Esta investigación explora las percepciones sobre la Inteligencia Artificial (IA) en el ámbito educativo, enfocándose en docentes de Lengua y Literatura de enseñanza secundaria —tanto en formación como en ejercicio— en la Región del Biobío, Chile. A partir de un enfoque cualitativo fenomenológico, se realizaron entrevistas semiestructuradas a diez participantes, analizadas con QDA Miner y luego sometidas a triangulación. Los docentes en ejercicio valoran la IA como una herramienta útil para la elaboración de instrumentos de evaluación y actividades pedagógicas; no obstante, manifiestan desconfianza respecto de la exactitud de la información y optan por utilizar fuentes tradicionales. Los docentes en formación aprecian la eficiencia de la IA en la planificación y búsqueda de información, pero les preocupa la falta de transparencia en las referencias utilizadas. En general, la IA se concibe como una herramienta potencialmente enriquecedora del quehacer docente, aunque requiere supervisión humana, ajustes constantes y capacitación para su efectiva integración en el contexto educativo.
To improve the mechanical stability and service durability of solar road structures, this study systematically investigates the mechanical response characteristics of photovoltaic panels with different geometric shapes—including triangles, rectangles, squares, regular pentagons, and regular hexagons—under consistent boundary and loading conditions using the discrete element method (DEM). All panels have a uniform thickness of 10 cm and equivalent surface areas to ensure shape comparability. Side lengths vary among the shapes: square panels with sides of 0.707 m, 1.0 m, and 1.5 m; triangle 1.155 m; rectangle (aspect ratio 1:2) 0.707 m; pentagon 1.175 m; and hexagon 0.577 m. Results show that panel geometry significantly influences stress distribution and deformation behavior. Although triangular panels exhibit higher ultimate bearing capacity and failure energy, they suffer from severe stress concentration and low stiffness. Regular hexagonal panels, due to their geometric symmetry, enable more uniform stress and displacement distributions, offering better stability and crack resistance. Size effect analysis reveals that larger panels improve load-bearing and energy dissipation capacity but exacerbate edge stress concentration and reduce overall stiffness, leading to more pronounced “thinning” deformation and premature failure. Failure mode analysis further indicates that shape governs crack initiation and path, while size determines crack propagation rate and failure extent—revealing a coupled shape–size mechanical mechanism. Regarding assembly, honeycomb arrangements demonstrate superior mechanical performance due to higher compactness and better load-sharing characteristics. The study ultimately recommends the use of small-sized regular hexagonal units and optimized splicing structures to balance strength, stiffness, and durability. These findings provide theoretical guidance and parameter references for the structural design of solar roads.
Engineering machinery, tools, and implements, Technological innovations. Automation
Preetham Goli, Srinivasa Rao Gampa, Amarendra Alluri
et al.
This paper presents a novel methodology for the optimal sizing of solar photovoltaic (PV) systems in distribution networks by determining the monthly optimum tilt and azimuth angles to maximize solar energy capture. Using one year of solar irradiation data, the Grey Wolf Optimizer (GWO) is employed to optimize the tilt and azimuth angles with the objective of maximizing monthly solar insolation. Unlike existing approaches that assume fixed azimuth angles, the proposed method calculates both tilt and azimuth angles for each month, allowing for a more precise alignment with solar trajectories. The optimized orientation parameters are subsequently utilized to determine the optimal number and placement of PV panels, as well as the optimal location and sizing of shunt capacitor (SC) banks, for the IEEE 69-bus distribution system. This optimization is performed under peak load conditions using the GWO, with the objectives of minimizing active power losses, enhancing voltage profile stability, and maximizing PV system penetration. The long-term impact of this approach is assessed through a 20-year energy and economic savings analysis, demonstrating substantial improvements in energy efficiency and cost-effectiveness.
Engineering machinery, tools, and implements, Technological innovations. Automation
DeepSeek-V3 and DeepSeek-R1 are leading open-source Large Language Models (LLMs) for general-purpose tasks and reasoning, achieving performance comparable to state-of-the-art closed-source models from companies like OpenAI and Anthropic -- while requiring only a fraction of their training costs. Understanding the key innovative techniques behind DeepSeek's success is crucial for advancing LLM research. In this paper, we review the core techniques driving the remarkable effectiveness and efficiency of these models, including refinements to the transformer architecture, innovations such as Multi-Head Latent Attention and Mixture of Experts, Multi-Token Prediction, the co-design of algorithms, frameworks, and hardware, the Group Relative Policy Optimization algorithm, post-training with pure reinforcement learning and iterative training alternating between supervised fine-tuning and reinforcement learning. Additionally, we identify several open questions and highlight potential research opportunities in this rapidly advancing field.
Ankur Tomar, Hengyue Liang, Indranil Bhattacharya
et al.
The emergence of AI-driven web automation through Large Language Models (LLMs) offers unprecedented opportunities for optimizing digital workflows. However, deploying such systems within industry's real-world environments presents four core challenges: (1) ensuring consistent execution, (2) accurately identifying critical HTML elements, (3) meeting human-like accuracy in order to automate operations at scale and (4) the lack of comprehensive benchmarking data on internal web applications. Existing solutions are primarily tailored for well-designed, consumer-facing websites (e.g., Amazon.com, Apple.com) and fall short in addressing the complexity of poorly-designed internal web interfaces. To address these limitations, we present Cybernaut, a novel framework to ensure high execution consistency in web automation agents designed for robust enterprise use. Our contributions are threefold: (1) a Standard Operating Procedure (SOP) generator that converts user demonstrations into reliable automation instructions for linear browsing tasks, (2) a high-precision HTML DOM element recognition system tailored for the challenge of complex web interfaces, and (3) a quantitative metric to assess execution consistency. The empirical evaluation on our internal benchmark demonstrates that using our framework enables a 23.2% improvement (from 72% to 88.68%) in task execution success rate over the browser_use. Cybernaut identifies consistent execution patterns with 84.7% accuracy, enabling reliable confidence assessment and adaptive guidance during task execution in real-world systems. These results highlight Cybernaut's effectiveness in enterprise-scale web automation and lay a foundation for future advancements in web automation.
Does government transparency affect innovation? I evaluate the launch of a government database with detailed technical information on the universe of wireless-enabled products on the U.S. market (N 347 thousand). The results show the launch approximately doubled the use of new technologies in the following ten years, an indicator of follow-on innovation. The increase affected both products in the same and new product classes, suggesting novelty; waned over several years, potentially due to an increase in secrecy and patenting; and boosted foreign more than U.S. domestic competitors. These results highlight the importance of information for private sector innovation.
Roberto Bacani Figueroa, Mai Huong Nguyen, Aliza Ali
et al.
Amid the growing need to keep learners abreast of rapid technological advancements brought about by the Fourth Industrial Revolution, this study explores perceived needs of students in virtual learning environments supported by emerging technologies. A survey was conducted across five leading open universities in Southeast Asia. The study aimed to identify student preferences regarding features of their virtual learning environments that could better prepare them as productive citizens and professionals. Findings indicate strong interest in interactive books and learning analytics, underscoring the importance of enhancing learner engagement and data-informed instruction. The results inform the development of a strategic roadmap to guide open universities in prioritizing technological and pedagogical innovations aligned with the evolving expectations of digital-age learners.
We provide a model of investment in innovation that is dynamic, features multiple heterogeneous research projects of which only one potentially leads to success, and in each period, the researcher chooses the set of projects to invest in. We show that if a search for innovation starts, it optimally does not end until the innovation is found -- which will be never with a strictly positive probability.
Janet van der Meulen, Dalia Mukhtar-Landgren, T. Koglin
ABSTRACT As climate ambitions have increased, questions regarding the sustainability of transport systems have been placed on the transport innovation agenda. Yet the relationship between economic competitiveness and sustainability agendas in national innovation policy is an uneven one. We aim to unpack this relationship by analysing the position of cycling in Swedish innovation policy, focusing on the funding of projects within the field of sustainable mobility. We apply a critical theoretical approach and build on Hartmut Rosa’s critical work on modernity and acceleration, Sheller and Urry’s theories on mobilities – including contributions from followers to this field – and critical innovation studies. The result of our analysis for cycling is threefold. First, the conceptualisation of ‘progress’ does not help to place cycling high on the innovation agenda. Second, the bicycle and cycling have difficulties appearing as ‘new’, in contrast to the car and driving. Third, the unreflexivity regarding automation, digitisation, and sharing prevents taking account of negative effects on cycling and obstructs a fundamental questioning of automobility. In our conclusion, we propose a different view of progress, of which the current interpretation seems to be preventing innovation policy from having a stronger sustainability agenda. An alternative interpretation of progress logically also questions the role and primacy of technological novelties.
A multi-loop resource nexus, which features an intricate network of various resource loops, is proposed to promote circular economies. To realize efficient transfer and transformation of resources, the chemical sciences are critical for innovations in low-carbon resource recovery technologies.
Olive fruits at different ripening stages give rise to various table olive products and oil qualities. Therefore, developing an efficient method for recognizing and sorting olive fruits based on their ripening stages can greatly facilitate post-harvest processing. This study introduces an automatic computer vision system that utilizes deep learning technology to classify the ‘Roghani’ Iranian olive cultivar into five ripening stages using color images. The developed model employs convolutional neural networks (CNN) and transfer learning based on the Xception architecture and ImageNet weights as the base network. The model was modified by adding some well-known CNN layers to the last layer. To minimize overfitting and enhance model generality, data augmentation techniques were employed. By considering different optimizers and two image sizes, four final candidate models were generated. These models were then compared in terms of loss and accuracy on the test dataset, classification performance (classification report and confusion matrix), and generality. All four candidates exhibited high accuracies ranging from 86.93% to 93.46% and comparable classification performance. In all models, at least one class was recognized with 100% accuracy. However, by taking into account the risk of overfitting in addition to the network stability, two models were discarded. Finally, a model with an image size of 224 × 224 and an SGD optimizer, which had a loss of 1.23 and an accuracy of 86.93%, was selected as the preferred option. The results of this study offer robust tools for automatic olive sorting systems, simplifying the differentiation of olives at various ripening levels for different post-harvest products.
Engineering machinery, tools, and implements, Technological innovations. Automation
Florian Tschernuth, Xin Shen, Alessandro Rossi
et al.
Continuous and breakthrough industrial innovations are essential for sustainability and the success of modern economies. As the development of innovative products and technologies is often performed within start-up enterprises, the facilitation of entrepreneurial spirit is of major political interest. However, central European countries like Italy and Germany lack behind as highly educated students rather prefer more conventional career paths. In this study we surveyed Bachelor, Master and PhD students in Germany and Italy to further understand the cultural and socio-geographic effects on career decisions. Although only minor differences could be detected among the peer-group, beneficial observations were derived, identifying key-motivators and cultural necessities.
Ming-An Chung, Chia-Chun Hsu, Siao-Rong Huang
et al.
This paper presents a small-size broadband slot monopole chip antenna for millimeter wave application. Using a 0.18 μm CMOS process, through metal_1, the artificial magnetic conductor (AMC) of the metal layer increased the impedance bandwidth of the chip antenna. The additional inverted C branch was used to achieve a better reflection coefficient. By adding an AMC and inverted C branch, the operating frequency of the chip antenna went to 33.8–110 GHz below the reflection coefficient of −10 dB, and its fractional bandwidth was 103.4%. The maximum gain was −6.3 dBi at 72 GHz. The overall chip size was 1.2 × 1.2 (mm<sup>2</sup>). Through measurement and verification, the proposed antenna reflection coefficient was close to the simulation trend and had better resonance. The frequency range of the chip antenna proposed in this paper covered the 5G NR FR2 band (24.2 GHz–52.6 GHz) and W-band (75 GHz–110 GHz). The proposed chip antenna can be applied to the Internet of Things, Industry 4.0, biomedical electronics, near field sensing and other related fields.
Engineering machinery, tools, and implements, Technological innovations. Automation
The remarkable capabilities and intricate nature of Artificial Intelligence (AI) have dramatically escalated the imperative for specialized AI accelerators. Nonetheless, designing these accelerators for various AI workloads remains both labor- and time-intensive. While existing design exploration and automation tools can partially alleviate the need for extensive human involvement, they still demand substantial hardware expertise, posing a barrier to non-experts and stifling AI accelerator development. Motivated by the astonishing potential of large language models (LLMs) for generating high-quality content in response to human language instructions, we embark on this work to examine the possibility of harnessing LLMs to automate AI accelerator design. Through this endeavor, we develop GPT4AIGChip, a framework intended to democratize AI accelerator design by leveraging human natural languages instead of domain-specific languages. Specifically, we first perform an in-depth investigation into LLMs' limitations and capabilities for AI accelerator design, thus aiding our understanding of our current position and garnering insights into LLM-powered automated AI accelerator design. Furthermore, drawing inspiration from the above insights, we develop a framework called GPT4AIGChip, which features an automated demo-augmented prompt-generation pipeline utilizing in-context learning to guide LLMs towards creating high-quality AI accelerator design. To our knowledge, this work is the first to demonstrate an effective pipeline for LLM-powered automated AI accelerator generation. Accordingly, we anticipate that our insights and framework can serve as a catalyst for innovations in next-generation LLM-powered design automation tools.
The objectives of the study were to identify the types and levels of innovations and the business categories of Female Social Entrepreneurs in Africa (FSEAs) and to determine how these FSEAs create social value in their societies. The database of Ashoka, an international organisation promoting exceptional social entrepreneurs, known as changemakers, were used to identify 142 FSEAs from 20 African countries. Schumpeter’s (1939) typology of innovation and Hamel and Breen’s (2007) hierarchy of innovation were used to determine the type and social value created and the FSEAs’ contribution to society. The results indicate that at 85%, most FSEAs have post-school qualifications, of which 43% have a degree and 24% post graduate qualifications. The business categories of the majority of FSEAs are in Education and Learning (30), Development and Prosperity (30), and Health and Fitness (21). Furthermore, the Schumpeterian type of innovation of the majority is Opening of New Markets (78) and Introduction of New Products or Services (46). The Hamel and Breen’s level of innovation of the majority of FSEAs is Product and Service Innovation (114). We found that the FSEAs identified and addressed important challenges in their communities through various types of innovation. This process created valuable social contributions to their communities, the broader society and, in some instances, other African countries.
This paper presents an automatic approach to creating taxonomies of technical terms based on the Cooperative Patent Classification (CPC). The resulting taxonomy contains about 170k nodes in 9 separate technological branches and is freely available. We also show that a Text-to-Text Transfer Transformer (T5) model can be fine-tuned to generate hypernyms and hyponyms with relatively high precision, confirming the manually assessed quality of the resource. The T5 model opens the taxonomy to any new technological terms for which a hypernym can be generated, thus making the resource updateable with new terms, an essential feature for the constantly evolving field of technological terminology.
Maria Krantz, Alexander Windmann, Rene Heesch
et al.
The increasing complexity of Cyber-Physical Systems (CPS) makes industrial automation challenging. Large amounts of data recorded by sensors need to be processed to adequately perform tasks such as diagnosis in case of fault. A promising approach to deal with this complexity is the concept of causality. However, most research on causality has focused on inferring causal relations between parts of an unknown system. Engineering uses causality in a fundamentally different way: complex systems are constructed by combining components with known, controllable behavior. As CPS are constructed by the second approach, most data-based causality models are not suited for industrial automation. To bridge this gap, a Uniform Causality Model for various application areas of industrial automation is proposed, which will allow better communication and better data usage across disciplines. The resulting model describes the behavior of CPS mathematically and, as the model is evaluated on the unique requirements of the application areas, it is shown that the Uniform Causality Model can work as a basis for the application of new approaches in industrial automation that focus on machine learning.
Automation systems are increasingly being used in dynamic and various operating conditions. With higher flexibility demands, they need to promptly respond to surrounding dynamic changes by adapting their operation. Context information collected during runtime can be useful to enhance the system's adaptability. Context-aware systems represent a design paradigm for modeling and applying context in various applications such as decision-making. In order to address context for automation systems, a state of the art assessment of existing approaches is necessary. Thus, the objective of this work is to provide an overview on the design and applications of context and context models for automation systems. A systematic literature review has been conducted, the results of which are represented as a knowledge graph.
This paper explores a potential paradigm shift in trade finance utilizing blockchain technology. Traditionally, the centralized operating model has governed trade finance and the manner in which traders handle business processes. However, such heavy reliance on centralized authorities has made for poor performance, the lack of flexibility and transparency, and vulnerability to malicious alteration. The blockchain, as a distributed ledger technology (DLT), has attracted growing attention and has the potential to disrupt legacy finance procedures such as payment by letter of credit (L/C). International trade players may benefit from the technological reengineering of financial processes through the implementation of blockchain- and smart contract-based platforms. From the conceptual perspective of a paradigm shift, this study analyzes the feasibility of blockchain innovation in trade finance through modern blockchain-based L/C initiatives. Moreover, this study also explores blockchain applications in terms of logistics tracking and how it integrates with trade finance procedures. This study contributes to the understanding of a blockchain paradigm shift with a multi-case study. The results may illuminate the potential future application of blockchain finance and provide researchers with an illustrative example of other finance-related capabilities. Studies of trade-related topics such as customs clearances, insurance, and logistics applications need to be addressed in the future to create a comprehensively trustless environment and facilitate the automation of trade.