Valery Mfondoum, Mylène Noubi Tchatchoua, Homère Ngandam
et al.
Продуктивным подходом к интеграции стратегического Форсайта и машинного обучения выступает модель Generalized Strategic Foresight Model embedding MLOps (GSF(M)²) — унифицированная структура управления, сочетающая интерпретационную глубину долгосрочного сценарного Форсайта с адаптивностью процедур машинного обучения в режиме реального времени. Модель устраняет структурные недостатки существующих систем принятия решений, где методы Форсайта генерируют упреждающие идеи, но лишены механизмов операционализации, тогда как алгоритмы машинного обучения автоматизируют процессы, но игнорируют стратегический и партисипативный контекст, а также социально-организационную специфику. Системный обзор литературы по методологии PRISMA (по 16 публикаций в каждом блоке — Форсайт и жизненный цикл машинного обучения) выявил методологические пробелы обоих направлений при сопоставлении с эталонными архитектурами.
GSF(M)² синтезирует преимущества обоих подходов, встраивая логику Форсайта в адаптивные процессы машинного обучения, а автоматизированные циклы обратной связи — в сценарное планирование. Результатом стала постоянно обучающаяся экосистема, позволяющая в режиме реального времени осуществлять корректировку сценариев, параметров моделей и стратегических вариантов. Синтез упреждающей аналитики, непрерывного сканирования стратегического горизонта и приоритизации на базе данных обеспечивает повышение эффективности разработки политики и институциональную гибкость в условиях международной и технологической неопределенности. GSF(M)² представляет собой первую двухуровневую структуру коэволюции стратегического Форсайта и адаптивных алгоритмов в единой рефлексивной архитектуре управления.
Vinay Chamola, Mritunjay Shall Peelam, Mohsen Guizani
et al.
The evolution from 1G to 6G networks has transformed global communication, progressing from basic voice calls in 1G to the immersive, AI-enabled experiences of 6G. As emerging AI-driven applications like autonomous systems, the Internet of Everything (IoE), and immersive technologies demand unprecedented capabilities, 7G networks are set to redefine connectivity by overcoming the limitations of earlier generations. This paper comprehensively reviews the innovations and challenges in 7G networks, focusing on integrating advanced AI and machine learning paradigms such as meta-learning, incremental learning, distributed intelligence, and reinforcement learning to enhance adaptability, resource allocation, and edge performance. The review also examines the role of Large Language Models (LLMs) in enabling real-time actionable intelligence and optimizing edge devices within 7G. The paper highlights the use of technologies, including blockchain for decentralized security, quantum computing for robust encryption, terahertz communication for ultra-fast data transfer, zero-energy solutions for sustainability, and generative AI for intelligent network optimization and automation. By addressing these challenges and exploring cutting-edge strategies, this paper envisions 7G networks as the foundation for a secure, intelligent, and sustainable digital future, equipped to combat emerging cyber warfare threats, enhance resilience against technological disruptions, and support innovations across smart cities, autonomous systems, healthcare, and industrial IoT.
Telecommunication, Transportation and communications
The importance of deep learning has heralded transforming changes across different technological domains, not least in the enhancement of robotic arm functionalities of object detection’s and grasping. This paper is aimed to review recent and past studies to give a comprehensive insight to focus in exploring cutting-edge deep learning methodologies to surmount the persistent challenges of object detection and precise manipulation by robotic arms. By integrating the iterations of the You Only Look Once (YOLO) algorithm with deep learning models, our study not only advances the innovations in robotic perception but also significantly improves the accuracy of robotic grasping in dynamic environments. Through a comprehensive exploration of various deep learning techniques, we introduce many approaches that enable robotic arms to identify and grasp objects with unprecedented precision, thereby bridging a critical gap in robotic automation. Our findings demonstrate a marked enhancement in the robotic arm’s ability to adapt to and interact with its surroundings, opening new avenues for automation in industrial, medical, and domestic applications. The impact of this research extends lays the groundwork for future developments in robotic autonomy, offering insights into the integration of deep learning algorithms with robotic systems. This also serves as a beacon for future research aimed at fully unleashing the potential of robots as autonomous agents in complex, real-world settings.
Emerging disruptive technologies such as artificial intelligence (AI) are fueling global rivalry by changing the power dynamics among countries. This article examines the implications of AI for the prospects of defense competition between major powers such as the United States and China. It presents possible scenarios of such competition through 2050 and their implications for smaller countries with limited geopolitical influence as they adapt to the increasingly complex context these processes create. The scenarios provide not only structured pictures of possible futures but also a strategic canvas for developing proactive national security policies in the changing international landscape. In the context of rapid technological advances and strategic competition, smaller countries face both challenges and opportunities as they navigate their own paths. The proposed recommendations aim to “level the playing field” and help such states not only address the challenges posed by AI in the military sphere but also seize the opportunities arising from technological shifts. The findings presented can serve as a basis for developing national security strategies even in the context of institutional and infrastructural limitations. Decision makers will be able to navigate and effectively act in a complex, changing arena, the dynamism of which is largely determined by AI technologies.
In contemporary academic settings, end-of-semester student feedback on a lecturer’s teaching abilities often fails to provide a comprehensive, real-time evaluation of their proficiency, and becomes less relevant with each new cohort of students. To address these limitations, an innovative feedback method has been proposed, utilizing image processing algorithms to dynamically assess the emotional states of students during lectures by analyzing their facial expressions. This real-time approach enables lecturers to promptly adapt and enhance their teaching techniques. Recognizing and engaging with emotionally positive students has been shown to foster better learning outcomes, as their enthusiasm actively stimulates cognitive engagement and information analysis. The purpose of this work is to identify emotions based on facial expressions using a deep learning model based on a convolutional neural network (CNN), where facial recognition is performed using the Viola–Jones algorithm on a group of students in a learning environment. The algorithm encompasses four key steps: image acquisition, preprocessing, emotion detection, and emotion recognition. The technological advancement of this research lies in the proposal to implement photonic hardware and create an optical neural network which offers unparalleled speed and efficiency in data processing. This approach demonstrates significant advancements over traditional electronic systems in handling computational tasks. An experimental validation was conducted in a classroom with 45 students, demonstrating that the level of understanding in the class as predicted was 43–62.94%, and the proposed CNN algorithm (facial expressions detection) achieved an impressive 83% accuracy in understanding students’ emotional states. The correlation between the CNN deep learning model and the students’ feedback was 91.7%. This novel approach opens avenues for the real-time assessment of students’ engagement levels and the effectiveness of the learning environment, providing valuable insights for ongoing improvements in teaching practices.
Engineering machinery, tools, and implements, Technological innovations. Automation
Toan Khac Nguyen, L. Minh Dang, Truong-Dong Do
et al.
Growth factors affect farm owners, environmental conditions, nutrient adaptation, and resistance to chrysanthemum diseases. Healthy chrysanthemum plants can overcome all these factors and provide farms owners with a lot of income. Chrysanthemum white rust disease is a common disease that occurs worldwide; if not treated promptly, the disease spreads to the entire leaf surface, causing the plant’s leaves to burn, turn yellow, and fall prematurely, reducing the photosynthetic performance of the plant and the appearance of the flower branches. In Korea, chrysanthemum white rust disease most often occurs during the spring and autumn seasons, when temperature varies during the summer monsoon, and when ventilation is poor in the winter. Deep neural networks were used to determine healthy and unhealthy plants. We applied the Raspberry Pi 3 module to recognize white rust and test four neural network models. The five main deep neural network processes utilized for a dataset of non-diseased and white rust leaves include: (1) data collection; (2) data partitioning; (3) feature extraction; (4) feature engineering; and (5) prediction modeling based on the train–test loss of 35 epochs within 20 min using Linux. White rust recognition is performed for comparison using four models, namely, DenseNet-121, ResNet-50, VGG-19, and MobileNet v2. The qualitative white rust detection system is achieved using a Raspberry Pi 3 module. All models accomplished an accuracy of over 94%, and MobileNet v2 achieved the highest accuracy, precision, and recall at over 98%. In the precision comparison, DenseNet-121 obtained the second highest recognition accuracy of 97%, whereas ResNet-50 and VGG-19 achieved slightly lower accuracies at 95% and 94%, respectively. Qualitative results were obtained using the Raspberry Pi 3 module to assess the performance of the seven models. All models had accuracies of over 91%, with ResNet-50 obtaining a value of 91%, VGG-19 reaching a value of 93%, DenseNet-121 reaching 95%, SqueezeNet obtaining over 95%, MobileNet obtaining over 96%, and MobileNetv2-YOLOv3 reaching 92%. The highest accuracy rate was 97% (MobileNet v2). MobileNet v2 was validated as the most effective model to recognize white rust in chrysanthemums using the Raspberry Pi 3 system. Raspberry Pi 3 module was considered, in conjunction with the MobileNet v2 model, to be the best application system. MobileNet v2 and Raspberry Pi require a low cost for the recognition of chrysanthemum white rust and the diagnosis of chrysanthemum plant health conditions, reducing the risk of white rust disease and minimizing costs and efforts while improving floral production. Chrysanthemum farmers should consider applying the Raspberry Pi module for detecting white rust, protecting healthy plant growth, and increasing yields with low-cost.
Engineering machinery, tools, and implements, Technological innovations. Automation
This study identifies relevant innovations and discusses value creation in the aviation industry between 2000 and 2019. Aviation experts with experience in innovation were selected and invited to complete a survey identifying the leading innovations in the industry. This study contributes to recent aviation history by offering a list of innovations and a discussion of technological path dependency and value proposition with examples. This overview is helpful to academics and practitioners to verify how these innovations have shaped the industry worldwide, making it more efficient, agile, sustainable, and safe. The innovations selected comprise consolidated technologies and emerging advances introduced in the timeframe proposed. 33 innovations primarily related to incremental and technical typologies that add value to products were mapped. In addition, this study provides insightful findings by classifying the value created for the aviation sector into five innovation clusters: (1) aircraft technology, adding value in terms of efficiency and sustainability; (2) innovation in passenger services, creating more personalized services and enhancing the customer experience; (3) innovation in flying, adding value in terms of safety and the security environment; (4) business and operational management, improving procedures and revenue; (5) and general applications, adding value in terms of Aviation 4.0 (increases in automation and data exchange, including cyber-physical systems, the Internet of Things (IOT) and cloud computing).
People are increasingly dependent on technology. On the other hand, companies’ large-scale investments to establish an ongoing loyalty with technology platforms and ecosystems show negative results. This is due to lower trust, concerns about risk, and increasing issues of privacy. Despite the continuous development of digital assistant applications to increase interactivity, however, there is no guarantee that the concept of interactivity is capable of gaining users’ trust and addressing their concerns. The purpose of the present study was to analyze the effects of controllability, synchronicity, bidirectionality on perceived performance and user satisfaction with digital assistant applications as moderated by perceived trust. Amos 22.0 was used to analyze a sample of 150 digital assistant users of brands Samsung Bixby, Google Assistant, Apple Siri, and other brands.Results show that bidirectionality is the most worrying feature in terms of perceived performance of digital assistants related to trust and privacy protection issues of personal data, whereas the other two features contribute to perceived performance and digital assistant users’ satisfaction. Perceived trust plays a role in moderating the relationship between controllability, synchronicity bi-directionality of perceived performance. Finally, perceived performance has an effect on digital assistant users’ satisfaction.
Neil Gunn Burnet, Jessica Scaife, Marina Romanchikova
et al.
The VoxTox research programme has applied expertise from the physical sciences to the problem of radiotherapy toxicity, bringing together expertise from engineering, mathematics, high energy physics (including the Large Hadron Collider), medical physics and radiation oncology. In our initial cohort of 109 men treated with curative radiotherapy for prostate cancer, daily image guidance computed tomography (CT) scans have been used to calculate delivered dose to the rectum, as distinct from planned dose, using an automated approach. Clinical toxicity data have been collected, allowing us to address the hypothesis that delivered dose provides a better predictor of toxicity than planned dose.
Neil McKeganey is the founding director of the Centre for Drug Misuse Research within the University of Glasgow and has directed the research programme of the Centre since 1994. For the last 15 years, Professor McKeganey has concentrated on research within the drug misuse field and has undertaken work on drug injectors and HIV, prostitution, and drugs and young people. He has acted as an advisor to the UK Home Office, the World Health Organisation and the United States Department of Justice.