Search Results (10,987)

Beamforming for linear antenna arrays (LAAs) has become a critical research topic in advanced 5G and emerging 6G wireless communication systems due to the increasing demand for high spectral efficiency, interference mitigation, and enhanced radiation performance. This paper proposes an enhanced hybrid optimization framework based on Harris Hawks Optimization integrated with the Covariance Matrix Adaptation Evolution Strategy (HHO–CMA-ES) for unified LAA synthesis. The proposed approach simultaneously optimizes excitation amplitudes, phase shifts, and inter-element positions to achieve substantial peak sidelobe level (PSLL) reduction while preserving desirable beam characteristics. The optimization performance of the proposed method is systematically evaluated against several widely used metaheuristic algorithms, including the Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Whale Optimization Algorithm (WOA), Flower Pollination Algorithm (FPA), and conventional Harris Hawks Optimization (HHO), under identical simulation conditions. Simulation results obtained for a 20-element LAA demonstrate the superior effectiveness of the proposed HHO–CMA-ES framework across multiple optimization scenarios. In amplitude-only optimization, the proposed method achieves a PSLL of −41.746 dB, corresponding to an improvement of approximately 29.3% compared with WOA and more than 88% relative to GA. For amplitude–phase optimization, HHO–CMA-ES improves PSLL by nearly 21% compared with WOA. In the amplitude–position scenario, the proposed approach achieves the best performance with a PSLL of −44.54 dB, yielding an approximately 63% improvement over PSO and more than 111% over GA. Furthermore, the proposed framework exhibits faster convergence, improved solution stability, and enhanced beamforming robustness, confirming its suitability for high-dimensional antenna synthesis problems in future 5G/6G communication systems. Full article

Due to the higher power density of silicon carbide (SiC) inverters in electric vehicles (EVs), effectively managing conducted electromagnetic interference (EMI) has become a vital aspect of inverter design. The complex power topology of SiC inverters increases the complexity of different types, phenomena, and mechanisms of conducted EMI, making the selection of appropriate suppression methods more challenging. Many studies have examined the mechanisms of conducted EMI and their suppression techniques. However, the fast switching transients of SiC devices can affect an automotive traction inverter at multiple physical levels, ranging from the gate-drive circuit and isolation interface to the external power terminals. These phenomena are closely related through their common switching excitation and parasitic coupling networks, but they should not all be interpreted as equivalent conducted-emission phenomena. To provide a structured engineering perspective, this review organizes the relevant disturbances using a source–path–victim framework and examines three representative paths: gate-loop crosstalk, common-mode (CM) coupling across the isolated gate-drive interface, and system-level CM/DM-conducted emissions. The corresponding mitigation techniques and their applicability to EV traction inverters are subsequently reviewed. Full article

The Jimsar shale reservoir is characterized by low porosity and low permeability. Multistage fracturing of horizontal wells is an effective development method. However, interference between multiple fractures and the presence of well-developed bedding planes limit the effective extension of fractures. To elucidate the initiation and propagation mechanisms of inter-bed fractures in horizontal multi-stage fracturing within shale reservoirs, this study focuses on the Jimusar Shale outcrop and employs large-scale true triaxial hydraulic fracturing physical simulation experiments to construct an experimental model of horizontal multi-stage fracturing. By analyzing fracture morphology, propagation paths, and bedding plane activation characteristics, the study elucidates the fracture propagation patterns under various conditions. Results indicate the following: (1) Increasing the fluid injection rate enhances the net pressure at the fracture tip, effectively overcoming the resistance of bedding planes and promoting the continued propagation of hydraulic fractures across bedding interfaces. (2) Stage spacing affects the coordinated propagation of hydraulic fractures by modifying the stress shadow effect between adjacent fractures. A smaller stage spacing intensifies stress interference, leading to the suppression of local fracture propagation and an increased likelihood of fracture communication. (3) The horizontal stress difference is the primary controlling factor governing fracture propagation direction and bedding penetration capability. A larger horizontal stress difference promotes stable fracture propagation along the direction of the maximum horizontal principal stress and facilitates penetration through bedding planes, whereas a lower horizontal stress difference favors bedding plane activation and fracture deflection, resulting in the formation of a more complex fracture network. The study has established an understanding of the fracture expansion laws under different parameter conditions, which can provide theoretical basis and technical support for the optimization design of multi-stage fracturing parameters in horizontal shale oil wells and the efficient modification of reservoirs. Full article

Objectives: To characterize age-stratified reference patterns in dual-task performance and dual-task cost across the adult lifespan using a standardized digital assessment. Methods: A cross-sectional observational study was conducted in 400 healthy participants aged 18 to 94 years from Spain and Ecuador. Participants performed a digital dual-task test combining a motor task (cursor control to hit a virtual target) and a cognitive task (arithmetic operations). Performance was assessed under single-task (ST) and dual-task (DT) conditions. Dual-task cost (DTC) was calculated separately for the motor and cognitive components, together with a composite Mean DTC. Participants were grouped into seven age categories for descriptive age-stratified analyses, and differences were assessed using one-way ANOVA with Bonferroni-adjusted post hoc comparisons. In addition, multiple linear regression analyses were performed with age treated as a continuous variable and sex as a covariate. Results: Motor and cognitive performance was lower across older age groups under both ST and DT conditions. DTC differed significantly across age groups (p < 0.001), with larger differences becoming apparent at older ages. Cognitive DTC showed a marked increase across older age groups, indicating substantial age-associated cognitive interference under dual-task conditions. Mean DTC increased from 28.1% in participants aged 18–29 years to 63.2% in those aged ≥80 years. Age remained significantly associated with Motor DTC (B = 0.506 per year; 95% CI 0.422–0.589; p < 0.001), Cognitive DTC (B = 0.582; 95% CI 0.497–0.667; p < 0.001), and Mean DTC (B = 0.544; 95% CI 0.483–0.604; p < 0.001) after adjustment for sex. Although adjusted DTC levels differed between sexes, no significant age × sex interaction was observed. Conclusions: Digital dual-task performance differed substantially across age groups, with higher dual-task costs observed at older ages. These findings provide age-stratified reference data for this specific digital cognitive-motor paradigm and support its further investigation as a scalable measure of cognitive-motor interference. Further psychometric, longitudinal, and clinical validation is required before the Dualtask Test can be established as a clinical biomarker or used to distinguish physiological age-associated differences from pathological cognitive decline. Full article

In this paper, a Michelson interferometer was employed to generate interference patterns, the modulated function was extracted by digital filtering and the Laplace transform was subsequently applied to obtain the complex function $F\left(s\right)=F(\sigma +\omega i)$, which changes its zero structure in response to physical perturbations. This study presents a novel method for detecting millimetric displacements based on analyzing the movement of zeros in the complex s-plane. The proposed technique demonstrates that an unperturbed system displays two zeros on the imaginary axis, whereas the introduction of a displacement $∆x$ generates a third zero with a real component. The position of this third zero in the s-plane directly correlates with the magnitude of the perturbation. Finally, the measurement vector $s_{\mathrm{m}\mathrm{e}\mathrm{a}\mathrm{s}}$, calculated as the vector difference between reference and perturbed zeros, quantifies the displacement with high precision. Experimentally, displacements ranging from 0.8 mm to 8.8 mm were introduced into one of the interferometer arms, allowing for the systematic observation of the zero migration within the complex plane. The obtained results demonstrate that this methodology transforms the displacement measurement problem into a singularity localization task in the complex domain, offering enhanced robustness against intensity fluctuations compared to conventional techniques based on Fourier analysis or fringe counting. Full article

To address the problems of uneven illumination distribution, missing edge details, and noise interference in underground coal mine images, a mine image enhancement algorithm based on local perception and multi-domain guided fusion is proposed. First, the algorithm constructs a dynamic nonlinear luminance mapping function based on local luminance features to adaptively adjust the enhancement amplitude. This addresses the issues of overexposure in strong light areas and missing details in dark areas caused by uneven illumination. Second, an adaptive gradient enhancement strategy is introduced to construct a gradient weight matrix. This matrix dynamically allocates enhancement weights according to local luminance differences, thereby suppressing noise and sharpening edges while maintaining luminance balance, achieving the collaborative optimization of luminance and details. Finally, a saturation stretching module based on color drift perception is designed to correct color deviations. Combined with a non-local means (NLM) denoising mechanism in the YUV space, it further improves the overall perceptual quality and color naturalness of the images. Extensive experimental validations were conducted on the public Low-Light(LOL) test set and a self-built coal mine image dataset. Quantitative evaluation results show that the proposed method achieves the best overall performance in both full-reference metrics (e.g., Peak Signal-to-Noise Ratio(PSNR), Structural Similarity Index Measure(SSIM)) and no-reference metrics (e.g., Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE)). Compared with the evaluated methods, it more effectively improves image contrast, preserves structural details, and suppresses noise. Ultimately, this method effectively improves the luminance uniformity, contrast, and edge detail resolution of images in low-light environments, providing a feasible theoretical reference for downstream tasks such as image enhancement and target detection in coal mine intelligent monitoring systems. Full article

To address the challenges of crop–weed instance segmentation and image-plane operation-point localization under visual similarity, background interference, leaf occlusion, and irregular plant morphology in cotton fields, a lightweight instance-segmentation model, GDMM-YOLO11, was developed. Based on YOLO11n-seg, the four backbone stage-transition downsampling convolutions at P2/4, P3/8, P4/16, and P5/32 were replaced with GhostConv to reduce redundant computation, a C3k2_DySnakeConv_Mona module was introduced to strengthen structural and multi-scale feature representation, and the original post-SPPF C2PSA block was replaced with mixed local channel attention (MLCA) to recalibrate high-level feature responses. Experiments were conducted on 2177 images containing 2856 annotated plant instances using a stratified 65%/15%/20% training–validation–test split. Across three independent runs with random seeds 3407, 3408, and 3409, GDMM-YOLO11 achieved a mask precision of 89.82 ± 2.60%, mask recall of 84.50 ± 2.49%, mask mAP@0.5 of 89.76 ± 0.66%, and mask mAP@0.5:0.95 of 68.50 ± 0.34%. Relative to YOLO11n-seg, the corresponding three-run mean values were numerically higher by 3.52, 1.07, 1.67, and 2.33 percentage points, respectively, while the parameter count decreased from 2.836 M to 2.533 M and the computational cost decreased from 9.6 to 8.8 GFLOPs. For image-plane operation-point localization, 505 of 528 ground-truth weed instances obtained valid same-class mask matches. The proposed skeleton-constrained fused-center method achieved a ground-truth-mask inclusion rate of 99.41%, a conditional Success@0.15 of 91.29%, and an end-to-end Success@0.15 of 87.31%. Paired comparisons with the mask-centroid baseline showed statistically significant improvements in ground-truth-mask inclusion and weed-boundary clearance, whereas differences in localization error and Success@0.10/0.15 were not statistically significant. TensorRT FP16 deployment on an NVIDIA Jetson AGX Orin achieved a model-only inference latency of 2.796 ± 0.115 ms, corresponding to 357.67 FPS. These results show that GDMM-YOLO11 provides a favorable accuracy–complexity trade-off while supporting image-plane operation-point generation and high-throughput model-only edge inference. Full article

Background: Older adults initiating chemotherapy are highly vulnerable to symptom burden and treatment interference. Nutritional insecurity (NI), barriers in food access, preparation, tolerance, and consumption, is a potentially modifiable determinant of treatment vulnerability, yet its role in shaping symptom pathways during chemotherapy is poorly understood. Objective: To characterize NI as a symptom phenotype and examine associations between NI and symptom burden, functional limitations, life interference, and inflammatory biomarkers in older adults undergoing chemotherapy. Methods: In a cross-sectional analysis nested within a prospective cohort, 79 adults ≥55 years newly diagnosed with cancer and receiving first-line chemotherapy completed NI screening and assessments of physical, psychological, and cognitive symptoms; functional limitations; inflammatory biomarkers (NPAR, NLR, MLR, PLR); and life interference at either pre-chemotherapy (T1) or final chemotherapy (TFinal). Participants were categorized as nutritionally secure (NS) or nutritionally insecure (NI). Group differences were evaluated using chi-square tests, Cramér’s V, Mann–Whitney U tests, ANOVA, and risk differences. Results: NI prevalence was 34.2%. Participant demographic and clinical characteristics were largely similar between NI and NS groups in both cohorts. NI participants reported higher prevalence of nearly all symptoms at both time points, with the strongest separation in continuous symptom burden at TFinal (mean rank 33.53 vs. 21.36; p = 0.005). Functional limitations showed minimal NI-related differences. Life interference was greater among NI participants in the T1 cohort but not in TFinal. Inflammatory biomarkers did not differ by NI status, and exploratory analyses showed no biomarker-symptom associations. Conclusions: NI represents a distinct symptom-heavy phenotype marked by elevated symptom burden and early-life interference, without corresponding functional decline or inflammatory abnormalities. Findings support universal NI surveillance in oncology and motivate longitudinal research to clarify NI–symptom–interference pathways and inform targeted interventions for older adults initiating chemotherapy. Full article

The manual identification of non-metallic pipelines in ground-penetrating radar (GPR) images is inefficient and heavily experience-dependent. Existing deep-learning methods suffer from performance degradation caused by image noise, signal attenuation, and false anomalies. To address these challenges, this paper proposes YOLOv8-WT, which introduces a novel WTConv-ATT module combining wavelet transform and multi-dimensional dynamic attention. This module performs multi-level wavelet decomposition to extract frequency-domain features and enhance global and low-frequency information perception; meanwhile spatial-channel-pixel attention adaptively generates feature fusion weights to suppress noise interference. Several existing well-established modules (Wise-IoU, C2f-FSDA, CBAM, EMA) are also integrated to further boost detection performance. Experimental results on merged public GPR datasets show that compared with the YOLOv8s baseline, the proposed model achieves increases of 1.27, 4.59, and 2.55 percentage points in Precision, Recall, and mAP50, reaching 92.98%, 91.83%, and 96.76%, respectively. YOLOv8-WT obtains promising detection performance for non-metallic pipelines under mixed-dataset conditions. Further validation is still required for unknown real-world GPR scenarios. Full article

This study proposes Multiple-Input Multiple-Output and Orthogonal Frequency-Division Multiplexing methods, as introduced in the IEEE 802.15.7a-2024 standard, to achieve higher data rates and longer transmission ranges in OCC systems where a camera is used to capture optical signals. OFDM is a multi-carrier modulation scheme extensively used in high-data-rate wireless communications to mitigate ISI caused by multipath propagation. In optical wireless communication (OWC) systems, OFDM has been widely adopted in both indoor and outdoor applications, including eHealth, smart home, and smart IoT systems. OWC technologies provide a secure and low-interference communication channel for IoT devices using visible light. In OWC-enabled edge computing, data processing is performed in nodes, reducing communication overhead and improving system scalability. Nevertheless, user mobility remains a major challenge for OWC systems, as time-varying optical channels significantly degrade signal processing performance. Furthermore, reliable signal detection under mobility is critical for improving the signal-to-noise ratio. To overcome these challenges, this paper proposes a deep learning-based LED detection scheme for a mobility-aware MIMO-OFDM system. Deep learning techniques are also utilized to identify OFDM frame boundaries and decode the transmitted data, replacing traditional signal processing approaches. Experimental results demonstrate that the proposed method enables long-range MIMO-OFDM communication over distances of up to 22 m while maintaining a low error rate at a receiver speed of 3 m/s. Full article

In recent decades, high-precision processing with ultrashort laser pulses has been progressively applied in highly efficient surface modification technologies for new multilayer nanomaterials widely used in industry. In this work, the ultrafast dynamics of layer-by-layer ablation of a multilayer Ni/Al thin-film at the initial stage (0–120 ps) after irradiation with a single 60 fs Gaussian-shaped femtosecond laser pulse, together with the morphology of the modified surface, were investigated using single-shot spatiotemporal resolved interferometry. Partial removal of the 46 nm thick upper Ni layer took place in the spallation mode with an expansion velocity of several hundred meters per second. The lower spallation threshold observed relative to bulk Ni is attributed to interference of rarefaction waves, reflected from the layer boundaries. A jet-like strong ejection of material during the complete removal of the upper nickel layer in the phase explosion mode was accompanied by an explosive expansion of the underlying overheated molten aluminum. The experimental study was supported by calculations of the spatiotemporal behavior of temperature in thin-film layers. The obtained results may help in studying the ablation mechanism of multilayer thin-films, as well as in developing simulation methods and laser processing technologies. Full article

Well-facilitated farmland plays an important role in stabilizing and increasing crop yields, advancing agricultural mechanization, and improving production efficiency. Field-road quality directly affects machinery access and the transport of agricultural inputs and harvested crops. Current acceptance inspections rely mainly on field surveys and spot measurements, resulting in limited spatial coverage, low efficiency, and poor reproducibility. Therefore, a method is needed to rapidly survey entire field-road networks, extract road extents and structural indicators, and generate verifiable inspection records. Centimeter-resolution UAV imagery enables flexible and repeatable data acquisition, but deriving acceptance-oriented indicators remains challenging. Narrow field roads are readily obscured by crops and shelterbelt shadows or confused with cropland textures, resulting in blurred boundaries, discontinuities, and false detections. We developed a lightweight UAV-based framework integrating field-road segmentation and structural-indicator quantification. MobileNetV2 replaced the DeepLabv3+ backbone, while a Normalization-based Attention Module and Content-Aware ReAssembly of FEatures enhanced interference suppression and spatial reconstruction. Morphological processing, skeleton extraction, Euclidean distance transformation, and skeleton-graph analysis were then used to quantify road width and network connectivity. Across three training runs with different random seeds, the model achieved mean mIoU, mPA, and precision values of 93.34%, 96.75%, and 98.90%, respectively. The model had 6.14 million parameters and an inference speed of 17.04 FPS. After averaging five measurements from each road segment, the R² values between the predicted and manually measured widths were 0.650, 0.486, and 0.662 for asphalt, concrete, and gravel roads, respectively. The corresponding width MAEs were 0.130, 0.140, and 0.100 m. Connectivity analysis yielded an index of 1.00 in the first validation area, while gap repair increased the index from 0.4682 to 0.4795 in the second area. The framework supports efficient, quantitative, and traceable acceptance inspection of field-road infrastructure in well-facilitated farmland. Full article

Accurate acquisition of biomass and yield information for protected vegetable crops is essential for crop growth assessment, environmental regulation, optimal harvest timing, and production planning. With advances in machine vision, spectral sensing, and artificial intelligence technologies, research in this field is shifting from destructive sampling and single-time-point estimation toward non-contact, multisource, continuous monitoring and dynamic prediction. Focusing on protected leafy and fruit vegetables, this review summarizes biomass and yield indicators and their ground-truth measurement methods and compares the characteristics of RGB imaging, three-dimensional vision, spectral sensing, and environmental data. It then reviews advances in biomass estimation, continuous growth monitoring, and harvest prediction for leafy vegetables, as well as flower and fruit sensing, fruit counting, individual fruit mass estimation, and stage-specific harvest yield prediction for fruit vegetables. Current research is expanding from static estimation at the individual-plant level to growth-process monitoring and stage-specific yield prediction, but challenges remain, including interference from complex environments, difficulties in the spatiotemporal alignment of multisource data, incomplete continuous information, and limited model adaptability. Future research should strengthen robust sensing under complex conditions, dynamic multisource fusion, and mechanistic–data-driven integration, thereby advancing the field from individual-plant estimation toward dynamic monitoring of cultivation units and production decision support. Full article

Background/Objectives: Functional limitations are one of many potential clinical consequences for people living with long COVID. However, medical, health, and demographic variables associated with functional limitations in this population have not been fully characterized. The objective of this study was to identify variables associated with having a functional limitation among United States (US) adults with long COVID in a nationally representative dataset. Methods: This was a cross-sectional analysis of US adults in the 2023 Medical Expenditure Panel Survey (MEPS). A multivariable logistic regression model was created to identify factors associated with reporting functional limitations versus no functional limitations in people with MEPS-defined long COVID. Results: In this study, 26% of adults with MEPS-defined long COVID reported functional limitations. Factors that were associated with functional limitations in people with long COVID included non-married status versus married (odds ratio [OR] = 1.8), having 2–4 chronic health conditions versus 0–1 conditions (OR = 2.8), being unemployed versus employed (OR = 3.5), having public health insurance versus uninsured (OR = 5.3), and reporting pain interference versus no pain (little pain interference, OR = 2.3; moderate interference, OR = 8.8; extreme/quite a bit of interference, OR = 16.9). No other associations were found. Conclusions: Functional limitations are common in people experiencing long COVID. Several factors were associated with functional limitations in this population of individuals with long COVID, although whether the functional limitation was caused by long COVID or another problem was unknown. Further research is needed to better understand factors associated with long COVID and to explore the scope and severity of functional limitations in long COVID. Full article

Food freshness is critical to ensuring food safety, minimizing food waste, and improving industrial efficiency. Traditional sensory evaluation and laboratory-based detection methods suffer from inherent limitations, including strong subjectivity, time-consuming procedures, and destructive sampling, which make it difficult to meet the requirements of modern supply chains for rapid, non-destructive, and real-time monitoring. This review comprehensively summarizes the mainstream research directions and recent advances in food freshness monitoring technologies across three complementary directions: (1) biomimetic or instrumental systems based on digital identification of food odors, tastes, and appearance characteristics, including electronic noses, electronic tongues, and machine vision technology; (2) biosensor-based technologies for ultrasensitive recognition of specific biomolecules (metabolites, proteins, and nucleic acids); (3) spectroscopic and imaging techniques for “fingerprint” capture and quantitative analysis of internal components. In addition, the preparation principles, instrumentation, and applications are stated in each section, along with critical performance aspects such as stability and anti-interference capability in complex matrices. We hope this review can provide a comprehensive understanding of sustainable and real-time food quality control for the advancement of food freshness assessment. Full article