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Time travel as a meditation on grief: mourning and memory in dark
No full textTime travel narratives across cultures often meditate on loss, portraying grief as a central element that shapes identity, relationships, and perceptions of time. Focusing on the German series Dark (2017–2020), this chapter argues that time travel provides a powerful lens for exploring the phenomenology of death, showing how prolonged grief and ambiguous loss inform both narrative and character development
Introduction: reflections on the literary imagination in progressive rock and metal
No full textThis introductory chapter offers a brief overview of the intersections between the broad-based genre formations known as progressive rock and metal in order to support the inclusion of these forms alongside each other within one text. The roles of the concept album and broader transmedia storytelling are discussed, recognizing that there is a strong identification between such presentational forms and the albums released by progressive rock and metal artists. The final section outlines the different sections and chapters that comprise the book, chapters that address the fundamental question which underpins this book: “How are narratives and fictions translated into the works of progressive rock and metal artists and bands?” Our authors and their objects of analytic inquiry offer global and diverse perspectives on these genres and their literary connections: from ancient times to the modern world, from children’s literature to epic poetry, from mythology to science fiction, and from esoteric fantasy to harsh political criticism. The musical treatments of these literary materials span the continents from South and North America through Europe and Asia. The collection presents critical perspectives on the enduring and complex relationships between words, music, and diverse cultures, as these are expressed in progressive rock and metal
Microtiming in early funk: a microrhythmic analysis of fourteen influential funk grooves
No full textBeginning with “Cold Sweat” by James Brown, which is arguably the first funk track, this article focuses on the microrhythmic analyses of fourteen influential early funk grooves from the period 1967–1974. All the tracks under scrutiny were created without the use of click tracks, and many were recorded live in the studio, meaning that the determination of microtiming deviations was not straightforward. For this reason, methodologies used for note onset detection, the creation of rhythmic reference grids, and the calculation of microtiming deviations are summarised. These analyses have resulted in an empirical database of over one thousand microtiming deviations. Clear, systematic patterns of microtiming were observed, original and quantifiable data that justified many of the theories previously suggested and discussed in the literature was found, and new information regarding microtiming deviations and patterns was revealed. Sixteenth note swing rhythms were found to be an element of every track investigated (bar one, which was recorded with a drum machine with a straight feel). The degrees of swing varied from imperceptible thorough to overt funk shuffles. Evidence of backbeat delay (the slightly late articulation of beats two and four) was found to be limited. Unless specific musical instructions were being provided by lead vocalists, rhythmic elements of the tracks investigated were not perturbed by vocals. Novel findings were made demonstrating that structural aspects of musical arrangements may be highlighted microrhythmically
AI in maritime security: applications, challenges, future directions, and key data sources
Get PDFThe growth and sustainability of today’s global economy heavily relies on smooth maritime operations. The increasing security concerns to marine environments pose complex security challenges, such as smuggling, illegal fishing, human trafficking, and environmental threats, for traditional surveillance methods due to their limitations. Artificial intelligence (AI), particularly deep learning, has offered strong capabilities for automating object detection, anomaly identification, and situational awareness in maritime environments. In this paper, we have reviewed the state-of-the-art deep learning models mainly proposed in recent literature (2020–2025), including convolutional neural networks, recurrent neural networks, Transformers, and multimodal fusion architectures. We have highlighted their success in processing diverse data sources such as satellite imagery, AIS, SAR, radar, and sensor inputs from UxVs. Additionally, multimodal data fusion techniques enhance robustness by integrating complementary data, yielding more detection accuracy. There still exist challenges in detecting small or occluded objects, handling cluttered scenes, and interpreting unusual vessel behaviours, especially under adverse sea conditions. Additionally, explainability and real-time deployment of AI models in operational settings are open research areas. Overall, the review of existing maritime literature suggests that deep learning is rapidly transforming maritime domain awareness and response, with significant potential to improve global maritime security and operational efficiency. We have also provided key datasets for deep learning models in the maritime security domain
Easy CNN & Computer Vision for everyone
No full textComputer Vision offers meaningful solutions in everyday life such as becoming the eyes of blind people, traffic control, cashier-free stores, autonomous vehicles, and delivery robots.With this book, we seek to share our expertise with anyone motivated to explore and make meaningful contributions to the field of Computer Vision.This book is for anyone who wants to explore the field of Computer Vision, especially those who feel they lack a strong theoretical background in deep learning or image processing. We have been on the same journey as you—we understand the excitement, confusion, and technical challenges you may be facing. This book is designed to make your learning journey smoother, more structured, and truly enjoyable. We truly believe that with the guidance provided in this book, everyone can develop and implement powerful Computer Vision models—no matter their starting point!In addition to simplifying the foundations, we also highlight recent trends such as multimodal learning—where vision is combined with text, audio, or tabular data—and the importance of staying current with cutting-edge architectures like YOLOv12, which was released just a month ago. We designed it to be beneficial for readers from beginner to advanced levels.The book begins with a brief overview of core concepts in Convolutional Neural Networks (CNNs) and Computer Vision, followed by step-by-step Python implementations using Jupyter Notebooks. All notebooks are organised in folders matching the chapter numbers, and each notebook is accompanied by detailed explanations under the relevant code blocks. You can access all materials via QR codes on the next page, which link directly to our GitHub repository and website
Enhancing supply chain management: a comparative study of machine learning techniques with cost–accuracy and ESG-based evaluation for forecasting and risk mitigation
Get PDFIn today’s volatile market environment, supply chain management (SCM) must address complex challenges such as fluctuating demand, fraud, and delivery delays. This study applies machine learning techniques—Extreme Gradient Boosting (XGBoost) and Recurrent Neural Networks (RNNs)—to optimize demand forecasting, inventory policies, and risk mitigation within a unified framework. XGBoost achieves high forecasting accuracy (MAE = 0.1571, MAPE = 0.48%), while RNNs excel at fraud detection and late delivery prediction (F1-score ≈ 98%). To evaluate models beyond accuracy, we introduce two novel metrics: Cost–Accuracy Efficiency (CAE) and CAE-ESG, which combine predictive performance with cost-efficiency and ESG alignment. These holistic measures support sustainable model selection aligned with the ISO 14001, GRI, and SASB benchmarks; they also demonstrate that, despite lower accuracy, Random Forest achieves the highest CAE-ESG score due to its low complexity and strong ESG profile. We also apply SHAP analysis to improve model interpretability and demonstrate business impact through enhanced Customer Lifetime Value (CLV) and reduced churn. This research offers a practical, interpretable, and sustainability-aware ML framework for supply chains, enabling more resilient, cost-effective, and responsible decision-making
Why are students not attending in-person classes post-COVID-19? An explorative study in student engagement
No full textAcross UK and global higher education, new trends in student engagement have emerged with in-person attendance having significantly decreased post-COVID-19. There are numerous theories suggesting reasons why a decrease in student attendance has occurred post COVID-19, such as a desire to learn online, the current cost of living crisis, and a further increase in poor mental health. Therefore, this research aims to explore this topic with elected programme-level Student Academic Representatives from three post-92 institutions to contribute to the national debate. The findings cement that the cost-of-living crisis has a significant impact on students? choices to attend classes. However, the research also demonstrates that teaching quality and content remain at the heart of student decision-making when it comes to attendance and that HEPs therefore have control over developing solutions to this challenge by fore fronting the lived experiences of their students
A lab-on-a-chip system integrating DNA purification and loop-mediated isothermal amplification for the quantification of the toxic diatom <i>Pseudo-nitzschia multistriata</i>
No full textMicrofluidic technology can expedite nucleic acid testing by converting the functions of bulky laboratory instruments and protracted bench methodologies into easy-to-use and inexpensive miniaturised systems without compromising speed and reliability. We developed a lab-on-a-chip (LOC) platform that integrates a dimethyl adipimidate (DMA)-based functionalised silica DNA isolation and pre-concentration method with a rapid and real-time loop-mediated isothermal amplification (LAMP) for detecting domoic acid-producing phytoplankton, Pseudo-nitzschia. An optimised design of a lab on a chip extraction module achieved a maximum DNA capture capacity of 61.73 ± 0.98 ng μL−1. The DMA-based method reduced reagent costs per sample by 97% compared to a commercial nucleic acid isolation kit. A subsequent on-chip LAMP process was capable of sensitively quantifying cytochrome P450 homologous to the dabD gene, coding for a component of the domoic acid toxin production pathway, with a limit-of-detection of 10 cells per mL. LAMP-based detection of the target gene was achieved using dry-preserved reagents with a shelf-life of five months without refrigeration. There was no significant difference in assay performance between the preserved LAMP and freshly prepared LAMP mixtures. The total analysis time at the LOD of 10 cells per mL, from sample to result, was achieved within one hour. Our results demonstrate the long-term stability of assay reagents, rapid turnaround, and cost-effectiveness, offering a simple and economical approach to environmental monitoring and environmental bio-hazard diagnostics
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