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Battery Energy Storage Systems (BESSs) play a vital role in modern power grids by optimally dispatching energy according to the price signal. This paper proposes a reinforcement learning-based model that optimizes BESS scheduling with the proposed Q-learning algorithm combined with an epsilon-greedy strategy. The proposed epsilon-greedy strategy-based Q-learning algorithm can efficiently manage energy dispatching under uncertain price signals and multi-day operations without retraining. Simulations are conducted under different scenarios, considering electricity price fluctuations and battery aging conditions. Results show that the proposed algorithm demonstrates enhanced economic returns and adaptability compared to traditional methods, providing a practical solution for intelligent BESS scheduling that supports grid stability and the efficient use of renewable energy. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar (SAR) has been extensively applied in remote sensing applications. Nevertheless, it is a challenge to process and interpret SAR images. The key to interpreting SAR images lies in transforming them into other forms of remote sensing images to extract valuable hidden remote sensing information. Currently, the conversion of SAR images to optical images produces low-quality results and incomplete spectral information. To address these problems, an end-to-end network model, S2MS-GAN, is proposed for converting SAR images into multispectral images. In this process, to tackle the issues of noise and image generation quality, a TV-BM3D module is introduced into the generator model. Through TV regularization, block-matching, and 3D filtering, these two modules can preserve the edges and reduce the speckle noise in SAR images. In addition, spectral attention is added to improve the spectral features of the generated MS images. Furthermore, we construct a very high-resolution SAR-to-MS image dataset, S2MS-HR, with a spatial resolution of 0.3 m, which is currently the most comprehensive dataset available for high-resolution SAR-to-MS image interpretation. Finally, a series of experiments are conducted on the relevant dataset. Both quantitative and qualitative evaluations demonstrate that our method outperforms several state-of-the-art models in translation performance. The solution effectively facilitates high-quality transitions of SAR images across different types. [ABSTRACT FROM AUTHOR]

Seismic stacking is a core technique in seismic data processing, aimed at enhancing the signal-to-noise ratio (SNR) of data by utilizing seismic data acquisition with multifold geometry. Traditional stacking methods always have certain limitations, such as the reliance on the accuracy of velocity analysis for dip moveout (DMO) in common midpoint (CMP) stacking. The seislet transform, a compression technique tailored to nonstationary seismic data, can compress and stack along the prediction direction of seismic data, which provides a new technical idea for high-fidelity seismic imaging based on the effectiveness of the compression. This paper introduces a high-order OC-seislet stacking method for low-SNR seismic data, capable of achieving the high-fidelity stacking of reflection and diffraction waves simultaneously. With the multi-scale analysis advantages of the seislet transform, this method addresses the dependency of DMO stacking on velocity analysis accuracy. In the frequency–wavenumber–scale domain, the correction compensation of the high-order CDF 9/7 basis function is used to obtain the compression coefficients of the high-order OC-seislet transform. This approach simultaneously stacks frequency–wavenumber information of reflection and diffraction waves with high fidelity while implementing DMO processing. After normalizing the weighting coefficients and applying soft thresholding for denoising, the final result is transformed back to the original time–space domain, yielding high-fidelity stacking sections. The results of applying this method to both synthetic and field data show that, compared with conventional DMO stacking methods, the high-order OC-seislet stacking technique reasonably represents dipping layers and fault amplitudes, and it can achieve a balance of a high SNR and high fidelity in stacked profiles. [ABSTRACT FROM AUTHOR]

Featured Application: The system can be applied in underground coal mines and effectively improve the problems of output harmonics, poor waveform quality, and the large space occupied due to the separate placement of the power frequency transformer and frequency converter. Mining frequency converters are the primary means for achieving variable frequency speed regulation of electromechanical equipment in coal mines, offering energy-saving benefits for coal mining enterprises. The common power supply method involves converting high voltage to low voltage using power frequency transformers before supplying equipment. However, this integration of power frequency transformers with supply devices occupies significant space, making it unsuitable for confined underground environments. Additionally, they suffer from poor output waveform quality and high harmonic content. To tackle these challenges, this paper presents a three-stage topology for high-frequency isolated frequency conversion and speed regulation, utilizing three-phase uncontrolled rectification, a single active isolated DC/DC converter, and an NPC three-level inverter. The control strategies for each stage are discussed in detail. Simulations and experimental results confirm the validity and feasibility of the proposed design, demonstrating enhanced stability and dynamic performance of the three-stage high-frequency isolated frequency converter. [ABSTRACT FROM AUTHOR]

With the rapid development of multimedia services and smart devices, 360-degree video has enhanced the user viewing experience, ushering in a new era of immersive human–computer interaction. These technologies are increasingly integrating everyday life, including gaming, education, and healthcare. However, the uneven spatiotemporal distribution of wireless resources presents significant challenges for the transmission of ultra-high-definition 360-degree video streaming. To address this issue, this paper proposes a multi-layer non-uniform coding-based adaptive transmission method for 360° video (MNCATM). This method optimizes video caching and transmission by dividing non-uniform tiles and leveraging users' dynamic field of view (FoV) information and the multi-bitrate characteristics of video content. First, the video transmission process is formalized and modeled, and an adaptive transmission optimization framework for a non-uniform video is proposed. Based on this, the optimization problem required by the paper is summarized, and an algorithm is proposed to solve the problem. Simulation experiments demonstrate that the proposed method, MNCATM, outperforms existing transmission schemes in terms of bandwidth utilization and user quality of experience (QoE). MNCATM can effectively utilize network bandwidth, reduce latency, improve transmission efficiency, and maximize user experience quality. [ABSTRACT FROM AUTHOR]

Quality control and grading of Korla fragrant pears significantly impact their commercial value. Rapid and non-destructive detection of soluble solids content (SSC) and firmness is crucial to improving this. This study proposes a method combining near-infrared spectroscopy (NIRS) with machine learning for the rapid, non-destructive detection of SSC and firmness in Korla pears. By analyzing absorbance in the 900–1800 nm range, six preprocessing methods—Savitzky–Golay derivative (SGD), standard normal variate (SNV), multiplicative scatter correction (MSC), Savitzky–Golay smoothing (SGS), vector normalization (VN), and min-max normalization (MMN)—were applied to the raw spectral data. uninformative variable elimination (UVE) and successive projections algorithm (SPA) were then used to extract effective wavelengths. Partial least squares regression (PLSR) models were developed for SSC and firmness based on the extracted data. The results showed that all preprocessing and wavelength-extraction methods improved model accuracy. The optimal SSC prediction model was MSC-SPA-PLSR (R = 0.93, RMSE = 0.195), and the best hardness prediction model was MSC-UVE-PLSR (R = 0.83, RMSE = 0.249). This research aids in establishing a non-destructive testing system, offering producers a rapid and accurate quality assessment tool, and provides the food industry with better production control measures to enhance standardization and market competitiveness of Korla pears. [ABSTRACT FROM AUTHOR]

Developing a size effect model that can encompass the surface effect and the fracture process zone (FPZ) is still challenging. Here, a combined size effect model (CSE model) is formulated by integrating the surface effect size model and the size effect model of fracture mechanics (SEFM model) associated with the FPZ for rocks under compression. Parametric studies indicate that an increased volume fraction of the interior zone as the sample size increases is responsible for the anomalous size effect (ASE). The normal size effect (NSE) is a result of the decrease in the surface layer's volume fraction and the interior zone's nominal strength as the sample size increases. Moreover, the mixed type ASE–NSE is caused by the competition among the reduced surface volume fraction, the weakened interior zone strength, and the increased interior zone volume fraction as the sample size increases. A validation study demonstrates that the CSE model accurately predicts the ASE, the NSE, and the mixed type ASE–NSE. It was observed that the determination coefficient R2 of the CSE model is greater than that of the SEFM model for the NSE, equivalent to that of the size effect model of surface effects for the ASE and close to that of the improved USEL (IUSEL) for the mixed type ASE–NSE. [ABSTRACT FROM AUTHOR]

Integrating multi-modal data in natural language processing has opened new pathways for the enhancement of dialogue summarization. However, existing models often struggle to effectively synthesize textual, auditory, and visual inputs. This paper introduces a Modality-Enhanced Multi-Channel Attention Network (MEMA), a novel approach designed to optimize the integration and interaction of diverse modalities for dialogue summarization. MEMA leverages symmetrical embedding strategies to balance the integrity and distinctiveness of each modality, ensuring a harmonious interaction within the unified architecture. By maintaining symmetry in the processing flow, MEMA enhances the contextual richness and coherence of the generated summaries. Our model demonstrates superior performance on the Multi-modal Dialogue Summarization (MDS) dataset, particularly in generating contextually enriched abstract summaries. The results underscore MEMA's potential to transform dialogue summarization by providing a more symmetrical and integrated understanding of multi-modal interactions, bridging the gap in multi-modal data processing, and setting a new standard for future summarization tasks. [ABSTRACT FROM AUTHOR]

The integration of PbS quantum dots (QDs) with graphene represents a notable advancement in enhancing the optoelectronic properties of quantum-dot-based devices. This study investigated the electrical transport properties of PbS quantum dot (QD)/graphene heterostructures, leveraging the high carrier mobility of graphene. We fabricated QD/graphene/SiO2/Si heterostructures by synthesizing p-type monolayer graphene via chemical vapor deposition and spin-coating PbS QDs on the surface. Then, we used a low-temperature electrical transport measurement system to study the electrical transport properties of the heterostructure under different temperature, gate voltage, and light conditions and compared them with bare graphene samples. The results indicated that the QD/graphene samples exhibited higher resistance than graphene alone, with both resistances slightly increasing with temperature. The QD/graphene samples exhibited significant hole doping, with conductivity increasing from 0.0002 Ω−1 to 0.0007 Ω−1 under gate voltage modulation. As the temperature increased from 5 K to 300 K, hole mobility decreased from 1200 cm2V−1s−1 to 400 cm2V−1s−1 and electron mobility decreased from 800 cm2V−1s−1 to 200 cm2V−1s−1. Infrared illumination reduced resistance, thereby enhancing conductivity, with a resistance change of about 0.4%/mW at a gate voltage of 125 V, demonstrating the potential of these heterostructures for infrared photodetector applications. These findings offer significant insights into the charge transport mechanisms in low-dimensional materials, paving the way for high-performance optoelectronic devices. [ABSTRACT FROM AUTHOR]

Numerical weather prediction of sea surface temperature (SST) is crucial for regional operational forecasts. Deep learning offers an alternative approach to traditional numerical general circulation models for numerical weather prediction. In our previous work, we developed a sophisticated deep learning model known as the Attention-based Context Fusion Network (ACFN). This model integrates an attention mechanism with a convolutional neural network framework. In this study, we applied the ACFN model to the South China Sea to evaluate its performance in predicting SST. The results indicate that for a 1-day lead time, the ACFN model achieves a Mean Absolute Error of 0.215 °C and a coefficient of determination ( R 2 ) of 0.972. In addition, in situ buoy data were utilized to validate the forecast results. The Mean Absolute Error for forecasts using these data increased to 0.500 °C for a 1-day lead time, with a corresponding R 2 of 0.590. Comparative analyses show that the ACFN model surpasses traditional models such as ConvLSTM and PredRNN in terms of accuracy and reliability. [ABSTRACT FROM AUTHOR]

This paper proposes a three-step joint rate optimization method for intelligent reflecting surface (IRS)-assisted coal mine wireless communication systems. Different from terrestrial IRS-assisted communication scenarios, in coal mines, IRSs can be installed flexibly on the tops of rectangular tunnels to address the issues of signals being blocked and interfered with by mining equipment. Therefore, it is necessary to optimize the IRS deployment position, the transmit power and IRS phase shifts to achieve the maximum effective achievable rate at user stations equipped with the proposed system. However, due to the complex channel models of coal mines, the optimization problem of IRS deployment position is non-convex. To solve this problem, two auxiliary variables along with logarithmic operations and Taylor approximation are introduced. On this basis, a three-step joint rate optimization involving the transmit power, IRS phase shifts and IRS deployment position is proposed to maximize the effective achievable rates at the user station. The simulation results show that compared with other rate optimization schemes, the effective achievable rates at the user station using the proposed joint rate optimization scheme can be improved by approximately 12.32 % to 54.17 % for different parameter configurations. It is also pointed out that the deployment position of the IRS can converge to the same optimal position independent of the initial deployment position. Moreover, we investigate the effects of the roughness of the tunnel walls in a coal mine on the effective achievable rates at the user station, and the simulation results indicate that the proposed three-step joint rate optimization scheme performs better in the coal mine scenario regardless of the roughness. [ABSTRACT FROM AUTHOR]

Cyanobacterial blooms have become a serious water pollution problem in many parts of the world, and the monitoring and study of the impacts of biotoxins on human health are of vital importance. In this study, the contents of microcystin-LR, 2-methylisoborneol, and geosmin were measured in water and sediment samples from Nanwan Reservoir, China, by means of bimonthly sampling between February and December 2023. The physicochemical and hydrochemical factors and phytoplankton dynamics in the reservoir were also investigated. The results showed that the overall mean concentration of microcystin-LR (0.729 μg/L) in summer approached the guiding standard (1 μg/L) set by the WHO for drinking water. Furthermore, the content of 2-methylisoborneol (143.5 ng/L) was 14 times higher than the national standard (10 ng/L). The results of laboratory cultures showed that lower light levels and medium temperatures were suitable for the growth of Microcystis and Planktothricoides but higher temperatures promoted the synthesis and release of microcystin-LR and 2-methylisoborneol. In addition, the results of co-cultures showed that the growth of Planktothricoides was inhibited by Microcystis. Our results suggest that cyanobacterial bloom and the presence of the metabolites 2-methylisoborneol and microcystin-LR can decrease the drinking water quality of Nanwan Reservoir. [ABSTRACT FROM AUTHOR]

In the foreseeable future, the development of petroleum-based polymeric materials may be limited, owing to the gradual consumption of disposable resources and the increasing emphasis on environmental protection policies. Therefore, it is necessary to focus on introducing environmentally friendly renewable biobased materials as a substitute for petroleum-based feed stocks in the preparation of different types of industrially important polymers. Plant polyphenols, a kind of natural aromatic biomolecule, exist widely in some plant species. Benefiting from their special macromolecular structure, high reactivity, and broad abundance, plant polyphenols are potent candidates to replace the dwindling aromatic monomers derived from petroleum-based resources in synthesizing high-quality polymeric materials. In this review, the most related and innovative methods for elaborating novel polymeric materials from plant polyphenols are addressed. After a brief historical overview, the classification, structural characteristics, and reactivity of plant polyphenols are summarized in detail. In addition, some interesting and innovative works concerning the chemical modifications and polymerization techniques of plant polyphenols are also discussed. Importantly, the main chemical pathways to create plant polyphenol-based organic/organic–inorganic polymeric materials as well as their properties and possible applications are systematically described. We believe that this review could offer helpful references for designing multifunctional polyphenolic materials. [ABSTRACT FROM AUTHOR]

Flexible grippers are a promising and pivotal technology for robotic grasping and manipulation tasks. Remarkably, magnetorheological (MR) materials, recognized as intelligent materials with exceptional performance, are extensively employed in flexible grippers. This review aims to provide an overview of flexible robotic grippers and highlight the application of MR materials within them, thereby fostering research and development in this field. This work begins by introducing various common types of flexible grippers, including shape memory alloys (SMAs), pneumatic flexible grippers, and dielectric elastomers, illustrating their distinctive characteristics and application domains. Additionally, it explores the development and prospects of magnetorheological materials, recognizing their significant contributions to the field. Subsequently, MR flexible grippers are categorized into three types: those with viscosity/stiffness variation capabilities, magnetic actuation systems, and adhesion mechanisms. Each category is comprehensively analyzed, specifying its unique features, advantages, and current cutting-edge applications. By undertaking an in-depth examination of diverse flexible robotic gripper types and the characteristics and application scenarios of MR materials, this paper offers a valuable reference for fellow researchers. As a result, it facilitates further advancements in this field and contributes to the provision of efficient gripping solutions for industrial automation. [ABSTRACT FROM AUTHOR]

Aiming at the issues of control delay and circuit parameter mismatch in three-phase modular multilevel converters (MMCs), this paper proposes a model assisted extended state observer-based deadbeat predictive current control (MAESO-based DPCC) strategy to regulate the AC-side current and internal circulating current. The model assisted ESO (MAESO) is employed to estimate the predicted values of the d- and q-axis components of the AC-side current, the internal circulating current, and system disturbance caused by the other certain and uncertain factors (including circuit parameter changes) of MMC at the time instant k + 1, and the required control input at the time instant k + 1 is then calculated based on the deadbeat control principle. The proposed control strategy not only maintains excellent steady-state performance and fast dynamic response characteristics similar to those of the traditional deadbeat predictive current control (DPCC) strategy but also has stronger robustness in the case of circuit parameter changes. The proposed control strategy was ultimately compared with the traditional DPCC strategy via experiments, and the experimental results verify the feasibility and effectiveness of the proposed control strategy. [ABSTRACT FROM AUTHOR]

Deciduous tooth agenesis is a severe craniofacial developmental defect because it affects masticatory function from infancy and may result in delayed growth and development. Here, we aimed to identify the crucial pathogenic genes and clinical features of patients with deciduous tooth agenesis. We recruited 84 patients with severe deciduous tooth agenesis. Whole-exome and Sanger sequencing were used to identify the causative variants. Phenotype–genotype correlation analysis was conducted. We identified 54 different variants in 8 genes in 84 patients, including EDA (73, 86.9%), PAX9 (2, 2.4%), LRP6 (2, 2.4%), MSX1 (2, 2.4%), BMP4 (1, 1.2%), WNT10A (1, 1.2%), PITX2 (1, 1.2%), and EDARADD (1, 1.2%). Variants in ectodysplasin A (EDA) accounted for 86.9% of patients with deciduous tooth agenesis. Patients with the EDA variants had an average of 15.4 missing deciduous teeth. Mandibular deciduous central incisors had the highest missing rate (100%), followed by maxillary deciduous lateral incisors (98.8%) and mandibular deciduous lateral incisors (97.7%). Our results indicated that EDA gene variants are major pathogenic factors for deciduous tooth agenesis, and EDA is specifically required for deciduous tooth development. The results provide guidance for clinical diagnosis and genetic counseling of deciduous tooth agenesis. [ABSTRACT FROM AUTHOR]

Hexachlorobenzene (HCB) is one of the most persistent environmental pollutants of global concern. Ni/Fe nanoparticles, with their small particle size, large surface area, and high reactivity, are a promising candidate for HCB degradation. In this work, we investigated the kinetics and products of the dechlorination of HCB by Ni/Fe nanoparticles and how the presence of heavy metal ions Cd(Ⅱ) and Zn(Ⅱ) influences the reaction. It is found that 400 μg/L HCB can be rapidly removed by 7.5 g/L Ni/Fe nanoparticles and the removal percentage reaches 99% in 48 h. The removal is facilitated by adsorption and sequential dechlorination of HCB, producing PCB, 1,2,3,4-TeCB, and 1,2,3-TCB as the main products, with 1,2,3,5/1,2,4,5-TeCB, 1,2,4-TCB, and 1,2-DCB as the minor products. The addition of heavy metal ions Cd(Ⅱ) and Zn(Ⅱ) does not significantly affect the removal rate of HCB but hinders the adsorption and degradation of the byproducts through competitive adsorption. Additionally, the concentration of both Cd(Ⅱ) and Zn(Ⅱ) decreases rapidly and achieves over 98% removal in 4 h. Our study reveals that Ni/Fe nanoparticles can remove HCB and heavy metals Cd(Ⅱ) and Zn(Ⅱ) concurrently, with the extent of HCB dechlorination reduced compared to that without heavy metal. These findings may inform the application of Ni/Fe nanoparticles in the treatment of water bodies and soil contaminated by both halogenated aromatics and heavy metal. [ABSTRACT FROM AUTHOR]

The increasing global nitrogen input poses a significant threat to aquatic environments, particularly in agricultural watersheds, where intensive human activities and insufficient water protection infrastructure exacerbate the risk of nitrogen pollution. Accurate identification of nitrogen pollution sources and the associated transformation processes is essential for protecting watershed ecosystems. In this study, a combination of hydrochemical analysis, correlation and principal component analysis, and stable nitrate isotopes (δ15N-NO3− and δ18O-NO3−) were employed to trace nitrogen transport pathways and source contributions in both surface water and groundwater within a typical agricultural watershed. The results revealed the presence of nitrogen pollution, including total nitrogen (TN), ammonia nitrogen (NH3-N), and nitrate nitrogen (NO3−-N), with significant spatial and seasonal variations in both surface water and groundwater. The spatiotemporal evolution of hydrochemical indicators and nitrate isotope compositions highlighted multiple potential sources of nitrogen, including soil input, agricultural input, and manure and sewage input. The results from stable isotope analysis in an R (SIAR) model indicated that ammonium fertilizers (7.1~78.4%) and manure and sewage (2.6~69.7%) were the primary sources of nitrates in surface water, while manure and sewage were the main sources in groundwater (67.9~73.7%). This research demonstrated that nitrification, seasonal variations, and human activities significantly impact nitrogen migration and transformation in agricultural watersheds. However, the issue of groundwater severely polluted by manure and sewage has received insufficient attention. To effectively control nitrogen pollution in agricultural watersheds, it is necessary to improve septic tanks and sewage networks, as well as implement scientific fertilization practices. [ABSTRACT FROM AUTHOR]

In order to realize effective laser communication on underwater mobile platforms, the active tracking and alignment technology of underwater laser communication (UWLC) is studied. Firstly, the servo control principle of the UWLC system is analyzed. Secondly, aiming at underwater disturbance, an adaptive fuzzy PID controller is designed to realize parameter self-tuning, thereby improving the system's anti-disturbance ability. The designed algorithm was simulated, and the simulation results show that the adaptive fuzzy PID algorithm has better anti-disturbance ability and tracking performance than the traditional PID. Finally, an experimental platform was built for dynamic tracking experiments, and the results show that the dynamic tracking accuracy of the designed control algorithm was improved by 30.29% compared with the traditional control algorithm, which provides a certain reference for the development of laser communication on underwater moving platforms. [ABSTRACT FROM AUTHOR]

Background: As large language models continue to expand in size and diversity, their substantial potential and the relevance of their applications are increasingly being acknowledged. The rapid advancement of these models also holds profound implications for the long-term design of stimulus-responsive materials used in drug delivery. Methods: The large model used Hugging Face's Transformers package with BigBird, Gemma, and GPT NeoX architectures. Pre-training used the PubChem dataset, and fine-tuning used QM7b. Chemist instruction training was based on Direct Preference Optimization. Drug Likeness, Synthetic Accessibility, and PageRank Scores were used to filter molecules. All computational chemistry simulations were performed using ORCA and Time-Dependent Density-Functional Theory. Results: To optimize large models for extensive dataset processing and comprehensive learning akin to a chemist's intuition, the integration of deeper chemical insights is imperative. Our study initially compared the performance of BigBird, Gemma, GPT NeoX, and others, specifically focusing on the design of photoresponsive drug delivery molecules. We gathered excitation energy data through computational chemistry tools and further investigated light-driven isomerization reactions as a critical mechanism in drug delivery. Additionally, we explored the effectiveness of incorporating human feedback into reinforcement learning to imbue large models with chemical intuition, enhancing their understanding of relationships involving -N=N- groups in the photoisomerization transitions of photoresponsive molecules. Conclusions: We implemented an efficient design process based on structural knowledge and data, driven by large language model technology, to obtain a candidate dataset of specific photoswitchable molecules. However, the lack of specialized domain datasets remains a challenge for maximizing model performance. [ABSTRACT FROM AUTHOR]

Oily sludge (OS) has long been regarded as a hazardous waste, and improper disposal may lead to serious environmental concerns and human health risks. Despite various methods having been proposed and applied to the treatment of OS, the oil occurrence states and properties in sludge are rarely characterized, which may directly link to the selection and effectiveness of treatment methods. Here, confocal laser scanning microscopy (CLSM), X-ray diffraction (XRD), gas chromatography (GC), and four components (SARA) analysis were utilized to characterize the changes in the oil occurrence states and compositions in OS samples before and after high-speed stirring (HSS) treatment. Our results show a substantial reduction in the oil concentration of OS after HSS treatment (from 32.98% to 1.65%), while SARA analysis reveals a similar oil composition before and after treatment, suggesting the broad applicability of HSS in removing oil and its insignificant selectivity towards various hydrocarbon components. This is further supported by the total petroleum hydrocarbon (TPH) analysis results, which show that the separated oil phase has a hydrocarbon composition similar to that of the original OS sample. The CLSM and fluorescence analysis suggest a homogeneous distribution of oil in the sludge, with relatively light components more concentrated in the pore systems between coarse mineral particles, whereas relatively heavy components tend to coexist with clay minerals. After HSS cleaning, both light and heavy components are removed to varying degrees, but light components are preferentially removed while heavy components tend to be retained in the sludge due to adsorption by clay minerals. This is consistent with TPH analysis, where a significant decrease in n-alkanes with lower carbon numbers (n-C14 to n-C20) was observed in the residual sample. Our findings demonstrate the dynamic response of oil occurrence states and compositions to the OS treatment process and highlight the importance of characterizing these fundamental properties prior to the selection of OS treatment methods. [ABSTRACT FROM AUTHOR]

MXenes nanosheets with high conductivity, hydrophilicity, and excellent reactive oxygen species (ROS) scavenging ability have shown promise in treating various degenerative diseases correlated with abnormal ROS accumulation. Herein, the therapeutic potential of Ti3C2Tx nanosheets, which is the most widely investigated MXene material, in delaying osteoarthritis (OA) progression is demonstrated. In vitro experiments indicate the strong ROS scavenging capacity of Ti3C2Tx nanosheets and their acceptable biocompatibility. Ti3C2Tx nanosheets effectively protect chondrocytes from cell death induced by oxidative stress. In addition, Ti3C2Tx nanosheets demonstrate a prominent anti-inflammatory effect and the ability to restore homeostasis between anabolic activities and catabolic activities in chondrocytes. Furthermore, RNA sequencing reveals the potential mechanism underlying the Ti3C2Tx nanosheet-mediated therapeutic effect. Finally, the in vivo curative effect of Ti3C2Tx nanosheets is verified using a rat OA model. Histological staining and immunohistochemical analyses indicate that Ti3C2Tx nanosheets effectively ameliorate OA progression. Conclusively, the in vitro and in vivo experiments suggest that Ti3C2Tx nanosheets could be a promising and effective option for OA treatment. [ABSTRACT FROM AUTHOR]

According to the requirements of the small double-pulse solid rocket motor, a compartmentalized isolation device has been designed. This device consists of a metal diaphragm and a support frame. An experimental study and numerical simulation were used to verify the bidirectional working process of the metal diaphragm during operation of the double-pulse motor. The results show that the pressure-bearing capacity of the metal diaphragm meets the requirements under the working conditions of pulse I without affecting pulse II, because the metal diaphragm can provide insulation and flame retardancy. The metal diaphragm can be cracked in the direction of the preset V-groove in a relatively short time under the working conditions of pulse II, which allows the gas to flow to the first pulse combustion chamber normally. This indicates that the metal diaphragm can meet the requirements of bidirectional working process in dual-pulse motors. [ABSTRACT FROM AUTHOR]

In recent years, the detrimental impact of traditional gas–liquid refrigerants on the environment has prompted a shift towards sustainable solid-state refrigeration technology. The elastocaloric effect, particularly in NiTi-based shape memory alloys (SMAs), presents a promising alternative due to its high coefficient of performance. However, conventional methods for alloy development are inefficient, often failing to meet the stringent requirements for practical applications. This study employed machine learning (ML) to accelerate the design of NiTi-based SMAs with an enhanced elastocaloric effect. Through active learning across four iterations, we identified nine novel NiTi-based SMAs exhibiting phase-transformation-induced entropy changes (ΔS) greater than 90 J/kg·K−1, surpassing most existing alloys. Our ML model demonstrates robust interpretability, revealing key relationships between material features and performance. This work not only establishes a more efficient pathway for alloy discovery but also aims to contribute significantly to the advancement of sustainable refrigeration technologies. [ABSTRACT FROM AUTHOR]

This study focuses on existing pruning equipment; cutting blades show cutting resistance and lead to high energy consumption. Using finite element (FEA) numerical simulation technology, the branch stress wave propagation mechanism during pruning was studied. The cutting performance of the bionic blade was evaluated with cutting energy consumption as the test index and the branch diameter and branch angle as the test factors, respectively. The test results showed that the blades imitating the mouthparts of the three-pecten bull and the beak of the woodpecker performed well in pruning, and the energy consumption during cutting was reduced by 18.2% and 16.3% compared to traditional blades, making these blades significantly better. These two blades also effectively reduced the cutting resistance and branch splitting by optimizing the edge angle design and increasing the slip-cutting action. In contrast, the imitation shark's tooth blade increased cutting energy consumption by 14.4% due to the large amount of cutting resistance in the cutting process when cutting larger-diameter branches, making it unsuitable for application in the pruning field. Therefore, the blades imitating the mouthparts of the three pectins and the beak of the woodpecker have significant advantages in reducing the cutting resistance and improving the pruning quality. These findings provide an important theoretical reference for the development of energy-efficient pruning equipment. [ABSTRACT FROM AUTHOR]

Evaluating the performance of multifunctionality is a necessary foundation for forest multifunctionality management. This study aims to comprehensively adopt multiple methods to construct a multifunctional evaluation system for natural spruce–fir forests and explore the impact of thinning intensity on the multifunctional management effect of spruce–fir. This article combines subjective and objective evaluation methods and selects three methods to construct an evaluation system: the Analytic Hierarchy Process, the combined entropy weight method, and the CRITIC method. The results showed that the consistency of the three evaluation methods is good, and according to the score based on the evaluation results, the multifunctional performance of the sample plot with a thinning intensity of 20% (average score of AHP method is 75.5; EWM is 91; CRITIC is 96.5) is significantly better than that of the sample plot with a thinning intensity of 40% (AHP is 65.3; EWM is 51; CRITIC is 48), both of which were significantly better than those of the untreated sample plot (AHP is 12.7; EWM is 18.7; CRITIC is 17.3). A coupling relationship model between multifunctional values and different functions, as well as a coupling relationship model between different functions and various indicators, were constructed based on the evaluation system. Finally, the forest stand with the highest multifunctional comprehensive value was selected as the reference for the target structure to construct the target structure system, which is convenient for actual management. This study found that there is a nurturing intensity (20%) that can best utilize the multiple functions of forests, which has practical significance for promoting forest multifunctionality in forest management. In addition, this study scientifically constructed and compared several evaluation systems for the multifunctional performance of forests, laying a certain foundation for forest multifunctional evaluation and future forest multifunctional management. [ABSTRACT FROM AUTHOR]

Biogas slurry with rich nutrients could be applied as fertilizer to improve nitrogen absorption and soil structure. Arbuscular mycorrhizal fungi (AMF) are beneficial soil microorganisms that establish mutualistic relationships with the plant roots. The purpose of this study was to study the effects of AMF and biogas slurry treatment on hybrid Pennisetum growth, soil chemical properties, and soil microorganisms. The results revealed that the biomass yield of hybrid Pennisetum was significantly increased after the application of biogas slurry, and it reached the peak values when the biogas slurry dosage was 900 t/hm2, which were 13,216.67 kg/hm2 and 13,733.33 kg/hm2 in AMF− and AMF+ treatment groups, respectively. Moreover, biogas slurry treatment has a significant promoting effect on other agronomic traits related to biomass yield. As for soil chemical indicators, the contents of total nitrogen, nitrate nitrogen, ammonia nitrogen, and available phosphorus in the soil increased with the increase in biogas slurry application, while the soil organic matter was decreased. The addition of arbuscular mycorrhizal fungi significantly increased the species diversity of soil fungi with no biogas slurry application. Furthermore, when biogas slurry was applied, it had no significant effect on soil microbial diversity and composition, no matter the AMF+ or AMF− treatment. The research results can provide a reference for the long-term utilization of biogas slurry and it also can be used in the actual production of hybrid Pennisetum. [ABSTRACT FROM AUTHOR]

Urban green space (UGS) is a crucial physical area that supports the functioning of urban ecosystems, and its changes affect urban ecological balance. In order to accurately analyze the dynamic processes and transfer targets of UGS during urbanization, this study proposes a new method of UGS assessment based on multi-temporal Landsat remote sensing data. This method is integrated with intensity analysis and landscape pattern indices so as to explore the spatio-temporal dynamics of the evolution process, landscape pattern, and driving forces of UGS from 2000 to 2022 in the resource-based city of Taiyuan in central China. The results of the case study show that rapid urbanization brought about a continuous reduction in UGS in the study area, but the trend of decreasing gradually slowed down; UGS patches have become more dispersed and isolated, bare land has been targeted for both gains and losses of UGS, and ecological restoration of bare land mitigated the rapid reduction of UGS. The results of this study not only confirm the applicability of this methodology for monitoring and assessing the evolution of UGS, but also reveal the identification of the targeting or avoidance of other categories during the conversion of UGS. Thus, the potential factors influencing changes in UGS can be analyzed to guide and safeguard sustainable development. [ABSTRACT FROM AUTHOR]

To investigate the mechanism of polypropylene fiber (PPF) on the rheological and thixotropic properties of cemented paste backfill containing mineral admixtures, the concept of water film thickness (WFT) was introduced. The packing density of the tailings-binder-PPF (TBP) system was measured in dry and wet conditions and the WFT was calculated accordingly. Additionally, the rheological parameters (yield stress, thixotropy, etc.) of the fiber-reinforced cemented paste backfill (FRCPB) were quantified. The results demonstrate that the wet packing test is a more appropriate method for measuring the packing density of the TBP system. The PPF length has a slight adverse effect on the packing density, and the packing density initially increases and then decreases with the PPF content. The reasons can be attributed to the filling effect and wedge effect of the fibers, respectively. In addition to the packing density, the thixotropy of FRCPB is also affected by the interaction of fibers. WFT is a crucial factor affecting the yield stress of FRCPB, with which it exhibits a strong linear relationship. The study identified that the optimum PPF content for enhancing the rheological and thixotropic properties of CPB is 0.2%, with a fiber length of 9 mm, balancing flowability and stability for practical application in mining backfill operations. These insights can guide the optimization of CPB mixtures, enhancing their flowability and stability during placement in mined-out spaces. By improving the fill quality and reducing the risk of blockage during backfill operations, the results offer practical benefits in increasing the safety and efficiency of underground mining activities. [ABSTRACT FROM AUTHOR]

Oxidative stress is a major factor that limits the development of the poultry industry. Ferulic acid (FA) has an antioxidant effect in birds, but the mechanism is not fully understood. In this study, we stimulated oxidative stress in 28-day-old female Linwu ducks by lipopolysaccharide (LPS) and fed them a diet supplemented with FA for 28 days. Results showed that FA alleviated LPS-induced growth performance regression, oxidative stress, and microbiota imbalance in ducks. An integrated metagenomics and metabolomics analysis revealed that s_Blautia_obeum, s_Faecalibacterium_prausnitzii, s_gemmiger_formicilis, and s_Ruminococcaceae_bacterium could be the biomarkers in the antioxidant effect of FA, which interacted with dihydro-3-coumaric acid, L-phenylalanine, and 13(S)-HODE, and regulated the phenylalanine metabolism and PPAR signaling pathway. This study revealed the mechanism of the antioxidant effect of FA, which provided evidence of applying FA as a new antioxidant in commercial duck production. [ABSTRACT FROM AUTHOR]

Moderate-Resolution Imaging Spectroradiometer (MODIS) Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectance (NBAR) products are being increasingly used for the quantitative remote sensing of vegetation. However, the assumption underlying the MODIS NBAR product's inversion model—that surface anisotropy remains unchanged over the 16-day retrieval period—may be unreliable, especially since the canopy structure of vegetation undergoes stark changes at the start of season (SOS) and the end of season (EOS). Therefore, to investigate the MODIS NBAR product's temporal effect on the quantitative remote sensing of crops at different stages of the growing seasons, this study selected typical phenological parameters, namely SOS, EOS, and the intervening stable growth of season (SGOS). The PROBA-V bioGEOphysical product Version 3 (GEOV3) Fractional Vegetation Cover (FVC) served as verification data, and the Pearson correlation coefficient (PCC) was used to compare and analyze the retrieval accuracy of FVC derived from the MODIS NBAR product and MODIS Surface Reflectance product. The Anisotropic Flat Index (AFX) was further employed to explore the influence of vegetation type and mixed pixel distribution characteristics on the BRDF shape under different stages of the growing seasons and different FVC; that was then combined with an NDVI spatial distribution map to assess the feasibility of using the reflectance of other characteristic directions besides NBAR for FVC correction. The results revealed the following: (1) Generally, at the SOSs and EOSs, the differences in PCCs before vs. after the NBAR correction mainly ranged from 0 to 0.1. This implies that the accuracy of FVC derived from MODIS NBAR is lower than that derived from MODIS Surface Reflectance. Conversely, during the SGOSs, the differences in PCCs before vs. after the NBAR correction ranged between –0.2 and 0, suggesting the accuracy of FVC derived from MODIS NBAR surpasses that derived from MODIS Surface Reflectance. (2) As vegetation phenology shifts, the ensuing differences in NDVI patterning and AFX can offer auxiliary information for enhanced vegetation classification and interpretation of mixed pixel distribution characteristics, which, when combined with NDVI at characteristic directional reflectance, could enable the accurate retrieval of FVC. Our results provide data support for the BRDF correction timescale effect of various stages of the growing seasons, highlighting the potential importance of considering how they differentially influence the temporal effect of NBAR corrections prior to monitoring vegetation when using the MODIS NBAR product. [ABSTRACT FROM AUTHOR]

As a prescription drug, retinoic acid is listed as a banned cosmetic additive in the EU and China regulations. Currently, spectrophotometric methods, including thin-layer chromatography (TLC), high-performance liquid chromatography (HPLC), and HPLC–MS/MS, are commonly used for the determination of retinoic acid. As these conventional methods require complex pretreatment and are time-consuming, chemical derivatization combined with paper spray ionization mass spectrometry was developed for the fast detection of retinoic acid in cosmetics. N,N-dimethylpiperazine iodide (DMPI) was utilized as a derivatization reagent. Carboxylic acid in retinoic acid was derivatized to carry a positive charge and was subjected to mass spectrometry analysis. Results showed that compared with non-derivatized compounds, the detection limit was increased by about 50 times. The linearity in the range of 0.005–1 μg·mL−1 was good. The limit of detection (LOD) was 0.0013 μg·mL−1, and the limit of quantification (LOQ) was 0.0043 μg·mL−1. The recoveries of spiked samples were in the range of 95–105%, and the RSDs were below 5%. Derivatization and paper spray ionization MS render a quick, sensitive, and accurate method for the detection of retinoic acid in a complex matrix. [ABSTRACT FROM AUTHOR]

The intense changes in glaciers in the southeastern Tibetan Plateau (SETP) have essential impacts on regional water resource management. In order to study the seasonal fluctuations of glaciers in this region and their relationship with climate change, we focus on the Yigong Zangbo River Basin in the SETP, extract the annual and seasonal variations of glaciers in the basin during 2018–2023, and analyze their spatio-temporal characteristics through the seasonal-trend decomposition using the LOESS (STL) method. Finally, combining the Extreme Gradient Boosting (XGBoost) model and the Shapley additive explanations (SHAP) model, we assess the comprehensive impact of meteorological factors such as temperature and snowfall on glacier changes. The results indicate that glaciers in the Yigong Zangbo River Basin experienced remarkable mass loss during 2018–2023, with an average annual melting rate of −0.83 ± 0.12 m w.e.∙yr−1. The glacier mass exhibits marked seasonal fluctuations, with increases in January–March (JFM) and April–June (AMJ) and noticeable melting in July–September (JAS) and October–December (OND). The changes over these four periods are 2.12 ± 0.04 m w.e., 0.93 ± 0.15 m w.e., −1.58 ± 0.19 m w.e., and −1.32 ± 0.17 m w.e., respectively. Temperature has been identified as the primary meteorological driver of glacier changes in the study area, surpassing the impact of snowfall. This study uses advanced altimetry data and meteorological data to monitor and analyze glacier changes, which provides valuable data for cryosphere research and also validates a set of replicable research methods, which provides support for future research in related fields. [ABSTRACT FROM AUTHOR]

In the field of Generalized Zero-Shot Learning (GZSL), the challenge lies in learning attribute-based information from seen classes and effectively conveying this knowledge to recognize both seen and unseen categories during the training process. This paper proposes an innovative approach to enhance the generalization ability and efficiency of GZSL models by integrating a Convolutional Block Attention Module (CBAM). The CBAM blends channel-wise and spatial-wise information to emphasize key features, thereby improving the model's discriminative and localization capabilities. Additionally, the method employs a ResNet101 backbone for systematic image feature extraction, enhanced contrastive learning, and a similarity map generator with attribute prototypes. This comprehensive framework aims to achieve robust visual–semantic embedding for classification tasks. The proposed method demonstrates significant improvements in performance metrics in benchmark datasets, showcasing its potential in advancing GZSL applications. [ABSTRACT FROM AUTHOR]

The thermal stability and aging kinetics of polyimides have garnered significant research attention. As a newly developed class of high thermal stability polyimide, the thermal aging characteristics and degradation kinetics of phenylene-capped polyimide prepolymer (PMR350) have not yet been reported. In this article, the thermo-oxidative stability of PMR350 was investigated systematically. The thermal degradation kinetics of PMR350 resin under different atmospheres were also analyzed using the Flynn–Wall–Ozawa method, the Kissinger–Akahira–Sunose method, and the Friedman method. Thermogravimetric analysis (TGA) results revealed that the 5% thermal decomposition temperature (Td5%) of PMR350 in a nitrogen atmosphere was 29 °C higher than that in air, and the maximum thermal degradation rate was 0.0025%/°C, which is only one-seventh of that observed in air. Isothermal oxidative aging results indicated that the weight loss rate of PMR350 and the time-dependence relationship follow a first-order exponential growth function. PMR350 resin thermal decomposition reaction under air atmosphere includes one stage, with a degradation activation energy of approximately 57 kJ/mol. The reaction model g(α) fits the F2 model, and the integral form is given by g(α) = 1/(1 − α). In contrast, the thermal decomposition reaction under a nitrogen atmosphere consists of two stages, with degradation activation energies of 240 kJ/mol and 200 kJ/mol, respectively. The reaction models g(α) correspond to the A2 and D3 models, with the integral forms represented as g(α) = [−ln(1 − α)]2 and g(α) = [1 − (1 − α)1/3]2 due to the oxygen accelerating thermal degradation from multiple perspectives. Moreover, PMR350 resin maintained high hardness and modulus even after thermal aging at 350 °C for 300 h. The results indicate that the resin exhibits excellent resistance to thermal and oxygen aging. This study represents the first systematic analysis of the thermal stability characteristics of PMR350 resin, offering essential theoretical insights and data support for understanding the mechanisms of thermal stability modification in PMR350 and its engineering applications. [ABSTRACT FROM AUTHOR]

Accurate understanding of urban land use change information is of great significance for urban planning, urban monitoring, and disaster assessment. The use of Very-High-Resolution (VHR) remote sensing images for change detection on urban land features has gradually become mainstream. However, most existing transfer learning-based change detection models compute multiple deep image features, leading to feature redundancy. Therefore, we propose a Transfer Learning Change Detection Model Based on Change Feature Selection (TL-FS). The proposed method involves using a pretrained transfer learning model framework to compute deep features from multitemporal remote sensing images. A change feature selection algorithm is then designed to filter relevant change information. Subsequently, these change features are combined into a vector. The Change Vector Analysis (CVA) is employed to calculate the magnitude of change in the vector. Finally, the Fuzzy C-Means (FCM) classification is utilized to obtain binary change detection results. In this study, we selected four VHR optical image datasets from Beijing-2 for the experiment. Compared with the Change Vector Analysis and Spectral Gradient Difference, the TL-FS method had maximum increases of 26.41% in the F1-score, 38.04% in precision, 29.88% in recall, and 26.15% in the overall accuracy. The results of the ablation experiments also indicate that TL-FS could provide clearer texture and shape detections for dual-temporal VHR image changes. It can effectively detect complex features in urban scenes. [ABSTRACT FROM AUTHOR]

This study explores the impact of sensory experiences on tourists' experiences at replica destinations, aiming to clarify the intrinsic influence mechanisms between sensory experiences, perceived authenticity, well-being, and the intention to revisit. Utilizing embodied cognition theory, this study empirically tested its application through data collected via an online survey of tourists who visited a well-known replica destination, Window of the World in Shenzhen, within three months prior to completing the survey. The findings reveal that tourists' sensory experiences, except for smell and taste, are directly related to the intention to revisit. Positive sensory experiences influence perceived authenticity and well-being, subsequently leading to an increased intention to revisit. By providing empirical evidence on tourists' sensory experiences at replica destinations in different cultural contexts, this study enriches the existing body of research and broadens the scope of replica tourism studies. In addition, an increasing number of replica tourism destinations play an important role in protecting traditional tourist destinations, especially historical sites. This study provides valuable insights for the future development of replica tourism destinations in promoting sustainable tourism. At the same time, this study extends the application of embodied cognition theory to replica tourism, contributing to theoretical development and offering new insights into the role of sensory experiences in shaping tourist behavior. [ABSTRACT FROM AUTHOR]

Aiming to accurately identify apple targets and achieve segmentation and the extraction of branch and trunk areas of apple trees, providing visual guidance for a picking robot to actively adjust its posture to avoid branch trunks for obstacle avoidance fruit picking, the spindle-shaped fruit trees, which are widely planted in standard modern apple orchards, were focused on, and an algorithm for apple tree fruit detection and branch segmentation for picking robots was proposed based on an improved YOLOv8s model design. Firstly, image data of spindle-shaped fruit trees in modern apple orchards were collected, and annotations of object detection and pixel-level segmentation were conducted on the data. Training set data were then augmented to improve the generalization performance of the apple detection and branch segmentation algorithm. Secondly, the original YOLOv8s network architecture's design was improved by embedding the SE module visual attention mechanism after the C2f module of the YOLOv8s Backbone network architecture. Finally, the dynamic snake convolution module was embedded into the Neck structure of the YOLOv8s network architecture to better extract feature information of different apple targets and tree branches. The experimental results showed that the proposed improved algorithm can effectively recognize apple targets in images and segment tree branches and trunks. For apple recognition, the precision was 99.6%, the recall was 96.8%, and the mAP value was 98.3%. The mAP value for branch and trunk segmentation was 81.6%. The proposed improved YOLOv8s algorithm design was compared with the original YOLOv8s, YOLOv8n, and YOLOv5s algorithms for the recognition of apple targets and segmentation of tree branches and trunks on test set images. The experimental results showed that compared with the other three algorithms, the proposed algorithm increased the mAP for apple recognition by 1.5%, 2.3%, and 6%, respectively. The mAP for tree branch and trunk segmentation was increased by 3.7%, 15.4%, and 24.4%, respectively. The proposed detection and segmentation algorithm for apple tree fruits, branches, and trunks is of great significance for ensuring the success rate of robot harvesting, which can provide technical support for the development of an intelligent apple harvesting robot. [ABSTRACT FROM AUTHOR]

Background: Acute myeloid leukemia (AML) is the most common type of acute leukemia among adults with the recommend therapy of combination of cytarabine and idarubicin in the induction phase. The uncoordinated pharmacokinetics prevent adequate control of drug ratio following systemic administration. Therefore, the dual-loaded liposomes containing cytarabine and idarubicin for synergistic effects were proposed and investigated. Methods: The molar ratio of cytarabine and idarubicin for synergistic effects was investigated. The dual-loaded liposomes were prepared and characterized by particle size, zeta potential, encapsulation efficiency, cryo-Transmission electron microscopy (cryo-TEM), and in vitro stability. The in vitro cytotoxicity and cell uptake of liposomes were determined within CCRF-CEM cells. The PK experiments was carried out in male SD rats. The in vivo antitumor effect was carried out within CD-1 nude female mice. The antitumor mechanism of liposomes was investigated. Results: The synergistic molar ratios were found to be in the range of 20:1~40:1. The size distribution of the dual-loaded liposomes was approximately 100 nm with PDI ≤ 0.1, a zeta potential of approximately −30 mV, an entrapment efficiency of cytarabine and idarubicin of >95% with spherical structure and uniform distribution, and in vitro stability for 21 d. The drugs in the liposomes can be quickly uptaken by the leukemia cells. The PK experiments showed that the molar ratio of cytarabine to idarubicin in plasma was maintained at 30:1 within 4 h. The efficacy of liposomes was significantly enhanced. Conclusions: The dual-loaded liposomes containing cytarabine and idarubicin showed enhanced antitumor efficacy. [ABSTRACT FROM AUTHOR]

Methane is the second largest contributor to global surface air temperature rise. Reducing atmospheric methane will mitigate climate change and improve air quality. Since the main sink of methane is the hydroxyl radical (OH) in the atmosphere, increasing OH concentration will accelerate the methane oxidation process and reduce methane concentration. Because the primary source of OH is the reaction between water vapor and ozone, scientists have proposed a water vapor enhancement strategy to raise OH concentrations in the atmosphere and remove methane. We use a two-box model to evaluate interactions between OH and methane and a radiative kernel method to calculate radiative responses to water vapor content growth. This proves that increasing OH concentration does have a positive effect on methane reduction. If the concentration of OH is increased by 10% from its current value and maintained for 50 (100) years, 45 Tg yr−1 (67.5 Tg yr−1) more methane will be oxidized by OH, and the cumulative effects of the oxidation are equivalent to a 120.5 Gt (219.6 Gt) reduction in CO2 and will lower the global surface air temperature by 0.054 °C (0.099 °C). Our study also provides insights into a mixed picture of global and regional radiative responses to the growth of water vapor content. The reduced radiative forcing by methane removal cannot overpower the increased radiative forcing by water vapor from the global average point of view. However, due to OH's greater sensitivity to water vapor and weaker radiative response at higher latitudes, this perspective may be reversed if abrupt CH4 emissions from permafrost thaw occur. [ABSTRACT FROM AUTHOR]

In the field of statistics, uncertain regression analysis occupies an important position. It can thoroughly analyze data sets contained in complex uncertainties, aiming to quantify and reveal the intricate relationships between variables. It is worth noting that the traditional least squares method only takes into account the reduction in the deviations between predictions and observations, and fails to fully consider the inherent characteristics of the correlation uncertainty distributions under the uncertain regression framework. In light of this, this paper constructs a statistical invariant with symmetric uncertainty distribution based on the observations and the disturbance term. It also proposes the least squares estimation of unknown parameters and disturbance term in the uncertain regression model based on the least squares principle and, combined with the mathematical properties of the normal uncertainty distribution, gives a numerical algorithm for solving specific estimates. Finally, in order to verify the effectiveness of the least squares estimation method proposed in this paper, we also design two numerical examples and an empirical study of forecasting of electrical power output. [ABSTRACT FROM AUTHOR]

Heterosis of growth traits in economic fish has benefited the production of aquaculture for many years, yet its genetic and molecular basis has remained obscure. Nowadays, a new germplasm of hybrid Jinhu grouper (Epinephelus fuscoguttatus ♀ × E. tukula ♂), abbreviated as EFT, exhibiting paternal-biased growth heterosis, has provided an excellent model for investigating the potential regulatory mechanisms of heterosis. We integrated transcriptome and methylome to unravel the changes of gene expression, epigenetic modification, and subgenome dominance in EFT compared with maternal E. fuscoguttatus. Integration analyses showed that the heterotic hybrids showed lower genomic DNA methylation levels than the purebred parent, and the up-regulated genes were mostly DNA hypomethylation. Furthermore, allele-specific expression (ASE) detected paternal subgenome dominance-regulated paternal-biased heterosis, and paternal bias differentially expressed genes (DEGs) were wholly up-regulated in the muscle. Multi-omics results highlighted the role of lipid metabolism, particularly "Fatty acid synthesis", "EPA biosynthesis", and "Signaling lipids", in EFT heterosis formation. Coherently, our studies have proved that the eicosapentaenoic acid (EPA) of EFT was greater than that of maternal E. fuscoguttatus (8.46% vs. 7.46%). Finally, we constructed a potential regulatory network for control of the heterosis formation in EFT. Among them, fasn, pparg, dgat1, igf1, pomca, fgf8a, and fgfr4 were identified as key genes. Our results provide new and valuable clues for understanding paternal-biased growth heterosis in EFT, taking a significant step towards the molecular basis of heterosis. [ABSTRACT FROM AUTHOR]

Lung adenocarcinoma (LUAD) poses significant challenges due to its complex biological characteristics and high recurrence rate. The high recurrence rate of LUAD is closely associated with cellular dormancy, which enhances resistance to chemotherapy and evasion of immune cell destruction. Using single-cell RNA sequencing (scRNA-seq) data from LUAD patients, we categorized the cells into two subclusters: dormant and active cells. Utilizing high-density Weighted Gene Co-expression Network Analysis (hdWGCNA) and pseudo-time cell trajectory, aberrant expression of genes involved in protein O-glycosylation was detected in dormant cells, suggesting a crucial role for O-glycosylation in maintaining the dormant state. Intercellular communication analysis highlighted the interaction between fibroblasts and dormant cells, where the Insulin-like Growth Factor (IGF) signaling pathway regulated by O-glycosylation was crucial. By employing Gene Set Variation Analysis (GSVA) and machine learning, a risk score model was developed using hub genes, which showed high accuracy in determining LUAD prognosis. The model also demonstrated robust performance on the training dataset and excellent predictive capability, providing a reliable basis for predicting patient clinical outcomes. The group with a higher risk score exhibited a propensity for adverse outcomes in the tumor microenvironment (TME) and tumor mutational burden (TMB). Additionally, the 50% inhibitory concentration (IC50) values for chemotherapy exhibited significant variations among the different risk groups. In vitro experiments demonstrated that EFNB2, PTTG1IP, and TNFRSF11A were upregulated in dormant tumor cells, which also contributed greatly to the diagnosis of LUAD. In conclusion, this study highlighted the crucial role of O-glycosylation in the dormancy state of LUAD tumors and developed a predictive model for the prognosis of LUAD patients. [ABSTRACT FROM AUTHOR]

In capture–recapture experiments, the presence of overdispersion and heterogeneity necessitates the use of the negative binomial regression model for inferring population sizes. However, within this model, existing methods based on likelihood and ratio regression for estimating the dispersion parameter often face boundary and nonidentifiability issues. These problems can result in nonsensically large point estimates and unbounded upper limits of confidence intervals for the population size. We present a penalized empirical likelihood technique for solving these two problems by imposing a half-normal prior on the population size. Based on the proposed approach, a maximum penalized empirical likelihood estimator with asymptotic normality and a penalized empirical likelihood ratio statistic with asymptotic chi-square distribution are derived. To improve numerical performance, we present an effective expectation-maximization (EM) algorithm. In the M-step, optimization for the model parameters could be achieved by fitting a standard negative binomial regression model via the R basic function glm.nb(). This approach ensures the convergence and reliability of the numerical algorithm. Using simulations, we analyze several synthetic datasets to illustrate three advantages of our methods in finite-sample cases: complete mitigation of the boundary problem, more efficient maximum penalized empirical likelihood estimates, and more precise penalized empirical likelihood ratio interval estimates compared to the estimates obtained without penalty. These advantages are further demonstrated in a case study estimating the abundance of black bears (Ursus americanus) at the U.S. Army's Fort Drum Military Installation in northern New York. [ABSTRACT FROM AUTHOR]

Sea ice is a crucial component of the global climate system. The China–French Ocean Satellite Scatterometer (CFOSAT/SCAT, CSCAT) employs an innovative rotating fan beam system. This study applied principal component analysis (PCA) to extract classification features and developed an ensemble machine learning approach for sea ice detection. PCA identified key features from CSCAT's backscatter information, representing outer and sweet swath observations. The ensemble model's performances (OA and Kappa) for the Northern and Southern Hemispheres were 0.930, 0.899, and 0.844, 0.747, respectively. CSCAT achieved an accuracy of over 0.9 for close ice and open water but less than 0.3 for open ice, with misclassification of open ice as closed ice. The sea ice extent discrepancy between CSCAT and the National Snow and Ice Data Center (NSIDC) was −0.06 ± 0.36 million km2 in the Northern Hemisphere and −0.03 ± 0.48 million km2 in the Southern Hemisphere. CSCAT's sea ice closely matched synthetic aperture radar (SAR) imagery, indicating effective sea ice and open water differentiation. CSCAT accurately distinguished sea ice from open water but struggled with open ice classification, with misclassifications in the Arctic's Greenland Sea and Hudson Bay, and the Antarctic's sea ice–water boundary. [ABSTRACT FROM AUTHOR]

Carotenoids belonging to the class of tetraterpenoids have extensive applications in medicine, food, nutrition, cosmetics, and feed. Among them, lutein and zeaxanthin can prevent macular degeneration in the elderly, which is very important for protecting vision. Here, we introduce the first metabolomic analysis of Sphingopyxis sp. USTB-05, aiming to shed light on the biosynthesis of carotenoids. Sphingopyxis sp. USTB-05 has the complete methylerythritol 4-phosphate (MEP) pathway and carotenoid biosynthesis pathway, especially involved in the bioconversion of zeaxanthin, violaxanthin, and astaxanthin. Metabolomic profiling identified seven carotenes and six xanthophylls synthesized by Sphingopyxis sp. USTB-05. Zeaxanthin, in particular, was found to be the most abundant, with a content of 37.1 µg/g dry cells. Collectively, the results presented herein greatly enhance our understanding of Sphingopyxis sp. USTB-05 in carotenoids biosynthesis, and thus further accelerate its fundamental molecular investigations and biotechnological applications. [ABSTRACT FROM AUTHOR]

With the popularity of carbon-free vehicle obstacle avoidance races, the requirements for the accuracy and reliability of vehicle motion control are getting higher and higher. Aiming at the problems of trajectory deviation and debugging difficulties of the carbon-free vehicle during the movement process, the Revolute-Slider-Slider-Revolute (RSSR) mechanism is adopted as the steering device, which is aimed at improving the motion control precision and obstacle avoidance ability of the vehicle. Firstly, from the kinematics point of view, a kinematics model based on the spatial four-link mechanism is established, and the influence of each parameter change on the trajectory of the vehicle is analyzed. On this basis, the influence of each key parameter on the motion process is further explored through practical debugging, so as to derive a general law of the vehicle's motion under the drive of the RSSR mechanism. Through numerical simulation analysis, the accuracy of the theoretical model is verified, and the structural design of the vehicle is optimized accordingly. The actual debugging results show that the vehicle can realize smooth operation, which fully proves the practicality and effectiveness of the established mathematical model and the research of the RSSR mechanism. [ABSTRACT FROM AUTHOR]

The maize Snf2 gene family plays a crucial role in chromatin remodeling and response to environmental stresses. In this study, we identified and analyzed 35 members of the maize Snf2 gene family (ZmCHR1 to ZmCHR35) using the Ensembl Plants database. Each protein contained conserved SNF2-N and Helicase-C domains. Phylogenetic analysis revealed six groups among the Snf2 proteins, with an uneven distribution across subfamilies. Physicochemical analysis indicated that the Snf2 proteins are hydrophilic, with varied amino acid lengths, isoelectric points, and molecular weights, and are predominantly localized in the nucleus. Chromosomal mapping showed that these genes are distributed across all ten maize chromosomes. Gene structure analysis revealed diverse exon–intron arrangements, while motif analysis identified 20 conserved motifs. Collinearity analysis highlighted gene duplication events, suggesting purifying selection. Cis-regulatory element analysis suggested involvement in abiotic and biotic stress responses. Expression analysis indicated tissue-specific expression patterns and differential expression under various stress conditions. Specifically, qRT-PCR validation under drought stress showed that certain Snf2 genes were upregulated at 12 h and downregulated at 24 h, revealing potential roles in drought tolerance. These findings provide a foundation for further exploration of the functional roles of the maize Snf2 gene family in development and stress responses. [ABSTRACT FROM AUTHOR]

Understanding liquidity and liquidity risk is essential for effective risk management. We investigate liquidity spillover effects among ETFs that track the S&P sectors. In particular, using COVID-related news shocks as a natural experiment, we estimate the direction and magnitude of two-way net spillovers and their asymmetry across good and bad news regimes, where liquidity is measured by the daily quoted bid–ask spread and the Amihud illiquidity ratio. Our results confirm the liquidity links amongst ETFs and suggest that liquidity spillovers are more pronounced during bad news periods compared to good news periods. In addition, we document the variations in the results obtained using the bid–ask spread and the Amihud ratio, which provide insights into different dimensions of liquidity and liquidity risk, including volatility and trading volume. [ABSTRACT FROM AUTHOR]