Search Results (11,571)

The magnetic properties of molecular nanomagnets can be finely modulated by light, which provides great potential in optical switches, smart sensors, and data storage devices. Light-induced spin transition, structure changes, and radical formation could tune the static and dynamic magnetic properties of molecular nanomagnets with high spatial and temporal resolutions. Herein, we summarize the design strategies of photoresponsive molecular nanomagnets and review the recent advances in transition metal/lanthanide molecular nanomagnets with photomagnetic properties. The photoresponsive mechanism based on spin transition, photocyclization, and photogenerated radicals is discussed in detail, providing insights into the photomagnetic properties of molecular nanomagnets for advanced photoresponsive materials. Full article

Waveguide-integrated metasurfaces offer a promising platform for ultracompact on-chip optical systems, enabling applications such as fluorescence sensing, holography, and near-eye displays. In particular, integrated achromatic metalenses that couple guided modes to free-space radiation are highly desirable for single-molecule fluorescence sensing, where high numerical aperture (NA), efficient light focusing, and consistent focal volume overlap across excitation and emission wavelengths are critical. However, designing integrated high-NA metalenses with multi-wavelength operation remains fundamentally challenging due to the wavelength-dependent propagation of guided modes. Here, we present an inverse design framework that simultaneously optimizes the geometries and positions of silicon nitride nanofins atop a slab waveguide to achieve diffraction-limited focusing at three wavelengths with unity NA. The resulting metalens outperforms conventional segmented designs in focusing efficiency and sidelobe suppression, particularly at wavelengths corresponding to the excitation and emission bands of the model fluorophore Alexa Fluor 647. Numerical analysis shows that the design yields a high molecule detection efficiency suitable for epi-fluorescence single-molecule sensing. This work highlights the potential of inverse-designed metalenses as a versatile on-chip platform for advanced applications in fluorescence spectroscopy, augmented reality, or optical trapping. Full article

Background/Objectives: Bones are essential components of the human body, providing structural support, enabling mobility, storing minerals, and protecting internal organs. Bone fractures (BFs) are common injuries that result from excessive physical force and can lead to serious complications, including bleeding, infection, impaired oxygenation, and long-term disability. Early and accurate identification of fractures through radiographic imaging is critical for effective treatment and improved patient outcomes. However, manual evaluation of X-rays is often time-consuming and prone to diagnostic errors due to human limitations. To address this, artificial intelligence (AI), particularly deep learning (DL), has emerged as a powerful tool for enhancing diagnostic precision in medical imaging. Methods: This research introduces a novel convolutional neural network (CNN) model, the Multi-Level Feature Fusion Network (MLFNet), designed to capture and integrate both low-level and high-level image features. The model was evaluated using the Bone Fracture Multi-Region X-ray (BFMRX) dataset. Preprocessing steps included image normalization, resizing, and contrast enhancement to ensure stable convergence, reduce sensitivity to lighting variations in radiographic images, and maintain consistency. Ablation studies were conducted to assess architectural variations, confirming the model’s robustness and generalizability across data distributions. MLFNet’s high accuracy, interpretability, and efficiency make it a promising solution for clinical deployment. Results: MLFNet achieved an impressive accuracy of 99.60% as a standalone model and 98.81% when integrated into hybrid ensemble architectures with five leading pre-trained DL models. Conclusions: The proposed approach supports timely and precise fracture detection, optimizing the diagnostic process and reducing healthcare costs. This approach offers significant potential to aid clinicians in fields such as orthopedics and radiology, contributing to more equitable and effective patient care. Full article

Within transport systems, train stations cover a primary role as places where access to different modes of transport must be realised effectively, providing a valuable opportunity to make rail services, public transport, and soft mobility more attractive. This research seeks to shed some light on how Italian travellers perceive the quality of train stations, and to identify priorities for action in relation to design, building, and operation that might help revitalise their attractiveness. The methodology involved designing a questionnaire capable of identifying significant correlations between attitudinal and behavioural variables via an exploratory factor analysis, reaching around 400 respondents through a snowball sampling plan. The factor “sociality and daily life” showed the importance that people place on the vitality of urban places. Travellers also consider other factors, like the overall service quality, the cleanliness and safety of a train station, the walkability of connections within the node, and the possibility of reaching the station by bicycle. The profiling of respondents using a cluster analysis based on latent factors points to specific policies, showing how actions targeting stations can have positive effects on the use of rail transport and on the propensity towards intermodality and sustainable mobility. A safe, “living” place can mitigate the risk of social degradation, while promoting walking and cycling. Full article

Urban parks often represent the last viable habitats for wildlife in city centres, functioning as crucial refuges and biodiversity hotspots for a wide array of plant and animal species. This study investigates the issue of light pollution in urban parks in selected Polish cities from the perspective of sustainable urban development and dark-sky friendly ordinances. Field data conducted in 2024 and 2025 include measurements of Upward Light Output Ratio (ULOR), illuminance, luminance, correlated colour temperature (CCT), and spectral characteristics of light sources. In addition, an analysis of changes in the level of light pollution in the studied parks and their surroundings between 2012 and 2025 was performed using data from the VIIRS (Visible Infrared Imaging Radiometer Suite) located on the Suomi NPP satellite. Results highlight the mismatch between sustainable development objectives and the current practice of lighting in most of the analysed parks. The study emphasises the need for better integration of light pollution mitigation in urban spatial policies and provides recommendations for environmentally and socially responsible lighting design in urban parks. Full article

Fraudulent online payment operations represent a persistent challenge in digital commerce, particularly in sectors like air travel, where credit and debit card payments dominate. This study presents a novel fraud detection framework tailored to airline ticket purchases, combining a synthetic dataset generator with a modular, customizable feature engineering process. These are two machine learning models—support vector machines (SVMs) and the light gradient boosting machine (LightGBM)—for real-time fraud detection. A synthetic dataset was generated, including a rich set of engineered features reflecting realistic user, transaction, and flight-related attributes. While both models were evaluated using classification-evaluation metrics, LightGBM outperformed SVMs in terms of overall performance with an accuracy of 94.2% and a recall of 71.3% for fraudulent cases. The main contribution of this study is the design of a reusable, customizable feature engineering framework for fraud detection in the airline sector, along with the development of a lightweight, adaptable fraud detection system for merchants, especially small and medium-sized enterprises. These findings support the use of advanced machine learning methods to enhance security in digital airline transactions. Full article

Context and Objective: Food quality and shelf life are strongly influenced by the interaction between packaging properties and mass transport processes. This study explored how hurdle technology can be applied to food preservation, focusing on olive oil as a practical case due to its high sensitivity to oxidation and light. Methodology: An analogy was developed between transport phenomena in packaging and the fundamental laws of electricity, providing a simple physical basis for understanding preservation mechanisms. This was supported by parametric simulations and mathematical modeling, which were used to predict how different packaging materials and conditions influence product stability. Main Results: The application to olive oil showed that packaging properties such as resistance to oxygen and light permeation have a direct effect on preservation effectiveness. Model predictions highlighted clear differences in stability depending on the choice of packaging, demonstrating the critical role of material selection. Conclusions: The study presents an integrated framework that links packaging characteristics with food preservation outcomes. By combining physical analogies with modeling tools, it offers a practical basis for designing packaging solutions that extend shelf life and protect sensitive foods such as olive oil. Full article

Small-target detection in Unmanned Aerial Vehicle (UAV) aerial images remains a significant and unresolved challenge in aerial image analysis, hampered by low target resolution, dense object clustering, and complex, cluttered backgrounds. In order to cope with these problems, we present AeroLight, a novel and efficient detection architecture that achieves high-fidelity performance in resource-constrained environments. AeroLight is built upon three key innovations. First, we have optimized the feature pyramid at the architectural level by integrating a high-resolution head specifically designed for minute object detection. This design enhances sensitivity to fine-grained spatial details while streamlining redundant and computationally expensive network layers. Second, a Dynamic Feature Fusion (DFF) module is proposed to adaptively recalibrate and merge multi-scale feature maps, mitigating information loss during integration and strengthening object representation across diverse scales. Finally, we enhance the localization precision of irregular-shaped objects by refining bounding box regression using a Shape-IoU loss function. AeroLight is shown to improve mAP50 and mAP50-95 by 7.5% and 3.3%, respectively, on the VisDrone2019 dataset, while reducing the parameter count by 28.8% when compared with the baseline model. Further validation on the RSOD dataset and Huaxing Farm Drone dataset confirms its superior performance and generalization capabilities. AeroLight provides a powerful and efficient solution for real-world UAV applications, setting a new standard for lightweight, high-precision object recognition in aerial imaging scenarios. Full article

The complex topography of China’s Tibetan Plateau mountainous roads, characterized by diverse curve types and frequent traffic accidents, significantly impacts the safety and sustainability of the transportation system. To enhance driving safety on these mountain roads and promote low-carbon, resilient transportation development, this study investigates the mechanisms through which different curve types affect driving safety and proposes optimization strategies based on interpretable machine learning methods. Focusing on three typical curve types in plateau regions, drone high-altitude photography was employed to capture footage of three specific curves along China’s National Highway G318. Oblique photography was utilized to acquire road environment information, from which 11 data indicators were extracted. Subsequently, 8 indicators, including cornering preference and vehicle type, were designated as explanatory variables, the curve type indicator was set as the dependent variable, and the remaining indicators were established as safety assessment indicators. Linear models (logistic regression, ridge regression) and non-linear models (Random Forest, LightGBM, XGBoost) were used to conduct model comparison and factor analysis. Ultimately, three non-linear models were selected, employing an explainability-oriented dynamic ensemble learning strategy (X-DEL) to evaluate the three curve types. The results indicate that non-linear models outperform linear models in terms of accuracy and scene adaptability. The explainability-oriented dynamic ensemble learning strategy (X-DEL) is beneficial for the construction of driving safety models and factor analysis on Tibetan Plateau mountainous roads. Furthermore, the contribution of indicators to driving safety varies across different curve types. This research not only deepens the scientific understanding of safety issues on plateau mountainous roads but, more importantly, its proposed solutions directly contribute to building safer, more efficient, and environmentally friendly transportation systems, thereby providing crucial impetus for sustainable transportation and high-quality regional development in the Tibetan Plateau. Full article

The performance and beam quality of the new fourth-generation synchrotron light source with ultra-low emittance are highly susceptible to coupled-bunch instabilities. These instabilities arise from the interaction between the bunched electron beam and the surrounding vacuum chamber installations. To mitigate these effects, the installation of a transverse bunch-by-bunch feedback system is planned. This system will comprise a button-type beam position monitor (BPM) for beam signal detection, a digital feedback controller, a broadband power amplifier, and a broadband stripline kicker as the primary actuator. One of the critical challenges lies in the development of the stripline kicker, which must be optimized for high shunt impedance and wide bandwidth while minimizing beam-coupling impedance. This work focuses on the comprehensive design of the stripline kicker intended for transverse (horizontal and vertical) bunch-by-bunch feedback in the Siam Photon Source II (SPS-II) storage ring. The stripline kicker design also incorporates features to enable its use for beam excitation in the SPS-II tune measurement system. The optimization process involves analytical approximations and detailed numerical electromagnetic field analysis of the stripline’s 3D geometry, focusing on impedance matching, field homogeneity, power transmission, and beam-coupling impedance. The details of engineering design are discussed to ensure that it meets the fabrication possibilities and stringent requirements of the SPS-II accelerator. Full article

Solar greenhouses are an important component of modern facility agriculture, and the dynamic changes in their internal environment directly affect crop growth and yield. Among these factors, crop transpiration releases water vapor through transpiration, directly altering the indoor humidity balance and forming a dynamic coupling with factors such as temperature and light. The environment of solar greenhouses exhibits highly nonlinear and multivariate coupling characteristics, leading to insufficient prediction accuracy in existing models. However, accurate predictions are crucial for regulating crop growth and yield. However, current mainstream greenhouse environmental prediction models still have obvious limitations when dealing with such complexity: traditional machine learning models and single-variable-driven models have issues such as insufficient accuracy (average MAE is 15–20% higher than in this study) and weak adaptability to nonlinear environmental changes in multi-environmental factor coupling predictions, making it difficult to meet the needs of precision farming. A review of relevant research over the past five years shows that while LSTM-based models perform well in time series prediction, they ignore the spatial correlations between environmental factors. Models incorporating attention mechanisms can capture key variables but suffer from high computational costs. To address these issues, this study proposes a prediction model based on multi-strategy optimization and gradient-boosting (GBDT) algorithms. By introducing a multi-scale feature fusion module, it addresses the accuracy issues in multi-factor coupling prediction. Additionally, it employs a lightweight network design to balance prediction performance and computational efficiency, filling the gap in existing research applications under complex greenhouse environments. The model optimizes data preprocessing and model parameters through Sobol sequence initialization, adaptive t-distribution perturbation strategies, and Gaussian–Cauchy mixture mutation strategies and combines CatBoost for modeling to enhance prediction accuracy. Experimental results show that the MSCSO–CatBoost model performs excellently in temperature prediction, with the mean absolute error (MAE) and root mean square error (RMSE) reduced by 22.5% (2.34 °C) and 24.4% (3.12 °C), respectively, and the coefficient of determination (R²) improved to 0.91, significantly outperforming traditional regression methods and combinations of other optimization algorithms. Additionally, the model demonstrates good generalization capability in predicting multiple environmental variables such as temperature, humidity, and light intensity, adapting to environmental fluctuations under different climatic conditions. This study confirms that combining multi-strategy optimization with gradient-boosting algorithms can significantly improve the prediction accuracy of solar greenhouse environments, providing reliable support for precision agricultural management. Future research could further explore the model’s adaptive optimization in complex climatic regions. Full article

To significantly reduce fuel consumption and improve fuel economy in hybrid tractor under complex working conditions, an energy—saving strategy based on working condition prediction and Deep Deterministic Policy Gradient and Fuzzy control (DDPG-Fuzzy) was proposed. Firstly, a hybrid tractor system dynamics model containing diesel, motor, and power battery was established. Secondly, a working condition prediction model for plowing velocity and resistance was constructed based on the adaptive cubic exponential smoothing method. Finally, a two-layer control architecture was designed. The upper layer adopted the DDPG algorithm, which takes demand torque, equivalent fuel consumption, and the State of Charge (SOC) as state inputs to optimize energy consumption by generating the diesel benchmark torque through the policy network. The lower layer introduced a fuzzy control compensation mechanism that calculates the torque correction based on the plowing velocity error and the plowing resistance deviation to adjust the power allocation. In light of on this, an energy—saving strategy for hybrid tractor based on working condition prediction and DDPG-Fuzzy control was proposed. Under a standard 140 s plowing cycle, the results showed that the working condition prediction model achieved mean prediction accuracies of 97% for plowing velocity and 96.8% for plowing resistance. Under plowing conditions, the proposed strategy reduced the equivalent fuel consumption by 9.7% compared to the power-following strategy, and reduced SOC by 4.4% while maintaining it within a reasonable range. By coordinating the operation of the diesel and motor within high-efficiency regions, this approach enhances fuel economy under complex working conditions. Full article

This study introduces YOLOv13-Cone-Lite, an enhanced algorithm based on YOLOv13s, designed to meet the stringent accuracy and real-time performance demands for traffic cone detection in autonomous formula racing cars on enclosed tracks. We improved detection accuracy by refining the network architecture. Specifically, the DS-C3k2_UIB module, an advanced iteration of the Universal Inverted Bottleneck (UIB), was integrated into the backbone to boost small object feature extraction. Additionally, the Non-Maximum Suppression (NMS)-free ConeDetect head was engineered to eliminate post-processing delays. To accommodate resource-limited onboard terminals, we minimized superfluous parameters through structural reparameterization pruning and performed 8-bit integer (INT8) quantization using the TensorRT toolkit, resulting in a lightweight model. Experimental findings show that YOLOv13-Cone-Lite achieves a mAP₅₀ of 92.9% (a 4.5% enhancement over the original YOLOv13s), a frame rate of 68 Hz (double the original model’s speed), and a parameter size of 8.7 MB (a 52.5% reduction). The proposed algorithm effectively addresses challenges like intricate lighting and long-range detection of small objects and offers the automotive industry a framework to develop more efficient onboard perception systems, while informing object detection in other closed autonomous environments like factory campuses. Notably, the model is optimized for enclosed tracks, with open traffic generalization needing further validation. Full article

In recent years, structured light with novel propagation properties has attracted great attention. Among these structured beams, the Airy beam is one of the most representative and widely used beams. In this paper, we propose a kind of quadruple Gaussian Airy beam (QGAB) with fourfold symmetry. The QGAB is designed by the combination of Gaussian and Airy functions, and the polarization of the QGAB can be either singular or space-variant. We experimentally generate the QGABs and further study the propagation characteristics of the QGABs with different polarization states. The QGAB enriches the family of the structured beams, and the autofocusing and self-healing properties can be applied in regions such as optical communications, optical microscopes, and optical tweezers. Full article

Objectives: This work investigated the influence of anthropometric height on oculo-manual ability during a visuo-motor reaction time task. The aim was to determine whether aligning test configurations with individual stature changes performance outcomes. Methods: In the first phase, 450 participants completed a standardized reaction task using a fixed panel, and correlations were explored between anthropometric measures and performance. The results revealed significant inverse correlations between height and both reaction time total time, and reaction time intertime. A second experimental phase involved an additional group of 36 individuals, who completed the same task using both the fixed and adjustable panels, designed to align visual stimuli with each participant’s central line of sight and arm length. Results: A paired-sample t-test showed a statistically significant reduction in both reaction time total time, total time required to deactivate all 54 lights targets, ($32.1\pm 3.26$ s to $30.7\pm 2.58$ s, $p<0.05$) and reaction time intertime, average time interval between successive light deactivations out of a total of 54 lights, ($0.31\pm 0.123$ s to $0.21\pm 0.149$ s, $p<0.01$), time total time, total time required to deactivate all 54 lights targets, ($32.1\pm 3.26$ s to $30.7\pm 2.58$ s, $p<0.05$) and reaction time intertime, average time interval between successive light deactivations out of a total of 54 lights, ($0.31\pm 0.123$ s to $0.21\pm 0.149$ s, $p<0.01$) under the adjustable panel configuration. Conclusions: These findings suggest that standard testing configurations may disadvantage individuals with shorter stature and highlight the benefits of personalized setups for assessing and enhancing oculo-manual coordination. Full article