A bibliometric and thematic approach to agriculture 4.0

Audio-based digital biomarkers in diagnosing and managing respiratory diseases: a systematic review and bibliometric analysis

Advances in wearable sensors and artificial intelligence have greatly enhanced the potential of digitised audio biomarkers for disease diagnostics and monitoring. In respiratory care, evidence supporting their clinical use remains fragmented and inconclusive. This study aimed to assess the current research landscape of digital audio biomarkers in respiratory medicine through a bibliometric analysis and systematic review (PROSPERO CRD 42022336730). MEDLINE, Embase, Cochrane Library and CINAHL were searched for references indexed up to 9 April 2024. Eligible studies evaluated the accuracy of sound analysis for diagnosing and managing obstructive (asthma and COPD) or infectious respiratory diseases, excluding COVID-19. A narrative synthesis was conducted, and the QUADAS-2 tool was used to assess study quality and risk of bias. Of 14 180 studies, 81 were included. Bibliometric analysis identified fundamental (e.g. "diagnostic accuracy"+"machine learning") and emerging (e.g. "developing countries") themes. Despite methodological heterogeneity, audio biomarkers generally achieved moderate (60-79%) to high (80-100%) accuracies. 80% of studies (eight out of ten) reported high sensitivities and specificities for asthma diagnosis, 78% (seven out of nine) reported high sensitivities and 56% (five out of nine) reported high specificities for COPD, and 64% (seven out of eleven) reported high sensitivity or specificity values for pneumonia diagnosis. Breathing and coughing were the most common biomarkers, with artificial neural networks being the most common analysis technique. Future research on audio biomarkers should focus on testing their validity in clinically diverse populations and resolving algorithmic bias. If successful, digital audio biomarkers hold promise for complementing existing clinical tools in enabling more accessible applications in telemedicine, communicable disease monitoring, and chronic condition management.

A systematic review of measurement tools and senior engagement in urban nature: Health benefits and behavioral patterns analysis

The engagement of senior citizens with urban nature has been shown to provide multiple health benefits and mitigate health issues associated with demographic aging. This review utilized the PRISMA methodology to systematically analyze the relationship between monitoring tools, seniors' behaviors in urban nature, and influencing factors. The main findings are as follows: (1) 4 main types, including self-reports, on-site observations, sensors, and third-party data, and 24 sub-types of measurement tools: ranging from questionnaires to crowdsourced imagery services. Self-reports capture participants' awareness of behaviors, on-site observations record various types of behaviors, sensors collect indicators to detect the body's direct responses, and third-party data provide representative behavior data from large samples. (2) 4 categories and 45 types of behaviors: physical and sports behaviors, leisure and recreational behaviors, relaxation, and passive behaviors, social and care behaviors, based on their characteristics and purposes. Physical and sports behaviors are the most common types for the elderly in urban nature, with walking being the most frequently measured behavior. (3) 36 influencing factors: ranging from diabetes risk to balanced meal habits, classified into 4 categories from physical and vitality health to social and lifestyle health. Physical and vitality health are the most affected category, receiving more academic attention. Gardening is identified as having the most health benefits. This review provides a classification of tools and behaviors, and a detailed discussion of future trends in the field. It provides actionable insights for researchers, urban designers, city managers, and policymakers to select the appropriate measurement tool from 24 sub-tools to better understand behaviors of elderly people in urban nature. It can also help them select the right type of behavior from 45 sub-behaviors to investigate in line with their research goals to improve seniors' health and well-being.

Identifying hidden factors influencing soil Olsen-P in an alkaline calcareous soil using machine learning and geostatistical techniques

Phosphorus (P) deficiency is one of the major constraints for sustainable crop production in calcareous soils. This study aimed to elucidate the key soil characteristics modulating the variability of soil Olsen P in these typical soils. A comprehensive soil sampling initiative (1.5 samples per hectare) was conducted on a 100-ha farm, considering 31 attributes that included soil physical and chemical properties, and geographic attributes. Three machine learning algorithms-partial least squares regression (PLSR), random forest (RF), and cubist regression (CR)-were employed to understand key variables controlling soil Olsen P. Furthermore, the same data set was used to spatially map the variations in Olsen P levels using ordinary kriging. The results revealed that soil chemical factors, specifically exchangeable manganese and zinc, cation exchange capacity, and carbonate, played a crucial role in controlling P levels. Among the machine learning models, the best performing model was RF (R2 = 0.95, RMSE = 1.30 mg kg-1) followed by CR (R2 = 0.92 and RMSE = 1.43 mg kg-1). Additionally, the analysis using a Gaussian semi-variogram model showed a good performance (R2 = 0.78, RMSE = 2.05 m) in visualizing the spatial distribution of Olsen P, revealing its heterogeneity. The resulting pattern of Olsen P distribution may be attributed not only to soil properties but also to external factors, such as sediment transport through watercourses across the study area and atmospheric deposition from a nearby P mining site. Overall, the combination of geostatistical methods and machine learning approach demonstrates a significant potential in understanding the complexity of soil available P (Olsen-P) that could help to develop sustainable and precise P management.

Medical magnetic resonance imaging publications in Arab countries: A 25-year bibliometric analysis

Magnetic Resonance Imaging (MRI) is increasingly becoming a cornerstone in modern diagnostic healthcare, offering unparalleled capabilities in stroke, dementia, and cancer screening. Therefore, this study aims to map medical MRI literature affiliated with Arab countries, focusing on publication trends, top journals, author affiliations, study countries, and authors' collaboration, and keyword analysis. The scientific database used is the Scopus database. Microsoft Excel, VOSviewer software, and Biblioshiny for the Bibliometrix R package are the bibliometric tools used in this analysis. A total of 2592 publications were published between 1988 and 2022, with total citations of 22,115. Most of them were original articles (91,7%) and 89.9% were published in traditional journals. The number of total publications exhibited a steady increase over time, whereas total citations showed fluctuations, peaking in 2015 with 1571 citations for publications from that year. The most cited article was authored by Yaseen M. Arabi, receiving 286 citations. Saudi Arabia was the top active country. In addition, the most prolific author was Maha S Zaki, and the most prolific source was the "Egyptian Journal of Radiology and Nuclear Medicine". The most prolific affiliation was Cairo University. The "multiple sclerosis" and "case report" were the most trending keywords. The analysis revealed a significant growth in MRI research inside Arab countries, as shown by an increase in the total number of publications and international collaborations. Despite these developments, the results of this study suggest that there is still room for MRI research in the Arab region to advance. This can be achieved through increasing international collaboration and multidisciplinary work.

A bibliometric analysis of the role of nanotechnology in dark fermentative biohydrogen production

Recently, nanoparticles have drawn a lot of interest as catalysts to enhance the effectiveness and output of biohydrogen generation processes. This review article provides a comprehensive bibliometric analysis of the significance of nanotechnology in dark fermentative biohydrogen production. The study examines the scientific literature from the database of The Web of Science© while the bibliometric investigation utilized VOSviewer© and Bibliometrix software tools to conduct the analysis. The findings revealed that a total of 232 articles focused on studying dark fermentation for hydrogen production throughout the entire duration. The extracted data was used to analyze publication trends, authorship patterns, and geographic distribution along with types and effects of nanoparticles on the microbial community responsible for dark fermentative biohydrogen production. The findings of this bibliometric analysis provide valuable insights into the advancements and achievements in the utilization of nanoparticles in the dark fermentation process used to produce biohydrogen.

Extended reality in the automotive sector: A bibliometric analysis of publications from 2012 to 2022

The present study aims to present a bibliometric analysis of publications related to "Extended Reality" (XR) in the automotive sector. XR is revolutionizing the industry in all fields, and the automotive is one of the sectors that has had much to gain from this technology and its components (Virtual Reality, Augmented Reality, Mixed Reality). Articles on XR in the automotive field that were published from 2012 to 2022 were retrieved from the Scopus database. Extracted items were analysed in terms of the document type, document language, year of publication, country, authors, affiliations, sources, citations, keywords, and research domains. The open-source tool VOSviewer was used to visualize trends in research on XR applied to automotive. The analyses of 1584 documents revealed that the total number of publications has continually increased over the last 11 years. The country producing most of the articles in this field was Germany, followed by the United States and China. The most productive journal is Transportation Research Part F: Traffic Psychology and Behaviour and the institution that issued most of the articles is Technical University of Munich. From the analysis of author keywords, the prominent research areas currently involving the use of XR technologies in automotive can be highlighted: virtual prototyping, design, manufacturing, sales, training, driver or pedestrian behaviour analysis, and ergonomics. More recently, terms like artificial intelligence and autonomous vehicles have started to be used more frequently in studies in the field. The current study reveals an expanding corpus of literature on XR-based applications for the automotive sector using bibliometric methods. Researchers and stakeholders can use this study as a useful reference to comprehend the big picture and the state-of-the-art in this area.

An overview of image-based phenotyping as an adaptive 4.0 technology for studying plant abiotic stress: A bibliometric and literature review

Improving the tolerance of crop species to abiotic stresses that limit plant growth and productivity is essential for mitigating the emerging problems of global warming. In this context, imaged data analysis represents an effective method in the 4.0 technology era, where this method has the non-destructive and recursive characterization of plant phenotypic traits as selection criteria. So, the plant breeders are helped in the development of adapted and climate-resilient crop varieties. Although image-based phenotyping has recently resulted in remarkable improvements for identifying the crop status under a range of growing conditions, the topic of its application for assessing the plant behavioral responses to abiotic stressors has not yet been extensively reviewed. For such a purpose, bibliometric analysis is an ideal analytical concept to analyze the evolution and interplay of image-based phenotyping to abiotic stresses by objectively reviewing the literature in light of existing database. Bibliometricy, a bibliometric analysis was applied using a systematic methodology which involved data mining, mining data improvement and analysis, and manuscript construction. The obtained results indicate that there are 554 documents related to image-based phenotyping to abiotic stress until 5 January 2023. All document showed the future development trends of image-based phenotyping will be mainly centered in the United States, European continent and China. The keywords analysis major focus to the application of 4.0 technology and machine learning in plant breeding, especially to create the tolerant variety under abiotic stresses. Drought and saline become an abiotic stress often using image-based phenotyping. Besides that, the rice, wheat and maize as the main commodities in this topic. In conclusion, the present work provides information on resolutive interactions in developing image-based phenotyping to abiotic stress, especially optimizing high-throughput sensors in image-based phenotyping for the future development.

Predicting rainfall and irrigation requirements of corn in Ecuador

This work is a case study whose objective is prediction of irrigation needs of corn crops in different regions of Ecuador; being this a fundamental basic food for the country's economy, as in the remaining countries of the Andean area. The proposed methodology seeks to help improving the quality of corn crop. Specifically, we propose the application of regression models, within the framework of Functional Data Analysis (FDA), to predict the amount of rainfall (scalar response variable) in the places with the highest production of corn in Ecuador, as a function of functional covariates such as temperature and wind speed. From the estimation of the amount of rainfall, effective precipitation is calculated. This is the fraction of water used by the crops, from which the value of real evapotranspiration or ETc is obtained and, more importantly, the irrigation requirements at each stage of the corn crop, for its adequate physiological development. Application of regression models based on functional basis, Functional Principal Components (FPC) or Functional Partial Least Squares (FPLS) for scalar response variable, allows us to use the information of variables such as wind speed and temperature (of functional nature) in a better way than using multivariate models, for predicting the amount of rainfall, obtaining, as a result, very explicative models, defined by a high goodness of fit (R 2 = 0.97 , with 6 significant parameters and an error of 0.14) and practical utility. The model has been also applied to North Peru regions, obtaining rainfall prediction errors between 9% and 22%. Thus, the geographical limitations of the model could be the Andean regions with similar climate. In addition, this study proposes the application of FDA exploratory analysis and FDA outlier detection techniques as a common and useful practice in the specific domain of rainfall prediction studies, prior to applying the regression models.

Driving hospitality and tourism to foster sustainable innovation: A systematic review of COVID-19-related studies and practical implications in the digital era

Sustainable innovation is a solution for the hospitality and tourism (H&T) industries to cope with the COVID-19 crisis, build resilience, and ensure survival post-pandemic. The primary aim of our review is to systematically identify and critically review the literature on sustainable innovation in H&T amid COVID-19 (conducted in 2020 and 2021), in order to synthesize and classify prevalent types, antecedents, and outcomes of sustainable innovation. The authors present a comprehensive review of the 58 articles on sustainable innovation in H&T through the Web of Science (WoS) database, spanning over 2 years (2020–2021). This review demonstrates that since the beginning of COVID-19, H&T have strongly mobilized network technologies (especially social media and digital platforms) and data-processing technologies (especially Artificial Intelligence (AI) and Machine Learning (ML)) in comparison with physical-digital interface technologies (especially Virtual Reality (VR)), while physical-digital process technologies remain very limited in these industries. Several relevant antecedents of the adoption of sustainable innovation, more specifically digital technologies, have been identified at multiple levels of analysis, including the organizational, managerial, and stakeholder levels. Our research also reveals several consequences of the adoption of sustainable innovation in H&T. These consequences were congregated according to the three main dimensions related to sustainability in economic, social, and environmental outcomes. This study provides important practical implications for the H&T sectors in the digital era and post-pandemic. The current research is the first study to systematically and critically review sustainable innovation in an H&T context.