This study aimed to analyze the effect of a flow training program based on mindfulness applied to young and adult elite handball athletes. A quantitative, quasi-experimental, descriptive data analysis approach was carried out. The sample included 105 athletes (51 female and 54 male). The athletes were divided into two groups: (i) experimental (n = 53) and (ii) control (n = 52). The results of the repeated ANOVA indicated that the experimental group achieved significant improvements compared to the control group in decision making (n2p = 0.086), mental well-being (n2p = 0.045), dispositional flow state (n2p = 0.103), non-judgment (n2p = 0.040), refocusing (n2p = 0.052), and mindful traits in daily life (n2p = 0.058). A Pearson analysis demonstrated a positive correlation between dispositional flow state and mindfulness in sport and mental well-being. The correlation analysis also showed an inverse correlation between decision making and sense of control and mindful traits in daily life. The findings revealed that the program can be effective in decision making, mental well-being, dispositional flow state, mindfulness in sport, and mindful traits in daily lives of athletes.

Among the common applications of plenoptic cameras are depth reconstruction and post-shot refocusing. These require a calibration relating the camera-side light field to that of the scene. Numerous methods with this goal have been developed based on thin lens models for the plenoptic camera\'s main lens and microlenses. Our work addresses the often-overlooked role of the main lens exit pupil in these models, specifically in the decoding process of standard plenoptic camera (SPC) images. We formally deduce the connection between the refocusing distance and the resampling parameter for the decoded light field and provide an analysis of the errors that arise when the exit pupil is not considered. In addition, previous work is revisited with respect to the exit pupil\'s role, and all theoretical results are validated through a ray tracing-based simulation. With the public release of the evaluated SPC designs alongside our simulation and experimental data, we aim to contribute to a more accurate and nuanced understanding of plenoptic camera optics.

The use of satellite synthetic aperture radar (SAR) for moving target imaging has gained popularity recently. Researchers are focused on improving its imaging quality. To achieve high-quality and fast imaging, we have developed a dual-mode refocusing algorithm. We optimized the algorithm\'s target speed estimation and carried out data enhancement and quantization design for SAR image refocusing. The design is implemented on a Xilinx XC5VFX130T FPGA. The dual-mode image data are based on a slice size of 512 × 512 for slice mode and 256 × 256 for scan mode in a time-series function simulation. The serial-parallel conversion and pipeline design balances the operating speed and logic resources for optimal performance. Experiment results on slice data of real SAR images show that the system\'s processing speed can reach two frames per second, utilizing 69633 LUTs, 255 RAMs, and 296 DSPs.

Biomolecular NMR spectroscopy requires large magnetic field strengths for high spectral resolution. Today\'s highest fields comprise proton Larmor frequencies of 1.2 GHz and even larger field strengths are to be expected in the future. In protein triple resonance experiments, various carbon bandwidths need to be excited by selective pulses including the large aliphatic chemical shift range. When the spectrometer field strength is increased, the length of these pulses has to be decreased by the same factor, resulting in higher rf-amplitudes being necessary in order to cover the required frequency region. Currently available band-selective pulses like Q3/Q5 excite a narrow bandwidth compared to the necessary rf-amplitude. Because the maximum rf-power allowed in probeheads is limited, none of the selective universal rotation pulses reported so far is able to cover the full [Formula: see text]C aliphatic region on 1.2 GHz spectrometers. In this work, we present band-selective 90° and 180° universal rotation pulses (SURBOP90 and SURBOP180) that have a higher ratio of selective bandwidth to maximum rf-amplitude than standard pulses. Simulations show that these pulses perform better than standard pulses, e. g. Q3/Q5, especially when rf-inhomogeneity is taken into account. The theoretical and experimental performance is demonstrated in offset profiles and by implementing the SURBOP pulses in an HNCACB experiment at 1.2 GHz.

Cervical cytopathology image refocusing is important for addressing the problem of defocus blur in whole slide images. However, most of current deblurring methods are developed for global motion blur instead of local defocus blur and need a lot of supervised re-training for unseen domains. In this paper, we propose a refocusing method for cervical cytopathology images via multi-scale attention features and domain normalization. Our method consists of a domain normalization net (DNN) and a refocusing net (RFN). In DNN, we adopt registration-free cycle scheme for normalizing the unseen unsupervised domains into the seen supervised domain and introduce gray mask loss and hue-encoding mask loss to ensure the consistency of cell structure and basic hue. In RFN, combining the locality and sparseness characteristics of defocus blur, we design a multi-scale refocusing network to enhance the reconstruction of cell nucleus and cytoplasm, and introduce defocus intensity estimation mask to strengthen the reconstruction of local blur. We integrate hybrid learning strategy on the supervised and unsupervised domains to make RFN achieving well refocusing on the unsupervised domain. We build a cervical cytopathology image refocusing dataset and conduct extensive experiments to demonstrate the superiority of our method compared with current deblurring state-of-the-art models. Furthermore, we prove that the refocused images help improve the performance of subsequent high-level analysis tasks. We release the refocusing dataset and source codes to promote the development of this field.

Conventional refocusing pulses are optimised for a single spin without considering any type of coupling. However, despite the fact that most couplings will result in undesired distortions, refocusing in delay-pulse-delay-type sequences with desired heteronuclear coherence transfer might be enhanced considerably by including coupling evolution into pulse design. We provide a proof of principle study for a Hydrogen-Carbon refocusing pulse sandwich with inherent J-evolution following the previously reported ICEBERG-principle with improved performance in terms of refocusing performance and/or overall effective coherence transfer time. Pulses are optimised using optimal control theory with a newly derived quality factor and z-controls as an efficient tool to speed up calculations. Pulses are characterised in theory and experiment and compared to conventional concurrent refocusing pulses, clearly showing an improvement for the J-evolving pulse sandwich. As a side-product, also efficient J-compensated resfocusing pulse sandwiches - termed BUBU pulses following the nomenclature of previous J-compensated BUBI and BEBEtr pulse sandwiches - have been optimised.

Synthetic Aperture Radar (SAR) is widely applied to the field of ocean remote sensing. Clear SAR images are the basis for ocean information acquisitions, such as parameter retrieval of ocean waves and wind field inversion of the ocean surface. However, the SAR ocean images are usually blurred, which seriously affects the acquisition of ocean information. The reasons for the wave blurring in SAR images mainly include the following two aspects. One is that when SAR observes the ocean, the motion of ocean waves will have a greater impact on imaging quality. The other is that the ocean\'s surface is seriously decorrelated within the integration time. In order to obtain clear SAR images of ocean waves, a SAR imaging algorithm of ocean waves based on the optimum subaperture is proposed, aiming at the above two aspects. The optimum focus setting of the ocean waves is calculated, drawing support from the azimuth phase velocity of the dominant wave. The optimum subaperture is further calculated according to the proposed new evaluation, namely, F. Finally, according to the optimum focus setting and the optimum subaperture, the dominant wave is refocused, and a clear SAR image of the dominant wave can be obtained. The proposed algorithm was applied to airborne L-band and P-band SAR data. Furthermore, the proposed algorithm was compared with present methods, and the results sufficiently demonstrated the effectiveness and superiority of the proposed algorithm.

The increasing throughput of experiments in biomaterials research makes automatic techniques more and more necessary. Among all the characterization methods, microscopy makes fundamental contributions to biomaterials science where precisely focused images are the basis of related research. Although automatic focusing has been widely applied in all kinds of microscopes, defocused images can still be acquired now and then due to factors including background noises of materials and mechanical errors. Herein, we present a deep-learning-based method for the automatic sorting and reconstruction of defocused cell images. First, the defocusing problem is illustrated on a high-throughput cell microarray. Then, a comprehensive dataset of phase-contrast images captured from varied conditions containing multiple cell types, magnifications, and substrate materials is prepared to establish and test our method. We obtain high accuracy of over 0.993 on the dataset using a simple network architecture that requires less than half of the training time compared with the classical ResNetV2 architecture. Moreover, the subcellular-level reconstruction of heavily defocused cell images is achieved with another architecture. The applicability of the established workflow in practice is finally demonstrated on the high-throughput cell microarray. The intelligent workflow does not require a priori knowledge of focusing algorithms, possessing widespread application value in cell experiments concerning high-throughput or time-lapse imaging.

In this study, the possibilities of temperature responsive × reversed phase liquid chromatography (TRLC × RPLC) are assessed in terms of pharmaceutical impurity analysis. Due to the increased peak capacity per unit time they offer, two-dimensional LC approaches are gaining relevance for the analysis of complex drug formulations. Because the latter depicts a larger predisposition for the occurrence of an increased number of impurities, current 1D-HPLC approaches often prove insufficient. Since many LC × LC methods are limited by modulation, solvent compatibility, orthogonality, and sensitivity issues, the combination of TRLC × RPLC is explored in this work for pharmaceutical impurity analysis. As this combination of a purely aqueous separation with RPLC allows for systematic and optimization-free refocusing in the second dimension, it opens possibilities for generic LC × LC requiring minimal to no method development, in this way overcoming a major perceived contemporary hurdle of LC × LC. The approach is demonstrated with a representative mixture of 17 solutes comprising 11 corticosteroids and 6 progestogens. Orthogonality and peak capacities were assessed on three RP core-shell column selectivities (Poroshell EC-C18, phenyl-hexyl and PFP). Although the TRLC × EC-C18 combination offered somewhat better orthogonality, the combination with the PFP column proved the best for the separation at hand. Depending on the composition of the mixture, the use of full, shifted, or segmented gradients allowed facile optimization of the separation. The developed platform allowed detection of the impurities at the 0.05% level compared to a selected main compound, while also opening up possibilities for analysis of formulations comprising two active ingredients.

Recently, an acoustic lens has been proposed for volumetric focusing as an alternative to conventional reconstruction algorithms in Photoacoustic (PA) Imaging. Acoustic lens can significantly reduce computational complexity and facilitate the implementation of real-time and cost-effective systems. However, due to the fixed focal length of the lens, the Point Spread Function (PSF) of the imaging system varies spatially. Furthermore, the PSF is asymmetric, with the lateral resolution being lower than the axial resolution. For many medical applications, such as in vivo thyroid, breast and small animal imaging, multiple views of the target tissue at varying angles are possible. This can be exploited to reduce the asymmetry and spatial variation of system the PSF with simple spatial compounding. In this article, we present a formulation and experimental evaluation of this technique. PSF improvement in terms of resolution and Signal to Noise Ratio (SNR) with the proposed spatial compounding is evaluated through simulation. Overall image quality improvement is demonstrated with experiments on phantom and ex vivo tissue. When multiple views are not possible, an alternative residual refocusing algorithm is proposed. The performances of these two methods, both separately and in conjunction, are compared and their practical implications are discussed.