Technological Barriers Research Articles

Educational Empowerment and Reemployment of Younger Elderly in China

With the rapid progression of population aging in China and the relative shrinkage of the working-age population, the reemployment of younger elderly in the labor market is becoming increasingly salient. Drawing on the multi-perspective framework of active aging, empowerment theory, and human capital theory, this study examines the multidimensional challenges faced by younger elderly in reentering employment, including institutional barriers, sociocultural resistance, and individual psychological hurdles. It also highlights the systemic obstacles resulting from the interplay of factors such as the traditional retirement system, employment discrimination, the digital divide, and family responsibilities. In response to these issues, this study proposes an intervention framework centered on educational empowerment, which integrates multi-tier skills training and psychological support with digital technologies and community resources. This approach seeks to help younger elderly overcome technological barriers, rebuild their sense of self-efficacy, and gain broader support at both societal and policy levels. In practice, this study advocates the establishment of a multi-level educational system through governmental special funds, collaboration among enterprises and social organizations, blended learning, and flexible institutional incentives. By constructing a well-rounded policy and regulatory environment with a multi-stakeholder coordination mechanism, younger elderly can not only extend their working lives but also inject new vitality into sustainable social and economic development. This study aims to provide a feasible reference for advancing research in older adult education and active aging policies, as well as to offer insights into both macro-level and micro-level collaborative practices in China and other rapidly aging countries.

Characteristics and outcomes of medication-assisted treatment care models for pregnant women with opioid use disorder: A scoping review.

Pregnant women and their unborn children are often overlooked in the opioid crisis, despite increased rates of both maternal complications and neonatal opioid withdrawal syndrome. Although medications for opioid use disorder (MOUD) are considered the gold standard for pregnant women, many either do not have access to or are not offered MOUD as an option. To describe the characteristics of MOUD care models and assess the effectiveness of MOUD Care Models on initiation, adherence, and engagement in treatment for pregnant women with OUD. The Preferred Reporting Items for Systematic Reviews and Meta-Analysis protocol directed the process of this scoping review. A search for English, peer-reviewed, quantitative studies, published between 2012 and 2023 was conducted in eight scholarly databases. The Joanna Briggs Critical Appraisal tools were used to assess study quality. The integrated care models included medication management, psychotherapy support, behavioral health care, and medical care management, with added focus on prenatal and postpartum care. Synthesis revealed that treatment engagement, medication adherence, breastfeeding rates, and postdelivery MOUD referrals were higher in integrated care models compared with nonintegrated care models. Pregnant women with OUD in integrated care models have better outcomes compared with those in nonintegrated care models. Improvements in care models are necessary to tackle provider shortages, stigma, and financial, geographic, and technological barriers. Addressing these issues will enhance nurse practitioners' ability to provide comprehensive and accessible care to this vulnerable population.

Exploring the experiences and perceptions of artificial intelligence utilization among students of the department of nursing, Bayero University, Kano

Artificial intelligence (AI) is rapidly transforming various sectors, including healthcare and education. In nursing, AI has the potential to enhance educational outcomes and improve clinical practices. The study aim to explore the nuanced experiences and perceptions of nursing students at Bayero University, Kano, in relation to the use of AI in their academic and clinical environments. A qualitative narrative inquiry was conducted among nursing students at Bayero University, Kano. Focus group discussion was used to collect data from the participants. Thematic analysis was employed to identify and interpret themes related to their experiences and perceptions of AI. The study identified that the majority of students have engaged with AI technologies primarily for academic purposes, such as researching assignments and accessing clinical information. However, they demonstrated limited awareness of nursing-specific AI tools. Participants also reported significant challenges related to network connectivity and data access, which hindered their use of AI. Despite these challenges, the overall perception of AI was positive, with students acknowledging its potential to improve nursing education and practice. Concerns about technical errors, over-reliance, and job displacement were also noted. Nursing students at Bayero University, Kano, recognize the potential benefits of AI in education and clinical practice but face significant challenges due to infrastructural limitations. Their positive perception of AI suggests an openness to its integration into nursing, provided that technological and educational barriers are addressed. The findings highlight the need for improved technological infrastructure, targeted training on AI applications in nursing, and a balanced approach to integrating AI with human expertise. Keywords: Artificial intelligence, Nursing education, Perceptions, Experiences, Qualitative, Nigeria

Revolutionizing Primary Healthcare in Jordan: A Software Engineering Approach to General Practitioner Integration.

This paper examines the pervasive issues of stress and burnout among healthcare specialists in Jordan, which are intensified by the current system allowing patients unrestricted access to specialized clinics. Our comprehensive analysis includes an examination of systemic, technological, and cultural barriers that currently hinder effective healthcare delivery. This was done with a mixed-methods approach: gathering quantitative data reported by the World Health Organization and World Bank, and qualitative insights from stakeholder interviews. Based on our findings, we propose a restructured care pathway where general practitioners (GPs) serve as the primary contact point for patients. This model aims to distribute healthcare demands more evenly, thereby alleviating the pressure on specialists. We also explore the integration of health information systems to improve processes. The implementation of this GP-centric model demonstrates potential benefits in our pilot studies, including reduced wait times, improved patient satisfaction, and decreased burnout rates among specialists. The paper concludes with strategic recommendations for nationwide policy adjustments to foster a more sustainable and equitable healthcare system in Jordan.

“I am just a voice. I can neither help nor explain; I am simply transferring your words into another language”

The present paper explores the multifaceted challenges in the field of Public Service Interpreting in Greece, focusing on the experiences and perspectives of diverse participants involved in the migration context. Conducted by interpreting students at the Ionian University in Greece, this paper is based on a series of 12 in-depth interviews with various stakeholders: public servants, interpreters, refugees, and representatives of non-governmental organizations (NGOs) actively engaged in migration and refugee support services. By analyzing the insights gathered from these participants, the study aims to identify common patterns and key issues that hinder effective communication and service delivery. Such interview- and evidence-based approach explores the intricacies of public service interpreting (PSI) in Greece from a range of perspectives, highlighting challenges such as the shortage of available interpreters, reliance on ad hoc solutions, technological barriers, and poor working conditions, all of which arise during interactions in relevant contexts. The findings are expected to contribute to the discourse on improving interpreting practices and policies, ultimately enhancing the quality of public services for refugees and migrants in Greece.

Why industrial catching-upper usually fails? A tradeoff model among independent or cooperative R&D, technology licencing and acquisition

ABSTRACT This study examines why less developed economies’ (LDEs) or less lucrative industrialization fail to catch up. This study establishes a mathematical model to analyse how a potential entrant into the high-tech industry with a technology barrier that requires intellectual property usage right (IPUR) comprehensively utilizes binary internal-external technology capability and quaternarily trade-offs. The trade-offs involve exclusively independent R&D and three forms of technology capability transfer (TCT): pure TCT, IPUR licencing and intellectual property ownership right acquisition. The model reveals a substitutive-complementary relationship between internal-external technology sourcing, analyses entrant-incumbent bargaining influence in the entrant’s trade-off and clarifies why entry fails. Two technology thresholds indicate the importance of aggregate technology stock; only a high-tech candidate entrant enters profitably. Additionally, costly external TCT impacts entry. In particular, an entrant with an extremely poor endowment suffers from Matthew’s effect, amplifying entry difficulty and highlighting the significance of LDEs’ technology capability independence. Furthermore, we conduct numerical simulations to confirm our solutions of technology thresholds and Matthew’s effect, shedding light on the factors making LDEs’ entry less successful. High aggregate stock and high independence of the entrant’s technology capability are significant in ensuring successful entry or high profitability.

Identifying gaps in practical use of epoxy foam/aerogels: review - solutions and prospects

Abstract Epoxy foam/aerogel materials (EP-AGs) have potential in the aerospace, construction, and energy industries, allowing the development of lightweight high-performance products for a wide range of applications. Research interest in developing EP-AGs is increasing as it has the potential to create greener and more sustainable materials for making various products. Several commercial applications of EP-AGs and techniques for creating, processing, and drying them have already been reported. The introduction of EP-AGs into value-added materials is one of the most promising options but suffers from a lack of knowledge about the relationships between microstructure and properties. The current obstacles to their use in the industrial sector and for applications and challenges related to factory scale-up are also taken into account. EP-AGs are hindered by critical gaps in applicational and processing complexity, such as scaling up from laboratory to large-scale production, optimizing synthesis and processing techniques, and developing standardized testing protocols. The review focuses on the processing complexities and further difficulties associated with EP-AGs to improve casting burdens, cost-effectiveness, and accessibility in various applications. This review also examines the challenges in synthesizing EP-AGs used to make special materials, their practices, and the technological barriers one would face.

AskBeacon-performing genomic data exchange and analytics with natural language.

Enabling clinicians and researchers to directly interact with global genomic data resources by removing technological barriers is vital for medical genomics. AskBeacon enables large language models (LLMs) to be applied to securely shared cohorts via the Global Alliance for Genomics and Health Beacon protocol. By simply "asking" Beacon, actionable insights can be gained, analyzed, and made publication-ready. In the Parkinson's Progression Markers Initiative (PPMI), we use natural language to ask whether the sex-differences observed in Parkinson's disease are due to X-linked or autosomal markers. AskBeacon returns a publication-ready visualization showing that for PPMI the autosomal marker occurred 1.4 times more often in males with Parkinson's disease than females, compared to no differences for the X-linked marker. We evaluate commercial and open-weight LLM models, as well as different architectures to identify the best strategy for translating research questions to Beacon queries. AskBeacon implements extensive safety guardrails to ensure that genomic data is not exposed to the LLM directly, and that generated code for data extraction, analysis and visualization process is sanitized and hallucination resistant, so data cannot be leaked or falsified. AskBeacon is available at https://github.com/aehrc/AskBeacon.

Empirical investigation of barriers and benefits of smart farming

PurposeSmart farming (SF) holds immense potential in making farming viable and improving farmers' livelihoods. However, its adoption is still in the early stages, and the resulting impacts are underexplored. This study investigates the information systems/policy and implementation barriers to the adoption of SF and its implications for various socioeconomic aspects.Design/methodology/approachThis survey research used a structured questionnaire to collect data on farm details, farmer characteristics and usage of SF technologies from a sample of 197 farmers based in the State of Karnataka, India. Exploratory factor analysis with principal component extraction is used to validate the proposed questionnaire constructs. Results are analyzed using one-way and two-way ANOVAs and interaction plots.FindingsOur study throws light on the benefits and barriers of SF technologies. Advanced SF technologies displayed a significant positive effect on socioeconomic variables compared to startup SF technologies, which displayed no significant effect. Overcoming information systems/policy barriers for adopting startup and advanced SF technologies displayed significant positive effects on farmers' health and net income, whereas overcoming implementation barriers for adopting advanced SF technologies led to improvement in the farmers' gross income.Originality/valueAlthough the technical feasibility of SF has been explored in the literature, its adoption barriers and socioeconomic impacts have been underexplored. To the best of our knowledge, the interaction between different types of barriers and the level of SF adoption has not yet been investigated in the literature. Our study addresses those research gaps using data from a previously underexplored context, viz. developing nations.

Telepsychiatry in post-pandemic India: A mixed methods exploration of patient perspectives and preferences toward telemedicine versus in-person consultations.

Telepsychiatry in post-pandemic India: A mixed methods exploration of patient perspectives and preferences toward telemedicine versus in-person consultations.

Viscoelastic and flow behaviour of β-lactoglobulin/lactoferrin coacervates: Influence of temperature and ionic strength.

Viscoelastic and flow behaviour of β-lactoglobulin/lactoferrin coacervates: Influence of temperature and ionic strength.

The Impact of Digital Nomad Economy on Local Businesses Study of MSME Market Changes in Popular Destinations in Indonesia

This study examines the impact of the digital nomad economy on micro, small, and medium enterprises (MSMEs) in popular destinations in Indonesia, including Bali, Yogyakarta, and Lombok. Using a quantitative research approach, data were collected from 120 MSME owners and managers through a structured questionnaire measured on a 1-5 Likert scale. The findings, analyzed with SPSS version 25, reveal that the digital nomad economy creates significant opportunities for MSMEs, including revenue growth, an expanded customer base, and improved service quality. However, challenges such as increased competition, gentrification, and technological barriers were also identified. Regression analysis showed that adaptation strategies, customer engagement, and perceived challenges significantly influence business performance. This study underscores the need for capacity-building initiatives, inclusive growth policies, and enhanced access to technology to ensure sustainable benefits from the digital nomad economy for local MSMEs.

Advanced High-Throughput Phenotyping Techniques for Managing Abiotic Stress in Agricultural Crops—A Comprehensive Review

Abiotic stresses, such as drought, salinity, and heat, exacerbated by climate change, pose significant challenges to global agriculture. These stresses negatively impact crop physiology, leading to yield losses and complicating efforts to breed resilient varieties. While advancements in molecular biology and genomics have identified stress-resistance genes, their effective utilization in breeding programs depends on precise phenotypic evaluation under diverse stress conditions. High-throughput phenotyping (HTP) technologies have emerged as indispensable tools, enabling non-destructive, rapid assessment of critical traits like root architecture, chlorophyll content, and canopy temperature in controlled and field environments. Unlike existing reviews, this manuscript critically addresses technological barriers such as cost scalability, field adaptability, and the integration of artificial intelligence for real-time data analysis. Additionally, it provides a fresh perspective on multi-omics integration in phenomics to bridge the genotype–phenotype gap, ensuring a more holistic approach to precision agriculture. This review bridges gaps in crop improvement by identifying practical solutions to enhance the adoption of HTP in breeding programs. It ensures food security amidst the escalating impacts of climate change.

Navigating the Digital Frontier: Improving Electronic Evidence Management in Ukraine's Criminal Justice System

Objectives: This article aims to explore the prospects for improving the handling of electronic evidence within the criminal process of Ukraine. The specific objectives include analyzing current practices, identifying challenges and limitations faced by legal practitioners and law enforcement, and proposing actionable recommendations for enhancing the legal framework, training, technological integration, and interagency collaboration related to electronic evidence. Methods: The study employs a review methodology, which includes a literature review of existing academic articles, legal texts, and case law related to electronic evidence in Ukraine and other jurisdictions. It also involves comparative analysis of best practices from countries with advanced frameworks for handling electronic evidence. Results: The review reveals several key findings: there are significant gaps in the legal framework regarding the definitions and admissibility of electronic evidence, leading to inconsistencies in court proceedings. Additionally, there are training deficiencies among legal practitioners and law enforcement personnel in digital forensics and the legal implications of electronic evidence. Technological barriers hinder the adoption of advanced technologies for secure evidence collection and analysis, and there is insufficient collaboration between various agencies and stakeholders involved in the criminal justice process. Conclusion: The article concludes that improving the handling of electronic evidence in Ukraine's criminal process is essential for enhancing the integrity and effectiveness of the legal system. Ukraine may better prepare for the issues presented by the digital era by filling in the gaps in the legal framework, investing in technology and training, and encouraging interagency cooperation. Implementing these suggestions will strengthen the criminal justice system and enhance public confidence in its ability to handle electronic evidence.

Functional neuroimaging in disorders of consciousness: towards clinical implementation.

Functional neuroimaging has provided several new tools for improving both the diagnosis and prognosis in patients with DoC. These tools are now being used to detect residual and covert awareness in behaviourally non-responsive patients with an acquired severe brain injury and predict which patients are likely to recover. Despite endorsement of advanced imaging by multiple clinical bodies, widespread implementation of imaging techniques such as functional MRI (fMRI), electroencephalography (EEG), and positron emission tomography (PET) in both acute and prolonged disorders of consciousness patients has been hindered by perceived costs, technological barriers, and lack of expertise needed to acquire, interpret, and implement these methods. In this review we provide a comprehensive overview of neuroimaging in DoC, the different technical approaches employed (i.e. fMRI, EEG, PET), the imaging paradigms used (active, passive, resting state) and the types of inferences that have been made about residual cortical function based on those paradigms (e.g., perception, awareness, communication). Next, we outline how these barriers might be overcome, discuss which select patients stand to benefit the most from these neuroimaging techniques, and consider when during their clinical trajectory imaging tests are likely to be most useful. Moreover, we make recommendations that will help clinicians decide which advanced imaging technologies and protocols are likely to be most appropriate in any particular clinical case. Finally, we describe how these techniques can be implemented in routine clinical care to augment current clinical tools and outline future directions for the field as a whole.

Towards Climate-Smart Agriculture: Strategies for Sustainable Agricultural Production, Food Security, and Greenhouse Gas Reduction

Without transformative adaptation strategies, the impact of climate change is projected to reduce global crop yields and increase food insecurity, while rising greenhouse gas (GHG) emissions further exacerbate the crisis. While agriculture is a major contributor to climate change through unsustainable practices, it also offers significant opportunities to mitigate these emissions through the adoption of sustainable practices. This review examines climate-smart agriculture (CSA) as a key strategy for enhancing crop productivity, building climate resilience, and reducing GHG emissions, while emphasizing the need for strategic interventions to accelerate its large-scale implementation for improved food security. The analysis revealed that while nitrogen use efficiency (NUE) has improved in developed countries, the global NUE remains at 55.47%, emphasizing the need for precision nutrient management and integrated soil fertility strategies to enhance productivity and minimize environmental impacts. With 40% of the world’s agricultural land already degraded, sustainability alone is insufficient, necessitating a shift toward regenerative agricultural practices to restore degraded soil and water by improving soil health, enhancing biodiversity, and increasing carbon sequestration, thus ensuring long-term agricultural resilience. CSA practices, including precision agriculture, regenerative agriculture, biochar application, and agroforestry, improve soil health, enhance food security, and mitigate greenhouse gas emissions. However, result variability highlights the need for site-specific strategies to optimize benefits. Integrating multiple CSA practices enhances soil health and productivity more effectively than implementing a single practice alone. Widespread adoption faces socio-economic and technological barriers, requiring supportive policies, financial incentives, and capacity-building initiatives. By adopting climate-smart technologies, agriculture can transition toward sustainability, securing global food systems while addressing climate challenges.

Impacto de la Inteligencia Artificial en la Transformación Digital y Aprendizaje Educativo

This article explores the impact of Artificial Intelligence (AI) on digital transformation and educational learning, evaluating its effectiveness through an experimental design with control and experimental groups. A quantitative approach was used, conducting statistical tests such as ANOVA and t-tests for independent samples, finding significant differences in academic performance between students who used AI-based tools, such as ChatGPT, Copilot, and Gamma, and those who did not. The results showed an average increase of 18.1 points in the experimental group compared to 2.2 points in the control group, with a p-value < 0.05, validating the positive impact of AI on performance. Furthermore, the qualitative analysis, based on semi-structured interviews, revealed that students perceived improvements in content understanding and greater autonomy in their learning. Overall satisfaction with the AI tools was notably higher in the experimental group, highlighting student motivation and engagement. These findings suggest that the integration of AI not only enhances academic performance but also supports personalized and adaptive learning, although challenges are identified, such as the need to balance the development of technical and interpersonal skills and overcoming technological and cultural barriers.

Barreras en la implementación del plan de educación 2022: reflexiones para avanzar

Abstract This opinion article analyzes the implementation of the 2022 Study Plan in México, identifying barriers that hinder its consolidation. Some of the barriers mentioned are: unfinished principals, lack of resources and adequate infrastructure, resistance to change on the part of managers and teachers, difficulties in the training and professional development of teachers, technological barriers and access to information, ambiguities in the evaluation process. At the same time, some strategies and alternative solutions are proposed, better management in the proper use of resources, establishing the necessary mechanisms to know the professionalization needs of teachers and implementation of training in innovative technologies. It concludes by highlighting the importance of collaboration between all educational actors to overcome these barriers and achieve the improvement of the teaching-learning process of Basic Education students. Keywords: Study plan 2022, implementation, barriers, reflect, proposal for improvement.

Navigating the integration of artificial intelligence in the medical education curriculum: a mixed-methods study exploring the perspectives of medical students and faculty in Pakistan

BackgroundThe integration of artificial intelligence (AI) into medical education is poised to revolutionize teaching, learning, and clinical practice. However, successful implementation of AI-based tools in medical curricula faces several challenges, particularly in resource-limited settings like Pakistan, where technological and institutional barriers remain significant. This study aimed to evaluate knowledge, attitudes, and practices of medical students and faculty regarding AI in medical education, and explore the perceptions and key barriers regarding strategies for effective AI integration.MethodsA concurrent mixed-methods study was conducted over six months (July 2023 to January 2024) at a tertiary care medical college in Pakistan. The quantitative component utilized a cross-sectional design, with 236 participants (153 medical students and 83 faculty members) completing an online survey. Mean composite scores for knowledge, attitudes, and practices were analyzed using non-parametric tests. The qualitative component consisted of three focus group discussions with students and six in-depth interviews with faculty. Thematic analysis was performed to explore participants’ perspectives on AI integration.ResultsMajority of participants demonstrated a positive attitude towards AI integration. Faculty had significantly higher mean attitude scores compared to students (3.95 ± 0.63 vs. 3.81 ± 0.75, p = 0.040). However, no statistically significant differences in knowledge (faculty: 3.53 ± 0.66, students: 3.55 ± 0.73, p = 0.870) or practices (faculty: 3.19 ± 0.87, students: 3.23 ± 0.89, p = 0.891) were found. Older students reported greater self-perceived knowledge (p = 0.010) and more positive attitudes (p = 0.016) towards AI, while male students exhibited higher knowledge scores than females (p = 0.025). Qualitative findings revealed key themes, including AI’s potential to enhance learning and research, concerns about over-reliance on AI, ethical issues surrounding privacy and confidentiality, and the need for institutional support. Faculty emphasized the importance of training to equip educators with the necessary skills to effectively integrate AI into their teaching.ConclusionsThis study highlights both the enthusiasm for AI integration and the significant barriers that must be addressed to successfully implement AI in medical education. Addressing technological constraints, providing faculty training, and developing ethical guidelines are critical steps toward fostering the responsible use of AI in medical curricula. These findings underscore the need for context-specific strategies, particularly in resource-limited settings, to ensure that medical students and educators are well-prepared for the future of healthcare.

Analyzing The Protection Of Cloud Security Against Cloud Security Risks With Secure Coding Mechanisms

Cloud computing is a technology that supports data and applications by using a central server and theinternet. The user can use the Internet to access the application and data from any computer. By combiningprocessing, bandwidth, and storage memory, this technology enables far more efficient computing. Onemethod that supports all IT operations is cloud computing. Rather than relying on internal performance andinfrastructure, it utilizes on-demand IT services to remove the technological barriers that organizations mustovercome in order to design information. Because organizations save their most sensitive data on the cloud,data reliability is the main problem when discussing cloud computing.