Scientific Landscape Research Articles

Shedding Light on the Mid‐Infrared Complex Refractive Index of Anodic Aluminum Oxide

AbstractIn the current scientific landscape, the understanding of optical properties in the mid‐infrared (mid‐IR) range (3–30 µm) is crucial in simulations and models to explore the potential of materials for various applications. However, due to the challenges associated with mid‐IR characterization, accurate refractive index (n) and extinction coefficient (κ) data are often lacking in the literature. This study addresses this gap by investigating the mid‐IR n and κ spectra of anodic aluminum oxide (AAO) nanostructures anodized under different conditions, using two distinct approaches: IR ellipsometry and a theoretical model based on multilayer reflection and effective medium. The results demonstrate a strong agreement: the anodizing conditions have a significant influence on the optical properties of the AAO nanostructures. These differences enable accurate simulations of the emissivity spectra of AAO nanostructures on Al foils, which align closely with experimental measurements. This theoretical approximation is versatile and extensible to a broad range of materials. Different materials are tested, namely, a sapphire, a polycarbonate film, and a polyethylene terephthalate (PET) film achieving a useful qualitative description. This study paves the way for a novel approach in the engineering of new micro and nano‐optical materials, facilitating their evaluation for suitability in mid‐IR applications.

The educator and educational continuity in the zerosei integrated system in Italy. Virtues, skills and world context

In Italy, Legislative Decree 65/2017 establishes the integrated system of education and instruction from birth to six years, effectively establishing the institutional conditions for the creation of an educational proposal that has pedagogical roots dating back to the debates on educational continuity which have crossed the Italian scientific landscape since the 1980s. This proposal from the legislator has opened the way to numerous debates regarding a series of rather complex issues concerning pedagogical, political, organizational and financial issues. In the analysis and problematization of these themes, the question of the definition of the professional figure who can/should operate, precisely, in a zero-six context seems to emerge as emblematic. This issue appears to be particularly thorny since at the moment in Italy we are in a transition phase which sees on the one hand the push towards a new integrated educational system for early childhood, on the other the persistence of old training and organizational structures which on the one hand they can be considered a limit to change, on the other hand they can prove to be a solid reference base from which to start to achieve change. In the definition of operational paths to promote the integrated zerosei system, and in the definition of the related professional figures, the theme of educational continuity appears to be central, which emerges both in relation to the internal dimension of the educational structures and in relation to the contiguity between these structures, the services, the family and the territory.

Mapping the landscape of government data breaches: A bibliometric analysis of literature from 2006 to 2023

Data breaches in the government sector are severe threats to cybersecurity and illegal acts that flagrantly violate security and privacy laws. It undermines public trust in government data management. Unlike breaches in other sectors, these incidents can have far-reaching consequences and the potential to cause significant damage to governance and national security. This field has experienced rapid growth in recent years, which means an increase in urgency to address this challenge. This study conducted a bibliometric analysis to evaluate the scientific landscape of government data breaches. It analysed 107 articles published from 2006 to 2023 from the Scopus database. The key findings highlighted trends in scientific production, the most cited articles, country contributions, co-occurrence networks, thematic evolution, and factorial analysis. Ransomware has become one of the biggest challenges in cybersecurity, especially for governments. The use of increasingly sophisticated zero-day attack techniques makes it difficult for law enforcement to track the digital footprints of perpetrators. The regulations on data protection remain inadequate and require improvement. The existing legal framework is not strong enough to reduce data breaches in cyberspace. Therefore, to deal with cyber threats, such as ransomware, cyber risk management is essential and using cloud computing raises new challenges and opportunities in cybersecurity. These findings contribute to future research and practice as guidance for policymakers, system developers, researchers, and practitioners in improving cybersecurity protocols and shaping government regulations and policies.

Seamless Science: Lifting Experimental Mechanical Testing Lab Data to an Interoperable Semantic Representation

The scientific landscape is undergoing rapid transformations with the advent of the digital age which revolutionizes research methodologies. In materials science and engineering, an adoption of modern data management techniques is desirable to maximize the efficiency and accessibility of research efforts. Traditional practices in testing laboratories are usually inadequate for efficient data acquisition and utilization as they lead to local storage and difficulty in publication and correlation with other results. Electronic laboratory notebooks (ELNs) are promising prospects in this respect. Semantic concepts and ontologies enhance interoperability by standardizing experimental data representation. An in‐laboratory pipeline seamlessly integrating an ELN with transformation scripts to convert experimental into interoperable data in a machine‐actionable format is created in this study as a proof of concept. Tensile test results and the corresponding tensile test ontology are used exemplary. Linking ELN data to semantic concepts enriches the stored information while improving interpretability and reusability. Involving undergraduate students builds a bridge between theory and practice during their training and promotes their digital skills. This study underscores the potential of ELNs and knowledge representations as beneficial means toward improved data management practices that enhance collaborative research and education while ensuring compatibility with evolving standards and technologies.

Mapping the Scientific Landscape of Knowledge Management in IT SMEs: A Bibliometric Analysis

This study embarks on a comprehensive bibliometric analysis to elucidate the intricate relationship between knowledge management capabilities and perceived performance within information technology small and medium-sized enterprises (IT SMEs). Utilizing data from the Web of Science and Scopus databases, this research meticulously examines occurrences, word frequencies, and clustering analyses to identify pivotal thematic areas influencing IT SMEs. By employing advanced tools such as RStudio software and the Biblioshiny application, the study dissects average citations per year, presenting a dynamic view of research impact and evolving significance in the domain. Authorship dynamics are explored through Lotka’s law, and geographic contributions are analyzed, highlighting the global and collaborative essence of knowledge management research. Factorial analysis further reveals layered dimensions of research trends, offering guidance for researchers and practitioners in crafting nuanced strategies. The collective insights underscore the paramount role of knowledge management in enhancing IT SMEs’ performance and competitive edge, marking a significant stride toward understanding and implementing effective knowledge management strategies in the rapidly evolving IT landscape.

Liquid-solid composites with confined interface behaviors

Abstract In the evolving landscape of materials science, the expansion from traditional composite materials to liquid-solid composites has marked a significant shift. Composite materials, typically solid-state, have long been the cornerstone in many applications due to their structural stability and mechanical properties. However, the emergence of liquid-solid composites has introduced a new paradigm, leveraging the dynamic composite interfaces and fluidic nature of liquids. Recent years have witnessed the rapid development of liquid-solid composites, distinguishing themselves with their defect-free, molecularly smooth surfaces, and adaptive features. In this review, we introduce liquid-based confined interface materials, which represent a cutting-edge advancement, integrating confined liquids within solid frameworks at mesoscopic scales. Characterized by their confined competitive multiphase interfacial interactions, these materials offer practical functionalities like anti-fouling, multiphase flow control, and drag-reduction. We summarize the trendy development of the materials, and showcase important applications realized based on both controllable motions of the confined liquids and the solid frameworks. We also discuss their design and preparation and address the future challenges and outlooks, such as artificial intelligence in advancing functionalities.

Enhancing Science and Climate Literacy in Latin America and the Caribbean

The Intercontinental meeting on Science Literacy 2024 brought together experts, educators, and policymakers to discuss the changing landscape of science and climate literacy in Latin America and the Caribbean. This document synthesizes the ideas shared at the conference and aims to serve as a resource to promote science literacy measures. Key topics include the impact of digital transformation on education, the integration of climate literacy into school curricula, the importance of cultural relevance, and the challenges of accessibility and misinformation. Opportunities to improve scientific literacy through collaborative networks, public engagement, and supportive policies are also highlighted. The white paper presents strategies to integrate digital tools, promote inclusion, and promote lifelong learning. These recommendations will be integrated into the ongoing development of the Declaration on Science and Climate Literacy, initially published in 2021 by the Andean Road Countries for Science and Technology and its collaborators.

Transparency of sustainability disclosure in agri-food value chain management: Mapping the scientific landscape

Sustainability transparency in agri-food value chains is crucial for fostering accountability, enhancing consumer trust, facilitating compliance with regulatory standards, and ultimately contributing to the resilience and sustainability of food systems in the face of social, environmental, and economic challenges. This paper aims to conduct bibliometric mapping and a systematic review of the scientific landscape concerning transparency of sustainability disclosure in agri-food value chains by identifying the key transparency dimensions and relevant research gaps. An analysis of 841 Scopus-indexed publications, utilizing Scopus tools, SciVal, VOS Viewer, and Biblioshiny software, yields insights into source and author mapping from 2000 to 2022. Bibliometric analysis underlines the increase in research interests in transparency of sustainability disclosure in agri-food value chains after Sustainable Development Goals adoption and the COVID-19 pandemic, following upwards trend, especially in the UK, India, and the USA. The most influential topic clusters are supply chain, environmentally preferable purchasing, and green practices, as well as Bitcoin, Ethereum, and Internet of Things with the strongest co-occurrences between transparency (as the most recent notion in scientific landscape) and sustainability, traceability, supply chains, food supply, and blockchain. Systematic review highlights the evidence that transparency as boundary-spanning phenomenon is explored within the mono-country and chain researches, case studies and interviews methodologies from triple bottom line dimension mainly, only introducing governance criteria. Research gaps were identified regarding the role of transparency in different economic system and chains; sustainability conceptual framework used; transparency dimensions incorporation; technology-driven progress and other chain characteristics (traceability, resilience) intersection. AcknowledgmentsInna Makarenko is gratefully acknowledging the support of MSCA4Ukraine project no. 1233713.

Scientific landscape of oxidative stress in sarcopenia: from bibliometric analysis to hotspots review.

Sarcopenia is a significant healthcare challenge in the aging population. Oxidative stress (OS) is acknowledged to play a pivotal role in the pathological progression of sarcopenia. Numerous studies have demonstrated that mitigating or eliminating OS can ameliorate the pathological manifestations associated with sarcopenia. However, current clinical antioxidant therapies often fall short of anticipated outcomes. This bibliometric analysis aims to delineate prevailing research trends, thematic emphases, focal points, and developmental trajectories within the domain of OS in sarcopenia, while also endeavoring to explore prospective anti-oxidative stress strategies for future clinical interventions. Relevant publications were retrieved from the Web of Science (WOS) Core Collection database for the period 2000-2024. Citespace was employed for retrieving and analyzing trends and emerging topics. In the field of OS in sarcopenia, the number of publications has significantly increased from 2000 to 2024. The United States and China are the primary contributors to global publication output. The most productive research institution is INRAE. The most prolific author is Holly Van Remmen from the United States, while the most frequently cited author is Cruz-Jentoft AJ from Spain. Experimental Gerontology is the journal with the highest volume of published articles, whereas the Journal of Gerontology Series A: Biological Sciences and Medical Sciences holds the record for the highest number of citations. The research keywords in this field can be categorized into eight domains: "Physiology and anatomy", "Physiological mechanisms", "Pathology associations", "Experimental studies", "Nutrition and metabolism", "Sports and physical activities", "Age" and "Oxidation and antioxidation". Moreover, recent years have seen the emergence of "TNF-α," "insulin resistance", "mitochondrial autophagy", "signal pathways", and "mechanisms" as focal points in the realm of OS in sarcopenia, encompassing related fundamental research and clinical translation. This bibliometric and visualization provides a comprehensive analysis of the global research landscape in the field of OS in sarcopenia, identifies priorities, summarizes the current research status and suggests possible future research priorities. In addition, in order to benefit more sarcopenia patients, strengthening cooperation and communication between institutions and research teams is the key to the future development of this field. Given the expectation that research on OS in sarcopenia will remain a prominent area of interest in the future, this article could serve as a valuable resource for scholars seeking to shape future studies through an understanding of influential scholarly contributions and key research findings. https://www.crd.york.ac.uk, identifier CRD42024528628.

Citizen Science and Artificial Intelligence in Horizon 2020 and Horizon Europe Projects: Communication and Scientific Impact

The Horizon 2020 and Horizon Europe framework programs are the key funding programs for the European Union's policy on innovation, research, and development (R&D&I) in all scientific subject areas. These instruments promote open science by using citizen science as a collaborative methodology and artificial intelligence as a disruptive technology, thereby encouraging public participation and engagement in scientific research. This paradigm shift in the scientific landscape is the impetus for this descriptive and exploratory study analyzing the effectiveness of communication policies and the quality of the dissemination and scientific impact of 28 R&D&I projects developed using citizen science and artificial intelligence, which were selected from the Community Research and Development Information Service (CORDIS) repository. This case study employs a methodological procedure grounded in content analysis and bibliometric indicators to meet four specific objectives: to determine the main formats and channels used in the projects’ communication strategies, as well as which category the projects’ papers fall into; to analyze the effectiveness of the projects’ scientific dissemination using articles published in Scopus according to subject area; to analyze the quality of scientific impact of the 234 articles that the projects produced using the SCImago Journal Rank (SJR) indicator; and to evaluate their specific and comparative impact using the standardized indicators Field-weighted citation impact (FWCI) and CiteScore. The findings confirmed that there were substantial differences in terms of the effectiveness of communication and the quality of dissemination and scientific impact among the projects analyzed. In this context, communication science could help efficiently navigate the challenges and opportunities in scientific communication.

Understanding complex systems through differential causal networks

In the evolving landscape of data science and computational biology, Causal Networks (CNs) have emerged as a robust framework for modelling causal relationships among elements of complex systems derived from experimental data. CNs can efficiently model causal relationships emerging in a single system while comparing multiple systems, allowing to understand rewiring in different cells, tissues, and physiological states with a deeper perspective. Despite the existence of network models, namely differential networks, that have been used to compare coexpression and correlation structures, causality needs to be introduced in differential analysis to robustly provide direction to the edges of such networks, in order to better understand the flows of information, and also to better intervene in their functioning, for example for agricultural or pharmacological purposes. Resolved to reach this ambitious goal, we introduce Differential Causal Networks (DCNs), a novel framework that represents differences between two existing CNs. A DCN is obtained from experimental data by comparing two CNs, and it is a power tool for highlighting differences in causal relations. After a careful definition and design of DCNs, we test our algorithm to model possible differential causal relationships between genes responsible for the onset of type 2 diabetes mellitus-related pathologies considering patients’ sex at the tissue level. DCNs allowed us to shed light on causal differences between sexes across nine tissues. We also compare differences among three possible definitions of DCNs to highlight similarities and differences of biological importance. Code, Data and Supplementary Information are available at https://github.com/hguzzi/DifferentialCausalNetworks.

Time to take stock.

The reelection of Donald Trump for a second, nonconsecutive term as US president-mirroring only Grover Cleveland's 22nd and 24th presidencies after the Civil War-underscores a reality: Although his success stems partly from a willingness to tap into xenophobia, sexism, racism, transphobia, nationalism, and disregard for truth, his message resonates with a large portion of the American populace who feel alienated from America's governmental, social, and economic institutions. These include science and higher education. Winning back this disaffected group will require science leaders to foster and promote a more inclusive scientific landscape for all Americans and lay out how science can be successful under Trump. But it will also require the entire scientific enterprise to change behaviors that make science and its practitioners susceptible to persistent and future attacks.

Reviewability and supportability: New complementary principles to empower research software practices

In today's scientific landscape, research software has evolved from being a supportive tool to becoming a fundamental driver of discovery, particularly in life sciences. Beyond its roots in software engineering, research software now plays a crucial role in facilitating efficient data analysis and enabling the exploration of complex natural phenomena. The advancements in simulations and modeling through research software have significantly accelerated the pace of scientific research while reducing associated costs. This growing reliance underscores the importance of software in ensuring reproducibility – a cornerstone of scientific rigor and trustworthiness. Although verifying reproducibility presents challenges, well-developed and openly accessible research software enhances transparency and aids in the early detection of errors. Although verifying reproducibility can be challenging, well-developed and accessible research software improves transparency and facilitates error detection. This mini-review examines the characteristics of research software and summarizes the key events that have shaped its development, alongside changes in requirements and guidelines. Moreover, we propose two additional principles – reviewability and supportability – complementing the widely accepted FAIR principles (Findability, Accessibility, Interoperability, and Reusability). These new principles aim to improve the efficiency and effectiveness of software evaluation during the peer review process. Through this review, we aim to assist scientists, especially those without extensive software development expertise, in understanding best practices for developing research software and the underlying motivations driving these practices.

Higher education for sustainable development in the digital era: Mapping the bibliometric analysis

Modern higher education institutions in developed countries are becoming active agents of permanent societal changes. The aim of the study is to map the scientific landscape on the topic of higher education for sustainable development in the context of digitalization. In the research process, bibliometric analysis, an in-built Scopus instrument, and VOSviewer were used. The findings proved that the severe restrictions faced by universities during the COVID-19 pandemic became a powerful impulse to intensify the attention of scientists to the mentioned topic. Visualization maps of keywords and scientific publications showed the existing interdisciplinary nature and the main thematic directions of research on higher education for sustainable development in the digital era. They demonstrated a direct connection between the basic principles of the sustainable development concept, sustainable development goals, higher education, universities, digitization, and digital technologies. The largest number of works on the specified subject belong to scientists from China, Spain, the Czech Republic, Germany, and Ukraine. The study concluded that future publications in the analyzed field would cover the effectiveness of digital technologies in higher education, the development of competencies for the digital economy, inclusiveness, cybersecurity, as well as ethical aspects of the use of technologies to achieve the global sustainable development goals. AcknowledgmentThis analysis is carried out within the framework of the scientific project “Model of the Post-War Recovery of Border Universities of Ukraine in the Digitalization Era Under the Sustainable Development Concept” with the support of the Ministry of Education and Science of Ukraine (Order No 1190 dated 30.12.2022).

Scientific knowledge management in Brazil: an analysis of the Lattes Platform

The Lattes Platform (PL) is an essential resource for Brazil's scientific and technological landscape, centralizing researchers' curricular information and facilitating the management of scientific data. However, the lack of rigorous standardization in the data entered compromises the efficiency of retrieving and analyzing this information, limiting academic knowledge management. This study investigated the limitations of the PL and proposed a solution that integrates new technologies to optimize scientific data management. The research was conducted in two main stages: a systematic literature review on knowledge management and technologies applied to the PL, and a case study at the Federal Institute of Acre (IFAC). The review included the analysis of 79 articles and a survey of software records at the National Institute of Industrial Property (INPI). In the case study, semi-structured questionnaires were administered to IFAC staff to understand their needs and perceptions regarding the PL. The results reveal a significant demand for tools that automate data extraction and integrate the PL with other academic management systems. The analysis of software records at INPI highlighted solutions that can enhance the PL’s efficiency. The integration of Artificial Intelligence (AI), exemplified by the proposed LattesAI, emerges as a promising solution to overcome the platform's current limitations, automating processes and personalizing knowledge management. This innovation not only optimizes academic management but also strengthens Brazil's position in the global scientific landscape, promoting greater efficiency and collaboration in educational and research institutions. Future studies should focus on developing AI algorithms specifically for the PL, ensuring its continuous evolution in response to the demands of the academic environment.

Unleashing the Dynamic Strategies in the Era of Change – A Spotlight on the ISPRS Student Consortium's Global Contributions

Abstract. ISPRS Student Consortium (ISPRS SC) has played a significant role in bringing students and young researchers closer to the field of remote sensing and photogrammetry in the evolving landscape of geospatial sciences. Innovative strategies and events have been employed by the ISPRS SC to address contemporary challenges and harness emerging opportunities in the field of photogrammetry and remote sensing. Through an analysis of the global contributions of ISPRS SC, ranging from research initiatives to the online webinar series, this paper underscores the consortium's commitment to fostering collaboration, innovation, and knowledge exchange among young scholars and professionals worldwide. The paper highlights key thematic areas addressed by ISPRS SC such as advancements in remote sensing technologies, geospatial data analysis, and applications in diverse domains including environmental monitoring, agriculture, urban planning, and disaster management. Moreover, in this paper, we emphasize the role of the consortium in nurturing future leaders in geospatial sciences through ISPRS SC mentorship programs, networking opportunities, and capacity-building initiatives. By highlighting the initiatives led by the ISPRS Student Consortium, this paper underscores the key role of youth-driven innovation for positive change and shaping the future of geospatial sciences on a global scale.

AutoML and Automated Data Science by Democratizing AI through End-to-End Automation

The rise of data-driven decision-making has led to a significant demand for data science and machine learning (ML) solutions across industries. However, developing these solutions requires extensive expertise in data preprocessing, feature engineering, model selection, hyperparameter tuning and evaluation. AutoML (Automated Machine Learning) and Automated Data Science (AutoDS) have emerged as transformative approaches that aim to democratize data science by automating the endto-end ML pipeline. This paper explores the foundational concepts of AutoML, highlighting key techniques and algorithms, such as neural architecture search (NAS), hyperparameter optimization and meta-learning. We delve into AutoDS's broader scope, which seeks to fully automate tasks from data acquisition to deployment. Real-world applications, such as predictive modeling, anomaly detection and time series forecasting, are examined to demonstrate the impact of these technologies. Additionally, the paper analyzes the current frameworks and platforms facilitating automation, including Auto-sklearn, Google AutoML and H2O.ai and evaluates their performance across different tasks. While the potential to accelerate data science workflows and make AI accessible to non-experts is evident, challenges remain, particularly regarding transparency, interpretability and ethical considerations in fully automated systems. This research provides insights into current trends, future opportunities and the transformative role of AutoML and AutoDS in driving innovation in the data science landscape.

Rural Depopulation in the Context of 4.0 Technologies: Opportunities for Sustainability and Innovation Policies

This paper provides a systematic review of the scientific landscape of the interdisciplinary field of rural depopulation studies, considering the potential contribution of Fourth Industrial Revolution technologies or Industry 4.0 to enhance our contemporary understanding and policy approach to facing the complex challenge of rural transitions. Two levels of intersec search are examined: (a) an aggregated conceptual level of rural population studies and (b) a more policy-based level. Blockchain, the Internet of Things, additive manufacturing, and other 4.0 technologies highlight a scientific panorama in which social and sustainable concerns are integrated with the disruptive potential of these technologies.

Experiences and initiatives on pharmacokinetic modeling and simulation data analysis: Perspectives from the Brazilian Health Regulatory Agency (ANVISA)

The landscape of drug product development and regulatory sciences is evolving, driven by the increasing application of systems thinking and modeling and simulation (M&S) techniques, especially from a biopharmaceutics perspective. Patient-centric quality standards can be achieved within this context through the application of quality by design (QbD) principles and M&S, specifically by defining clinically relevant dissolution specifications (CRDS). To this end, it is essential to bridge in vitro results to drug product in vivo performance, emphasizing the need to explore the translational capacity of biopharmaceutics tools. Physiologically based M&S analyses offer a unique avenue for integrating the drug, drug product, and biological properties of a target organism to study their interactions on the pharmacokinetic response. Accordingly, Physiologically Based Biopharmaceutics Modeling (PBBM) has seen increasing use to support drug development and regulatory applications globally. In Brazil, a Model-Informed Drug Development (MIDD) policy and strategic project are not yet established, limiting applicability of M&S techniques. Drawing from the experience of the ANVISA-Academia PBBM Working Group (WG), this article assesses the opportunities and challenges for pharmacometrics (PMx) in Brazil and proposes strategies to advance the adoption of M&S analyses into regulatory decision-making.

Unveiling Weak Signals of Emergence in Underwater Sensing Research Trends

Detecting emerging research trends is crucial as it allows for the proactive identification and monitoring of novel and influential topics in the scientific community. Monitoring research trends aids researchers, institutions, and policymakers in allocating resources, fostering innovation, and staying competitive in rapidly changing scientific landscapes. The growing significance of underwater sensing technologies in various domains has propelled research endeavours aimed at understanding the characteristics of academic discourse in this field. In this work, we comprehensively analyzed the academic research topics related to underwater sensing technologies using advanced computational methodologies. Leveraging natural language processing, topic modelling, and weak signal detection techniques, and focusing on underwater sensing as the case technology, we dissect a large corpus of scholarly articles published between 2007 and 2021 to unveil underlying thematic patterns and emergent trends within this domain while shedding light on signals of emerging technologies. Among the eighty extracted topics, six research topics were identified and recognized as emerging weak signals, and validated by experts. Notably, deep learning for underwater imaging was the only topic that transitioned from being weak to a strong signal in the final period.