentration affects capital structure decisions independently of the risks associated with relationship-specific investments. Robustness tests are conducted using a three-stage least squares approach to account for the simultaneity between revenue concentration, asset redeployability and capital structure.FindingsOur findings demonstrate that revenue concentration is negatively associated with financial leverage, even after accounting for asset redeployability. This suggests that revenue concentration affects capital structure decisions independently of the risks associated with customer-specific investments. Furthermore, we performed robustness tests to address potential simultaneity issues between revenue concentration, asset redeployability and capital structure.Research limitations/implicationsThe study relies on available data sources, which may have inherent limitations in terms of accuracy, completeness or consistency. The quality of the data used in the analysis could impact the robustness of the findings. Time Period: The study focuses on more recent years, which might limit the ability to compare the findings with studies conducted over different time periods. Historical trends or structural changes that could impact the relationship between revenue concentration and financial leverage might not be fully captured.Practical implicationsFirms with higher revenue concentration tend to have lower financial leverage. Recent years show a negative relationship between profitability and market leverage compared to earlier periods. Revenue concentration has a distinct effect on financial leverage, not fully explained by risks from relationship-specific investments or asset redeployability. Insights for firms in managing capital structure decisions, considering revenue concentration and its implications for leverage.Originality/valueThis research is one of the first papers that investigates the impact of revenue concentration on the capital structure choices of firms. By exploring the relationship between revenue concentration and financial leverage, the study contributes to the existing literature by shedding light on an underexplored area. Thus, this study adds originality to the field by addressing a research gap and contributing to the understanding of the relationship between revenue concentration and capital structure choices.

Exploring interactivity effects on learners’ sense of agency, cognitive load, and learning outcomes in immersive virtual reality: A mixed methods study

This study explored the effects of IVR interactivity on learners’ sense of agency, cognitive load, and learning outcomes. The research questions were: 1. “How does interactivity influence the learners’ sense of agency?” and 2. “How does interactivity influence the learners’ cognitive load and learning outcomes?” A mixed-methods experimental design was applied. Safety training interventions, including individually performed IVR scenarios, were held for 68 participants in groups of two to four persons in two work organizations. Single- and repeated-measure questionnaires were the main source of the quantitative data. Qualitative data collection by video recordings and stimulated recall interviews was carried out on 23 persons in total. The results indicate that high interactivity enables a stronger sense of agency for the learners and yields learning benefits by supporting generative cognitive processing. Based on the results, interactivity and learner involvement may be particularly important for achieving affective training goals. Considering the sociocultural and individual factors in training design and pre-briefing the learners are also recommended.

DORA: an interactive map for the visualization and analysis of ancient human DNA and associated data.

The ability to sequence ancient genomes has revolutionized the way we study evolutionary history by providing access to the most important aspect of evolution-time. Until recently, studying human demography, ecology, biology, and history using population genomic inference relied on contemporary genomic datasets. Over the past decade, the availability of human ancient DNA (aDNA) has increased rapidly, almost doubling every year, opening the way for spatiotemporal studies of ancient human populations. However, the multidimensionality of aDNA, with genotypes having temporal, spatial and genomic coordinates, and integrating multiple sources of data, poses a challenge for developing meta-analyses pipelines. To address this challenge, we developed a publicly-available interactive tool, DORA, which integrates multiple data types, genomic and non-genomic, in a unified interface. This web-based tool enables browsing sample metadata alongside additional layers of information, such as population structure, climatic data, and unpublished samples. Users can perform analyses on genotypes of these samples, or export sample subsets for external analyses. DORA integrates analyses and visualizations in a single intuitive interface, resolving the technical issues of combining datasets from different sources and formats, and allowing researchers to focus on the scientific questions that can be addressed through analysis of aDNA datasets.

Breast cancer and the steadily increasing maternal age: are they colliding?

BackgroundPregnancy-related cancers are mostly breast cancers, and their incidence is likely to increase as a result of the modern trend of delaying childbearing. In particular, advanced maternal age increases breast cancer risk, and younger breast cancer patients are more likely to die and metastasize. This study compared a population with a high incidence of delayed childbearing with another population with a lower mean age at childbirth in order to determine whether breast cancer diagnosis and childbearing age overlap.MethodsWe retrospectively analyzed multiple data sources. The Surveillance, Epidemiology, and End Results (SEER) program, the United States National Center for Health Statistics as part of the National Vital Statistics System, the United Nations Population Division, the GLOBOCAN Cancer Observatory, the CLIO-INFRA project database, the Human Fertility Database, and anonymized local data were used.ResultsAs women’s age at delivery increased, the convergence between their age distribution at breast cancer diagnosis and childbearing increased. In addition, the overlap between the two age distributions increased by more than 200% as the average age at delivery increased from 27 to 35 years.ConclusionsAs women’s average childbearing age has progressively risen, pregnancy and breast cancer age distributions have significantly overlapped. This finding emphasizes the need for increased awareness and educational efforts to inform women about the potential consequences of delayed childbearing. By providing comprehensive information and support, women can make more informed decisions about their reproductive health and cancer prevention strategies.

Learner corpus research: a critical appraisal and roadmap for contributing (more) to SLA research agendas

Abstract Over the past decade, learner corpora have gained recognition as valuable data sources in Second Language Acquisition (SLA) research. This development can be attributed to significant progress in Learner Corpus Research (LCR). However, there is still substantial work to be done. This article highlights key issues essential for sustaining the relevance of learner corpora in SLA. More particularly, I focus on the need for more diverse types of learner corpora, stress the importance of detailed metadata, and advocate for multifactorial study designs. I then revisit ongoing debates regarding the role of the native speaker in LCR and propose a practical solution to address this thorny issue. Finally, I also readdress the need for improvement in the quantitative methods and statistics, arguing that the importance of robust quantitative analysis cannot be overstated. In conclusion, I envision an ambitious learner corpus compilation project that adheres to the FAIR principles, with the goal of further elevating study quality in LCR.

THE EFFECT OF FOREIGN DIRECT INVESTMENT ON THE ECONOMIC DEVELOPMENT OF NIGERIA

The study examined the effect of Foreign Direct Investment (FDI) on development in Nigeria. The ultimate goal of developing countries is to attain development. Most studies on the effect of Foreign Direct Investment on the development of the Nigerian economy used growth proxied by GPP as a measure of development. This study however uses the Human Development Index (HDI) which is a complementary measure of development as it considers the three aspect of development (health, knowledge and standard of living). Current data from secondary data source. were used Secondary data were collected and OLS estimation method was used for the analysis of data. The result shows that the stock of FDI inflow has a positive and statistically significant effect on Economic development in Nigeria. Therefore to experience sustainable development, government should aim at providing infrastructure and conducive trade and investment environment for both foreign and local investors.

An assessment of federal alcohol policies in Canada and priority recommendations: Results from the 3rd Canadian Alcohol Policy Evaluation Project.

To systematically assess the Canadian federal government's current alcohol policies in relation to public health best practices. The 2022 Canadian Alcohol Policy Evaluation (CAPE) Project assessed federal alcohol policies across 10 domains. Policy domains were weighted according to evidence for their relative impact, including effectiveness and scope. A detailed scoring rubric of best practices was developed and externally reviewed by international experts. Policy data were collected between June and December 2022, using official legislation, government websites, and data sources identified from previous iterations of CAPE as sources. Contacts within relevant government departments provided any additional data sources, reviewed the accuracy and completeness of the data, and provided amendments as needed. Data were scored independently by members of the research team. Final policy scores were tabulated and presented as a weighted overall average score and as unweighted domain-specific scores. Compared to public health best practices, the federal government of Canada scored 37% overall. The three most impactful domains-(1) pricing and taxation, (2) marketing and advertising controls, and (3) impaired driving countermeasures-received some of the lowest scores (39%, 10%, and 40%, respectively). Domain-specific scores varied considerably from 0% for minimum legal age policies to 100% for controls on physical availability of alcohol. Many evidence-informed alcohol policies have not been adopted, or beenadopted only partially, by the Canadian federal government. Urgent adoption of the recommended policies is needed to prevent and reduce the enormous health, social, and economic costs of alcohol use in Canada.

Towards a Discourse Move Analysis of Research Article Abstracts in Nepali Journals: A Cross-Disciplinary Study

An abstract is an important component of a research article to attract the readers’ attention and provide a concise, informative, and accurate summary of the paper. Many academic manuscripts are usually rejected due to ineffective abstracts. This study aims to investigate and analyse the discourse moves in research article abstracts in the journals published in Nepal. To achieve this objective, 120 article abstracts from journals of eight different domains were selected, and the contents were analysed using Bitchener’s (2018) framework. The results showed that all the abstracts followed the broad categories of introduction, purpose, methods, products, and conclusion, but there were differences in frequency of occurrence using the parameters specified under each category. The findings of the study showed that discourse moves were frequently used by the majority of journals for writing the context and background, authors' motivations for the research, research gaps, goals of the research, research approach and design, data sources and analysis processes, findings, and contributions of the findings to related fields. Finally, the study concluded that the findings of the study can be significant for designing field-specific academic writing courses that lead the authors to standardize their writing and prevent the article from being rejected.

A conjugated structure discloses interaction between two fault systems in eastern Taiwan during 2022 Guangfu earthquake

The Longitudinal Valley (LV) in the eastern Taiwan is located at the convergent boundary between the Philippine Sea Plate and the Eurasian Plate. Within the LV, two significant fault systems, the Longitudinal Valley Fault (LVF) in the eastern part and the Central Range Fault (CeRF) in the western part, have been recognized. However, understanding the precise mechanisms and spatial relationships of the two fault systems in the middle segment of the LV has remained a challenge. In this study, we integrate various data sources, including geodetic measurements, seismic data, field investigations, and simulation results, all obtained during the 2022 Guangfu earthquake event. Our goal is to shed light on the complex structural interactions between the two fault systems. We introduce a coupled structural model consisting of a main fault plane and a coupled ramp plane. The deep-seated fault plane is constrained using seismic data, while the shallow ramp plane is determined through field investigations. Geodetic measurements are utilized to constrain the surface deformation resulting from dislocation model. The simulation outcomes reveal that the 2022 Guangfu earthquake was primarily triggered by the activity along the CeRF. The CeRF experienced an eastward reverse slip of approximately 58 cm along the fault plane. This fault not only intersects the LVF beneath the Coastal Range but also initiates the development of a ramp plane along pre-existing fault planes of the LVF. This intricate interplay results in approximately 20 cm of vertical surface deformation within the Coastal Range. The linear connection observed in the simulation residuals is associated with pre-existing faults within the LVF system. This alignment corresponds to the ground distribution of ramp plane, suggesting that older fault systems within this area exhibit characteristics of reactivation in response to earthquake-induced processes. Our study not only pinpoints the potential location of the CeRF intersecting with the LVF in the middle segment of the LV but also provides insight into one of the interaction mechanisms between the CeRF and the LVF. These findings hold significant implications for assessing seismogenic structural activity and enhancing the mitigation of regional earthquake hazards in the future.

Towards Improved Privacy in Digital Marketing: A Unified Approach to User Modeling with Deep Learning on a Data Monetization Platform

This paper introduces an innovative method for safeguarding user privacy in digital marketing campaigns through the application of deep learning techniques on a data monetization platform. This framework empowers users to maintain authority over their personal data while enabling marketers to pinpoint suitable target audiences. The system consists of several key stages Data representation learning in hyperbolic space captures latent user interests across various data sources with hierarchical structures. Subsequently, Generative Adversarial Networks generate synthetic user interests from these embedding. To preserve user privacy, Federated Learning is utilized for decentralized user monetization, Data privacy, modeling training, ensuring data remains undisclosed to marketers. Lastly, a hyperbolic embedding, Federated learning targeting strategy, rooted in recommendation systems, utilizes learned user interests to identify optimal target audiences for digital marketing campaigns. In sum, this approach offers a comprehensive solution for privacy-preserving user modeling in digital marketing.

JWST MIRI and NIRCam unveil previously unseen infrared stellar populations in NGC 6822

ABSTRACT NGC 6822 is a nearby (∼490 kpc) non-interacting low-metallicity (0.2 Z⊙) dwarf galaxy which hosts several prominent H ii regions, including sites of highly embedded active star formation. In this work, we present an imaging survey of NGC 6822 conducted with the Near Infrared Camera (NIRCam) and Mid-Infrared Instrument (MIRI) onboard JWST. We describe the data reduction, source extraction, and stellar population identifications from combined near- and mid-infrared (IR) photometry. Our NIRCam observations reach 7 mag deeper than previous JHKs surveys of this galaxy, which were sensitive to just below the tip of the red giant branch (TRGB). These JWST observations thus reveal for the first time in the near-IR the red clump stellar population and extend nearly 3 mag deeper. In the mid-IR, we observe roughly 2 mag below the TRGB with the MIRI F770W and F1000W filters. With these improvements in sensitivity, we produce a catalogue of ∼900 000 point sources over an area of ∼6.0 × 4.3 arcmin2. We present several NIRCam and MIRI colour–magnitude diagrams and discuss which colour combinations provide useful separations of various stellar populations to aid in future JWST observation planning. Finally, we find populations of carbon- and oxygen-rich asymptotic giant branch stars which will assist in improving our understanding of dust production in low-metallicity, early Universe analogue galaxies.

Fulfillment Of Citizenship Rights For The Children Of Indonesian Migrant Workers Who Are Working Illegally In Malaysia

The right to citizenship is a fundamental right for every human being. However, there are still individuals who have not been able to access their citizenship rights, namely the children of Indonesian migrant workers who are working illegally in Malaysia. The author wants to know about the protection of citizenship rights for the children of Indonesian migrant workers who work illegally in Malaysia, as well as the state's responsibility in fulfilling the citizenship rights of these children. This research used a qualitative normative method. It relied on literature and written sources for data. The results indicate that the children of Indonesian migrant workers who work illegally in Malaysia, according to principles and applicable laws, are Indonesian citizens. However, they do not have documents to prove their citizenship. The Indonesian government has a responsibility to ensure the possession of citizenship documents for the children of Indonesian migrant workers who work illegally in Malaysia.

Open e-commerce 1.0, five years of crowdsourced U.S. Amazon purchase histories with user demographics

This is a first-of-its-kind dataset containing detailed purchase histories from 5027 U.S. Amazon.com consumers, spanning 2018 through 2022, with more than 1.8 million purchases. Consumer spending data are customarily collected through government surveys to produce public datasets and statistics, which serve public agencies and researchers. Companies now collect similar data through consumers’ use of digital platforms at rates superseding data collection by public agencies. We published this dataset in an effort towards democratizing access to rich data sources routinely used by companies. The data were crowdsourced through an online survey and shared with participants’ informed consent. Data columns include order date, product code, title, price, quantity, and shipping address state. Each purchase history is linked to survey data with information about participants’ demographics, lifestyle, and health. We validate the dataset by showing expenditure correlates with public Amazon sales data (Pearson r = 0.978, p < 0.001) and conduct analyses of specific product categories, demonstrating expected seasonal trends and strong relationships to other public datasets.

Fostering Privacy in Collaborative Data Sharing via Auto-encoder Latent Space Embedding

Securing privacy in machine learning via collaborative data sharing is essential for organizations seeking to harness collective data while upholding confidentiality. This becomes especially vital when protecting sensitive information across the entire machine learning pipeline, from model training to inference. This paper presents an innovative framework utilizing Representation Learning via autoencoders to generate privacy-preserving embedded data. As a result, organizations can distribute these representations, enhancing the performance of machine learning models in situations where multiple data sources converge for a unified predictive task downstream.

Intraoperative Frozen Section Evaluation of Pancreatic Specimens and Related Liver Lesions.

Frozen sections are essential in the surgical management of patients, especially those with pancreatic masses, because frozen sections can provide answers intraoperatively and aid in treatment decisions. Pancreas frozen sections are challenging because of the small tissue size, processing artifacts, neoadjuvant treatment effects, and concurrent pancreatitis-like obstructive changes. The authors present a review of intraoperative evaluation of pancreatic specimens. To provide an approach to the diagnosis of pancreatic adenocarcinoma on frozen sections and to discuss commonly encountered pitfalls. Indications for pancreas frozen sections and specific margin evaluation will be discussed. We will also review frozen section diagnosis of subcapsular liver lesions and tumors other than metastases of pancreatic ductal adenocarcinoma. Data sources included a literature review and the personal experiences of the authors. The features for diagnosis of pancreatic adenocarcinoma include disordered architecture, glands at abnormal locations, and atypical cytology. It is important to be aware of the pitfalls and clues on frozen section. The evaluation of resection margins can be challenging, and in the setting of the resection of cystic tumors, the key is the diagnosis of high-grade dysplasia or cancer. Finally, it is vital to remember the differential diagnosis for subcapsular liver lesions because not all lesions will be metastases of adenocarcinomas or bile duct adenomas. Frozen sections remain a useful tool for the intraoperative management of patients with pancreatic tumors.

Crowdsourcing RTK: a new GNSS positioning framework for building spatial high-resolution atmospheric maps based on massive vehicle GNSS data

High-quality spatial atmospheric delay correction information is essential for achieving fast integer ambiguity resolution (AR) in precise positioning. However, traditional real-time precise positioning frameworks (i.e., NRTK and PPP-RTK) depend on spatial low-resolution atmospheric delay correction through the expensive and sparsely distributed CORS network. This results in limited public appeal. With the mass production of autonomous driving vehicles, more cost-effective and widespread data sources can be explored to create spatial high-resolution atmospheric maps. In this study, we propose a new GNSS positioning framework that relies on dual base stations, massive vehicle GNSS data, and crowdsourced atmospheric delay correction maps (CAM). The map is easily produced and updated by vehicles equipped with GNSS receivers in a crowd-sourced way. Specifically, the map consists of between-station single-differenced ionospheric and tropospheric delays. We introduce the whole framework of CAM initialization for individual vehicles, on-cloud CAM maintenance, and CAM-augmented user-end positioning. The map data are collected and preprocessed in vehicles. Then, the crowdsourced data are uploaded to a cloud server. The massive data from multiple vehicles are merged in the cloud to update the CAM in time. Finally, the CAM will augment the user positioning performance. This framework forms a beneficial cycle where the CAM’s spatial resolution and the user positioning performance mutually improve each other. We validate the performance of the proposed framework in real-world experiments and the applied potency at different spatial scales. We highlight that this framework is a reliable and practical positioning solution that meets the requirements of ubiquitous high-precision positioning.

Mapping Opium Poppy Cultivation: Socioeconomic Insights from Satellite Imagery

Over 30 million people globally consume illicit opiates. In recent decades, Afghanistan has accounted for 70–90% of the world’s illicit supply of opium. This production provides livelihoods to millions of Afghans, while also funneling hundreds of millions of dollars to insurgent groups every year, exacerbating corruption and insecurity, and impeding development. Remote sensing and field surveys are currently used in official estimates of total poppy cultivation area. These aggregate estimates are not suited to study the local socioeconomic conditions surrounding cultivation. Few avenues exist to generate comprehensive, fine-grained data under poor security conditions, without the use of costly surveys or data collection efforts. Here, we develop and test a new unsupervised approach to mapping cultivation using only freely available satellite imagery. For districts accounting for over 90% of total cultivation, our aggregate estimates track official statistics closely (correlation coefficient of 0.76 to 0.81). We combine these predictions with other grid-level data sources, finding that areas with poppy cultivation have poorer outcomes such as infant mortality and education, compared to areas with exclusively other agriculture. Surprisingly, poppy-growing areas have better healthcare accessibility. We discuss these findings, the limitations of mapping opium poppy cultivation, and associated ethical concerns.