Search Results (3,569)

This paper presents a W-band low-voltage traveling-wave tube (TWT) incorporating a spoof surface plasmon polariton (SSPP) slow-wave structure (SWS) and a dual-sheet beam. The SSPP-based SWS adopts a periodic double-F-groove configuration, which provides strong field localization, increases the interaction impedance, and reduces the phase velocity, thereby enabling a low synchronization voltage. Owing to its symmetric open geometry, the SWS naturally forms a dual-sheet beam tunnel, which enhances the effective beam current without increasing the aperture size. Eigenmode calculations indicate that, within the 92–97 GHz band, the normalized phase velocity is between 0.198 and 0.208, and the interaction impedance exceeds 2.65 Ω. Moreover, an energy-coupling structure was developed to ensure efficient signal transmission. Three-dimensional particle-in-cell (PIC) simulations predict a peak output power of 366.1 W and an electronic efficiency of 6.15% at 95.5 GHz for a 2 × 250 mA dual-sheet beam at 11.9 kV, with stable amplification and without self-oscillation observed. The proposed low-voltage, high-efficiency W-band TWT offers a manufacturable and easily integrable solution for next-generation millimeter-wave systems, supporting high-capacity wireless backhaul, airborne communication, radar imaging, and sensing platforms where compactness and reduced power-supply demands are critical. Full article

Fiber Bragg grating (FBG) sensing systems face significant challenges in resolving overlapping spectral signatures when multiple sensors operate within limited wavelength ranges, severely limiting sensor density and network scalability. This study introduces a novel Transformer-based neural network architecture that effectively resolves spectral overlap in both uniform and mixed-linewidth FBG sensor arrays, operating under bidirectional drift. The system uniquely combines dual-linewidth configurations with reflection and transmission mode fusion to enhance demodulation accuracy and sensing capacity. By integrating cloud computing, the model enables scalable deployment and near-real-time inference even in large-scale monitoring environments. The proposed approach supports self-healing functionality through dynamic switching between spectral modes during fiber breaks and enhances resilience against spectral congestion. Comprehensive evaluation across twelve drift scenarios demonstrates exceptional demodulation performance under severe spectral overlap conditions that challenge conventional peak-finding algorithms. This breakthrough establishes a new paradigm for high-density, distributed FBG sensing networks applicable to land monitoring, soil stability assessment, groundwater detection, maritime surveillance, and smart agriculture. Full article

The need for sustainable and long-term environmental monitoring has driven the development of energy-autonomous sensors, which either operate passively or integrate energy harvesting (EH) solutions. In many applications, the energy cost of data transmission is a critical factor in autonomous sensing systems. To address this challenge, optical passive sensors, which exploit changes in reflectivity to monitor physical parameters, offer self-sustained operation without requiring an external power source. Similarly, RF-based passive sensors, both chipless and with minimal circuitry, enable wireless monitoring with low power consumption. When more energy is available, EH techniques can be combined with active optical sensors. Infrared laser-based CO₂ sensors, as well as drone-mounted optical systems, demonstrate how EH can power precise environmental measurements. Beyond optics, other sensing modalities also benefit from EH, further expanding the range of self-powered environmental monitoring technologies. This review discusses the trade-offs between passive and EH-assisted sensing strategies, with a focus on optical implementations. The outlook highlights emerging solutions to enhance sensor autonomy while minimizing the energy cost of data transmission, paving the way for sustainable and scalable environmental monitoring. Full article

Many gesture recognition systems with innovative interfaces have emerged for smart home control. However, these systems tend to be energy-intensive, bulky, and expensive. There is also a lack of real-time demonstrations of gesture recognition and subsequent evaluation of the user experience. Photovoltaic light sensors are self-powered, battery-free, flexible, portable, and easily deployable on various surfaces throughout the home. They enable natural, intuitive, hover-based interaction, which could create a positive user experience. In this paper, we present the development and evaluation of a real-time, hover gesture recognition system that can control multiple smart home devices via a self-powered photovoltaic interface. Five popular supervised machine learning algorithms were evaluated using gesture data from 48 participants. The random forest classifier achieved high accuracies. However, a one-size-fits-all model performed poorly in real-time testing. User-specific random forest models performed well with 10 participants, showing no significant difference in offline and real-time performance and under normal indoor lighting conditions. This paper demonstrates the technical feasibility of using photovoltaic surfaces as self-powered interfaces for gestural interaction systems that are perceived to be useful and easy to use. It establishes a foundation for future work in hover-based interaction and sustainable sensing, enabling human–computer interaction researchers to explore further applications. Full article

Aiming at the core problem of high false and missed alarm rate and insufficient interference resistance of existing smoke and fire detection algorithms in complex scenes, this paper proposes a target detection network based on improved feature pyramid structure. By constructing a Context Guided Convolutional Block instead of the traditional convolutional operation, the detected target and the surrounding environment information are fused with secondary features while reconfiguring the feature dimensions, which effectively solves the problem of edge feature loss in the down-sampling process. The Poly Kernel Inception Block is designed, and a multi-branch parallel network structure is adopted to realize multi-scale feature extraction of the detected target, and the collaborative characterization of the flame profile and smoke diffusion pattern is realized. In order to further enhance the logical location sensing ability of the target, a Manhattan Attention Mechanism Unit is introduced to accurately capture the spatial and temporal correlation characteristics of the flame and smoke by establishing a pixel-level long-range dependency model. Experimental tests are conducted using a self-constructed high-quality smoke and fire image dataset, and the results show that, compared with the existing typical lightweight smoke and fire detection models, the present algorithm has a significant advantage in detection accuracy, and it can satisfy the demand for real-time detection. Full article

Situated within the Embodied Learning in Early Mathematics and Science project in Australia, this paper explores the relationships between ‘embodied activities’ used by a preschool teacher and the children’s development of number sense over six months. Using an instrumental case study approach, qualitative data from multiple sources including self-reported data from the teacher, activity descriptions, two task-based interviews with nine children, and number-knowledge data extracted from a mathematics achievement assessment (pretest and post-test) was analysed. Pattern searching techniques across text and video revealed connections between the embodied activities implemented by the teacher and the children’s development of subitizing and counting skills, mathematical drawing, and number magnitude knowledge. We propose that attending to specific aspects of children’s physical development, particularly finger dexterity and drawing skills, in experiences that focus on representing number concepts, can support their development of number sense. Full article

Multi-core fiber (MCF) has drawn increasing attention for its potential application in distributed acoustic sensing (DAS) due to the compact optical structure of integrating several fiber cores in the same cladding, which indicates an intrinsic space-division-multiplexed (SDM) capability in a single piece of fiber. In this paper, a dual-channel DAS integrated with MCF is presented, of which the equivalent self-noise characteristic is analyzed. The equivalent self-noise of the system can be effectively suppressed by signal superposition with the phase matching method. Considering that the noise correlation among the cores is not zero, the signal-to-noise (SNR) gain after signal superposition is less than the theoretical value. The dual-channel DAS system is set up by a piece of 2 km long seven-core MCF, in which the dual-sensing channels are constructed by a four-core series and three-core series, respectively. The total noise correlation coefficient of the seven cores is 11.28, while the equivalent self-noise of the system can be suppressed by 6.32 dB with signal superposition. An equivalent self-noise suppression method based on a linear delay phase matching scheme is proposed for noise decorrelation in the DAS MCF system. After noise decorrelation, the suppression of the equivalent self-noise of the system can reach the theoretical value of 8.45 dB with a time delay of 1 ms, indicating a noise correlation among the seven cores of almost zero. The feasibility of the equivalent self-noise suppression method for the DAS system is verified for both single-frequency and broadband signals, which is of great significance for the detection of weak vibration signals based on a DAS system. Full article

Multi-temporal remote sensing image matching plays a crucial role in tasks such as detecting changes in urban buildings, monitoring agriculture, and assessing ecological dynamics. Due to temporal variations in images, significant changes in land features can lead to low accuracy or even failure when matching results. To address these challenges, in this study, a remote sensing image matching framework is proposed based on multi-perception and enhanced feature description. Specifically, the framework consists of two core components: a feature extraction network that integrates multiple perceptions and a feature descriptor enhancement module. The designed feature extraction network effectively focuses on key regions while leveraging depthwise separable convolutions to capture local features at different scales, thereby improving the detection capabilities of feature points. Furthermore, the feature descriptor enhancement module optimizes feature point descriptors through self-enhancement and cross-enhancement phases. The enhanced descriptors not only extract the geometric information of the feature points but also integrate global contextual information. Experimental results demonstrate that, compared to existing remote sensing image matching methods, our approach maintains a strong matching performance under conditions of angular and scale variation. Full article

This study verified the effects of a Virtual Reality (VR) play program on young children’s playfulness using a Solomon four-group experimental design. Targeting 120 children aged four and five in South Korea, a 10-week, child-friendly non-immersive VR program was conducted, measuring five subdomains of playfulness based on Barnett’s framework: physical, social, and cognitive spontaneity, manifestation of enjoyment, and sense of humor. Statistical analysis revealed that the VR program had a significant positive effect across all subdomains of playfulness. The biggest influence on playfulness was sense of humor, followed by physical spontaneity and social spontaneity with an overall effect size of 0.290. Furthermore, the lack of interaction effects with the pretest confirmed the study’s internal validity, proving the VR program was the clear causal factor. These results provide empirical evidence that VR play can enhance the emotional, cognitive, and social development of young children. This study offers a practical basis for integrating VR-based play into early childhood education curricula and suggests its potential to improve peer relationships, confidence, and self-expression. Future research is needed, including the development of content to enhance cognitive spontaneity and longitudinal studies. Full article

Object detection in synthetic aperture radar (SAR) imagery poses significant challenges due to low resolution, small objects, arbitrary orientations, and complex backgrounds. Standard object detectors often fail to capture sufficient semantic and geometric cues for such tiny targets. To address this issue, a new Convolutional Neural Network (CNN) framework called Deformable Vectorized Detection Network (DVDNet) has been proposed, specifically designed for detecting small, oriented, and densely packed objects in SAR images. The DVDNet consists of Grouped-Deformable Convolution for adaptive receptive field adjustment to diverse object scales, a Local Binary Pattern (LBP) Enhancement Module that enriches texture representations and enhances the visibility of small or camouflaged objects, and a Vector Decomposition Module that enables accurate regression of oriented bounding boxes via learnable geometric vectors. The DVDNet is embedded in a two-stage detection architecture and is particularly effective in preserving fine-grained features critical for mall object localization. The performance of DVDNet is validated on two SAR small target detection datasets, HRSID and SSDD, and it is experimentally demonstrated that it achieves 90.9% mAP on HRSID and 87.2% mAP on SSDD. The generalizability of DVDNet was also verified on the self-built SAR ship dataset and the remote sensing optical dataset HRSC2016. All these experiments show that DVDNet outperforms the standard detector. Notably, our framework shows substantial gains in precision and recall for small object subsets, validating the importance of combining deformable sampling, texture enhancement, and vector-based box representation for high-fidelity small object detection in complex SAR scenes. Full article

The innate immune system protects against infection and cellular damage by recognizing conserved pathogen-associated molecular patterns (PAMPs) and damage-associated molecular patterns (DAMPs). Emerging evidence suggests that aberrant epigenetic modifications—such as altered DNA methylation and histone marks—can serve as immunogenic signals that activate pattern recognition receptor (PRR)-mediated immune surveillance. This review explores the concept that epigenetic marks may function as DAMPs or even mimic PAMPs. I highlight how unmethylated CpG motifs, which are typically suppressed using host methylation, are recognized as foreign via Toll-like receptor 9 (TLR9). I also examine how cytosolic DNA sensors, including cGAS, detect mislocalized or hypomethylated self-DNA resulting from genomic instability. In addition, I discuss how extracellular histones and nucleosomes released during cell death or stress can act as DAMPs that engage TLRs and activate inflammasomes. In the context of cancer, I review how epigenetic dysregulation can induce a “viral mimicry” state, where reactivation of endogenous retroelements produces double-stranded RNA sensed by RIG-I and MDA5, triggering type I interferon responses. Finally, I address open questions and future directions, including how immune recognition of epigenetic alterations might be leveraged for cancer immunotherapy or regulated to prevent autoimmunity. By integrating recent findings, this review underscores the emerging concept of the epigenome as a target of innate immune recognition, bridging the fields of immunology, epigenetics, and cancer biology. Full article

The evolution of remote sensing technology has led to significant improvements in high-resolution and hyperspectral image acquisition, enhancing applications like environmental monitoring and disaster assessment. However, the high dimensionality, nonlinearity, and heterogeneity of these images pose challenges for intelligent interpretation. While deep learning models (e.g., CNN) require balancing efficiency and parameter optimization, meta-heuristic algorithms establish self-organizing, parallelized search mechanisms capable of achieving asymptotic approximation towards the global optimum of parameters without requiring gradient information. In this paper, we first propose an improved Gannet Optimization Algorithm (GOA), named TEGOA, which uses the T-distribution perturbation and elite retention to address CNN’s parameter dependency. The experiment on CEC2017 shows that TEGOA has a better performance on composition functions. Hence, it is suitable for solving complex optimization problems. Then, we propose a classification model TEGOA-CNN, which combines TEGOA with CNN to increase the accuracy and efficiency of remote sensing sence classification. The experiments of TEGOA-CNN on two well-known datasets, UCM and AID, showed a higher performance in classification accuracy of remote sensing images. Particularly, TEGOA-CNN achieves 100% classification accuracy on 10 out of the 21 surface categories of UCM. Full article

As China’s housing market shifts from quantity expansion to quality improvement, consumer expectations for both functionality and aesthetics in residential products are rising. Drawing on the Stimulus–Organism–Response (S-O-R) framework, this study develops a perceptual mechanism model to examine how display design identity and facility service satisfaction influence millennials’ willingness to purchase improved housing, mediated by an elevated sense of style and moderated by upward social comparison. Based on structural equation modeling with 491 valid responses, the findings reveal that facility service satisfaction has a significant direct effect on purchase intention, while display design identity affects behavior indirectly through an elevated sense of style. Moreover, the elevated sense of style serves as a critical mediator in multiple pathways, and its effect is significantly moderated by upward social comparison. This study contributes to the housing consumption literature by clarifying how functional and symbolic factors jointly shape purchase intentions, especially under the influence of social comparison dynamics. It also highlights the role of artistic display design as a symbolic stimulus that enhances style perception and self-identity among younger consumers, offering practical insights for improved housing design and marketing strategies. Full article

This research provides a new systematic solution to the essential issue of self-noise interference in underwater acoustic sensing signals induced by unmanned surface vehicles (USVs) operating at sea. The self-noise pertains to the near-field interference noise generated by the growing diversity and volume of acoustic equipment utilized by USVs. The generating mechanism of self-noise is clarified, and a self-noise propagation model is developed to examine its three-dimensional coupling properties within spatiotemporal fluctuation environments in the time-frequency-space domain. On this premise, the YOLOv11 object identification framework is innovatively applied to the delay-Doppler (DD) feature maps of self-noise, thereby overcoming the constraints of traditional time-frequency spectral approaches in recognizing noise with delay spread and overlapping characteristics. A comprehensive comparison with traditional models like YOLOv8 and SSD reveals that the suggested delay-Doppler YOLO (DD-YOLO) algorithm attains an average accuracy of 87.0% in noise source identification. An enhanced denoising method, termed optimized time-frequency regularized overlapping group shrinkage (OTFROGS), is introduced, using structural sparsity alongside non-convex regularization techniques. Comparative experiments with traditional denoising methods, such as the normalized least mean square (NLMS) algorithm, wavelet threshold denoising (WTD), and the original time-frequency regularized overlapping group shrinkage (TFROGS), reveal that OTFROGS outperforms them in mitigating USV self-noise. This study offers a dependable technological approach for optimizing the performance of USV acoustic systems and proposes a theoretical framework and methodology applicable to different underwater acoustic sensing contexts. Full article

Background: Sense of coherence constitutes a significant personal resource that underpins the harmonious professional functioning of nurses employed in psychiatric inpatient wards. It serves as a protective factor, enabling effective coping with the psychophysical burden arising from a demanding and stress-inducing work environment, while also supporting the maintenance of a high level of job satisfaction. Regular assessment of the sense of coherence among psychiatric nursing staff is essential for the early identification of individuals at risk of developing occupational burnout. The aim of the present study was to determine the relationship between the level of sense of coherence and the degree of occupational burnout among nurses working in inpatient psychiatric units. Methods: The study employed a cross-sectional design and utilized standardized psychometric instruments, including The Sense of Coherence Questionnaire (SOC-29) to assess the level of coherence, and the Maslach Burnout Inventory (MBI) to measure occupational burnout. Additionally, a self-developed questionnaire was used to collect sociodemographic data. The research was conducted in five psychiatric hospitals in Poland between January and June 2023. The sample consisted of 555 nurses (449 women and 106 men) employed in inpatient psychiatric wards. Statistical analyses included descriptive statistics, Pearson’s correlation coefficients to examine relationships between variables, and multiple linear regression to identify predictors of burnout dimensions. Significance level set at p < 0.05. Results: The mean global sense of coherence score among psychiatric nurses was 124.68 (SD = 45.81), with manageability scoring highest among subscales (43.83, SD = 16.28). Average occupational burnout scores were emotional exhaustion 28.75 (SD = 16.39), depersonalization 13.55 (SD = 9.71), and reduced personal accomplishment 23.61 (SD = 11.11). Significant negative correlations were found between sense of coherence (and its components) and all burnout dimensions (p < 0.001). Manageability was the strongest predictor of lower emotional exhaustion (β = −0.73), depersonalization (β = −0.65), and reduced personal accomplishment (β = −0.65), while meaningfulness predicted depersonalization (β = 0.37, p = 0.012). These results indicate that higher sense of coherence, especially manageability, is linked to reduced burnout among psychiatric nurses. Conclusions: The study revealed significant negative associations between sense of coherence and all dimensions of occupational burnout, with manageability emerging as the strongest protective factor. Nurses with higher levels of sense of coherence reported lower emotional exhaustion, depersonalization, and reduced personal accomplishment. These findings highlight the importance of incorporating sense of coherence assessment into strategies for identifying individuals at increased risk of burnout. Full article