Integration Of Tools Research Articles

Educación 4.0 en el ámbito universitario. Una revisión sistemática de literatura

Education 4.0 represents an emerging paradigm in higher education, oriented toward technological innovation and the integration of advanced digital tools, with the aim of transforming teaching and learning models. This study presents a systematic review of scientific literature published between 2020 and 2023, analyzing the challenges, advances, and opportunities involved in the adoption of Education 4.0 in universities. The methodology used consisted of a systematic review that employed search strings across multiple academic databases, applying specific inclusion and exclusion criteria. Research exploring innovative pedagogical practices was reviewed, considering each work’s contribution to advancing the field. The results highlight those reforms in higher education, supported by governmental policies, are key to implementing this new paradigm. The incorporation of disruptive technologies such as artificial intelligence and virtual laboratories into educational plans is identified as an opportunity to enhance the quality of education and personalize teaching according to students’ needs. However, continuous teacher training is also required to ensure the effective integration of these tools.

Undoing the past to prepare for the future: Lessons learned from the data exchange project at UC Berkeley

Realigning data and integration tools after years of inconsistent management can seem overwhelming at best and impossible at worst. To meet the demands of today's tools and analytics, it is a worthwhile investment to strategically improve data quality and accessibility. Whether your institution is preparing for a customer relationship management (CRM) conversion or just trying to get a better handle on managing data, this paper will examine UC Berkeley's initiative to create business and technical solutions to increase transparency, governance and data intake while decreasing customisations and confusion for stakeholders.

An integrated tool for cost-effectively applying nutrient management practices for corn, soybeans, and wheat.

Harmful algal blooms (HABs) problem in Lake Erie has become critical recently-primarily triggered by phosphorus losses from cropland in the Maumee River watershed (major crops of corn/soybeans/wheat). Implementing agricultural best management practices (BMPs) is crucial to reduce excess nutrient loadings. Nutrient management is the management of nutrient applications for crop production that maximizes nutrient use efficiencies and minimizes nutrient losses. However, an integrated watershed-scale tool is needed for cost-effectively applying nutrient management practices for corn/soybeans/wheat considering the 4Rs (Right nutrient source, Right rate, Right time, and Right place of nutrient applications). In this study, by combining an improved Soil and Water Assessment Tool (SWAT) for nutrient management (SWAT-NM) and an improved BMP Cost Evaluation Tool (BMP-COST) for economic evaluations of nutrient management (BMP-COST-NM) considering the 4Rs for corn/soybeans/wheat, an integrated tool SWAT-COST-NM was created. SWAT-COST-NM was demonstrated in the AXL watershed (a typical agricultural area in the Maumee River watershed). The impacts of single nutrient management practices (single-NM, which separately changed the rate, place, time, or nutrient source of fertilizer applications) and combined-NM practices (multiple single-NM practices combined as one nutrient management practice) for corn/soybeans/wheat were evaluated. Tradeoffs in yearly net costs, crop yields, and March-July/yearly nutrient losses (Total Phosphorus-TP, Dissolved Reactive Phosphorus-DRP, and Total Nitrogen-TN) existed. Nutrient management did not necessarily lead to sufficient increases in crop yields to generate extra revenues that match or exceed the additional costs of the activities (compared to existing practices). One of the combined-NM practices could simultaneously reduce March-July TP, DRP, and TN losses by 5.89%, 8.19%, and 8.23%, respectively, while increasing crop yields with additional income (0.50 $/ha/yr of cropped area). SWAT-COST-NM, which can quantify various factors and tradeoffs when evaluating the impacts of nutrient management practices for corn/soybeans/wheat, can assist decision-makers in cost-effectively applying nutrient management practices considering the 4Rs.

Examining In-service Senior High School English Teachers’ Perspectives on Corpus Use and the Effects of Corpus Training

Given the benefits of incorporating corpora into language learning, particularly in developing students’ abilities to observe and analyze language data, this study investigated Taiwanese in-service senior high school English teachers’ corpus literacy, their application of corpus tools in teaching, and the effects of an online corpus workshop. Conducted in two stages, the first involved collecting 151 teachers’ perceptions of corpus literacy and its applications from 141 schools across Taiwan. The second stage invited teachers across Taiwan to participate in an online corpus workshop, where corpus-based teaching and two tools (SKELL and Sketch Engine) were introduced, along with hands-on activities. Following the workshop, the participants completed a post-survey. The analysis of the pre-survey responses revealed a positive attitude toward but limited understanding of corpus use among teachers before attending the workshop. The Wilcoxon Signed Rank test, used to analyze the pre- and post-survey responses, showed significant improvements in the teachers’ corpus literacy and application skills after the workshop. The findings of this study offer valuable insights into corpus use among in-service teachers in various contexts. Future research should explore the further integration of corpus tools into classrooms and include in-depth interviews for more comprehensive insights.

Digital Twin Methodology in Food Processing: Basic Concepts and Applications.

The goal of this article is to present a concise review about digital twin (DT) methodology and its application in food processing. We aim to identify the building blocks, current state and bottlenecks, and to discuss future developments of this approach. DT methodology appears as a powerful approach for digital transformation of food production, via integration of modelling and simulation tools, sensors, actuators and communication platforms. This methodology allows developing virtual environments for real-time monitoring and controlling of processes, as well as providing actionable metrics for decision-making, which are not possible to obtain by physical sensors. So far, main applications were focused on refrigerated transport and storage of fresh produces, and thermal processes like cooking and drying. DT methodology can provide useful solutions to food industry towards productivity and sustainability, but requires of multidisciplinary efforts. Wide and effective implementation of this approach will largely depend on developing high-fidelity digital models with real-time simulation capability.

Conceptualizing agile business practices for enhancing SME resilience to economic shocks

This paper examines the role of agile business practices in fortifying small and medium-sized enterprises (SMEs) against economic shocks, emphasizing resilience as a critical factor for sustainable growth. Given SMEs' vulnerability to unpredictable economic shifts, the study explores how agility — defined by rapid adaptation, flexibility, and innovation — enables these enterprises to navigate and recover from disruptions effectively. Through a review of resilience-building frameworks, the paper identifies core agile practices, such as adaptive decision-making, lean management, and iterative product development, as pivotal to enhancing SMEs' operational and strategic resilience. Findings indicate that SMEs leveraging agile methodologies experience improved responsiveness to market changes, reduced operational risks, and greater capacity to maintain continuity during crises. Additionally, the study discusses the integration of digital tools, cross-functional teams, and customer-centric approaches as enablers of agility that strengthen resilience. Conclusions underscore the necessity for SMEs to adopt agile frameworks not only as a response mechanism but also as a proactive strategy for long-term stability and competitiveness in volatile markets. This research contributes to the growing discourse on SME resilience, offering actionable insights for stakeholders seeking to embed agility as a fundamental attribute in organizational practices.

Leveraging artificial intelligence to enhance psychological capital in learning organizations: a viewpoint

Purpose This viewpoint article presents perspectives on the intersection of AI and the diverse elements of Psychological Capital (PsyCap) and is a foundation for discussion on this juncture. It also exhibits the advantages of integrating AI and PsyCap in learning organizations and the challenges and future directions associated with the same. Design/methodology/approach The article exhibits the juncture of Artificial Intelligence (AI) and Psychological Capital (PsyCap) in context to learning organizations. It also highlights the various AI tools that can be used to manage diverse PsyCap attributes in learning organizations. The approach is to present a starting point for discussion on the intersection of the two concepts and how learning organizations can leverage them to create sustained competitive advantage. Findings Artificial Intelligence Systems can be used to enhance self-efficacy, promote optimism, cultivate hope and build resilience in learning organizations. A strategic approach with continued evaluation of AI tools is the key. However, challenges like privacy and unethical issues, biasness and management of human- AI intervention have to be taken into consideration for effective integration of AI tools and Psychological Capital. Originality/value The research in the domain of Artificial Intelligence and related technologies is not a new phenomenon. However, there is a possibility to understand that AI is not a competitor to human ingenuity but a facilitator to it. Through this article an effort is made to ignite the conversation on the topic and it can be a starting point for future research on the integration of the two perspectives.

Use of Digital Tools in Arbovirus Surveillance: Scoping Review

Background The development of technology and information systems has led to important changes in public health surveillance. Objective This scoping review aimed to assess the available evidence and gather information about the use of digital tools for arbovirus (dengue virus [DENV], zika virus [ZIKV], and chikungunya virus [CHIKV]) surveillance. Methods The databases used were MEDLINE, SCIELO, LILACS, SCOPUS, Web of Science, and EMBASE. The inclusion criterion was defined as studies that described the use of digital tools in arbovirus surveillance. The exclusion criteria were defined as follows: letters, editorials, reviews, case reports, series of cases, descriptive epidemiological studies, laboratory and vaccine studies, economic evaluation studies, and studies that did not clearly describe the use of digital tools in surveillance. Results were evaluated in the following steps: monitoring of outbreaks or epidemics, tracking of cases, identification of rumors, decision-making by health agencies, communication (cases and bulletins), and dissemination of information to society). Results Of the 2227 studies retrieved based on screening by title, abstract, and full-text reading, 68 (3%) studies were included. The most frequent digital tools used in arbovirus surveillance were apps (n=24, 35%) and Twitter, currently called X (n=22, 32%). These were mostly used to support the traditional surveillance system, strengthening aspects such as information timeliness, acceptability, flexibility, monitoring of outbreaks or epidemics, detection and tracking of cases, and simplicity. The use of apps to disseminate information to society (P=.02), communicate (cases and bulletins; P=.01), and simplicity (P=.03) and the use of Twitter to identify rumors (P=.008) were statistically relevant in evaluating scores. This scoping review had some limitations related to the choice of DENV, ZIKV, and CHIKV as arboviruses, due to their clinical and epidemiological importance. Conclusions In the contemporary scenario, it is no longer possible to ignore the use of web data or social media as a complementary strategy to health surveillance. However, it is important that efforts be combined to develop new methods that can ensure the quality of information and the adoption of systematic measures to maintain the integrity and reliability of digital tools’ data, considering ethical aspects.

Engaging Adolescents: Innovative ESL Teaching Strategies for Teen Learners

This study investigated the effectiveness of innovative ESL teaching strategies tailored specifically for teenage learners, focusing on the integration of digital tools and culturally relevant materials. Participants aged 13 to 19 were subjected to a mixed-methods approach, combining quantitative assessments of language skills and qualitative feedback through interviews and focus groups. Results indicated significant improvements in both test scores and student engagement, highlighting the crucial role of adapting educational strategies to the developmental needs and interests of teenagers. The research confirms that interactive and relevant teaching approaches can profoundly enhance learning outcomes in ESL contexts.

Chatbot Demonstrates Moderate Interrater Reliability in Billing for Hand Surgery Clinic Encounters.

Artificial intelligence offers opportunities to improve the burden of health care administrative tasks. Application of machine learning to coding and billing for clinic encounters may represent time- and cost-saving benefits with low risk to patient outcomes. Gemini, a publicly available large language model chatbot, was queried with 139 de-identified patient encounters from a single surgeon and asked to provide the Current Procedural Terminology code based on the criteria for different encounter types. Percent agreement and Cohen's kappa coefficient were calculated. Gemini demonstrated 68% agreement for all encounter types, with a kappa coefficient of 0.586 corresponding to moderate interrater reliability. Agreement was highest for postoperative encounters (n = 43) with 98% agreement and lowest for new encounters (n = 27) with 48% agreement. Gemini recommended billing levels greater than the surgeon's billing level 31 times and lower billing levels 10 times, with 4 wrong encounter type codes. A publicly available chatbot without specific programming for health care billing demonstrated moderate interrater reliability with a hand surgeon in billing clinic encounters. Future integration of artificial intelligence tools in physician workflow may improve the accuracy and speed of billing encounters and lower administrative costs.

Are AI tools better than traditional tools in literature searching? Evidence from E-commerce research

This article examines the potential implications of Artificial Intelligence (AI) for literature search, comparing AI-based tools to conventional research methods. It also addresses the scarcity of academic literature on specific AI tools for research writing, posing four critical questions regarding accuracy, quality, uniqueness, and qualified uniqueness. Employing Algorithmic Theory and Data Dependency Theory, this project scrutinizes AI performance in algorithms, machine learning models, and data quality. Testing nine e-commerce topics using Scopus, Web of Science, Elicit, and SciSpace, the authors conclude that while conventional methods excel in accuracy and quality, AI tools show promise in uniqueness, complementing literature reviews. The findings also emphasize the judicious integration of AI tools and advocate for further research into new applications and diverse fields. Ultimately, this research offers highly relevant insights into leveraging AI tools to enhance conventional literature search practices in research and professional domains.

Digital Democracy: How Technology Can Drive Higher Voter Turnout in Elections

This article pinpoints the transformative role of technology in improving the rate at which people vote during elections. An important concept developed in this paper is that of digital democracy, and as such, a thorough analysis will be done on technological advancements, which have so far proved very instrumental in promoting civic participation and entailing more involvement in the democratic process by all concerned through online voter registration, mobile phone applications, and social media campaigns. The article, therefore, pinpoints studies showing that states that have adopted online registration actually report higher rates of active voters, while mobile apps present information that makes voting more manageable. Social media also offers an effective outreach opportunity, particularly among younger voters, to create in them a sense of community and urgency about participation in electoral activity. The integration of educational tools, such as virtual workshops and gamified learning, enables further development of the capability to push participants and voters forward in an accessible and engaging manner. The article leverages these advantages of technological developments as a means toward creating a better-informed and more active electorate contributing to the health of democracy.

Artificial intelligence, medications, pharmacogenomics, and ethics.

Artificial Intelligence (AI) and Machine Learning (ML) are revolutionizing various scientific and clinical disciplines including pharmacogenomics (PGx) by enabling the analysis of complex datasets and the development of predictive models. The integration of AI and ML with PGx has the potential to provide more precise, data-driven insights into new drug targets, drug efficacy, drug selection, and risk of adverse events. While significant effort to develop and validate these tools remain, ongoing advancements in AI technologies, coupled with improvements in data quality and depth is anticipated to drive the transition of these tools into clinical practice and delivery of individualized treatments and improved patient outcomes. The successful development and integration of AI-assisted PGx tools will require careful consideration of ethical, legal, and social issues (ELSI) in research and clinical practice. This paper explores the intersection of PGx with AI, highlighting current research and potential clinical applications, and ELSI including privacy, oversight, patient and provider knowledge and acceptance, and the impact on patient-provider relationship and new roles.

Exploring the role of Artificial Intelligence in Acute Kidney Injury management: a comprehensive review and future research agenda.

This study reviews the studies utilizing Artificial Intelligence (AI) and AI-driven tools and methods in managing Acute Kidney Injury (AKI). It categorizes the studies according to medical specialties, analyses the gaps in the existing research, and identifies opportunities for future research directions. PRISMA guidelines were adopted using the three most common databases (PubMed, Scopus, and EBSCO), which resulted in 27 eligible studies, published between 2012 and 2023. The study showed significant heterogeneity in the design of the models, with variations in clinical settings, patient characteristics, cohort regions, and statistical methods. Most models were developed for AKI in hospitalized patients, particularly those undergoing surgery or in intensive care units. Compact models with a subset of significant predictors were deemed more clinically applicable than full models with all predictors. The findings suggest that AI tools, such as machine learning (ML) algorithms, have high prediction capabilities despite the dynamic and complex association among the influencing factors and AKI. Based on these findings and the recognized need for broader inclusivity, future studies should consider adopting a more inclusive approach by incorporating diverse healthcare settings, including resource-limited or developing countries. This inclusivity will lead to a more holistic understanding of AKI management challenges and facilitate the development of adaptable and universally applicable AI-driven solutions. Additionally, further investigations should focus on refining AI models to enhance their accuracy and interpretability, promoting seamless integration and implementation of AI-based tools in real-world clinical practice. Addressing these key aspects will elevate the effectiveness and impact of AI-driven approaches in managing AKI.

Rediscovering publicly available single-cell data with the DISCO platform.

Single-cell RNA sequencing (scRNA-seq) has emerged as the key technique for studying transcriptomics at the single-cell level. In our previous work, we presented the DISCO database (https://www.immunesinglecell.org/) that integrates publicly available human scRNA-seq data. We now introduce an enhanced version of DISCO, which has expanded fourfold to include >100 million cells from >17 thousand samples. It provides uniformly realigned read count tables, curated metadata, integrated tissue and phenotype specific atlases, and harmonized cell type annotations. It also hosts a single-cell enhanced knowledgebase of cell type ontology and gene signatures relating to cell types and phenotypes. Lastly, it offers a suite of tools for data retrieval, integration, annotation, and mapping, allowing users to construct customized atlases and perform integrated analysis with their own data. These tools are also available in a standalone R package for offline analysis.

From analytics to empathy and creativity: Charting the AI revolution in marketing practice and education

The rapid advancement of artificial intelligence (AI) increasingly demands an understanding of its impact on marketing practice and education. Our hybrid literature review synthesizes 312 peer-reviewed articles on AI in marketing and consumer behavior, using scientometrics and the TCCM (Theory, Context, Characteristics, Methodology) framework. We identify five research areas: human–AI interaction in services, natural language processing (NLP) and computer vision for consumer insights, AI for e-commerce and decision support, marketing automation and creativity, and AI ethics. AI’s evolution is marked by a transition from analytical to empathetic and intuitive technologies like affective computing and generative AI. We highlight the changing dynamics between humans and AI, AI integration in marketing practices and education, and the transformed AI-enhanced marketing workplace. We underscore the significance of ethical considerations, the well-being of users, and the integration of generative AI tools. This review provides a comprehensive guide for forthcoming research, practical applications, and educational advancements in AI-enhanced marketing.

Understanding Multi-Cloud and Hybrid Cloud Architectures in Data Management

This comprehensive article explores the growing adoption of multi-cloud and hybrid cloud architectures in modern data management practices. It delves into the benefits, challenges, and best practices associated with these complex cloud strategies. The article presents key statistics and projections from industry leaders, highlighting the rapid growth and increasing importance of multi-cloud and hybrid cloud solutions. It discusses how these architectures enhance data redundancy, vendor flexibility, cost optimization, and performance while addressing critical issues such as data sovereignty, scalability, and legacy system integration. The piece also examines the challenges organizations face in implementing these strategies, including data integration, security, skill gaps, compliance, and cost management. Finally, it outlines best practices for effective implementation, emphasizing the need for comprehensive data governance, robust integration tools, cloud-agnostic technologies, and continuous optimization.

The Influence that the Complexity of the Three-Dimensional Eye Model Used to Generate Simulated Eye-tracking Data Has on the Gaze Estimation Errors Achieved Using the Data

Simulated eye-tracking data are an integral tool in the development of eye-tracking methods. Most of the simulated data used in eye-tracking-related research has been generated using low-complexity eye models that include a single spherical corneal surface. This study investigated the influence of eye-model complexity on the ability of simulated eye-tracking data to predict real-world outcomes. The experimental procedures of two pertinent comparative eye-tracking studies were replicated in a simulated environment using various eye-model complexities. The simulated outcomes were then evaluated against the findings of the comparative studies that were derived from real-world outcomes. The simulated outcomes of both comparative studies were significantly influenced by the eye-model complexity. Eye models that included an aspheric corneal surface best replicated experimental eye-tracking outcomes, while including a posterior corneal surface did not improve the ability of simulated data to replicate real-world outcomes. Using a wide-angle eye model that accurately replicates the peripheral optics of the eye did not improve simulated outcomes relative to a paraxial eye model.

Enhanced Molecular Imaging through a Versatile Peptide Nanofiber for Self-assembly and Precise Recognition.

Designing molecules for multivalent targeting of specific disease markers can enhance binding stability which is critical in molecular imaging and targeted therapy. Through rational molecular design, the nanostructures formed by self-assembly of targeting peptides are expected to achieve multivalent targeting by increasing the density of recognition ligands. However, the balance between targeting peptide self-assembly and molecular recognition remains elusive. In this study, we designed a targeting-peptide-based imaging probe system TAP which consist of the signal unit, the recognition motif, the assembly motif and a Pro-leverage. It is verified that TAP could specifically binds to PD-L1-positive tumor cells in a multivalent manner to produce biological effects, and could also be combined with imaging probes through unique self-assembly strategies. By the balance between the peptide self-assembly and targeting recognition, the specificity and stability can be improved while the accumulation capacity of the probes at the tumor site can be greatly enhanced compared with the conventional strategy, thus reducing side effects, providing an effective tool for diagnostic and therapeutic integration of tumors.

Digital Mental Health Innovations in the Face of Climate Change: Navigating a Sustainable Future.

Climate change poses substantial challenges to mental health, with increased frequency of extreme weather events and environmental degradation exacerbating stress, anxiety, trauma, and existential concerns. Digital innovations, particularly artificial intelligence and digital phenotyping, offer promising avenues to mitigate climate-related mental health burdens. The integration of digital tools into climate-related mental health care necessitates careful consideration of issues of access to and adoption of solutions. Future research should evaluate the effectiveness and scalability of digital interventions to address the mental health impacts of climate change through collaborative efforts involving clinicians, environmental health specialists, bioethicists, policy makers, and technology developers.