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Restoring high-quality images from degraded hazy observations is a fundamental and essential task in the field of computer vision. While deep models have achieved significant success with synthetic data, their effectiveness in real-world scenarios remains uncertain. To improve adaptability in real-world environments, we construct an entirely new computational framework by making efforts from three key aspects: imaging perspective, structural modules, and training strategies. To simulate the often-overlooked multiple degradation attributes found in real-world hazy images, we develop a new hazy imaging model that encapsulates multiple degraded factors, assisting in bridging the domain gap between synthetic and real-world image spaces. In contrast to existing approaches that primarily address the inverse imaging process, we design a new dehazing network following the “localization-and-removal” pipeline. The degradation localization module aims to assist in network capture discriminative haze-related feature information, and the degradation removal module focuses on eliminating dependencies between features by learning a weighting matrix of training samples, thereby avoiding spurious correlations of extracted features in existing deep methods. We also define a new Gaussian perceptual contrastive loss to further constrain the network to update in the direction of the natural dehazing. Regarding multiple full/no-reference image quality indicators and subjective visual effects on challenging RTTS, URHI, and Fattal real hazy datasets, the proposed method has superior performance and is better than the current state-of-the-art methods.

The abundance of molecules on early Earth likely enabled a wide range of prebiotic chemistry, with peptides playing a key role in the development of early life forms and the evolution of metabolic pathways. Among peptides, those with enzyme-like activities occupy a unique position between peptides and enzymes, combining both structural flexibility and catalytic functionality. However, their full potential remains largely untapped. Further exploration of these enzyme-like peptides at the nanoscale could provide valuable insights into modern nanotechnology, biomedicine, and even the origins of life. Hence, this review introduces the groundbreaking concept of “peptide nanozymes (PepNzymes)”, which includes single peptides exhibiting enzyme-like activities, peptide-based nanostructures with enzyme-like activities, and peptide-based nanozymes, thus enabling the investigation of biological phenomena at nanoscale dimensions. Through the rational design of enzyme-like peptides or their assembly with nanostructures and nanozymes, researchers have found or created PepNzymes capable of catalyzing a wide range of reactions. By scrutinizing the interactions between the structures and enzyme-like activities of PepNzymes, we have gained valuable insights into the underlying mechanisms governing enzyme-like activities. Generally, PepNzymes play a crucial role in biological processes by facilitating small-scale enzyme-like reactions, speeding up molecular oxidation-reduction, cleavage, and synthesis reactions, leveraging the functional properties of peptides, and creating a stable microenvironment, among other functions. These discoveries make PepNzymes useful for diagnostics, cellular imaging, antimicrobial therapy, tissue engineering, anti-tumor treatments, and more while pointing out opportunities. Overall, this research provides a significant journey of PepNzymes’ potential in various biomedical applications, pushing them towards new advancements.

Nanomaterials with peroxidase-like activity and photothermal conversion efficiency have garnered significant attention for their ability to generate cytotoxic hydroxyl radicals and provide synergistic therapeutic effects. Selecting nanozymes with suitable properties and carriers is crucial for maximizing efficacy. While the mucin family is known for its mucoadhesive, glycosylated structures that enhance drug bioavailability and targeting, its potential in nanozymes remains underexplored. Here, we utilize mucin-2 to facilitate osmium nanoclusters (Os@Mucin), creating protein-corona-like nanozymes. This configuration bestows Os@Mucin with excellent peroxidase-like activity (769 U/mg) and photothermal conversion efficiency (22.83%, 808 nm). Mucin-2 promotes Os uptake by cells, allowing Os@Mucin to exhibit tumor environment-responsive peroxidase-like activity, further enhanced under photothermal conditions for targeted cytotoxicity and synergistic effects. In vivoexperiments demonstrate that this integration effectively treats triple-negative breast cancer. This study innovatively highlights the potential of the mucin family and underscores the promising role of Os nanozymes in tumor therapy.

Alcoholic liver disease (ALD) is one of the causes of hepatocellular carcinoma, accompanied by intestinal leakage and microbial changes. Pueraria has protective effects on liver injury. The aim of this study was to investigate the mechanism of pueraria in the treatment of ALD. UPLC-Q/TOF-MS was used to analyze the composition of the pueraria extract (PUE). Acute and chronic ALD models were established to evaluate the antialcoholic and hepatoprotective effects of PUE. As a result, PUE treatment reduced the serum levels of ALT, AST, TC, and TG and inflammatory factors and alleviated liver inflammation and drunk state. PUE decreased the gene expression of ADH1 and the serum level of acetaldehyde (ACH) to inhibit the generation of ACH from ethanol metabolism, increased the gene level of ALDH2 to accelerate the decomposition of ACH, and thereby alleviated liver inflammation and intestinal barrier damage. Meanwhile, 16 S rDNA revealed that PUE altered the microbiota composition, reduced the amount of Proteobacteriaand Desulfobacterota, and thus inhibited the generation of lipopolysaccharide and its downstream-like TLR4/MyD88/NF-κB pathway. PUE also increased the abundance of Bacteroides, Ruminococcus, and Prevotellaand producted short-chain fatty acids to protect the intestinal wall. Treatment with fecal microbiota transplantation further confirmed that PUE gut microbiota dependently alleviated ALD. Therefore, PUE regulated gut microbiota and inhibited ethanol metabolism to alleviate ALD through the liver–gut–brain axis. It has good prospects in the future.

Ulcerative colitis (UC) is a common chronic, relapsing inflammatory bowel condition. Procyanidins (PC) are known for their antiangiogenic, anti-inflammatory, antioxidant, and antimetastatic properties. However, there is comparatively limited information on how PC interacts with UC. In this study, 5 mg/10 mL/kg body weight of PC was administered to mice with dextran sulfate sodium (DSS)-induced colitis mice. PC treatment prolonged the survival period of mice, ameliorated UC symptoms, reduced damage to the intestinal mucosal barrier, and increased the protein expression of ZO-1 and occludin in the DSS-treated mice. Importantly, PC treatment significantly reduced gene expression related to Th17 cell differentiation, including STAT3, SMAD3, TGF-β, and JAK1. The results of the flow cytometry analysis indicated significant increase in the number of Treg cells and a concomitant decrease in the proportion of Th17 cells in the colon following PC treatment. Additionally, PC increased the abundance of gut microbiota such as Bacteroidota, Oscillospiraceae, Muribaculaceae,and Desulfovibrionaceae, as well as the concentrations of acetate acid, propionate acid, and butyrate acid in the feces. PC also activated short-chain fatty acid receptors, such as G-protein coupled receptor 43 in the colon, which promoted the proliferation of Treg cells. The depletion of gut microbiota and subsequent transplantation of fecal microbiota demonstrated that PC’s effects on gut microbiota were effective in improving UC and restoring intestinal Th17/Treg homeostasis in a microbiota-dependent manner. This suggests that PC could be a promising functional food for the prevention and treatment of UC in the future.

Bipolar nanopores, with asymmetric charge distributions, can induce significant ionic current rectification (ICR) at ultrashort lengths, finding potential applications in nanofluidic devices, energy conversion, and other related fields. Here, with simulations, we investigated the characteristics of ion transport and modulation of the ICR inside bipolar nanopores. With bipolar nanopores of half-positive and half-negative surfaces, the most significant ICR phenomenon appears at various concentrations. In these cases, the ICR ratios are independent of electrolyte types. In other cases where nanopores have oppositely charged surfaces of different lengths, ICR ratios are related to the mobility of anions and cations. The pore length and surface charge density can enhance ICR. As the pore length increases, ICR ratios first increase and then approach their saturation, which is determined by the surface charge density. External surface charges of nanopores can promote the ICR phenomenon mainly due to the enhancement of ion enrichment inside the nanopores by external surface conductance. The effective width of exterior charged surfaces under various conditions is also explored, which is inversely proportional to the pore length and salt concentration and linearly related to the pore diameter, surface charge density, and applied voltage. Our results may provide guidance for the design of bipolar porous membranes.

Testing internal ballistic parameters has long been a challenging issue in the field of internal ballistics. Therefore, a novel method based on a double-layer and double-grid structure strain tester was proposed to assess the key parameters of internal ballistics for small-caliber guns. This approach offers a simultaneous testing of multiple internal ballistic parameters through a single test system. It enables the acquisition of the entire test curve without compromising the structural integrity of the gun. Moreover, the method boasts a long service life, compact size, low cost, simple operation, and excellent electromagnetic compatibility. The core idea was to test the strain on the outer wall of the barrel using the double-layer and double-grid strain tester. In particular, the chamber pressure, projectile bottom pressure, and projectile velocity were calculated according to the thick-walled cylinder theory, the projectile bottom pressure solving model, and the projectile motion equation. Initially, the mechanism of this test method was analyzed. Subsequently, a strain tester was designed for a 30 mm caliber gun to test the outer wall strain. Finally, the internal ballistic parameters obtained from the strain test were compared with traditional measurements, which showed largely consistent results. During the ascent phase of the pressure curves, the root mean square (RMS) errors for the chamber pressure and the projectile bottom pressure curves are 1.71 and 3.60 MPa, respectively. The errors in the maximum values of chamber pressure, projectile bottom pressure, and projectile velocity are 1.1%, 0.9%, and 1.2%, respectively, thus verifying the feasibility of the strain test method.

The surface of solar cell products is critically sensitive to existing defects, leading to the loss of efficiency. Finding any defects in the solar cell is a significantly important task in the quality control process. Automated visual inspection systems are widely used for defect detection and reject faulty products. Numerous methods are proposed to deal with defect detection and solar cell inspection. However, they suffer from high computation complexity and hardware requirement cost issues. To overcome these issues, in this study, an effective visual inspection method is proposed to detect different types of defects in solar cells. The proposed method is based on adapted morphological and edge detection algorithms. This method uses multiple uses of morphological and canny edge detection with adapted parameters to extract and highlight the objects on a solar cell. Then the detected objects are classified, and different defect types and components are detected using their features and classification algorithm. Experimental results indicate that the proposed method can meet the requirements for effectiveness and real-time processing and presents promising results compared to other existing algorithms.

AbstractIn recent years, especially as compared to mutual funds, exchange traded fund (ETF) markets have grown and advanced significantly compared to other financial asset classes because of relative advantages. We found that inclusion of real estate investment trusts (REITs) in ETF assets under management (AUM) positively impacts REIT capital structure (financial leverage), cost of equity capital, and stock market liquidity. As percentages of REIT outstanding shares included in ETF AUM increased, we found corresponding reductions in financial leverage (both book and market leverage), reduced costs of equity capital, and greater market liquidity. This should be of particular interest to REIT and ETF managers as well as REIT and ETF investors. Partially because regulatory statutes incentivize REITs to rely more heavily on external equity financing, REIT stocks included as ETF AUM showed greater reductions in leverage compared to non-REIT stocks also held as ETF AUM. Our results, including applying difference-in-differences models, were robust with respect to these findings, REIT type, and firm fixed effects.

Food safety concerns have become a significant threat to human health and well-being, catching global attention in recent years. As a result, it is imperative to research conceptually novel biosensing and effective techniques for food matrices detection. Currently, DNA-templated metal nanoclusters (DNA-MNCs) are considered as one of the most promising nanomaterials due to their excellent properties in biosensing. While DNA-MNCs have garnered increasing interest, the reviews of design strategies, applications, and futuristic prospects for biosensing have been hardly found especially in food safety. The synthesis of DNA-MNCs and their use as biosensing materials in food contamination detection, including pathogenic bacteria, toxins, heavy metals, residues of pesticides, and others were comprehensively reviewed. In addition, we summarize the properties of DNA-MNCs briefly and discuss the challenges and future trends. The application of DNA-MNCs powered biosensing has been demonstrated and actively studied, which is a promising paradigm for food safety testing that can supplement or even replace current existing methods. Despite the challenges of difficulty regulating accurately, poor stability, low quantum yield, and difficult commercial transformation, the application prospects of DNA-MNCs biosensors are promising. This review aims to provide insights and directions for the future development of DNA-MNCs based food detection technology.

This study evaluated the potential of electronic nose (e-nose) and electrochemical fingerprinting for identifying and discriminating between four commercial braising seasoning packets (Huachang, Jideli, Chuanzhen, Gusong) applied to chicken meat samples. E-nose analysis of marinated chicken aromas showed divergent sensor resistance profiles reflecting variations in volatile compounds between seasoning formulations. Principal component analysis of e-nose data differentiated samples into two distinct groups, with Chuanzhen and Gusong clustered separately from Huachang and Jideli. Differential pulse voltammetry also discriminated samples based on phytochemical fingerprints. Chuanzhen and Gusong chicken exhibited higher oxidation peaks indicating greater transfer of phenolic antioxidants from spices. Gas chromatography-mass spectrometry quantified key aromatic compounds in seasoning packets, revealing higher levels of sulfur, nitrogen, and phenolic volatiles in Chuanzhen and Gusong. Multivariate models accurately predicted seasoning identities from analytical data. This demonstrates the utility of rapid global aroma and electrochemical profiling combined with targeted GC-MS analysis to characterize complex food formulations and detect differences based on spice compositions.

Internet of Things (IoT) devices frequently encounter various challenges, including limited power, spectrum, and memory resources, as well as harsh environments conditions. Therefore, the development of an efficient transmission scheme is crucial for ensuring reliable and secure communication in IoT networks. In this article, an adaptive semi-grant-free (SGF) transmission scheme is proposed for reliable uplink nonorthogonal multiple access systems with enhanced security, in which a ratio-based user scheduling criterion and a hybrid successive interference cancellation technique are employed to suppress the activity of untrusted nodes while ensuring reliable transmission. To evaluate the superiority of the adaptive scheme, a conventional static transmission scheme and a worst-case eavesdropping scenario are used as benchmarks. Simulation results show that the adaptive scheme outperforms the conventional schemes in terms of outage and intercept probability. In addition, the closed-form results of grant-based user's and grant-free user's outage probability and untrusted node's intercept probability are derived. Compared to existing literature, this work provides a comprehensive view of security-reliability tradeoff analysis of SGF transmissions.

Traditional image dehazing methods based on physical model do not consider the coupling of different color channels, which will produce artifacts during dehazing. To solve this problem, an improved image fog removal method based on total variational model is proposed. First of all, in order to improve the accuracy of atmospheric light estimation, dark channel principle is used to calculate atmospheric light, which can effectively improve the effect of fog removal. Secondly, based on the atmospheric scattering model, alternate least-squares processing is carried out on the de-fogging image, and the transmission of the three RGB color channels is coupled to effectively remove the artifacts existing in the image. The experiment is conducted in the LIVE image de-fogging database to compare with the classical de-fogging method. The experimental results show that the proposed algorithm not only reduces the artifacts in images, but also increases the peak signal-to-noise ratio and structural similarity by 2.1 and 0.5 respectively, and improves the quality evaluation of unreferenced images by 1.7, which proves the effectiveness of the proposed algorithm.

Learning-based approaches inspired by the scattering model for enhancing underwater imagery have gained prominence. Nevertheless, these methods often suffer from time-consuming attributable to their sizable model dimensions. Moreover, they face challenges in adapting unknown scenes, primarily because the scattering model's original design was intended for atmospheric rather than marine condition. To address these obstacles, we begin by investigating the inherent differences in imaging characteristics between atmospheric and marine conditions based on statistical distributions. Building on these observations, we introduce an efficient and effective algorithm called Deep Scene Curve, abbreviated as DSC. This method comprises two essential steps: scene-irrelevant zero-mean adjustment and scene-oriented hyperparameter estimation. The first step transforms scene features into a unified zero-mean space, thereby reducing interference from scene-specific attributes. In the second step, we employ a lightweight neural network to estimate scene-oriented hyperparameters for a defined pixel-level curve based on underwater observations. This approach enables us to generate a deep curve that excels in both adaptability and efficiency, as substantiated by extensive experiments. Notably, our method achieves a significant 56% improvement in average inference time while reducing FLOPs by 92% compared to existing techniques. Furthermore, our extensive experiments in low-light image enhancement tasks highlight the potential advantages of DSC.

Due to the range-angle-dependent beampattern, frequency diverse array (FDA) has attracted extensive attention. However, the inherent time-varying and range-angle coupling problems of the FDA are still troublesome. In this article, a quasi-time-invariant focusing beamforming scheme for retrodirective (RD)-FDA is proposed to alleviate the above tough problems. Specifically, linear and semicircular RD-FDAs are explored to achieve automatic beam alignment of the desired target without phase weights. By establishing an artificial neural network (ANN) framework, frequency offsets can be optimized to match the radiation requirements. By minimizing the loss value of the network, the desired focusing pattern can be realized. Then, optimized frequency offsets are periodically inverted to limit the signal phase, which allows flexible control of the spatial variation range of the mainlobe beam. Simulation results show the effectiveness and superiority of the proposed scheme to achieve quasi-time-invariant beamforming.

Reducing pollutant emissions from electricity production in the power system positively impacts the control of greenhouse gas emissions. Boosting kernel search optimizer (BKSO) is introduced in this research to solve the combined economic emission dispatch (CEED) problem. Inspired by the foraging behavior in the slime mould algorithm (SMA), the kernel matrix of the kernel search optimizer (KSO) is intensified. The proposed BKSO is superior to the standard KSO in terms of exploitation ability, robustness, and convergence rate. The CEC2013 test function is used to assess the improved KSO's performance and compared to 11 well-known optimization algorithms. BKSO performs better in statistical results and convergence curves. At the same time, BKSO achieves better fuel costs and fewer pollution emissions by testing with four real CEED cases, and the Pareto solution obtained is also better than other MAs. Based on the experimental results, BKSO has better performance than other comparable MAs and can provide more economical, robust, and cleaner solutions to CEED problems.

The antenna array synthesis problem has long been known as a tough issue, which attracts considerable interest to explore high-performance low-complexity optimization techniques. In this article, an efficient artificial neural network (ANN) for time-modulated arrays (TMAs) synthesis is proposed. By defining the equivalent excitation properly, TMA synthesis can be first transformed to a generalized phased array optimization. Next, a two-stage ANN framework composed of two encoders and a universal decoder is established to optimize the equivalent excitation, and then a single-input single-output (SISO) sinc−1-ANN is proposed to solve inverse of $\text {sinc}\left ({\cdot }\right)$ efficiently. To achieve fast and accurate pattern prediction, the decoder is pretrained to be a real-time array analyzer, while the encoder is designed as an array synthesizer to develop online training. By minimizing the loss function related to radiation pattern and equivalent excitation, the desired pattern can be achieved. Then, with the help of the pretrained SISO sinc−1-ANN, the static excitation coefficient, switch-on duration, and starting time are acquired. Simulation results of different types of desired TMA patterns are provided to verify the superiority, effectiveness, and efficiency of the proposed approach.

Non-nucleic acid targets have posed a serious challenge to food safety. The detection of non-nucleic acid targets can enable us to monitor food contamination in a timely manner. In recent years, the CRISPR/Cas system has been extensively explored in biosensing. However, there is a lack of a summary of CRISPR/Cas-powered detection tailored to non-nucleic acid targets involved in food safety. This review comprehensively summarizes the recent advances on the construction of CRISPR/Cas-powered detection and the promising applications in the field of food safety related non-nucleic acid targets. The current challenges and futuristic perspectives are also proposed accordingly. The rapidly evolving CRISPR/Cas system has provided a powerful propellant for non-nucleic acid target detection via integration with aptamer and/or DNAzyme. Compared with traditional analytical methods, CRISPR/Cas-powered detection is conceptually novel, essentially eliminates the dependence on large instruments, and also demonstrates the capability for rapid, accurate, sensitive, and on-site testing.

The possibility to design wearable devices based on electrical impedance tomography (EIT) has recently given a boost to this technology. However, the coverage of the sensor array will vary with the size difference of the measured object, resulting in an incomplete measurement set. If this measurement set is directly invested in the solution of the inverse problem, the imaging quality will be significantly degraded. In this case, it is necessary to take measures to reduce the impact of missing measurement data on reconstruction. In this article, a new method (MinR-DFF) is proposed which provides a novel solution to the problem of local measurement information loss due to incomplete electrode arrays and achieves high-quality image reconstruction. The effectiveness of the proposed method is verified by numerical simulation and solid–liquid two-phase experiments. By using this method, the correct images of the phantoms are successfully recovered with an incomplete electrode array and both the correlation coefficients (CORs) and the relative errors (REs) do not differ from the results using the complete measurement set by more than 5%.

Multicomponent synergistic regulation and defect design have been effective strategies to enhance the electrocatalytic activity of transition-metal oxides. In this work, NiO and NiMoO4nanocomposites on multi-walled carbon nanotubes (termed NiO–NiMoO4/mCNTs) are synthesized through a two-step method. Physical characterizations show NiO–NiMoO4/mCNTs have a well-defined NiO–NiMoO4structure, large specific surface area, and abundant oxygen vacancies. For oxygen evolution reaction (OER), NiO–NiMoO4/mCNTs deliver lower overpotential (277 mV) than NiO/mCNTs, NiO, and commercial RuO2nanocrystals at 10 mA cm–2. For hydrogen evolution reaction (HER), NiO–NiMoO4/mCNTs still show the best HER activity, manifested by the smallest onset potential and the lowest Tafel. Density functional theory calculations show that the adsorption energies of hydrogen- and oxygen-containing intermediates on the NiO–NiMoO4/mCNTs surface have changed, which can lower the energy barriers required for HER and OER. The excellent electrocatalytic activity of bifunctional NiO–NiMoO4/mCNTs for OER and HER can be attributed to the synergy effect between NiO, NiMoO4, and mCNTs. A symmetrical two-electrode water electrolyzer with NiO–NiMoO4/mCNTs as both the cathode and anode are constructed, which can reach a current density of 10 mA cm–2and only requires 1.57 V.

ABSTRACTTFEB (transcription factor EB) regulates multiple genes involved in the process of macroautophagy/autophagy and plays a critical role in lifespan determination. However, the detailed mechanisms that regulate TFEB activity are not fully clear. In this study, we identified a role for HSP90AA1 in modulating TFEB. HSP90AA1 was phosphorylated by CDK5 at Ser 595 under basal condition. This phosphorylation inhibited HSP90AA1, disrupted its binding to TFEB, and impeded TFEB’s nuclear localization and subsequent autophagy induction. Pro-autophagy signaling attenuated CDK5 activity and enhanced TFEB function in an HSP90AA1-dependent manner. Inhibition of HSP90AA1 function or decrease in its expression significantly attenuated TFEB’s nuclear localization and transcriptional function following autophagy induction. HSP90AA1-mediated regulation of a TFEB ortholog was involved in the extended lifespan of Caenorhabditis elegansin the absence of its food source bacteria. Collectively, these findings reveal that this regulatory process plays an important role in modulation of TFEB, autophagy, and longevity.Abbreviations: AL: autolysosome; AP: autophagosome; ATG: autophagy related; BafA1: bafilomycin A1; CDK5: cyclin-dependent kinase 5; CDK5R1: cyclin dependent kinase 5 regulatory subunit 1; CR: calorie restriction; FUDR: 5-fluorodeoxyuridine; HSP90AA1: heat shock protein 90 alpha family class A member 1; MAP1LC3: microtubule associated protein 1 light chain 3; NB: novobiocin sodium; SQSTM1: sequestosome 1; TFEB: transcription factor EB; WT: wild type.

Foodborne viruses have been recognized as important threats to food safety and human health. Rapid and accurate detection is one of the most crucial measures for food safety control. With the development of biology, chemistry, nanoscience, and related interdisciplines, detection strategies have been devised and advanced continuously. This review mainly focuses on the progress of detection methods for foodborne viruses. The current detection methods for foodborne viruses are summarized, including traditional electron microscopy and cultural isolation, immunoassay, molecular technology, biosensors, and newly emerging CRISPR/Cas-based detection technology. Furthermore, a comparison of the detection methods was objectively discussed. This review provides a comprehensive account of foodborne virus detection methods from fundamentals to state-of-the-art and illustrates the advantages and disadvantages of the current methods and proposes the future trends and directions for foodborne virus detection. It is hoped that this review can update current knowledge and present blueprints in order to accelerate futuristic development.

Lignin, the second largest component of biomass, is considered as an important alternative source of fossil reserves for the production of fuels and chemicals. Here, we developed a novel method to oxidatively degrade organosolv lignin into value-added four-carbon esters, particularly diethyl maleate (DEM), with the cooperative catalyst consisting of 1-(3-sulfobutyl) triethylammonium hydrogen sulfate ([BSTEA]HSO4) and 1-butyl-3-methylimidazolium ferric chloride ([BMIM]Fe2Cl7). Under optimized conditions (1.00 MPa initial O2pressure, 160 °C, 5 h), the lignin aromatic ring was effectively cleaved by oxidation to form DEM with a yield of 15.85% and a selectivity of 44.25% in the presence of the synergistic catalyst of [BMIM]Fe2Cl7-[BSMIM]HSO4(1/3, mol/mol). The structure and composition analysis of lignin residues and liquid products confirmed that the aromatic units in lignin were effectively and selectively oxidized. Furthermore, the catalytic oxidation of lignin model compounds was explored for obtaining a possible reaction pathway of oxidative cleavage of lignin aromatic units to DEM. This study provides a promising alternative method for the production of traditional petroleum-based chemicals.

Electrical impedance tomography (EIT) is a noninvasive, cost-effective, and structurally simple technology that enables applications in many fields by measuring changes in electrical parameters. However, the nonlinearity and ill-posedness of the EIT image reconstruction process hinder the complete recovery of the electrical parameters of the field from the measured data, making it still challenging. A Calderón’s method-guided deep neural network (CGDNN) which consists of Calderón’s method as a preliminary imaging module and deep neural network as an image segmentation module is proposed in this article. The preliminary imaging module of CGDNN avoids the computation of the sensitivity matrix and provides a fast and stable nonlinear mapping from measurement data to reconstruction images to facilitate image-to-image mapping by deep neural networks. The preliminary imaging module and the image segmentation module are connected by multichannel to avoid the manual selection of optimal preimage. In order to obtain more accurate reconstruction results, a network structure of multilevel U-Net with dense skip connections is applied. Simulation data and experimental data are used to evaluate the feasibility and effectiveness of CGDNN. The results show that CGDNN can obtain high-quality electrical properties distribution images quickly and accurately compared with traditional methods and deep learning image reconstruction methods.

Images captured from low-light scenes often suffer from severe degradations, including low visibility, color casts, intensive noises, etc. These factors not only degrade image qualities, but also affect the performance of downstream Low-Light Vision (LLV) applications. A variety of deep networks have been proposed to enhance the visual quality of low-light images. However, they mostly rely on significant architecture engineering and often suffer from the high computational burden. More importantly, it still lacks an efficient paradigm to uniformly handle various tasks in the LLV scenarios. To partially address the above issues, we establish Retinex-inspired Unrolling with Architecture Search (RUAS), a general learning framework, that can address low-light enhancement task, and has the flexibility to handle other challenging downstream vision tasks. Specifically, we first establish a nested optimization formulation, together with an unrolling strategy, to explore underlying principles of a series of LLV tasks. Furthermore, we design a differentiable strategy to cooperatively search specific scene and task architectures for RUAS. Last but not least, we demonstrate how to apply RUAS for both low- and high-level LLV applications (e.g., enhancement, detection and segmentation). Extensive experiments verify the flexibility, effectiveness, and efficiency of RUAS.

The boosted heterogeneous interfaces in titanium nitride (TiN) nanotube/polydimethylsiloxane (PDMS) composite contributed to strong polarization loss relaxation ability.The TiN nanotubes/PDMS composite possessed both good impedance matching behavior and strong dielectric loss ability in wide temperature spectrum.The TiN nanotubes/PDMS composite exhibited excellent EMWA performances (effective absorption bandwidth value of 3.23 GHz and minimum reflection loss value of − 44.15 dB) at the varied temperature from 298 to 573 K.

Stripping units take a key role in the neutral particle analyzer (NPA). A renovated gas-stripping unit was constructed for the newly designed E//B NPA. Using H2as the working gas, we measured the gas inlet pressure (P0) and vacuum chamber pressure (P3). The pressure distribution inside the gas-stripping room was calculated with Ansys Fluent, using the measured P0and P3as boundary conditions. The stripping efficiency of the stripping unit was then simulated utilizing the Geant4 Monte Carlo code for the H and D particles. The pressure P0=40Pa, which is one-sixth of what found in the previous design and corresponds to a thickness of 1.27×1017atoms/cm2, was obtained as the optimum working pressure for the upgraded stripping unit. An 50 kV electron cyclotron resonance (ECR) ion source platform was designed and constructed for E//B NPA calibration, and its performance has been measured. Using the ECR ion source platform, we measured the efficiency of the stripping unit through an inverse experiment with proton beams. We compared the current ratios of measurements with and without H2gas to Geant4 simulation results. We found adequate agreement between the overall trends of the experiment and the simulation. The significant deviation for incident energies below 20 keV may result from the scattering effects of low-energy protons, leading to reduced accuracy in single-scattering physics in Geant4 simulations. Applying the scattering corrections observed in the reverse experiments obtains more accurate stripping efficiencies for H and D atoms in the energy range of 20–200 keV and the global efficiency with the maximum values of 95.0% for H atoms and 78.9% for D atoms at 200 keV.

Direct electrolytic CO2capture solution (e.g., bicarbonate), which bypasses the energy-intensive processes of CO2desorption, offers a unique route for CO2conversion to fuels or value-added chemicals. Nonprecious Ni single-atom catalysts (SACs) anchored on metal–organic frameworks (MOFs) possess abundant porous structures and exhibit a high selectivity for CO production. However, these MOF-derived Ni SACs are usually synthesized by a series of complex procedures, and their abundant micropores (<2 nm) also reduce the local reactant transport in the catalysts. Herein, we report a simple one-step pyrolysis method to prepare a MOF-derived Ni SAC that can efficiently boost bicarbonate conversion to CO. The abundant mesopores around 35.4 nm significantly enhance the transport of local reactants in the catalysts. At a high current density of 100 mA/cm2, the tailored catalyst shows 67.2% Faradaic efficiency of CO, which, to the best of our knowledge, exceeds the state-of-the-art precious Ag nanoparticle catalysts reported so far. This study highlights the significance of developing nonprecious catalysts for employment in large-scale bicarbonate electrolysis conversion devices.

Besides the dominant NaCl, natural seawater/river water contains trace multivalent ions, which can provide effective screening of surface charges. Here, in both negatively and positively charged nanopores, influences from divalent ions as counterions and co-ions have been investigated with respect to the performance of osmotic energy conversion (OEC) under natural salt gradients. As counterions, trace Ca2+ions can suppress the electric power and conversion efficiency significantly. The reduced OEC performance is due to the bivalence and low diffusion coefficient of Ca2+ions instead of the uphill transport of divalent ions discovered in the previous work. Effectively screened charged surfaces by Ca2+ions induce an enhanced diffusion of Cl–ions which simultaneously decreases the net ion penetration and ionic selectivity of the nanopore. As co-ions, Ca2+ions have weak effects on the OEC performance. The promotion from charged exterior surfaces in OEC processes for ultrashort nanopores is also studied, with an effective region of ∼200 nm in width beyond pore boundaries independent of the presence of Ca2+ions. Our results shed light on the physical details of the nanofluidic OEC process under natural seawater/river water conditions, which can provide a useful guide for high-performance osmotic energy harvesting.

Enhancing the quality of low-light (LOL) images plays a very important role in many image processing and multimedia applications. In recent years, a variety of deep learning techniques have been developed to address this challenging task. A typical framework is to simultaneously estimate the illumination and reflectance, but they disregard the scene-level contextual information encapsulated in feature spaces, causing many unfavorable outcomes, e.g., details loss, color unsaturation, and artifacts. To address these issues, we develop a new context-sensitive decomposition network (CSDNet) architecture to exploit the scene-level contextual dependencies on spatial scales. More concretely, we build a two-stream estimation mechanism including reflectance and illumination estimation network. We design a novel context-sensitive decomposition connection to bridge the two-stream mechanism by incorporating the physical principle. The spatially varying illumination guidance is further constructed for achieving the edge-aware smoothness property of the illumination component. According to different training patterns, we construct CSDNet (paired supervision) and context-sensitive decomposition generative adversarial network (CSDGAN) (unpaired supervision) to fully evaluate our designed architecture. We test our method on seven testing benchmarks [including massachusetts institute of technology (MIT)-Adobe FiveK, LOL, ExDark, and naturalness preserved enhancement (NPE)] to conduct plenty of analytical and evaluated experiments. Thanks to our designed context-sensitive decomposition connection, we successfully realized excellent enhanced results (with sufficient details, vivid colors, and few noises), which fully indicates our superiority against existing state-of-the-art approaches. Finally, considering the practical needs for high efficiency, we develop a lightweight CSDNet (named LiteCSDNet) by reducing the number of channels. Furthermore, by sharing an encoder for these two components, we obtain a more lightweight version (SLiteCSDNet for short). SLiteCSDNet just contains 0.0301M parameters but achieves the almost same performance as CSDNet. Code is available at https://github.com/KarelZhang/CSDNet-CSDGAN. [ABSTRACT FROM AUTHOR]

Neural crest (NC) is the primitive neural structure in embryonic stage, which develops from ectodermal neural plate cells and epithelial cells. When the neural fold forms into neural tube, neural crest also forms a cord like structure above the neural tube and below the ectoderm. Neural crest cells (NCC) have strong migration and proliferation abilities. A number of tissue cells differentiate from neural crest cells, such as melanocytes, central and peripheral neurons, glial cells, craniofacial cells, osteoblasts, chondrocytes and smooth muscle cells. The migration and differentiation of neural crest cells are regulated by a gene network where a variety of genes, transcriptional factors, signal pathways and growth factors are involved.

Enhancing visual quality for underexposed images is an extensively concerning task that plays an important role in various areas of multimedia and computer vision. Most existing methods often fail to generate high-quality results with appropriate luminance and abundant details. To address these issues, we develop a novel framework, integrating both knowledge from physical principles and implicit distributions from data to address underexposed image correction. More concretely, we propose a new perspective to formulate this task as an energy-inspired model with advanced hybrid priors. A propagation procedure navigated by the hybrid priors is well designed for simultaneously propagating the reflectance and illumination toward desired results. We conduct extensive experiments to verify the necessity of integrating both underlying principles (i.e., with knowledge) and distributions (i.e., from data) as navigated deep propagation. Plenty of experimental results of underexposed image correction demonstrate that our proposed method performs favorably against the state-of-the-art methods on both subjective and objective assessments. In addition, we execute the task of face detection to further verify the naturalness and practical value of underexposed image correction. What is more, we apply our method to solve single-image haze removal whose experimental results further demonstrate our superiorities. [ABSTRACT FROM AUTHOR]

Continuous-variable entanglement plays a very important role in quantum information processes. In quantum information protocols based on continuous variable entanglement, long-term data acquisition and information processing are necessary, such as gate sequence for quantum computation and gravitational wave detection. The degree of entanglement is mainly affected by the coating parameters of the output coupler. In this paper, we theoretically analyze the effect of different transmittances of the output coupler on the degree of entanglement, which provides a theoretical basis for further realizing high-degree and long-term stable of entanglement.

In the traditional quantum wolf pack algorithm, the wolf pack distribution is simplified, and the leader wolf is randomly selected. This leads to the problems that the development and exploration ability of the algorithm is weak and the rate of convergence is slow. Therefore, a quantum wolf pack evolutionary algorithm of weight decision‐making based on fuzzy control is proposed in this paper. First, to realize the diversification of wolf pack distribution and the regular selection of the leader wolf, a dual strategy method and sliding mode cross principle are adopted to optimize the selection of the quantum wolf pack initial position and the candidate leader wolf. Second, a new non‐linear convergence factor is adopted to improve the leader wolf's search direction operator to enhance the local search capability of the algorithm. Meanwhile, a weighted decision‐making strategy based on fuzzy control and the quantum evolution computation method is used to update the position of the wolf pack and enhance the optimization ability of the algorithm. Then, a functional analysis method is adopted to prove the convergence of the quantum wolf pack algorithm, thus realizing the feasibility of the algorithm's global convergence. The performance of the quantum wolf pack algorithm of weighted decision‐making based on fuzzy control was verified through six standard test functions. The optimization results are compared with the standard wolf pack algorithm and the quantum wolf pack algorithm. Results show that the improved algorithm had a faster rate of convergence, higher convergence precision, and stronger development and exploration ability.

Herein, to alleviate interfacial thermal residual stress (ITRS) while improving the interfacial toughness and strength of carbon fiber reinforced polymer composites (CFRPs) the aldehyde-functionalized polyrotaxane (afPR) was synthesized and incorporated into naphthalenediimide (NDI) at the CFRP interphase. The formation of afPR was confirmed by nuclear magnetic resonance (NMR) and Fourier transform infrared (FTIR). Unsized CFs (uCF), NDI modified CFs (NCF), and the CFs (N0.5afPR-CF) modified by NDI with 0.5 wt ratio of afPR were prepared and characterized. The chemical activity and roughness of N0.5afPR-CF were increased. N0.5afPR-CF/EP exhibited a 76.14 % enhancement in interfacial shear strength (IFSS), an enormous increase in energy absorption by 426.15 %, a remarkable increase in interfacial toughness by 342.19 %, and a significant alleviation of 73.13 % in thermal residual stress (ITRS) compared to unsized CF reinforced composites (uCF/EP), due to the molecular pulley effect of afPR. The toughening and strengthening mechanism of N0.5afPR-CF/EP had also been proposed.