Search Results (274)

Seed varietal purity and physiological viability are critical determinants of crop yield and quality. However, non-destructive assessment faces significant challenges in fine-grained variety discrimination and the perception of internal defects. This study proposes S3-Net, an AI-driven multimodal sensing framework that integrates vision–language alignment with dual-spectral sensor fusion for autonomous seed quality evaluation. We introduce a Knowledge–Vision Alignment (KVA) module that incorporates encyclopedic morphological descriptions to guide feature learning, significantly enhancing few-shot generalization. Complementarily, a Dual-Spectral Fusion (DSF) module combines high-resolution RGB textures with penetrative Short-Wave Infrared (SWIR) sensing to jointly characterize external and internal traits. Experimental results on a custom multimodal dataset of 6000 samples across 12 crop categories demonstrate that S3-Net achieves 96.9% accuracy for species identification and 95.8% for viability detection. Notably, S3-Net outperforms ResNet-50 by 40.3% in extreme 1-shot scenarios. With a stable inference throughput of 95 fps, the system meets the high-throughput demands of industrial-scale applications, providing a robust and efficient solution for intelligent agricultural phenotyping. Full article

With the wave of new campus construction gradually receding, the focus of green campus planning and design is shifting toward the low-carbon retrofitting of the existing built environment. University campuses often face challenges such as dispersed land use, inadequate spatial planning, disorganized road layouts, suboptimal landscape design, and low energy efficiency. Grounded in a review of current research on campus carbon emissions, this study integrates green technology indicators with planning and design approaches to establish a multi-scale, context-adaptive planning framework for carbon control, spanning five dimensions: intensive land use, spatial layout, transportation systems, landscape development, and facility integration. Employing a combined approach of bibliometric analysis and case studies, this research examines and compares typical university campuses both domestically and internationally to validate the effectiveness of the synergistic “technology-system-behavior” pathway in mitigating high-carbon lock-in. Through a systematic comparative analysis of representative low-carbon campuses, the synthesized results indicate that under optimal operational conditions, the clustered reorganization of functional zones demonstrates the potential to reduce transportation carbon emissions by approximately 25%; comprehensive retrofitting of building envelopes can decrease building energy consumption intensity by an estimated 30%; a multimodal coordinated transport system can increase the share of non-motorized travel to around 65%; establishing high carbon-sequestration plant communities can enhance carbon sink capacity by up to 30%; and smart facility integration can reduce overall campus carbon emissions by a projected range of 25–40%. It should be noted that these quantitative outcomes represent high-probability potential ranges, with actual performance subject to behavioral and operational fluctuations. This study provides theoretical support and practical pathways for achieving “near-zero carbon campuses” and underscores the important demonstrative role that higher education institutions can play in addressing climate change. Full article

Bird’s-eye-view (BEV) perception has emerged as a key representation for unified scene understanding in autonomous driving. However, current BEV methods relying solely on monocular cameras suffer from severe degradation under adverse weather and dynamic scenes due to limited depth cues and illumination dependency. To address these challenges, we propose a robust multi-modal BEV perception framework that integrates dual-source 4D millimeter-wave radar and multi-view camera images. The proposed architecture systematically exploits Doppler velocity and temporal information from 4D radar to model dynamic object motion, while introducing a deformable fusion strategy in the BEV space for accurate semantic alignment across modalities. Our design includes four key modules: a Doppler-Aware Radar Encoder (DARE) that enhances motion-sensitive features via velocity-guided attention; a Fog-Aware Feature Denoising Module (FADM) that suppresses modality inconsistency in low-visibility conditions through cross-modal attention and residual enhancement; a Multi-Modal Temporal Fusion Module (TFM) that encodes radar temporal sequences using a Transformer encoder for motion continuity modeling; and a confidence-aware multi-task loss that jointly supervises semantic segmentation, motion estimation, and object detection. Extensive experiments on the DualRadar dataset and adverse-weather simulations demonstrate that our method achieves significant gains over state-of-the-art baselines in BEV segmentation accuracy, detection robustness, and motion stability. The proposed framework offers a scalable and resilient solution for real-world autonomous perception, especially under challenging environmental conditions. Full article

Aesthetic medicine has progressed from the early 2000s fascination with bio-stimulation to the current dominance of hyaluronic acid (HA) fillers, prized for immediate, predictable, and reversible volumizing effects. Recently, demand for more natural results, stronger emphasis on skin quality, and increased post-pandemic self-scrutiny have renewed interest in regenerative strategies, sometimes called the “second wave of bio-stimulation.” This trend highlights the need for clearer terminology and a cautious, evidence-based reading of proposed biological mechanisms. This narrative review proposes a framework in which bio-regeneration denotes a hypothesized, controlled induction of physiological processes, fibroblast activation, collagen and elastin synthesis, extracellular matrix remodeling, and immune modulation, potentially producing sustained improvements in dermal structure and function beyond simple filling. Among emerging technologies, polyethylene glycol diglycidyl ether (PEGDE) cross-linking is reported to create a stable, flexible HA scaffold with homogeneous tissue integration, favorable rheology, thermal stability, and a reduced inflammatory profile, supporting safer multimodal use with energy-based devices. The framework is illustrated with PEGDE-crosslinked HA combined with low-concentration calcium hydroxyapatite (CaHA), exemplified by a PEGDE-HA filler containing CaHA microspheres plus glycine and L-proline. These formulations aim to deliver immediate correction via HA and delayed stimulatory effects possibly driven by gradual CaHA exposure and macrophage-associated signaling. Available clinical, imaging, and histological observations, including prospective ultrasound and biopsy assessments, suggest progressive dermal thickening and predominant type I collagen expression, without pathological inflammation or granuloma formation. Although evidence remains preliminary and largely non-comparative, findings are compatible with controlled remodeling and resolving inflammation; however, the underlying mechanism and any ‘regenerative’ versus ‘reparative’ classification require controlled comparative studies. Full article

Objectives: To strengthen the recognition of juvenile xanthogranuloma (JXG) by analyzing ultrasound findings. Methods: This study retrospectively enrolled these patients with pathologically confirmed JXG from January 2011 to March 2025. The clinical, imaging, pathological features, and prognosis of all included patients were analyzed. All the imaging features were evaluated in consensus by two radiologists. Results: Fourteen patients were included in the study. A total of 78.6% presented with solitary masses. The age of the patients ranged from 2 months to 48 years. Those aged ≤1 year accounted for 64.3% of the sample. The lesions were predominantly located on the head and face, and the skin of most patients was yellowish-orange. The ultrasound manifestations are mostly hypoechoic masses with clear boundaries and regular shapes. Contrast-enhanced ultrasound shows a slight homogeneous enhancement, and on shear wave elastography, it appears to be relatively hard. Conclusions: JXGs are more common in infants or young children and present with yellowish-orange, cutaneous lesions. Ultrasound revealed homogeneous, well-circumscribed, regular hypoechoic nodules. Multimodal imaging may be helpful for preoperative diagnosis. Full article

By setting up ultrasonic fields in solid and liquid environments, the propagation characteristics of ultrasonic waves were investigated, and a sensing experiment device with related physical field settings was constructed. A comparison of results between multi-mode microfiber and single-mode fiber interferometric sensors found that the multi-mode microfiber maintains the original ultrasonic waveform output and has much higher sensitivity than the single-mode fiber sensor. The sensor in the paper had a detection limit of approximately 540 Pa and a bandwidth of approximately 5 MHz. The photoacoustic experiment with the microfiber ultrasound sensor had the highest resolution, which is about 10 times that of a single-mode fiber sensor. In summary, the multi-mode microfiber interferometric sensor was applied to ultrasonic detection. Full article

Background/Objectives: Macular telangiectasia type 2 (MacTel2) is a progressive parafoveal retinal disorder with emerging evidence supporting broader neurodegenerative and metabolic involvement. Given the vulnerability of cochlear structures to systemic and microvascular stressors, this study aimed to investigate whether MacTel2 is associated with measurable auditory dysfunction. Methods: This prospective case–control study included 42 participants: 21 patients with clinically and multimodally confirmed MacTel2 and 21 age- and sex-matched healthy controls. All participants underwent standardized audiological assessment, including tympanometry, conventional and extended high-frequency pure-tone audiometry (0.5–16 kHz), distortion product otoacoustic emissions (DPOAE; 0.5–8 kHz), and click-evoked auditory brainstem response (ABR). Hearing loss was graded using the World Health Organization (WHO) classification based on PTA4 (0.5, 1, 2, and 4 kHz), and a clinically relevant cutoff of PTA4 > 25 dB HL was additionally applied. DPOAE responses were considered absent when the signal-to-noise ratio (SNR) was <6 dB. Results: The MacTel2 and control groups were comparable with respect to age and sex distribution. Patients with MacTel2 demonstrated significantly higher air-conduction thresholds than controls across both conventional and extended high frequencies, with the largest differences observed in the extended high-frequency range (10–16 kHz). PTA4 values were significantly higher in the MacTel2 group in both better- and worse-hearing ears, and the prevalence of clinically relevant hearing loss (PTA4 > 25 dB HL) was significantly greater among MacTel2 patients. DPOAE amplitudes were markedly reduced at all tested frequencies (0.5–8 kHz) in the MacTel2 group, and frequency-specific DPOAE absence/reduction (SNR < 6 dB) was substantially more frequent in MacTel2 than in controls. In contrast, ABR wave I and wave V latencies and the I–V interpeak interval did not differ significantly between groups, suggesting preserved brainstem-level auditory conduction. Within the MacTel2 cohort, no significant correlations were observed between the disease grade and audiological measures. Conclusions: MacTel2 was associated with significantly impaired peripheral auditory function, characterized by elevated conventional and extended high-frequency thresholds and pronounced reductions or the absence of DPOAE responses, while ABR parameters remained comparable to those of controls. These findings support a predominantly cochlear (outer hair cell-related) involvement in MacTel2 and suggest that auditory screening including conventional pure-tone audiometry, with consideration of extended high-frequency audiometry and otoacoustic emissions when feasible, may be clinically relevant in this population. Full article

A water-rich muscle-equivalent tissue-mimicking phantom within a polymeric matrix was experimentally evaluated through a multimodal characterization methodology to determine whether it reproduces the coupled dielectric–thermal behavior of hydrated biological tissue under exposure to electromagnetic waves. The material was analyzed using thermogravimetric analysis, microwave dielectric spectroscopy from 1.5 to 4.0 GHz, VIS–NIR spectroscopy between 350 and 1200 nm, and terahertz time-domain reflection. The thermogravimetric results confirmed dominant water content, with primary mass loss below 200 °C, establishing hydration as the governing factor of its thermal response. Next, the microwave dielectric measurements show that the phantom exhibits a relative permittivity of 37.4 and an electrical conductivity of 2.4 S/m. On the other hand, the VIS–NIR spectra show smooth broadband absorption with limited spatial variability, and principal component analysis reveals macroscopic optical homogeneity without structural spectral distortion. In the THz regime, strong broadband attenuation characteristic of water-rich matrices is observed, and reflection-mode measurements enable robust assessment of temporal stability through time- and frequency-domain signatures. Finally, a microwave thermal validation demonstrates stable behavior under low-power excitation, whereas under hyperthermia-level irradiation, a significant thermal drift of −3.985 °C/h was reached under non-adiabatic conditions, identifying hydration-mediated moisture redistribution as the principal limitation during prolonged high-power exposure. Collectively, these results demonstrate cross-regime dielectric–thermal consistency while explicitly defining the hydration-driven constraints governing long-term stability, providing a validated reference material for broadband electromagnetic and thermal biomedical experimentation. Full article

A Digital Twin is a virtual environment that simulates, predicts, and optimizes the performance of its physical counterpart. Digital Twin models hold great potential in wireless networking testing and development. This paper aims to envision our concept of simulating the operation of different sensors in vehicle test-track conditions. Vehicle parameters are embedded into the edge computing entity, which uses them to generate a test configuration for the Digital Twin. This configuration is then applied in simulated sensor-output prediction, ultimately producing event data for the vehicle entity. The sensor suite—comprising radar, cameras, GPS and LiDAR—is modeled to provide the multi-modal input required for generating simulated perception data in the Digital Twin. To ensure realistic perception behavior, the physical vehicle is represented within a digital environment that reproduces the actual test track. This allows LiDAR occlusions to be attributed to genuine environmental structures (e.g., trees, buildings, other vehicles) rather than simulation artifacts. Within the Digital Twin, the objective is to evaluate how sensor signals—such as radar waves and LiDAR light pulses—propagate through the environment and how real-world obstacles may weaken or distort them. Historical datasets are used to calibrate and validate the Digital Twin, ensuring that the simulated sensor behavior aligns with real-world observations; the data collected during previous test runs can be used for visualization and analysis. Weather conditions are modeled to evaluate how rain, fog and snow impact sensor performance within the Digital Twin environment, to learn about the effects and predict sensor operation in different weather conditions. In this article, we examine the Digital Twin of our test track as a development environment for designing, deploying and testing ITS-enhanced road-weather services and warnings. These services integrate real-world road-weather observations, forecast data, roadside sensors and on-board vehicle measurements to support safe driving and optimize vehicle trajectories for both passenger and autonomous vehicles. This research is expected to benefit stakeholders involved in automotive testing, simulation and road-weather service development. Full article

Based on the electron cyclotron maser instability, gyrotrons are capable of generating high-power electromagnetic vortex waves. In conventional axisymmetric configurations, the electron beam typically lifts the azimuthal degeneracy between co-rotating and counter-rotating modes, leading to a state of intense mutual suppression. This study elucidates a fundamental transition from such competitive dynamics to a stable cooperative coexistence, driven by symmetry-breaking perturbations. Using a time-dependent self-consistent interaction theory, we investigate the intermodal dynamics of the counter-rotating ${\mathrm{T}\mathrm{E}}_{6,2}$ mode pair in a terahertz gyrotron. Our results reveal that the azimuthal intermodal phase beating dictates a reciprocal energy exchange that ensures single-mode dominance. However, electron beam misalignment introduces a significant azimuthal non-uniformity in the coupling strength. This non-uniformity effectively neutralizes the competitive disparity between the two modes. At a critical offset, the system undergoes a “territorial division,” where the orthogonal vortex modes spatially segregate by dominating distinct azimuthal segments of the annular beam. This spatial segregation eliminates nonlinear cross-suppression, allowing for the stable coexistence of both rotational states. These findings offer a new perspective on multi-mode interactions in non-ideal systems and establish a robust theoretical framework for the active manipulation of vortex waves in high-performance THz radiation sources. Full article

To address the tradeoff between environmental robustness and fine-grained accuracy in single-sensor human behavior recognition, this paper proposes a non-contact system fusing 77 GHz SIFT (mmWave) radar and a 40 kHz ultrasonic array. The system leverages radar’s long-range penetration and low-visibility adaptability, paired with ultrasound’s centimeter-level short-range precision and electromagnetic clutter immunity. A synchronized data acquisition platform ensures multi-modal signal consistency, while wavelet transform (for radar) and STFT (for ultrasound) extract complementary time–frequency features. The proposed Attention-CNN-BiLSTM architecture integrates local spatial feature extraction, bidirectional temporal dependency modeling, and salient cue enhancement. Experimental results on 1600 synchronized sequences (four behaviors: standing, sitting, walking, falling) show a 98.6% mean class accuracy with subject-wise generalization, outperforming single-sensor baselines and traditional deep learning models. As a privacy-preserving, lighting-agnostic solution, it offers promising applications in smart homes, healthcare monitoring, and intelligent surveillance, providing a robust technical foundation for contactless behavior recognition. Full article

Breast cancer remains the most frequently diagnosed cancer in women worldwide, with outcomes strongly dependent on stage at detection. Conventional imaging modalities such as mammography, ultrasound and MRI are limited by reduced sensitivity in dense breasts, radiation exposure, high cost and restricted availability in low-resource settings. This review critically examines microwave imaging (MWI) as a non-invasive, radiation-free and an emerging resource-efficient breast imaging modality that exploits dielectric contrast between healthy and malignant breast tissues. We first summarise experimental and clinical evidence on breast dielectric properties and their implications for numerical phantoms and device design. We then review passive, active (tomographic and radar-based) and hybrid MWI systems, including key clinical prototypes such as SAFE, MammoWave, MARIA and Wavelia, and analyse associated image-reconstruction algorithms from classical inverse scattering to advanced beamforming, Huygens-based methods and AI based reconstruction. Finally, we discuss outstanding challenges—tissue heterogeneity, calibration, hardware constraints and computational complexity—and identify future directions including AI-assisted reconstruction, multimodal hybrid imaging and large-scale clinical validation needed to translate MWI into routine breast cancer screening and diagnosis. Full article

Steel pipes play a vital role in energy and industrial transportation systems, where undetected defects such as cracks and wall thinning may lead to severe safety hazards. Although ultrasonic guided waves enable long-range inspection, their defect imaging performance is often limited by dispersion, multimode interference, and strong noise. In this work, a multi-frequency fusion imaging method integrating time-reversal, topological derivative, and sparse Bayesian learning is proposed for guided wave-based defect detection in steel pipes. Multi-frequency guided waves are employed to enhance defect sensitivity and suppress frequency-dependent ambiguity. Time-reversal focusing is used to concentrate scattered energy at defect locations, while the topological derivative provides a global sensitivity map as physics-guided prior information. These results are further fused within a sparse Bayesian learning framework to achieve probabilistic defect imaging and uncertainty quantification. Dispersion compensation based on the semi-analytical finite element method is introduced to ensure accurate wavefield reconstruction at different frequencies. Domain randomization is also incorporated to improve robustness against uncertainties in material properties, temperature, and measurement noise. Numerical simulation results verify that the proposed method achieves high localization accuracy and significantly outperforms conventional TR-based imaging in terms of resolution, false alarm suppression, and stability. The proposed approach provides a reliable and robust solution for guided wave inspection of steel pipelines and offers strong potential for engineering applications in nondestructive evaluation and structural health monitoring. Full article

This study presents a multimodal framework that uses smartphone motion sensors and generative AI to create audio comics from live news headlines. The system operates without direct touch or voice input, instead responding to simple hand-wave gestures. The system demonstrates potential as an alternative input method, which may benefit users who find traditional touch or voice interaction challenging. In the experiments, we investigated the generation of comics on based on the latest tech-related news headlines using Really Simple Syndication (RSS) on a simple hand wave gesture. The proposed framework demonstrates extensibility beyond comic generation, as various other tasks utilizing large language models and multimodal AI could be integrated by mapping them to different hand gestures. Our experiments with open-source models like LLaMA, LLaVA, Gemma, and Qwen revealed that LLaVA delivers superior results in generating panel-aligned stories compared to Qwen3-VL, both in terms of inference speed and output quality, relative to the source image. These large language models (LLMs) collectively contribute imaginative and conversational narrative elements that enhance diversity in storytelling within the comic format. Additionally, we implement an AI-in-the-loop mechanism to iteratively improve output quality without human intervention. Finally, AI-generated audio narration is incorporated into the comics to create an immersive, multimodal reading experience. Full article

Background: Soft tissue tumors (STTs) represent a heterogeneous group of rare lesions that frequently mimic bone sarcomas in both clinical and radiologic appearance. Accurate differentiation between benign and malignant lesions is critical for appropriate treatment planning, yet conventional imaging often remains inconclusive. Ultrasound (US) elastography, a non-invasive method that quantifies tissue stiffness, has recently emerged as a potential adjunct to standard musculoskeletal imaging for improving diagnostic confidence and guiding biopsy. Methods: A systematic review was conducted in accordance with PRISMA guidelines. PubMed, Web of Science, and Cochrane Library were searched using the keywords “elastography”, “sonoelastography”, and “soft tissue tumor”. Twelve studies encompassing 1554 patients met the inclusion criteria, assessing the diagnostic accuracy of strain, compression, and shear wave elastography for differentiating benign from malignant STTs. Results: Elastography alone demonstrated limited specificity when used as a single diagnostic technique. However, its integration into multiparametric ultrasound approaches—combining grayscale, Doppler, and contrast-enhanced imaging—significantly improved diagnostic performance. Several studies reported sensitivities and specificities exceeding 85% when elastographic parameters were incorporated into composite diagnostic scores. Conclusions: Ultrasound elastography shows promise as a quantitative imaging biomarker for the preoperative evaluation of musculoskeletal tumors, particularly in distinguishing soft tissue from bone-related lesions. Although not a substitute for histopathological confirmation, its application within multimodal ultrasound protocols may reduce unnecessary biopsies, enhance diagnostic accuracy, and facilitate tailored management of bone and soft tissue sarcomas. Full article