Complex Landscape Research Articles

Navigating Global Research Trends on Seafoods-Related Foodborne Diseases: A Decadal Bibliometric Analysis

Ensuring seafood safety has become a critical area of focus due to its strong association with foodborne diseases. Despite the critical nature of this field, an in-depth understanding of global research trends in this field remains limited. Our study conducted an extensive bibliometric analysis to track the evolution and thematic focus of research on seafood safety from 2013 to 2023. Utilizing Scopus for publication and citation data, along with VOSviewer for visualizing keyword networks, this study highlights key developments and scholarly efforts aimed at mitigating risks associated with seafoods consumption. The analysis pinpoints the United States, China, the United Kingdom, France, South Korea and Malaysia as leading contributors. Their extensive research outputs and collaborations have markedly shaped the field's evolution. The overall positive trajectory in research volume experienced a minor setback in 2022, suggesting a complex and dynamic research landscape. "Food Control" is identified as a principal journal for the dissemination of research works, anchoring a dynamic co-citation network that fosters extensive international dialogue. We categorize seven principal research clusters: epidemiology, molecular detections, microbial resistance, bacteriophage applications, toxin detection methodologies, predictive modelling and biofilm studies. These clusters encompass the scopes and the breadth of current investigations. This decade-long review highlights the necessity for cohesive global research strategies to enhance the knowledge and control of foodborne pathogens in seafoods. This strategy is critical for improving food safety measures and safeguarding public health globally.

Learning Strategies for Chinese as Foreign Language Learners in College: A Qualitative Study

Embarking on the journey of learning Chinese as a foreign language in college entails grappling with a myriad of distinctive challenges, ranging from navigating its intricate writing system to mastering its tonal pronunciation and unraveling its rich cultural nuances. In this complex landscape of language acquisition, comprehending the nuanced learning strategies wielded by Chinese foreign language learners becomes paramount. These strategies not only illuminate the pathways to effective teaching but also hold the key to enhancing student outcomes. Despite the burgeoning interest in Chinese language education, there exists a conspicuous dearth of qualitative studies delving into the intricacies of learning strategies within college settings. To address this gap, this study adopts a qualitative research paradigm, employing semi-structured interviews as its methodological backbone. Through these interviews, the study endeavors to excavate profound insights into the diverse repertoire of learning strategies embraced by Chinese foreign language learners in the collegiate milieu. At its core, the study's primary objective is twofold: to meticulously explore and rigorously analyze the spectrum of learning strategies woven into the fabric of Chinese foreign language learning at the college level. By peering into the multifaceted approaches employed by students, the study aspires to engender a nuanced comprehension of effective pedagogical methodologies. Moreover, the findings are poised to serve as a compass, guiding the design and implementation of tailored language learning interventions tailored to the unique needs and aspirations of Chinese language learners in college environments.

Multi-region hierarchical surrogate-assisted quantum-behaved particle swarm optimization for expensive optimization problems

Surrogate-assisted evolutionary algorithms (SAEAs) have been successfully applied to solve computationally expensive optimization problems. However, most SAEAs struggle to achieve good results in solving complex multimodal problems, especially high-dimensional ones. Moreover, for problems with complex landscapes, SAEAs typically require constructing complex global surrogates to model the landscape and performing many iterations to identify the surrogate’s optimum, thereby reducing the efficiency of SAEAs. To deal with these issues, this paper proposes a multi-region hierarchical surrogate-assisted quantum-behaved particle swarm optimization (MHS-QPSO) algorithm for expensive optimization problems. To better balance exploration and exploitation, a search behavior selection strategy is proposed, enabling MHS-QPSO to appropriately switch between global and local searches. For the global search, the search space is divided into multiple regions that can adaptively adjust the size of the areas. A surrogate is constructed in each region, requiring only a small number of QPSO iterations to find the optimum of each surrogate. Furthermore, a novel reliability-based criterion is proposed to screen candidate solutions in different regions for exact evaluations, which can save the number of exact function evaluations and can rapidly improve the fitting accuracy of the surrogates in regions with superior fitness. During local searches, a dynamic boundary adjustment strategy is introduced to guide the QPSO to faster approach the potential optimal region. Experimental results on seven benchmark functions with dimensions from 10 to 100, and on a complex real application, demonstrate that MHS-QPSO significantly outperforms several state-of-the-art algorithms within a limited computational budget. Code for MHS-QPSO is available at https://github.com/quanshuzhang/MHS-QPSO.git.

Legal Support of Artificial Intelligence in Countering Anti-Money Laundering and Terrorism Financing Regimes in the BRICS Plus Countries

The increasing integration of artificial intelligence technologies into the financial structures of the BRICS Plus countries (comprising the original member countries of Brazil, Russia, India, China, and South Africa as well as the four new member countries of Egypt, Ethiopia, Iran, and the United Arab Emirates) presents both opportunities and challenges in combating economic crimes, which include money laundering and terrorism financing. This article explores the complex regulatory landscape that governs the application of artificial intelligence in these efforts. It examines how artificial intelligence can enhance the performance of anti-money laundering and counter-terrorism financing frameworks by enabling the evaluation of massive datasets, the identification of anomalous transaction patterns, and the automation of compliance procedures. Simultaneously, the article addresses the highly challenging situations that arise when using artificial intelligence. For instance, these technologies can make it difficult to understand the fluctuation of illicit price ranges, thereby complicating efforts to determine their origins and destinations. Through a comparative analysis of the frameworks throughout the BRICS Plus countries, this research highlights the varying levels of regulatory readiness of these frameworks and proposes pathways for harmonizing artificial intelligence-driven economic security measures. The overarching goal of an artificial intelligence model is to enhance both the effectiveness and the integrity of the financial sectors in the BRICS Plus consortium, necessitating a collaborative approach to combating financial crimes in an increasing number of digital economies across the world.

A review on achromatopsia: Exploring genetic basis, diagnostic innovations, and therapeutic prospects

Achromatopsia (ACHM) is a rare inherited retinal disorder characterized by partial or complete color blindness, which impacts individuals' quality of life and daily functioning. This review delves into the genetic basis, diagnostic innovations, and therapeutic prospects associated with ACHM. The main objective is to comprehensively explore the current understanding of ACHM, including genetic mutations, diagnostic techniques, and management approaches, while also discussing potential therapeutic interventions. Achromatopsia primarily results from mutations in genes encoding cone photoreceptor proteins, such as GNAT2, ATF6, PDE6H, PDE6C, CNGA3, and CNGB3. Various diagnostic methods, including electroretinography (ERG) and optical coherence tomography (OCT), are pivotal in confirming ACHM diagnosis by assessing cone function and structural abnormalities in dysfunctional cone cells. This review also highlights gene therapy interventions aimed at restoring cone function, highlighting promising advancements in preclinical and clinical settings. Additionally, it discusses the clinical features of ACHM, such as reduced visual acuity, photophobia, and color vision impairment, emphasizing the need for tailored management strategies to increase patients' quality of life. This review underscores the complex genetic landscape of ACHM and the importance of accurate diagnosis through advanced diagnostic modalities such as ERG and OCT. While there is currently no cure for ACHM, management approaches focus on symptom alleviation and improving patients' daily functioning. Gene therapy has emerged as a promising avenue for potential treatment, with ongoing research aiming to address the underlying genetic defects and restore cone photoreceptor function. The findings of this review hold clinical relevance by providing insights into the pathogenesis, diagnosis, and management of ACHM, guiding healthcare professionals in delivering personalized care to affected individuals. Continued research and advancements in gene therapy offer hope for future therapeutic interventions, highlighting the importance of updating the latest developments in the field of ACHM.

Prevalence and Associated Risk Factors of Helminth Infections in the Digestive Tract of Camels in Xinjiang, China.

Camels, vital to economies in Asia, Africa, and the Arabian Peninsula, have been domesticated for over 4000 years. They thrive in arid regions like Xinjiang, China, but face challenges from internal and external parasites, particularly gastrointestinal parasites, which impact health, meat and milk quality, and production efficiency. This study investigates the prevalence of gastrointestinal helminth infections in camels from five major regions in Xinjiang. We collected 435 fresh fecal samples and used the saturated saline flotation method and McMaster's method for detection. Molecular examination followed. The overall prevalence was 18.2% (95% confidence interval [CI]: 14.7-22.2%), with Urumqi showing the highest prevalence (29%, 95% CI: 23.4-35.1%) compared to other regions (odds ratio [OR]: 4.62, 95% CI: 2.63-8.41%). Younger camels (≤3 years old) were more likely infected by the parasites after adjusting for the region differences (OR: 10.53, 5.12-24.65%). However, we found no evidence that the prevalence was different between male and female camels. PCR analysis identified Trichostrongylus spp., Chabertia ovina, and Haemonchus contortus as predominant parasites, with observed co-infections indicating a complex parasitic landscape. The findings provide essential epidemiological data for effective parasite control strategies.

The role of kinesin superfamily proteins in hepatocellular carcinoma.

The most prevalent form of primary liver cancer, hepatocellular carcinoma (HCC) poses a significant global health challenge due to its limited therapeutic options. Researchers are currently focused on the complex molecular landscape that governs the initiation and progression of HCC in order to identify new avenues for diagnosis, prognosis, and treatment. In the context of HCC, the Kinesin Superfamily Proteins (KIFs) have become critical regulators of cellular processes, prompting a growing interest in their function among the diverse array of molecular actors implicated in cancer. The KIFs, a family of microtubule-based molecular motors, are renowned for their essential roles in the dynamics of mitotic spindles and intracellular transport. Beyond their well-established functions in normal cellular physiology, emerging evidence indicates that dysregulation of KIFs significantly contributes to the pathogenesis of HCC. Novel therapeutic targets and diagnostic markers are revealed through the unique opportunity to comprehend the complex interplay between KIFs and the molecular events that drive HCC.

What Next for Actor Network Theory? Inventing around Latour on a planet in distress

This is a reflection piece on the theme ‘what will happen to actor-network theory (ANT) after Latour?’ We distinguish three scenarios but focus on the one we see as the least probable but most interesting for sociology and related disciplines. With inspiration from the anthropologist Marilyn Strathern, we think of this third scenario as a way of “inventing around” Latour. In this scenario, classical ANT tenets are prolonged and refracted through Latour's later work on modes of existence and critical zones. This mutant ANT is subject to further transformations through encounters with empirical situations and concepts from many other places. We use two brief cases for illustration. The first considers the empirical potentials and possible pitfalls of Latour's modes of existence in relation to the emergence of carbon markets as a form of Anthropocene politics. The second examines recent urban upheavals and transformations in Bangkok through the lens of urban critical zones, which make visible problems of co-existence that shade into pluriversal cosmopolitics because the city is full of beings that are both modern and amodern. These cases point the way to a scenario in which ANT is still unfinished, and far from “uncritical”. It is an “indisciplinary” scenario, where ANT with sociology and adjacent disciplines venture into complex landscapes of planetary ecological disruptions and postcolonial tensions without any false protection from certified theory and pre-defined politics.

Exploring Nanocluster Potential Energy Surfaces via Deep Reinforcement Learning: Strategies for Global Minimum Search.

The search for global minimum (GM) configurations in nanoclusters is complicated by intricate potential energy landscapes replete with numerous local minima. The complexity of these landscapes escalates with increasing cluster size and compositional diversity. Evolutionary algorithms, such as genetic algorithms, are hampered by slow convergence rates and a propensity for prematurely settling on suboptimal solutions. Likewise, the basin hopping technique faces difficulties in navigating these complex landscapes effectively, particularly at larger scales. These challenges highlight the need for more sophisticated methodologies to efficiently scan the potential energy surfaces of nanoclusters. In response, our research has developed a novel deep reinforcement learning (DRL) framework specifically designed to explore the potential energy surfaces (PES) of nanoclusters, aiming to identify the GM configurations along with other low-energy states. This study demonstrates the framework's effectiveness in managing various nanocluster types, including both mono- and multimetallic compositions, and its proficiency in navigating complex energy landscapes. The model is characterized by remarkable adaptability and sustained efficiency, even as cluster sizes and feature vector dimensions increase. The demonstrated adaptability of DRL in this context underscores its considerable potential in materials science, particularly for the efficient discovery and optimization of novel nanomaterials. To the best of our knowledge, this is the first DRL framework designed for the GM search in nanoclusters, representing a significant innovation in the field.

A novel approach to commercial property valuation: Successive term indexing and its competitive implications

The development of innovative valuation models in the commercial real estate sector is crucial for enhancing competitiveness, as they provide stakeholders with the accuracy and adaptability needed to navigate and capitalize in an increasingly complex market landscape. This paper aims to improve initial valuations provided by real estate agents by updating their timeliness while also enhancing their accuracy. Leveraging statistical techniques based on hedonic regression, it introduces a novel mechanism, successive term indexing, enabling the reassessment of widely varying commercial properties with initial assessments that are at least one term old, where a term typically encompasses one year. The model's novelty lies in its approach to indexing individual property characteristics across two successive terms, using a k-means algorithm to categorize all numerical variables and stepwise selection. The former enables differing index values for varying sizes, while the latter allows dynamic evaluation of the significance of regressors in time. Applied to data from a banking institution, the model showcases strong predictive accuracy with unique reassessment ratios ranging from -3.8% to 5.2% for 2022. With its nuanced analysis of market dynamics and indexing capabilities, the mechanism intersects the elements of PPIs and AVMs, presenting a significant methodological advancement and a practical, simple-to-use tool for valuation.

The Role of Artificial Intelligence in Higher Education

The integration of Artificial Intelligence (AI) in higher education has sparked a transformative shift in pedagogical methodologies, student engagement, and academic integrity across the UK and Ireland (Dogru, et.al, 2023). This qualitative study delves into the multifaceted implications of utilising AI in academic assignments from the perspective of higher education students, drawing on semi-structured interviews carried out with higher education students. The findings reveal a variety of perceived benefits, including enhanced study efficiency, personalised learning support (Hanaba et al., 2020; Yang, 2021), and the potential of AI to level the academic playing field for students with diverse needs. However, these advantages are in contrast with students’ perceptions of a wide range of significant concerns and challenges, notably the ethical dilemmas surrounding academic honesty, the potential for student dependency on AI leading to diminished knowledge and skill development, as well as the issue of equity in relation to students’ access to AI resources. Moreover, the study stresses a critical gap in institutional guidance regarding AI use, with students voicing a need for clear, consistent guidance from universities. This paper highlights the complex landscape of AI in academia, advocating for a balanced approach that harnesses AI's potential while addressing ethical, educational, and equity challenges. The study emphasises a need for immediate and more in-depth research into the use of AI in higher education from the perspective of both students and university staff in Ireland/UK.

StopKB: a comprehensive knowledgebase for nonsense suppression therapies.

Nonsense variations, characterized by premature termination codons, play a major role in human genetic diseases as well as in cancer susceptibility. Despite their high prevalence, effective therapeutic strategies targeting premature termination codons remain a challenge. To understand and explore the intricate mechanisms involved, we developed StopKB, a comprehensive knowledgebase aggregating data from multiple sources on nonsense variations, associated genes, diseases, and phenotypes. StopKB identifies 637 317 unique nonsense variations, distributed across 18 022 human genes and linked to 3206 diseases and 7765 phenotypes. Notably, ∼32% of these variations are classified as nonsense-mediated mRNA decay-insensitive, potentially representing suitable targets for nonsense suppression therapies. We also provide an interactive web interface to facilitate efficient and intuitive data exploration, enabling researchers and clinicians to navigate the complex landscape of nonsense variations. StopKB represents a valuable resource for advancing research in precision medicine and more specifically, the development of targeted therapeutic interventions for genetic diseases associated with nonsense variations. Database URL: https://lbgi.fr/stopkb/.

Foraging distance distributions reveal how honeybee waggle dance recruitment varies with landscape

Honeybee (Apis mellifera) colonies use a unique collective foraging system, the waggle dance, to communicate and process the location of resources. Here, we present a means to quantify the effect of recruitment on colony forager allocation across the landscape by simply observing the waggle dance on the dancefloor. We show first, through a theoretical model, that recruitment leaves a characteristic imprint on the distance distribution of foraging sites that a colony visits, which varies according to the proportion of trips driven by individual search. Next, we fit this model to the real-world empirical distance distribution of forage sites visited by 20 honeybee colonies in urban and rural landscapes across South East England, obtained via dance decoding. We show that there is considerable variation in the use of dancing information in colony foraging, particularly in agri-rural landscapes. In our dataset, reliance on dancing increases as arable land gives way to built-up areas, suggesting that dancing may have the greatest impact on colony foraging in the complex and heterogeneous landscapes of forage-rich urban areas. Our model provides a tool to assess the relevance of this extraordinary behaviour across modern anthropogenic landscape types.

Cryptocurrency Portfolio Allocation under Credibilistic CVaR Criterion and Practical Constraints

The cryptocurrency market offers attractive but risky investment opportunities, characterized by rapid growth, extreme volatility, and uncertainty. Traditional risk management models, which rely on probabilistic assumptions and historical data, often fail to capture the market’s unique dynamics and unpredictability. In response to these challenges, this paper introduces a novel portfolio optimization model tailored for the cryptocurrency market, leveraging a credibilistic CVaR framework. CVaR was chosen as the primary risk measure because it is a downside risk measure that focuses on extreme losses, making it particularly effective in managing the heightened risk of significant downturns in volatile markets like cryptocurrencies. The model employs credibility theory and trapezoidal fuzzy variables to more accurately capture the high levels of uncertainty and volatility that characterize digital assets. Unlike traditional probabilistic approaches, this model provides a more adaptive and precise risk management strategy. The proposed approach also incorporates practical constraints, including cardinality and floor and ceiling constraints, ensuring that the portfolio remains diversified, balanced, and aligned with real-world considerations such as transaction costs and regulatory requirements. Empirical analysis demonstrates the model’s effectiveness in constructing well-diversified portfolios that balance risk and return, offering significant advantages for investors in the rapidly evolving cryptocurrency market. This research contributes to the field of investment management by advancing the application of sophisticated portfolio optimization techniques to digital assets, providing a robust framework for managing risk in an increasingly complex financial landscape.

Digital twins and biodiversity: a conceptual framework of opportunities and challenge

PurposeThe intersection of digital twins and ecological diversity offers a new and complex landscape that requires interdisciplinary investigation. Recognizing the urgent need for a nuanced approach to the ethical, technological and practical challenges in this area, this paper presents a conceptual framework that serves as a roadmap for future research and policymaking.Design/methodology/approachThis study employs a conceptual methodology deeply rooted in interdisciplinary perspectives. A systematic literature review was conducted using the Scopus database to identify relevant articles. The selected articles were rigorously analyzed to derive key dimensions, which were then validated through expert panel reviews and a pilot study.FindingsOur framework identifies seven critical dimensions: Data Acquisition and Simulation, Impact Assessment, Ecological Protection and Management, Ethical and Legal Considerations, Social and Cultural Impacts, Technological Feasibility and Limitations and Policies and Regulations. These dimensions provide a comprehensive structure for understanding and addressing the intersection of digital twins and biodiversity conservation.Originality/valueThis study contributes a foundational guide for sustainable and ethical engagement between digital twins and biodiversity conservation. It offers a novel, interdisciplinary framework that integrates diverse perspectives and provides practical insights for scholars, policymakers and practitioners in this emerging field.

Examining the financial and commercial literacy of Australian Indigenous small business owners

AbstractFinancial and commercial literacy are essential skills for small business owners to navigate an increasingly complex financial landscape. This study examines the financial and commercial literacy of 592 Indigenous and non‐Indigenous small business owners. We found that small business owners had low levels of financial and commercial literacy with significant differences for Indigenous small business owners located in remote areas and for non‐Indigenous small business owners who are female, young, and who have lower levels of education. We demonstrate the need for more support to improve financial literacy among small business owners to enhance the long‐term sustainability of their enterprises.

Temporal Convolutional Network for Carbon Tax Projection: A Data-Driven Approach

This study introduces a novel application of a temporal convolutional network (TCN) for projecting carbon tax prices, addressing the critical need for accurate forecasting in climate policy. Utilizing data from the World Carbon Pricing Database, we demonstrate that the TCN significantly outperformed traditional time series models in capturing the complex dynamics of carbon pricing. Our model achieved a 31.4% improvement in mean absolute error over ARIMA baselines, with an MAE of 2.43 compared to 3.54 for ARIMA. The TCN model also showed superior performance across different time horizons, demonstrating a 30.0% lower MAE for 1-year projections, and enhanced adaptability to policy changes, with only a 39.8% increase in prediction error after major shifts, compared to ARIMA’s 95.6%. These results underscore the potential of deep learning for enhancing the precision of carbon price projections, thereby supporting more informed and effective climate policy decisions. Our findings have significant implications for policymakers and stakeholders in the realm of carbon pricing and climate change mitigation strategies, offering a powerful tool for navigating the complex landscape of environmental economics.

Analyzing the Mutational Landscape of Prostate Cancer Susceptibility Genes through Next-Generation Sequencing (NGS) Analysis

Prostate cancer, a significant public health concern, exhibits a complex genetic landscape influenced by a variety of susceptibility genes. This study deciphers the intricate genetic makeup of prostate cancer by examining mutations in 14 prostate cancer susceptibility genes. The Next-Generation Sequencing (NGS) approach was employed on a cohort of 45 prostate cancer patients to detect mutations. The gnomAD database was used to analyze mutational frequencies in the Asian population suffering from prostate cancer. The cBioPortal database was employed to check the presence of observed mutations in The Cancer Genome Atlas (TCGA) dataset. In addition, this study employed RT-qPCR and Immunohistochemistry (IHC) techniques were utilized to check the functional consequences of the observed pathogenic mutations. Finally, Metascape and DrugBank resources were used for gene enrichment and drug prediction analyses. Results of the NGS analysis revealed a total of 29 mutations (pathogenic and benign) within the examined prostate cancer cohort, distributed across four key genes (BRCA1/2, TP53, and PMS2) out of the total analyzed 14 genes. Pathogenic mutations in BRCA1/2 and TP53 genes are of particular interest due to their fundamental roles in DNA repair, cell cycle regulation, and tumor suppression. Functional consequence analyses (RT-qPCR and IHC) demonstrated the down-regulation of BRCA1/2 and TP53 genes in prostate cancer samples with pathogenic mutations, reinforcing the disruption of their tumor suppressor roles. Lastly, drug prediction analysis uncovered promising therapeutic options by identifying drugs capable of enhancing the mRNA expression of these genes. This opens new avenues for tailored treatment strategies aimed at restoring normal cellular functions of the BRCA1/2 and TP53 genes. In conclusion, this study provides a comprehensive view of genetic mutations in prostate cancer susceptibility genes, ranging from benign to pathogenic. It emphasizes the genetic complexity of prostate cancer and offers insights into potential mechanisms driving this malignancy. These findings lay the groundwork for further research, personalized treatment approaches, and enhanced clinical management of prostate cancer.

Utilizing drawings to understand how our backgrounds and experiences change our plant mental models

Societal Impact StatementOur culture, life experiences, and the places we live teach us about the world around us and the plants within it. This information gives each individual distinct mental models. By delving into plant mental models of young adults, this study offers insights into the complex landscape of plant knowledge, with a special emphasis on Indigenous communities. Beyond broadening our understanding, these findings provide valuable resources for educators and researchers striving to bridge the gap in plant literacy. Moreover, the methods used offer a blueprint for similar investigations globally, which will foster a deeper understanding of plant awareness across diverse populations.Summary Plant mental models, or what people think about when they think of a given plant, give insight into a person's knowledge and understanding of plants. However, little to no research has been conducted on plant mental models of young adults or comparing mental models across demographics. This study assessed college students' plant mental models in the demographic categories of race, gender, rurality, and college type (tribal vs. non‐tribal), as well as experience in art, photography, and plant identification, to determine the impact of these factors on plant mental models. A total of 697 students from three tribal and three non‐tribal colleges enrolled in a specific introductory biology course participated. Participants completed four drawings, which represented their mental model of a plant, flower, grass, and tree. They also completed a short survey about demographics and experience with art, photography, and plant identification. Tree drawings had the most detail, and grass drawings had the least. Gender was the only demographic factor that impacted the detail drawn, with females including more detail for flowers and trees and males for grass. Surprisingly, experience with art, photography, and plant identification did not impact the detail included in the drawings. Overall, participants plant mental models across demographics were simplistic and lacked detail, potentially indicating a lack of plant awareness.

Integrating digital and interactive approaches in adolescent health literacy: a comprehensive review.

Adolescent health literacy is critical for navigating the complex landscape of modern healthcare and making informed decisions that influence long-term health outcomes. This comprehensive review synthesizes current research on integrating digital tools and interactive learning approaches to enhance health literacy among adolescents. We explore the use of digital technologies, such as mobile apps and virtual reality, which cater to the preferences of this tech-savvy generation, offering personalized and accessible health information. The effectiveness of interactive learning methodologies, including simulations and role-playing, is also examined, highlighting their potential to increase engagement and retention of health-related knowledge. We address the importance of mental health literacy and the role of social media and peer education in disseminating health information effectively. Our review identifies gaps in the current literature, particularly the need for studies that consider long-term outcomes and the impact of socioeconomic and cultural factors on health literacy initiatives. We propose a multidimensional approach to health literacy education that incorporates innovative technologies and interactive methods to meet the diverse needs of adolescents in various contexts. The findings suggest that an integrated approach, including digital and critical health literacy, is essential for developing comprehensive health education programs that are both informative and engaging for adolescents.