Search Results (68)

Electric dipole antennas can be designed in a variety of geometries and applied across a wide range of configurations. Appropriately designed dipole antennas can provide deep tissue penetration and low radiofrequency (RF) power deposition in magnetic resonance imaging (MRI), making them attractive for applications requiring safe and effective RF transmission in deep regions. On clinical 3 T MRI systems, however, conventional dipoles are too large in size for practical imaging of the head. Inspired by telecommunications designs, the present work adapts meandered dipoles (where the conductor is folded to shorten the antenna) with the resonance frequency controlled through trace geometry. Additionally, multi-channel configurations are considered to improve RF power transmission. A straight dipole was progressively transformed into meandered geometries and characterized using benchtop measurements and electromagnetic simulations. Analyses evaluated frequency response, near-field behavior, power-flow directionality, and distributions of local tissue heating and transmitted RF magnetic field in multi-channel arrays. A four-channel parallel-transmit (pTx) prototype was also used to show the feasibility of dipole-based head imaging at 3 T. The present work demonstrates a practical implementation of compact, low-heating dipole arrays for head MRI, with potential for extension to ultra-high-field or multinuclear imaging. Full article

With the rapid expansion of global photovoltaic (PV) installed capacity, hot spot defects have become a major hidden danger that reduces power generation efficiency and threatens the safe and stable operation of PV stations. Unmanned aerial vehicle (UAV) infrared remote sensing is a key technology for the efficient intelligent monitoring of large-scale PV stations. However, detecting tiny hotspots in such infrared images poses severe challenges. Most of these defects are ultra-small targets with extremely low pixel size and weak contrast, which are easily submerged by complex background noise, leading to prominent issues including low detection accuracy and high miss rates. To address these issues, we propose a lightweight detection network based on YOLO11n, named PHSNet, for PV hotspot detection in UAV infrared images. Its core designs include the dynamic convolution integrated C3k2 (Dy-C3k2) for small target feature enhancement, context-guided downsampling (CG-Down) to alleviate feature loss during downsampling, optimized detection layers, and a lightweight shared deconvolutional detection head (LSDECD) for small target adaptation in low-altitude aerial scenes, forming a full-link optimization architecture for tiny target feature perception. Experiments on a dedicated dataset (4025 images, 25,181 annotations, 92% targets < 20 pixels) show that PHSNet achieves 0.73 AP₅₀ and 0.315 AP, surpassing YOLO11n by 0.1 in AP₅₀ and 0.058 in AP, respectively. With only 1.8 M parameters and 98.8 FPS, it outperforms mainstream lightweight models, including YOLOv8n and RT-DETR-R18, strikes a superior accuracy–efficiency balance, and provides an efficient solution for real-time intelligent monitoring and edge deployment of PV stations. Full article

Magnetic polymeric microspheres are pivotal solid-phase carriers in chemiluminescence enzyme immunoassays (CLEIA). However, their practical clinical application is frequently hindered by non-specific adsorption, irreversible aggregation, and the intrinsic susceptibility of exposed outermost Fe₃O₄ nanoparticles to oxidation. To overcome these critical bottlenecks, we rationally engineered highly original monodisperse P(St-MMA)@Fe₃O₄@P(St-MMA) sandwich-structured microspheres. The bespoke amphiphilic outer shell acts as an impenetrable shield against hydration and oxidation, while maintaining a topologically size-matched mesoporous network (average pore size of 13.11 nm) for optimal antibody anchoring. Strikingly, this architecture ensures exceptional long-term colloidal stability, completely preventing macroscopic agglomeration for over six months in buffer solutions. When evaluated in a carcinoembryonic antigen (CEA), CLEIA, our microspheres achieved an ultra-low limit of detection (LOD) of 0.055 ng·mL⁻¹ and high analytical recovery (93.37–108.25%). In a head-to-head comparison with industry-standard commercial magnetic beads, the engineered microspheres delivered stronger chemiluminescent signals and lower background noise, demonstrating excellent intra-assay (CV < 4.37%) and inter-assay (CV < 10%) precision. This work establishes a scalable, highly stable materials platform that effectively resolves the persistent oxidation limitations, holding immense practical importance for next-generation ultrasensitive clinical in vitro diagnostics. Full article

Background/Objectives: Excess consumption of energy-dense foods (EDF; ultra-processed snacks, sweets, and sugar-sweetened beverages) among preschool-aged children is a public health concern, particularly in low-income families. Caregiver parenting style, psychological stress, and food-parenting practices (FPP) may shape children’s EDF consumption, yet little is known about how these factors operate in real time. This exploratory pilot study examined (1) associations between baseline characteristics and EDF feeding episodes across 1 week and (2) whether caregivers’ momentary stress during EDF episodes related to FPP used. Methods: In total, 22 caregivers of Head Start children (ages 3–5) completed baseline measures and 7 days of ecological momentary assessment (up to seven prompts/day). At each prompt, caregivers reported child EDF consumption in the past hour; if confirmed, they reported FPP used and rated momentary stress. Aim 1 used Poisson regression to model caregiver-level EDF episode counts. Aim 2 tested momentary stress–practice associations during EDF episodes using GEE, with within-person and between-person stress modeled separately. Results: Authoritarian parenting was associated with a higher weekly rate of EDF episodes (RR = 1.43, 95% CI 1.23–1.66, p < 0.001); authoritative parenting trended lower (RR = 0.90, p = 0.065). Higher baseline stress was associated with more EDF episodes (RR = 1.25, p = 0.001). Momentarily, elevated stress above a caregiver’s own average increased odds of using food as a reward (OR = 1.08 per +10 points, p = 0.011), while higher average momentary stress was associated with co-eating (OR = 1.59, p = 0.042). Domain-level FPP composites showed no association with momentary stress. Conclusions: Authoritarian parenting and higher caregiver stress were associated with increased EDF feeding, and momentary stress was linked to reward-based feeding during those episodes. These hypothesis-generating findings suggest potential behavioral targets for just-in-time adaptive intervention, pending replication in adequately powered studies. Full article

Indoor positioning in hospitals is challenging because global navigation satellite systems signals are unavailable and existing solutions struggle with complex indoor propagation and high maintenance requirements. Fingerprinting-based methods using Wi-Fi, Bluetooth Low Energy (BLE), or magnetic field depend on extensive site surveys, while time or angle-based systems such as ultra-wide band, angle of arrival, and Wi-Fi round trip time require additional infrastructure. Recent machine learning approaches improve performance but remain limited by Pedestrian Dead Reckoning (PDR) drift and unstable spatial representations. This study proposes an AI-generated spatial pattern matching framework that integrates an AI-based PDR model with BLE Received Signal Strength Indicator (RSSI) to construct a user RSSI surface. Spatial similarity between user-generated patterns and the pre-built radio map is evaluated using Surface Correlation (SC), and a bi-directional candidate generation strategy with SC-based heading correction is employed to mitigate inertial drift. Experiments in a real hospital setting show that the proposed method achieves robust and accurate localization even in complex indoor environments where conventional fingerprinting and PDR techniques often fail. The results indicate that combining AI-driven inertial modeling with SC-based spatial pattern matching offers a practical and infrastructure-friendly solution for hospital indoor positioning. Full article

Electrophoretic Displays (EPDs) are widely adopted in e-readers and portable devices due to their ultra-low power consumption and eye-friendly reflective characteristics. However, inherent hardware limitations, such as low resolution, slow response speed, and display degradation, frequently result in blurred strokes and degraded text readability. While traditional driving waveform optimizations can mitigate these issues, they are device-dependent and require extensive manual calibration. To address these challenges, this paper proposes an Improved Whale Optimization Algorithm-based Multi-scale Fusion Attention-enhanced SwinIR (IWOA-MFA-SwinIR) model for super-resolution and recognition of text images on EPDs. Structurally, the model incorporates a multi-scale fused attention (MFA) module that synergistically integrates channel, spatial, and gated attention mechanisms to precisely capture high-frequency text details while suppressing background noise within the SwinIR architecture. Furthermore, to enhance model robustness and eliminate manual tuning, an Improved Whale Optimization Algorithm (IWOA) is employed to adaptively optimize critical hyperparameters, including embedding dimension (d), attention head count (h), learning rate ($lr$), and dimensionality reduction coefficient (r). Experiments conducted on the TextZoom and EPD datasets demonstrate that the proposed model achieves state-of-the-art performance. In the ablation study, it attains a Peak Signal-to-Noise Ratio (PSNR) of 24.406, a Structural Similarity Index (SSIM) of 0.8837, and a Character Recognition Accuracy (CRA) of 89.81%. In the comparative evaluation, the proposed model consistently outperforms the second-best comparison model across three difficulty levels, yielding approximately a 1% improvement in PSNR, a 0.8% improvement in SSIM, and an 8% improvement in CRA. This confirms the proposed model’s superiority over mainstream comparative models in restoring text fidelity and improving recognition rates. Full article

This paper presents an orthogonal-heading wavelength-resolution SAR (WRSAR) target detection framework that fuses multi-heading image stacks for foliage-penetrating (FOPEN) vehicle detection. First, a low-rank–sparse decomposition is applied to very-high-frequency (VHF), ultra-wideband (UWB) WRSAR stacks to suppress vegetation clutter and enhance target contrast. The clutter-suppressed sparse stacks acquired from orthogonal headings are then fused to enrich target scattering characteristics. Finally, a Rayleigh-entropy statistic computed on the fused sparse stack is used to represent discontinuous positional changes. Based on the non-negative nature of WRSAR amplitudes for both clutter and FOPEN targets, we introduce a non-negative constrained tensor robust principal component analysis (NCTRPCA) to improve sparsity in the stack components. Furthermore, since Shannon differential entropy has no tunable parameter, we replace Shannon entropy with RE in this work and derive its closed-form expression for the proposed detector. Experiments on the publicly available multi-heading, multi-temporal CARABAS II dataset show that the proposed orthogonal-heading WRSAR fusion achieves higher FOPEN vehicle detection performance than recent state-of-the-art methods while maintaining moderate computational cost. Full article

To clarify how flow-channel configuration and wall spacing govern the hydrodynamic performance of an oscillating-hydrofoil biomimetic pumping device, this study conducted a systematic numerical investigation under confined-flow conditions. Using a finite-volume solver with an overset-grid technique, we compared pumping performance across three channel configurations and a range of channel–wall distances. The results showed that bidirectional-channel confinement suppresses wake deflection and irregular vorticity evolution, enabling symmetric and periodic vortex organization and thereby improving pumping efficiency by approximately 33.6% relative to the single-channel case and by 62.7% relative to the no-channel condition. Wall spacing exhibited a distinctly non-monotonic influence on performance, revealing two high-performance regimes: under extreme confinement (gap ratio $h/c=$ 1.4), the device attains peak pumping and thrust efficiencies of 19.9% and 30.7%, respectively, associated with a strongly guided jet-like transport mode; and under moderate spacing ($h/c=$ 2.2–2.6), both efficiencies remain high due to an improved balance between directional momentum transport and reduced vortex-evolution losses. These findings identify key confinement-driven mechanisms and provide practical guidance for optimizing flow-channel design in ultralow-head oscillating-hydrofoil pumping applications. Full article

The petroleum industry faces intensifying challenges related to the depletion of easily accessible reservoirs and the growing energy demand, necessitating the adoption of advanced chemical agents that can operate under extreme conditions. Cationic gemini surfactants, characterized by their unique dimeric architecture consisting of two hydrophilic head groups and two hydrophobic tails, have emerged as superior alternatives to conventional monomeric surfactants due to their enhanced interfacial activity and physicochemical resilience. This review provides a comprehensive analysis of the literature concerning the molecular structure, synthesis, and functional applications of cationic gemini surfactants across the entire oil value chain, from extraction to refining. The analysis reveals that gemini surfactants exhibit critical micelle concentrations significantly lower than their monomeric analogs and maintain stability in high-temperature and high-salinity environments. They demonstrate exceptional efficacy in enhanced oil recovery through ultra-low interfacial tension reduction and wettability alteration, while simultaneously serving as effective drag reducers, wax inhibitors, and dual-action biocidal corrosion inhibitors in transportation pipelines. Cationic gemini surfactants represent a transformative class of multifunctional materials for the oil industry. Full article

Background: Photon-counting computed tomography (PCCT) represents a major technological advance in clinical CT imaging, offering superior spatial resolution, enhanced material discrimination, and potential radiation dose reduction compared to conventional energy-integrating detector systems. As the first clinically approved PCCT scanner becomes available, establishing a comprehensive characterization of its image quality is essential to understand its performance and clinical impact. Methods: Image quality was evaluated using a commercial quality assurance phantom with acquisition protocols typically used for three anatomical regions—head, abdomen/thorax, and inner ear—representing diverse clinical scenarios. Each region was scanned using both ultra-high-resolution (UHR, 120 × 0.2 mm slices) and conventional (144 × 0.4 mm slices) protocols. Conventional metrics, including signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), slice thickness accuracy, and uniformity, were assessed following international standards. Task-based analysis was also performed through target transfer function (TTF), noise power spectrum (NPS), and detectability index (d′) to evaluate diagnostic relevance. Results: UHR protocols provided markedly improved spatial resolution, particularly in the inner ear imaging, as confirmed by TTF analysis, though with increased noise and reduced low-contrast detectability in certain conditions. CT numbers showed linear correspondence with known attenuation coefficients across all protocols. Conclusions: This study establishes a detailed technical characterization of the first clinical PCCT scanner, demonstrating significant improvements in terms of spatial resolution and accuracy of the quantitative image analysis, while highlighting the need for noise–contrast optimization in high-resolution imaging. Full article

With the large-scale integration of wind power into the grid, the accuracy of wind farm cluster power prediction has become a key factor for the sustainability of modern power systems. Reliable ultra-short-term forecasts support the secure dispatch of high-penetration renewable energy, reduce wind curtailment, and improve the low-carbon and economical operation of power systems. Aiming at the problem of significant differences in wind turbine characteristics, this paper proposes a prediction method based on an improved density-based spatial clustering of applications with noise (DBSCAN) and a hybrid deep learning model. First, the wind speed signal is decomposed at multiple scales using successive variational modal decomposition (SVMD) to reduce non-stationarity. Subsequently, the DBSCAN parameters are optimized by the fruit fly optimization algorithm (FOA), and dimensionality reduction is performed by principal component analysis (PCA) to achieve efficient clustering of wind turbines. Next, the representative turbines with the highest correlation are selected in each cluster to reduce computational complexity. Finally, the SVMD-TCN-BiGRU-MSA-GJO hybrid model is constructed, and long-term dependence is extracted using a temporal convolutional network (TCN); the temporal features are captured by bidirectional gated recurrent units (BiGRUs); the feature weights are optimized by a multi-head self-attention mechanism (MSA), and the hyper-parameters are, in turn, optimized by golden jackal optimization (GJO). The experimental results show that this method reduces the MAE, RMSE, and MAPE by 14.02%, 12.9%, and 13.84%, respectively, and improves R² by 3.9% on average compared with the traditional model, which significantly improves prediction accuracy and stability. These improvements enable more accurate scheduling of wind power, lower reserve requirements, and enhanced stability and sustainability of power system operation under high renewable penetration. Full article

Children with Congenital Zika Virus Syndrome (CZVS) experience severe neurological and nutritional impairments. Although immediate clinical consequences are well-documented, long-term anthropometric and nutritional outcomes remain poorly understood. This study assessed longitudinal anthropometric and nutritional outcomes in children affected by CZVS. A cohort of 38 children aged ≥ 5 years diagnosed with CZVS was followed at a reference center in Northeast Brazil. Anthropometric measures (weight, height, BMI, head circumference) were collected using standardized methods, including digital scales and anthropometric tape measures. Growth was analyzed using WHO Anthro and WHO Anthro Plus software (version 3.2.2). Dietary intake was evaluated through two 24 h recalls and analyzed with NutWIN 2.5 software. Nutritional status was classified using WHO growth standards, and associations between dietary intake and BMI were statistically examined. Children showed significant linear growth improvement (p = 0.007) without corresponding weight gain, leading to worsening BMI classifications (p = 0.017). Dietary evaluations revealed limited dietary diversity, frequent intake of ultra-processed foods, inadequate fruit consumption, and widespread insufficiencies in caloric and micronutrient intake (zinc, calcium, iron, vitamin D). Low carbohydrate intake was significantly associated with inadequate BMI (p = 0.030). Multidisciplinary nutritional interventions addressing medical, dietary, educational, and socioeconomic factors are essential for improving health outcomes in children with CZVS. Full article

Breast ultrasound imaging functions as a vital radiation-free detection tool for breast cancer, yet its low contrast, speckle noise, and interclass variability make automated interpretation difficult. In this paper, we introduce UltraScanNet as a specific deep learning backbone that addresses breast ultrasound classification needs. The proposed architecture combines a convolutional stem with learnable 2D positional embeddings, followed by a hybrid stage that unites MobileViT blocks with spatial gating and convolutional residuals and two progressively global stages that use a depth-aware composition of three components: (1) UltraScanUnit (a state-space module with selective scan gated convolutional residuals and low-rank projections), (2) ConvAttnMixers for spatial channel mixing, and (3) multi-head self-attention blocks for global reasoning. This research includes a detailed ablation study to evaluate the individual impact of each architectural component. The results demonstrate that UltraScanNet reaches 91.67% top-1 accuracy, a precision score of 0.9072, a recall score of 0.9174, and an F1-score of 0.9096 on the BUSI dataset, which make it a very competitive option among multiple state-of-the-art models, including ViT-Small (91.67%), MaxViT-Tiny (91.67%), MambaVision (91.02%), Swin-Tiny (90.38%), ConvNeXt-Tiny (89.74%), and ResNet-50 (85.90%). On top of this, the paper provides an extensive global and per-class analysis of the performance of these models, offering a comprehensive benchmark for future work. The code will be publicly available. Full article

Strawberry, as an important global economic crop, its disease prevention and control directly affects yield and quality. Traditional detection means rely on manual observation or traditional machine learning algorithms, which have defects such as low efficiency, high false detection rate, and insufficient adaptability to tiny disease spots and complex environment. To solve the above problems, this study proposes a strawberry disease recognition method based on improved YOLOv8. By systematically acquiring 3146 image data covering seven types of typical diseases, such as gray mold and powdery mildew, a high-quality dataset containing different disease stages and complex backgrounds was constructed. Aiming at the difficulties in disease detection, the YOLOv8 model is optimized in two stages: on the one hand, the ultra-small scale detection head (32 × 32) is introduced to enhance the model’s ability to capture early tiny spots; on the other hand, the convolution and attention fusion module (CAFM) is combined to enhance the feature robustness in complex field scenes through the synergy of local feature extraction and global information focusing. Experiments show that the mAP50 of the improved model reaches 0.96 and outperforms mainstream algorithms such as YOLOv5 and Faster R-CNN in both recall and F1 score. In addition, the interactive system developed based on the PyQT5 framework can process images, videos and camera inputs in real time, and the disease areas are presented intuitively through visualized bounding boxes and category labels, which provides farmers with a lightweight and low-threshold field management tool. This study not only verifies the effectiveness of the improved algorithm but also provides a practical reference for the engineering application of deep learning in agricultural scenarios, which is expected to promote the further implementation of precision agriculture technology. Full article

Background/Objectives: Forced degradation studies are critical for identifying potential degradation pathways of monoclonal antibodies (mAbs), particularly under thermal stress. Due to their structural complexity and sensitivity to elevated temperatures, mAbs are prone to fragmentation, aggregation, and post-translational modifications. This study aimed to evaluate and compare the degradation profiles of biosimilar anti-VEGF mAb and its originator counterparts sourced from both the United States (U.S.) and the European Union (EU) under thermal stress conditions. To our knowledge, this represents one of the few studies conducting a direct head-to-head comparability assessment across biosimilar and two geographically sourced originators, integrating orthogonal analytical approaches. Methods: Biosimilar candidate and originator products (U.S. and EU) were incubated at 37 °C and 50 °C for 3, 7, and 14 days. Fragmentation profiles were assessed using validated non-reduced and reduced capillary electrophoresis–sodium dodecyl sulfate (CE-SDS) methods. Additionally, size-exclusion ultra-performance liquid chromatography (SE-UPLC) and liquid chromatography–tandem mass spectrometry (LC-MS/MS) assays were performed on samples stressed for 14 days to provide deeper insights into degradation pathways. Results: Non-reduced CE-SDS analysis indicated a time- and temperature-dependent increase in low-molecular-weight fragments and a corresponding decrease in the intact form, with more pronounced effects observed at 50 °C. Reduced CE-SDS revealed a more rapid increase in total impurity levels at 50 °C, accompanied by a decrease in total light and heavy chain content. SE-UPLC showed enhanced aggregation under thermal stress, more pronounced at 50 °C. LC-MS/MS analysis identified increased asparagine deamidation in the PENNY peptide and pyroglutamic acid formation (pE) at the N-terminus of the heavy chain. Conclusions: The degradation profiles of the biosimilar and originator mAbs were highly comparable under thermal stress, with no significant qualitative differences detected. By applying a multi-tiered analytical characterization technique, this study provides a comprehensive comparability assessment that underscores the robustness of biosimilarity even under forced degradation conditions. Full article