Search Results (1,489)

This study investigates how new R&D institutions mediate policy–market disjunctures to foster integrated innovation and entrepreneurship ecosystems. Employing a longitudinal case analysis (2013–2023) of the Jiangsu Industrial Technology Research Institute (JITRI), we delineate a three-phase evolutionary process: (1) an initial government-dominated phase, stimulating foundational capability development through contract R&D; (2) a subsequent marketization phase, enabling systemic resource integration via co-creation centers and global networks; and (3) a culminating synergy phase, where policy–market alignment facilitates ecosystem optimization through crowdsourced R&D and cross-domain collaboration. Three core mechanisms underpin this adaptation: policy–market coupling (providing external momentum), endogenous capability development (absorption to innovation), and dynamic resource orchestration (acquisition to optimization). JITRI’s hybrid governance model demonstrates that stage-contingent interventions—specifically, policy anchoring in early stages followed by market-responsive resource allocation—effectively transmute inherent tensions into productive synergies. These findings yield implementable frameworks for structuring innovative ecosystems and underscore the necessity for comparative studies to establish broader theoretical generalizability. Full article

Parkinson’s disease (PD) is a progressive neurological disorder with motor and non-motor symptoms that are inadequately addressed by current pharmacological and surgical therapies. Brain–computer interfaces (BCIs), particularly those based on electroencephalography (eBCIs), provide a promising, non-invasive approach to personalized neurorehabilitation. This narrative review explores the clinical potential of BCIs in PD, discussing signal acquisition, processing, and control paradigms. eBCIs are well-suited for PD due to their portability, safety, and real-time feedback capabilities. Emerging neurophysiological biomarkers—such as beta-band synchrony, phase–amplitude coupling, and altered alpha-band activity—may support adaptive therapies, including adaptive deep brain stimulation (aDBS), as well as motor and cognitive interventions. BCIs may also aid in diagnosis and personalized treatment by detecting these cortical and subcortical patterns associated with motor and cognitive dysfunction in PD. A structured search identified 11 studies involving 64 patients with PD who used BCIs for aDBS, neurofeedback, and cognitive rehabilitation, showing improvements in motor function, cognition, and engagement. Clinical translation requires attention to electrode design and user-centered interfaces. Ethical issues, including data privacy and equitable access, remain critical challenges. As wearable technologies and artificial intelligence evolve, BCIs could shift PD care from intermittent interventions to continuous, brain-responsive therapy, potentially improving patients’ quality of life and autonomy. This review highlights BCIs as a transformative tool in PD management, although more robust clinical evidence is needed. Full article

An accurate extrinsic calibration between LiDAR and cameras is essential for effective sensor fusion, directly impacting the perception capabilities of autonomous driving systems. Although prior calibration approaches using planar and point features have yielded some success, they suffer from inherent limitations. Specifically, methods that rely on fitting planar contours using depth-discontinuous points are prone to systematic errors, which hinder the precise extraction of the 3D positions of feature points. This, in turn, compromises the accuracy and robustness of the calibration. To overcome these challenges, this paper introduces a novel 3D calibration plate incorporating the gradient depth, localization markers, and corner features. At the point cloud level, the gradient depth enables the accurate estimation of the 3D coordinates of feature points. At the image level, corner features and localization markers facilitate the rapid and precise acquisition of 2D pixel coordinates, with minimal interference from environmental noise. This method establishes a rigorous and systematic framework to enhance the accuracy of LiDAR–camera extrinsic calibrations. In a simulated environment, experimental results demonstrate that the proposed algorithm achieves a rotation error below 0.002 radians and a translation error below 0.005 m. Full article

As semiconductor manufacturing advances into the angstrom-scale era characterized by three-dimensional integration, conventional metrology technologies face fundamental limitations regarding accuracy, speed, and non-destructiveness. Although optical spectroscopy has emerged as a prominent research focus, its application in complex manufacturing scenarios continues to confront significant technical barriers. This review establishes three concrete objectives: To categorize AI–optical spectroscopy integration paradigms spanning forward surrogate modeling, inverse prediction, physics-informed neural networks (PINNs), and multi-level architectures; to benchmark their efficacy against critical industrial metrology challenges including tool-to-tool (T2T) matching and high-aspect-ratio (HAR) structure characterization; and to identify unresolved bottlenecks for guiding next-generation intelligent semiconductor metrology. By categorically elaborating on the innovative applications of AI algorithms—such as forward surrogate models, inverse modeling techniques, physics-informed neural networks (PINNs), and multi-level network architectures—in optical spectroscopy, this work methodically assesses the implementation efficacy and limitations of each technical pathway. Through actual application case studies involving J-profiler software 5.0 and associated algorithms, this review validates the significant efficacy of AI technologies in addressing critical industrial challenges, including tool-to-tool (T2T) matching. The research demonstrates that the fusion of AI and optical spectroscopy delivers technological breakthroughs for semiconductor metrology; however, persistent challenges remain concerning data veracity, insufficient datasets, and cross-scale compatibility. Future research should prioritize enhancing model generalization capability, optimizing data acquisition and utilization strategies, and balancing algorithm real-time performance with accuracy, thereby catalyzing the transformation of semiconductor manufacturing towards an intelligence-driven advanced metrology paradigm. Full article

The segmentation of individual trees holds considerable significance in the investigation and management of forest resources. Utilizing smartphone-captured imagery combined with image-based 3D reconstruction techniques to generate corresponding point cloud data can serve as a more accessible and potentially cost-efficient alternative for data acquisition compared to conventional LiDAR methods. In this study, we present a Sparse 3D U-Net framework for single-tree segmentation which is predicated on a multi-head attention mechanism. The mechanism functions by projecting the input data into multiple subspaces—referred to as “heads”—followed by independent attention computation within each subspace. Subsequently, the outputs are aggregated to form a comprehensive representation. As a result, multi-head attention facilitates the model’s ability to capture diverse contextual information, thereby enhancing performance across a wide range of applications. This framework enables efficient, intelligent, and end-to-end instance segmentation of forest point cloud data through the integration of multi-scale features and global contextual information. The introduction of an iterative mechanism at the attention layer allows the model to learn more compact feature representations, thereby significantly enhancing its convergence speed. In this study, Dongsheng Bajia Country Park and Jiufeng National Forest Park, situated in Haidian District, Beijing, China, were selected as the designated test sites. Eight representative sample plots within these areas were systematically sampled. Forest stand sequential photographs were captured using an iPhone, and these images were processed to generate corresponding point cloud data for the respective sample plots. This methodology was employed to comprehensively assess the model’s capability for single-tree segmentation. Furthermore, the generalization performance of the proposed model was validated using the publicly available dataset TreeLearn. The model’s advantages were demonstrated across multiple aspects, including data processing efficiency, training robustness, and single-tree segmentation speed. The proposed method achieved an F1 score of 91.58% on the customized dataset. On the TreeLearn dataset, the method attained an F1 score of 97.12%. Full article

This paper presents a technique for extracting the initial parameters of the longitudinal phase space of freshly injected bunches in an electron storage ring. This technique combines simulation of single-bunch longitudinal phase space evolution with a bunch-by-bunch data acquisition and processing system, enabling high-precision determination of initial phase space parameters during electron storage ring injection—including the initial phase, initial bunch length, initial energy offset, initial energy spread, and initial energy chirp. In our experiments, a high-speed oscilloscope captured beam injection signals, which were then processed by the bunch-by-bunch data acquisition system to extract the evolution of the injected bunch’s phase and length. Additionally, a single-bunch simulation software package was developed, based on mbtrack2 and PyQt5, that is capable of simulating the phase space evolution of bunches under different initial parameters after injection. By employing a genetic algorithm to iteratively align simulation results with experimental data, the remaining initial phase space parameters of the injected bunch can be accurately determined. Full article

We present a corrosion-resistant quadrupole ion trap mass spectrometer (QIT-MS) designed for trace detection of volatiles in sulfuric acid aerosols, with a specific focus on phosphine (PH₃). Here, we detail the gas calibration methodology using permeation tube technology for generating certified ppb-level PH₃/H₂S/CO₂ mixtures, and report results from mass spectra with sufficient resolution to distinguish isotopic envelopes that validate the detection of PH₃ at a concentration of 62 ppb. Fragmentation patterns for PH₃ and H₂S agree with NIST data, and signal-to-noise performance confirms ppb sensitivity over 2.6 h acquisition periods. We further assess spectral interferences from oxygen isotopes and propose a detection scheme based on isolated phosphorus ions (P⁺) to enable specific and interference-resistant identification of PH₃ and other reduced phosphorus species of astrobiological interest in Venus-like environments. This work extends the capabilities of QIT-MS for trace gas analysis in chemically aggressive atmospheric conditions. Full article

High-speed communication can be achieved using laser inter-satellite links. However, laser terminals are highly sensitive to environmental conditions, which can lead to link disconnections. Therefore, an acquisition method capable of determining pointing errors is essential. In this study, a fast space–time fusion acquisition method was developed. This method establishes the relationship between satellite position, capture time, and azimuth and elevation angles. The performance of the proposed acquisition time optimization method was verified in a practical engineering application. Experimental results show that the pointing error was reduced by five times, the acquisition rate increased by 40%, the acquisition speed improved by 300 times, and multiple interference factors were effectively addressed. Full article

This paper presents a robust hardware system designed for future detection of faults in wind turbines by analyzing audible noise signals. Predictive maintenance strategies have increasingly relied on acoustic monitoring as a non-invasive method for identifying anomalies that may indicate component wear, misalignment, or impending mechanical failures. The proposed device captures and processes sound signals in real-time using strategically positioned microphones, ensuring high-fidelity data acquisition without interfering with turbine operation. Signal processing techniques are applied to extract relevant acoustic features, facilitating future identification of abnormal sound patterns that may indicate mechanical issues. The system’s effectiveness was validated through rigorous field tests, demonstrating its capability to enhance the reliability and efficiency of wind turbine maintenance. Experimental results showed an average transmission latency of 131.8 milliseconds, validating the system’s applicability for near real-time audible noise monitoring in wind turbines operating under limited connectivity conditions. Full article

Trajectory planning is crucial for solar aircraft endurance. The multi-wing morphing solar aircraft can enhance solar energy acquisition through wing deflection, which simultaneously incurs aerodynamic losses, complicating energy coupling and challenging existing planning methods in efficiency and long-term optimization. This study presents an energy-optimal trajectory planning method based on Hierarchical Reinforcement Learning for morphing solar-powered Unmanned Aerial Vehicles (UAVs), exemplified by a Λ-shaped aircraft. This method aims to train a hierarchical policy to autonomously track energy peaks. It features a top-level decision policy selecting appropriate bottom-level policies based on energy factors, which generate control commands such as thrust, attitude angles, and wing deflection angles. Shaped properly by reward functions and training conditions, the hierarchical policy can enable the UAV to adapt to changing flight conditions and achieve autonomous flight with energy maximization. Evaluated through 24 h simulation flights on the summer solstice, the results demonstrate that the hierarchical policy can appropriately switch its bottom-level policies during daytime and generate real-time control commands that satisfy optimal energy power requirements. Compared with the minimum energy consumption benchmark case, the proposed hierarchical policy achieved 0.98 h more of full-charge high-altitude cruise duration and 1.92% more remaining battery energy after 24 h, demonstrating superior energy optimization capabilities. In addition, the strong adaptability of the hierarchical policy to different quarterly dates was demonstrated through generalization ability testing. Full article

Earthwork construction presents significant challenges due to its unique characteristics, including irregular topography, inhomogeneous geotechnical properties, dynamic operations involving heavy equipment, and continuous terrain updates over time. Existing methods often fail to accurately capture these complexities, support semantic attributes, simulate realistic equipment–environment interactions, and update the model dynamically during construction. Moreover, most current digital solutions lack an integrated framework capable of linking geotechnical semantics with construction progress in a continuously evolving terrain. This study introduces a novel, voxel-based digital twin framework tailored for earthwork construction. Unlike previous studies that relied on surface, mesh, or layer-based representations, our approach leverages semantically enriched voxelization to encode spatial, material, and behavioral attributes at a high resolution. The proposed framework connects the physical and digital representations of the earthwork environment and is structured into five modules. The data acquisition module gathers terrain, geotechnical, design, and construction data. Virtual models are created for the earthwork in as-planned and as-built models. The digital twin core module utilizes voxels to create a realistic earthwork environment that integrates the as-planned and as-built models, facilitating model–equipment interaction and updating models for progress monitoring. The visualization and simulation module enables model–equipment interaction based on evolving as-built conditions. Finally, the monitoring and analysis module provides volumetric progress insights, semantic material information, and excavation tracking. The key innovation of this framework lies in multi-resolution voxel modeling, semantic mapping of geotechnical properties, and supporting dynamic updates during ongoing construction, enabling model–equipment interaction and material-specific construction progress monitoring. The framework is validated through real-world case studies, demonstrating its effectiveness in providing realistic representations, model–equipment interactions, and supporting progress information and operational insights. Full article

Structural Health Monitoring (SHM) is an essential technique for continuously assessing structural conditions using integrated sensor systems during operation. SHM technologies have evolved to address the increasing demand for efficient maintenance strategies in advanced engineering fields, such as civil infrastructure, aerospace, and transportation. However, developing a miniaturized, cost-effective, and multi-sensor solution based on Wireless Sensor Networks (WSNs) remains a significant challenge, particularly for SHM applications in weight-sensitive aerospace structures. To address this, the present study introduces a novel WSN-based Multi-Sensor System (MSS) that integrates multiple sensing capabilities onto a 3 × 3 cm flexible Printed Circuit Board (PCB). The proposed system combines a Piezoelectric Transducer (PZT) for impact detection; a strain gauge for mechanical deformation monitoring; an accelerometer for capturing dynamic responses; and an environmental sensor measuring temperature, pressure, and humidity. This high level of functional integration, combined with real-time Data Acquisition (DAQ) and precise time synchronization via Bluetooth Low Energy (LE), distinguishes the proposed MSS from conventional SHM systems, which are typically constrained by bulky hardware, single sensing modalities, or dependence on wired communication. Experimental evaluations on composite panels and aluminum specimens demonstrate reliable high-fidelity recording of PZT signals, strain variations, and acceleration responses, matching the performance of commercial instruments. The proposed system offers a low-power, lightweight, and scalable platform, demonstrating strong potential for on-board SHM in aircraft applications. Full article

Inclusive music education refers to the use of musical experiences and practices as tools for promoting participation, equity, and meaningful engagement among all learners, including those with Special Educational Needs (SEN). Music education has long been recognized not only for its value in emotional expression and cultural transmission but also for its cognitive and relational benefits. This entry examines the inclusive and transformative potential of music, highlighting how it can foster equitable, accessible, and culturally relevant learning environments. Drawing from pedagogy, neuroscience, and educational technology, the entry explores how music contributes to cognitive, emotional, and social development, with a focus on learners with SEN. It emphasizes the importance of early exposure to music, the strong connections between music and language acquisition, and the need to challenge persistent misconceptions about innate musical talent. The findings demonstrate that when supported by digital tools and educational games, music education becomes a powerful driver of inclusion, enhancing participation, relational dynamics, and cognitive engagement. The entry concludes by advocating for a reimagining of music not as a secondary subject, but as a foundational component of holistic and inclusive education, capable of building more empathetic, connected, and equitable societies. Full article

With the advancement of the third-generation BeiDou Navigation Satellite System (BDS-3), BeiDou dual-frequency multi-constellation (DFMC) receivers exhibit distinct advantages in accuracy and reliability due to their dual-frequency capabilities. However, the integration time imposes constraints on further improvements in sensitivity. To address this limitation, this study proposes a dual-frequency cooperative acquisition strategy targeting the B1C and B2a signals, with the objective of enhancing acquisition performance in weak signal environments. A dual-channel acquisition architecture was designed, incorporating an inter-frequency Doppler assistance technique to improve acquisition efficiency. Simulation results demonstrate that, compared to conventional fixed short integration time architectures, the proposed cooperative acquisition approach increases the receiver’s acquisition sensitivity by 5.7 dB. Real-world experiments further confirm the effectiveness of this strategy, achieving successful acquisition of the PRN28 signal with 5 ms of coherent integration, thereby highlighting its practical utility. This research offers an innovative solution for high-sensitivity signal acquisition in challenging environments for BeiDou DFMC receivers and provides valuable insights for the advancement of high-precision BeiDou applications. Full article

This paper investigates integrating a sensory data model for managing an existing 50-year-old building. A primary challenge in retrofitting older structures is the optimal deployment of high-quality sensors, systematic data acquisition, and subsequent data management. To address this, the study implemented a network of over 50 sensors connected via 270 m of wired infrastructure, deliberately avoiding wireless transmission to ensure data reliability. This configuration generates 5568 data points daily, which are archived on a dedicated server. The data is planned for integration into the Campus Geographical Information System (GIS), enabling private and public access. A methodology was employed, involving the strategic placement of sensors based on building use patterns, continuous data monitoring, and iterative sensor performance evaluation. The findings from the study indicate that integrating sensory data through this structured approach significantly enhances building management capabilities. Specifically, the results demonstrate improved energy efficiency and environmental performance, which is particularly relevant for public and educational facilities. The research highlights that a data-driven, monitoring-based management system can optimize operational functions and inform future retrofitting strategies for aging buildings. Full article