Future Landscape Research Articles

Cryptocurrencies in the family context: trust, information and financial stability

Background: The rapid rise of cryptocurrencies has sparked debate about their role in the financial system, particularly across generations. Objectives: This study aimed to identify and explain differences in attitudes towards cryptocurrencies between Generation Z (students) and Generation Y (their parents) in Poland, focusing on trust in the financial system, perceptions of cryptocurrency value, and visions of the future financial landscape. Methodology: A survey was conducted in June 2023 with 40 students aged 23-24 and one of their parents each (80 total participants). Respondents answered questions about their trust in traditional and crypto finance, views on cryptocurrency potential, and expectations for future financial systems. Results: Generation Z showed significantly greater openness and trust in cryptocurrencies compared to Generation Y. The younger generation was more likely to view cryptocurrencies as inflation hedges and potential components of future payment systems. Generation Y demonstrated higher trust in the current financial system and fiat currency. Conclusions: The study reveals substantial intergenerational differences in cryptocurrency perceptions, with implications for financial education, regulation, and innovation. These divergences may shape the future evolution of financial markets and monetary systems.

Transforming undergraduate dental education: the impact of artificial intelligence

Artificial intelligence (AI) is a rapidly evolving area, having had a transformative effect within some areas of medicine and dentistry. In dentistry, AI systems are contributing to clinical decision-making, diagnostics and treatment planning. Ongoing advances in AI technology will lead to further expansion of its existing applications and more widespread use within the field of dentistry. The predicted transformation of current practice within dentistry brought about by AI necessitates the education of undergraduate dental students on the topic of AI. AI will act as a complementary tool to clinical expertise and skill, rather than a replacement. AI education frameworks should integrate with existing traditional dental education. It is important that dental students develop a sound understanding of the ethical considerations surrounding AI, ensuring its safe and responsible use. This article aims to consider the status of current undergraduate dental education and the possible impact of AI, examining both the challenges and opportunities, and the practicalities, of incorporating AI into existing undergraduate dental curricula. Understanding and envisioning the future landscape shaped by AI will allow for development of a future dental workforce who are skilled, confident and responsible AI users.

Fusion Model of Quantum Wavelet Transform and Neural Network for Video Coding on the Internet of Things Environment

Solving the video compression problem requires a multi-faceted approach, balancing quality, efficiency, and computational demands. By leveraging advancements in technology and adapting to the evolving needs of video applications, it is possible to develop compression methods that meet the challenges of the present and future digital landscape. To address these objectives, machine learning and AI approaches can be utilized to predict and remove redundancies more effectively, optimizing compression algorithms dynamically based on content. Still, state-of-the art neural network-based video compression models need large and diverse datasets to generalize well across different types of video content. Wavelets can provide both time (spatial) and frequency localization, making them highly effective for video compression. This dual localization allows wavelet transforms to handle both rapid changes in video content and slow-moving scenes efficiently, leading to better compression ratios. Yet, some wavelet coefficients may be more critical for maintaining visual quality than others. Inaccurate quantization can lead to noticeable degradation. For the first time, the suggested model combine Quantum Wavelet Transform (QWT) and Neural Networks (NN) for video compression. This fusion model aims to achieve higher compression ratios, maintain video quality, and reduce computational complexity by utilizing QWT’s efficient data representation and NN’s powerful pattern recognition and predictive capabilities. Quantum bits (qubits) can encode large amounts of information in their quantum states, enabling more efficient data representation. This is especially useful for encoding large video files. Furthermore, quantum entanglement allows for correlated data representation across qubits, which can be exploited to capture intricate details and redundancies in video data more effectively than classical methods. The experimental results reveal that QWT achieves a compression ratio of almost twice that of traditional WT for the same video, maintaining superior visual quality due to more efficient redundancy elimination.

Advancements in the Early Identification and Treatment of Myocardial Infarction in the Emergency Department: A Comprehensive Review of Machine Learning Approaches

Background: Early detection and prompt treatment of myocardial infarction (MI) in the emergency department (ED) are pivotal for reducing morbidity and mortality. Chest pain is a common presenting symptom in the ED, necessitating effective risk stratification and decision-making to distinguish between acute coronary syndromes (ACS) and benign conditions. Methods: This systematic review evaluates the application of machine learning (ML) algorithms in identifying myocardial infarction among patients presenting with nonspecific chest pain in the ED. A comprehensive search of databases including PubMed, Cochrane Library, and Embase was performed for studies published until 2023, which investigated ML methodologies in this context. Results: The review highlights a substantial interest in machine learning applications, demonstrating that ML techniques have significant potential to enhance diagnostic accuracy and prognostic capabilities compared to traditional clinical decision tools such as the TIMI and HEART scores. ML algorithms exhibited higher sensitivity and specificity in detecting MI, ultimately alleviating diagnostic burdens on emergency physicians. However, challenges remain in integrating these technologies into routine clinical practice due to issues related to data quality, model interpretability, and acceptance among healthcare providers. Conclusion: While machine learning holds promise for transforming the assessment of chest pain in the emergency department, further research is necessary to address existing limitations, including bias, data integration, and generalizability. The future landscape of emergency medicine could benefit from robust ML models that can assist clinicians in decision-making, leading to improved patient outcomes and more efficient healthcare delivery

Impact of Electric Vehicles on the Future E-Commerce Landscape: Challenges & Advantages

The E-commerce landscape has seen a paradigm shift with an increasing demand for door- step delivery and even same day delivery. The convenience of shopping over a handheld mobile devise from the convenience of one’s preferred location is a major driver for the phenomenon growth, development, and expansion of online shopping. The COVID pandemic led lockdowns acted as catalysts expediting this expansion. But with this progress came the menace of rapid expansion of delivery trucks, vans, 3-wheelers, and 2-wheelers that increased not only the congestions within the cities but also added massively to the already deteriorating air quality. Pollution (both air and noise) and congestion, are the two immediate fallouts of the expansion in delivery fleet. This made the fleet owners and businesses turn to the obvious alternative – EVs or electrical vehicles that have the potential to not only cut down harmful emissions but also enhance financial and operational efficiencies. Modern businesses cannot help but improve efficiencies in order to sustain in an immensely competitive and dynamic business environment. This article adds to the literature on EVs and generates insight into the practicability of adopting EVs in the last mile logistics. It highlights that despite being both environment and pocket friendly, the benefits of economy and efficiency looks futile in the face of inadequate availability of charging infrastructure that heightens range anxiety.

A multi‐proxy reconstruction of Lateglacial and Holocene mountain vegetation dynamics in Uri, Central Switzerland

In the context of climate change, a long‐term perspective is essential to understand future trajectories of mountain vegetation. We analysed the sediment record of Golzerensee (1411 m a.s.l.), a mountain lake in the transitional zone between the montane and subalpine vegetation belt in Uri, Central Switzerland. We reconstructed past vegetation responses to anthropogenic impact, fire activity and climatic changes dating back to c. 14 550 cal. a BP using pollen, stomata, macrofossil and microcharcoal analyses. The tree line, composed of Betula and Pinus sylvestris, reached Golzerensee during the Allerød c. 13 200 years ago, but tundra vegetation re‐expanded during the Younger Dryas cooling (12 800–11 700 cal. a BP). With the rapid temperature increase at the onset of the Holocene, a closed forest established within a few decades around the study site. Concurrently, temperate taxa (Ulmus, Tilia, Acer, Corylus avellana) increased in the lowlands, likely reaching the elevation of the lake. Abies alba established locally during the Holocene Thermal Maximum (HTM) at 9600 cal. a BP and dominated closed, montane forests, delaying the westward expansion of Picea abies that established only from c. 7700 cal. a BP onwards. The concomitant occurrence of cultural indicator taxa, as well as an increase in charcoal influx and fungal dung spore concentrations suggest that human impact has facilitated the expansion of Picea abies by negatively affecting important competitors such as Abies alba through fire and browsing disturbance. With rising temperatures due to current climate change, Abies alba and other temperate species may regain their former relevance, leading to diverse mixed forests that were abundant during the HTM c. 10 000–5000 cal. a BP. However, future forest composition and landscape diversity will also be influenced by browsing disturbance and land use management.

The impact of distributed systems on the architecture and design of computer systems: advantages and challenges

A distributed system can encompass a variety of configurations, including mainframes, personal computers, workstations, and minicomputers. The varying degrees of software flexibility and the ability to execute tasks in parallel facilitate simultaneous data processing across multiple processors. The higher the resilience of an application, the quicker it can recover after a system failure. Organisations increasingly adopt distributed computing systems as they face increased data generation and demand for enhanced application performance. These systems enable businesses to scale effectively in response to growing data volumes. Integrating additional hardware into a distributed system is generally simpler than upgrading a centralised system reliant on powerful servers. Distributed systems comprise numerous nodes that collaborate towards a common objective. This article aims to provide a comprehensive overview of distributed systems, their architectural frameworks, and essential components. This study examines how distributed systems influence the architecture and design of computer systems. The research methods consist of reviewing existing literature and analysing case studies on implementing distributed systems. Key findings indicate that the evolution of distributed systems is ongoing, driven by emerging technologies and the increasing demand for efficient, scalable, and secure solutions. Innovations such as edge computing, blockchain technology, 5G, and the integration of AI and machine learning are among the notable trends shaping the future landscape of distributed systems. Looking ahead, designers and architects need to stay informed about these advancements to create reliable and adaptable distributed systems that can address the dynamic needs of users and organisations

The nutritional feed gap: Seasonal variations in ruminant nutrition and knowledge gaps in relation to food security in Southern Africa

AbstractLivestock production is critical to food security and rural livelihoods across Southern Africa. Despite progress in livestock science research in recent years, the seasonal availability and quality of feed remains one of the key challenges to livestock productivity in Southern Africa. In particular, dry weather conditions, the lack of rain and lower temperatures in the dry season cause herbaceous plants to die back and browse species to defoliate, limiting the abundance, quality, and variety of feed available. This creates a ‘Nutritional Feed Gap’, defined here as the combined effect of the sharp reduction in both forage quantity and quality from the wet to the dry season and the risk that it poses to ruminant production systems and the food security of the people and communities reliant on them. Understanding the nature and extent of how seasonality impacts ruminant production potential can thus contribute towards mitigating negative impacts of extreme weather and climate change on food systems. In this review, we characterise this nutritional feed gap in terms of forage abundance and nutrition as well as discussing how climate change may shape the future nutritional landscape. Whilst some forage nutrient concentrations varied little by season, crude protein and phosphorus were consistently found to decrease from the wet season to the dry season. We also identify a shortfall in primary research that assess both forage quality and quantity simultaneously, which forms part of a broader knowledge gap of our limited understanding of the impact of limiting factors to ruminant production on short and long-term food security across Southern Africa.

Transforming Cognition and Human Society in the Digital Age

AbstractSince the onset of the digital revolution, humankind has experienced an unprecedented acceleration of changes triggered by technological advancements. Frequently used digital media have unquestionably penetrated our everyday life, shaping human cognition in multiple ways. The rise of artificial intelligence, which coevolved with a new, interdisciplinary field of cognitive science, has amplified these effects, contributing new ways of affecting human society, in terms of efficient human-machine interaction and knowledge generation and accumulation, at an exponential rate. Simultaneously, cultural shifts driven by globalization and social media have fostered new modes of communication, identity formation, and knowledge dissemination on a global scale. Understanding the intricate dynamics of techno-cultural evolution and its influence on cognition is essential for comprehending the contemporary society and preparing it for the future challenges. We need to adapt for the current and future information environments and digital landscapes, improving human resilience to new technologies and addressing core human vulnerabilities. Thanks to human flexibility, enabled by neural plasticity, that may be feasible, ideally combined with alleviation of known negative effects of digital technologies.

AI regulation: still a to-do item on New Zealand’s political agenda

The body of AI regulations and country strategies has been steadily growing in the past few years. The EU recently adopted the world’s first comprehensive AI regulation, the AI Act. The US has published several high-level guidance documents, such as the 2023 Executive Order on AI, and has begun to introduce AI-specific laws and amendments to existing laws at the state and substate levels. China has also adopted several laws in the AI space. Many other countries have at least developed and published AI strategies. New Zealand (NZ) is a latecomer to AI regulation. As of yet, there is no overarching AI strategy, AI-specific legislation, much less a comprehensive AI regulatory framework in place. This paper surveys key domestic actors and regulatory initiatives that will form the basis of the country’s future AI regulatory landscape.

Wildfire management decisions outweigh mechanical treatment as the keystone to forest landscape adaptation

BackgroundModern land management faces unprecedented uncertainty regarding future climates, novel disturbance regimes, and unanticipated ecological feedbacks. Mitigating this uncertainty requires a cohesive landscape management strategy that utilizes multiple methods to optimize benefits while hedging risks amidst uncertain futures. We used a process-based landscape simulation model (LANDIS-II) to forecast forest management, growth, climate effects, and future wildfire dynamics, and we distilled results using a decision support tool allowing us to examine tradeoffs between alternative management strategies. We developed plausible future management scenarios based on factorial combinations of restoration-oriented thinning prescriptions, prescribed fire, and wildland fire use. Results were assessed continuously for a 100-year simulation period, which provided a unique assessment of tradeoffs and benefits among seven primary topics representing social, ecological, and economic aspects of resilience.ResultsProjected climatic changes had a substantial impact on modeled wildfire activity. In the Wildfire Only scenario (no treatments, but including active wildfire and climate change), we observed an upwards inflection point in area burned around mid-century (2060) that had detrimental impacts on total landscape carbon storage. While simulated mechanical treatments (~ 3% area per year) reduced the incidence of high-severity fire, it did not eliminate this inflection completely. Scenarios involving wildland fire use resulted in greater reductions in high-severity fire and a more linear trend in cumulative area burned. Mechanical treatments were beneficial for subtopics under the economic topic given their positive financial return on investment, while wildland fire use scenarios were better for ecological subtopics, primarily due to a greater reduction in high-severity fire. Benefits among the social subtopics were mixed, reflecting the inevitability of tradeoffs in landscapes that we rely on for diverse and countervailing ecosystem services.ConclusionsThis study provides evidence that optimal future scenarios will involve a mix of active and passive management strategies, allowing different management tactics to coexist within and among ownerships classes. Our results also emphasize the importance of wildfire management decisions as central to building more robust and resilient future landscapes.

Metabolic glycan labelling with bio-orthogonal targeting and its potential in drug delivery.

New modes of targeted drug delivery are emerging with promise of enhancing therapeutic efficacy while reducing side effects. This review examines the landscape of metabolic glycan labelling-a technique gaining traction for its potential in specific drug targeting. By exploiting the natural glycan synthetic pathway of monosaccharides, unnatural sugar analogues are incorporated into glycoproteins, allowing for the presentation of unique functional groups on cells. This enables specific targeting using 'clickable' probes with complementary click chemistry functional groups. The selection of sugar analogues and chemical tags are crucial components explored in this review, alongside considerations for cell lines, tissues, and cargo selection. The review discusses non-therapeutic and therapeutic applications of metabolic glycan labelling, as well as its potential beyond labelling of cell surfaces. The review also highlights underexplored areas of metabolic glycan labelling by assessing the limited literature addressing labelling efficiency, turnover rates, the impact of sugar supplements in cell culture, and the critical cell to functionalised sugar ratio. Furthermore, this review delves into the future landscape and goals of metabolic glycan labelling, envisioning its potential in targeted drug delivery.

Home dialysis: there's no place like home.

The rapid rise in incidence and prevalence of end-stage kidney disease (ESKD) over the past 50 years was matched by a drop-off in use of home dialysis and a proliferation of in-center hemodialysis across the United States. There is renewed interest in improving access to home dialysis modalities for patients with ESKD. The aim of this review is to update kidney care providers with clinical outcome data and new guidelines that promote patient-centered choices, and to address barriers to home dialysis uptake and continued use. Recent literature, including changing practice guidelines for prescribing home dialysis and examining the goals of treatment from the patient and caregiver perspectives will be reviewed. We will examine the impact of newer care models to promote home dialysis for incident ESKD patients. Assisted home dialysis, home dialysis in a nursing home and the impact of new payment models rewarding home dialysis providers will be examined. This concise review of recent pertinent literature should give the dialysis provider confidence in advising their patients on the benefits of home dialysis, a glimpse into the future landscape for home dialysis, and hopefully transform kidney care providers into unbiased patient advocates.

The Evolution and Future Trajectory of Diesel Engine Technology: Applications, Environmental Challenges, and Innovative Solutions for Sustainability

Diesel engines, introduced by Rudolf Diesel in 1897, have played a significant role in the global transportation and energy sectors due to their reliability and efficiency. However, increasing environmental concerns and the need for sustainable solutions have driven the evolution of diesel technology, leading to innovations that reduce emissions and improve performance. This paper reviews the historical development and diverse applications of diesel engines, particularly in marine vessels, trains, and aircraft. It explores the technological advancements that have enhanced fuel efficiency and reduced pollutants, such as the introduction of alternative fuels like biodiesel and hybrid diesel-electric systems. The review also highlights the importance of emissions control technologies like diesel particulate filters (DPF) and selective catalytic reduction (SCR). Furthermore, the paper discusses the long-term projections for diesel engines considering evolving government policies and the push towards carbon neutrality. While diesel technology faces challenges in achieving sustainability, continued innovation and regulatory support will ensure its relevance in future energy landscapes.

The Impact of Artificial Intelligence on the Future of Work and Employment

Artificial intelligence (AI) is rapidly changing how we work and live. It’s everywhere, from helping doctors diagnose illnesses to powering algorithms that decide what we see online. This paper dives into how AI is shaping the job market—both the good and the challenging sides. On one hand, AI brings incredible benefits. It boosts productivity, sparks innovation, and opens new career opportunities in fields like healthcare, finance, education, and manufacturing. These advancements can lead to economic growth and better ways of doing things. However, there’s concern about AI replacing jobs traditionally done by people. Tasks that were once done by humans, especially routine and manual work, are now being automated by AI. This shift raises questions about job security and the skills workers need to thrive in a tech-driven world. This paper looks closely at how AI is transforming jobs across different industries. It examines real-world examples and studies to understand how many jobs might be lost versus how many new jobs AI could create. It also explores the wider impacts on society, like potential income gaps between skilled and unskilled workers. To tackle these challenges, the paper suggests strategies. These include investing in education and training programs to equip workers with the skills they need for AI-powered jobs. It also calls for thoughtful policies that balance the benefits of AI with the need to protect workers and ensure fairness in the job market. By bringing together current research and insights, this paper aims to give a clear picture of how AI is changing employment globally. It seeks to guide discussions among policymakers, businesses, and educators on how to best integrate AI into our future job landscape, ensuring it benefits everyone. In conclusion, while AI offers exciting possibilities for innovation and efficiency, its impact on jobs requires careful consideration and proactive planning. By working together, we can harness AI’s potential to create a future where technology enhances human lives and work opportunities

Korea’s State Practice of International Law: a Multifaceted Evaluation through Regional Perspectives

Abstract This article provides a multifaceted evaluation of South Korea’s state practice of international law through diverse regional perspectives from Asian scholars. It examines Korea’s role as a middle power in shaping regional and global legal frameworks, the impact of its historical and geopolitical context on legal approaches, and its contributions to various areas of international law. The analysis highlights Korea’s active engagement in international organizations, commitment to human rights and environmental governance, and efforts to balance traditional alliances with new partnerships. The paper also emphasizes the importance of legal education in sustaining Korea’s engagement with international law and explores how Korea’s experiences offer valuable lessons for regional legal development. By synthesizing these varied viewpoints, this study offers insights into Korea’s evolving role in the international legal order and its potential to influence the future landscape of international law in Northeast Asia and beyond.

AI-Driven Diagnostics and Imaging: Transforming Early Detection and Precision in Healthcare

Artificial intelligence is revolutionizing medical imaging and diagnostics, marking a transformative era in healthcare delivery. This comprehensive article explores the evolution from early computer-aided diagnosis systems to sophisticated deep-learning architectures, examining their impact across radiology, pathology, and clinical workflows. The article covers breakthrough technologies, including vision transformers, multi-modal integration, and explainable AI frameworks, highlighting their contributions to improved diagnostic accuracy and efficiency. The article encompasses the clinical benefits of early disease detection, workflow optimization, and cost reduction while addressing crucial challenges in regulatory compliance, ethical considerations, and data privacy. Looking ahead, the review examines emerging trends in federated learning, infrastructure requirements, and the economic implications of AI implementation in healthcare settings, providing insights into the future landscape of AI-driven medical imaging.

Reviving coral reef ecotourism through green insurance investments

ABSTRACT Coral reefs are a vital landscape for ecotourism though they remain precarious due to climate change and natural disasters. This research seeks to elucidate insights as to how green investments in the form of coral reef insurance are conceptualized and operationalized in some parts of the world, where early innovators have been witnessed. Drawing from sentiment analysis of 116 journal articles and other gray literature (book chapters, industry reports, etc), the findings suggest that while there is optimism towards the use of insurance as a vehicle for reviving coral reef ecotourism, there remains some skepticism and lack of a willingness to pay motivation in other parts of the world. Theoretical and managerial implications for future landscapes of reef insurance are proposed.

Targeting menin for precision therapy in high-risk acute myeloid leukemia.

This mini-review provides an overview of the current evidence for Revumenib, a first-in-class menin inhibitor, in treating AML with KMT2A rearrangements or NPM1 mutations. This therapy represents a promising advancement by selectively disrupting leukemogenic pathways. The clinical promise of Revumenib in genetically defined AML highlights its potential role in shaping the future treatment landscape. This mini-review underscores the need for ongoing trials to define optimal dosing, safety protocols, and combination therapies, with the ultimate goal of establishing Revumenib as a standard of care for high-risk AML subsets.

VR and Physiological Assessment Technologies Applied on the Touring Perception of Chinese Classical Gardens

This paper reviews on the research status and new progress in the field of Chinese classical gardens by the application of new technologies including virtual reality and physiological measurements. Firstly, it introduces the background and significance of current research in the field of spatial creation in Chinese classical gardens. Then, this paper combs relevant literature on the application of VR and physiological research methods on landscape and architectural studies in recent years. Finally, the possibilities of applying physiological data and virtual reality in the exploration of the spatial perception of Chinese classical gardens is discussed. The results could provide inspiration sources and technical references for the empirical studies of future landscape research on Chinese classical gardens.