Search Results (86)

Background: Upper-limb performance in handball depends on accurate shoulder sensorimotor control under high loads and fatigue. This study examined between-cohort differences associated with concentric versus eccentric exercise-induced fatigue in shoulder proprioception, kinesthesia, functional stability, and isometric force output in professional male handball players. Methods: This was a retrospective, quasi-experimental (non-randomized) between-cohort comparison of two previously collected cohorts who completed either a concentric (n = 46) or eccentric (n = 33) fatigue protocol, with pre- and post-fatigue assessments of joint repositioning sense (absolute angular error, AAE), threshold to detection of passive movement (TTDPM), Y Balance Test Upper Quarter (YBT-UQ), and the Athletic Shoulder (ASH) test. Results: Fatigue significantly increased AAE across all tested angles (Time: all p < 0.001), with a contraction-specific effect at end-range internal rotation (IR45°), where AAE increased more after concentric than eccentric fatigue (Time × Fatigue Type: p = 0.017; Δ = +1.34° (+61.8%) vs. +0.20° (+7.4%)). TTDPM increased after fatigue (p = 0.001) with no interaction (p = 0.968). YBT-UQ performance decreased after fatigue for all dominant-limb outcomes and for non-dominant inferolateral, superolateral, and composite scores (all p ≤ 0.018), but not for non-dominant anteromedial reach (p = 0.986); no Time × Fatigue Type interactions were detected for YBT-UQ outcomes (all p > 0.05). ASH force output decreased across all positions and both limbs (all p ≤ 0.002), with the dominant-limb Y position showing a greater decline following eccentric fatigue (Time × Fatigue Type: p = 0.030; e.g., ASH Y dominant Δ = −0.49 (−4.6%) vs. −1.43 N·kg⁻¹ (−13.3%)). Conclusions: Exercise-induced fatigue impairs shoulder sensorimotor function and upper-limb performance in handball. Contraction-mode differences were small and task-specific in this between-cohort comparison, emerging primarily at end-range proprioception and selected isometric strength positions. These findings may inform the design of training programs that emphasize fatigue-resistant sensorimotor control and end-range strength, while causal inferences regarding contraction mode are not warranted given the non-randomized design. Full article

This work presents the design of a novel electrochemical sensor for highly sensitive determination of LEV, utilizing a sensing platform based on a newly synthesized, high-purity manganese (III) porphyrin complex [5,10,15,20-tetrayltetrakis(2-methoxybenzene-4,1-diyl) tetraisonicotinateporphyrinato] manganese (III) porphyrin (MnTMIPP). The successful synthesis of the MnTMIPP complex was verified using ultraviolet–visible (UV–Vis) and infrared spectroscopy (IR). The sensing electrode was fabricated by depositing the synthesized material onto an indium tin oxide (ITO) electrode via a drop-coating method. Under optimized experimental conditions, the proposed sensor demonstrated a wide dynamic range, from 10⁻⁹ M to 10⁻³ M, with a low calculated detection limit of 4.82 × 10⁻¹⁰ M. Furthermore, the MnTMIPP/ITO electrode displayed interesting metrological performance: high selectivity, reproducibility, and stability. Successful application in spiked river water and saliva samples with satisfactory recovery rates confirms the sensor’s potential as a reliable and cost-effective platform for monitoring LEV in real-world environments. Full article

Existing Generative Adversarial Networks (GANs) frequently yield remote sensing images with blurred fine details, distorted textures, and compromised spatial structures when applied to super-resolution (SR) tasks, so this study proposes a Multi-Attention Fusion Generative Adversarial Network (MAF-GAN) to address these limitations: the generator of MAF-GAN is built on a U-Net backbone, which incorporates Oriented Convolutions (OrientedConv) to enhance the extraction of directional features and textures, while a novel co-calibration mechanism—incorporating channel, spatial, gating, and spectral attention—is embedded in the encoding path and skip connections, supplemented by an adaptive weighting strategy to enable effective multi-scale feature fusion, and a composite loss function is further designed to integrate adversarial loss, perceptual loss, hybrid pixel loss, total variation loss, and feature consistency loss for optimizing model performance; extensive experiments on the GF7-SR4×-MSD dataset demonstrate that MAF-GAN achieves state-of-the-art performance, delivering a Peak Signal-to-Noise Ratio (PSNR) of 27.14 dB, Structural Similarity Index (SSIM) of 0.7206, Learned Perceptual Image Patch Similarity (LPIPS) of 0.1017, and Spectral Angle Mapper (SAM) of 1.0871, which significantly outperforms mainstream models including SRGAN, ESRGAN, SwinIR, HAT, and ESatSR as well as exceeds traditional interpolation methods (e.g., Bicubic) by a substantial margin, and notably, MAF-GAN maintains an excellent balance between reconstruction quality and inference efficiency to further reinforce its advantages over competing methods; additionally, ablation studies validate the individual contribution of each proposed component to the model’s overall performance, and this method generates super-resolution remote sensing images with more natural visual perception, clearer spatial structures, and superior spectral fidelity, thus offering a reliable technical solution for high-precision remote sensing applications. Full article

The growing demand for compact, low-power infrared (IR) sensors necessitates advanced solutions for on-chip spectral selectivity, particularly for integration with Thermal Metal-Oxide-Semiconductor (TMOS) devices. This paper investigates the design and analysis of CMOS-compatible metal–insulator–metal (MIM) metamaterial absorbers tailored for selective absorption in the long-wave infrared (LWIR) region. We present a design methodology utilizing an equivalent-circuit model, which provides intuitive physical insight into the absorption mechanism and significantly reduces computational costs compared to full-wave electromagnetic simulations. An important rule in this design methodology is demonstrating how the resonance wavelength of these absorbers can be precisely tuned across the LWIR spectrum by engineering the geometric parameters of the top metallic patterns and, critically, by optimizing the dielectric substrate’s refractive index and thickness, which assist in designing small period MIM absorber units which are important in infrared thermal sensor pixels. Our results demonstrate that the resonance wavelength of these absorbers can be precisely tuned across the LWIR spectrum by engineering the geometric parameters of the top metallic patterns and by optimizing the dielectric substrate’s refractive index and thickness. Specifically, the selection of silicon as the dielectric material, owing to its high refractive index and low losses, facilitates compact designs with high-quality factors. The transmission line model provides intuitive insight into how near-perfect absorption is achieved when the absorber’s input impedance matches the free-space impedance. This work presents a new approach for the methodology of designing MIM absorbers in the mid-infrared and long-wave infrared (LWIR) regions, utilizing the intuitive insights provided by equivalent circuit modeling. This study validates a highly efficient design approach for high-performance, spectrally selective MIM absorbers for LWIR radiation, paving the way for their monolithic integration with TMOS sensors to enable miniaturized, cost-effective, and functionally enhanced IR sensing systems. Full article

Infrared (IR) thermal sensors on CMOS-SOI-MEMS platforms enable scalable, low-cost thermal imaging but require optimized optical, thermal, and mechanical performance. This paper presents a multiphysics modeling framework to study the integration of Metasurface absorbers into a Thermal CMOS-SOI-MEMS IR sensor. Using finite-difference time-domain (FDTD) simulations, we demonstrate near-unity absorption at targeted wavelengths (e.g., 4.26 µm for CO₂ sensing, 10 µm for thermal imaging) compared to conventional absorbers. The absorbed power, calculated from blackbody irradiance, drives thermal finite element analysis (FEA), confirming high thermal isolation and maximized temperature rise (ΔT) while quantifying the thermal time constant’s sensitivity to Metasurface mass. An analytical RC circuit model, validated against 3D FEA, accurately captures thermal dynamics for rapid design iterations. Mechanical modal and harmonic analyses verify structural integrity, with natural frequencies above 20 kHz, ensuring resilience against mechanical resonances and environmental vibrations. This holistic framework quantifies trade-offs between optical efficiency, thermal responsivity, and mechanical stability, providing a predictive tool for designing high-performance, uncooled IR sensors compatible with CMOS processes. Full article

Remote sensing image super-resolution (SR) plays a crucial role in enhancing the quality and resolution of satellite and aerial imagery, which is essential for various applications, including environmental monitoring and urban planning. While recent image super-resolution networks have achieved strong results, remote-sensing images present domain-specific challenges—complex spatial distribution, large cross-scale variations, and dynamic topographic effects—that can destabilize multi-scale fusion and limit the direct applicability of generic SR models. These features make it difficult for single-scale feature extraction methods to fully capture the complex structure, leading to the presence of artifacts and structural distortion in the reconstructed remote sensing images. Therefore, new methods are needed to overcome these challenges and improve the accuracy and detail fidelity of remote sensing image super-resolution reconstruction. This paper proposes a novel Multi-scale Windowed Spatial and Channel Attention Network (MSWSCAN) for high-fidelity remote sensing image super-resolution. The proposed method combines multi-scale feature extraction, window-based spatial attention, and channel attention mechanisms to effectively capture both global and local image features while addressing the challenges of fine details and structural distortion. The network is evaluated on several benchmark datasets, including WHU-RS19, UCMerced and RSSCN7, where it demonstrates superior performance in terms of peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) compared to state-of-the-art methods. The results show that the MSWSCAN not only enhances texture details and edge sharpness but also reduces reconstruction artifacts. To address cross-scale variations and dynamic topographic effects that cause texture drift in multi-scale SR, we combine windowed spatial attention to preserve local geometry with a channel-aware fusion layer (FFL) that reweights multi-scale channels. This stabilizes cross-scale aggregation at a runtime comparable to DAT and yields sharper details on heterogeneous land covers. Averaged over WHU–RS19, RSSCN7, and UCMerced_LandUse at ×2/×3/×4, MSWSCAN improves PSNR (peak signal-to-noise ratio, dB)/SSIM (structural similarity index measure, 0–1) by +0.10 dB/+0.0038 over SwinIR and by +0.05 dB/+0.0017 over DAT. In conclusion, the proposed MSWSCAN achieves state-of-the-art performance in remote sensing image SR, offering a promising solution for high-quality image enhancement in remote sensing applications. Full article

A fluorescence sensor was designed based on nitrogen-doped graphene quantum dots confined in a metal–organic framework and molecularly imprinted polymer for the selective determination of 5-hydroxymethylfurfural (HMF). Morphological, structural, and spectroscopic characterizations, such as SEM, STEM, BET, FT-IR, and XRD, verified successful synthesis and imprinting with enhanced surface area and structural durability. The sensor demonstrated intense fluorescence at around 420 nm, which was quenched through photoinduced electron transfer (PET) by HMF, exhibiting a linear relationship up to 35 µmol L⁻¹ and a detection limit of 30 nmol L⁻¹. It offered high imprinting efficiency, selectivity, and stability. The sensing platform also displayed efficient anti-interference performance toward interference species and presented excellent recovery in actual food samples such as honey, juice, and coffee, thus revealing the applicability of the sensing device for real-world HMF measurement in complicated matrices. Full article

Levees are critical for flood defence, but their integrity is threatened by hazards such as piping and seepage, especially during high-water-level periods. Traditional manual inspections for these hazards and associated emergency response elements, such as personnel and assets, are inefficient and often impractical. While UAV-based remote sensing offers a promising alternative, the effective fusion of multi-modal data and the scarcity of labelled data for supervised model training remain significant challenges. To overcome these limitations, this paper reframes levee monitoring as an unsupervised anomaly detection task. We propose a novel, fully automated framework that unifies geophysical hazards and emergency response elements into a single analytical category of “abnormal targets” for comprehensive situational awareness. The framework consists of three key modules: (1) a state-of-the-art registration algorithm to precisely align infrared and visible images; (2) a generative adversarial network to fuse the thermal information from IR images with the textural details from visible images; and (3) an adaptive, unsupervised segmentation module where a mean-shift clustering algorithm, with its hyperparameters automatically tuned by Bayesian optimization, delineates the targets. We validated our framework on a real-world dataset collected from a levee on the Pajiang River, China. The proposed method demonstrates superior performance over all baselines, achieving an Intersection over Union of 0.348 and a macro F1-Score of 0.479. This work provides a practical, training-free solution for comprehensive levee monitoring and demonstrates the synergistic potential of multi-modal fusion and automated machine learning for disaster management. Full article

Precision and real-time detection of hydrogen peroxide (H₂O₂) are essential in pharmaceutical, industrial, and defence sectors due to its strong oxidizing nature. In this study, silver (Ag)-doped CeO₂/Ag₂O-modified glassy carbon electrode (Ag-CeO₂/Ag₂O/GCE) has been developed as a non-enzymatic electrochemical sensor for the sensitive and selective detection of H₂O₂. The synthesized Ag-doped CeO₂/Ag₂O nanocomposite was characterized using various advanced techniques, including X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FT-IR), field-emission scanning electron microscopy (FE-SEM), and high-resolution transmission electron microscopy (HR-TEM). Their optical, magnetic, thermal, and chemical properties were further analyzed using UV–vis spectroscopy, electron paramagnetic resonance (EPR), thermogravimetric-differential thermal analysis (TG-DTA), and X-ray photoelectron spectroscopy (XPS). Electrochemical sensing performance was evaluated using cyclic voltammetry and amperometry. The Ag-CeO₂/Ag₂O/GCE exhibited superior electrocatalytic activity for H₂O₂, attributed to the increased number of active sites and enhanced electron transfer. The sensor displayed a high sensitivity of 2.728 µA cm⁻² µM⁻¹, significantly outperforming the undoped CeO₂/GCE (0.0404 µA cm⁻² µM⁻¹). The limit of detection (LOD) and limit of quantification (LOQ) were found to be 6.34 µM and 21.1 µM, respectively, within a broad linear detection range of 1 × 10⁻⁸ to 0.5 × 10⁻³ M. The sensor also demonstrated excellent selectivity with minimal interference from common analytes, along with outstanding storage stability, reproducibility, and repeatability. Owing to these attributes, the Ag-CeO₂/Ag₂O/GCE sensor proved effective for real sample analysis, showcasing its potential as a reliable, non-enzymatic platform for H₂O₂ detection. Full article

This paper presents a comparative analysis of ten state-of-the-art robotic de-mining systems, grouped into (i) sensor-centric platforms for high-precision detection and (ii) rapid mechanical-contact vehicles for clearance. Building on these findings, we propose a lightweight tracked platform (~1.9 T) equipped with a four-channel sensing suite-RGB/IR camera, 32-layer LiDAR, pulsed-induction metal detector, and 2.45 GHz microwave thermography—integrated in an adaptive Bayesian “detect → confirm → neutralize” loop. The modular end-effector permits either pinpoint mechanical intervention or deployment of a linear charge. Modelling indicates an expected detection sensitivity ≥ 95% with a false-positive rate ≤ 5% in humanitarian demining mode and a clearance throughput above 1.5 ha·h⁻¹ in breaching mode. Ongoing work includes CFD analysis of the thermal front, fabrication of a prototype, and performance testing in accordance with IMAS 10.20. Full article

The burgeoning demand for reliable fault detection in high-voltage power equipment necessitates advanced sensing materials capable of identifying trace sulfur hexafluoride SF₆ decomposition products (SDPs). In this work, the first-principles calculations were employed to comprehensively evaluate the potential of Ir- and Pt-doped InTe (Ir-InTe and Pt-InTe) monolayers as high-performance gas sensors for the four specific SDPs (H₂S, SO₂, SOF₂, SO₂F₂). The results reveal that Ir and Pt atoms are stably incorporated into the hollow sites of the InTe monolayer, significantly reducing the intrinsic bandgap from 1.536 eV to 0.278 eV (Ir-InTe) and 0.593 eV (Pt-InTe), thereby enhancing the material’s conductivity. Furthermore, Ir-InTe exhibits selective chemisorption for H₂S, SO₂, and SOF₂, with adsorption energies exceeding −1.35 eV, while Pt-InTe shows chemisorption capability for all four SDPs. These interactions are further supported by significant charge transfer and orbital hybridization. Crucially, these interactions induce notable bandgap changes, with Ir-InTe showing up to a 65.5% increase (for SOF₂) and Pt-InTe showing an exceptional 105.2% increase (for SO₂F₂), alongside notable work function variations. Furthermore, recovery time analysis indicates that Ir-InTe is suitable for reusable H₂S sensing at 598 K (0.24 s), whereas Pt-InTe offers recyclable detection of SO₂ (5.27 s) and SOF₂ (0.16 s) at the same temperature. This work provides theoretical guidance for the development of next-generation InTe-based gas sensors for the fault diagnosis in high-voltage power equipment. Full article

The use of pressure-sensitive paints (PSPs), an optical oxygen sensing technique, to visualise and measure the surface pressure on vehicle models in wind tunnel testing is becoming increasingly prevalent. Porphyrins have long been the standard luminophore for PSP formulations, with the majority employing the red-emitting platinum(II)-5,10,15,20-tetrakis-(2,3,4,5,6-pentafluorphenyl)-porphyrin. nIR-emitting luminophores, such as Pt(II) tetraphenyl tetrabenzoporphyrins, possess distinct advantages over visible emitting luminophores. In particular, they have wider spectrally useful ‘windows’, facilitating the insertion of a secondary visible emitting temperature-sensitive luminophore to be used for internal calibration without spectral crosstalk that detrimentally impacts PSP performance. In this work, we explore the effect of changing the loading quantity of an nIR-emitting para-CF₃ Pt(II) benzoporphyrin luminophore on the performance of PSP formulations. An optimal luminophore loading of 1.28% wt/wt benzoporphyrin luminophore to polystyrene binder was identified, resulting in a low temperature sensitivity at 100 kPa of 0.61%/K and a large pressure sensitivity at 293 K of 0.740%/kPa. These strong performance metrics, for a polystyrene-based PSP, demonstrate the efficacy of benzoporphyrin luminophores as an attractive luminophore option for the development of a new generation of high-performance PSP formulations that outperform current commercially available ones. Full article

This work represents the first use of a phosphonium salt-functionalized β-Cyclodextrin polymer (β-CDP) as a highly selective sensing membrane for monitoring the safety of drinking water against perchlorate ions (ClO₄⁻) using electrochemical impedance spectroscopy (EIS). Structural confirmation via ¹H NMR, ¹³C NMR, ³¹P NMR, and FT-IR spectroscopies combined with AFM and contact angle measurements demonstrate how the enhanced solubility of modified cyclodextrin improves thin film quality. The innovation lies in the synergistic combination of two detection mechanisms: the “Host-Guest” inclusion in the cyclodextrin cavity and anionic exchange between the bromide ions of the phosphonium groups and perchlorate anions. Under optimized functionalization conditions, EIS reveals high sensitivity and selectivity, achieving a record-low detection limit (LOD) of ~10⁻¹² M and a wide linear range of detection (10⁻¹¹ M–10⁻⁴ M). Sensing mechanisms at the functionalized transducer interfaces are examined through numerical fitting of Cole-Cole impedance spectra via a single relaxation equivalent circuit. Real water sample analysis confirms the sensor’s practical applicability, with recoveries between 96.9% and 109.8% and RSDs of 2.4–4.8%. Finally, a comparative study with reported membrane sensors shows that β-CDP offers superior performance, wider range, higher sensitivity, lower LOD, and simpler synthesis. Full article

To address the growing demand for environmentally friendly flexible sensors, here, a composite hydrogel of nanocellulose (NC) and polyvinyl alcohol (PVA) was designed and fabricated using Camellia oleifera shells as a sustainable alternative to petroleum-based raw materials. Firstly, NC was extracted from Camellia oleifera shells and modified with 2-chloropropyl chloride to obtain a nanocellulose-based initiator (Init-NC) for atomic transfer radical polymerization (ATRP). Subsequently, sulfonyl betaine methacrylate (SBMA) was polymerized by Init-NC initiating to yield zwitterion-functionalized nanocellulose (NC-PSBMA). Finally, the NC-PSBMA/PVA hydrogel was fabricated by blending NC-PSBMA with PVA. A Fourier transform infrared spectrometer (FT-IR), proton nuclear magnetic resonance spectrometer (¹H-NMR), X-ray diffraction (XRD), scanning electron microscope (SEM), transmission electron microscope (TEM), universal mechanical testing machine, and digital source-meter were used to characterize the chemical structure, surface microstructure, and sensing performance. The results indicated that: (1) FT-IR and ¹H NMR confirmed the successful synthesis of NC-PSBMA; (2) SEM, TEM, and alternating current (AC) impedance spectroscopy verified that the NC-PSBMA/PVA hydrogel exhibits a uniform porous structure (pore diameter was 1.1737 μm), resulting in significantly better porosity (15.75%) and ionic conductivity (2.652 S·m⁻¹) compared to the pure PVA hydrogel; and (3) mechanical testing combined with source meter testing showed that the tensile strength of the composite hydrogel increased by 6.4 times compared to the pure PVA hydrogel; meanwhile, it showed a high sensitivity (GF = 1.40, strain range 0–5%; GF = 1.67, strain range 5–20%) and rapid response time (<0.05 s). This study presents a novel approach to developing bio-based, flexible sensing materials. Full article

Recent advances in unmanned aerial vehicle (UAV) technology combined with deep learning techniques have greatly improved agricultural monitoring. However, accurately processing images at low resolutions remains challenging for precision cultivation of succulents. To address this issue, this study proposes a novel method that combines cutting-edge super-resolution reconstruction (SRR) techniques with object detection and then applies the above model in a unified drone framework to achieve large-scale, reliable monitoring of succulent plants. Specifically, we introduce MambaIR, an innovative SRR method leveraging selective state-space models, significantly improving the quality of UAV-captured low-resolution imagery (achieving a PSNR of 23.83 dB and an SSIM of 79.60%) and surpassing current state-of-the-art approaches. Additionally, we develop Succulent-YOLO, a customized target detection model optimized for succulent image classification, achieving a mean average precision (mAP@50) of 87.8% on high-resolution images. The integrated use of MambaIR and Succulent-YOLO achieves an mAP@50 of 85.1% when tested on enhanced super-resolution images, closely approaching the performance on original high-resolution images. Through extensive experimentation supported by Grad-CAM visualization, our method effectively captures critical features of succulents, identifying the best trade-off between resolution enhancement and computational demands. By overcoming the limitations associated with low-resolution UAV imagery in agricultural monitoring, this solution provides an effective, scalable approach for evaluating succulent plant growth. Addressing image-quality issues further facilitates informed decision-making, reducing technical challenges. Ultimately, this study provides a robust foundation for expanding the practical use of UAVs and artificial intelligence in precision agriculture, promoting sustainable farming practices through advanced remote sensing technologies. Full article