Real-world Studies Research Articles

Drug Survival, Retention, and Persistence of Dupilumab in Adults and Adolescents with Atopic Dermatitis: A Narrative Literature Review.

Atopic dermatitis (AD) is a chronic, relapsing inflammatory skin condition that can have a negative impact on a patient's quality of life. Long-term effectiveness is required to manage the symptoms of AD (skin inflammation, eczematous lesions, and itching). Because some of the systemic immunosuppressants used to treat AD have been associated with serious adverse events (AEs), other safer, more effective options, including dupilumab, have been proven effective long-term for treatment of adult and adolescent patients with moderate-to-severe AD. The long-term safety and effectiveness of a drug are usually confirmed in real-world studies by evaluating its performance over time. Measures such as drug survival, drug retention, drug persistence, or retention rates reflect whether treatment may be considered as satisfactory by both patients and physicians, meeting key clinical needs. This review aimed to describe the survival, retention, or persistence of dupilumab therapy in adults and adolescents with moderate-to-severe AD by conducting a PubMed search in March 2023 and screening for relevant publications. Globally, real-world studies with dupilumab have regularly reported high drug survival rates after 1, 2, and 3years of observation, being consistently at 80-90%, with low rates of treatment discontinuation due to lack of efficacy or AEs. These findings are notably higher than 1- and 2-year drug survival rates of systemic immunosuppressants (including cyclosporine [37% and 20%, respectively] and methotrexate [41% and 33%, respectively]). Overall, real-world data on drug survival have confirmed that dupilumab provides long-term sustained efficacy and acceptable safety in patients with moderate-to-severe AD.

Factors Predicting COVID-19 Vaccine Effectiveness and Longevity of Humoral Immune Responses

The COVID-19 pandemic, caused by SARS-CoV-2, prompted global efforts to develop vaccines to control the disease. Various vaccines, including mRNA (BNT162b2, mRNA-1273), adenoviral vector (ChAdOx1, Ad26.COV2.S), and inactivated virus platforms (BBIBP-CorV, CoronaVac), elicit high-titer, protective antibodies against the virus, but long-term antibody durability and effectiveness vary. The objective of this study is to elucidate the factors that influence vaccine effectiveness (VE) and the longevity of humoral immune responses to COVID-19 vaccines through a review of the relevant literature, including clinical and real-world studies. Here, we discuss the humoral immune response to different COVID-19 vaccines and identify factors influencing VE and antibody longevity. Despite initial robust immune responses, vaccine-induced immunity wanes over time, particularly with the emergence of variants, such as Delta and Omicron, that exhibit immune escape mechanisms. Additionally, the durability of the humoral immune responses elicited by different vaccine platforms, along with the identification of essential determinants of long-term protection—like pre-existing immunity, booster doses, hybrid immunity, and demographic factors—are critical for protecting against severe COVID-19. Booster vaccinations substantially restore neutralizing antibody levels, especially against immune-evasive variants, while individuals with hybrid immunity have a more durable and potent immune response. Importantly, comorbidities such as diabetes, cardiovascular disease, chronic kidney disease, and cancer significantly reduce the magnitude and longevity of vaccine-induced protection. Immunocompromised individuals, particularly those undergoing chemotherapy and those with hematologic malignancies, have diminished humoral responses and benefit disproportionately from booster vaccinations. Age and sex also influence immune responses, with older adults experiencing accelerated antibody decline and females generally exhibiting stronger humoral responses compared to males. Understanding the variables affecting immune protection is crucial to improving vaccine strategies and predicting VE and protection against COVID-19.

Oral Semaglutide Use in Type2 Diabetes: A Pooled Analysis of Clinical and Patient-Reported Outcomes from Seven PIONEER REAL Prospective Real-World Studies.

Oral semaglutide is a glucagon-like peptide1 receptor agonist (GLP-1RA) approved for improving glycemic control in adults with type2 diabetes (T2D). The PIONEER REAL program evaluates clinical and patient-reported outcomes of oral semaglutide treatment as part of routine clinical practice across 13 countries. Here, data from Canada, Denmark, Italy, the Netherlands, Sweden, Switzerland, and the UK are pooled and analyzed to address treatment satisfaction as well as glycated hemoglobin (HbA1C) and body weight changes in relevant subgroup analyses. This pooled analysis encompasses seven country-specific, non-interventional, multicenter, phase4, prospective, single-arm clinical studies assessing the use of oral semaglutide in adults with T2D. Primary endpoint was the change in HbA1C from baseline to end of study (EOS), and secondary endpoints included changes in body weight and treatment satisfaction. For the analyses, results were stratified by age, T2D duration, and oral semaglutide dose at EOS as well as baseline HbA1C, body weight, and body mass index. Oral semaglutide treatment was initiated by 1615 participants. At EOS, 1222 (76%) participants out of the 1483 (92%) who completed the study were on treatment. Estimated changes in HbA1C and body weight from baseline to week38 were - 1.0%-point (95%CI - 1.08 to - 0.97; P < 0.0001) and - 5.0% (CI - 5.37 to - 4.72; P < 0.0001). Treatment satisfaction increased significantly during the study. Shorter T2D duration interacted with higher HbA1C reduction and body weight loss. Interaction was also observed between higher baseline HbA1C and more pronounced decrease in HbA1C. No significant interactions were detected between clinical outcomes and age or physician setting. The PIONEER REAL pooled analysis shows that people initiating oral semaglutide treatment experience improved glycemic control and body weight loss across age groups and T2D duration. This occurs regardless of specialist or primary care practice setting and is accompanied by an increased treatment satisfaction. NCT04559815 (Canada), NCT04537637 (Denmark), NCT05230615 (Italy), NCT04601740 (the Netherlands), NCT04601753 (Sweden), NCT04537624 (Switzerland), NCT04862923 (UK).

Optimising Business Operations: Integrating Internet of Things (IoT) with Management Information Systems (MIS)

The current landscape of business operations is largely dominated by the integration of the Internet of Things (IoT) with Management Information Systems (MIS), which results in higher efficiency and innovative solutions for common and complex organisational challenges [1] [2]. Owing to this, the following research paper aims to explore the multifaceted benefits and challenges associated with this integration. Alongside this, it seeks to provide insights for not just future research within the field, but also for practitioners and scholars alike. The study begins with a comprehensive introduction to the relevance of IoT and MIS, highlighting their evolution and the complexities that may arise in their integration. After this, it moves further, delving into the technological foundations of IoT, and examining its core components, architecture, and emerging trends. Discussing the theoretical frameworks that surround information systems, it then seeks to illustrate both historical and contemporary integration issues and conducts a deep-seated analysis of current research on IoT- MIS Integration, extrapolating on the advantages, risks, and tangible benefits. The qualitative research is backed up by real-world case studies of well-known companies such as GE and Walmart, which present a strong insight into successful IoT-MIS integration and the challenges encountered during implementation. These examples reveal key takeaways, best practices, and strategic recommendations for businesses looking to harness the full potential of this integration. Lastly, strategic recommendations are presented, including best practices for effective integration and mitigation strategies for associated risks. The paper eventually concludes by outlining future research directions, identifying emerging trends in IoT and MIS integration, and formulating relevant research questions. With the aforementioned, the paper aims to contribute to the overall comprehension of IoT-MIS integration and presents a balanced view of its potential usability in modern-day content. Furthermore, the findings thus attained offer a valuable guide tool for firms willing to navigate the complexities of digital transformation, allowing them to emphasize the quintessential importance of strategic planning, investment in technology, and a commitment to robust security measures. Thus, this study serves as a roadmap for organizations which aspire to thrive in an increasingly interconnected and data-centric business environment.

Exploring Global Arts Movements and Their Impact on Health Education

This review investigates the intersection between global art movements and health education, exploring how art can play a transformative role in promoting health awareness and education, particularly in under-resourced communities. Drawing on philosophical theories of creativity, behavior change, and real-world case studies, this research highlights how arts initiatives have contributed to public health awareness and education. The paper addresses the integration of arts in health education, the challenges faced, and the opportunities for creating a more holistic approach to health promotion. The review concludes by emphasizing the importance of cross-disciplinary partnerships to further integrate arts into health education for a more profound global impact. Keywords: Global arts movements, Health education, Public health, Arts initiatives, Social change.

Sequential Diagnostic Approach Using FIB-4 and ELF for Predicting Advanced Fibrosis in Metabolic Dysfunction-Associated Steatotic Liver Disease

Background and Aims: Multiple non-invasive tests (NITs) for identifying advanced fibrosis in patients with non-alcoholic fatty liver disease (NAFLD) are available, but, due to the limitations of single NITs, the American Association for the Study of Liver Disease (AASLD) guidelines suggest a two-step strategy, combining the Fibrosis-4 Index (FIB-4) score with the Enhanced Liver Fibrosis (ELF) test to improve diagnostic accuracy and minimize unnecessary liver biopsies. However, few real-world studies have used such a sequential approach. We here evaluated the diagnostic accuracy of the ELF test in patients with recently established metabolic dysfunction-associated steatotic liver disease (MASLD) and assessed the clinical utility of applying a two-step strategy, including the ELF test following the FIB-4 score assessment, in patients with MASLD. Methods: We enrolled 153 patients diagnosed with MASLD who underwent liver biopsy at the Dong-A University Hospital between June 2018 and August 2023. The degree of fibrosis was determined based on liver biopsy results. Various NITs were used, including the Aminotransferase-to-Platelet Ratio Index (APRI), FIB-4 score, NAFLD Fibrosis score (NFS) and ELF test. The diagnostic efficacy of these NITs was evaluated based on the area under the receiver operating characteristic curve (AUROC). Additionally, the performance of each test was further examined both when applied individually and in a two-step approach, where FIB-4 was used followed by ELF testing. Key metrics such as sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy were used for this analysis. Results: Overall, 153 patients with MASLD (mean age: 46.62 years; 52.3% men; 28.1% with type 2 diabetes) were included. The performance of the NITs in identifying advanced fibrosis was as follows: the AUROC of the APRI, FIB-4, NFS, and ELF tests were 0.803 (95% confidence interval (CI), 0.713–0.863), 0.769 (95% CI, 0.694–0.833), 0.699 (95% CI, 0.528–0.796), and 0.829 (95% CI, 0.760–0.885), respectively. The combination of the FIB-4 score ≥ 1.30 and the ELF score ≥ 9.8 showed 67.86% sensitivity, 90.40% specificity, a PPV of 75.18%, an NPV of 86.78%, an accuracy of 83.64%, and an AUROC of 0.791 for predicting the diagnosis of advanced fibrosis. This approach excluded 28 patients (71.8%) from unnecessary liver biopsies. Conclusions: Our study demonstrated that ELF testing maintained diagnostic accuracy in assessing liver fibrosis in patients with MASLD in real-world practice. This test was used as a second step in the evaluation, reducing clinically unnecessary invasive liver biopsies and referrals to tertiary institutions. This approach allows assessment of MASLD severity in primary care settings without requiring additional equipment.

Spatio-temporal attention based collaborative local–global learning for traffic flow prediction

Traffic flow prediction is crucial for intelligent transportation systems (ITS), providing valuable insights for traffic control, route planning, and operation management. Existing work often separately models the spatial and temporal dependencies and primarily relies on predefined graphs to represent spatio-temporal dependencies, neglecting the traffic dynamics caused by unexpected events and the global relationships among road segments. Unlike previous models that primarily focus on local feature extraction, we propose a novel collaborative local–global learning model (LOGO) that employs spatio-temporal attention (STA) and graph convolutional networks (GCN). Specifically, LOGO simultaneously extracts hidden traffic features from both local and global perspectives. In local feature extraction, a novel STA is devised to directly attend to spatio-temporal coupling interdependencies instead of separately modeling temporal and spatial dependencies, and to capture in-depth spatio-temporal traffic context with an adaptive graph focusing on the dynamics in traffic flow. In global feature extraction, a global correlation matrix is constructed and GCNs are utilized to propagate messages on the obtained matrix to achieve interactions between both adjacent and similar road segments. Finally, the obtained local and global features are concatenated and fed into a gated aggregation to forecast future traffic flow. Extensive experiments on four real-world traffic datasets sourced from the Caltrans Performance Measurement System (PEMS03, PEMS04, PEMS07, and PEMS08) demonstrate the effectiveness of our proposed model. LOGO achieves the best performance over 18 state-of-the-art baselines and the best prediction performance with the highest improvement of 6.06% on the PEMS07 dataset. Additionally, two real-world case studies further substantiate the robustness and interpretability of LOGO.

Advancements in the treatment of autoimmune diseases: Integrating artificial intelligence for personalized medicine

The incorporation of artificial intelligence (AI) into medical practice has considerably improved the treatment of autoimmune disorders, opening new avenues for personalized therapy. This study examines advances in AI-driven therapeutic options for autoimmune illnesses, including both current and developing treatments. Traditional therapies for autoimmune illnesses, such as immunosuppressive therapy and biologics, attempt to alleviate symptoms but frequently fall short of offering personalized care. Emerging approaches, such as precision medicine and artificial intelligence, are altering the landscape by harnessing massive volumes of patient data to better customize therapies. AI holds the ability to transform autoimmune disease therapy by enhancing diagnosis, discovering biomarkers, optimizing drug development, and personalized treatment procedures. Real-world applications and case studies are examined to demonstrate how machine learning algorithms have improved treatment tactics for rheumatoid arthritis, systemic lupus erythematosus, and multiple sclerosis. While AI has many advantages, like enhanced diagnosis accuracy and personalized therapy, it also has drawbacks, such as data privacy, the requirement for vast datasets, algorithmic bias, and a lack of explain ability. This study emphasizes the advantages of AI, such as improved patient stratification and predictive modelling, while also discussing its drawbacks, such as ethical problems and the possibility of data exploitation. AI presents intriguing prospects for treating autoimmune diseases, but more research and cooperation are required to overcome current difficulties and completely integrate AI into clinical practice.

Real-world evidence of effectiveness and safety of pasireotide in the treatment of acromegaly: a systematic review and meta-analysis.

Pasireotide long-acting release (PAS-LAR) is a second-generation somatostatin receptor ligand (SRL) approved for acromegaly treatment. This meta-analysis aimed to evaluate the real-world effectiveness and safety of PAS-LAR in patients with acromegaly resistant to first-generation somatostatin receptor ligands (fgSRL). A systematic literature search was conducted in PubMed and Web of Science for real-world studies on PAS-LAR in acromegaly published between 2014 and 2023. Random-effects meta-analyses were performed on biochemical control rates, tumor shrinkage, and metabolic parameters. Twelve studies comprising 409 patients were included. The pooled rate of insulin-like growth factor 1 (IGF-1) control was 57.9% [95% CI: 48.4-66.8] and the percentage of patients with tumor shrinkage was 33.3% [95%CI: 19.7-50.4]. Significant reductions were observed in growth hormone standardized mean difference (SMD) 0.6 ng/mL [95% CI: 0.3 to 1.0] and IGF-1 levels SMD 0.9 ULN [95% CI: 0.4 to 1.4]. However, as expected, a worsening in glucose metabolism was noted as an increase in fasting glucose SMD - 0.8mg/dL [95% CI: -1.0 to -0.5, p < 0.01], glycated hemoglobin SMD - 0.5% [95% CI: -0.7 to -0.2]. and type 2 diabetes mellitus prevalence SMD - 11.5% (95% CI: -17.5 to -5.5). PAS-LAR demonstrated higher effectiveness in real-world settings, with over 60% of patients achieving IGF-1 control compared to the around 30% efficacy observed in clinical trials. These findings suggest that PAS-LAR is an effective option for acromegaly patients resistant to fgSRL, but careful monitoring of glucose levels is essential. The high heterogeneity observed across studies emphasizes the need for identifying PAS-LAR response biomarkers to set-up individualized treatment approaches for optimizing patient outcomes.

Optimizing API Project Efficiency: Agile Configurations, Advanced Design Patterns, and Testing Strategies

In the rapidly evolving digital era, Application Programming Interfaces (APIs) have emerged as the cornerstone of modern software development, enabling seamless communication and data exchange between disparate systems. Optimizing API project efficiency is paramount for organizations aiming to stay competitive and agile in response to market demands. This paper explores strategies for enhancing API efficiency through agile configurations, advanced design patterns, and comprehensive testing methodologies. We delve into the significance of decoupling configurations to achieve agile adaptability, discuss various API design paradigms like RESTful APIs and GraphQL, and examine their specific use cases and benefits. Additionally, we emphasize the importance of robust testing and security practices, highlighting industry-standard tools and technologies that facilitate these processes. Real-world case studies are presented to illustrate practical implementations, and the tangible improvements achieved. By integrating these advanced strategies, organizations can enhance their API performance, scalability, and maintainability, thereby delivering superior value to users and stakeholders in an increasingly interconnected world. Keywords: API efficiency, agile adaptability, decoupling configurations, RESTful API, GraphQL, API testing, microservices, API gateway, performance optimization, configuration management, API security, authentication, automated testing, real-world case studies

Fortifying Application Security: Integrating OAuth2 Single Sign-On with Precision Role-Based Access Control

In today's digital era, securing applications has become paramount due to the increasing sophistication of cyber threats and the proliferation of data breaches. This paper explores how integrating OAuth2 Single Sign-On (SSO) with precision Role-Based Access Control (RBAC) can significantly enhance application security. By unifying authentication and authorization mechanisms, organizations can streamline user access while mitigating risks associated with over-privileged accounts and credential fatigue. We delve into the OAuth2 protocol architecture, dissect the core components of RBAC, and present strategies for their effective integration. Through real-world case studies and comparative analyses, we highlight the benefits, challenges, and future trends of this integration. Our findings underscore the necessity for robust security frameworks that adapt to evolving threats, emphasizing the role of OAuth2 SSO and RBAC in fortifying application security. Keywords Application Security, OAuth2, Single Sign-On, Role-Based Access Control, Authentication, Authorization, Identity Providers, JSON Web Tokens, Zero Trust Security, Adaptive Authentication, Machine Learning, Identity Management, User Experience, Credential Fatigue, Cyber Threats, Access Control

DGNMDA: Dual Heterogeneous Graph Neural Network Encoder for miRNA-Disease Association Prediction

In recent years, numerous studies have highlighted the pivotal importance of miRNAs in personalized healthcare, showcasing broad application prospects. miRNAs hold significant potential in disease diagnosis, prognosis assessment, and therapeutic target discovery, making them an integral part of precision medicine. They are expected to enable precise disease subtyping and risk prediction, thereby advancing the development of precision medicine. GNNs, a class of deep learning architectures tailored for graph data analysis, have greatly facilitated the advancement of miRNA-disease association prediction algorithms. However, current methods often fall short in leveraging network node information, particularly in utilizing global information while neglecting the importance of local information. Effectively harnessing both local and global information remains a pressing challenge. To tackle this challenge, we propose an innovative model named DGNMDA. Initially, we constructed various miRNA and disease similarity networks based on authoritative databases. Subsequently, we creatively design a dual heterogeneous graph neural network encoder capable of efficiently learning feature information between adjacent nodes and similarity information across the entire graph. Additionally, we develop a specialized fine-grained multi-layer feature interaction gating mechanism to integrate outputs from the neural network encoders to identify novel associations connecting miRNAs with diseases. We evaluate our model using 5-fold cross-validation and real-world disease case studies, based on the HMDD V3.2 dataset. Our method demonstrates superior performance compared to existing approaches in various tasks, confirming the effectiveness and potential of DGNMDA as a robust method for predicting miRNA-disease associations.

Mitigating anticipated medicine shortages through inter-facility medicine sharing

This study develops an optimisation model to facilitate inter-facility medicine sharing in response to anticipated medicine shortages. These facilities include hospitals and medical representatives. We adopt the concept of collective response proposed in our study literature. The optimisation model is developed according to the real-world practices of inter-facility medicine sharing. We utilise case studies of particular healthcare networks to demonstrate the efficacy of the developed model. The efficacy encompasses the model’s application to real-world case studies, as well as its validity and reliability within a specific system. The results show that the developed model is able to determine which facilities should share the requested amount of medicines; and to reduce total lead times by at least one day compared to the ones obtained in the current practice. The model can be used as a decision-support tool for healthcare practitioners when responding to shortages. The study presents the managerial implications of medicine sharing at the network level and supports the development of collaboration amongst facilities in response to medicine shortages.

Systematic Review on Influenza Burden in Emerging Markets in 2018–2023—An Evidence Update to Guide Influenza Vaccination Recommendations

Background: Influenza is a contagious respiratory illness responsible for seasonal epidemics and with potential to cause pandemics. The decline in influenza-related studies published since 2018 resulted in data gaps, particularly in emerging markets. Methods: This systematic review searched for studies in six databases and gray literature sources to define the clinical burden of influenza and influenza-like illness (ILIs) and their associated sequelae among humans across emerging markets. Eligible studies were published in English, Spanish, or Chinese between January 2018 and September 2023 and conducted in Asia, the Middle East, Africa, and Latin America. Results: In total, 256 articles were included, mostly on lab-confirmed influenza infections (n = 218). Incidences of lab-confirmed influenza cases in Asia (range 540–1279 cases/100,000 persons) and Sub-Saharan Africa (range 34,100–47,800 cases/100,000 persons) were higher compared to Latin America (range 0.7–112 cases/100,000 persons) and the Middle East and North Africa (range 0.1–10 cases/100,000 persons). Proportions of lab-confirmed influenza cases and influenza-associated outcomes (i.e., hospitalization, ICU admission and death) varied widely across regions. Temporal variation in influenza trend was observed before and during the COVID-19 pandemic. Conclusions: In conclusion, influenza causes significant disease burden in emerging markets. Robust large real-world studies using a similar methodology are needed to have more accurate estimates and compare studies within age groups and regions. Continuous monitoring of influenza epidemiology is important to inform vaccine programs in emerging markets with heavy influenza disease burden.

Modeling severe uncontrolled asthma: transitioning away from health states

BackgroundModels developed to date to simulate long-term outcomes of asthma have been criticized for lacking granularity and ignoring disease heterogeneity. ObjectiveTo propose an alternative approach to modeling asthma and apply it to model long-term outcomes in a population with moderate-to-severe type 2 asthma (patients with raised fractional exhaled nitric oxide or eosinophils) and treated with conventional therapy. MethodsA discretely integrated condition event (DICE) approach was adopted, simulating individual profiles with asthma over patients’ lifetime in terms of exacerbations, asthma-related death, and death unrelated to asthma. The timing of these events is dependent on profile characteristics including lung function, asthma control, exacerbation history, and other baseline characteristics or contextual factors. Predictive equations were derived from a clinical trial to model time to exacerbation, change in asthma control, lung function, and utility. Real-world studies were used to supplement data gaps. Outcomes evaluated included life expectancy, quality-adjusted life-years (QALY), number of exacerbations, and lung function over time. ResultsAverage annual rates of severe and moderate exacerbations were 1.82 and 3.08 respectively, with rates increasing over time. Lung function declined at a higher rate compared with the general population. Average life expectancy was 75.2 years, compared with 82.4 years in a matched general population. The majority of life-years were spent with uncontrolled asthma and impaired lung function. ConclusionPatients with moderate-to-severe type 2 asthma and a history of exacerbations suffer from frequent exacerbations and reduced lung function and life expectancy. Capturing multiple conditions to simulate long-term outcomes in patients with asthma may provide more realistic projections of exacerbation rates.

Getting Heard: Practical Strategies for Capturing Attention and Influencing Outcomes at Work

Getting your ideas heard and influencing outcomes at work can be challenging given modern distractions and information overload. This practitioner-focused brief explores evidence-based communication strategies for capturing attention and gaining buy-in. Drawing from research in cognitive psychology, persuasion, and leadership storytelling, it provides guidance on crafting compelling messages through narrative structure, formatting for visual engagement, and crafting an persuasive opening statement, evidence, and resolution. The brief also offers tips for delivering ideas interactively to different learning styles while empowering participation over passivity. Real-world case studies from Patagonia, Anthropic, and Discovery Education demonstrate effective application across industries. By understanding human cognition and facilitating collaborative discovery, communicators can inspire others towards shared goals through respect, understanding and inspiration over directives.

D5 digital circular workflow: five digital steps towards matchmaking for material reuse in construction

The intersection of digital transformation and circular construction practices presents significant potential to mitigate environmental impacts through optimised material reuse. We propose a five-step (D5) digital circular workflow that integrates these digital innovations towards reuse, validated through real-world case studies. We assessed a variety of digital tools for enhancing the reuse of construction materials, including digital product passports, material classification assisted by artificial intelligence (AI), reality capture, computational design, design inspired by generative AI, digital fabrication techniques, extended reality, and blockchain technology. Using action research through a multiple case study approach, we disassembled several buildings that were set for demolition and subsequently designed and executed construction projects using the salvaged materials. Our findings indicate that digital transformation for detection, disassembly, distribution, design, and finally deployment significantly support the application of circular economy principles. We demonstrate the potential of the proposed workflow for industry implementation and scalability.

Peer Review of Real-World Data Studies and Open Science-Connecting the Last Mile.

Peer Review of Real-World Data Studies and Open Science-Connecting the Last Mile.

Advancing Sustainable Chemistry Education: Insights from real-world case studies

Advancing Sustainable Chemistry Education: Insights from real-world case studies

A deep dive into cybersecurity solutions for AI-driven IoT-enabled smart cities in advanced communication networks

The integration of the Internet of Things (IoT) and artificial intelligence (AI) in urban infrastructure, powered by advanced information communication technologies (ICT), has paved the way for smart cities. While these technologies promise enhanced quality of life, economic growth, and improved public services, they also introduce significant cybersecurity challenges. This article comprehensively examines the complex factors in securing AI-driven IoT-enabled smart cities within the framework of future communication networks. Our research addresses critical questions about the evolving threat, multi-layered security approaches, the role of AI in enhancing cybersecurity, and necessary policy frameworks. We conduct an in-depth analysis of cybersecurity solutions across service, application, network, and physical layers, evaluating their effectiveness and integration potential with existing systems. The study offers a detailed examination of AI-driven security approaches, particularly ML and DL techniques, assessing their applicability and limitations in smart city environments. We incorporate real-world case studies to illustrate successful strategies and show areas requiring further research, especially considering emerging communication technologies. Our findings contribute to the field by providing a multi-layered classification of cybersecurity solutions, assessing AI-driven security approaches, and exploring future research directions. Additionally, we investigate the essential role played by policy and regulatory frameworks in safeguarding smart city security. Based on our analysis, we offer recommendations for technical implementations and policy development, aiming to create a holistic approach that balances technological advancements with robust security measures. This study also provides valuable insights for scholars, professionals, and policymakers, offering a comprehensive perspective on the cybersecurity challenges and solutions for AI-driven IoT-enabled smart cities in advanced communication networks.