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The number of vision sensors continues to increase with the rapid development of intelligent systems. The effective transmitting and processing of the sensing data become difficult due to the sensing, computing and memory units being physically separated. In-sensor computing architecture inspired by biological visual systems with efficient information processing has attracted increasing attention for overcoming these performance limitations. Bipolar cells in the retina can generate ON/OFF information processing channels to amplify marginal information. The synaptic structure is plastic and can enhance the output information that is repeated many times. In recent years, numerous new material and device strategies to implement in-sensor computing by mimicking the functions of bipolar cells and synapses have been reported: ON/OFF optical responses have been realized on two-dimensional materials by band-modulation and tunneling; synaptic responses, such as short-term plasticity and long-term plasticity, have been realized by phase transition and carrier regulating. In this review, we will summarize the biological vision processes, analyse the physical mechanisms behind the in-sensor computational vision sensors (ICVSs), and then overview the emerging physical artificial neural networks implemented with ICVSs. After that, we will discuss ICVS design based on biological mechanisms beyond ON/OFF bipolar-cell-response and synaptic response. [ABSTRACT FROM AUTHOR]

This article discusses the development of a heterogeneous copper-based catalyst, Cu3N NC, for the protosilylation of unsaturated compounds. The catalyst was found to be effective in the synthesis of various silanes from alkynes, alkenes, and imines under base- and ligand-free conditions. The Cu3N NC catalyst demonstrated high activity and durability, and it could be reused multiple times without significant loss of activity. The article also presents the protosilylation of alkynes with electron-withdrawing groups, the protosilylation of alkenes, and the protosilylation of imines using the Cu3N NC catalyst. The findings contribute to the development of sustainable technologies for the production of valuable silanes. [Extracted from the article]

This paper investigates the nonlinear free vibration of spinning pre-twisted functionally graded material (FGM) plates in thermal environment. Based on the Kirchhoff plate theory and von Kármán large-deformation theory, the nonlinear dynamic model is established via the Lagrange equation, where the warping effect is taken into account. The material properties are related to temperature and change according to a power-law exponent distribution. The theoretical solution of nonlinear-free vibration is obtained using the multiple scale method. The effects of power-law exponent, temperature difference, spinning speed, hub radius ratio, and twist angle on nonlinear-free vibration characteristics of the spinning pre-twisted cantilever FGM plates are discussed. The results show that the spinning pre-twisted cantilever FGM plate exhibits nonlinear hardening-spring vibration behavior, while the characteristics weaken with the increase of spinning speed. The twist angle and setting angle have coupling effects on the nonlinear vibration characteristics of spinning pre-twisted FGM plates. [ABSTRACT FROM AUTHOR]

Integration of multiple data sources presents a challenge for accurate prediction of molecular patho-phenotypic features in automated analysis of data from human model systems. Here, we applied a machine learning-based data integration to distinguish patho-phenotypic features at the subcellular level for dilated cardiomyopathy (DCM). We employed a human induced pluripotent stem cell-derived cardiomyocyte (iPSC-CM) model of a DCM mutation in the sarcomere protein troponin T (TnT), TnT-R141W, compared to isogenic healthy (WT) control iPSC-CMs. We established a multimodal data fusion (MDF)-based analysis to integrate source datasets for Ca2+ transients, force measurements, and contractility recordings. Data were acquired for three additional layer types, single cells, cell monolayers, and 3D spheroid iPSC-CM models. For data analysis, numerical conversion as well as fusion of data from Ca2+ transients, force measurements, and contractility recordings, a non-negative blind deconvolution (NNBD)-based method was applied. Using an XGBoost algorithm, we found a high prediction accuracy for fused single cell, monolayer, and 3D spheroid iPSC-CM models (≥92 ± 0.08 %), as well as for fused Ca2+ transient, beating force, and contractility models (>96 ± 0.04 %). Integrating MDF and XGBoost provides a highly effective analysis tool for prediction of patho-phenotypic features in complex human disease models such as DCM iPSC-CMs. [ABSTRACT FROM AUTHOR]

Particulate organic carbon (POC) plays a crucial role in the carbon cycle of inland lake ecosystems. The utilization of remote sensing satellite data provides an effective approach for monitoring the temporal and spatial variations in POC concentration within inland water. However, Erhai Lake, situated in the unique natural environment of the Yunnan-Kweichow Plateau, poses distinct challenges due to the complex and diverse origin of its water quality elements. Existing methods face difficulties in accurately detecting POC concentration in Erhai Lake. In this study, 112 water samples and 574 in-situ remote sensing reflectance curves were collected from Erhai Lake during April, May, and June 2023. The analysis of the optical characteristics of the water in Erhai Lake revealed the closest relationship between the concentration of total particulate matter and POC. Consequently, a POC concentration inversion algorithm was developed, utilizing bands 8, 12, and 16 of Sentinel-3 Ocean and Land Colour Instrument (OLCI). Various POC concentration inversion algorithms were evaluated utilizing a separate dataset. The results indicate that the three-band method offers superior accuracy in POC concentration inversion for Erhai Lake, with a root mean square error (RMSE) of 0.13 mg/L and a mean absolute percentage error (MAPE) of 28.80%. The three-band method was effectively applied to OLCI images from April, May, and June 2023, enabling the analysis of the spatiotemporal distribution of POC in Erhai Lake. [ABSTRACT FROM AUTHOR]

Feature selection in classification is a complex optimization problem that cannot be solved in polynomial time. Bi-objective feature selection, aiming to minimize both selected features and classification errors, is challenging due to the conflict between objectives, while one of the most effective ways to tackle this is to use multi-objective evolutionary algorithms. However, very few of these have ever reflected an evolutionary multi-tasking framework, despite the implicit parallelism offered by the population-based search characteristic. In this paper, a dynamic multi-tasking-based multi-objective evolutionary algorithm (termed DTEA) is proposed for handling bi-objective feature selection in classification, which is not only suitable for datasets with relatively lower dimensionality of features, but is also suitable for datasets with relatively higher dimensionality of features. The role and influence of multi-tasking on multi-objective evolutionary feature selection were studied, and a dynamic tasking mechanism is proposed to self-adaptively assign multiple evolutionary search tasks by intermittently analyzing the population behaviors. The efficacy of DTEA is tested on 20 classification datasets and compared with seven state-of-the-art evolutionary algorithms. A component contribution analysis was also conducted by comparing DTEA with its three variants. The empirical results show that the dynamic-tasking mechanism works efficiently and enables DTEA to outperform other algorithms on most datasets in terms of both optimization and classification. [ABSTRACT FROM AUTHOR]

The present study applies a Deep Reinforcement Learning (DRL) algorithm to Active Flow Control (AFC) of a two-dimensional flow around a confined square cylinder. Specifically, the Soft Actor-Critic (SAC) algorithm is employed to modulate the flow of a pair of synthetic jets placed on the upper and lower surfaces of the confined squared cylinder in flow configurations characterized by Re of 100, 200, 300, and 400. The investigation starts with an analysis of the baseline flow in the absence of active control. It is observed that at Re = 100 and Re = 200, the vortex shedding exhibits mono-frequency characteristics. Conversely, at Re = 300 and Re = 400, the vortex shedding is dominated by multiple frequencies, which is indicative of more complex flow features. With the application of the SAC algorithm, we demonstrate the capability of DRL-based control in effectively suppressing vortex shedding, while significantly diminishing drag and fluctuations in lift. Quantitatively, the data-driven active control strategy results in a drag reduction of approximately 14.4%, 26.4%, 38.9%, and 47.0% for Re = 100, 200, 300, and 400, respectively. To understand the underlying control mechanism, we also present detailed flow field comparisons, which showcase the adaptability of DRL in devising distinct control strategies tailored to the dynamic conditions at varying Re. These findings substantiate the ability of DRL to control chaotic, multi-frequency dominated vortex shedding phenomena, underscoring the robustness of DRL in complex AFC problems. [ABSTRACT FROM AUTHOR]

Using a simple acid-base neutralization method, a Ch-PW solid catalyst was synthesized by mixing choline hydroxide (ChOH) and phosphotungstic acid (HPW) at a 2:1 molar ratio in an aqueous solution. This catalyst was combined with a 20 wt.% potassium peroxymonosulfate (PMS) solution, using acetonitrile (ACN) as the extraction solvent to create an extraction catalytic oxidative desulfurization system. The optimal desulfurization conditions were determined through response surface methodology, targeting the highest desulfurization rate: 0.99 g of Ch-PW, 1.07 g of PMS, 2.5 g of extraction solvent, at a temperature of 50.48 °C. The predicted desulfurization rate was 90.79%, compared to an experimental rate of 93.64%, with a deviation of 3.04%. A quadratic model correlating the desulfurization rate with the four conditions was developed and validated using ANOVA, which also quantified the impact of each factor on the desulfurization rate: PMS > ACN > Ch-PW > temperature. GC-MS analysis identified the main oxidation product as DBTO2, and the mechanism of desulfurization in this system was further explored. [ABSTRACT FROM AUTHOR]

To address the cost and efficiency issues of manually analysing requirement dependency in requirements engineering, a requirement dependency extraction method based on part-of-speech features and an improved stacking ensemble learning model (P-Stacking) is proposed. Firstly, to overcome the problem of singularity in the feature extraction process, this paper integrates part-of-speech features, TF-IDF features, and Word2Vec features during the feature selection stage. The particle swarm optimization algorithm is used to allocate weights to part-of-speech tags, which enhances the significance of crucial information in requirement texts. Secondly, to overcome the performance limitations of standalone machine learning models, an improved stacking model is proposed. The Low Correlation Algorithm and Grid Search Algorithms are utilized in P-stacking to automatically select the optimal combination of the base models, which reduces manual intervention and improves prediction performance. The experimental results show that compared with the method based on TF-IDF features, the highest F1 scores of a standalone machine learning model in the three datasets were improved by 3.89%, 10.68%, and 21.4%, respectively, after integrating part-of-speech features and Word2Vec features. Compared with the method based on a standalone machine learning model, the improved stacking ensemble machine learning model improved F1 scores by 2.29%, 5.18%, and 7.47% in the testing and evaluation of three datasets, respectively. [ABSTRACT FROM AUTHOR]

The h-BN/diamond mix-dimensional heterostructure has broad application prospects in the fields of optoelectronic devices and power electronic devices. In this paper, the electronic properties and band offsets of hexagonal boron nitride (h-BN)/(H, O, F, OH)-diamond (111) heterostructures were studied by first-principles calculations under biaxial strain. The results show that different terminals could significantly affect the interface binding energy and charge transfer of h-BN/diamond heterostructure. All heterostructures exhibited semiconductor properties. The h-BN/(H, F)-diamond systems were indirect bandgap, while h-BN/(O, OH)-diamond systems were direct bandgap. In addition, the four systems all formed type-II heterostructures, among which h-BN/H-diamond had the largest band offset, indicating that the system was more conducive to the separation of electrons and holes. Under biaxial strain the bandgap values of the h-BN/H-diamond system decreased, and the band type occurred direct–indirect transition. The bandgap of h-BN/(O, F, OH)-diamond system increased linearly in whole range, and the band type only transformed under large strain. On the other hand, biaxial strain could significantly change the band offset of h-BN/diamond heterostructure and promote the application of this heterostructure in different fields. Our work provides theoretical guidance for the regulation of the electrical properties of h-BN/diamond heterostructures by biaxial strain. [ABSTRACT FROM AUTHOR]

Deep reinforcement learning (DRL) has emerged as a promising approach for handling highly dynamic and nonlinear active flow control (AFC) problems. However, the computational cost associated with training DRL models presents a significant performance bottleneck. To address this challenge and enable efficient scaling on high-performance computing architectures, this study focuses on optimizing DRL-based algorithms in parallel settings. We validate an existing state-of-the-art DRL framework used for AFC problems and discuss its efficiency bottlenecks. Subsequently, by deconstructing the overall framework and conducting extensive scalability benchmarks for individual components, we investigate various hybrid parallelization configurations and propose efficient parallelization strategies. Moreover, we refine input/output (I/O) operations in multi-environment DRL training to tackle critical overhead associated with data movement. Finally, we demonstrate the optimized framework for a typical AFC problem where near-linear scaling can be obtained for the overall framework. We achieve a significant boost in parallel efficiency from around 49% to approximately 78%, and the training process is accelerated by approximately 47 times using 60 central processing unit (CPU) cores. These findings are expected to provide valuable insight for further advancements in DRL-based AFC studies. [ABSTRACT FROM AUTHOR]

A van der Waals (vdW) heterostructure is formed by combining multiple materials through vdW bonds. It can combine the advantages of electronic, optical, thermal, and magnetic properties of different 2D materials and has the potential to develop into the next generation of high‐performance functional devices. Herein, the current research advances of vdW heterostructures are reviewed. First, current fabrication methods and physical structures of vdW heterostructures are summarized. The 2D/nD (n = 0,1,2,3) mixed‐dimensional heterostructures are discussed in detail. Second, a new type of vdW heterostructure is introduced based on two‐dimensional electron gas with a nanoscale junction interface. Finally, the application prospects of vdW heterostructures in photoelectric and memory devices are further outlined by combing new applications in the neural networks. This review shows that vdW heterostructures have great advantages in high integration, energy harvesting, and logical operations, and it provides directions and suggestions for the future research and application of environmentally friendly, high‐performance, and smart functional devices. [ABSTRACT FROM AUTHOR]

This study investigates the utilization of a trapezoidal cavity with a corrugated bottom wall containing a Carreau hybrid nanofluid composed of water, aluminum, and copper nanoparticles. A heated, rotating cylinder is placed at the center of the enclosure in the presence of an external magnetic field. The impact of Forchheimer and Brinkman porous medium models on the hybrid nanofluid is examined. Three different inlet–outlet placement configurations are considered to investigate their influence on heat transfer. The governing equations for fluid flow and heat transfer are solved numerically. Through simulations, a range of flow-controlling variables is systematically adjusted, including the Darcy number, Reynolds number, Hartmann number, nanoparticle volume fraction, undulation on the hot bottom wall, power law index, and rotational speed of the inner heated cylinder. The results demonstrate that the hybrid nanofluid and rotating cylinder significantly enhance heat transfer within the trapezoidal cavity. Higher values of the Darcy number, Reynolds number, and nanoparticle volume fraction lead to increased heat transfer rates. The placement configuration of the inlet and outlet ports also affects heat transfer performance, with the bottom-top configuration yielding the best results. Furthermore, a comparative analysis of flow profiles and heat distribution is conducted using the multiple expression programing technique. The proposed model accurately predicts the flow and heat transfer characteristics in the trapezoidal cavity, as validated through comparison with provided data sets. [ABSTRACT FROM AUTHOR]

Nanoemulsions have garnered great attention as a chemical additive for enhanced oil recovery (EOR) technology worldwide due to their small size and unique physicochemical properties. Herein, this work introduces a novel nonionic surfactant-stabilized nanoemulsion prepared using a low-energy method for EOR projects in the Chang 8 tight reservoir. Spontaneous imbibition experiments were conducted in oil-saturated cores, and secondary imbibition experiments were carried out in water-bearing cores. Additionally, core flooding experiments were performed to study the EOR performance of both nanoemulsion and complex surfactant. Finally, the EOR mechanisms were thoroughly investigated, specifically focusing on oil-washing capacity, reduced interfacial tension, and wettability alteration. The designed nanoemulsion, with droplets averaging 10.5 ± 0.7 nm in synthetic formation brine at ambient temperature, exhibits excellent thermal and long-term stability even under reservoir temperature conditions. The oil recovery efficiency of the nanoemulsion system in oil-saturated cores through spontaneous imbibition was 31.28%, representing a 6.42% improvement over the complex surfactant solution. In the secondary imbibition experiments, the nanoemulsion mobilized 8.53% of the residual oil, compared to 4.11% for the complex surfactant. Core flooding experiments revealed that the nanoemulsion system achieved a total oil recovery of 62.69%, outperforming the complex surfactant system by 8.68% during the chemical flooding stage and 1.82% during the subsequent brine flooding stage. The EOR mechanisms of nanoemulsion in tight reservoirs can be attributed to four aspects: reduction of oil/water interfacial tension, wettability alteration, miscibility with crude oil, and increased sweep volume. The generation of the Marangoni effect and the adsorption and diffusion characteristics are essential differences between the nanoemulsion system and the complex surfactant system. The presented findings in this paper could aid in promoting the large-scale use of nanoemulsions in Chang 8 tight reservoirs. [ABSTRACT FROM AUTHOR]

Evolutionary algorithms have been widely used for tackling multi-objective optimization problems, while feature selection in classification can also be seen as a discrete bi-objective optimization problem that pursues minimizing both the classification error and the number of selected features. However, traditional multi-objective evolutionary algorithms (MOEAs) can encounter setbacks when the dimensionality of features explodes to a large scale, i.e., the curse of dimensionality. Thus, in this paper, we focus on designing an adaptive MOEA framework for solving bi-objective feature selection, especially on large-scale datasets, by adopting hybrid initialization and effective reproduction (called HIER). The former attempts to improve the starting state of evolution by composing a hybrid initial population, while the latter tries to generate more effective offspring by modifying the whole reproduction process. Moreover, the statistical experiment results suggest that HIER generally performs the best on most of the 20 test datasets, compared with six state-of-the-art MOEAs, in terms of multiple metrics covering both optimization and classification performances. Then, the component contribution of HIER is also studied, suggesting that each of its essential components has a positive effect. Finally, the computational time complexity of HIER is also analyzed, suggesting that HIER is not time-consuming at all and shows promising computational efficiency. [ABSTRACT FROM AUTHOR]

The genesis of peralkaline granites has fundamental implications for understanding their source characters and tectonic settings, yet it remains highly controversial after decades of research. Two major challenges in studies of peralkaline granites are how the peralkaline magmas evolve and how to trace their magmatic evolution. Here we use K isotopes in granites from Zhoushan archipelago of southeast China to explore these two issues. The Zhoushan archipelago hosts two groups of Cretaceous high-silica granites, which are the peralkaline rocks (PAG) and the calc-alkaline rocks (CAG). The CAG rocks exhibit a small variation (δ41K = −0.54 ± 0.06 ‰ to −0.36 ± 0.06 ‰) in K isotopic compositions, in contrast to the larger variation for the PAG rocks (δ41K = −0.67 ± 0.07 ‰ to −0.27 ± 0.05 ‰). The PAG rocks show strong correlations between δ41K values and various whole-rock major elements (i.e., Al 2 O 3 , Na 2 O, and K 2 O contents), but these correlations are absent for the CAG rocks. We propose that the separation of Na-rich alkali-feldspar, rather than low-Na alkali-feldspar and/or plagioclase, controlled the K isotope variability in PAG. The alkali-feldspar in PAG is enriched in Na, among which the alkali-feldspar with Or (orthoclase; mol. %) <15 is characterized by high δ41K ranging from −0.34 ‰ to 0.36 ‰. Geochemical modeling demonstrates that 23–33 % fractional crystallization of such Na-rich alkali-feldspar is required to generate the geochemical trends observed in PAG. Those findings suggest that crystallization of Na-rich alkali-feldspars dominates the magma evolution for peralkaline magmatism, supporting strong fractional crystallization in high-silica peralkaline magma. K isotopes show promise to trace detailed magmatic processes and provide constraints on the generation of peralkaline granites. [ABSTRACT FROM AUTHOR]

We analyze the effect of wall slip on the fully developed reverse mixed convection of Jeffery nanofluids between two inclined parallel plates with uniform wall heat flux conditions. The theory of tri‐hybrid water‐based nanoparticles with unique shapes, namely, cylindrical (copper), spherical (titanium oxide), and platelet (aluminum oxide) for heat transfer enhancement is utilized since it has better heat performance applicable in a dynamic of fuels and coolant in automobiles as compared with regular Newtonian fluid and nanofluid. The equations describing the above transport phenomena are nondimensional through appropriate scale transformations. Analytical solutions for velocity, temperature, and pressure distributions are obtained. Four different flow regimes including no reversal, bottom reversal, top reversal, and on both walls reversal are found for different combinations of buoyancy and pressure. Particularly, we notice that the wall slip significantly affects flow reversal. Furthermore, we notice that Magyari 's conclusion that the results of the homogeneous nanofluid flow model can be recovered from the corresponding Newtonian fluid model has great limitations. Besides, the effect of several physical factors on velocity and temperature distributions and important physical quantities, including skin friction coefficient and Nusselt number, are analyzed and graphically discussed. [ABSTRACT FROM AUTHOR]

The multi-objective evolutionary algorithm optimization (MOEA) is a challenging but critical approach for tackling complex data analysis problems. However, prevailing MOEAs often rely on single strategies to obtain optimal solutions, leading to concerns such as premature convergence and insufficient population diversity, particularly in high-dimensional data scenarios. In this paper, we propose a novel adversarial population generation algorithm, APG-SMOEA, which synergistically combines the benefits of MOEAs and Generative Adversarial Networks (GANs) to address these limitations. In order to balance the efficiency and quality of offspring selection, we introduce an adaptive population entropy strategy, which includes control parameters based on population entropy and a learning pool for storing and retrieving optimal solutions. Additionally, we attempt to alleviate the training complexity and model collapse problems common in GANs with APG-SMOEA. Experimental results on benchmarks demonstrate that the proposed algorithm is superior to the existing algorithms in terms of solution quality and diversity of low-dimensional or high-dimensional complex data. [ABSTRACT FROM AUTHOR]

Optimal integration of transcriptomics data and associated spatial information is essential towards fully exploiting spatial transcriptomics to dissect tissue heterogeneity and map out inter-cellular communications. We present SEDR, which uses a deep autoencoder coupled with a masked self-supervised learning mechanism to construct a low-dimensional latent representation of gene expression, which is then simultaneously embedded with the corresponding spatial information through a variational graph autoencoder. SEDR achieved higher clustering performance on manually annotated 10 × Visium datasets and better scalability on high-resolution spatial transcriptomics datasets than existing methods. Additionally, we show SEDR's ability to impute and denoise gene expression (URL: https://github.com/JinmiaoChenLab/SEDR/). [ABSTRACT FROM AUTHOR]

6-Diazo-5-oxo-L-norleucine (DON) is a potent glutamine antagonist with toxic side effects; in order to reduce these effects, multiple prodrugs have been designed. However, there are currently no reports of a DON prodrug with a defined mechanism to achieve high tumor selectivity. To improve the selective toxicity of DON to tumor cells while reducing systemic toxicity, a hypoxia-activated prodrug, termed HDON, was designed. HDON achieved remarkable tumor suppression of 76.4 ± 5.2% without leading to weight loss in an H22 murine liver cancer model with high hypoxia. Moreover, to augment the therapeutic efficacy of HDON, combretastatin A4 nanoparticles were used to aggravate tumor hypoxia of MC38 murine colon cancer and 4T1 murine breast cancer, activate HDON to DON, and stimulate a robust anti-tumor immune response while selectively killing in tumor cells in vivo , achieving significantly elevated tumor suppression rates of 98.3 ± 3.4% and 98.1 ± 3.1%, with cure rates of 80.0% and 20.0%, respectively. [Display omitted] • A hypoxia-activated prodrug of 6-diazo-5-oxo-L-norleucine termed HDON was designed. • HDON exhibited stability and low toxicity under normal physiological conditions. • HDON reduced to DON in hypoxic tumors, enabling effective metabolic blockade. [ABSTRACT FROM AUTHOR]

Dedicated bus lane (DBL) is one of the effective measures to improve transit system efficiency and further promote transit priority. Then, the performance assessment of DBLs turns out to be an essential task for better reflecting the interests of all stakeholders. To this end, this paper presents a data-driven and multidimensional framework to objectively and accurately assess the detailed performance of DBLs from the perspective of passengers, government, and operators, respectively, in which multiple data sources (i.e., IC card and GPS data) and corresponding mining technologies are adopted to establish a series of criteria, enabling a before-and-after comparison for DBL opening. The proposed model has been applied to evaluate the efficiency of the DBL system in the city of Chongqing, China. The results reveal the benefits of opening DBLs, especially for government interests, in which DBLs use less public resources to expand more public welfare. But its role in strengthening transit system competitiveness is diminished, because the opening of DBLs did not significantly increase speed and volume. [ABSTRACT FROM AUTHOR]

Simple Summary: The circadian clock plays a crucial role in helping plants to effectively acclimate to ever-changing environmental conditions. The proper functioning of the circadian clock is integral to the growth and development of plants. In crops, the circadian clock system exerts a multifaceted influence on yield and the response to abiotic stress. This review provides a comprehensive overview of the mechanisms through which the components of a crop's circadian clock impact its response to abiotic stress and yield. We propose that the circadian clock may orchestrate the balance between abiotic stresses and yield in crops, which is useful for the future molecular design of crop breeding. Plants have evolved a circadian clock to adapt to ever-changing diel and seasonal environmental conditions. The circadian clock is generally considered an internal system that has evolved to adapt to cyclic environmental cues, especially diel light and temperature changes, which is essential for higher plants as they are sessile organisms. This system receives environmental signals as input pathways which are integrated by circadian core oscillators to synchronize numerous output pathways, such as photosynthesis, the abiotic stress response, metabolism, and development. Extreme temperatures, salinity, and drought stresses cause huge crop losses worldwide, imposing severe pressure on areas of agricultural land. In crop production, the circadian system plays a significant role in determining flowering time and responding to external abiotic stresses. Extensive studies over the last two decades have revealed that the circadian clock can help balance the tradeoff between crop yield-related agronomic traits and adaptation to stress. Herein, we focus on summarizing how the circadian clock coordinates abiotic stress responses and crop yield. We also propose that there might be an urgent need to better utilize circadian biology in the future design of crop breeding to achieve high yields under stress conditions. [ABSTRACT FROM AUTHOR]

This study aims to prepare functional fillers using isophorone diisocyanate (IPDI), amine-terminated butadiene acrylonitrile (ATBN), and graphene oxide (GO) to improve the performance of epoxy resin (EP) composite materials by enhancing filler dispersion and toughening. Unexpected results were obtained during the experimental and testing phases, achieving multiple bonding mechanisms. Successful grafting of IPDI and ATBN onto GO was confirmed using TEM, FT-IR, and XPS. The prepared composites underwent T-peel, shear, tensile, and impact testing. Compared to pure EP, the T-peel strength increased by 579%, the shear strength increased by 99%, and Al elements were detected in the T-peel samples. The tensile strength increased by 134%, and the impact strength increased by 65%, with an analysis of the impact morphology to explore the mechanism of IPDI and ATBN enhancement. Additionally, the dynamic mechanical performance of EP and its composites was tested, and various forms of Tg were analyzed. This functional filler can be applied to various resin matrices and effectively improve their performance. [ABSTRACT FROM AUTHOR]

This article investigates the fluid dynamics and heat transfer properties in a trapezoidal enclosure containing a heated cylindrical object. It involves the interaction of multiple physical processes such as the magnetic field, thermal radiation, porous materials, and aqueous copper oxide nanoparticles. The governing partial differential equations are analyzed numerically through the continuous Galerkin finite element algorithm. The analysis takes into account various physical parameter factors, including the Richardson number (0 – 5) , the Hartmann number (5 − 40) , the Darcy number (0.001 − 0.1) , thermal radiation parameter (0.5 − 2) , and nanoparticle volume concentration (0.01 − 0.1). The physical mechanism of thermal and mass transfer in the enclosure caused by various factors is fully explored. In addition, the multiple expression programming (MEP) technique is implemented to report a comparative analysis of flow profiles and thermal distribution. The findings demonstrated that at low Ri, the primary flow within the cavity is driven by the shear friction generated by the moving walls. The growing importance of radiative heat transfer reduces the effectiveness of convective heat transfer, resulting in a decline in the average Nusselt number with R. The heat transfer rate rises up to 27.7% as ϕ augments; however, its value declines by 9.37% against Ha. The expected results obtained by the MEP approach are very consistent with the numerical ones. There is no doubt that the new MEP concept provides a valuable tool for researchers to predict the heat transfer behavior of any data set in cavities of different shapes. It is expected to provide new idea for the development of efficient cooling systems and the improvement of energy efficiency in various engineering applications. [ABSTRACT FROM AUTHOR]

Adsorption‐based removal of carbon dioxide (CO2) from gas mixtures has demonstrated great potential for solving energy security and environmental sustainability challenges. However, due to similar physicochemical properties between CO2 and other gases as well as the co‐adsorption behavior, the selectivity of CO2 is severely limited in currently reported CO2‐selective sorbents. To address the challenge, we create a bioinspired design strategy and report a robust, microporous metal–organic framework (MOF) with unprecedented [Mn86] nanocages. Attributed to the existence of unique enzyme‐like confined pockets, strong coordination interactions and dipole‐dipole interactions are generated for CO2 molecules, resulting in only CO2 molecules fitting in the pocket while other gas molecules are prohibited. Thus, this MOF can selectively remove CO2 from various gas mixtures and show record‐high selectivities of CO2/CH4 and CO2/N2 mixtures. Highly efficient CO2/C2H2, CO2/CH4, and CO2/N2 separations are achieved, as verified by experimental breakthrough tests. This work paves a new avenue for the fabrication of adsorbents with high CO2 selectivity and provides important guidance for designing highly effective adsorbents for gas separation. [ABSTRACT FROM AUTHOR]

Adsorption‐based removal of carbon dioxide (CO2) from gas mixtures has demonstrated great potential for solving energy security and environmental sustainability challenges. However, due to similar physicochemical properties between CO2 and other gases as well as the co‐adsorption behavior, the selectivity of CO2 is severely limited in currently reported CO2‐selective sorbents. To address the challenge, we create a bioinspired design strategy and report a robust, microporous metal–organic framework (MOF) with unprecedented [Mn86] nanocages. Attributed to the existence of unique enzyme‐like confined pockets, strong coordination interactions and dipole‐dipole interactions are generated for CO2 molecules, resulting in only CO2 molecules fitting in the pocket while other gas molecules are prohibited. Thus, this MOF can selectively remove CO2 from various gas mixtures and show record‐high selectivities of CO2/CH4 and CO2/N2 mixtures. Highly efficient CO2/C2H2, CO2/CH4, and CO2/N2 separations are achieved, as verified by experimental breakthrough tests. This work paves a new avenue for the fabrication of adsorbents with high CO2 selectivity and provides important guidance for designing highly effective adsorbents for gas separation. [ABSTRACT FROM AUTHOR]

Antibiotic resistance genes (ARGs) and antibiotic resistant bacteria are considered as emerging contaminants, whose proliferation and dissemination are highly related to the climate conditions. Freeze-thaw cycles (FTC) is a natural phenomena that widely occurred in terrestrial ecosystem for high latitude environments, and frequent FTC has been revealed under global climate change. However, the effects of FTC on the resistome in soil are still not clear. Thus, a simulation experiment was performed to illustrate the profiles of ARGs and mobile genetic elements (MGEs) under FTC exposure. It was found that, 56.4–73.2% of the shared 71 core ARG subtypes were enriched after FTC treatment, and the abundance of ARGs in total had a significant positive linear correlation with the number of FTC in spite of the declined number of ARG subtypes (r = 0.986, p = 0.002). This can explained by the selection pressure exert by freeze-thaw on bacterial community structure and promoted horizontal gene transfer (HGT). The bacterial classeses within phylum Actinobacteria, one of the dominate bacterial phylum accounting for 36–54% of the total bacteria, were enriched because of their spore and filamentous producing attributes, and enormous ARGs were presented in this kind of antibiotic-synthesising bacteria. Moreover, the survived Actinobacteria could serve as ARG donors and receptors, which profit the HGT of ARGs. The present study revealed the facilitation posed by FTC on the proliferation and dissemination of ARGs, and the accordingly findings provide new sights for well understanding of the environmental behaviour of ARGs. [ABSTRACT FROM AUTHOR]

Mixed convection in the lid-driven cavity model widely represented real engineering applications, especially electronic cooling and wind across buildings. In past decades, avalanche experiments and numerical calculations have been done on the lid-driven cavity model. Unfortunately, one unusual flow behavior in the cavity mixed convections—multiple steady solutions, i.e., identical boundary conditions and governing parameters whereas different initial conditions or loading perturbations may lead to two or more flow states, and it was hardly investigated. Multiple steady enclosure flow behaviors essentially complicate the convective transport of heat and air, which has been vividly analyzed by streamlines, heatlines and isotherms. In the present work, the flow mechanisms and evolution driven by mixed convections in lid-driven cavities for multiple aspect ratios will be investigated through the numerical methodology of computational fluid dynamics. The effects of dimensionless parameter (Grashof number, Reynolds number, and Richardson number) on multiple flow motions have been disclosed, regarding of hysteresis effect and their destabilization mechanism. Investigations on these complicated flow motions essentially facilitate the optimization of engineering flows similar to lid driven enclosures, whatever mechanical cooling of electronics, or wind across street canyons. [ABSTRACT FROM AUTHOR]

Estimating the gamma dose rate at one meter above ground level and determining the distribution of radioactive pollution from aerial radiation monitoring data are the core technical issues of unmanned aerial vehicle nuclear radiation monitoring. In this paper, a reconstruction algorithm of the ground radioactivity distribution based on spectral deconvolution was proposed for the problem of regional surface source radioactivity distribution reconstruction and dose rate estimation. The algorithm estimates unknown radioactive nuclide types and their distributions using spectrum deconvolution and introduces energy windows to improve the accuracy of the deconvolution results, achieving accurate reconstruction of multiple continuous distribution radioactive nuclides and their distributions, as well as dose rate estimation of one meter above ground level. The feasibility and effectiveness of the method were verified through cases of single-nuclide (137Cs) and multi-nuclide (137Cs and 60Co) surface sources by modeling and solving them. The results showed that the cosine similarities between the estimated ground radioactivity distribution and dose rate distribution with the true value were 0.9950 and 0.9965, respectively, which could prove that the proposed reconstruction algorithm would effectively distinguish multiple radioactive nuclides and accurately restore their radioactivity distribution. Finally, the influences of statistical fluctuation levels and the number of energy windows on the deconvolution results were analyzed, showing that the lower the statistical fluctuation level and the more energy window divisions, the better the deconvolution results. [ABSTRACT FROM AUTHOR]

Fiber Optic Gyroscope Inertial Navigation System (FOG-INS) is a navigation system using fiber optic gyroscopes and accelerometers, which can offer high-precision position, velocity, and attitude information for carriers. FOG-INS is widely used in aerospace, marine ships, and vehicle navigation. In recent years, it has also played an important role in underground space. For example, in the deep earth, FOG-INS can be used in directional well drilling, which can enhance recovery in resource exploitation. While, in shallow earth, FOG-INS is a high-precision positioning technique that can guide construction in trenchless underground pipelaying. This article extensively reviews the application status and latest progress of FOG-INS in underground space from three aspects—FOG inclinometer, FOG drilling tool attitude measurement while drilling (MWD) unit, and FOG pipe-jacking guidance system. First, measurement principles and product technologies are introduced. Second, the research hot spots are summarized. Finally, the key technical issues and future trends for development are put forward. The findings of this work are useful for further research in the field of FOG-INS in underground space, which not only provides new ideas and directions for scientific research, but also offers guidance for subsequent engineering applications. [ABSTRACT FROM AUTHOR]

The excellent performance of bolometers in the infrared and terahertz regions has attracted great attention. Understanding the transport process of charged particles is an efficient approach to determine detector performance. However, the lack of studies on the fine‐scale spatial motion of microscopic particles in bolometers has prevented a full understanding of the physical process. Herein, using micro‐nano‐scale optoelectronic performance correlation measurements, it is described how prevalent defect states at the grain boundaries (GBs) decrease current responses. Ions at the GBs of the polycrystalline perovskite bolometer contribute to the photocurrent via thermal excitons. In addition, the built‐in electric field established by ion migration fluctuates periodically with the strength of the light‐heating process due to the interaction between the bolometric effect and the Coulomb force. Additionally, the first ion‐bolometric detector is demonstrated with a significant photovoltage response to infrared and THz waves (75.3 kV W−1 at 1064 nm and 2.3 kV W−1 at 0.22 THz). An examination of the THz images shows the potential for large‐area THz imaging applications. The ion‐bolometric effect combines the broad spectral characteristics of the bolometer effect with the temperature sensitivity due to ion migration and provides a unique perspective on detector technology. [ABSTRACT FROM AUTHOR]

Increased meteorological drought intensity with rising atmospheric demand for water (hereafter vapor pressure deficit [VPD]) increases the risk of tree mortality and ecosystem dysfunction worldwide. Ecosystem‐scale water‐use strategy is increasingly recognized as a key factor in regulating drought‐related ecosystem responses. However, the link between water‐use strategy and ecosystem vulnerability to meteorological droughts is poorly established. Using the global flux observations, historic hydroclimatic data, remote‐sensing products, and plant functional‐trait archive, we identified potentially vulnerable ecosystems, examining how ecosystem water‐use strategy, quantified by the percentage bias (δ) of the empirical canopy conductance sensitivity to VPD relative to the theoretical value, mediated ecosystem responses to droughts. We found that prevailing soil water availability substantially impacted δ in dryland regions where ecosystems with insufficient soil moisture usually showed conservative water‐use strategy, while ecosystems in humid regions exhibited more pronounced climatic adaptability. Hyposensitive and hypersensitive ecosystems, classified based on δ falling below or above the theoretical sensitivity, respectively, achieved similar net ecosystem productivity during droughts, employing different structural and functional strategies. However, hyposensitive ecosystems, risking their hydraulic system with a permissive water‐use strategy, were unable to recover from droughts as quickly as hypersensitive ones. Our findings highlight that processed‐based models predicting current functions and future performance of vegetation should account for the greater vulnerability of hyposensitive ecosystems to intensifying atmospheric and soil droughts. [ABSTRACT FROM AUTHOR]

This paper considers storm surge prediction using a neural network and considering multiple physical characteristics. Based on the factors that influence storm surges and historical observation data, we divide the input to the neural network into time features extracted from the prediction target and the auxiliary features that affect storm surges, and construct a feature gate within multiple recurrent neural network (RNN) cells. Historical hurricane data are used to assess the effectiveness and accuracy of the proposed model. Comparative analysis against a long short-term memory (LSTM) storm surge prediction model is conducted to verify the prediction performance of the proposed method. The comparison results show that the multi-RNN model is superior to the LSTM model in terms of four evaluation metrics and for all lead times. In particular, the multi-RNN model accurately predicts the maximum storm surge water level, and the prediction results are more consistent with the rise and fall of the water. A comparison of the storm surge forecasts using inputs from different time intervals under different evaluation indices confirms the generalization and stability of our proposed model. The experiments of storm surge prediction at six stations further confirm the wide applicability of the model. [ABSTRACT FROM AUTHOR]

Due to frequent human disturbance and the influence of crop growth and development, the migration of soil organic carbon (SOC) in sloping farmland is considerably different to those in other ecosystems. The impacts of maize over its entire growth period on the SOC loss in sloping farmland on purple soils under different erosion stages were investigated, in 2016. This was undertaken using rainfall simulation tests on 15° slopes with a rainfall intensity of 1.5 mm·min−1, in Sichuan Province, China. In this study, erosion development, fluctuating increasing trends in the surface runoff yield, interflow runoff yield, sediment yield, and dissolved organic carbon (DOC) migration flux were observed. Opposite trends were observed in the DOC mass concentration, total soil organic carbon (TOC) content of the sediment, the SOC content of sediment particle state, the DOC content of the sediment, and the SOC enrichment ratio. The DOC migration flux in the surface runoff and in interflow of the rill erosion stage was 1.39–2.84, 3.22–7.78 times significantly higher than that of the sheet erosion stage at each maize growth stage, respectively (p < 0.05). The average DOC mass concentration in the surface runoff, the total DOC content of the sediment, and the SOC enrichment ratio in the sheet erosion stage increased by 100.58–146.44%, 44.44–126.15%, 141.32–191.26%, respectively, compared with the rill erosion stage. Under the experimental conditions, we found that DOC loss mainly occurred at the seedling and mature stages for maize. We also found that maize growth could promote the production of soil interflow, leading to intense soil loss occurring at the subsurface. Compared with DOC mass concentrations in the surface runoff, there was an increase of 4.90–28.29% in the soil interflow, indicating that soil interflow plays a more important role in DOC loss. The growth of maize could impact formation of surface runoff and interflow, reducing the loss of SOC caused by soil erosion. This study helps to understand the carbon loss process in agricultural production in purple soil areas. [ABSTRACT FROM AUTHOR]

The intermediate di-[EMIM]Cl was synthesized from methyl imidazole and 1,4-dichlorobutane and then reacted with cobalt chloride at 105 °C to prepare a bi-[EMIM]CoCl3 ionic liquid catalyst. The di-[EMIM]CoCl3 catalyst coupled with oxone to remove sulfur-containing organics in octane. The di-[EMIM]CoCl3 catalyst was characterized by HNMR, FTIR, TG, and SEM–EDS. The dibenzothiophene (DBT) was dissolved in octane to prepare a model oil with an initial sulfur content of 500 ppm. Six grams of the model oil was added. The results showed that the optimal dosages of di-[EMIM]CoCl3, oxone (20 wt%), and [BMIM]BF4 extractant were 1 g, 2 g, and 2 g, respectively. At the optimum temperature of 45 °C, 100% of sulfur was removed after 40 min. After di-[EMIM]CoCl3 was recycled five times, the sulfur removal percentage remained above 91%. The sulfur removal percentages for different sulfur-containing organics followed the order of dibenzothiophene (DBT) > benzothiophene (BT) > 4,6-dimethyldibenzothiophene (4,6-DMDBT). The oxidation product was determined to be DBTO2 by GC–MS analysis, and the oxidation mechanism was discussed. The active oxygen atoms of oxone oxidized DBT to form DBTO, and then persulfate oxidized DBTO to DBTO2 via an identical oxidation mechanism. [ABSTRACT FROM AUTHOR]

Objectives: To evaluate the discriminatory capability of spectral CT-based radiomics to distinguish benign from malignant solitary pulmonary solid nodules (SPSNs). Materials and methods: A retrospective study was performed including 242 patients with SPSNs who underwent contrast-enhanced dual-layer Spectral Detector CT (SDCT) examination within one month before surgery in our hospital, which were randomly divided into training and testing datasets with a ratio of 7:3. Regions of interest (ROIs) based on 40-65 keV images of arterial phase (AP), venous phases (VP), and 120kVp of SDCT were delineated, and radiomics features were extracted. Then the optimal radiomics-based score in identifying SPSNs was calculated and selected for building radiomics-based model. The conventional model was developed based on significant clinical characteristics and spectral quantitative parameters, subsequently, the integrated model combining radiomics-based model and conventional model was established. The performance of three models was evaluated with discrimination, calibration, and clinical application. Results: The 65 keV radiomics-based scores of AP and VP had the optimal performance in distinguishing benign from malignant SPSNs (AUC65keV-AP = 0.92, AUC65keV-VP = 0.88). The diagnostic efficiency of radiomics-based model (AUC = 0.96) based on 65 keV images of AP and VP outperformed conventional model (AUC = 0.86) in the identification of SPSNs, and that of integrated model (AUC = 0.97) was slightly further improved. Evaluation of three models showed the potential for generalizability. Conclusions: Among the 40-65 keV radiomics-based scores based on SDCT, 65 keV radiomics-based score had the optimal performance in distinguishing benign from malignant SPSNs. The integrated model combining radiomics-based model based on 65 keV images of AP and VP with Zeff-AP was significantly superior to conventional model in the discrimination of SPSNs. [ABSTRACT FROM AUTHOR]

Lewis acidic ionic liquid-loaded Fe3O4@SiO2 magnetic catalytic microspheres were prepared. When coupled with persulfate, the microspheres catalytically oxidized and removed dibenzothiophene (DBT) from octane. The microspheres used for catalytic desulfurization were characterized and analyzed by SEM, TEM, FT-IR, VSM, and TG-DTA. The results indicated that the prepared catalytic desulfurization microspheres had a uniform particle size distribution, an overall diameter of 200 nm, and a core diameter of Fe3O4 of about 180 nm. The saturation magnetization of the catalytic desulfurization microspheres was 39.8 emug−1, which allowed them to be rapidly magnetically separated. The optimal experimental conditions were 1.5 g catalyst, 1.2 g PMS (20%) oxidant, 30 °C, an initial sulfur content of 500 ppm, 6.0 g octane, and 3.0 g acetonitrile. Under these conditions, the desulfurization rate reached 98.5% after 25 min, which decreased by about 10% after five recycles. GC-MS analysis indicated the oxidation product was dibenzothiophene sulfone (DBTO2). In the desulfurization mechanism, DBT was first extracted by acetonitrile. Then, in the acetonitrile phase, Co2+ catalyzed hydrogen persulfate to form active oxygen atoms that oxidized DBT and eventually formed DBTO2. [ABSTRACT FROM AUTHOR]

Polarization plays a key role in fundamental science, and the improvement in miniaturization and practicability of polarization conversion devices could provide more degrees of freedom for light–matter interactions. Metasurfaces that can manipulate arbitrary polarization states at subwavelength scales can significantly reduce the complexity of meta‐optical systems. Here, a general design of an all‐silicon diatomic metasurface operating in the terahertz band that can generate a tailorable linear polarization state by the superposition of two meta‐atoms with individual geometric parameters is experimentally demonstrated. By periodically arranging polarization‐converting and polarization‐maintaining meta‐atoms, the existence of interference effects enables the proposed diatomic meta‐platform to act as an optimal linear polarization operator. The gradient arrangement of the meta‐molecules under the profile of the propagation phase is deduced by using the advanced Jones matrix, so that the polarization filtering and wavefront manipulation can be realized simultaneously, including the generation of tightly converged vortex and bifocal focusing beams. This demonstration of generating tailorable linear polarization states located on the Poincaré sphere directly from arbitrarily polarized waves can significantly facilitate the development of functional polarization meta‐devices. [ABSTRACT FROM AUTHOR]

Generating oxygen vacancies is an effective way to improve photoelectrochemical (PEC) water splitting performance of semiconductor materials owing to the formation of shallow donor level and the supply of additional electron donor. Herein, oxygen vacancies were introduced into In 2 O 3 nanorods by a hydrothermal reduction method using NaBH 4 solution as the reductant, and the effects of hydrothermal reduction time on the oxygen vacancy concentration, optoelectronic property and PEC water splitting activity over In 2 O 3 nanorods were systematically investigated. The results of LSV, EIS and MS showed that the reduced samples displayed superior PEC performance and In 2 O 3-x -1 exhibited a highest photocurrent density of 0.97 mA cm−2 at 1.23 V vs. RHE under the irradiation of visible light, which was roughly 4 times of bare In 2 O 3. The remarkable performance of In 2 O 3-x -1 is mainly ascribed to the introduction of oxygen vacancies, which leads to better light absorption capacity, higher carrier concentration, and increased electron transport efficiency at the electrode/electrolyte interface. • Oxygen vacancies were introduced into In 2 O 3 nanorods by a wet chemical reduction. • The concentration of oxygen vacancies have significantly influence on the PEC performance. • In 2 O 3-x -1 showed the highest PEC performance due to a suitable oxygen vacancy content. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a new functional for the conformal spectrum of the conformal Laplacian on a closed manifold M of dimension at least 3. For this new functional we provide a Korevaar type result. The main body of the paper deals with the case of the sphere but a section is devoted to more general closed manifolds. The functional introduced here has been investigated in the recent work [S. Perez-Ayala, Extremal eigenvalues of the conformal Laplacian under Sire–Xu normalization, preprint (2019); arXiv:2011.06018]. [ABSTRACT FROM AUTHOR]

NK-lysin (NKL) is a family of antimicrobial proteins with an important role in innate and adaptive immunity. In this study, a non-canonical NK-lysin (NKLnc) was identified in the Japanese flounder (Paralichthys olivaceus), which shares low sequence identities (15.8–20.6%) with previously reported fish NKLs and was phylogenetically separated from the canonical NKLs in teleost. NKLnc expression was upregulated in flounder tissues during bacterial infection, and interference with NKLnc expression impaired the ability of flounder cells to eliminate invading bacteria. When expressed in Escherichia coli, NKLnc was detrimental to the host cells. P35, a peptide derived from the saposin B domain (SapB) of NKLnc, bound major bacterial surface molecules and killed both Gram-negative and Gram-positive bacteria by inflicting damage to bacterial cell structure and genomic DNA. The bactericidal activity, but not the bacteria-binding capacity, of P35 required the structural integrity of the alpha 2/3 helices in SapB. Furthermore, P35 induced the migration of flounder peripheral blood leukocytes, inhibited bacterial dissemination in fish tissues, and facilitated fish survival after bacterial challenge. Together our study reveals that NKLnc plays an important part in flounder immune defense, and that NKLnc peptide exerts an antimicrobial effect via multiple mechanisms by targeting both bacteria and fish cells. [ABSTRACT FROM AUTHOR]

Background:  Posterior or anterior sagittal anorectoplasty (ASARP) is the mainstream for correcting rectovestibular fistula (RVF). However, the intermediate RVF has the potential risk of wound complications when applying ASARP due to its high rectal pouch, long fistula tract, and difficulty separating the rectum and vagina. We developed laparoscopic-assisted anorectoplasty (LAARP) for surgical correction of RVF, which has acceptable preliminary outcomes. The purpose of this study is to evaluate the safety and efficacy of LAARP in comparison with ASARP for patients with RVF.Materials and Methods:  Twenty-five patients with RVF who underwent LAARP between October 2017 and December 2020 were retrospectively reviewed. The outcomes were compared with 43 patients who underwent ASARP between April 2015 and August 2018. The age, weight at operation, sacral ratio, operative time, and postoperative hospital stay were evaluated. The results were assessed for complications, perineum appearance, and bowel function.Results:  The two groups were comparable in terms of demographics. The median operative time of the LAARP group was significantly longer than that of the ASARP group (113 vs. 95 minutes; p = 0.015). The mean length of the resected rectum in the LAARP group was also longer than that in the ASARP group (6.75 ± 5.07 vs. 3.31 ± 3.06 cm; p = 0.001). Compared with the LAARP group, complications in the ASARP group were more frequent (4.0 vs. 27.9%, p = 0.036). No intraoperative or postoperative wound-related complications occurred in the LAARP group. However, in the ASARP group, one patient had an intraoperative vaginal injury and four had postoperative anastomosis-related complications. The incidence of redo operation in the ASARP group was significantly higher than that in the LAARP group (p = 0.000). Cosmetic satisfaction was higher in the LAARP group (96.0 vs. 76.7%; p < 0.05). In terms of voluntary bowel movement, soiling, and constipation, the LAARP group had similar results compare with the ASARP group.Conclusion:  The LAARP technique has shown several unique strengths in treating intermediate type RVF, including lower risks of complications, and minimal muscular injury, with a comparable bowel function. [ABSTRACT FROM AUTHOR]

The magnetized Jeffery–Hamel flow due to a point sink or source in convergent and divergent channels is studied. Such viscous flow plays an important role in understanding rivers and canals, human anatomy, and the connection between capillaries and arteries. The simplified governing equation ruled by the Reynolds number, the Hartmann number and the divergent–convergent angle with appropriate boundary conditions is solved by the newly proposed Coiflet wavelet-homotopy analysis method (CWHAM). A highly accurate solution is obtained, whose accuracy is rigidly checked. Our proposed method combines the advantages of the homotopy analysis method for strong nonlinearity and the Coiflet wavelet method for excellent local expression capability so that it holds higher computational efficiency, larger applicable range of physical parameters, and better nonlinear processing capability. Different from the previously published research on the CWHAM, we focus on establishing the solution process of nonlinear flow problems with extreme physical parameters, which is not considered before. [ABSTRACT FROM AUTHOR]

In order to solve the problem of low electrical conductivity of carbon nanofiber membranes, a novel triple crosslinking strategy, including pre-rolling, solvent and chemical imidization crosslinking, was proposed to prepare carbon nanofiber membranes with a chemical crosslinking structure (CNMs-CC) derived from electrospinning polyimide nanofiber membranes. The physical-chemical characteristics of CNMs-CC as freestanding anodes for lithium-ion batteries were investigated in detail, along with carbon nanofiber membranes without a crosslinking structure (CNMs) and carbon nanofiber membranes with a physical crosslinking structure (CNMs-PC) as references. Further investigation demonstrates that CNMs-CC exhibits excellent rate performance and long cycle stability, compared with CNMs and CNMs-PC. At 50 mA g−1, CNMs-CC delivers a reversible specific capacity of 495 mAh g−1. In particular, the specific capacity of CNMs-CC is still as high as 290.87 mAh g−1 and maintains 201.38 mAh g−1 after 1000 cycles at a high current density of 1 A g−1. The excellent electrochemical performance of the CNMs-CC is attributed to the unique crosslinking structure derived from the novel triple crosslinking strategy, which imparts fast electron transfer and ion diffusion kinetics, as well as a stable structure that withstands repeated impacts of ions during charging and discharging process. Therefore, CNMs-CC shows great potential to be the freestanding electrodes applied in the field of flexible lithium-ion batteries and supercapacitors owing to the optimized structure strategy and improved properties. [ABSTRACT FROM AUTHOR]

Global climate change profoundly influences the patterns of vegetation growth. However, the disparities in vegetation responses induced by regional climate characteristics are generally weakened in large-scale studies. Meanwhile, distinct climatic drivers of vegetation growth result in the different reactions of different vegetation types to climate variability. Hence, it is an extraordinary challenge to detect and attribute vegetation growth changes. In this study, the spatiotemporal distribution and dynamic characteristics of climate change effects on vegetation growth from 2000 to 2020 were investigated by the normalized difference vegetation index (NDVI) dataset during the growing season (April–October). Meanwhile, we further detected the climate-dominated factor between different vegetation types (i.e., forest, shrub, and grass) within the Chaohe watershed located in temperate northern China. The results revealed a continuous greening trend over the entire study period, despite slowing down since 2007 (p < 0.05). Growing-season precipitation (P) was identified as the dominant climatic factor of the greening trend (p < 0.05), and approximately 34.83% of the vegetated area exhibited a significant response to increasing P. However, continued warming-induced intensive evaporation demand caused the vegetation growth to slow down. Hereinto, the areas with a significantly positive response of forest growth to temperature decreased from 24.38% to 18.06% (p < 0.05). In addition, solar radiation (SW) corresponds to the vegetation trend in the watershed (p < 0.05), and the significantly positive SW-influenced areas increased from 9.24% and 2.64% to 11.78% and 3.37% in forests and shrubland, respectively (p < 0.05). Our findings highlight the nonlinearity of long-term vegetation growth trends with climate variation and the cause of this divergence, which provide vital insights into forecasting vegetation responses to future climate change. [ABSTRACT FROM AUTHOR]

In this paper, the space–time fractional Whitham–Broer–Kaup equations are investigated. By means of new fractional scaling transformations, the fractional nonlinear system of different time and space orders is transformed to the integer one. The multiple solitary solutions and periodic solutions are obtained, respectively. All those solutions are given exactly by introducing new scaling transformations, which makes our study unique and different from most existing studies. It is expected that exact solutions for nonlinear wave system of fractional order can be handled in the similar way. [ABSTRACT FROM AUTHOR]

Oxidative transformation of indoles is a promising approach for synthesizing valuable nitrogen‐containing compounds; however, conventional methods use toxic oxidants and additives. Herein, we report for the first time the catalytic oxidative trimerization and the oxygenative cleavage (Witkop oxidation) of indoles using molecular oxygen under additive‐free conditions accomplished by a copper nitride nanocube (Cu3N NC) catalyst. The Cu3N NC catalyst exhibits excellent activity to produce various indolin‐3‐ones and 2‐ketoanilides with high functional group tolerance. Owing to this excellent catalytic performance, the Cu3N NC catalyst is distinguished from the conventional copper catalysts, demonstrating the high catalytic potential of metal nitride in organic synthesis. [ABSTRACT FROM AUTHOR]

Offshore floating wind turbine (FOWT) represents one of the most promising frontiers for the development of wind energy. However, its safety and the dynamic response characteristics are compromised by the occurrence of typhoons, which, unlike monsoons, exhibit characteristics like extreme wind speeds, anomalous turbulence, and wind direction shifts during their moving. In this paper, the severe typhoon Hagupit is taken as the impacting storm, with modeling characteristics, including the typhoon profile, turbulent wind spectra, and wave factors, based on the actual measured data. The typhoon wind field model is further developed within the integrated analysis software OpenFAST, allowing the derivation of the response and load characteristics of a FOWT during various stages of a typhoon passage, also analyzing the impact of yaw error. The results indicate that the Front Eye-wall Stage (FEWS) exerts the greatest influence on platform motion and load response, with heightened risk of damage to turbine blades and mooring lines. The resulting response spectrum reflects a coupling characteristic of wave frequency, the first or second structural natural frequency, and typhoon frequency. The effect of yaw error on the safety of the wind turbine system is significant. Within a certain range of yaw error angles, not only does such misalignment increase the platform's response, but also amplifies the load borne by the upper structure and lower mooring system of the platform, while this effect weakens once the yaw error surpasses a certain threshold. [ABSTRACT FROM AUTHOR]

• SWA dominated the changes in water cost of poplar plantation under dry-heat events. • Dry-heat events caused GPP-ET decoupling in high SWA conditions. • Water use strategy was sensitive to the interaction between SWA and VPD. • Land-atmosphere feedback increases the potential risk of poplar plantation. Terrestrial ecosystems are increasingly threatened by more frequent and intensified compound dry-heat conditions, exacerbated by land-atmosphere feedback under changing climate. The water-use strategy (WUS), which determines the trade-off between carbon assimilation and water consumption, is a key metric for exploring how terrestrial ecosystems respond to environmental stresses. However, how water availability influences their water-use strategies in response to dry-heat conditions is still not well understood. Here, we compared the response of WUS to dry-heat conditions in two poplar plantations with different soil water availability (SWA) in North China, represented by the Priestley-Taylor coefficient (α), and applied an interpretable machine learning algorithm to disentangle the independent and interactive effects of the main biophysical factors on WUS under dry, heat, and their compound conditions. We found that the plantation with higher SWA (high-SWA plantation) showed a more permissive WUS (i.e., increased marginal water cost of carbon gain) under dry-heat conditions than the low-SWA one due to the decoupling of evapotranspiration and gross primary productivity, and less sensitive to α and vapor pressure deficit (VPD) variations. During dry conditions, heat made the WUS more permissive in the high-SWA plantation and more conservative in the low-SWA one. Moreover, the α-VPD interaction was progressively strengthened as intensified dry-heat stresses until reaching a threshold (α = 0.7, VPD = 2.2 kPa in the low-SWA plantation and α = 0.85, VPD = 2.6 kPa in the high-SWA plantation). Thus, our study emphasizes that land-atmosphere feedback exacerbates the vulnerability of poplar plantations to dry-heat conditions. [ABSTRACT FROM AUTHOR]

Organic and inorganic composite contamination is a critical barrier to the efficiency of membrane distillation (MD) processes. This study investigated the impacts of humic acid (HA), sodium chloride (NaCl), and calcium chloride (CaCl2) as representative organic and inorganic contaminants on the performance of MD. The findings indicated that Ca2+ caused more severe membrane clogging and irreversible fouling compared to Na+. The XDLVO theory and molecular dynamics simulations were employed to understand the HA–cation interaction. The results demonstrated that Ca2+ instigated a bridging effect, leading to lower energy barriers and higher binding energies, thereby enhancing HA adsorption onto the membrane. Conversely, Na+ facilitated the electrostatic shielding effect, resulting in reduced particle size and promoting agglomeration around HA molecules. In the HA–Na+–Ca2+ system, a clear interplay and competition between Na+ and Ca2+ were observed. The aggregation effect of Na+ hindered the bridging between HA and Ca2+, leading to the precipitation of NaCl. Overall, this research provides valuable insights into the fouling mechanisms within MD caused by organic-inorganic composite contaminants. • Na+ reduced HA size through electrostatic shielding, while Ca2+ induced HA aggregation via bridging effect. • Ca2+ made membrane fouling more sever and irreversible than Na+. • Molecular dynamics simulations showed that Na+ accumulated around HA molecules, hindering HA–Ca2+ bridging in the HA–Na+–Ca2+ system. • XDLVO theory revealed that cations reduced the energy barrier between contaminants and membrane. [ABSTRACT FROM AUTHOR]