Future Trajectories Research Articles

Algorithmic Trading and AI: A Review of Strategies and Market Impact

This review explores the dynamic intersection of algorithmic trading and artificial intelligence (AI) within financial markets. It delves into the evolution, strategies, and broader market impact of algorithmic trading fueled by AI technologies. Examining the symbiotic relationship between advanced algorithms and AI, the review navigates through the various strategies employed, shedding light on their implications for market efficiency, liquidity, and overall stability. From high-frequency trading to machine learning-driven predictive analytics, this review unveils the multifaceted landscape of algorithmic trading in the era of AI, presenting both opportunities and challenges for financial markets. The review begins by tracing the historical development of algorithmic trading, emphasizing the paradigm shift with the integration of AI. From traditional programmatic trading to the emergence of sophisticated algorithms driven by machine learning and deep learning, the evolution sets the stage for a comprehensive understanding of the subject. An in-depth analysis of diverse algorithmic trading strategies unfolds, covering areas such as trend following, statistical arbitrage, market making, and sentiment analysis. The incorporation of AI introduces adaptive learning capabilities, enabling algorithms to evolve and optimize strategies based on real-time market conditions. Exploring the impact of algorithmic trading on financial markets, the review examines how AI-driven strategies contribute to market efficiency, liquidity provision, and price discovery. It dissects the implications for traditional market structures, regulatory considerations, and the potential risks associated with algorithmic dominance. Acknowledging the transformative power of algorithmic trading with AI, the review critically assesses the challenges and ethical considerations. From algorithmic bias to systemic risks, the review delves into the darker corners of this technological advancement, prompting a reflection on the need for responsible and transparent practices. The review concludes by peering into the future trajectory of algorithmic trading fueled by AI. Anticipated innovations, regulatory responses, and the evolving landscape of financial markets are discussed, offering insights into the ongoing transformation and potential disruptions in the realm of algorithmic trading. In essence, this review provides a nuanced perspective on the intricate relationship between algorithmic trading and AI, offering a comprehensive understanding of their strategies and the transformative impact on financial markets.

Advancements in GitHub Automation and Workflow: A Comprehensive Exploration

The landscape of software development has been significantly transformed by the integration of automation and streamlined workflows, with GitHub emerging as a pivotal platform in this evolution. This research paper delves into the multifaceted realm of GitHub automation and workflow, investigating key aspects that contribute to enhanced efficiency, collaboration, and code quality in modern development practices. The paper begins by exploring the utilization of GitHub Actions for establishing robust Continuous Integration and Continuous Deployment (CI/CD) pipelines. Best practices for designing reliable workflows are discussed, along with insights into customizing workflows to suit diverse project requirements through the use of custom actions and templates. The automation of code reviews is scrutinized, with a focus on tools and practices that facilitate objective and efficient evaluation of code quality. The paper also investigates GitHub's role in security automation, covering strategies for vulnerability detection and integration of security checks into the development lifecycle. Collaborative workflows on GitHub, including branch management, pull requests, and code reviews, are explored as pivotal components fostering teamwork. The integration of GitHub into DevOps practices is analyzed, showcasing its synergy with popular DevOps tools and platforms. As a forward-looking endeavor, the research paper concludes by examining emerging trends and technologies in GitHub automation. By encapsulating a comprehensive overview of the current state and future trajectories of GitHub automation and workflow, this paper aims to contribute to the collective understanding of best practices, challenges, and opportunities in contemporary software development.

AI in Test Automation: Overcoming Challenges, Embracing Imperatives

This scholarly article delves into the intersection of Artificial Intelligence (AI) and Test Automation, thoroughly examining the challenges inherent in implementing AI methodologies and elucidating imperatives critical for successful integration within contemporary software testing frameworks. The research entails a comprehensive exploration of challenges, ranging from intricacies in data quality to algorithmic biases, tool complexities, and integration challenges, drawing on empirical evidence from case studies and real-world scenarios. The paper articulates imperatives essential for overcoming challenges and ensuring the efficacy of AI in test automation. It emphasizes the significance of structured training programs, meticulous data management strategies, and the cultivation of an organizational culture conducive to the seamless integration of AI technologies. Through a rigorous analysis of successful case studies, the article provides a scholarly basis for the formulation of strategies and solutions to surmount challenges faced by organizations adopting AI in testing practices. A visual matrix aligning challenges with corresponding imperatives adds scholarly rigor to the article, offering a comprehensive framework for understanding the intricate relationships between challenges and the imperative strategies required for resolution. Furthermore, the exploration of emerging trends and innovations anticipates the future trajectory of AI-driven test automation, contributing valuable insights for strategic planning in the realm of software testing. This scholarly work underscores the importance of a systematic and informed approach to AI in Test Automation. By addressing challenges with academic rigor and embracing imperative strategies grounded in empirical evidence, organizations can position themselves at the forefront of AIdriven testing practices, advancing the field with a scholarly foundation for continued exploration and innovation.

Research Progress of Extracellular Vesicles-Loaded Microneedle Technology.

Microneedles (MNs), renowned for their painless and minimally invasive qualities, exhibit significant potential for facilitating effective drug delivery, vaccination, and targeted sample extraction. Extracellular vesicles (EVs), serving as cargo for MNs, are naturally occurring nanovesicles secreted by cells and characterized by novel biomarkers, low immunogenicity, and cell-source-specific traits. MNs prove instrumental in extracting EVs from the sample fluid, thereby facilitating a promising diagnostic and prognostic tool. To harness the therapeutic potential of EVs in tissue repair, MNs with sustained delivery of EVs leverage micron-sized channels to enhance targeted site concentration, demonstrating efficacy in treating various diseases, such as Achillea tendinopathy, hair loss, spinal cord injury, and diabetic ulcers. EV-loaded MNs emerge as a promising platform for repair applications of skin, cardiac, tendon, hair, and spinal cord tissues. This review commences with an overview of MNs, subsequently delving into the role of EVs as cargo for MNs. The paper then synthesizes the latest advancements in the use of EV-loaded MNs for tissue regenerative repair, extending to research progress in extracting EVs from MNs for disease diagnosis and prognostic evaluations. It aims to offer valuable insights and forecast future research trajectories with the hope of inspiring innovative ideas among researchers in this field.

ADVANCING WATER QUALITY PREDICTION: THE ROLE OF MACHINE LEARNING IN ENVIRONMENTAL SCIENCE

This article delves into the burgeoning domain of machine learning (ML) applications within environmental science, with a specific focus on water quality prediction. Amidst escalating environmental challenges, the precision and efficiency of ML models have emerged as pivotal tools for analyzing complex datasets, offering nuanced insights and forecasts about water quality trends. We explore the integration of ML in environmental monitoring, highlighting its comparative advantage over traditional statistical methods in handling vast, multifaceted data streams. This exploration encompasses a critical evaluation of various ML algorithms tailored for predictive accuracy in water quality assessment, including supervised and unsupervised learning models. The article also addresses the challenges inherent in ML applications, such as data quality and model interpretability, and anticipates future trajectories in this rapidly evolving field. The potential for ML to revolutionize environmental policy-making and resource management through enhanced predictive capabilities is a central theme, underscoring the transformative impact of these technologies in environmental science.

Spatial planning constraints will mitigate the fragmentation trajectory of natural and semi-natural landscapes: a case of Lushan City, China

ContextThe biodiversity faces an underlying threat from landscape fragmentation resulting from rapid urbanization. Examining the future trajectory of landscape fragmentation is imperative to understanding the impact of current spatial planning constraints on natural and semi-natural habitat preservation, ecosystem services, and sustainability.MethodsWe employed a Patch-generating Land Use Simulation (PLUS) model to simulate and predict the land use and landscape pattern changes in Lushan City under two distinct scenarios: “Planning Constraints (PC)” and “Natural Development (ND)”. We then identified an appropriate landscape fragmentation index (LFI) that effectively captured the fragmentation essence. To determine the optimal scale, we adopted an experimental approach using both the moving window (MW) method and the semi-variance function. By constructing a LFI spatiotemporal sequence and conducting trend analysis, we identified the potential fragmentation areas (PFA) with significant fragmentation tendencies.ResultsThe spatial planning constraints will (1) prevent the encroachment of construction land into 2.14 km2 of cropland, 0.21 km2 of forest, and 0.13 km2 of grassland; (2) shift the highly fragmented area from the northeastern portion of Lushan to the planned area defined by the development boundary; (3) mitigate and decelerate the trend of landscape fragmentation in natural and semi-natural landscapes, decrease PFA by 7.74 km2 and preserve 15.61 km2 of natural landscapes. (4) still leave 29.42% of forest and 22.82% of grassland at risk of fragmentation.ConclusionsSpatial planning constraints will effectively control the potential fragmentation in natural and semi-natural landscapes by changing the spatial distribution of LFI and PFA. This control mechanism will greatly exclude the anthropogenic impact and ensure the conservation of habitats. The habitats remaining within PFA should be focused in future eco-management optimization.

About Latent Roles in Forecasting Players in Team Sports

Forecasting players in sports has grown in popularity due to the potential for a tactical advantage and the applicability of such research to multi-agent interaction systems. Team sports contain a significant social component that influences interactions between teammates and opponents. However, it still needs to be fully exploited. In this work, we hypothesize that each participant has a specific function in each action and that role-based interaction is critical for predicting players’ future moves. We create RolFor, a novel end-to-end model for Role-based Forecasting. RolFor uses a new module we developed called Ordering Neural Networks (OrderNN) to permute the order of the players such that each player is assigned to a latent role. The latent role is then modeled with a RoleGCN. Thanks to its graph representation, it provides a fully learnable adjacency matrix that captures the relationships between roles and is subsequently used to forecast the players’ future trajectories. Extensive experiments on a challenging NBA basketball dataset back up the importance of roles and justify our goal of modeling them using optimizable models. When an oracle provides roles, the proposed RolFor compares favorably to the current state-of-the-art (it ranks first in terms of ADE and second in terms of FDE errors). However, training the end-to-end RolFor incurs the issues of differentiability of permutation methods, which we experimentally review. Finally, this work restates differentiable ranking as a difficult open problem and its great potential in conjunction with graph-based interaction models.

Melting trajectory of the asymmetrically-heated conical thermal head for ice-melting probes

The development of ice-melting probes is driven by the scientific need to explore the subglacial aquatic environments, such as the subglacial lakes in Antarctica and subglacial water on some extraterrestrial planets. However, during the downward melting, a deviation of the melting trajectory might occur. If left unaddressed, the accumulated deviation will eventually lead to missing the probe's target. Therefore, it is necessary to build a theoretical model to describe the deviation for predicting the future melting trajectory and building the deviation correction system. The deviation comes from any asymmetric heating condition on the melting head. Generally, the Close-Contact Melting (CCM) theory is used to model the probe's melting process without any deviation, and there is only one attempt so far that applies the CCM theory to asymmetric heating conditions. Based on this initial attempt, we successfully expanded the CCM theory with asymmetric temperature profiles to conical thermal heads. Additionally, we simplified the model solving which leads to fewer influence factors compared to existing studies. The model indicates that the melting trajectory of a conical head is linear under asymmetric heating conditions, and the inclination angle of the trajectory is related to the ratio between the head's temperatures on each side and the head's cone angle. To validate the theoretical results, laboratory experiments have also been conducted with an innovative optical positioning method.

A sustainable trajectory for the 2030 Agenda: Targeting historical lock‐ins to accelerate progress on all SDGs

AbstractThe 2030 Agenda states that the 17 sustainable development goals (SDGs) make parts of an indivisible whole, and research on SDG implementation highlights how managing goal interactions—synergies and trade‐offs—is key for implementation to be successful. Here, we present an empirically based and transparent method for developing “consistent scenarios” that identifies which SDGs will be unattainable if historical patterns of interaction between SDGs are maintained. Using historical data on SDG indicators and correlation analysis together with cross‐impact balance analysis, we identify such scenarios for a set of high‐ to low‐income countries. Finding no consistent scenario where progress can be made on all 17 SDGs for any of the countries, our findings demonstrate the importance of policies that target interactions and resolve trade‐offs rather than individual goals to unlock a sustainable future trajectory. This reflects what was novel about the SDGs as a framework for global development; that the goals are indivisible and achieving them all require systemic changes that alter how social and economic processes interact with the environment.

Decolonising Studies of Religion in So-Called Australia: Truth-Telling, Collective Reflections and Future Trajectories

ABSTRACT Studies of religion in the lands now called (by some) Australia have thus far focused mainly on so-called Abrahamic traditions of Christianity, Judaism and Islam, the southern states of New South Wales and Victoria and on theories from the global North. This is despite the fact that Australia is a religiously and spiritually diverse nation, and that diverse worldviews in Australia have long been and continue to be shaped by First Peoples, and South–South flows of migration across the Pacific and Indian oceans, particularly in Australia’s far northern regions. This article begins with truth-telling of the historical and contemporary reality of the religious and spiritual composition of Australia, challenging the myth of a White Christian nation. It then includes a series of scholarly and personal reflections, by the paper’s authors, on why and how there is a need to decolonise studies of religion in Australia. These author narratives, recounting lived experiences from their diverse positionalities including their First Peoples, Indonesian, Chinese, African, and/or European backgrounds, further reflect Australia’s multifaith and ‘multicultural Real’ (Hage 1998). The paper concludes with an analysis of these narratives and posits ways forward to dislocate coloniality in the study of religion across the Asia-Pacific region.

Vessel trajectory prediction based on spatio-temporal graph convolutional network for complex and crowded sea areas

In order to improve the navigation ability of vessels and ensure the safety of maritime traffic, vessel trajectory prediction plays a crucial role in the intelligent navigation and collision avoidance. Especially for complex and crowded waters, autonomous vessels must have high situational awareness to detect other vessels, and predict their future trajectories and assess collision risks. In this work, a deep attention-aware spatio-temporal graph convolutional network based on AIS data (DAA-SGCN) is proposed to predict the future trajectories of vessels. It mainly includes three modules: motion information encoding of vessel trajectories, the spatio-temporal feature extraction module and the trajectory prediction module. The LSTM is used to extract the motion features of vessels, the spatio-temporal graph is constructed based on deep attention mechanism. The spatial social interaction features are extracted by ST-GCN, and the temporal correlation are extracted by RT-CNN, to obtain the high-level spatio-temporal features of vessel trajectories. Then, the feature embedding is fed into the trajectory prediction module to predict the future trajectories of vessels. On the basis of a large number of experiments, the prediction performance of the DAA-SGCN compared to the optimal baseline model is improved by about 74% and 69% in ADE and FDE metrics.

In Situ Transmission Electron Microscopy Methods for Lithium-Ion Batteries.

In situ Transmission Electron Microscopy (TEM) stands as an invaluable instrument for the real-time examination of the structural changes in materials. It features ultrahigh spatial resolution and powerful analytical capability, making it significantly versatile across diverse fields. Particularly in the realm of Lithium-Ion Batteries (LIBs), in situ TEM is extensively utilized for real-time analysis of phase transitions, degradation mechanisms, and the lithiation process during charging and discharging. This review aims to provide an overview of the latest advancements in in situ TEM applications for LIBs. Additionally, it compares the suitability and effectiveness of two techniques: the open cell technique and the liquid cell technique. The technical aspects of both the open cell and liquid cell techniques are introduced, followed by a comparison of their applications in cathodes, anodes, solid electrolyte interphase (SEI) formation, and lithium dendrite growth in LIBs. Lastly, the review concludes by stimulating discussions on possible future research trajectories that hold potential to expedite the progression of battery technology.

Non-probability sampling network based on anomaly pedestrian trajectory discrimination for pedestrian trajectory prediction

Pedestrian trajectory prediction in first-person view is an important support for achieving fully automated driving in cities. However, existing pedestrian trajectory prediction methods still have significant shortcomings in terms of pedestrian trajectory diversity, dynamic scene constraints, and dependence on long-term trajectory prediction. We proposes a non-probability sampling network based on pedestrian trajectory anomaly recognition (ADsampler) to predict multiple possible future pedestrian trajectories. First, by incorporating pose and optical flow information, ADsampler models the multi-dimensional motion characteristics of pedestrians based on observed trajectory information and discriminates trajectory states. The sampling range in the Gaussian latent space is determined based on the recognition results. Next, velocity and yaw information of the car are introduced to model the car's motion state. A subtraction fusion network is employed to remove redundant image feature constraints in highly dynamic scenes. Finally, ADsampler utilizes a novel trajectory decoding network that combines the position encoding capability of GRU with the long-term dependency capturing ability of Transformer to decode and predict the fused features. we evaluate our model on crowded videos in the public datasets JAAD, PIE, ETH and UCY. Experiments demonstrate that the proposed method outperforms state-of-the-art approaches in prediction accuracy.

Evolution of a Distinct SARS-CoV-2 Lineage Identified during an Investigation of a Hospital Outbreak.

The SARS-CoV-2 virus steadily evolves, and numerous antigenically distinct variants have emerged over the past three years. Tracking the evolution of the virus would help us understand the process that generates the diverse variants and predict the future evolutionary trajectory of SARS-CoV-2. Here, we report the evolutionary trajectory of a unique Omicron lineage identified during an outbreak investigation that occurred in a residence unit in the healthcare system. The new lineage had four distinct non-synonymous and two distinct synonymous mutations apart from its parental lineage. Since this lineage of virus was exclusively found during the outbreak, we were able to track the detailed evolutionary history of the entire lineage along the transmission path. Furthermore, we estimated the evolutionary rate of the SARS-CoV-2 Omicron variant from the analysis of the evolution of the lineage. This new Omicron sub-lineage acquired 3 mutations in a 12-day period, and the evolutionary rate was estimated as 3.05 × 10-3 subs/site/year. This study provides more insight into an ever-evolving virus.

Brave New World: Navigating New Paradigms in the Educational Landscape for the Evolving Learner in a Rapidly Changing Era

This literature review explores the significant transformations in the educational landscape prompted by technological advancements and changing learner profiles in the digital era. The review examines the evolution of learner profiles, highlighting the shift towards multisensory learning, digital fluency, and a preference for interactive, personalized learning experiences. It delves into integrating innovative technologies such as virtual reality (VR), augmented reality (AR), and blockchain in education and discusses their implications for teaching and learning. The review also considers the impact of neuroscience on educational practices, emphasizing the importance of understanding brain functioning and neuroplasticity in developing effective teaching strategies. The unprecedented shift to online and blended learning models, accelerated by the COVID-19 pandemic, is analyzed for its long-term effects on educational delivery and pedagogy. Additionally, the review addresses the growing emphasis on interdisciplinary learning, sustainability, mental health, and well-being within educational curricula. The implications of these transformative trends for educators, policymakers, and learners are discussed, highlighting the need for adaptive and responsive approaches to education in the face of rapid technological and societal changes. The review concludes with reflections on the future trajectory of education, anticipating continued technical integration, evolving pedagogical theories, and an increased focus on preparing learners for global challenges and lifelong learning.

The positive impact of transit-oriented-development characteristics on Metro Station usage: A case study of Tehran's metro stations and TOD index calculation

Transit-Oriented Development (TOD) facilitates the creation of more walkable cities. To implement TODs, urban planners need a mechanism to delineate TOD levels so they can envision future planning trajectories. This study offers an approach to developing scores for TODs within Tehran, a metropolitan area with metro stations and Bus Rapid Transit that facilitate public transportation usage. TODs exhibit distinct characteristics when compared to other areas within a city. Furthermore, TODs vary in their levels of integration across transit, commerce, and residential, necessitating policies specific to each TOD. The main objective is 1) To identify the factors that best define TODs for metro stations and 2) To develop a TOD index to measure the TOD score. We used three different spatial units of measurement to define a range of metrics to identify how effective TODs work with different characteristics. The spatial units include: 1. Thiessen polygons 2. the walkable distance of the transit nodes and 3. Thiessen polygons without the walkable distances. The main criteria we used are travel behavior, walkability, accessibility, transportation inclusivity, network attribute, trip generation rate, and land use. The metrics are weighted based on the correlation results with the usage rate. A Negative Binomial regression was developed to find the metrics that are significantly influential on the usage rate of the metro stations. Results indicate that all criteria except the land use are significantly correlated with the usage rate of metro stations.

How Would Structural Change in Electricity and Hydrogen End Use Impact Low-Carbon Transition of an Energy System? A Case Study of China

Driven by global targets to reduce greenhouse gas emissions, energy systems are expected to undergo fundamental changes. In light of carbon neutrality policies, China is expected to significantly increase the proportion of hydrogen and electricity in its energy system in the future. Nevertheless, the future trajectory remains shrouded in uncertainty. To explore the potential ramifications of varying growth scenarios pertaining to hydrogen and electricity on the energy landscape, this study employs a meticulously designed bottom-up model. Through comprehensive scenario calculations, the research aims to unravel the implications of such expansions and provide a nuanced analysis of their effects on the energy system. Results show that with an increase in electrification rates, cumulative carbon dioxide emissions over a certain planning horizon could be reduced, at the price of increased unit reduction costs. By increasing the share of end-use electricity and hydrogen from 71% to 80% in 2060, the unit carbon reduction cost will rise by 17%. Increasing shares of hydrogen could shorten the carbon emission peak time by approximately five years, but it also brings an increase in peak shaving demand.

The Impact Of Applied Linguistics On The Field Of Grammar: A Comparative Analytical Study With The Letter 'و' (Waw) As A Model/ أثر اللغويات التطبيقية في علم النحو: حرف الواو أنموذجًا دراسة تحليلية مقارنة

Language serves as a crucial tool for human interaction. It transcends its basic structure of words, rules, and syntax, reflecting the complex subtleties of culture, history, and the social development of its users. Within the study of language, applied linguistics stands out as an essential discipline that delves deeper than theoretical discussions. It critically examines how language is utilized, its manifestations in various contexts, and its multifaceted applications in human communications across different fields. Central to this research is the exploration of the transformative influence of applied linguistics on the domain of grammar. This study places particular emphasis on the Arabic letter 'و' (Waw). Given its frequent occurrence and versatile role in texts, ranging from the esteemed Quran to classical Arabic literature and detailed grammatical discussions, the comprehensive and precise understanding of the letter 'و' is of utmost importance. Such understanding facilitates both rigorous textual analysis and profound comprehension of linguistic constructs within the Arabic language. To discern the depth of this influence, this research adopts a comparative methodology. It juxtaposes the time-honored traditional grammatical approaches with contemporary perspectives offered by applied linguistics. Through this comparative lens, the study sets out to achieve two primary objectives: firstly, to identify how applied linguistics might present alternative or even more enriched interpretations of 'و' distinct from classical views; and secondly, to elucidate the tangible implications of these interpretations in practical, real-world linguistic applications. In culmination, this rigorous investigation serves a dual purpose. Not only does it shed light on the nuanced roles and interpretations of the Arabic letter 'و', but it also positions applied linguistics as a beacon guiding the future trajectory of grammatical studies. The research accentuates the indispensability of a symbiotic relationship between traditional grammar and modern applied linguistic techniques, advocating for their combined use in achieving a holistic and evolved understanding of the dynamic Arabic language system.

Evaluation of Ship Recycling Industry in View of Present Global Circumstances and Way Forward

Ship recycling, commonly referred to as ship dismantling, is an inherently sustainable practice with global implications. It involves the reverse engineering process of disassembling obsolete ships to recover reusable materials in a safe and environmentally friendly manner. Over the past two decades, ships have predominantly been beached in South Asian countries such as Bangladesh, India, China, and Pakistan, allowing local yards to dismantle vessels with moderate preventive measures and substantial manual labor. Ship recycling stands as a significant and environmentally conscious industry, witnessing the annual recycling of approximately 1000 obsolete ships globally. Currently, this industry is predominantly led by three South Asian countries—India, Bangladesh, and Pakistan. In Bangladesh, the industry commenced with the dismantling of stranded ships, evolving into a large and profitable sector that generates substantial employment opportunities for thousands of semi-skilled workers in poverty-prone areas. While the ship recycling industry presents opportunities, it also poses challenges for coastal zone management, encompassing environmental, law enforcement, and worker safety concerns. The surge in demand for ship recycling has prompted increased regulatory scrutiny at both national and international levels. This pressure culminated in the development of the Hong Kong International Convention (HKC) by the IMO in 2009, mandating safety and environmental protection standards at recycling yards. Achieving a perfectly green ship recycling process in line with European standards is inherently costly. However, a viable and almost green ship recycling approach, integrating the current common practice of beaching methods in Asian countries, is entirely feasible. Despite limitations and challenges, the industry significantly contributes to Bangladesh's GDP and the overall socio-economic landscape. Currently, there has been a visible reduction in Bangladesh's market share due to stringent environmental regulations, while competitors have experienced increases. This study represents an analytical effort to evaluate the current global ship recycling scenario, exploring its potential, challenges, and the future trajectory of both local and global ship recycling industries.

From Baking Competitions To Forced Repatriations: Patriotic Alternative And The Hybridity Of The Radical Right

Today, Patriotic Alternative is Britain’s largest and fastest growing radical right group. This article presents evidence in support of Patriotic Alternative being a highly original and innovative radical right group that is demarcated by its hybridity: one that brings together both authentic and inauthentic forms and ideologies of traditional nationalism. Having set out the research design, this article begins with an exploration of the origins, formation and leadership of Patriotic Alternative. From here, it does the same in relation to its ideology and activism. Next, Patriotic Alternative’s hybridity is considered in an attempt to better understand how, as a radical-right group, it combines authentic forms of traditional nationalism with newer, wholly contemporary – and thereby inauthentic – forms. Lastly, a number of conclusions are drawn about the potential future trajectory of Patriotic Alternative as also the British radical right and extreme right-wing more generally. Given Patriotic Alternative has been subjected to little if indeed any scholarly inquiry to date, this article makes a timely and much needed scholarly contribution.