An experimental and theoretical study of the photoionization properties of polysulfanes (HS, = 2-4).

We prepared polysulfanes (HS, = 2 to 4) photon irradiation of hydrogen sulfide (HS) ices and measured their photoionization efficiency spectra. The ionization energies of HS, HS and HS were determined to be 9.08 ± 0.

Signal Processing for Novel Noise Radar Based on de-chirp and Delay Matching.

Modern radar technology requires high-quality signals and detection performance. However, traditional frequency-modulated continuous wave (FMCW) radar often has poor anti-jamming capabilities, and the high sampling rates associated with large time-bandwidth product signals can lead to increased system hardware costs and reduced data processing efficiency. This paper constructed a composite radar waveform based on noise frequency modulation (NFM) and linear frequency modulation (LFM) signals, enhancing the signal's complexity and anti-jamming capability.

High-Resolution and Robust One-Bit Direct-of-Arrival Estimation via Reweighted Atomic Norm Estimation.

In recent years, one-bit quantization has attracted widespread attention in the field of direction-of-arrival (DOA) estimation as a low-cost and low-power solution. Many researchers have proposed various estimation algorithms for one-bit DOA estimation, among which atomic norm minimization algorithms exhibit particularly attractive performance as gridless estimation algorithms. However, current one-bit DOA algorithms with atomic norm minimization typically rely on approximating the trace function, which is not the optimal approximation and introduces errors, along with resolution limitations.

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.

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.

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.

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.

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.

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.

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.

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 β-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).

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 (Pb defects) through coordination with the -SH and -COOH groups in PA, but also finely tunes the crystallinity of Cs/FA-based perovskite film.

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.

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.

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.

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.

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.

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.

Deep Edge Guided Recurrent Residual Learning for Image Super-Resolution.

In this paper, we consider the image super-resolution (SR) problem. The main challenge of image SR is to recover high-frequency details of a low-resolution (LR) image that are important for human perception. To address this essentially ill-posed problem, we introduce a Deep Edge Guided REcurrent rEsidual (DEGREE) network to progressively recover the high-frequency details.

Learning to Segment Human by Watching YouTube.

An intuition on human segmentation is that when a human is moving in a video, the video-context (e.g., appearance and motion clues) may potentially infer reasonable mask information for the whole human body.

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.

Human Parsing with Contextualized Convolutional Neural Network.

In this work, we address the human parsing task with a novel Contextualized Convolutional Neural Network (Co-CNN) architecture, which well integrates the cross-layer context, global image-level context, semantic edge context, within-super-pixel context and cross-super-pixel neighborhood context into a unified network. Given an input human image, Co-CNN produces the pixelwise categorization in an end-to-end way. First, the cross-layer context is captured by our basic local-to-global-to-local structure, which hierarchically combines the global semantic information and the local fine details across different convolutional layers.

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.

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.

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.