Search Results (2,959)

Promoting breakthrough innovation is a critical strategy for overcoming technological bottlenecks and addressing “chokepoint” challenges, especially for emerging economies. This paper constructs a two-tier innovation network comprising collaborative R&D and technology transaction subnetworks. Using panel data from Chinese A-share listed companies between 2008 and 2022, we empirically examine the impact of network embeddedness on firm breakthrough innovation in the artificial intelligence industry and explore the moderating effect of enterprise digitalization. The results reveal a U-shaped relationship between embeddedness breadth and breakthrough innovation, and an inverted U-shaped relationship between embeddedness depth and breakthrough innovation. The heterogeneous resource acquisition mediates these nonlinear relationships. As a firm’s digitalization intensity increases, the U-shaped and inverted U-shaped relationships between embeddedness dimensions and breakthrough innovation are significantly amplified. This study deepens our understanding of the mechanisms and boundary conditions by which network embeddedness affects firm innovation and provides new theoretical insights for fostering breakthrough innovation in emerging economies. Full article

Effective patent recommendation plays a crucial role in bridging the gap between enterprise technological demands and patent supply. However, semantic mismatches and incomplete demand expressions often hinder accurate supply–demand matching. This research proposes a demand-driven patent recommendation method. First, content analysis and topic clustering were used to construct an enterprise demand element system, dividing the demand content into five elements: materials, methods, efficacy, products, and applications. Based on the completeness of these elements, enterprise demands were further classified into explicit and implicit types. Second, an enterprise technical problem space and a patent solution space were established, identifying ten types of enterprise technical problems and fifteen types of patent solution categories. These were connected through supply–demand elements to build corresponding correlation systems for explicit and implicit demands. Finally, according to different types of supply–demand correlations and demand characteristics, differentiated patent recommendation methods were designed. Taking various demands in the lithium battery industry as empirical cases, the results show that the proposed method based on demand classification and supply–demand element association effectively achieves accurate patent matching and addresses the challenges caused by incomplete demand information. The study provides an intelligent, content-based recommendation pathway for enterprise technology acquisition and patent transformation, offering theoretical and practical significance for enhancing patent commercialization and improving the efficiency of technological achievement transformation. Full article

Patenting is essential for protecting intellectual property, fostering technological innovation, and maintaining competitive advantages in the global market. In Mexico, strategic planning in science, technology, and innovation requires reliable forecasting tools. This study evaluates computational models for predicting applied and granted patents between 1990 and 2024, including statistical (ARIMA), machine learning (Regression Trees, Random Forests, and Support Vector Machines), and deep learning (Long Short-Term Memory, LSTM) approaches. The workflow involves historical data acquisition, exploratory analysis, decomposition, model selection, forecasting, and evaluation using the Root Mean Square Error (RMSE), the determination coefficient ($R^2$), and the Mean Absolute Percentage Error (MAPE) as performance metrics. To ensure generalization and robustness in the training stage, we use the cross-validation rolling origin. On the test stage, LSTM achieves the highest accuracy (RMSE = 106.91, $R^2=0.97$, and MAPE = 0.63 for applied patents; RMSE = 283.20, $R^2=0.93$, and MAPE = 2.65 for granted patents). However, cross-validation shows that ARIMA provides more stable performance across multiple scenarios, highlighting a trade-off between short-term accuracy and long-term reliability. These results demonstrate the potential of machine learning and deep learning as forecasting tools for industrial property management. Full article

In the context of digital transformation for industrial heritage conservation propelled by China’s National Industrial Heritage Management Measures, evidence regarding the trade-offs among accuracy, completeness, and efficiency within the acquisition–registration–integration pipeline, as well as transferable methodologies, remains inadequate. Addressing key challenges in information integration for industrial architectural heritage in Inner Mongolia—such as fragile media, weak sustainability, and severe information silos—demands a systematic solution. This paper proposes a BIM-based three-dimensional digital preservation framework centered on “Space-Time-Value” and empirically validates its workflow effectiveness and database interoperability. Focusing on the Inner Mongolia University of Technology Architecture Building, a prime exemplar of adaptive reuse in the region, we employed terrestrial 3D laser scanning and Unmanned Aerial Vehicle (UAV) oblique photogrammetry to acquire a 13.8-billion-point cloud. Using Autodesk Revit, we developed an LOD400 model (comprising 12 component types and 349 parametric families), achieving systematic integration of structural data, spatial evolution information, and non-geometric attributes. Comparative evaluation shows that this workflow outperforms baselines in geometric accuracy, facade completeness, and processing efficiency, while significantly enhancing the integration and retrieval capabilities for heterogeneous data. The research establishes a “Multi-source Data Integration + Sustainable Utilization” digital paradigm for industrial architectural heritage, providing a replicable methodology for whole-life-cycle management and adaptive reuse. Full article

The emergence of STEAM education, which integrates the Arts into Science, Technology, Engineering, and Mathematics (STEM), reflects a growing recognition of the need to develop both technical proficiency and creative capacity in learners. This shift emphasizes the importance of preparing students to tackle complex, real-world problems through innovative and interdisciplinary thinking. Drawing on an integrative review of 108 scholarly articles, from Scopus, ERIC, and Web of Science, we included peer-reviewed articles published between 2010 and 2024; this paper traces the conceptual evolution of STEAM education and examines its pedagogical implications for the psychomotor domain. It critically explores how incorporating the Arts reshapes traditional understandings of skill acquisition by highlighting hands-on, embodied, and creative approaches to problem-solving. The article, therefore, explores the concept of psycho-productive competency to capture the interplay between psychomotor skills and cognitive–emotional dimensions of learning. Findings underscore the need for teaching strategies and learning environments that move beyond technical demonstration to foster creativity, innovation, and holistic development. This re-examination of the psychomotor domain positions educational practice in line with the demands of a rapidly changing, knowledge-driven world. Full article

In this review we offer a guide to organ-on-chip (OoC) technologies, covering the full experimental pipeline, from organoid derivation and culture, through microfluidic device fabrication and design strategies, to perfusion systems and data acquisition with AI-assisted analysis. At each stage, we highlight both the advantages and limitations, providing a balanced perspective that aids experimental planning and decision-making. By integrating insights from stem cell biology, bioengineering, and computational analytics, this review presents a compilation of the state of the art of OoC research. It emphasizes practical considerations for experimental design, reproducibility, and functional readouts while also exploring applications in human and veterinary medicine. Furthermore, key technical challenges, standardization issues, and regulatory considerations are discussed, offering readers a clear roadmap for advancing both foundational studies and translational applications of OoC systems. Full article

Pavement surface defects such as potholes pose significant safety risks and accelerate infrastructure deterioration. Accurate and automated detection of such defects requires both advanced sensing technologies and robust deep learning models. In this study, we propose PoFormer, a Transformer–CNN hybrid framework designed for precise segmentation of pavement potholes from heterogeneous image datasets. The architecture leverages the global feature extraction ability of Transformers and the fine-grained localization capability of CNNs, achieving superior segmentation accuracy compared to state-of-the-art models. To construct a representative dataset, we combined open source images with high-resolution field data acquired using a multi-sensor pavement inspection vehicle equipped with a line-scan camera and infrared/laser-assisted lighting. This sensing system provides millimeter-level resolution and continuous 3D surface imaging under diverse environmental conditions, ensuring robust training inputs for deep learning. Experimental results demonstrate that PoFormer achieves a mean IoU of 77.23% and a mean pixel accuracy of 84.48%, outperforming existing CNN-based models. By integrating multi-sensor data acquisition with advanced hybrid neural networks, this work highlights the potential of 3D imaging and sensing technologies for intelligent pavement condition monitoring and automated infrastructure maintenance. Full article

Emergency response to construction safety accidents is the focus of this study. Despite the abundance of data and materials available for emergency response in construction safety, the unstructured nature of the knowledge and the disordered state of storage have limited the timely application of this knowledge in decision-making for emergency response. In this study, scenario-response theory, natural language processing, and deep learning technologies were employed to construct a domain knowledge graph for emergency response in the field of safety accidents. First, based on scenario-response theory and domain-specific materials, four categories of scenario domains and 14 types of scenario elements were identified. Second, according to the mapping relationships between scenario elements and emergency response knowledge, 14 entity types and 10 relationship types were determined, thereby forming the knowledge structure pattern of this field. Subsequently, 4877 entities and 5783 relationships were extracted by means of the BERT-BiLSTM-CRF model and the BERT-CNN model, with F1 values reaching approximately 0.8. Finally, the Neo4j graph database was adopted for data storage, and a domain knowledge graph was constructed. Based on this graph, services such as knowledge association, knowledge retrieval, and intelligent question-answering were implemented. These services effectively addressed key challenges in information acquisition and decision support for on-site safety management, thereby significantly enhancing response efficiency and quality while strengthening overall safety management practices within the construction industry. Full article

Hyperspectral imaging (HSI) is increasingly used in studies for medical applications as it provides both structural and functional information of biological tissue, enhancing diagnostic accuracy and clinical decision-making. Recently, HSI cameras (HSICs) have been integrated with medical endoscopes (HSIEs), capturing hypercube data beyond conventional white light imaging endoscopes. However, there are currently no cleared or approved HSIEs by the U.S. Food and Drug Administration (FDA). HSI accuracy depends on technologies and experimental parameters, which must be assessed for reliability. Importantly, the reflectance spectrum of a target can vary across different cameras and under different environmental or operational conditions. Thus, before reliable clinical translation can be achieved, a fundamental question must be addressed: can the same target yield consistent spectral measurements across different HSI systems and under varying acquisition conditions? This study investigates the impact of eight parameters—ambient light, exposure time, camera warm-up time, spatial and temporal averaging, camera focus, working distance, illumination angle, and target angle—on spectral measurements using two HSI techniques: interferometer-based spectral scanning and snapshot. Controlled experiments were conducted to evaluate how each parameter affects spectral accuracy and whether normalization can mitigate these effects. Our findings reveal that several parameters significantly influence spectral measurements, with some having a more pronounced impact. While normalization reduced variations for most parameters, it was less effective at mitigating errors caused by ambient light and camera warm-up time. Additionally, normalization did not eliminate spectral noise resulting from low exposure time, small region of interest, or a spectrally non-uniform light source. From these results, we propose practical considerations for optimizing HSI system performance. Implementing these measures can minimize variations in reflectance spectra of identical targets captured by different cameras and under diverse conditions, thereby supporting the reliable translation of HSI techniques to clinical applications. Full article

Marine magnetic surveys are vast and time-consuming, and researchers have long been seeking an economical mode for large-area data acquisition. A towed magnetic measurement system was developed based on the motion characteristics of the wave glider. By modifying the SeaSPY2 magnetometer, a twin-body towed configuration was developed, in which an S-shaped towing cable mitigates motion-induced impacts from the platform, and a high-precision GNSS positioning module was integrated into the system. Sea trials were conducted in the coastal waters near Qingdao. The results indicated that the system achieved an average cruising speed of 0.56 m/s, with the towed body’s pitch and roll angles controlled within ±5° and ±1°, respectively. The dynamic noise was measured at 0.0639 nT (Level 1), and the internal consistency for repeated survey lines and cross lines was 1.832 nT and 1.956 nT, respectively, meeting the requirements of marine magnetic survey standards. The system offers unmanned operation, zero carbon emissions, and a minimal environmental footprint, and long endurance, supporting applications such as nearshore exploration, mapping in sensitive marine areas, and underwater magnetic target detection. The research provides a novel unmanned technological solution for deep-sea magnetic surveys and lays the foundation for low-cost, cluster-based operations. Full article

The power system is evolving with the integration of new technologies, including electronic devices and renewable energy sources, which are increasingly used to support new applications, reduce dependence on fossil fuels, and drive system innovation. However, this shift brings a significant drawback: a reduction in power quality (PQ). The literature extensively discusses the impact of poor PQ on electrical assets and explores potential solutions to this new challenge. Building on this foundation, this paper introduces new PQ indices derived from existing metrics and validated on both synthetic and real signals to assess their effectiveness. The aim is to provide researchers and system operators with simple and efficient tools for the clear identification of PQ issues in monitored networks. These new indices are designed to be flexible and independent of acquisition conditions, making them suitable for a wide range of frequencies (e.g., 50 Hz–150 kHz) and applications. After an overview of the PQ landscape, the paper demonstrates the use of these indices on various voltage waveforms, including a case study from a measurement campaign. The promising results indicate that, when combined with existing indices, these new metrics can form a strong foundation for a deeper understanding and more accurate classification of PQ issues in power networks. Full article

With the increasing application of oxide films in nuclear fuel assemblies, the accurate measurement of thin-film thickness has become increasingly critical. Traditional spectral interferometry techniques have limitations when dealing with new materials and complex structures; therefore, this study proposes a multi-channel wide-spectrum high-resolution analysis technique. This technique optimizes the utilization of photosensitive elements through multi-channel spectral sampling, combined with precision spectroscopic components and an independent optical focusing and imaging system. Simultaneously, it adopts optical correction technologies such as coma optimization and astigmatism correction to improve imaging quality and spectral resolution. Additionally, it enhances data accuracy by means of multi-channel calibration based on the least squares method and non-linear correction. The technique enables high-precision measurement ranging from the nanometer to the millimeter scale, resulting in a significantly wider measurement range compared to traditional spectrometers. Simulation verification shows that this technique outperforms existing technologies in information acquisition, analysis accuracy, and detection efficiency, and has broad application prospects in fields such as semiconductor chip manufacturing and optical coating. In the future, focus will be placed on expanding the spectral range, improving resolution, and enhancing real-time measurement capabilities. Full article

Fine-needle aspiration cytology (FNAC) is the cornerstone of thyroid nodule evaluation, standardized by the Bethesda System. However, indeterminate categories (Bethesda III–IV) remain a major challenge, often leading to unnecessary surgery or delayed molecular testing. Deep learning (DL) has recently emerged as a promising adjunct in thyroid cytopathology, with applications spanning triage support, Bethesda category classification, and integration with molecular data. Yet, routine adoption is limited by preanalytical variability (staining, slide preparation, Z-stack acquisition, scanner heterogeneity), annotation bias, and domain shift, which reduce generalizability across centers. Most studies remain retrospective and single-institution, with limited external validation. This article provides a technical overview of DL in thyroid cytology, emphasizing preanalytical sources of variability, architectural choices, and potential clinical applications. We argue that standardized datasets, multicenter prospective trials, and robust explainability frameworks are essential prerequisites for safe clinical deployment. Looking forward, DL systems are most likely to enter practice as diagnostic co-pilots, Bethesda classifiers, and multimodal risk-stratification tools. With rigorous validation and ethical oversight, these technologies may augment cytopathologists, reduce interobserver variability, and help transform thyroid cytology into a more standardized and data-driven discipline. Full article

Hydroponics offers a promising alternative to soil-based agriculture, enabling higher yields, resource efficiency, and improved crop quality. This study compares traditional hydroponic setups with systems enhanced through the Internet of Things (IoT) framework using the Nutrient Film Technique and a proportional–integral controller, focusing on growth performance and environmental control. Systems incorporating Internet of Things technology achieved a growth rate of 0.94 cm/day versus 0.16 cm/day for conventional setups, due to precise water temperature control, optimized lighting, data acquisition, targeted nutrients, and reduced pest incidence. The integration of Industry 4.0 principles further enhances sustainable production and resource management. Statistical validation under diverse conditions is recommended. Future work will add environmental sensors, refine mechanical design, and explore machine learning for adaptive control, highlighting the potential of Internet of Things–based hydroponics to transform agriculture through intelligent, efficient, and eco-friendly cultivation. Full article

Based on cross-sectional survey data from 574 grain farmers in Hebei Province, China, this study systematically analyzed, using an ordered Logit model and Bootstrap mediation effect tests, the mechanism by which information acquisition influences farmers’ adoption of green production technologies. The results showed that the diversity of information acquisition channels, content quality, and source credibility were all significantly and positively correlated with the degree of technology adoption, with content quality exhibiting the strongest correlation. Perceived usefulness played a partial mediating role between information acquisition and adoption behavior. Digital skills significantly and positively moderated the path through which information acquisition affects technology adoption—farmers with higher digital skills were more adept at converting information into technical knowledge and practices. Further heterogeneity analysis revealed that farmers with high digital skills in plain areas benefited more noticeably from information acquisition. Therefore, it is recommended that county-level agricultural technology extension centers take the lead in developing visualized technical materials to improve the quality of information content; conduct special digital skills training for elderly farmers to enhance their ability to acquire and identify information; and in regional practices, implement the supporting service of “targeted information & high-standard farmland” in plain areas while establishing a “technology demonstration household” dissemination network in mountainous areas. These measures will collectively form a differentiated and implementable technology promotion system, providing a feasible, practical path for advancing agricultural green transformation. Full article