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Knowledge tracing is a crucial task in personalized learning that models student mastery based on historical data to predict future performance. Currently, deep learning models in knowledge tracing predominantly use one-hot encodings of question, knowledge, and student IDs, showing promising results. However, they face a significant limitation: a vocabulary gap that impedes the processing of new IDs not seen during training. To address this, our paper introduces a novel method that incorporates aggregated features, termed ‘side information’, that captures essential attributes such as student ability, knowledge mastery, and question difficulty. Our approach utilizes side information to bridge the vocabulary gap caused by ID-based one-hot encoding in traditional models. This enables the model, once trained on one dataset, to generalize and make predictions on new datasets with unfamiliar students, knowledge, or questions without the need for retraining. This innovation effectively bridges the vocabulary gap, reduces the dependency on specific data representations, and improves the overall performance of the model. Experimental evaluations on five distinct datasets show that our proposed model consistently outperforms baseline models, using fewer parameters and demonstrating seamless adaptability to new contexts. Additionally, ablation studies highlight that including side information, especially regarding students and questions, significantly improves knowledge tracing effectiveness. In summary, our approach not only resolves the vocabulary gap challenge but also offers a more robust and superior solution across varied datasets.

Citrate is a major determinant of fruit flavor quality. Currently, citrus species and/or varieties with significant alterations in citrate level have greatly advanced the molecular basis of citrate accumulation in fruit. However, in-depth dissections of the molecular mechanism specific to citrate accumulation are still limited due to the lack of mutants, especially within one single variety. In this study, a fresh-sweet ‘Shatangju’ mutant (Citrus reticulata cv.) was obtained during a survey of citrus resources in Guangdong, China, and the phenotype, fruit morphology, and primary flavor profiles were comparatively analyzed. Unlike the wild-type ‘Shatangju’ (WT), the mutant (MT) material exhibited a dwarfed and multi-branched tree shape, delayed flowering and fruit ripening at maturity, a prolonged fruit tree-retention time, and a decreased single fruit weight at maturity. Dynamic measurement of the metabolite levels further suggested that the contents and fluctuation patterns of vitamin C, malate, quinate, and oxalate showed no obvious difference between MT and MT fruits, while the citrate level in MT fruits significantly decreased over various developmental stages, ranging from 0.356 to 1.91 mg g−1 FW. In addition, the accumulation patterns of the major soluble sugars (sucrose, fructose, and glucose), as well as the sugar/acid ratio, were also altered in MT fruits during development. Taken together, this study provides a novel acid-free ‘Shatangju’ mutant, which can serve as a powerful tool for the research of fruit flavor quality, especially for the comprehensive understanding of the molecular mechanism of citrate accumulation in fruits.

As a major agricultural hazard, drought frequently occurs due to a reduction in precipitation resulting in a continuously propagating soil moisture (SM) deficit. Assessment of the high spatial-resolution SM-derived drought index is crucial for monitoring agricultural drought. In this study, we generated a downscaled random forest SM dataset (RF-SM) and calculated the soil water deficit index (RF-SM-SWDI) at 30 m for agricultural drought monitoring. The results showed that the RF-SM dataset exhibited better consistency with in situ SM observations in the detection of extremes than did the SM products, including SMAP, SMOS, NCA-LDAS, and ESA CCI, for different land cover types in the U.S. and yielded a satisfactory performance, with the lowest root mean square error (RMSE, below 0.055 m3/m3) and the highest coefficient of determination (R2, above 0.8) for most observation networks, based on the number of sites. A vegetation health index (VHI), derived from a Landsat 8 optical remote sensing dataset, was also generated for comparison. The results illustrated that the RF-SM-SWDI and VHI exhibited high correlations (R ≥ 0.5) at approximately 70% of the stations. Furthermore, we mapped spatiotemporal drought monitoring indices in California. The RF-SM-SWDI provided drought conditions with more detailed spatial information than did the short-term drought blend (STDB) released by the U.S. Drought Monitor, which demonstrated the expected response of seasonal drought trends, while differences from the VHI were observed mainly in forest areas. Therefore, downscaled SM and SWDI, with a spatial resolution of 30 m, are promising for monitoring agricultural field drought within different contexts, and additional reliable factors could be incorporated to better guide agricultural management practices.

Surface downward longwave radiation (SDLR) is crucial for maintaining the global radiative budget balance. Due to their ease of practicality, SDLR parameterization models are widely used, making their objective evaluation essential. In this study, against comprehensive ground measurements collected from more than 300 globally distributed sites, four SDLR parameterization models, including three popular existing ones and a newly proposed model, were evaluated under clear- and cloudy-sky conditions at hourly (daytime and nighttime) and daily scales, respectively. The validation results indicated that the new model, namely the Peng model, originally proposed for SDLR estimation at the sea surface and applied for the first time to the land surface, outperformed all three existing models in nearly all cases, especially under cloudy-sky conditions. Moreover, the Peng model demonstrated robustness across various land cover types, elevation zones, and seasons. All four SDLR models outperformed the Global Land Surface Satellite product from Advanced Very High-Resolution Radiometer Data (GLASS-AVHRR), ERA5, and CERES_SYN1de-g_Ed4A products. The Peng model achieved the highest accuracy, with validated RMSE values of 13.552 and 14.055 W/m2 and biases of −0.25 and −0.025 W/m2 under clear- and cloudy-sky conditions at daily scale, respectively. Its superior performance can be attributed to the inclusion of two cloud parameters, total column cloud liquid water and ice water, besides the cloud fraction. However, the optimal combination of these three parameters may vary depending on specific cases. In addition, all SDLR models require improvements for wetlands, bare soil, ice-covered surfaces, and high-elevation regions. Overall, the Peng model demonstrates significant potential for widespread use in SDLR estimation for both land and sea surfaces.

When undertaking long-distance missions at sea, vessels aim to achieve an extended operational range through drag reduction and energy efficiency, while enhanced wave resilience also provides substantial benefits. In this work, the Delft-372 catamaran is utilized to investigate the feasibility of drag reduction and roll mitigation for catamaran formation sailing in waves, analyzing the effects of three different formation configurations and varying spacings. The overset grid method was employed to simulate vessel motions, while the Volume of Fluid (VOF) method captured the free surface. First, the numerical results of the catamaran’s resistance, pitch, and heave motion amplitudes under different wave conditions were compared with experimental data to verify the accuracy of the CFD numerical method, and a grid convergence analysis was performed. Next, numerical models of the Delft-372 catamaran were constructed in parallel, tandem, and lateral formations under wave conditions. The results of the single-ship simulation were employed as a benchmark to analyze the impact of different formation configurations and varying lateral and longitudinal spacings on the resistance, pitch, and heave motions of the catamarans. The study also examined the effects of wave interference between vessels and the combined influence of external waves on individual and overall hydrodynamic performance. Results indicated that the tandem formation outperformed the parallel and lateral formations, with optimal performance observed at the longitudinal distance of 1 LPP. Generally, during navigation, the follower catamaran should ideally be positioned in the trough of the stern wave of the leader catamaran.

Computer vision (CV) techniques have been widely studied and applied in the shipping industry and maritime research. The existing literature has primarily focused on enhancing image recognition accuracy and precision for water surface targets by refining CV models themselves. This paper introduces innovative methods to further improve the accuracy of detection and recognition using CV models, including using ensemble learning and integrating shipping domain knowledge. Additionally, we present a novel application of CV techniques in the maritime domain, expanding the research perspective beyond the traditional focus on the accurate detection and recognition of water surface targets. Specifically, a novel solution integrating a CV model and the transfer learning method is proposed in this paper to address the challenge of relatively low-speed and high-charge internet services on ocean-going vessels, aiming to improve the online video viewing experience while conserving network resources. This paper is of importance for advancing further research and application of CV techniques in the shipping industry.

To address the issue of automated apple harvesting in orchards, we propose a YOLOv5-RACF algorithm for identifying apples and calculating apple diameters. This algorithm employs the robot operating dystem (ROS) to control the robot’s locomotion system, Lidar mapping, and navigation, as well as the robotic arm’s posture and grasping operations, achieving automated apple harvesting and placement. The tests were conducted in an actual orchard environment. The algorithm model achieved an average apple detection accuracy (mAP@0.5) of 98.748% and a (mAP@0.5:0.95) of 90.02%. The time to calculate the diameter of one apple was 0.13 s, with a measurement accuracy within an error range of 1–3 mm. The robot takes an average of 9 s to pick an apple and return to the initial pose. These results demonstrate the system’s efficiency and reliability in real agricultural environments.

In unmanned aerial vehicle (UAV) path planning, evolutionary algorithms are commonly used due to their ability to handle high-dimensional spaces and wide generality. However, traditional evolutionary algorithms have difficulty with population initialization and may fall into local optima. This paper proposes an improved genetic algorithm (GA) based on expert strategies, including a novel rapidly exploring random tree (RRT) initialization algorithm and a cross-variation process based on expert guidance and the wolf pack search algorithm. Experimental results on baseline functions in different scenarios show that the proposed RRT initialization algorithm improves convergence speed and computing time for most evolutionary algorithms. The expert guidance strategy helps algorithms jump out of local optima and achieve suboptimal solutions that should have converged. The ERRT-GA is tested for task assignment, path planning, and multi-UAV conflict detection, and it shows faster convergence, better scalability to high-dimensional spaces, and a significant reduction in task computing time compared to other evolutionary algorithms. The proposed algorithm outperforms most other methods and shows great potential for UAV path planning problems.

In the domain of supervised learning, interpolation and extrapolation serve as crucial methodologies for predicting data points within and beyond the confines of a given dataset, respectively. The efficacy of these methods is closely linked to the nature of the dataset, with increased challenges when multivariate feature vectors are handled. This paper introduces a novel prediction framework that integrates interpolation and extrapolation techniques. Central to this method are two main innovations: an optimization model that effectively classifies new multivariate data points as either interior or exterior to the known dataset, and a hybrid prediction system that combines k-nearest neighbor (kNN) and linear regression. Tested on the port state control (PSC) inspection dataset at the port of Hong Kong, our framework generally demonstrates superior precision in predictive outcomes than traditional kNN and linear regression models. This research enriches the literature by illustrating the enhanced capability of combining interpolation and extrapolation techniques in supervised learning.

Based on the first principles, the structural stability, mechanical characteristics, electronic structure, and thermodynamic properties of AlCu2M (M = Ti, Cr, Zr, Sc, Hf, Mn, Pa, Lu, Pm) are investigated. The calculated results indicate that the AlCu2Pa crystal structure is more stable and that AlCu2Pa should be easier to form. All of the AlCu2M compounds have structural stability in the ground state. Elastic constants are used to characterize the mechanical stability and elastic modulus, while the B/G values and Poisson ratio demonstrate the brittleness and ductility of AlCu2M compounds. It is demonstrated that all computed AlCu2M compounds are ductile and mechanically stable, with AlCu2Hf having the highest bulk modulus and AlCu2Mn having the highest Young’s modulus. AlCu2Mn has the highest intrinsic hardness among AlCu2M compounds, according to calculations of their intrinsic hardness. The electronic densities of states are discussed in detail; it was discovered that all AlCu2M compounds form Al-Cu and Al-M covalent bonds. Additionally, we observe that the Debye temperature and minimum thermal conductivity of AlCu2Mn and AlCu2Sc are both larger than those of others, indicating stronger chemical bonds and higher thermal conductivities, which is consistent with the elastic modulus results.

The performance of corrosion-induced cracking of reinforced concrete members under transverse constraints was studied. Based on the theory of elastic-plastic mechanics and the hypothesis of uniform corrosion of a steel bar, a three-layer hollow cylinder model was established to predict the critical corrosion of the steel bar at the time of the cracking of the concrete cover. Taking the constraint of stirrups on surrounding concrete into consideration, it can be used to predict the corrosion rate of members with stirrups at the time of the cracking of the concrete cover, which further expands the application range of the corrosion-induced cracking models of concrete. On this basis, the critical corrosion rate of concrete under different stirrup ratios at the time of cracking was measured. The calculated results of the model are in accordance with experimental data. For corner steel bars, when the stirrup spacing is less than 100 mm, the existence of stirrups can effectively delay the occurrence of rust expansion cracks and enhance the durability of the structure. On the basis of this study, the problem of corrosion expansion and cracking of the concrete cover caused by non-uniform corrosion of steel bars along longitudinal and radial directions needs to be further studied in the future.

In group management, accurate assessment of individual performance is crucial for the fair allocation of resources such as bonuses. This paper explores the complexities of gauging each participant’s contribution in multi-participant projects, particularly through the lens of self-reporting—a method fraught with the challenges of under-reporting and over-reporting, which can skew resource allocation and undermine fairness. Addressing the limitations of current assessment methods, which often rely solely on self-reported data, this study proposes a novel equitable allocation policy that accounts for inherent biases in self-reporting. By developing a data-driven mathematical optimization model, we aim to more accurately align resource allocation with actual contributions, thus enhancing team efficiency and cohesion. Our computational experiments validate the proposed model’s effectiveness in achieving a more equitable allocation of resources, suggesting significant implications for management practices in team settings.

Global changes cause widespread forest fragmentation, which, in turn, has given rise to many ecological problems; this is especially true if the forest carbon stock is profoundly impacted by fragmentation levels. However, the way in which forest carbon uptake changes with different fragmentation levels and the main pathway through which fragmentation affects forest carbon uptake are still unclear. Remote sensing data, vegetation photosynthesis models, and fragmentation models were employed to generate a time series GPP (gross primary productivity) dataset, as well as forest fragmentation levels for forest landscapes in Zhejiang province, China. We analyzed GPP variation with forest fragmentation levels and identified the relative importance of the phenology (carbon uptake period—CUP) and physiology (maximum daily GPP—GPPmax) control pathways of GPP under different fragmentation levels. The results showed that the normalized mean annual GPP data of highly fragmented forests during the period from 2000 to 2018 were significantly higher than those of other fragmentation levels, while there was almost no significant difference in the annual GPP trend of forest landscapes with all fragmentation levels. Moreover, the percentage area of the control variable, GPPmax, gradually increased with fragmentation levels; the mean GPPmax between 2000 and 2018 of high-level fragmentation was higher than that of other fragmentation levels. Our results demonstrate that the carbon uptake capacity per unit area was enhanced in highly fragmented forest areas, and the maximum photosynthetic capacity (physiology-based process) played an important role in controlling carbon uptake, especially in highly fragmented forest landscapes. Our study calls for a better and deeper understanding of the potential of forest carbon uptake, and it is necessary to explore the mechanism by which forest fragmentation changes the vegetation photosynthetic process.

d-Lactic acid serves as a pivotal platform chemical in the production of poly d-lactic acid (PDLA) and other value-added products. This compound can be synthesized by certain bacteria, including Klebsiella pneumoniae. However, industrial-scale lactic acid production in Klebsiella pneumoniae faces challenges due to growth inhibition caused by lactic acid stress, which acts as a bottleneck in commercial microbial fermentation processes. To address this, we employed a combination of evolutionary and genetic engineering approaches to create an improved Klebsiella pneumoniae strain with enhanced lactic acid tolerance and production. In flask fermentation experiments, the engineered strain achieved an impressive accumulation of 19.56 g/L d-lactic acid, representing the highest production yield observed in Klebsiella pneumoniae to date. Consequently, this strain holds significant promise for applications in industrial bioprocessing. Notably, our genome sequencing and experimental analyses revealed a novel correlation between UTP-glucose-1-phosphate uridylyltransferase GalU and lactic acid resistance in Klebsiella pneumoniae. Further research is warranted to explore the potential of targeting GalU for enhancing d-lactic acid production.

Stochastic optimization models always assume known probability distributions about uncertain parameters. However, it is unrealistic to know the true distributions. In the era of big data, with the knowledge of informative features related to uncertain parameters, this study aims to estimate the conditional distributions of uncertain parameters directly and solve the resulting contextual stochastic optimization problem by using a set of realizations drawn from estimated distributions, which is called the contextual distribution estimation method. We use an energy scheduling problem as the case study and conduct numerical experiments with real-world data. The results demonstrate that the proposed contextual distribution estimation method offers specific benefits in particular scenarios, resulting in improved decisions. This study contributes to the literature on contextual stochastic optimization problems by introducing the contextual distribution estimation method, which holds practical significance for addressing data-driven uncertain decision problems.

This paper undertakes a revisit of the sunk cost fallacy, which refers to the tendency of people to persist investing resources into something, even if it is destined to have no good outcome. We emphasize that the utilities associated with different alternatives are not static for decision makers, which is exactly opposite to the traditional perspective. This paper argues that the utility of an option may change due to the choice of another option, suggesting that decisions considered irrational by the traditional analytical method, i.e., sunk cost fallacy, may be rational. We propose a novel analytical method for decision making with sunk cost when considering the utility change and validate the effectiveness of this method through mathematical modeling and computational experiments. This paper mathematically describes such decision-making problems, analyzing the impact of changes in the utilities across different alternatives on decision making with a real-world example. Furthermore, we develop a two-stage stochastic optimization model for such decision-making problems and employ the sample average approximation (SAA) method to solve them. The results from computational experiments indicate that some decisions traditionally considered irrational are, in fact, rational when the utility of an option changes as a result of choosing another option. This paper, therefore, highlights the significance of incorporating utility changes into the decision-making process and stands as a valuable addition to the literature, offering a refreshed and effective decision-making method for improved decision making.

Soybean is grown worldwide for its high protein and oil content. Weeds compete fiercely for resources, which affects soybean yields. Because of the progressive enhancement of weed resistance to herbicides and the quickly increasing cost of manual weeding, mechanical weed control is becoming the preferred method of weed control. Mechanical weed control finds it difficult to remove intra-row weeds due to the lack of rapid and precise weed/soybean detection and location technology. Rhodamine B (Rh-B) is a systemic crop compound that can be absorbed by soybeans which fluoresces under a specific excitation light. The purpose of this study is to combine systemic crop compounds and computer vision technology for the identification and localization of soybeans in the field. The fluorescence distribution properties of systemic crop compounds in soybeans and their effects on plant growth were explored. The fluorescence was mainly concentrated in soybean cotyledons treated with Rh-B. After a comparison of soybean seedlings treated with nine groups of rhodamine B solutions at different concentrations ranging from 0 to 1440 ppm, the soybeans treated with 180 ppm Rh-B for 24 h received the recommended dosage, resulting in significant fluorescence that did not affect crop growth. Increasing the Rh-B solutions reduced crop biomass, while prolonged treatment times reduced seed germination. The fluorescence produced lasted for 20 days, ensuring a stable signal in the early stages of growth. Additionally, a precise inter-row soybean plant location system based on a fluorescence imaging system with a 96.7% identification accuracy, determined on 300 datasets, was proposed. This article further confirms the potential of crop signaling technology to assist machines in achieving crop identification and localization in the field.

The stability of roadway sidewalls is crucial to ensuring people’s safety and production efficiency in coal mining. This paper investigated the deformation and failure of deep roadway sidewalls, particularly the effects of height-width ratios and lateral pressure coefficients. Our research results indicate that brittle failure occurred in the diabase sidewall rock of the Datai coal mine, and a V-shaped pit was formed as a result of shear damage caused by high stress. When the height-width ratio of a roadway increases from 0.25 to 2.00, the tensile and shear plastic failure area of the sidewall increases, and vertical stress is transferred to a deep part of the roadway sidewall. There are two stress concentration zones and two stress peak points in the sidewall of a roadway. When the lateral pressure coefficient increases from 0.10 to 1.00, the tensile plastic zone of rock mass in the sidewall first decreases and gradually reaches stability. On the other hand, the shear failure area increases and then decreases. Similarly, the sidewall horizontal displacement decreases and then increases. Additionally, the vertical stress concentration position is located near the roadway sidewall.

The research on baggage flow plays a pivotal role in achieving the efficient and intelligent allocation and scheduling of airport service resources, as well as serving as a fundamental element in determining the design, development, and process optimization of airport baggage handling systems. This paper examines baggage checked in by departing passengers at airports. The crrent state of the research on baggage flow demand is first reviewed and analyzed. Then, using examples of objective data, it is concluded that while there is a significant correlation between airport passenger flow and baggage flow, an increase in passenger flow does not necessarily result in a proportional increase in baggage flow. According to the existing research results on the influencing factors of baggage flow sorting and classification, the main influencing factors of baggage flow are divided into two categories: macro-influencing factors and micro-influencing factors. When studying the relationship between the economy and baggage flow, it is recommended to use a comprehensive analysis that includes multiple economic indicators, rather than relying solely on GDP. This paper provides a brief overview of prevalent transportation flow prediction methods, categorizing algorithmic models into three groups: based on mathematical and statistical models, intelligent algorithmic-based models, and combined algorithmic models utilizing artificial neural networks. The structures, strengths, and weaknesses of various transportation flow prediction algorithms are analyzed, as well as their application scenarios. The potential advantages of using artificial neural network-based combined prediction models for baggage flow forecasting are explained. It concludes with an outlook on research regarding the demand for baggage flow. This review may provide further research assistance to scholars in airport management and baggage handling system development.

In light of recent advancements in synchrotron radiation technology and nano-technology, there has been a marked increase in the need for ultra-precision nano-positioning mechanisms. This paper presents a method that integrates physical models with multi-objective optimization for developing an optical angle nano-positioning mechanism. We begin by examining the actual motion law of the mechanism, based on kinematic principles. The outcomes from this kinematic analysis facilitate a static analysis of the flexible hinge, identified as a critical component of the mechanism. Subsequently, we establish a dynamic model for the entire mechanism. By employing the physical model as a base and combining it with the optimization algorithm, we identify the optimal design parameters for the mechanism. The design achieves a resolution of 50 nrad and meets the specified requirements. The first-order inherent frequency of the mechanism is approximately 43.75 Hz. There is a discrepancy of 2.63% from the finite element modal analysis results and a 3.33% difference from the theoretical analysis results, validating the reliability of the design method proposed in this study.

In this study, verification was conducted through experiments to identify problems caused by traditional attachment methods in order to highlight the need for a suitable attachment method for new tile types according to changes in materials, production technology, and demand. The stability of adhesion strength was evaluated by subdividing the size of the adhesion area and adhesion strength measurement method for the country-type attachment method. The adhesion area on the back of the tile was divided into 60% and 80%, and the test specimens used in the experiment were tested for partial adhesion strength (Ta-1), overall adhesion strength (Ta-2), and adhesion strength after splitting (Ta-3), and the results were derived. As a result of conducting the adhesion test presented in the current national building standard tile specification (KCS 41 48 01) for 80% of the backfill area, the average adhesive strength was 0.85 N/mm2, and the standard strength was 0.39 N/mm2. However, as a result of the arithmetic average test of the adhesive strength of all tiles or cutting of the entire tile, rather than the partial adhesion test method of the mortar-attached part, it was confirmed that the adhesive strength was about −20% less than the current KCS 0.39 N/mm2.

Currently, deep learning is extensively utilized for ship target detection; however, achieving accurate and real-time detection of multi-scale targets remains a significant challenge. Considering the diverse scenes, varied scales, and complex backgrounds of ships in optical remote sensing images, we introduce a network model named YOLO-RSA. The model consists of a backbone feature extraction network, a multi-scale feature pyramid, and a rotated detection head. We conduct thorough tests on the HRSC2016 and DOTA datasets to validate the proposed algorithm. Through ablation experiments, we assess the impact of each improvement component on the model. In comparative experiments, the proposed model surpasses other models in terms of Recall, Precision, and MAP on the HRSC2016 dataset. Finally, in generalization experiments, our proposed ship detection model exhibits excellent detection performance across various scenarios. The method can accurately detect multi-scale ships in the image and provide a basis for marine ship monitoring and port management.

In this study, asymmetric Al2O3-SiO2 Janus nanoparticles with a dumbbell-like structure were synthesized by a facile chemical process in the aqueous phase. Prior to synthesis, Al2O3 nanoparticles in hydrosol were amino-modified using 3-aminopropyl triethoxysilane (KH550) and then carboxyl acid-functionalized using a ring-opening reaction of the amine functions with succinic anhydride, imparting unique anionic properties to the Al2O3 end. SiO2 nanoparticles were rendered hydrophobic through modification with hexamethyldisilazane (HMDS) and further functionalized with 3-chloropropyl triethoxysilane (KH230). The two nanoparticle hydrosols were then mixed, and the asymmetric Al2O3-SiO2 Janus nanoparticles were synthesized via the reaction between the –NH2 and −CH2Cl groups. The prepared Janus nanoparticles were primarily characterized by dynamic light scattering (DLS), Zeta potential (ZP), and transmission electron microscopy (TEM). The results indicated that about 90% of the modified Al2O3 and SiO2 nanoparticles were covalently coupled in a one-to-one manner to form the dominant dumbbell-like structure. These Janus nanoparticles exhibit amphiphilic properties, making them highly promising surfactants for emulsifying oil–water mixtures.

African swine fever virus (ASFV) and porcine reproductive and respiratory syndrome virus (PRRSV) infections lead to severe respiratory diseases in pigs, resulting in significant economic losses for the global swine industry. While numerous studies have focused on specific gene functions or pathway activities during infection, an investigation of shared immune responses in porcine alveolar macrophages (PAMs) after ASFV and PRRSV infections was lacking. In this study, we conducted a comparison using two single-cell transcriptomic datasets generated from PAMs under ASFV and PRRSV infection. Pattern recognition receptors (PRRs) RIG-I (DDX58), MDA5 (IFIH1), and LGP2 (DHX58) were identified as particularly recognizing ASFV and PRRSV, triggering cellular defense responses, including the upregulation of four cytokine families (CCL, CXCL, IL, and TNF) and the induction of pyroptosis. Through weighted gene co-expression network analysis and protein–protein interaction analysis, we identified thirteen gene and protein interactions shared by both scRNA-seq analyses, suggesting the ability to inhibit both ASFV and PRRSV viral replication. We discovered six proteins (PARP12, PARP14, HERC5, DDX60, RSAD2, and MNDA) in PAMs as inhibitors of ASFV and PRRSV replication. Collectively, our findings showed detailed characterizations of the immune responses in PAMs during ASFV and PRRSV infections, which may facilitate the treatments of these viral diseases.

In recent years, frequent global climate change has led to extreme weather events, such as high temperatures and droughts. Under the backdrop of climate change, the potential distribution zones of plants will undergo alterations. Therefore, it is necessary to predict the potential geographical distribution patterns of plants under climate change. Schima superba, a plant species with significant ecological and economic value, plays a crucial role in ecological restoration and maintaining environmental stability. Therefore, predicting potential changes in its suitable habitat in Zhejiang Province is significant. The MaxEnt model and combined data from 831 monitoring sites where Schima superba is distributed in Zhejiang Province with 12 selected bioclimatic variables were used to predict habitat suitability adaptability. We found that (1) the average AUC value of the MaxEnt model in repeated experiments was 0.804, with a standard deviation of 0.014, which indicates high reliability in predictions. (2) The total suitable habitat area for Schima superba in Zhejiang Province (suitability value > 0.05) is 87,600 km2, with high-suitability, moderate-suitability, and low-suitability areas covering 29,400 km2, 25,700 km2, and 32,500 km2, respectively. (3) Likewise, elevation, precipitation, and temperature are the dominant climatic variables that influence the distribution of Schima superba. Schima superba mainly occurs in areas with an elevation above 500 m and precipitation over 140 mm during the hottest season. The probability of Schima superba distribution reaches its peak at elevations between 1200 and 1400 m. Here, the precipitation ranges from 300 to 350 mm with high humidity, between 160 and 170 mm during the hottest season, and an annual temperature range between 28 and 31 °C. Therefore, our results indicate that climate change significantly affects the suitable habitat area of Schima superba. We also reveal the ecological characteristics and adaptation mechanisms of Schima superba in different geographical regions of Zhejiang Province. Future research should focus on the relationship between plant adaptation strategies and environmental changes, as well as applications in ecosystem protection and sustainable development, to promote the development and application of plant habitat adaptability research.

It is a difficult undertaking to reliably estimate global terrestrial evapotranspiration (ET) using the Visible Infrared Imaging Radiometer Suite (VIIRS) at high spatial and temporal scales. We employ deep neural networks (DNN) to enhance the estimation of terrestrial ET on a global scale using satellite data. We accomplish this by merging five algorithms that are process-based and that make use of VIIRS data. These include the Shuttleworth–Wallace dual-source ET method (SW), the Priestley–Taylor-based ET algorithm (PT-JPL), the MOD16 ET product algorithm (MOD16), the modified satellite-based Priestley–Taylor ET algorithm (MS-PT), and the simple hybrid ET algorithm (SIM). We used 278 eddy covariance (EC) tower sites from 2012 to 2022 to validate the DNN approach, comparing it to Bayesian model averaging (BMA), gradient boosting regression tree (GBRT) and random forest (RF). The validation results demonstrate that the DNN significantly improves the accuracy of daily ET estimates when compared to three other merging methods, resulting in the highest average determination coefficients (R2, 0.71), RMSE (21.9 W/m2) and Kling–Gupta efficiency (KGE, 0.83). Utilizing the DNN, we generated a VIIRS ET product with a 500 m spatial resolution for the years 2012–2020. The DNN method serves as a foundational approach in the development of a sustained and comprehensive global terrestrial ET dataset. The basis for characterizing and analyzing global hydrological dynamics and carbon cycling is provided by this dataset.

Weeds have a serious impact on lettuce cultivation. Weeding is an efficient way to increase lettuce yields. Due to the increasing costs of labor and the harm of herbicides to the environment, there is an increasing need to develop a mechanical weeding robot to remove weeds. Accurate weed recognition and crop localization are prerequisites for automatic weeding in precision agriculture. In this study, an intra-row weeding system is developed based on a vision system and open/close weeding knives. This vision system combines the improved you only look once v5 (YOLOv5) identification model and the lettuce–weed localization method. Compared with models including YOLOv5s, YOLOv5m, YOLOv5l, YOLOv5n, and YOLOv5x, the optimized SPH-YOLOv5x model exhibited the best performance in identifying, with precision, recall, F1-score, and mean average precision (mAP) value of 95%, 93.32%, 94.1% and 96%, respectively. The proposed weed control system successfully removed the intra-row weeds with 80.25% accuracy at 3.28 km/h. This study demonstrates the robustness and efficacy of the automatic system for intra-row weed control in lettuce.

Recent studies have revealed a putative relationship between diet and glioma development and prognosis, but few studies have examined the association between overall diet and glioma risk. This study, conducted in China, employed a hospital-based case-control approach. The researchers utilized an a priori method based on dietary data to evaluate compliance scores for five healthy dietary patterns (the Mediterranean diet, the Dietary Approaches to Stop Hypertension (DASH) diet, the Mediterranean-DASH diet Intervention for Neurodegenerative Delay (MIND) diet, the Paleolithic diet, and the Planetary Health Diet) in 1012 participants. At the same time, data-driven methods were used to explore the association between dietary patterns and glioma via principal component analysis (PCA). In the multivariate model, adhering to the Mediterranean diet (odds ratio (OR) = 0.29; 95% confidence interval (95% CI): 0.17–0.52), the DASH diet (OR = 0.09; 95% CI: 0.04–0.18), the MIND diet (OR = 0.25; 95% CI: 0.14–0.44), and the Paleolithic diet (OR = 0.13; 95% CI: 0.06–0.25) was associated with a reduced glioma risk. The results of PCA suggested that increasing the intake of plant-based foods and fish and limiting foods rich in carbohydrates, fats, and salts were associated with a reduced glioma risk. There was a substantial nonlinear dose–response association between glioma and the Mediterranean diet score. However, the DASH diet score, the MIND diet score, and the Paleolithic diet score exhibited linear dose–response relationships. Therefore, this study finds that dietary patterns may be an influencing factor for glioma risk.

Remote sensing images are very vulnerable to cloud interference during the imaging process. Cloud occlusion, especially thick cloud occlusion, significantly reduces the imaging quality of remote sensing images, which in turn affects a variety of subsequent tasks using the remote sensing images. The remote sensing images miss ground information due to thick cloud occlusion. The thick cloud removal method based on a temporality global–local structure is initially suggested as a solution to this problem. This method includes two stages: the global multi-temporal feature fusion (GMFF) stage and the local single-temporal information restoration (LSIR) stage. It adopts the fusion feature of global multi-temporal to restore the thick cloud occlusion information of local single temporal images. Then, the featured global–local structure is created in both two stages, fusing the global feature capture ability of Transformer with the local feature extraction ability of CNN, with the goal of effectively retaining the detailed information of the remote sensing images. Finally, the local feature extraction (LFE) module and global–local feature extraction (GLFE) module is designed according to the global–local characteristics, and the different module details are designed in this two stages. Experimental results indicate that the proposed method performs significantly better than the compared methods in the established data set for the task of multi-temporal thick cloud removal. In the four scenes, when compared to the best method CMSN, the peak signal-to-noise ratio (PSNR) index improved by 2.675, 5.2255, and 4.9823 dB in the first, second, and third temporal images, respectively. The average improvement of these three temporal images is 9.65%. In the first, second, and third temporal images, the correlation coefficient (CC) index improved by 0.016, 0.0658, and 0.0145, respectively, and the average improvement for the three temporal images is 3.35%. Structural similarity (SSIM) and root mean square (RMSE) are improved 0.33% and 34.29%, respectively. Consequently, in the field of multi-temporal cloud removal, the proposed method enhances the utilization of multi-temporal information and achieves better effectiveness of thick cloud restoration.

Nanmu (Phoebe zhennan) has a unique fragrance and is a high-quality tree species for forest conservation. The types and contents of volatile compounds in different tissues of nanmu wood are different, and the study of its volatile metabolites can help us to understand the source of its fragrance and functions. In order to explore the metabolites related to the wood fragrance of nanmu and to find out the unique volatile substances in the heartwood, gas chromatography–mass spectrometry (GC-MS) was performed to analyze the non-targeted metabolomics in five radial tissues from the sapwood to the heartwood of nanmu. A total of 53 volatile metabolites belonging to 11 classes were detected in all tissues, including terpenes, aromatic hydrocarbons, organoheterocyclics, phenols, esters, organic acids, alcohols, alkaloids, alkane, indoles derivatives, and others. And most of the volatile metabolites were identified for the first time in nanmu wood. Among them, terpenes and aromatic hydrocarbons were the main volatile components. In addition, 22 differential metabolites were screened from HW and SW, HW, and TZ via metabolomic analysis. Among these DAMs, three volatile metabolites (cadinene, a sesquiterpenoid; p-cymene, a monoterpenoid; 1,3,5-triisopropylbenzene, an aromatic hydrocarbon) contributed heavily to the characteristic fragrance of the heartwood. Additionally, the expression of transcripts showed that the unigenes in the terpenoid biosynthesis pathway were especially up-regulated in the SW. Therefore, we speculated that fragrance-related metabolites were synthesized in SW and then deposited in heartwood during sapwood transformed to heartwood. The expression levels of transcription factors (e.g., WRKY, C2H2, NAC) acted as the major regulatory factors in the synthesis of terpenoid. The results lay the foundations for further studies on the formation mechanism of fragrance components in nanmu wood and also provide a reference for the further development and utilization of nanmu wood.

As power sources for electric vehicles, lithium-ion batteries (LIBs) have many advantages, such as high energy density and wide temperature range. In the algorithm design process for LIBs, various battery models with different model structures are needed, among which the electrochemical model is widely used due to its high accuracy. However, the electrochemical model is composed of multiple nonlinear partial differential equations (PDEs) that make the simulating process time-consuming. In this paper, a physics-informed neural network single-particle model (PINN SPM) is proposed to improve the accuracy of the single-particle model (SPM) under high C-rates, while ensuring high solving speed. In PINN SPM, an SPM-Net is designed to solve the distribution of lithium-ion concentration in the electrolyte. In the neural network learning process, a loss function is designed based on the physical constraints brought by the PDEs, which reduces the error of the neural network under dynamic working conditions. Finally, the PINN SPM proposed in this paper can achieve a maximum relative error of up to 1.2% compared with the high-fidelity data generated from the P2D model under various conditions. Additionally, the PINN SPM is 20.8% faster than traditional numerical solution methods with the same computational resources.

With the development of vehicular communication technology, the electromagnetic compatibility requirements of vehicular communication systems are becoming more demanding. The traditional four-level electromagnetic compatibility evaluation model is widely applied in many scenarios. However, this model neglects the mutual interference of electronic devices inside a vehicle, and it cannot evaluate whether reduced radio receiver sensitivity, antenna isolation, and communication distance satisfy the system requirements for vehicular communication, thus making it unsuitable for digital communication systems. With the development of remote sensing technology, high-precision digital maps are easy to acquire and thus widely used. In this work, a modified five-level evaluation model based on digital maps is proposed, where digital maps are employed to support receiver sensitivity, antenna isolation, and communication performance evaluation. Through remote sensing technology and digital maps, a terrain profile is obtained, and a more accurate vehicle communication propagation model is established. In the experiment, an actual armored vehicular communication system example is applied to verify the performance of the proposed five-level evaluation model. Compared with the free-space propagation model, the error of the actual power received by the receiver is reduced by 0.97%, and the error of the communication distance where the sensitivity of the receiver is reduced by more than the system EMC threshold is reduced by 16.78%. The calculated antenna isolation degree is basically consistent with the actual measurement data. The model is able to evaluate the electromagnetic compatibility of an armored vehicular communication system more quickly, accurately, and comprehensively compared to previous evaluation models.

Bidirectional pulsed current (BPC) heating has proven to be an effective method for internal heating. However, current research has primarily focused on the impact of symmetrical BPC on battery heat generation, while neglecting the influence of different BPC parameters. To address this gap, this paper investigates the effects of various BPC parameters on battery heat generation. Initially, an electro-thermal coupled model of the battery is constructed based on the results of electrochemical impedance spectroscopy (EIS) tests conducted at different temperatures and amplitudes at 20% state of charge (SOC). The validation results of the model demonstrate that the absolute errors of voltage and temperature are generally less than 50 mV and 1.2 °C. Subsequently, the influence of BPC parameters on battery heat generation is examined under different terminal voltage constraints, temperatures, and frequencies. The findings at 20% SOC reveal that symmetrical BPC does not consistently correspond to the maximum heating power. The proportion of charge time and discharge time in one cycle, corresponding to the maximum heating power, varies depending on the charge and discharge cut-off voltages. Moreover, these variations differ across frequencies and temperatures. When the terminal voltage is constrained between 3 V and 4.2 V, the maximum heat power corresponds to a discharge time share of 0.55 in one cycle. In conclusion, the results underscore the complex relationship between BPC parameters and battery heat generation, which can further enhance our understanding of effective heating strategies for batteries.

Price competition and quality competition are the main ways to increase and maintain international competitiveness in the world. However, competitive strategies can vary significantly from product to product. With the utility function and trade gravity model as the research framework in this paper, we use the top 10 countries in terms of the trade volume of forest products from 2012 to 2021 as samples to systematically explore the differential impacts of price and quality on the international competitiveness of forest products. The results of the panel data model that show the regression coefficient of forest product price in terms of international competitiveness are significantly negative, while the regression coefficient of forest product quality is significantly positive, but the absolute value of the regression coefficient of product quality is higher than that of forest product price. Thus, the price and quality of forest products are key factors affecting international competitiveness in general, with the quality of forest products having a higher impact on international competitiveness than the price. However, a further analysis of different forest product categories and countries revealed significant differences in the significance and magnitude of price and quality impacts on international competitiveness. The quality of forest products contributes more to international competitiveness in Brazil, Canada, Germany, Russia, Sweden and the United States. Conversely, the price of forest products in China, Finland, Italy and Poland contributes more to international competitiveness. Therefore, an objective choice of price, quality or a quality:price ratio strategy, taking into account the industry and characteristics of forest products in each country, can contribute to the sustainable improvement of the international competitiveness of forest products.

Subtropical suburban secondary evergreen broadleaved forests are essential in regulating the ecological environment’s quality and promoting urban sustainable development. In the suburbs of Hangzhou City, well-preserved secondary evergreen broadleaved forest communities were selected to establish a 6 ha forest dynamic monitoring plot. Community surveys and environmental factor measurements were conducted in this area. This study investigated the beta diversity patterns at different scales by considering the environmental and spatial factors to explore the driving beta diversity. Using a similar paired-site beta diversity decomposition method, the study aimed to investigate the differences in species composition and the mechanisms of multiple species coexistence within the secondary evergreen broadleaved forest communities. The results showed that the beta diversity of the suburban secondary evergreen broadleaved forest communities decreased with the increasing spatial scale. Both the dispersal limitation and the environmental filtering were found to drive the formation of beta diversity patterns in these subtropical suburban forests. At relatively smaller scales (

Lodging is one of the most important factors affecting the high and stable yield of wheat worldwide. Solid-stemmed wheat has higher stem strength and lodging resistance than hollow-stemmed wheat does. There are many solid-stemmed varieties, landraces, and old varieties of durum wheat. However, the transfer of solid stem genes from durum wheat is suppressed by a suppressor gene located on chromosome 3D in common wheat, and only hollow-stemmed lines have been created. However, synthetic hexaploid wheat can serve as a bridge for transferring solid stem genes from tetraploid wheat to common wheat. In this study, the F1, F2, and F2:3 generations of a cross between solid-stemmed Syn-SAU-119 and semisolid-stemmed Syn-SAU-117 were developed. A single dominant gene, which was tentatively designated Su-TdDof and suppresses stem solidity, was identified in synthetic hexaploid wheat Syn-SAU-117 by using genetic analysis. By using bulked segregant RNA-seq (BSR-seq) analysis, Su-TdDof was mapped to chromosome 7DS and flanked by markers KASP-669 and KASP-1055 within a 4.53 cM genetic interval corresponding to 3.86 Mb and 2.29 Mb physical regions in the Chinese Spring (IWGSC RefSeq v1.1) and Ae. tauschii (AL8/78 v4.0) genomes, respectively, in which three genes related to solid stem development were annotated. Su-TdDof differed from a previously reported solid stem suppressor gene based on its origin and position. Su-TdDof would provide a valuable example for research on the suppression phenomenon. The flanking markers developed in this study might be useful for screening Ae. tauschii accessions with no suppressor gene (Su-TdDof) to develop more synthetic hexaploid wheat lines for the breeding of lodging resistance in wheat and further cloning the suppressor gene Su-TdDof.

Biomass porous carbon is a low-cost, environmentally friendly material with no secondary pollution and has great potential in the field of dye pollutant adsorption. In this work, we used lignin, a renewable resource abundant in nature, to completely replace phenol and develop a lignin-based phenolic resin carbon (LPFC) adsorbent with high dye removal capacity, high recyclability, and low production cost. The samples were characterized by scanning electron microscopy, Fourier transform infrared spectroscopy, and Raman spectroscopy. Then the effects of adsorbent dosage (1 g/L, 2 g/L, 3 g/L, 4 g/L, and 5 g/L), temperature (30 °C, 45 °C, and 60 °C), initial dye concentration (100, 200, 300, 400, 500, 600, 700, and 800 mg/L), and pH (3, 4, 6, 8, 10, and 12) on the adsorption capacity were investigated during the adsorption process. The experimental results showed that the pore structure of LPFC was richer and more graphitized than that of phenolic resin carbon (PFC). The adsorption performance of LPFC on CR was better than that of PFC. The adsorption characteristics of LPFC were investigated from the adsorption isotherm and kinetic perspectives. The Langmuir isothermal adsorption model and the proposed second-order kinetic model were able to fit the adsorption data better. The adsorption process preferred monolayer adsorption, and the proposed second-order model predicted a maximum adsorption capacity of 425.53 mg/g. After five cycles, the removal of CR by LPFC only decreased from 92.1 to 79.2%. It can be seen that LPFC adsorbents have great potential in the field of wastewater treatment and can effectively realize the high-value application of lignin.

Ultrasonic-assisted inner diameter machining is a slicing method for hard and brittle materials. During this process, the sawing force is the main factor affecting the workpiece surface quality and tool life. Therefore, based on indentation fracture mechanics, a theoretical model of the cutting force of an ultrasound-assisted inner diameter saw is established in this paper for surface quality improvement. The cutting experiment was carried out with alumina ceramics (99%) as an exemplar of hard and brittle material. A six-axis force sensor was used to measure the sawing force in the experiment. The correctness of the theoretical model was verified by comparing the theoretical modeling with the actual cutting force, and the influence of machining parameters on the normal sawing force was evaluated. The experimental results showed that the ultrasonic-assisted cutting force model based on the six-axis force sensor proposed in this paper was more accurate. Compared with the regular tetrahedral abrasive model, the mean value and variance of the proposed model’s force prediction error were reduced by 5.08% and 2.56%. Furthermore, by using the proposed model, the sawing processing parameters could be updated to improve the slice surface quality from a roughness Sa value of 1.534 µm to 1.129 µm. The proposed model provides guidance for the selection of process parameters and can improve processing efficiency and quality in subsequent real-world production.

With the accelerated growth of the UAV industry, researchers are paying close attention to the flight safety of UAVs. When a UAV loses its GPS signal or encounters unusual conditions, it must perform an emergency landing. Therefore, real-time recognition of emergency landing zones on the ground is an important research topic. This paper employs a semantic segmentation approach for recognizing emergency landing zones. First, we created a dataset of UAV aerial images, denoted as UAV-City. A total of 600 UAV aerial images were densely annotated with 12 semantic categories. Given the complex backgrounds, diverse categories, and small UAV aerial image targets, we propose the STDC-CT real-time semantic segmentation network for UAV recognition of emergency landing zones. The STDC-CT network is composed of three branches: detail guidance, small object attention extractor, and multi-scale contextual information. The fusion of detailed and contextual information branches is guided by small object attention. We conducted extensive experiments on the UAV-City, Cityscapes, and UAVid datasets to demonstrate that the STDC-CT method is superior for attaining a balance between segmentation accuracy and inference speed. Our method improves the segmentation accuracy of small objects and achieves 76.5% mIoU on the Cityscapes test set at 122.6 FPS, 68.4% mIoU on the UAVid test set, and 67.3% mIoU on the UAV-City dataset at 196.8 FPS on an NVIDIA RTX 2080Ti GPU. Finally, we deployed the STDC-CT model on Jetson TX2 for testing in a real-world environment, attaining real-time semantic segmentation with an average inference speed of 58.32 ms per image.

In this paper, A new gold self-relay catalysis enabling intramolecular annulation and intermolecular Michael addition of 3-yne-1,2-diols with aurone-derived azadienes (or para-quinone methides) is reported, efficiently furnishing a range of unsymmetrical furanized triarylmethanes with substituent diversity in good yields. The overall process was governed by the π- and σ-Lewis acid capability of gold complexes, providing a catalytic strategy for constructing triarylmethane skeletons.

This study presents an innovative, intelligent obstacle avoidance module intended to significantly enhance the collision prevention capabilities of the robotic arm mechanism onboard a high-speed rail tunnel lining inspection train. The proposed module employs a fusion of ORB-SLAM3 and Normal Distribution Transform (NDT) point cloud registration techniques to achieve real-time point cloud densification, ensuring reliable detection of small-volume targets. By leveraging spatial filtering, cluster computation, and feature extraction, precise obstacle localization information is further obtained. A fusion of multi-modal data is achieved by jointly calibrating 3D LiDAR and camera images. Upon validation through field testing, it is demonstrated that the module can effectively detect obstacles with a minimum diameter of 0.5 cm, with an average deviation controlled within a 1–2 cm range and a safety margin of 3 cm, effectively preventing collisions. Compared to traditional obstacle avoidance sensors, this module provides information across more dimensions, offering robust support for the construction of powerful automated tunnel inspection control systems and digital twin lifecycle analysis techniques for railway tunnels.

Spikelet number and grain number per spike are two crucial and correlated traits for grain yield in wheat. Photoperiod-1 (Ppd-1) is a key regulator of inflorescence architecture and spikelet formation in wheat. In this study, near-isogenic lines derived from the cross of a synthetic hexaploid wheat and commercial cultivars generated by double top-cross and two-phase selection were evaluated for the number of days to heading and other agronomic traits. The results showed that heading time segregation was conferred by a single incomplete dominant gene PPD-D1, and the 2 kb insertion in the promoter region was responsible for the delay in heading. Meanwhile, slightly delayed heading plants and later heading plants obviously have advantages in grain number and spikelet number of the main spike compared with early heading plants. Utilization of PPD-D1 photoperiod sensitivity phenotype as a potential means to increase wheat yield potential.

As a natural polymer, lignin is only less abundant in nature than cellulose. It has the form of an aromatic macromolecule, with benzene propane monomers connected by molecular bonds such as C-C and C-O-C. One method to accomplish high-value lignin conversion is degradation. The use of deep eutectic solvents (DESs) to degrade lignin is a simple, efficient and environmentally friendly degradation method. After degradation, the lignin is broken due to β-O-4 to produce phenolic aromatic monomers. In this work, lignin degradation products were evaluated as additives for the preparation of polyaniline conductive polymers, which not only avoids solvent waste but also achieves a high-value use of lignin. The morphological and structural characteristics of the LDP/PANI composites were investigated using 1H NMR, Fourier-transform infrared spectroscopy, scanning electron microscopy, transmission electron microscopy, thermogravimetric analysis and elemental analysis. The LDP/PANI nanocomposite provides a specific capacitance of 416.6 F/g at 1 A/g and can be used as a lignin-based supercapacitor with good conductivity. Assembled as a symmetrical supercapacitor device, it provides an energy density of 57.86 Wh/kg, an excellent power density of 952.43 W/kg and, better still, a sustained cycling stability. Thus, the combination of polyaniline and lignin degradate, which is environmentally friendly, amplifies the capacitive function on the basis of polyaniline.

The use of green and sustainable biomass-derived compounds to obtain excellent electrochemical properties is important to address growing environmental and energy issues. In this paper, cheap and abundant watermelon peel was used as a raw material to successfully synthesize nitrogen-phosphorus double-doped bio-based porous carbon by a one-step carbonization method and explore it as a renewable carbon source for low-cost energy storage devices. The supercapacitor electrode exhibited a high specific capacity of 135.2 F/g at a current density of 1 A/g in a three-electrode system. A variety of characterization methods and electrochemical tests indicate that porous carbon prepared by this simple method has great potential as electrode materials for supercapacitors.

To efficiently improve the productivity of strawberries under growing environmental change, the photosynthesis, root growth, and nitrogen allocation of strawberries (Fragaria × ananassa Duch. cv. Toyonoka) were investigated in a factorial design of CO2, temperature, and nitrogen supply. Elevated CO2 decreased the maximum CO2 assimilation rate (Amax), maximum CO2 carboxylation capacity per unit leaf area (Vcmax), and maximum CO2 carboxylation capacity per unit leaf mass (Vcm-m) by 20%, 24%, and 44%, respectively. Meanwhile, it reduced the SPAD value, maximal fluorescence level in the dark-adapted state (Fm), and maximal photochemical efficiency of PSII (Fv/Fm). Moreover, root branches, root number, root dry weight, and nitrogen-use efficiency were further increased in response to elevated CO2 under low nitrogen. When elevated CO2 was applied together with nitrogen nutrients, the Vcm-m and root nitrogen concentration (RNC) declined by 32% and 12%, respectively, but the total root dry weight (TRDW) increased by 88%. If the nitrogen nutrient was individually applied, the TRDW decreased by 16%, while the RNC increased by 21%. When the high temperature was individually applied, the TRDW increased by 104%, but the RNC decreased by 5%. Overall, elevated CO2 exacerbated photosynthetic down-regulation and significantly affected nitrogen redistribution among strawberry organs, reducing leaf nitrogen concentration and accelerating leaf senescence. However, it could increase seed quantity and improve its quality as well. In other words, under nitrogen-deficient conditions, elevated CO2 could improve the survival of offspring via the cost of the mother plant’s growth capacity.

In this study, the relationship between viscosity of synthetic polymer rubberized gel (SPRG) used for waterproofing and adhesive strength on varying degrees of wetted surfaces is examined. First, SPRG specimens of different ratios of viscosity modifiers were prepared (3% to 10%, interval of 1% per specimen type). These specimens were installed onto substrate surfaces of varying degrees of humidity ratio (0%, 10%, 20%, 30%, 50%, 70% and 100%). Upon following the standard installation procedure of the SPRG specimens, standard pull-off tests were conducted. Results of the test showed that a consistent relationship exists between the viscosity of synthetic polymer rubberized gel (SPRG) and its adhesive strength on different degrees of wetted surfaces. Linear regression analysis and subsequent calculation of the respective coefficient of determination leads to the result that viscosity is a relevant factor for achieving stable adhesion when it comes to the usage of SPRG waterproofing. The findings of this study could have significant implications for the development and application of adhesive gel waterproofing materials in various industries.

As the number and length of high-speed railway tunnels increase in China, implicit defects such as insufficient lining thicknesses, voids, and poor compaction have become increasingly common, posing a serious threat to train operation safety. It is, therefore, imperative to conduct a comprehensive census of the defects within the tunnel linings. In response to this problem, this study proposes a high-speed railway tunnel detection method based on vehicle-mounted air-coupled GPR. Building on a forward simulation of air-coupled GPR, the study proposes the F-K filtering and BP migration algorithms based on the practical considerations of random noise and imaging interference from the inherent equipment. Through multi-dimensional quantitative comparisons, these algorithms are shown to improve the spectrum entropy values and instantaneous amplitude ratios by 4.6% and 11.6%; and 120% and 180%, respectively, over the mean and bandpass filtering algorithms, demonstrating their ability to suppress clutter and enhance the internal signal prominence of the lining. The experimental results are consistent with the forward simulation trends, and the verification using the ground-coupled GPR detection confirms that air-coupled GPR can meet the requirements of high-speed railway tunnel lining inspections. A comprehensive GPR detection model is proposed to lay the foundation for a subsequent defect census of high-speed railway tunnels.

Increased uncertainty in the trade environment has become a reality. However, so far, there is no well-established indicator system to quantify the international competitiveness of forest products in the context of increased uncertainty in the trade environment. Based on expanding the concept of international competitiveness, we constructed an evaluation indicator system of international competitiveness including market performance and competitive advantage, which highlighted market stability and market sustainability indicators. We obtained a comprehensive international competitiveness index of the forest products by Deviation Maximization Method. This study aims to compare and evaluate the international competitiveness of forest products in the top 10 exporting countries using a comprehensive international competitiveness index. The results showed that it is more accurate and comprehensive to use the comprehensive international competitiveness index to evaluate the international competitiveness of forest products, compared to using only a single index. Additionally, the changes to the composite index of international competitiveness went hand-in-hand with the uncertainties the observed countries face, indicating that the indicator system is applicable to the measurement of international competitiveness in an uncertain environment. Large differences exist in the level of international competitiveness of forest products among observed countries. German paper products and wood chips, Chinese wood furniture, wood-based panels and wood products, U.S. logs and wood pulp, and Canadian sawn wood were the most competitive. On the whole, China, Germany and Italy have the highest level of overall international competitiveness in forest products, with Brazil and Poland showing the most significant increases.

Lignin is an abundant and environmentally friendly biopolymer that contains a large number of phenolic hydroxyl functional group. In this paper, alkaline lignin was modified using different acidic DES (choline chloride/p-toluenesulfonic acid and choline chloride/lactic acid) at 130 ℃ (TC-lignin and LC-lignin) and the conformational relationship between the modified products and the antioxidant activity was investigated. Lignin was characterized by 31P NMR, gel permeation chromatography (GPC) and Fourier transform infrared spectroscopy (FT-IR), and its antioxidant activity was evaluated. The results showed that the alkaline lignin products modified by acidic DES formed relatively homogenous dispersions and were characterized by a relatively low molecular weight and a high content of phenolic hydroxyl groups (e.g., TC-lignin, aliphatic-OH: 3.52 mmol/g, G-OH: 4.18 mmol/g, Mw: 3726, Mn: 2053, PDI: 1.81). The antioxidant activity (free radical scavenging rate, 90.35%) of TC-lignin was somewhat higher than that of LC-lignin (free radical scavenging rate, 89.12%) and both were higher than that of the commercially available antioxidant BHT (free radical scavenging rate, 88.79%). More specifically, we discussed the possible mechanisms of antioxidant reactions of lignin model substances in DPPH solutions. In addition, LC-lignin has an excellent UV-blocking capacity due to the specific phenolic hydroxyl and phenyl propane structure. A simple method is proposed for the modification of industrial lignin to make it suitable for use as an antioxidant and UV-resistant product.

Huanglongbing (HLB) is one of the most severe citrus diseases in the world, causing huge economic losses. However, efficient methods of protecting citrus from HLB have not yet been developed. microRNA (miRNA)-mediated regulation of gene expression is a useful tool to control plant diseases, but the miRNAs involved in regulating resistance to HLB have not yet been identified. In this study, we found that miR171b positively regulated resistance to HLB in citrus. Upon infection with HLB bacteria, the bacteria were detected in the second month in the control plants. However, in the miR171b-overexpressing transgenic citrus plants, the bacteria could not be detected until the 24th month. RNA-seq data indicated that multiple pathways, such as photosynthesis, plant–pathogen interaction, the MAPK signaling pathway, etc., might be involved in improving the resistance to HLB in miR171b-overexpressing plants compared with the control. Finally, we determined that miR171b could target SCARECROW-like (SCL) genes to downregulate their expression, which then led to promoted resistance to HLB stress. Collectively, our results demonstrate that miR171b plays a positive regulatory role in resistance to citrus HLB, and provides a new insight into the role of miRNAs in the adaptation of citrus to HLB stress.