Complex Landscape Research Articles

Advancing Fraud Detection in Banking: Integration of Data Pipelines, Machine Learning, and Cloud Computing

This article examines the transformation of fraud detection in the banking sector through the integration of advanced data pipelines, machine learning (ML), artificial intelligence (AI), and cloud computing technologies. We analyze how modern data pipelines enable real-time processing of vast transactional datasets, significantly improving the timeliness and accuracy of fraud detection. The article explores the application of ML models in identifying suspicious patterns and the role of AI-driven systems in continuously adapting to evolving fraud schemes while reducing false positives. We evaluate the impact of cloud platforms such as AWS, Azure, and Google Cloud in providing scalable, cost-efficient infrastructures for processing massive datasets and supporting seamless integration with ML models. The article presents case studies of successful implementations by major banks, demonstrating substantial reductions in processing times and improved detection efficiency. Additionally, we address key challenges including data quality maintenance, model interpretability, and false positive mitigation. The article concludes by discussing future innovations such as Federated Learning and Explainable AI (XAI), which promise to enhance cross-institutional collaboration and decision-making transparency in fraud detection. This comprehensive analysis provides valuable insights for financial institutions seeking to enhance their fraud detection capabilities in an increasingly complex digital landscape.

Exploring determinants influencing artificial intelligence adoption, reference to diffusion of innovation theory

An organization's ability to accept innovation heavily depends on its leadership caliber. Although leadership is known to affect all three stages of adoption (initiation, adoption, and routinization), literature has yet to examine the specific leadership components that contribute to each of these phases. Drawing from existing literature on transformational leadership and AI adoption, this article examines how transformational leadership can facilitate the successful implementation of AI technologies. This paper explores the intersection of transformational leadership and artificial intelligence (AI) adoption within organizations. This study used data from 250 companies to develop and evaluate a framework combining the Diffusion of Innovation (DOI) theory with transformational leadership (TL). The results were analyzed using structural equation modeling (SEM) techniques. The study examined the determinants influencing the adoption of AI as a new technology. This study found TL is a crucial driver of adoption, whereas elements like vision and intellectual stimulation are essential for the Intention to adopt. Also, this research indicates that adopting a significant innovation like AI is intricately linked to leaders' vision and ability to respect and recognize the feelings and needs of others (both indicators of offering individual assistance). Additionally, practical implications and recommendations for leaders are provided to navigate the complex landscape of AI adoption.

Deciphering the topological landscape of glioma using a network theory framework

Glioma stem cells have been recognized as key players in glioma recurrence and therapeutic resistance, presenting a promising target for novel treatments. However, the limited understanding of the role glioma stem cells play in the glioma hierarchy has drawn controversy and hindered research translation into therapies. Despite significant advances in our understanding of gene regulatory networks, the dynamics of these networks and their implications for glioma remain elusive. This study employs a systemic theoretical perspective to integrate experimental knowledge into a core endogenous network model for glioma, thereby elucidating its energy landscape through network dynamics computation. The model identifies two stable states corresponding to astrocytic-like and oligodendrocytic-like tumor cells, connected by a transition state with the feature of high stemness, which serves as one of the energy barriers between astrocytic-like and oligodendrocytic-like states, indicating the instability of glioma stem cells in vivo. We also obtained various stable states further supporting glioma’s multicellular origins and uncovered a group of transition states that could potentially induce tumor heterogeneity and therapeutic resistance. This research proposes that the transition states linking both glioma stable states are central to glioma heterogeneity and therapy resistance. Our approach may contribute to the advancement of glioma therapy by offering a novel perspective on the complex landscape of glioma biology.

Vegetation traits and biogeomorphic complexity shape the resilience of salt marshes to sea-level rise

The adaptive capacity of ecosystems, or their ability to function despite altered environmental conditions, is crucial for resilience to climate change. However, the role of landscape complexity or species traits on adaptive capacity remains unclear. Here, we combine field experiments and morphodynamic modelling to investigate how ecosystem complexity shapes the adaptive capacity of intertidal salt marshes. We focus on the importance of tidal channel network complexity for sediment accumulation, allowing vertical accretion to keep pace with sea-level rise. The model showed that landscape-scale ecosystem complexity, more than species traits, explained higher sediment accumulation rates, despite complexity arising from these traits. Landscape complexity, reflected in creek network morphology, also improved resilience to rising water levels. Comparing model outcomes with real-world tidal networks confirmed that flow concentration, sediment transport and deposition increase with drainage complexity. These findings emphasize that natural pattern development and persistence are crucial to preserve resilience to climate change.

Epigenetic Landscape of DNA Methylation in Pancreatic Ductal Adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) remains one of the most lethal malignancies, characterized by its aggressive progression and dismal prognosis. Advances in epigenetic profiling, specifically DNA methylation analysis, have significantly deepened our understanding of PDAC pathogenesis. This review synthesizes findings from recent genome-wide DNA methylation studies, which have delineated a complex DNA methylation landscape differentiating between normal and cancerous pancreatic tissues, as well as across various stages and molecular subtypes of PDAC. These studies identified specific differentially methylated regions (DMRs) that not only enhance our grasp of the epigenetic drivers of PDAC but also offer potential biomarkers for early diagnosis and prognosis, enabling the customization of therapeutic approaches. The review further explores how DNA methylation profiling could facilitate the development of subtype-tailored therapies, potentially improving treatment outcomes based on precise molecular characterizations. Overall, leveraging DNA methylation alterations as functional biomarkers holds promise for advancing our understanding of disease progression and refining PDAC management strategies, which could lead to improved patient outcomes and a deeper comprehension of the disease’s underlying biological mechanisms.

Forest cover is more important than its integrity or landscape configuration in determining habitat use by mammals in a human-modified landscape in Colombia

Human activities shape the structure of landscapes in different ways and hence modify animal communities depending on the type and intensity of these activities. The Montes de Maria subregion of Colombia has experienced a heavy transformation of most areas despite covering one of the last remnants of dry forest, a critically endangered ecosystem. However, the effects of this transformation have been little explored. Here, we used a multispecies occupancy model (MSOM) to understand the relative influence of three components of land-use change – deforestation (remaining forest amount), degradation of forest integrity (forest quality) and fragmentation (landscape configuration) on mammalian habitat use across a mosaic of tropical dry forest in Colombia. Our data suggest that the percentage of forest cover was substantially important for herbivores, and consistently showed a moderate effect on the entire community and some individual species. High variability in species-specific responses to the examined variables hindered broad taxonomic generalizations, nevertheless, we detected a moderate positive effect of forest cover in both diet specialists and generalists species, as well as in species with small home ranges. Although omnivores responses, tended to use less complex landscapes (mosaics of land uses), there was high uncertainty in this response. The lack of substantial effects on most species, and the absence of threatened species across this anthropogenic landscape, suggests that the current community is composed of species tolerant to habitat modifications, but not only diet generalist species. This is most likely the result of a long filtering process caused by land use transformation and hunting which could have caused non-sensitive species to distribute relatively homogenously across this landscape. Our results suggest that conservation strategies in the study area should focus on conserving and expanding as much forest as possible rather than only improving the quality of already existing forest patches.

“Human Rights And Social Justice In India: Challenges, Progress, And Pathways To Equity”

Human rights and social justice are foundational concepts for a just and equitable society, ensuring that all individuals receive fair treatment, dignity, and equality. In India, the principles of human rights and social justice are central to addressing widespread socio-economic inequalities, cultural diversity, and the challenges posed by a complex legal and social landscape. This article explores the evolution, implementation, and ongoing struggles of these concepts within the Indian context. It delves into key challenges such as poverty, caste discrimination, gender inequality, and access to resources, which continue to hinder the realization of equitable rights for all citizens. By examining legislative measures, policy reforms, and community-led initiatives, the article highlights India’s progress toward inclusive development while acknowledging persistent gaps. Through a comparative and analytical approach, the study proposes pathways for achieving greater equity and recommends actionable strategies for strengthening India’s commitment to human rights and social justice

Research on identification and zoning control of territorial spatial risk pattern based on deep learning: A case study of Shenzhen, China

In the era of globalization, digitalization, and diversification, currently societal cities face risks intertwined by uncertainty and complexity. Thus, it is crucial to address risks concerns to achieve modernization of spatial management. Moreover, how to scientifically identify the risk pattern of territorial space and how to map and control it in space becomes a critical and urgent issue to be addressed for achieving modernization of spatial management. Understanding the intricate relationship between land use, environmental factors, and population density is essential for identifying risk patterns that can inform effective disaster management strategies. By employing advanced analytical methods, this study aims to provide a comprehensive framework for recognizing and mitigating territorial spatial risks in an increasingly complex urban landscape. This study used a case study to conduct the unsupervised algorithm of stack autoencoder self-organizing map to analyze the distribution and spatial clustering of Shenzhen's land spatial risk pattern and reveal the driving mechanism of its risk pattern formation in detail. It was found that: (1) The overall territorial spatial risk pattern of Shenzhen presents a distribution trend of "high in the west and low in the east, high in the south and low in the north"; that is, I and II risk areas are clustered in the hilly areas in the northeast and IV and V risk areas in the coastal areas in the southwest; (2) Population density, land use intensity, seawater intrusion, geological disasters, relief amplitude, soil type, soil erosion intensity are the main driving forces for the formation of Shenzhen's territorial spatial risk pattern. In particular, seawater intrusion, population density and land use intensity, soil type were found having significantly synergistic impact on the risk pattern.

Regional Disparities and Trends in Venous Thromboembolism Mortality in Spain (1999–2022)

AimThis study aims to analyze trends in venous thromboembolism (VTE) mortality in the Spanish Autonomous Communities (ACs) from 1999 to 2022, with a focus on identifying regional disparities and informing targeted public health interventions. MethodsAge-standardized mortality rates (ASMRs) were calculated using the European standard population as a reference. Joinpoint regression analysis was employed to identify significant changes in mortality trends, and geographical analysis was conducted using Bayesian inference to assess regional variations in mortality risk. ResultsFrom 1999 to 2022, Spain recorded 59,515 VTE-related deaths, with a higher proportion in women (59.4%) compared to men (40.6%). On a nationwide scale, VTE ASMRs showed a general decline for both men and women, with rates decreasing from 11.0 to 5.1 per 100,000 in men and from 9.2 to 4.4 per 100,000 in women. However, this decline was not uniform across all regions or demographic groups. Notably, mortality rates among younger individuals, particularly men, increased during the study period, highlighting the need for targeted interventions. Analysis revealed significant regional disparities, with higher mortality risks observed in Extremadura, Aragon, Navarre, and Andalusia, particularly for women. ConclusionsThis study provides valuable insights into the complex landscape of VTE mortality in Spain. While the overall decline in mortality is encouraging, persistent regional disparities and rising rates among younger individuals underscore the need for ongoing surveillance and targeted interventions.

Inter-firm imitation of artificial intelligence: Towards innovation and competitive edge in business

In an era where artificial intelligence (AI) is rapidly transforming businesses, executives are at the forefront of steering their organizations through the complexities of technology adoption. This article provides a practitioner-focused perspective on how executives can strategically manage the adoption and integration of AI to drive innovation and maintain a competitive edge through the dynamics of inter-firm imitation of AI. By leveraging theories of imitation, knowledge transfer, and competitive advantage, it discusses how firms adopt AI technologies by emulating their competitors and subsequent effects on their innovation capabilities. Research examines the implications for firm strategy, including the potential for competitive parity, role of absorptive capacity, and the influence of industry characteristics on the effectiveness of imitative AI-driven innovation. The paper introduces a conceptual framework that integrates imitation, AI adoption, and innovation outcomes, offering a strategic roadmap for firms navigating the complex landscape of AI technology. Ultimately, the article provides managerial recommendations for firms to harness AI imitation as a tool for sustained innovation and competitive differentiation, drawing insights from six distinct case studies.

Determining the effect of long non-coding RNA maternally expressed Gene 3 (lncRNA MEG3) on the transcriptome profile in cervical cancer cell lines

This study investigates the role of the long non-coding RNA Maternally Expressed Gene3 (lncRNA MEG3) gene in cervical cancer, as evidenced by its downregulation in cancerous cell lines. The study demonstrates the effects of the overexpression of lncRNA MEG3 in cervical cancer cell lines, particularly in C33A and CaSki. Through comprehensive analyses, including Next-Generation Sequencing (NGS), alterations in global mRNA expression were analyzed. In C33A cells, 67 genes were upregulated, while 303 genes were downregulated. Similarly, in CaSki cells, 221 genes showed upregulation and 248 genes displayed downregulation. Gene ontology and KEGG pathway analyses were conducted to gain insight into potential mechanisms. Furthermore, the study delves into gene regulatory networks, uncovering intricate interactions among genes. The RNA sequencing data were confirmed for eight genes: PAX3, EGR2, ROR1, NRP1, OAS2, STRA6, CA9, and EDN2 by Real-time PCR. The findings illuminate the complex landscape of gene expression alterations and pathways impacted by the overexpression of lncRNA MEG3. The impact of MEG3 on the overall cervical cancer cells' mRNA profile is reported for the first time. New biomarkers for the prognosis of cervical cancer are also reported in this study. Moreover, identifying specific genes within the regulatory networks provides valuable insights into potential therapeutic targets for managing cervical cancer.

Digital transformation and industry 4.0 in pharma manufacturing: the role of iot, ai, and big data

The pharmaceutical industry is undergoing a significant transformation driven by the integration of digital technologies, collectively known as Industry 4.0. This shift is redefining how drugs are developed, manufactured, and distributed. Key technologies such as the Internet of Things (IoT), Artificial Intelligence (AI), and big data analytics are at the forefront of this change, enabling smart manufacturing, real-time process optimization, and enhanced supply chain management. IoT facilitates the creation of interconnected production environments where sensors and devices continuously monitor critical parameters, ensuring optimal conditions and predictive maintenance. AI accelerates drug discovery through predictive modeling, automates quality control processes, and employs predictive analytics to enhance maintenance and process improvement. Big data empowers data-driven decision-making, ensures regulatory compliance through comprehensive analysis, and supports the shift toward personalized medicine by enabling customized drug production. Despite the significant benefits, the adoption of these technologies poses challenges, including integration with existing systems, data security concerns, and navigating a complex regulatory landscape. This review explores these technologies' impact on pharmaceutical manufacturing, highlighting successful case studies and best practices. Additionally, it discusses the future directions, including the move towards fully autonomous systems and the importance of collaboration between tech companies, manufacturers, and regulators to drive innovation and ensure compliance. The continued evolution of digital technologies in pharma manufacturing promises to enhance efficiency, reduce costs, and deliver more personalized treatments.

Improving Data Security in Cloud Computing through the Implementation of a Hybrid Encryption Algorithm

As cloud computing continues to gain popularity, the security of sensitive data stored in the cloud remains a paramount concern for organizations and individuals alike. Traditional encryption methods, while effective, often struggle to balance security, efficiency, and usability. This paper presents a hybrid encryption algorithm that combines the strengths of symmetric and asymmetric encryption to enhance data security in cloud computing environments. The proposed hybrid approach leverages symmetric encryption for fast and efficient data encryption, while asymmetric encryption is utilized for secure key exchange. By implementing this dual-layer encryption strategy, we can significantly improve data confidentiality and integrity during transmission and storage. Additionally, the algorithm incorporates robust key management practices, ensuring that encryption keys are safeguarded against unauthorized access. Through a series of performance evaluations and security assessments, we demonstrate that our hybrid encryption algorithm not only provides superior security but also maintains acceptable levels of performance, making it suitable for real-time applications. The results indicate that this approach effectively mitigates risks associated with data breaches and unauthorized access, positioning it as a viable solution for enhancing data security in cloud computing. This research contributes to the ongoing discourse on cloud security, offering a practical framework for organizations to protect their sensitive information in increasingly complex digital landscapes.

Korean policy strategies for autonomous vehicles in platform-based mobility systems

ABSTRACT This study presents a comprehensive analysis of the economic and policy dimensions crucial for the integration of autonomous vehicles within platform-based mobility systems. Utilizing the Mini-Delphi method, we gathered expert insights to form strategic policy recommendations that address the challenges and opportunities in the autonomous vehicle sector. The research underscores the potential economic impacts of autonomous mobility services and suggests robust policy frameworks to support the sustainable growth of platform-based logistics and transportation services. Our findings aim to guide policymakers and industry stakeholders in navigating the complex landscape of digital transformation in mobility.

Artificial Intelligence Scene Creation with Virtual Technology for the DigitalFilm

Artificial Intelligence-based scene creation leverages advanced algorithms to generate realistic and immersive environments for various applications, including gaming, virtual reality, and simulations. By utilizing techniques such as procedural generation, deep learning, and computer vision, these systems can automatically create complex landscapes, detailed textures, and dynamic elements that respond to user interactions. This not only enhances the user experience but also significantly reduces the time and effort required for manual scene design, enabling developers to focus on creativity and innovation. In the realm of digital media production, the quest for lifelike scenes and efficient evaluation methodologies has led to the exploration of novel techniques. This paper investigates the synergy between point estimation and artificial intelligence (AI) in advancing digital scene creation and evaluation. Through comprehensive simulations and analyses, we assess the effectiveness of point estimation in quantifying scene attributes such as visual realism, dynamic interactivity, physical accuracy, and artistic expression. Additionally, we delve into the utilization of AI algorithms for automating scene classification, thereby streamlining decision-making processes and optimizing resource allocation in production workflows. Through comprehensive simulations and analyses, we assess the effectiveness of point estimation in quantifying scene attributes such as visual realism (mean score: 0.85), dynamic interactivity (mean score: 0.72), physical accuracy (mean score: 0.93), and artistic expression (mean score: 0.65).

Shedding light on pollination deficits: Cueing into plant spectral reflectance signatures to monitor pollination delivery across landscapes

AbstractPollination underlies plant yield, health and reproductive success in agricultural and natural systems worldwide. It is therefore concerning that declining animal pollinator populations compounded by growing demands for food are leading to rising pollination deficits, with globally significant economic and environmental implications.Despite this urgent issue, accurate and scalable tools to quantify and track pollination across useful spatiotemporal scales are lacking. Here, we propose to shed new light on pollination deficits, looking to remote sensing platforms as a transformative mapping and monitoring tool and a solution for pollinator conservation and crop management.Providing a synthesis of our current understanding of pollination‐triggered floral senescence and underlying ultrastructural and metabolic changes, we propose how spectral reflectance technologies could be applied to accurately detect pollination events in real‐time and at the landscape scale.Synthesis and applications: We highlight where research efforts can be targeted to produce scalable methods for identifying field‐relevant bioindicators of pollination. We provide guidance on how spectral imaging accompanied by machine learning and coupled with autonomous operation technologies will enable applications to detect pollination delivery across complex landscapes. Ultimately, such an ecological application will transform our quantitative understanding of pollination services and, by directly linking plant yields and health, will reveal pollination deficits at high resolution to help mitigate risks to food security and ecosystem functioning.

Innovative approaches in complex data forensics: error rate assessment and its impact on cybersecurity

In the realm of cybersecurity, the integrity of forensic investigations is paramount, especially as the volume and complexity of data continue to escalate. This paper explores innovative approaches to complex data forensics, focusing on the methodologies used to assess error rates in data retrieval and analysis. High error rates in forensic processes can compromise the reliability of findings, leading to erroneous conclusions that may impact security measures and legal proceedings. This research examines various techniques for error rate assessment, including statistical methods and data validation protocols, which serve to quantify the accuracy of forensic analysis. Furthermore, the paper discusses the profound implications that high error rates can have on the integrity of forensic findings, emphasizing the need for meticulous attention to detail in data handling and processing. To counter these challenges, we present strategies aimed at enhancing data reliability, such as implementing rigorous quality assurance processes, leveraging machine learning algorithms for anomaly detection, and utilizing advanced encryption methods to protect data integrity throughout the forensic lifecycle. By addressing the critical role of error rate assessment in data forensics, this research contributes to the broader discourse on cybersecurity and underscores the necessity of adopting robust methodologies to ensure accurate and reliable forensic outcomes in an increasingly complex digital landscape.

Conceptual framework for enhancing decision-making in higher education through data-driven governance

In an increasingly complex and competitive landscape, higher education institutions must leverage data-driven governance to enhance decision-making processes. This paper presents a conceptual framework aimed at integrating data analytics into the governance structures of higher education, facilitating informed decision-making, and improving institutional effectiveness. The framework emphasizes the importance of collecting, analyzing, and utilizing data across various dimensions, including academic performance, student engagement, financial management, and operational efficiency. By adopting a data-driven approach, institutions can identify trends, forecast outcomes, and make strategic decisions that align with their mission and goals. The study outlines key components of the framework, including data governance, analytics capabilities, stakeholder engagement, and institutional culture. Data governance ensures that data quality, integrity, and security are maintained, while analytics capabilities empower decision-makers with actionable insights. Engaging stakeholders—faculty, administrators, students, and external partners—fosters a collaborative environment where data-driven decisions are embraced and supported. Furthermore, the framework addresses the challenges institutions face when implementing data-driven governance, such as resistance to change, data silos, and inadequate technological infrastructure. Strategies for overcoming these challenges include investing in professional development, enhancing data literacy, and promoting a culture of continuous improvement. The framework also highlights the significance of ethical considerations and transparency in data usage, ensuring that decision-making processes are equitable and accountable. The findings indicate that by embracing data-driven governance, higher education institutions can enhance their responsiveness to changing demands, optimize resource allocation, and improve overall institutional performance. Ultimately, the conceptual framework serves as a guide for higher education leaders seeking to harness the power of data in their decision-making processes, fostering a proactive and adaptive institutional environment.

The Triumph of Soft Law in the Financial Sector: Can Non-binding Law Have Binding Domestic Legal Effects?

Examining the seemingly simple question of whether guidelines in the financial sector are mandatory reveals a highly complex landscape for supervision of the financial sector across the European Union, where international and national financial law enter collision and the participants come to the table with differing mandates/expectations. The article addresses the results, with focus on banking regulation, wherein the European Banking Authority and, in Estonia, the Financial Supervision and Resolution Authority are among the main guideline-issuers. The balance achieved is critical because the fines that the supervisory and resolution authorities can impose on banks for non-compliance with mandatory rules can reach 10% of these entities’ total annual net turnover from the preceding business year. An effort to consider all of the relevant factors for understanding which side of the mandatory/advisory line the guidelines stand on, the article constitutes an attempt to initiate vital discussion of the environment created in the financial sector for soft law (i.e., guidelines), which may under certain circumstances take on a larger role than that soft law’s legal nature allows.

Leveraging cryptocurrencies and digital assets for Small and Medium-Sized Enterprises (SMEs): Opportunities and challenges

This review paper explores the potential opportunities and challenges associated with leveraging cryptocurrencies and digital assets for small and medium-sized enterprises (SMEs). The paper highlights the benefits of adopting digital currencies, including financial inclusion, reduced transaction costs, access to new markets, and enhanced transaction transparency and security. However, it also addresses SMEs' challenges, such as regulatory and legal uncertainties, volatility, and cybersecurity risks. Strategic approaches for SMEs are discussed to mitigate these challenges, including risk management, using stablecoins, and adopting best practices for integrating cryptocurrencies into business operations. The paper offers recommendations for SMEs considering digital assets. The future outlook for cryptocurrencies in the SME sector is considered, emphasizing the potential for further growth as regulatory clarity improves and technological innovations advance. This paper serves as a guide for SMEs looking to navigate the complex landscape of cryptocurrency adoption while maximizing the benefits and minimizing the risks.