Three-month functional training programme improves knee joint function in athletes post-ACL reconstruction surgery.

OBJECTIVE: Rehabilitation and recovery duration following anterior cruciate ligament reconstructive surgery play a pivotal role in restoring optimal knee functionality in athletes. This study aimed to explore the impact of a 3-month functional training programme aligned with enhanced recovery after surgery on recuperation subsequent to anterior cruciate ligament reconstructive surgery. DESIGN: A quasi-experimental study. SUBJECTS: A cohort of 34 patients aged 14 to 24, who underwent anterior cruciate ligament reconstructive surgery and adhered to enhanced recovery after surgery protocols during the perioperative period, were allocated to an experimental group and a control group according to their eligibility, capacity, and willingness to engage in the functional training programme. METHODS: The participants in the experimental group underwent a 3-month regimen of functional training following anterior cruciate ligament reconstructive surgery, whereas the control group followed a conventional recovery approach. Evaluations were conducted both prior to and following the 3-month recovery interval, utilizing the Y-Balance Test, Functional Movement Screening, and Isokinetic Knee Test. RESULTS: Assessment outcomes of the Y-Balance Test, Isokinetic Knee Test, and Functional Movement Screening exhibited significant enhancement (p < 0.05) within the experimental group, as opposed to the control group. These findings underscore that those athletes who undertook the 3-month functional training regimen within the experimental group exhibited heightened dynamic balance capabilities, increased knee joint mobility, and enhanced stability compared with their counterparts in the control group. CONCLUSION: Consequently, this underscores the efficacy of the 3-month functional training protocol aligned with enhanced recovery after surgery, as a means to effectively facilitate recuperation subsequent to anterior cruciate ligament reconstructive surgery.

A Sparse Recovery Algorithm for Suppressing Multiple Linear Frequency Modulation Interference in the Synthetic Aperture Radar Image Domain.

In synthetic aperture radar (SAR) signal processing, compared with the raw data of level-0, level-1 SAR images are more readily accessible and available in larger quantities. However, an amount of level-1 images are affected by radio frequency interference (RFI), which typically originates from Linear Frequency Modulation (LFM) signals emitted by ground-based radars. Existing research on interference suppression in level-1 data has primarily focused on two methods: transforming SAR images into simulated echo data for interference suppression, or focusing interference in the frequency domain and applying notching filters to reduce interference energy. However, these methods overlook the effective utilization of the interference parameters or are confined to suppressing only one type of LFM interference at a time. In certain SAR images, multiple types of LFM interference manifest bright radiation artifacts that exhibit varying lengths along the range direction while remaining constant in the azimuth direction. It is necessary to suppress multiple LFM interference on SAR images when original echo data are unavailable. This article proposes a joint sparse recovery algorithm for interference suppression in the SAR image domain. In the SAR image domain, two-dimensional LFM interference typically exhibits differences in parameters such as frequency modulation rate and pulse width in the range direction, while maintaining consistency in the azimuth direction. Based on this observation, this article constructs a series of focusing operators for LFM interference in SAR images. These operators enable the sparse representation of dispersed LFM interference. Subsequently, an optimization model is developed that can effectively suppress multi-LFM interference and reduce image loss with the assistance of a regularization term in the image domain. Simulation experiments conducted in various scenarios validate the superior performance of the proposed method.

Real Aperture Radar Super-Resolution Imaging for Sea Surface Monitoring Based on a Hybrid Model.

In recent years, super-resolution imaging techniques have been intensely introduced to enhance the azimuth resolution of real aperture scanning radar (RASR). However, there is a paucity of research on the subject of sea surface imaging with small incident angles for complex scenarios. This research endeavors to explore super-resolution imaging for sea surface monitoring, with a specific emphasis on grounded or shipborne platforms. To tackle the inescapable interference of sea clutter, it was segregated from the imaging objects and was modeled alongside I/Q channel noise within the maximum likelihood framework, thus mitigating clutter's impact. Simultaneously, for characterizing the non-stationary regions of the monitoring scene, we harnessed the Markov random field (MRF) model for its two-dimensional (2D) spatial representational capacity, augmented by a quadratic term to bolster outlier resilience. Subsequently, the maximum a posteriori (MAP) criterion was employed to unite the ML function with the statistical model regarding imaging scene. This hybrid model forms the core of our super-resolution methodology. Finally, a fast iterative threshold shrinkage method was applied to solve this objective function, yielding stable estimates of the monitored scene. Through the validation of simulation and real data experiments, the superiority of the proposed approach in recovering the monitoring scenes and clutter suppression has been verified.

How sustainable environment is influenced by the foreign direct investment, financial development, economic growth, globalization, innovation, and urbanization in China.

This study aims to investigate the impact of foreign direct investment (FDI) inflow, financial development, economic growth, globalization, innovation, and urbanization on the carbon dioxide emissions in China by using long-ran time series dataset from 1970 to 2021 which for the first time used the newly developed dynamic autoregressive distributed lags (ARDL) simulation model for results analysis. Dynamic ARDL simulation model removes the shortcomings of the traditional ARDL models by predicting the actual change (positive and negative shocks) in the independent variables and its impact on the main dependent variables by 5000 simulations through graphical representation. The findings of the long-run dynamic ARDL simulations indicate that FDI inflow, globalization, and innovation negatively and significantly impact the environmental degradation while financial development, economic growth, and urbanization cause to increase the environmental degradation in China. Recommendations are suggested based on the findings of this study.

Golgi fucosyltransferase 1 reveals its important role in α-1,4-fucose modification of N-glycan in CRISPR/Cas9 diatom Phaeodactylum tricornutum.

Phaeodactylum tricornutum (Pt) is a critical microbial cell factory to produce a wide spectrum of marketable products including recombinant biopharmaceutical N-glycoproteins. N-glycosylation modification of proteins is important for their activity, stability, and half-life, especially some special modifications, such as fucose-modification by fucosyltransferase (FucT). Three PtFucTs were annotated in the genome of P. tricornutum, PtFucT1 was located on the medial/trans-Golgi apparatus and PtFucT2-3 in the plastid stroma. Algal growth, biomass and photosynthesis efficiency were significantly inhibited in a knockout mutant of PtFucT1 (PtFucT1-KO). PtFucT1 played a role in non-core fucose modification of N-glycans. The knockout of PtFucT1 might affect the activity of PtGnTI in the complex and change the complex N-glycan to mannose type N-glycan. The study provided critical information for understanding the mechanism of protein N-glycosylation modification and using microalgae as an alternative ecofriendly cell factory to produce biopharmaceuticals.

Dynamic Compressive Mechanical Properties of UR50 Ultra-Early-Strength Cement-Based Concrete Material under High Strain Rate on SHPB Test.

UR50 ultra-early-strength cement-based self-compacting high-strength material is a special cement-based material. Compared with traditional high-strength concrete, its ultra-high strength, ultra-high toughness, ultra-impact resistance, and ultra-high durability have received great attention in the field of protection engineering, but the dynamic mechanical properties of impact compression at high strain rates are not well known, and the dynamic compressive properties of materials are the basis for related numerical simulation studies. In order to study its dynamic compressive mechanical properties, three sets of specimens with a size of Φ100 × 50 mm were designed and produced, and a large-diameter split Hopkinson pressure bar (SHPB) with a diameter of 100 mm was used to carry out impact tests at different speeds. The specimens were mainly brittle failures. With the increase in impact speed, the failure mode of the specimens gradually transits from larger fragments to small fragments and a large amount of powder. The experimental results show that the ultra-early-strength cement-based material has a greater impact compression brittleness, and overall rupture occurs at low strain rates. Its dynamic compressive strength increases with the increase of strain rates and has an obvious strain rate strengthening effect. According to the test results, the relationship curve between the dynamic enhancement factor and the strain rate is fitted. As the impact speed increases, the peak stress rises, the energy absorption density increases, and its growth rate accelerates. Afterward, based on the stress-strain curve, the damage variables under different strain rates were fitted, and the results show that the increase of strain rate has a hindering effect on the increase of damage variables and the increase rate.

A Perspective on Developing a Plant 'Holobiont' for Future Saline Agriculture.

Soil salinity adversely affects plant growth and has become a major limiting factor for agricultural development worldwide. There is a continuing demand for sustainable technology innovation in saline agriculture. Among various bio-techniques being used to reduce the salinity hazard, symbiotic microorganisms such as rhizobia and arbuscular mycorrhizal (AM) fungi have proved to be efficient. These symbiotic associations each deploy an array of well-tuned mechanisms to provide salinity tolerance for the plant. In this review, we first comprehensively cover major research advances in symbiont-induced salinity tolerance in plants. Second, we describe the common signaling process used by legumes to control symbiosis establishment with rhizobia and AM fungi. Multi-omics technologies have enabled us to identify and characterize more genes involved in symbiosis, and eventually, map out the key signaling pathways. These developments have laid the foundation for technological innovations that use symbiotic microorganisms to improve crop salt tolerance on a larger scale. Thus, with the aim of better utilizing symbiotic microorganisms in saline agriculture, we propose the possibility of developing non-legume 'holobionts' by taking advantage of newly developed genome editing technology. This will open a new avenue for capitalizing on symbiotic microorganisms to enhance plant saline tolerance for increased sustainability and yields in saline agriculture.

Laminarin, a Major Polysaccharide in Stramenopiles.

During the processes of primary and secondary endosymbiosis, different microalgae evolved to synthesis different storage polysaccharides. In stramenopiles, the main storage polysaccharides are ß-1,3-glucan, or laminarin, in vacuoles. Currently, laminarin is gaining considerable attention due to its application in the food, cosmetic and pharmaceuticals industries, and also its importance in global biogeochemical cycles (especially in the ocean carbon cycle). In this review, the structures, composition, contents, and bioactivity of laminarin were summarized in different algae. It was shown that the general features of laminarin are species-dependence. Furthermore, the proposed biosynthesis and catabolism pathways of laminarin, functions of key genes, and diel regulation of laminarin were also depicted and comprehensively discussed for the first time. However, the complete pathways, functions of genes, and diel regulatory mechanisms of laminarin require more biomolecular studies. This review provides more useful information and identifies the knowledge gap regarding the future studies of laminarin and its applications.

EnlightenGAN: Deep Light Enhancement Without Paired Supervision.

Deep learning-based methods have achieved remarkable success in image restoration and enhancement, but are they still competitive when there is a lack of paired training data? As one such example, this paper explores the low-light image enhancement problem, where in practice it is extremely challenging to simultaneously take a low-light and a normal-light photo of the same visual scene. We propose a highly effective unsupervised generative adversarial network, dubbed EnlightenGAN, that can be trained without low/normal-light image pairs, yet proves to generalize very well on various real-world test images. Instead of supervising the learning using ground truth data, we propose to regularize the unpaired training using the information extracted from the input itself, and benchmark a series of innovations for the low-light image enhancement problem, including a global-local discriminator structure, a self-regularized perceptual loss fusion, and the attention mechanism. Through extensive experiments, our proposed approach outperforms recent methods under a variety of metrics in terms of visual quality and subjective user study. Thanks to the great flexibility brought by unpaired training, EnlightenGAN is demonstrated to be easily adaptable to enhancing real-world images from various domains. Our codes and pre-trained models are available at: https://github.com/VITA-Group/EnlightenGAN.

A Multi-Attribute Decision Method under Uncertainty Environment Conditions-The Green Supplier Evaluation Perspective.

Due to the continuous changes of political environment, consumption habits, technological progress and other factors, the external environment of enterprises is full of uncertainty. The turbulence of external environment is not conducive to the long-term operation and development of enterprises, but also brings great challenges to the selection of suppliers. This makes the competition of enterprises focus on how to choose long-term cooperation suppliers in the uncertain external environment. In addition, due to the deterioration of the global environment, governments pay more and more attention to environmental pollution, and consumers are more and more inclined to green consumption, which makes many companies pay more and more attention to environmental indicators when selecting suppliers. In the case of external environment turbulence and serious environmental pollution, the evaluation and selection of green suppliers in uncertain environment is particularly important for the long-term development of enterprises. What's more, when the supplier's capability gap is small, the decision-maker often hesitates among several suppliers. In this paper, the hesitant fuzzy is used to describe the hesitant psychology of decision-makers in selecting suppliers, the variance fluctuation is used to describe the characteristics of hesitant fuzzy numbers, and the probability is used to measure the uncertainty of the environment. A green supplier evaluation model under the uncertainty environment is proposed, which comprehensively evaluates the green suppliers under the uncertain environment. Furthermore, it is compared with other methods that do not consider the uncertainty and the adaptability of evaluation method and right confirmation method, so as to reflect the influence of uncertainty to green supplier evaluation and the importance of adaptability of evaluation method and right confirmation method.

Emergency patient flow forecasting in the radiology department.

The accurate forecast of radiology emergency patient flow is of great importance to optimize appointment scheduling decisions. This study used a multi-model approach to forecast daily radiology emergency patient flow with consideration of different patient sources. We constructed six linear and nonlinear models by considering the lag effects and corresponding time factors. The autoregressive integrated moving average and least absolute shrinkage and selection operator (Lasso) were selected from the category of linear models, whereas linear-and-radial support vector regression models, random forests and adaptive boosting were chosen from the category of nonlinear models. The models were applied to 4-year daily emergency visits data in the radiology department of West China Hospital in Chengdu, China. The mean absolute percentage error of six models ranged from 8.56 to 9.36 percent for emergency department patients, whereas it varied from 10.90 to 14.39 percent for ward patients. The best-performing model for total radiology visits was Lasso, which yielded a mean absolute percentage error of 7.06 percent. The arrival patterns of emergency department and total radiology emergency patient flows could be modeled by linear processes. By contrast, the nonlinear model performed best for ward patient flow. These findings will benefit hospital managers in managing efficient patient flow, thus improving service quality and increasing patient satisfaction.

Interfacial Contact Passivation for Efficient and Stable Cesium-Formamidinium Double-Cation Lead Halide Perovskite Solar Cells.

Perovskite solar cells (PSCs) have achieved extremely high power conversion efficiencies (PCEs) of over 25%, but practical application still requires further improvement in the long-term stability of the device. Herein, we present an in situ interfacial contact passivation strategy to reduce the interfacial defects and extraction losses between the hole transporting layer and perovskite. The existence of PbS promotes the crystallization of perovskite, passivates the interface and grain boundary defects, and reduces the nonradiation recombination, thereby leading to a champion PCE of 21.07% with reduced hysteresis, which is one of the best results for the methylammonium (MA)-free, cesium formamidinium double-cation lead-based PSCs. Moreover, the unencapsulated device retains more than 93% and 82% of its original efficiencies after 1 year's storage under ambient conditions and thermal aging at 85°C for 1,000 h in a nitrogen atmosphere, likely due to the usage of MA-free perovskite and the enhanced surface hydrophobicity.

Defect mitigation using d-penicillamine for efficient methylammonium-free perovskite solar cells with high operational stability.

Trap-dominated non-radiative charge recombination is one of the key factors that limit the performance of perovskite solar cells (PSCs), which was widely studied in methylammonium (MA) containing PSCs. However, there is a need to elucidate the defect chemistry of thermally stable, MA-free, cesium/formamidinium (Cs/FA)-based perovskites. Herein, we show that d-penicillamine (PA), an edible antidote for treating heavy metal ions, not only effectively passivates the iodine vacancies (Pb2+ defects) through coordination with the -SH and -COOH groups in PA, but also finely tunes the crystallinity of Cs/FA-based perovskite film. Benefiting from these merits, a reduction of non-radiative recombination and an increase in photoluminescence lifetime have been achieved. As a result, the champion MA-free device exhibits an impressive power conversion efficiency (PCE) of 22.4%, an open-circuit voltage of 1.163 V, a notable fill factor of 82%, and excellent long-term operational stability. Moreover, the defect passivation strategy can be further extended to a mini module (substrate: 4 × 4 cm2, active area: 7.2 cm2) as well as a wide-bandgap (∼1.73 eV) Cs/FA perovskite system by delivering PCEs of 16.3% and 20.2%, respectively, demonstrating its universality in defect passivation for efficient PSCs.

Focusing Bistatic Forward-looking Synthetic Aperture Radar Based on An Improved Hyperbolic Range Model and A Modified Omega-K algorithm.

For parallel bistatic forward-looking synthetic aperture radar (SAR) imaging, the instantaneous slant range is a double-square-root expression due to the separate transmitter-receiver system form. The hyperbolic approximation provides a feasible solution to convert the dual square-root expression into a single-square-root expression. However, some high-order terms of the range Taylor expansion have not been considered during the slant range approximation procedure in existing methods, and therefore, inaccurate phase compensation occurs. To obtain a more accurate compensation result, an improved hyperbolic approximation range form with high-order terms is proposed. Then, a modified omega-K algorithm based on the new slant range form is adopted for parallel bistatic forward-looking SAR imaging. Several simulation results validate the effectiveness of the proposed imaging algorithm.

Proposal-Free Network for Instance-Level Object Segmentation.

Instance-level object segmentation is an important yet under-explored task. Most of state-of-the-art methods rely on region proposal methods to extract candidate segments and then utilize object classification to produce final results. Nonetheless, generating reliable region proposals itself is a quite challenging and unsolved task. In this work, we propose a Proposal-Free Network (PFN) to address the instance-level object segmentation problem, which outputs the numbers of instances of different categories and the pixel-level information on i) the coordinates of the instance bounding box each pixel belongs to, and ii) the confidences of different categories for each pixel, based on pixel-to-pixel deep convolutional neural network. All the outputs together, by using any off-the-shelf clustering method for simple post-processing, can naturally generate the ultimate instance-level object segmentation results. The whole PFN can be easily trained without the requirement of a proposal generation stage. Extensive evaluations on the challenging PASCAL VOC 2012 semantic segmentation benchmark demonstrate the effectiveness of the proposed PFN solution without relying on any proposal generation methods.

Robust Single Image Super-Resolution via Deep Networks With Sparse Prior.

Single image super-resolution (SR) is an ill-posed problem, which tries to recover a high-resolution image from its low-resolution observation. To regularize the solution of the problem, previous methods have focused on designing good priors for natural images, such as sparse representation, or directly learning the priors from a large data set with models, such as deep neural networks. In this paper, we argue that domain expertise from the conventional sparse coding model can be combined with the key ingredients of deep learning to achieve further improved results. We demonstrate that a sparse coding model particularly designed for SR can be incarnated as a neural network with the merit of end-to-end optimization over training data. The network has a cascaded structure, which boosts the SR performance for both fixed and incremental scaling factors. The proposed training and testing schemes can be extended for robust handling of images with additional degradation, such as noise and blurring. A subjective assessment is conducted and analyzed in order to thoroughly evaluate various SR techniques. Our proposed model is tested on a wide range of images, and it significantly outperforms the existing state-of-the-art methods for various scaling factors both quantitatively and perceptually.

Parsing Based on Parselets: A Unified Deformable Mixture Model for Human Parsing.

Human parsing, namely partitioning the human body into semantic regions, has drawn much attention recently for its wide applications in human-centric analysis. Previous works often consider solving the problem of human pose estimation as the prerequisite of human parsing. We argue that these approaches cannot obtain optimal pixel-level parsing due to the inconsistent targets between the different tasks. In this work, we directly address the problem of human parsing by using the novel Parselet representation as the building blocks of our parsing model. Parselets are a group of parsable segments which can generally be obtained by low-level over-segmentation algorithms and bear strong semantic meaning. We then build a deformable mixture parsing model (DMPM) for human parsing to simultaneously handle the deformation and multi-modalities of Parselets. The proposed model has two unique characteristics: (1) the possible numerous modalities of Parselet ensembles are exhibited as the "And-Or" structure of sub-trees; (2) to further solve the practical problem of Parselet occlusion or absence, we directly model the visibility property at some leaf nodes. The DMPM thus directly solves the problem of human parsing by searching for the best graph configuration from a pool of Parselet hypotheses without intermediate tasks. Fast rejection based on hierarchical filtering is employed to ensure the overall efficiency. Comprehensive evaluations on a new large-scale human parsing dataset, which is crawled from the Internet, with high resolution and thoroughly annotated semantic labels at pixel-level, and also a benchmark dataset demonstrate the encouraging performance of the proposed approach.

Deep Human Parsing with Active Template Regression.

In this work, the human parsing task, namely decomposing a human image into semantic fashion/body regions, is formulated as an active template regression (ATR) problem, where the normalized mask of each fashion/body item is expressed as the linear combination of the learned mask templates, and then morphed to a more precise mask with the active shape parameters, including position, scale and visibility of each semantic region. The mask template coefficients and the active shape parameters together can generate the human parsing results, and are thus called the structure outputs for human parsing. The deep Convolutional Neural Network (CNN) is utilized to build the end-to-end relation between the input human image and the structure outputs for human parsing. More specifically, the structure outputs are predicted by two separate networks. The first CNN network is with max-pooling, and designed to predict the template coefficients for each label mask, while the second CNN network is without max-pooling to preserve sensitivity to label mask position and accurately predict the active shape parameters. For a new image, the structure outputs of the two networks are fused to generate the probability of each label for each pixel, and super-pixel smoothing is finally used to refine the human parsing result. Comprehensive evaluations on a large dataset well demonstrate the significant superiority of the ATR framework over other state-of-the-arts for human parsing. In particular, the F1-score reaches 64.38 percent by our ATR framework, significantly higher than 44.76 percent based on the state-of-the-art algorithm [28].

Image-Specific Prior Adaptation for Denoising.

Image priors are essential to many image restoration applications, including denoising, deblurring, and inpainting. Existing methods use either priors from the given image (internal) or priors from a separate collection of images (external). We find through statistical analysis that unifying the internal and external patch priors may yield a better patch prior. We propose a novel prior learning algorithm that combines the strength of both internal and external priors. In particular, we first learn a generic Gaussian mixture model from a collection of training images and then adapt the model to the given image by simultaneously adding additional components and refining the component parameters. We apply this image-specific prior to image denoising. The experimental results show that our approach yields better or competitive denoising results in terms of both the peak signal-to-noise ratio and structural similarity.

Learning Super-Resolution Jointly From External and Internal Examples.

Single image super-resolution (SR) aims to estimate a high-resolution (HR) image from a low-resolution (LR) input. Image priors are commonly learned to regularize the, otherwise, seriously ill-posed SR problem, either using external LR-HR pairs or internal similar patterns. We propose joint SR to adaptively combine the advantages of both external and internal SR methods. We define two loss functions using sparse coding-based external examples, and epitomic matching based on internal examples, as well as a corresponding adaptive weight to automatically balance their contributions according to their reconstruction errors. Extensive SR results demonstrate the effectiveness of the proposed method over the existing state-of-the-art methods, and is also verified by our subjective evaluation study.