Search Results (7,554)

Background/Objectives: The recent development of four-dimensional X-ray velocimetry (4DXV) technology (three-dimensional space and time) provides a unique opportunity to obtain preclinical quantitative functional lung images. Only single-scan measurements in non-survival studies have been obtained to date; thus, methodologies enabling animal survival for repeated imaging to be accomplished over weeks or months from the same animal would establish new opportunities for the assessment of pathophysiology drivers and treatment response in advanced preclinical drug-screening efforts. Methods: An anesthesia protocol developed for animal recovery to allow for repetitive, longitudinal scanning of individual animals over time. Test–retest imaging scans from the lungs of healthy mice were performed over 8 weeks to assess the repeatability of scanner-derived quantitative imaging metrics and variability. Results: Using a murine model of fibroproliferative lung disease, this longitudinal scanning approach captured heterogeneous progressive changes in pulmonary function, enabling the visualization and quantitative measurement of averaged whole lung metrics and spatial/regional change. Radiation dosimetry studies evaluated the effects of imaging acquisition protocols on X-ray dosage to further adapt protocols for the minimization of radiation exposure during repeat imaging sessions using these newly developed image acquisition protocols. Conclusions: Overall, we have demonstrated that the 4DXV advanced imaging scanner allows for repeat measurements from the same animal over time to enable the high-resolution, noninvasive mapping of quantitative lung airflow dysfunction in mouse models with heterogeneous pulmonary disease. The animal anesthesia and image acquisition protocols described will serve as the foundation on which further applications of the 4DXV technology can be used to study a diverse array of murine pulmonary disease models. Together, 4DXV provides a novel and significant advancement for the longitudinal, noninvasive interrogation of pulmonary disease to assess spatial/regional disease initiation, progression, and response to therapeutic interventions. Full article

Water electrolysis represents a viable and scalable green hydrogen production technology, which mitigates carbon emissions and contributes to environmental sustainability. Transition metal-based layered double hydroxides (LDHs) exhibit excellent oxygen evolution reaction (OER) efficiency, attributed to their adjustable interlayer spacing combined with abundant active sites. Here, we report a uniform multimetallic catalyst, demonstrating robust and efficient OER performance for high-performance water splitting. SEM and TEM confirmed its ultrathin hierarchical nanosheet structure. The characteristic peaks of LDH in XRD and Raman spectra further verified the successful synthesis of the LDH material. Fe-CoZn LDH delivers exceptional OER performance in 1 M KOH, requiring overpotentials of just 209, 238, and 267 mV to reach 10, 100, and 400 mA cm⁻², respectively. The catalyst also demonstrates exceptional hydrogen evolution reaction (HER) performance, achieving 10 mA cm⁻² at 119 mV. It also has excellent stability, with stable operation for up to 100 h under 100 mA cm⁻² in 1 M KOH electrolyte solution. Full article

The rapid advancement of kinetic pursuit technologies has significantly increased the difficulty of evasion for high-speed UAVs (HSUAVs), particularly in scenarios where two collaboratively operating pursuers approach from the same direction with optimized initial space intervals. This paper begins by deriving an optimal initial space interval to enhance cooperative pursuit effectiveness and introduces an evasion difficulty classification framework, thereby providing a structured approach for evaluating and optimizing evasion strategies. Based on this, an intelligent maneuver evasion strategy using semantic classification progressive learning with twin delayed deep deterministic policy gradient (SCPL-TD3) is proposed to address the challenging scenarios identified through the analysis. Training efficiency is enhanced by the proposed SCPL-TD3 algorithm through the employment of progressive learning to dynamically adjust training complexity and the integration of semantic classification to guide the learning process via meaningful state-action pattern recognition. Built upon the twin delayed deep deterministic policy gradient framework, the algorithm further enhances both stability and efficiency in complex environments. A specially designed reward function is incorporated to balance evasion performance with mission constraints, ensuring the fulfillment of HSUAV’s operational objectives. Simulation results demonstrate that the proposed approach significantly improves training stability and evasion effectiveness, achieving a 97.04% success rate and a 7.10–14.85% improvement in decision-making speed. Full article

This paper proposes a radar mainlobe anti-jamming method based on Space-Time Coding (STC) and Blind Source Separation (BSS). Addressing the performance degradation issue of traditional BSS methods under low Signal-to-Noise Ratio (SNR) and insufficient spatial resolution, this study first establishes the airborne SAR imaging geometric model and the jamming signal mixing model. Subsequently, STC technology is introduced to construct more equivalent phase centers and increase the system’s spatial Degrees of Freedom (DOF). Leveraging the increased DOFs, a JADE-based blind source separation algorithm is then employed to separate the mixed jamming signals. The separation of these signals significantly enhances the anti-jamming capability of the radar system. Simulation results demonstrate that the proposed method effectively improves BSS performance. As compared to traditional BSS schemes, this method provides an additional jamming suppression gain of approximately 10 dB in point target scenarios and about 3 dB in distributed target scenarios, significantly enhancing the radar system’s mainlobe anti-jamming capability in complex jamming environments. This method provides a new insight into radar mainlobe anti-jamming by combining the STC scheme and BSS technology. Full article

To address the limitations of traditional cotton leaf nitrogen content estimation methods, which include low efficiency, high cost, poor portability, and challenges in vegetation index acquisition owing to environmental interference, this study focused on emerging non-destructive nutrient estimation technologies. This study proposed an innovative method that integrates multi-color space fusion with deep and machine learning to estimate cotton leaf nitrogen content using smartphone-captured digital images. A dataset comprising smartphone-acquired cotton leaf images was processed through threshold segmentation and preprocessing, then converted into RGB, HSV, and Lab color spaces. The models were developed using deep-learning architectures including AlexNet, VGGNet-11, and ResNet-50. The conclusions of this study are as follows: (1) The optimal single-color-space nitrogen estimation model achieved a validation set R² of 0.776. (2) Feature-level fusion by concatenation of multidimensional feature vectors extracted from three color spaces using the optimal model, combined with an attention learning mechanism, improved the validation R² to 0.827. (3) Decision-level fusion by concatenating nitrogen estimation values from optimal models of different color spaces into a multi-source decision dataset, followed by machine learning regression modeling, increased the final validation R² to 0.830. The dual fusion method effectively enabled rapid and accurate nitrogen estimation in cotton crops using smartphone images, achieving an accuracy 5–7% higher than that of single-color-space models. The proposed method provides scientific support for efficient cotton production and promotes sustainable development in the cotton industry. Full article

The historical pipe organ, an instrument of vast scale and complex construction, has a significant connection to liturgical celebration and to the history of European churches. It is also one of the few musical instruments considered to be a work of architecture. The evolution of organ building, especially in the seventeenth to nineteenth centuries, required deep knowledge of musical culture and technology. The significance of this relationship is illustrated by the example of the former and present organs of the church of St. Mary Magdalene in Wroclaw (Breslau). The first church organs appeared here in the Middle Ages, and as will be shown, in subsequent eras, their location, form, and décor were changed according to evolving cultural and liturgical mandates as well as changes to the structure of the church architecture. The history of the specific organs of the church of St. Mary Magdalene is the product of a rich history of monumental construction, reconstruction, conservation, and restoration, and it is poised to offer a continuation of this tradition in the present and future of the parish and in music history with proposed restorations and renovations of their historic space and instruments. Full article

In educational technology environments, the ability to customize digital text presentation offers opportunities to enhance learning experiences through typographical adjustments. This study investigated how digital letter spacing (LS) manipulations affect reading comprehension (RC), reading speed, calibration of comprehension (CoC), and preferences in emergent readers. We examined 163 second graders and 126 third graders as they read digital texts in Hebrew under two conditions: standard LS (100%) and increased LS (150%). The results revealed developmental differences in response to spacing manipulations: increased LS significantly improved RC in second graders, whereas it showed an opposite trend for third graders. Reading rate remained stable across conditions for both groups. Children also demonstrated more accurate CoC under their individually optimal LS condition, suggesting that their comprehension monitoring was responsive to typographical features that supported reading. Preferences analysis indicated a higher numerical proportion of participants favoring the standard LS condition over the increased LS condition in both grades. These findings imply that by integrating adaptive typographical features into educational technology, educators can enhance performance in developing readers, supporting differentiated instruction in increasingly digital learning environments. Full article

In free space optical (FSO) communications, signals are affected by turbulence when transmitted through the atmosphere. Fluctuations in intensity caused by atmospheric turbulence lead to an increase in the bit error rate of FSO systems. Deep learning (DL), as a current research hotspot, offers a promising approach to improve the accuracy of signal detection. In this paper, we propose a signal detection method based on a bidirectional gated recurrent unit (BiGRU) neural network for FSO communications. The proposed detection method considers the temporal correlation of received signals due to the properties of the BiGRU neural network, which is not available in existing detection methods based on DL. In addition, the proposed detection method does not require channel state information (CSI) for channel estimation, unlike maximum likelihood (ML) detection technology with perfect CSI. Numerical results demonstrate that the proposed BiGRU-based detector achieves significant improvements in bit error rate (BER) performance compared with a multilayer perceptron (MLP)-based detector. Specifically, under weak turbulence conditions, the BiGRU-based detector achieves an approximate 2 dB signal-to-noise ratio (SNR) gain at a target BER of ${10}^{-6}$ compared to the MLP-based detector. Under moderate turbulence conditions, it achieves an approximate 6 dB SNR gain at the same target BER of ${10}^{-6}$. Under strong turbulence conditions, the proposed detector obtains a 6 dB SNR gain at a target BER of ${10}^{-4}$. Additionally, it outperforms conventional methods by more than one order of magnitude in BER under the same turbulence and SNR conditions. Full article

Bud eye identification is a critical step in the intelligent seed cutting process for potatoes. This study focuses on the challenges of low testing accuracy and excessive weighted memory in testing models for potato bud eye detection. It proposes an improved potato bud eye detection method based on YOLOv5s, referred to as the YOLO-SCA model, which synergistically optimizing three main modules. The improved model introduces the ShuffleNetV2 module to reconstruct the backbone network. The channel shuffling mechanism reduces the model’s weighted memory and computational load, while enhancing bud eye features. Additionally, the CBAM attention mechanism is embedded at specific layers, using dual-path feature weighting (channel and spatial) to enhance sensitivity to key bud eye features in complex contexts. Then, the Alpha-IoU function is used to replace the CloU function as the bounding box regression loss function. Its single-parameter control mechanism and adaptive gradient amplification characteristics significantly improve the accuracy of bud eye positioning and strengthen the model’s anti-interference ability. Finally, we conduct pruning based on the channel evaluation after sparse training, accurately removing redundant channels, significantly reducing the amount of computation and weighted memory, and achieving real-time performance of the model. This study aims to address how potato bud eye detection models can achieve high-precision real-time detection under the conditions of limited computational resources and storage space. The improved YOLO-SCA model has a size of 3.6 MB, which is 35.3% of the original model; the number of parameters is 1.7 M, which is 25% of the original model; and the average accuracy rate is 95.3%, which is a 12.5% improvement over the original model. This study provides theoretical support for the development of potato bud eye recognition technology and intelligent cutting equipment. Full article

With the widespread adoption of space–air–ground integrated networks (SAGINs) in next-generation wireless communications, intelligent reflecting surfaces (IRSs) have emerged as a key technology for enhancing system performance through passive link reinforcement. This paper addresses the prevalent issue of channel state information (CSI) uncertainty in practical systems by constructing an IRS-assisted multi-hop SAGIN communication model. To capture the performance degradation caused by channel estimation errors, a norm-bounded uncertainty model is introduced. A simulated annealing (SA)-based phase optimization algorithm is proposed to enhance system robustness and improve worst-case communication quality. Simulation results demonstrate that the proposed method significantly outperforms traditional multiple access strategies (SDMA and NOMA) under various user densities and perturbation levels, highlighting its stability and scalability in complex environments. Full article

Erbium (${\mathrm{Er}}^{3+}$) emitters are relevant for optical applications due to their narrow emission line directly in the telecom C-band due to the ⁴I_13/2 → ⁴I_15/2 transition at 1.54 μm. Additionally, they are promising candidates for future quantum technologies when embedded in thin film silicon-on-insulator (SOI) to achieve fabrication scalability and CMOS compatibility. In this paper we integrate Er³⁺ emitters in SOI metasurfaces made of closely spaced arrays of nanodisks, to study their spontaneous emission via room and cryogenic temperature confocal microscopy, off-resonance and in-resonance photoluminescence excitation at room temperature and time-resolved spectroscopy. This work demonstrates the possibility to adopt CMOS-compatible and fabrication-scalable metasurfaces for controlling and improving the collection efficiency of the spontaneous emission from the Er³⁺ transition in SOI and that they could be adopted in similar technologically advanced materials. Full article

Planned events in urban public spaces often face design challenges, and consequent poor performance, due to limited consideration of spatial criteria during the planning process. Our previous work revealed that event designers tend to have no urban design, or similar, training. Consequently, this paper reports on a Virtual Reality (VR)/Mixed Reality (MR) tool developed as a ‘proof of concept’ to support event designers in evaluating and modifying event layouts using urban design principles. Building on a previous study that identified key design-based criteria, including pedestrian flow, permeability, and geometry, this research applies those criteria through interactive, immersive environments. A VR experiment involving three sessions with users demonstrated how the tool facilitates spatial analysis and encourages reflective design thinking. Insights from the sessions highlight the value of visual representation in decision-making and suggest directions for future tool development, such as expanding the criteria set and incorporating real-time data. The study concludes by proposing that immersive technologies can enhance collaborative and responsive temporary event design for public spaces. Full article

Smart tourism destinations, shaped by the integration of tourism and information technology, have become a central theme in international academic research. This study employs bibliometric methods using CiteSpace to conduct co-authorship, co-citation, keyword co-occurrence, and burst analyses, with the aim of mapping the knowledge structure and research evolution of the field. Drawing on 232 articles from the Web of Science Core Collection (2013–2025), the results reveal a shift from technology-centered approaches toward themes of visitor experience, collaborative governance, and sustainable development. The Universitat d’Alacant (Spain) and The Hong Kong Polytechnic University (China) have emerged as leading research hubs, with Ivars-Baidal and colleagues as major contributors. Foundational studies by Buhalis and Gretzel continue to shape the domain. Keyword trends highlight increasing attention to technological efficiency and sustainable ethics. Overall, the study traces the developmental trajectory of smart tourism destinations, proposes a systematic knowledge framework, and identifies future directions for theoretical integration and methodological innovation. The findings provide both conceptual insights for academic research and strategic guidance for destination governance and policy. Full article

Lunar dust remains one of the most critical unresolved challenges to long-duration lunar missions. Its sharp, abrasive, and electrostatically charged particles are easily inhaled and can penetrate deep into the lungs, reaching the bloodstream and the brain. Despite airlocks and HEPA filtration systems, dust will inevitably infiltrate lunar habitats and threaten astronaut health. We present a novel patent protected helmet design. This system uses a multilayered, synergistic mitigation approach combining mechanical and electrostatic defenses. The mechanical system delivers HEPA-filtered, ionized air across the user’s face, while the electrostatic barrier repels charged particles away from the respiratory zone. These two systems work together to prevent dust from entering the user’s breathing space. Designed for use inside lunar habitats, this helmet represents a potential solution to an unaddressed, life-threatening problem. It allows astronauts to eat, talk, and sleep while maintaining a protected respiratory zone and provides targeted inhalation-level protection in an environment where dust exposure is otherwise unavoidable. This concept is presented at Technology Readiness Level 2 (TRL 2) to prompt early engagement and feedback from the scientific and engineering communities. Full article

Biomimetic structures inspired by evolutionary optimized biological systems offer promising solutions to overcome current limitations in passive daytime radiative cooling (PDRC) technology, which efficiently scatters solar radiation through atmospheric windows and radiates surface heat into space without additional energy consumption. While structural biomimicry provides excellent optical performance and feasibility, its complex manufacturing and high costs limit scalability due to micro–nano fabrication constraints. Material-based biomimicry, utilizing environmentally friendly and abundant raw materials, offers greater scalability but requires improvements in mechanical durability. Adaptive biomimicry enables intelligent regulation with high responsiveness but faces challenges in system complexity, stability, and large-scale integration. These biologically derived strategies provide valuable insights for advancing radiative cooling devices. This review systematically summarizes recent progress, elucidates mechanisms of key biological structures for photothermal regulation, and explores their application potential across various fields. It also discusses current challenges and future research directions, aiming to promote deeper investigation and breakthroughs in biomimetic radiative cooling technologies. Full article