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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.

During the operation of solid oxide fuel cells (SOFCs), the Ni-8YSZ anodes are subjected to thermal mismatch and reoxidation, accompanied by the risk of damage and failure. These damages and failures are generally induced by small defects at the microscopic level, leading to the degradation of the structural bearing capacity. Therefore, the distribution and quantification of the stresses in the real microstructure of Ni-8YSZ electrodes is essential. In this study, the real Ni-8YSZ microstructure was reconstructed based on nano-computed tomography, and the stress distribution of the real microstructure was analyzed based on the finite element method under reoxidation and different operating temperatures. The failure probability of 8YSZ at different degrees of reoxidation was evaluated according to the Weibull method, and the amount of damaged 8YSZ elements was statistically counted. The study results indicate a high level of stress in the thin necks and relatively sharp areas of the microstructure. The 8YSZ has a high failure probability at a reoxidation extent of 5–10%.

Spermatozoa cryopreservation protocols have been established for yellow catfish (Pelteobagrus fulvidraco), but cryopreservation can still cause cellular damage and affect spermatozoa viability and fertility. Therefore, the aim of this paper was to evaluate the effects of adding or not adding cryoprotectants during low-temperature storage on the ultrastructural damage, oxidative damage, and DNA damage of thawed yellow catfish spermatozoa. The mixed semen of three male yellow catfish was divided into a fresh spermatozoa group, a frozen spermatozoa group (DMSO+) with a cryoprotectant (10% DMSO), and a frozen spermatozoa group without a cryoprotectant (DMSO−). Ultrastructural of the spermatozoa after thawing were observed under an electron microscope and the spermatozoa were assayed for SOD, MDA, and T-AOC enzyme activities, as well as for DNA integrity. In terms of movement parameters, compared with DMSO−, the addition of DMSO has significantly improved sperm motility, curve line velocity (VCL), and straight line velocity (VSL). The ultrastructural results showed that although thawed spermatozoa exhibited increased damage than fresh spermatozoa, 10% DMSO effectively reduced the damage to the plasma membrane, mitochondria, and flagellum of spermatozoa by cryopreservation, and most of the spermatozoa were preserved with intact structure. The results of oxidative damage showed that compared with frozen spermatozoa, 10% DMSO significantly increased the activities of SOD and T-AOC enzymes and clearly reduced the activity of the MDA enzyme. The antioxidant capacity of spermatozoa was improved, lipid peroxidation was reduced, and the oxidative damage caused by cryopreservation was mitigated. The DNA integrity of spermatozoa showed that 10% DMSO clearly reduced the DNA fragmentation rate. In conclusion, 10% DMSO can effectively reduce the ultrastructural damage, oxidative damage, and DNA damage of yellow catfish spermatozoa during cryopreservation; it can also further optimize the cryopreservation protocol for yellow catfish spermatozoa. Meanwhile, it also provides a theoretical basis for the future optimization of the cryopreservation protocols.

The current healthcare system suffers from information fragmentation. Achieving an efficient medical process necessitates coordination among healthcare institutions at different levels to enhance efficiency and reduce costs, with information synergy playing a crucial role in this endeavor. Due to concerns about information security and the costs associated with information construction, healthcare institutions are often reluctant to collaborate on information sharing. To address this issue, we propose an analytical framework for information synergy among different levels of healthcare institutions, which clarifies the impact of synergy benefits on the collaborative behavior of medical system members. We construct an evolutionary game model to study the dynamic strategy choices of the relevant participants. Our analysis reveals that general hospitals, as the main actors in the healthcare system, have a lower willingness to further invest in information construction and sharing. Their willingness is significantly influenced by the volume of information they can utilize from primary healthcare institutions. Moreover, interventions from the government and society can effectively encourage all members of the medical system to adopt collaborative information strategies. Establishing a comprehensive and unified information platform, actively promoting hierarchical diagnosis and treatment, and setting reasonable assessment indicators for the level of information available from healthcare institutions can significantly reduce the costs associated with information construction. These measures will enhance the benefits of information synergy, thereby facilitating the exchange of information within the healthcare system.

The high performance of deep learning networks relies on large datasets and powerful computational resources. However, collecting enough diseased training samples is a daunting challenge. In addition, existing few-shot learning models tend to suffer from large size, which makes their deployment on edge devices difficult. To address these issues, this study proposes a pruning-based lightweight few-shot learning (Prune-FSL) approach, which aims to utilize a very small number of labeled samples to identify unknown classes of crop diseases and achieve lightweighting of the model. First, the disease few-shot learning model was built through a metric-based meta-learning framework to address the problem of sample scarcity. Second, a slimming pruning method was used to trim the network channels by the γ coefficients of the BN layer to achieve efficient network compression. Finally, a meta-learning pruning strategy was designed to enhance the generalization ability of the model. The experimental results show that with 80% parameter reduction, the Prune-FSL method reduces the Macs computation from 3.52 G to 0.14 G, and the model achieved an accuracy of 77.97% and 90.70% in 5-way 1-shot and 5-way 5-shot, respectively. The performance of the pruned model was also compared with other representative lightweight models, yielding a result that outperforms those of five mainstream lightweight networks, such as Shufflenet. It also achieves 18-year model performance with one-fifth the number of parameters. In addition, this study demonstrated that pruning after sparse pre-training was superior to the strategy of pruning after meta-learning, and this advantage becomes more significant as the network parameters are reduced. In addition, the experiments also showed that the performance of the model decreases as the number of ways increases and increases as the number of shots increases. Overall, this study presents a few-shot learning method for crop disease recognition for edge devices. The method not only has a lower number of parameters and higher performance but also outperforms existing related studies. It provides a feasible technical route for future small-sample disease recognition under edge device conditions.

Traditional range–instantaneous Doppler (RID) methods for maneuvering target imaging are hindered by issues related to low resolution and inadequate noise suppression. To address this, we propose a novel ISAR imaging method enhanced by deep learning, which incorporates the fundamental architecture of CapsNet along with two additional convolutional layers. Pre-training is conducted through the deep learning network to establish the mapping function for reference. Subsequently, the trained network is integrated into the electromagnetic simulation software, Feko 2019, utilizing a combination of geometric forms such as corner reflectors and Luneberg spheres for analysis. The results indicate that the derived ISAR imaging effectively identifies the ISAR program associated with complex aerial targets. A thorough analysis of the imaging results further corroborates the effectiveness and superiority of this approach. Both simulation and empirical data demonstrate that this method significantly enhances imaging resolution and noise suppression.

Despite the abundance of shale-oil and tight-oil reserves in the Fengcheng Formation within the Mahu Sag, exploration and development efforts for both types of reservoir are still in their early stages. A comprehensive examination and comparison of the pore structures of these reservoirs can establish rational classification and evaluation criteria. However, there is a dearth of comparative analyses focusing on the pore structures of shale-oil and tight-oil reservoirs within the Fengcheng Formation. This study addresses this gap by systematically analyzing and comparing the pore structures of these reservoirs using various techniques such as X-ray diffraction (XRD), scanning electron microscopy (SEM), low-temperature nitrogen adsorption, and mercury intrusion capillary pressure experiments (MICP). The results show that the shale oil within the Fengcheng Formation exhibits a higher content of carbonic acid compared to the tight-oil samples. Furthermore, it demonstrates smaller displacement pressure and median pressure, a larger sorting coefficient, and superior permeability in contrast to tight oil. Notably, the shale oil within the Fengcheng Formation is characterized by abundant striated layer structures and micro-fractures, which significantly contribute to the microstructural disparities between shale-oil and tight-oil reservoirs. These differences in microstructures between shale oil and tight oil within the Fengcheng Formation in the Mahu Sag region delineate distinct criteria for evaluating sweet spots in shale-oil and tight-oil reservoirs.

Engineering construction disturbs the Earth’s surface and exacerbates soil erosion, resulting in sediment contributions at the watershed scale, the spatiotemporal variation of which remains to be clarified. Based on a typically disturbed catchment, soil samples were collected from sources such as forests, grasslands, spoil heaps, and exposed slopes. Sediment deposition was sampled in 2022 and 2023 along the main channel and fingerprinting technology was employed to calculate the relative contributions of different sources. The results indicated that the optimal composite fingerprints comprising Na₂O, Li, Sr, and Ce could effectively resolve the contributions of different sources. Natural sources were the main sediment contributors, but the average contribution decreased from 72.96% to 58.73% over two periods. In contrast, the contribution of spoil heaps and exposed slopes increased from 27.04% to 41.27% and the area percentage increased from 0.18% to 0.30%. The spoil heap represents the relatively large area of disturbance and its contact length with the river determines the sediment contribution rates, which varied spatially in a quadratic trend along the channel. Meanwhile, the sediment contribution of relatively small and dispersed exposed slopes could be quantified using a linear equation of the disturbance weighting indicator (DWI) composed of disturbed area and flow distance. These results would be helpful in assessing the environmental impact of engineering disturbances and optimizing mitigation measures.

The development of electronic devices has a tendency to become more complicated in structure, more integrated in function, and smaller in size. The heat flow density of components continues to escalate, which urgently requires the development of heat sink materials with high thermal conductivity and a low coefficient of expansion. Diamond/aluminum composites have become the research hotspot of thermal management materials with excellent thermophysical and mechanical properties, taking into account the advantages of light weight. In this paper, diamond/Al composites are prepared by combining aluminum as matrix and diamond reinforcement through the discharge plasma sintering (SPS) method. The micro-interfacial bonding state of diamond and aluminum is changed by adjusting the particle size of diamond, and the macroscopic morphology performance of the composites is regulated. Through this, the flexible design of diamond/Al performance can be achieved. As a result, when 150 μm diamond powder and A1-12Si powder were used for the composite, the thermal conductivity of the obtained specimens was up to 660.1 W/mK, and the coefficient of thermal expansion was 5.63 × 10−6/K, which was a good match for the semiconductor material. At the same time, the bending strength is 304.6 MPa, which can satisfy the performance requirements of heat-sinking materials in the field of electronic packaging.

Modern laver fluffiness is achieved by applying high-speed gas to a laver, which is generated by the opening and closing of a laver fluffiness control valve in a fluffiness system. To address the problems of the slow response speed and poor stability of valves used in the laver processing industry at present, this paper proposes a novel principle of a high-speed switching control valve, which has the advantages of a fast response speed, high stability, and long service life. The structure and working principle of the control valve are introduced, and the calculation equation of the valve’s flow area is established. The flow field inside the control valve with different openings was numerically calculated in this study using Fluent. The flow regulation characteristics and flow field performance of the control valve were also analyzed. The results show that, with an increase in the valve opening, the influence of the flow area at the valve throttle on the valve flow rate was weakened. When the valve was opened, a vortex appeared in both the upper and lower cavities, and jet flow occurred at the throttle of the middle flow channel. As the valve opening increased, the pressure in the upper cavity reduced, while the pressure in the lower cavity increased. The vortex in the flow field intensified, and the jet phenomenon at the valve throttle gradually disappeared. At the same time, the main stream in the lower cavity gradually changed from an annular flow to a direct flow toward the valve outlet. Furthermore, the impact, collision, and vortex formation of the fluid caused energy loss of the fluid, leading to a decrease in the outlet flow of the control valve.

In this paper, the solid-state shear milling (S3M) strategy featuring a very strong three-dimensional shear stress field was adopted to prepare the high-performance polyoxymethylene (POM)/molybdenum disulfide (MoS2) functional nanocomposite. The transmission electron microscope and Raman measurement results confirmed that the bulk MoS2 particle was successfully exfoliated into few-layer MoS2 nanoplatelets by the above simple S3M physical method. The polarized optical microscope (PLM) observation indicated the pan-milled nanoscale MoS2 particles presented a better dispersion performance in the POM matrix. The results of the tribological test indicated that the incorporation of MoS2 could substantially improve the wear resistance performance of POM. Moreover, the pan-milled exfoliated MoS2 nanosheets could further substantially decrease the friction coefficient of POM. Scanning electron microscope observations on the worn scar revealed the tribological mechanism of the POM/MoS2 nanocomposite prepared by solid-state shear milling. The tensile test results showed that the pan-milled POM/MoS2 nanocomposite has much higher elongation at break than the conventionally melt-compounded material. The solid-state shear milling strategy shows a promising prospect in the preparation of functional nanocomposite with excellent comprehensive performance at a large scale.

Pure lanthanum hexaboride (LaB6) ceramics were prepared using powders of different grain sizes. The ceramics could reach a relative density of 98.2% at high temperatures and pressures, but had a low flexural strength (136.9 MPa). LaB6 ceramics were synthesized using ZrO2-Al2O3-TiO2 (ZAT) as sintering additives. The ceramics demonstrate high density and excellent mechanical properties. The hot pressure sintering (HPS) method was utilized in the synthesis of the ceramics. Investigations were conducted on the effects of ZAT content, as well as the effects of the sintering temperature and pressure on the sintering behavior, microstructure, and mechanical and electrical properties of LaB6 ceramics. LaB6 ceramics fabricated with a ZAT addition of 6 wt.%, at a sintering temperature of 1700 °C, and under a pressure of 50 MPa, exhibited superior sintering and electrical properties, including a relative density of 97%, a conductivity of 7.2 MS/m, a flexural strength of 281.5 MPa, and a Vickers hardness of 21.2 GPa. The LaB6 ceramics synthesized in this research exhibit promising potential as electron-emitting cathodes for field emission applications.

Recrystallization of polyoxymethylene (POM) in solvent is an effective post-treatment method for manufacturing a better POM product. Herein, the crystallization process of POM in methanol was investigated with the use of a series of equipment. The results reveal that POM crystallization in methanol yields two kinds of particle morphologies, including small particles with lamellar structures branching and growing in all directions and large particles resulting from melt agglomeration. The mechanism of POM crystallization in methanol with two distinct pathways was proposed, in which solution cooling crystallization of POM at higher temperature yields small particles while melt crystallization yields large particles. Furthermore, both non-isothermal and isothermal crystallization kinetics of POM were determined. The Avrami equation was employed to derive the crystallization rate constant via data fitting. The activation energy of crystallization was then obtained using the Arrhenius formula. The kinetics suggest that recrystallization of POM in methanol may dissolve and remove substances hindering raw material crystallization, achieving a faster crystallization rate for products.

Arterial stiffness has been proved to be an important parameter in the evaluation of cardiovascular diseases, and Pulse Wave Velocity (PWV) is a strong indicator of arterial stiffness. Compared to regional PWV (PWV among different arteries), local PWV (PWV within a single artery) outstands in providing higher precision in indicating arterial properties, as regional PWVs are highly affected by multiple parameters, e.g., variations in blood vessel lengths due to individual differences, and multiple reflection effects on the pulse waveform. However, local PWV is less-developed due to its high dependency on the temporal resolution in synchronized signals with usually low signal-to-noise ratios. This paper presents a method for the noninvasive simultaneous measurement of two local PWVs in both left and right radial arteries based on the Fiber Bragg Grating (FBG) technique via correlation analysis of the pulse pairs at the fossa cubitalis and at the wrist. Based on the measurements of five male volunteers at the ages of 19 to 21 years old, the average left radial PWV ranged from 9.44 m/s to 12.35 m/s and the average right radial PWV ranged from 11.50 m/s to 14.83 m/s. What is worth mentioning is that a stable difference between the left and right radial PWVs was observed for each volunteer, ranging from 2.27 m/s to 3.04 m/s. This method enables the dynamic analysis of local PWVs and analysis of their features among different arteries, which will benefit the diagnosis of early-stage arterial stiffening and may bring more insights into the diagnosis of cardiovascular diseases.

Diamond/aluminum composites have attracted significant attention as novel thermal management materials, with their interfacial bonding state and configuration playing a crucial role in determining their thermal conductivity and mechanical properties. The present work aims to evaluate the bending strength and thermal conductivity of CNT-modified Ti-coated diamond/aluminum composites with multi-scale structures. The Fe catalyst was encapsulated on the surface of Ti-coated diamond particles using the solution impregnation method, and CNTs were grown in situ on the surface of Ti-coated diamond particles using the plasma-enhanced chemical vapor deposition (PECVD) method. We investigated the influence of interface structure on the thermal conductivity and mechanical properties of diamond/aluminum composites. The results show that the CNT-modified Ti-coated diamond/aluminum composite exhibits excellent bending strength, reaching up to 281 MPa, compared to uncoated diamond/aluminum composites and Ti-coated diamond/aluminum composites. The selective bonding between diamond and aluminum was improved by the interfacial reaction between Ti and diamond particles, as well as between CNT and Al. This led to the enhanced mechanical properties of Ti-coated diamond/aluminum composites while maintaining acceptable thermal conductivity. This work provides insights into the interface’s configuration design and the performance optimization of diamond/metal composites for thermal management.

In the global transition towards sustainable energy, microgrids are emerging as a core component of distributed energy systems and a pivotal technology driving this transformation. By integrating renewable energy sources such as solar and wind power, microgrids not only enhance energy efficiency and reduce reliance on traditional energy sources but also bolster grid stability and mitigate the risk of widespread power outages. Consequently, microgrids demonstrate significant potential in improving the reliability of power supply and facilitating flexibility in energy consumption. However, the operational planning and optimization of microgrids are faced with complex challenges characterized by multiple objectives and constraints, making the reduction in operational costs a focal point of research. This study fully considers an operational model for a microgrid that incorporates distributed energy resources and comprehensive costs, integrating a battery storage system to ensure three-phase balance. The microgrid model includes photovoltaic power generation, wind power generation, fuel cells, micro-gas turbines, energy storage systems, and loads. The objectives of operating and maintaining this microgrid primarily involve optimizing dispatch, energy consumption, and pollution emissions, aiming to reduce carbon emissions and minimize total costs. To achieve these goals, the study introduces a carbon emission constraint strategy and proposes an improved Multi-Strategy Integrated Sparrow Search Algorithm (MISSA). By applying the MISSA to solve the operational problems of the microgrid and comparing it with other algorithms, the results demonstrate the effectiveness of the carbon emission constraint strategy in the microgrid’s operation. Furthermore, the results prove that the MISSA can achieve the lowest comprehensive operational costs for the microgrid, confirming its effectiveness in addressing the operational challenges of the microgrid.

Sperm cryopreservation is a valuable tool for breeding, conservation, and genetic improvement in aquatic resources, while oxidative damage will cause a decline in sperm quality during this progress. Melatonin (MT), a natural antioxidant hormone, is used as an additive in sperm cryopreservation to reduce cellular damage from oxidative stress. Here, we aimed to investigate the effect of adding MT to the freezing medium in sperm cryopreservation of brown-marbled grouper (Epinephelus fuscoguttatus). Different concentrations of MT (0, 0.1, 0.25, and 0.5 mg/mL) were tested. We evaluated sperm motility, viability, apoptosis, mitochondrial membrane potential (MMP), and fertilization ability to assess the effects of MT supplementation. Our results demonstrated that the addition of MT to the extender improved the post-thaw motility, MMP, and fertilization ability of brown-marbled grouper sperm. The total motility, curvilinear velocity, straight linear velocity, and average path velocity in MT-treated groups (0.1 and 0.25 mg/mL) exhibited significantly higher values than that of the control group. A higher MMP (p < 0.05) was observed in the group treated with 0.25 mg/mL MT, suggesting that supplementation of MT in the extender might be able to protect mitochondrial membrane integrity effectively. Regarding fertilizing ability, 0.25 mg/mL MT yielded a significantly higher hatching rate than the control. An adverse effect was found with the concentration of MT up to 0.5 mg/mL, suggesting the possible toxicity of a high-dose addition. In this study, we optimized the sperm cryopreservation protocol of brown-marbled grouper, which might be valuable for sperm cryopreservation and sample commercialization of groupers and other fish.

The online calibration method for current transformers is an important research direction in the field of smart grids. This article constructs a transformer error prediction model based on spatiotemporal integration. This model draws inspiration from the structure of forgetting gates in gated loop units and combines it with a graph convolutional network (GCN) that is good at capturing the spatial relationships within the graph attention network to construct an adaptive GCN. The spatial module formed by this adaptive GCN is used to model the spatial relationships in the circuit network, and the attention mechanism and gated time convolutional network are combined to form a time module to learn the temporal relationships in the circuit network. The layer that combines the time and space modules is used, which consists of a gating mechanism for spatiotemporal fusion, and a transformer error prediction model based on a spatiotemporal correlation analysis is constructed. Finally, it is verified on a real power grid operation dataset, and compared with the existing prediction methods to analyze its performance.

Rigid robots have found wide-ranging applications in manufacturing automation, owing to their high loading capacity, high speed, and high precision. Nevertheless, these robots typically feature joint-based drive mechanisms, possessing limited degrees of freedom (DOF), bulky structures, and low manipulability in confined spaces. In contrast, continuum robots, drawing inspiration from biological structures, exhibit characteristics such as high compliance, lightweight designs, and high adaptability to various environments. Among them, cable-driven continuum robots (CDCRs) driven by multiple cables offer advantages like higher dynamic response compared to pneumatic systems and increased working space and higher loading capacity compared to shape memory alloy (SMA) drives. However, CDCRs also exhibit some shortcomings, including complex motion, drive redundancy, challenging modeling, and control difficulties. This study presents a comprehensive analysis and summary of CDCR research progress across four key dimensions: configuration design, kinematics and dynamics modeling, motion planning, and motion control. The objective of this study is to identify common challenges, propose solutions, and unlock the full potential of CDCRs for a broader range of applications.

The effect of laser irradiation and tensile stress on the microstructure and soft magnetic properties of the FeSiBNbCu nanocrystalline alloy prepared using a continuous laser has been investigated. The experimental results indicate that a decreased laser scanning speed provides more thermal energy to induce nanocrystals and encourage grain growth. When the scanning speed is excessively high, the crystallization process will cease due to a lack of energy to drive diffusion phase transitions. Nevertheless, the introduction of tensile stress could significantly promote crystallization in FeSiBNbCu alloy samples irradiated at these high laser scanning speeds. This phenomenon can be attributed to the augmentation of compressive thermal stress at the interface between the laser-treated track and the untreated region. This heightened compressive stress promotes the diffusivity of atoms, and, as a result, the transformation from amorphous to crystalline states can be enhanced. As the applied tensile stress increases, both grain size and crystalline volume fraction exhibit a proportional augmentation. Consequently, these changes manifest in the soft magnetic properties. The crystalline volume fraction can reach 62%, and the coercivity is 2.9 A/m at the optimized scanning speed; these values correspond to 54% and 3.3 A/m under specific tensile stress loading.

Potato diseases and pests have a serious impact on the quality and yield of potatoes, and timely prevention and control of potato diseases and pests is essential. A rich knowledge reserve of potato diseases and pests is one of the most important prevention and control measures; however, valuable knowledge is buried in the massive data of potato diseases and pests, making it difficult for potato growers and managers to obtain and use it in a timely manner and to develop the potential of knowledge. Therefore, this paper explores the construction method of a knowledge graph for automatic knowledge extraction, which extracts the knowledge of potato diseases and pests scattered in heterogeneous data from multiple sources, organises it into a semantically related knowledge base, and provides potato growers with professional knowledge and timely guidance to effectively prevent and control potato diseases and pests. In this paper, a data corpus on potato diseases and pests, called PotatoRE, is first constructed. Then, a model of ALBert-BiLSTM-Self_Att-CRF is designed to extract knowledge from the corpus to form a triplet structure, which is imported into the Neo4j graph database for storage and visualisation. Furthermore, the performance of the model constructed in this paper is compared and verified using the datasets PotatoRE and People’s Daily. The results show that compared to the SOTA models of ALBert BiLSTM-CRF and ALBert BiGRU-CRF, the accuracy of our model has been improved by 2.92% and 3.12%, respectively, using PotatoRE. Compared to the Bert BiLSTM-CRF model on two datasets, our model not only improves the accuracy, recall, and F1 values, but also has a higher efficiency. The model in this paper solves the problem of the difficult recognition of nested entities. On this basis, through comparative experiments, the TransH model is used to effectively represent the constructed knowledge graph, which lays the foundation for achieving inference, extension, and automatic updating of the knowledge base. The achievements of the thesis have made certain contributions to the automatic construction of large-scale knowledge bases.

The high performance of deep learning networks relies mainly on massive data. However, collecting enough samples of crop disease is impractical, which significantly limits the intelligent diagnosis of diseases. In this study, we propose Heterogeneous Metric Fusion Network-based Few-Shot Learning (HMFN-FSL), which aims to recognize crop diseases with unseen categories using only a small number of labeled samples. Specifically, CBAM (Convolutional Block Attention Module) was embedded in the feature encoders to improve the feature representation capability. Second, an improved few-shot learning network, namely HMFN-FSL, was built by fusing three metric networks (Prototypical Network, Matching Network, and DeepEMD (Differentiable Earth Mover’s Distance)) under the framework of meta-learning, which solves the problem of the insufficient accuracy of a single metric model. Finally, pre-training and meta-training strategies were optimized to improve the ability to generalize to new tasks in meta-testing. In this study, two datasets named Plantvillage and Field-PV (covering 38 categories of 14 crops and containing 50,403 and 665 images, respectively) are used for extensive comparison and ablation experiments. The results show that the HMFN-FSL proposed in this study outperforms the original metric networks and other state-of-the-art FSL methods. HMFN-FSL achieves 91.21% and 98.29% accuracy for crop disease recognition on 5way-1shot, 5way-5shot tasks on the Plantvillage dataset. The accuracy is improved by 14.86% and 3.96%, respectively, compared to the state-of-the-art method (DeepEMD) in past work. Furthermore, HMFN-FSL was still robust on the field scenes dataset (Field-PV), with average recognition accuracies of 73.80% and 85.86% on 5way-1shot, 5way-5shot tasks, respectively. In addition, domain variation and fine granularity directly affect the performance of the model. In conclusion, the few-shot method proposed in this study for crop disease recognition not only has superior performance in laboratory scenes but is also still effective in field scenes. Our results outperform the existing related works. This study provided technical references for subsequent few-shot disease recognition in complex environments in field environments.

The structure of a material is an important factor in determining its physical properties. Here, we adjust the structure of the Ni50Mn37Ga13 spun ribbons by changing the wheel speed to regulate the exchange bias effect of the material. The characterization results of micromorphology and structure show that as the wheel speed increases, the martensite lath decreases from 200 nm to 50 nm, the structure changed from the NM to a NM and 10M mixed martensitic structure containing mainly NM, then changed to NM and 10M where 10M and NM are approaching. Meanwhile, HE first increased and then decreased as the wheel speed increased. The optimum exchange bias effect (HE = 7.2 kOe) occurs when the wheel speed is 25 m∙s−1, mainly attributed to the enhanced ferromagnetism caused by part of 10M in NM martensite, which enhanced the exchange coupling of ferromagnetism and antiferromagnetism. This work reveals the structural dependence of exchange bias and provides a way to tune the magnitude of the exchange bias of Heusler alloys.

The difference in the strengthening effect of different thickness reinforcement plates on an LY12 aluminum alloy central defect plate and the load distribution between the reinforcement plate and central defect plate in the reinforcement plate structure were studied. Through a method of equivalent transverse load in a small interval [a + Δa], the equivalent cyclic load of the central defect plate in the reinforced plate structure under different crack lengths was calculated, and then the distribution of the internal load of the reinforced plate structure with a different thickness with the crack propagation was solved. The secondary development program of the finite element software Abaqus was written in Python language, so that Abaqus software can solve the problem of high cycle fatigue. The finite element simulation of the different thickness-reinforced plate structure is carried out by this program. Through the output data, the equivalent cyclic load of the central defect plate in the reinforced plate structure under different crack lengths is calculated. Through three different fitting methods, the mathematical relationship between the equivalent cyclic load Δσ and the crack length a at both ends of the central defect plate in the reinforced plate structure is described. Based on the mathematical relationship and the finite element output data, the fatigue crack propagation life of the reinforced plate structures with different thicknesses is calculated. It is found that, under the same crack conditions, with the increase in the thickness of the reinforced plate, the bearing load of the cracked plate decreases and the life of the cracked plate increases. With the expansion of the crack, the bearing load ratio of the reinforced plate increases. The simulation method is compared with the experimental results to verify its effectiveness.

Zearalenone and its metabolites are mycotoxins generated by Fusarium species while crops are growing and can typically be found in various foods, posing a risk to human health. Governments have implemented stricter regulations concerning the permissible levels of zearalenone in food products to safeguard public health. Stricter regulations on zearalenone levels in food have been implemented. However, detecting zearalenone and its metabolites remains challenging due to sample complexity and interference. Surprisingly few reviews of sample preparation methods for zearalenone in food have appeared in the past decade. In this overview, we outline the most recent developments in the sample pre-treatment technology of zearalenone and its metabolites in food samples based on chromatography–mass spectrometry methods since 2012. This review covers some prominent technologies, such as liquid–liquid extraction-based methods, solid-phase extraction-based methods, and QuEChERS (quick, easy, cheap, effective, rugged, and safe) extraction, providing valuable insights into their advantages and limitations for potential applications. The assessment of the methods discussed, along with an overview of current challenges and prospects, will guide researchers in advancing the field and ensuring safer food quality for consumers worldwide.

The target simulation of airplanes is an important research topic. It is particularly important to find the right balance between high performance and low cost. In order to balance the contradictions between realistic target simulations and controllable costs, the scientific formulation of the performance parameters of target simulation is the key to achieving high performance. This paper proposes an intelligent simulation technology based on RCS imaging simulation through the combination of 60° variation corner reflector and a Luneberg lens reflector. It is designed to simulate several important RCS characteristics of the aircraft. At the same time, the different RCS images are automatically shifted to the corresponding gear position to achieve the purpose of simulation, and the price is low and the performance is good. It can be used for the training of radar target searching.

The land subsidence occurring over a goaf area after coal mining is a protracted process. The accurate prediction of long-term land subsidence over goaf areas relies heavily on the availability of long-term land subsidence monitoring data. However, the scarcity of continuous long-term land subsidence monitoring data subsequent to the cessation of mining significantly hinders the accurate prediction of long-term land subsidence in goafs. To address this challenge, this study proposes an innovative method based on interferometric synthetic aperture radar (InSAR) for predicting long-term land subsidence of goafs following coal mining. The proposed method employs a concatenation approach that integrates multiple short-term monitoring data from different coal faces, each with distinct cessation times, into a cohesive and uniform long-term sequence by normalizing the subsidence rates. The method was verified using actual monitoring data from the Yangquan No. 2 mine in Shanxi Province, China. Initially, coal faces with the same shapes but varying cessation times were selected for analysis. Using InSAR monitoring data collected between June and December of 2016, the average subsidence rate corresponding to the duration after coal mining cessation on each coal face was back-calculated. Subsequently, a function relating subsidence rate to the duration after coal mining cessation was fitted to the data. Finally, the relationship between cumulative subsidence and the duration after coal mining cessation was derived by integrating the function. The results indicated that the relationship between subsidence rate and duration after coal mining cessation followed an exponential function for a given coal face, whereas the relationship between cumulative subsidence and duration after coal mining cessation conformed to the Knothe time function. Notably, after the cessation of coal mining, significant land subsidence persisted in the goaf of the Yangquan No. 2 mine for a duration ranging from 5 to 10 years. The cumulative subsidence curve along the long axis of the coal face ultimately exhibited an inclined W-shape. The proposed method enables the quantitative prediction of residual land subsidence in goafs, even in cases where continuous long-term land subsidence monitoring data are insufficient, thus providing valuable guidance for construction decisions above the goaf.

With the burgeoning of the microelectronics industry, in order to improve the transmission speed between chips in large-scale integrated circuits to meet the demands of high integration, it is necessary for interlayer insulation materials to possess a lower dielectric constant (k). Polyimide (PI) has been widely used as interlayer insulation materials for large-scale integrated circuits, and the exploration on reducing their dielectric constant has attracted extensive attention in recent years. In this work, porous PI-based composites with a low dielectric constant are mainly reviewed. The application of porous SiO2, graphene derivatives, polyoxometalates, polyhedral oligomeric silsesquioxane and hyperbranched polysiloxane in reducing the dielectric constant of PI is emphatically introduced. The key technical problems and challenges in the current research of porous polyimide materials are summarized, and the development prospect of low k polyimide is also expounded.

The Arithmetic Optimization Algorithm (AOA) is a meta-heuristic algorithm inspired by mathematical operators, which may stagnate in the face of complex optimization issues. Therefore, the convergence and accuracy are reduced. In this paper, an AOA variant called ASFAOA is proposed by integrating a double-opposite learning mechanism, an adaptive spiral search strategy, an offset distribution estimation strategy, and a modified cosine acceleration function formula into the original AOA, aiming to improve the local exploitation and global exploration capability of the original AOA. In the proposed ASFAOA, a dual-opposite learning strategy is utilized to enhance population diversity by searching the problem space a lot better. The spiral search strategy of the tuna swarm optimization is introduced into the addition and subtraction strategy of AOA to enhance the AOA’s ability to jump out of the local optimum. An offset distribution estimation strategy is employed to effectively utilize the dominant population information for guiding the correct individual evolution. In addition, an adaptive cosine acceleration function is proposed to perform a better balance between the exploitation and exploration capabilities of the AOA. To demonstrate the superiority of the proposed ASFAOA, two experiments are conducted using existing state-of-the-art algorithms. First, The CEC 2017 benchmark function was applied with the aim of evaluating the performance of ASFAOA on the test function through mean analysis, convergence analysis, stability analysis, Wilcoxon signed rank test, and Friedman’s test. The proposed ASFAOA is then utilized to solve the wireless sensor coverage problem and its performance is illustrated by two sets of coverage problems with different dimensions. The results and discussion show that ASFAOA outperforms the original AOA and other comparison algorithms. Therefore, ASFAOA is considered as a useful technique for practical optimization problems.

With the gradual increase in the mining depth of coal resources, the destruction of the rock structure of the inter-layered rock of the near coal seam under the influence of mining has led to the frequent occurrence of water-inrush disasters in mines, which seriously affects the safety of mine production and the safety of the people in the underground. Therefore, it is important to study the mechanism of the water inrush of the rock between the coal seams under the influence of mining to control the occurrence of water inrush disasters and protect the loss of groundwater resources. This paper takes the Hanjiawan coal mine with typical stratigraphic characteristics as the background for research and studies the structural characteristics of interlayer rock breakage and the solid–liquid coupling inrush water disaster mechanism during the mining of 2−2 and 3−1 coals. The study shows that according to the damage degree and destruction depth of the inter-layered rock caused by the mining of the upper and lower coal seams, combined with the slip line theory and the “three bands” collapse theory, the inter-layered rock is classified into a completely fractured inter-layer, a fractured–broken stacked inter-layer, and a fractured–broken–fractured combined inter-layered rock using L≤hm+Hk2′, L>hm+Hk2′, and L≥hm+Hli2′ as the discriminating criteria. Combined with the structural classification of inter-layer rock and the discriminating criteria, we used similar simulation experiments and on-site research to analyze the evolution law and distribution characteristics of four types of inter-layer rock water-inrush fractures in different mines and put forward the classification of inter-layer rock water-inrush channels based on the width, length, and penetration of the fractures. Based on the characteristics of the water-inrush channel of inter-layer rock, we constructed the network-boundary inrush water calculation model of inter-layered rock and network-attach-boundary inrush water calculation model, solved the water movement of the water-inrush channel in the model by transforming the flat flow state, fracture to flow state, and pore-fracture flow state, and finally revealed the mechanism of the disaster by which water-inrush of inter-layer rocked was induced. Finally, we revealed its mechanism of inducing the inter-layer rock inrush water disaster. Our research enriches the theory and research ideas of the water-inrush disaster, provides theoretical support and a basis for the control of water-inrush disasters in similar conditions, and ensures the safe production of mines.

The strip filling method in underground reservoir needs high strength to achieve the requirements of water storage. In order to address the challenges associated with costly and weak filling materials, this study aimed to develop an economically efficient and high-strength gangue-based geopolymer backfill material (GBGBM). To achieve this, the Taguchi method was employed to design a series of 25 experiments, each consisting of four factors and five levels. This study focused on investigating the effects of different gangue gradation levels, sand ratios, water binder ratios (w/b), and aggregate binder ratios (a/b) on the working characteristics and unconfined compressive strength (UCS) of the GBGBM. The optimal combination of the GBGBM was determined by employing a signal-to-noise ratio (S/N)-based extreme difference and variance analysis. The results revealed that the w/b ratio exerted the most substantial influence on both the slump and UCS. Specifically, when employing a gradation of 50%, a sand ratio of 55%, an a/b ratio of 2.5, and a w/b ratio of 0.64, the slump measured 251.2 mm, the UCS at 3d reached 5.27 MPa, and the UCS at 28d amounted to 17.65 MPa. These findings indicated a remarkable improvement in early UCS by 131.14% and the late UCS by 49.45% compared to gangue-based cement backfill material (GBCBM). Additionally, this study examined the hydration products and microstructures of both GBGBM and GBCBM using X-ray diffraction (XRD), scanning electron microscopy (SEM), and mercury intrusion porosimetry (MIP) analyses. Significantly, the GBGBM exhibited notable advantages over the GBCBM, including a 78.16% reduction in CO2 emissions, a 73.45% decrease in energy consumption, and a 24.82% reduction in cost. These findings underscore the potential of GBGBM as a sustainable and cost-effective alternative to GBCBM.

Black soldier fly (Hermetia illucens L) larvae (BSFL) possess remarkable antibiotic degradation abilities due to their robust intestinal microbiota. However, the response mechanism of BSFL intestinal microbes to the high concentration of antibiotic stress remains unclear. In this study, we investigated the shift in BSFL gut microbiome and the functional genes that respond to 1250 mg/kg of tetracycline via metagenomic and metatranscriptomic analysis, respectively. The bio-physiological phenotypes showed that the survival rate of BSFL was not affected by tetracycline, while the biomass and substrate consumption of BSFL was slightly reduced. Natural BSFL achieved a 20% higher tetracycline degradation rate than the germ-free BSFL after 8 days of rearing. Metagenomic and metatranscriptomic sequencing results revealed the differences between the entire and active microbiome. Metatranscriptomic analysis indicated that Enterococcus, Vagococcus, Providencia, and Paenalcaligenes were the active genera that responded to tetracycline. Furthermore, based on the active functional genes that responded to tetracycline pressure, the response mechanisms of BSFL intestinal microbes were speculated as follows: the Tet family that mediates the expression of efflux pumps expel tetracycline out of the microbes, while tetM and tetW release it from the ribosome. Eventually, tetracycline was degraded by deacetylases and novel enzymes. Overall, this study provides novel insights about the active intestinal microbes and their functional genes in insects responding to the high concentration of antibiotics.

Potato canopy nitrogen content (CNC) is an imperative metric for assessing potato growth status and guiding field management. While the spectral index can be utilized to estimate CNC, its efficacy is influenced by the environment and crop type. To address this issue, we utilized hyperspectral indices (HIs) optimization for CNC estimation. Using the inverse and first-order differential (FD) transformations of the original data (OD), HIs comprising two-band combinations in 400–1000 nm, such as RSI, DSI, NDSI, SASI, and PSI, were constructed to analyze the correlation between CNC and HIs. Based on this analysis, prediction models for potato CNC were created using the most optimal HIs. The results showed that FD transformation significantly improved the correlations between CNC and HIs, among which FD−PSI(R654, R565) had the highest correlation with CNC. We further employed the optimal HIs as variables to establish univariate and multivariate regression models to estimate the potato CNC. Among the univariate models, the accuracy of the OD−DSI model was the highest, with an R2 of 0.79 and RMSE of 0.22. Meanwhile, the FD−MLR model demonstrated the highest accuracy compared to the other multivariate models, with an R2 of 0.84, an RMSE of 0.20 during validation, and a greater prediction accuracy than the OD−DSI model. FD−MLR can be used to map the CNC distribution map of monitored potato planting plots to guide precision fertilization.

(1) Background: There is growing interest in using insects to treat nutrient-rich organic wastes, such as the black soldier fly (BSF), one of the most efficient organic waste recyclers for upcycling nutrients into the food system. Although biochar (BC) was shown to enhance nutrient retention and the final product quality during the composting of livestock and poultry manure in many previous studies, little information is available on the effect of BC on livestock manure bioconversion by black soldier fly larvae (BSFL). (2) Methods: This study investigated the effect of adding a small amount of BC to chicken manure (CM) on the bioconversion system of the black soldier fly (including N2O and NH3 emissions and the final distribution of nitrogen during the treatment process). (3) Results: The lowest N2O and NH3 emission and highest residual nitrogen in the substrate were observed in the 15% BC treatment. The highest bioconversion rate of CM (8.31%) and the peak of larval biomass was obtained in the 5% BC treatment. (4) Conclusions: The results demonstrate the feasibility of adding 5% BC to reduce pollution and achieve a satisfactory BSFL-based CM bioconversion efficiency.

At present, convolutional neural networks (CNNs) have been widely applied to the task of skin disease image segmentation due to the fact of their powerful information discrimination abilities and have achieved good results. However, it is difficult for CNNs to capture the connection between long-range contexts when extracting deep semantic features of lesion images, and the resulting semantic gap leads to the problem of segmentation blur in skin lesion image segmentation. In order to solve the above problems, we designed a hybrid encoder network based on transformer and fully connected neural network (MLP) architecture, and we call this approach HMT-Net. In the HMT-Net network, we use the attention mechanism of the CTrans module to learn the global relevance of the feature map to improve the network’s ability to understand the overall foreground information of the lesion. On the other hand, we use the TokMLP module to effectively enhance the network’s ability to learn the boundary features of lesion images. In the TokMLP module, the tokenized MLP axial displacement operation strengthens the connection between pixels to facilitate the extraction of local feature information by our network. In order to verify the superiority of our network in segmentation tasks, we conducted extensive experiments on the proposed HMT-Net network and several newly proposed Transformer and MLP networks on three public datasets (ISIC2018, ISBI2017, and ISBI2016) and obtained the following results. Our method achieves 82.39%, 75.53%, and 83.98% on the Dice index and 89.35%, 84.93%, and 91.33% on the IOU. Compared with the latest skin disease segmentation network, FAC-Net, our method improves the Dice index by 1.99%, 1.68%, and 1.6%, respectively. In addition, the IOU indicators have increased by 0.45%, 2.36%, and 1.13%, respectively. The experimental results show that our designed HMT-Net achieves state-of-the-art performance superior to other segmentation methods.

In the northeast plains of China, the intensive utilization of agricultural soils has been a persistent issue, and finding ways to utilize soil resources efficiently and sustainably through a scientifically-driven management system has become a crucial challenge for agricultural production. Conservation tillage is a crucial technology for sustainable agriculture. Currently, plow and rotary tillage are the dominant methods used in Mollisols, but there is limited information on the effects of different conservation tillage practices in this region. The objective of this study was to investigate the short-term impact of tillage and sowing methods on soil physical properties and corn plant growth and to examine the relationship between soil physical properties and plant characteristics during various stages of growth. This study consisted of four tillage and sowing methods: plow tillage and precision seeder sowing (PTS), rotary tillage and precision seeder sowing (RTS), no-tillage and no-tillage seeder sowing (NTS), and no-tillage and precise sowing in stubble field (STS) (all four treatments involved total straw return). The results indicated that the soil penetration resistance (SPR) in the 10–40 cm soil layer under the PTS treatment was significantly lower (by 11.9% to 18%) compared to the other treatments (p < 0.05). On average, the soil moisture content in the NTS treatment was 2.7% and 1.4% higher than that of the PTS and RTS treatments. Additionally, soil temperature was 5.6% to 8.6% lower under the STS treatment compared to the other treatments during late corn growth. The RTS treatment also significantly reduced the bulk density of surface soil. High SPR impeded early crop growth but did not impact mid-crop development, while low soil temperature was one of the main factors affecting late corn growth and development as temperatures decreased. Based on the comparisons, we found that the short-term implementation of conservation tillage did not result in a significant decrease in corn yield. We believe that the short-term implementation of NTS tillage sowing practices in Mollisol regions is a feasible option.

Rind pattern is one of the most important appearance qualities of watermelon, and the mining of different genes controlling rind pattern can enrich the variety of consumer choices. In this study, a unique intermittent rind stripe was identified in the inbred watermelon line WT20. The WT20 was crossed with a green stripe inbred line, WCZ, to construct F2 and BC1 segregating populations and to analyze the genetic characterization of watermelon stripe. Genetic analysis showed that the intermittent stripe was a qualitative trait and controlled by a single dominant gene, ClIS. Fine mapping based on linkage analysis showed that the ClIS gene was located on the 160 Kb regions between 25.92 Mb and 26.08 Mb on watermelon chromosome 6. Furthermore, another inbred watermelon line with intermittent stripe, FG, was re-sequenced and aligned on the region of 160 Kb. Interestingly, only two SNP variants (T/C, A/T) were present in both WT20 and FG inbred lines at the same time. The two SNPs are located in 25,961,768 bp (T/C) and 25,961,773 bp (A/T) of watermelon chromosome 6, which is located in the promoter region of Cla019202. We speculate that Cla019202 is the candidate gene of ClIS which controls the intermittent stripe in watermelon. In a previous study, the candidate gene ClGS was proved to control dark green stripe in watermelon. According to the verification of the two genes ClIS and ClGS in 75 watermelon germplasm resources, we further speculate that the ClGS gene may regulate the color of watermelon stripe, while the ClIS gene regulates the continuity of watermelon stripe. The study provides a good entry point for studying the formation of watermelon rind patterns, as well as providing foundation insights into the breeding of special appearance quality in watermelon.

Aflatoxin B1 (AFB1) is a common mycotoxin contaminant in cereals that causes severe economic losses and serious risks to the health of humans and animals. In this paper, we investigated the characteristics of AFB1 degradation by black soldier fly larvae (BSFL) combined with commensal intestinal microorganisms. Germ-free BSFL and non-sterile BSFL were reared on peanut meal spiked with AFB1 for 10 days. The result showed that germ-free BSFL and non-sterile BSFL could achieve 31.71% and 88.72% AFB1 degradation, respectively, which indicated the important role of larvae gut microbiota in AFB1 degradation. Furthermore, twenty-five AFB1-degrading bacteria were isolated from BSFL gut, and S. acidaminiphila A2 achieved the highest AFB1 degradation, by 94%. When S. acidaminiphila A2 was re-inoculated to BSFL, the detrimental effect of AFB1 on the growth performance of BSFL was alleviated, and complete AFB1 degradation in peanut meal was obtained. In conclusion, the present study may provide a strategy to degrade AFB1 in feedstuff through bioconversion with BSFL in combination with gut-originated AFB1-degrading bacteria, while providing a sustainable insect protein and fat source to animals.

In this study, the effect of rotary-die equal-channel angular pressing (RD-ECAP) on the microstructure and texture evolution of GW103 alloy is studied. RD-ECAP processes were carried out for 1, 4 and 12 passes at 450 °C. The mechanical properties and friction behavior of RD-ECAP-processed Mg-10Gd-3Y (wt%) alloy (GW103) are discussed. The results reveal that the size of dynamic recrystallized grains and second-phase particles are significantly refined to about 1.3 μm and 1 μm, respectively. The texture evolution of the processed samples is studied by X-ray diffraction and electron backscattered diffraction techniques. The multiple texture components formed are not observed after the conventional ECAP process. Moreover, different dynamic recrystallization (DRX) mechanisms are systemically analyzed and discussed in view of the texture evolution of ECAP processed samples. The final textures obtained after 12 passes are identified as two types: The C-texture type induced by continuous and discontinuous DRX, and the random texture components induced by reorientation of the initial 1¯0> fiber. Based on the grain refinement, precipitate strengthening and texture weakening mechanisms, a high-performance ternary alloy of Mg-Gd-Y was firstly obtained through 12 passes RD-ECAP processing, with a combination of high yield strength of 312 MPa and a high ductility of 22%. In addition, the friction behaviors are also studied. The multi-pass-processed samples exhibit a relatively lower friction coefficient under a load of 10 N at room temperature.

Tert-butylperoxy-2-ethylhexanoate (TBPEH) and tert-butyl peroxybenzoate (TBPB) promote the radical acylation of allyl ester with benzaldehyde to synthesize new carbonyl-containing compounds under solvent-free and metal-free conditions. This reaction is compatible with electron-donating and halogen groups and has excellent atom utilization and chemical selectivity. Furthermore, the synthetic compounds can further apply to the preparation of lactone, piperidine, tetrazole and oxazole.

Under the background of tighter resource and environmental constraints, whether and how land transfer can promote the green development of agriculture has become a realistic question that needs to be answered urgently. This paper analyzes the internal mechanism between land transfer and agricultural green production by using property rights theory and sustainable development theory. Taking the data of the “investigation on household energy consumption and green agricultural development” in Sichuan Province in 2022 as a sample, it empirically analyzes the impact of land transfer on agricultural green production by using OLS and 2SLS models. The results show that: (1) land inflow significantly improves the level of agricultural green production, with a unit impact of 22.3%; (2) whereas land outflow will inhibit agricultural green production, with a unit impact of 5.46%; (3) the family’s long-term agricultural labor, social capital, migrant experience, non-agricultural income, and household clean energy use have a promoting effect on agricultural green production; (4) age, education level, health level and agricultural subsidies inhibit agricultural green production; (5) the heterogeneity analysis found that the inflow of land would significantly promote the level of green agricultural production of farmers who have environmental awareness, have been village cadres, have purchased agricultural insurance, and have not suffered from agricultural disasters; (6) agricultural training, farmers’ digital literacy, and agricultural related loans have a positive and strengthened regulatory role in the impact of land transfer on agricultural green production. Based on this, this paper gets policy enlightenment from the government, market, and farmers.

As an important technology of thick coal seam mining, fully mechanized mining with a large mining height has high mining efficiency. In order to study the roof safety control of large mining height working face, the 122106 working face of Caojiatan coal mine is taken as the engineering background. The numerical simulation method is used to analyze the control ability of roof subsidence when the support strength is 1.2 MPa, 1.4 MPa, 1.6 MPa, 1.8 MPa, 2.0 MPa, and 2.2 MPa. The results show that the support strength of hydraulic support is negatively correlated with roof subsidence. Through theoretical analysis of the mechanical model of the support and surrounding rock under the filling condition, it is shown that the height of the gap between the filling body and roof is the main influencing factor of roof subsidence: the smaller the height of the gap between the filling body and roof, the better the control effect on the roof. Through numerical simulation, the roof subsidence and surface subsidence under different filling rates are analyzed. The results show that when the filling rate increases to 80% the control of roof subsidence achieves better results. Taking production safety and economic benefits into consideration, when the reasonable support strength of the working face is determined to be 2.0 MPa and the filling rate is 80%, the safety control of the working face roof can be ensured.

The pressure relief of coal pillars in close-distance multi-coal seam goaf is a complex engineering problem with the characteristics of “dynamic mine pressure”. Hence, this paper studies such problems. First, the influence factors of the coal pillar in the goaf on the mine pressure of the mining face of the lower coal seam under this condition were theoretically analyzed, and it was concluded that vertical stress is the most important element, followed by horizontal stress. Next, a physical similarity simulation experiment was designed to study the stress distribution law of the coal pillar floor in the goaf before and after pressure release and the damage depth. Finally, a technology and monitoring method for coal pillar blasting pressure alleviation in goaf were introduced and implemented in engineering practice. After the pressure is alleviated, the surrounding rock stress of the lower coal seam mining face is redistributed, and the vertical stress is decreased by 20%. The adjacent rock’s deformation is improved. This technology’s cost and safety advantages are extraordinary and helpful for mining coal seams over close distances.

Atmospheric carbon dioxide (CO2) is recognized as the most important component of the greenhouse gases, the concentration of which has increased rapidly since the pre-industrial era due to anthropogenic emissions of greenhouse gases (GHG). The accurate monitoring of carbon dioxide is essential to study the global carbon cycle and radiation budget on Earth. The Aerosol and Carbon Detection Lidar (ACDL) instrument onboard the Atmospheric Environmental Monitoring Satellite (AEMS) was successfully launched in April 2022, which allows a new perspective to quantify the global spatial distribution of atmospheric CO2 with high accuracy. In this work, the impact of the Doppler shift on CO2 measurements for an integrated-path differential absorption (IPDA) light detection and ranging (lidar) system was evaluated to meet the weighted column-averaged mixing ratio of carbon dioxide (XCO2) measurement requirements of less than one part per million (ppm). The measurement uncertainties due to the Doppler shift were first evaluated in airborne IPDA observations. The result shows that most of the Doppler shift is in the range of 6–8 MHz, resulting in 0.26-0.39 ppm deviations in the XCO2 results. The deviations between the XCO2 retrievals and in situ measurements decreased to 0.16 ppm after the correction of the Doppler shift from 11:28:29 to 11:28:49 in the flight campaign. In addition, the online Doppler shift accounts for 98% of the deviations between XCO2 retrievals and in situ measurements. Furthermore, the impact of the Doppler shift on ACDL measurements is also assessed. The differences between the XCO2 retrievals with and without Doppler shift are used to quantify measurement uncertainties due to the Doppler effect. The simulations reveal that a pointing misalignment of 0.067 mrad can lead to a mean bias of about 0.30 ppm (0.072%) in the CO2 column. In addition, CO2 measurements are more sensitive to the Doppler shift at high altitudes for IPDA lidar, so the largest differences in the CO2 columns are found on the Qinghai–Tibet Plateau in China.

The emergence and popularization of various fifth-generation fighter jets with supersonic cruise, super maneuverability, and stealth functionalities have raised higher and more comprehensive challenges for the tactical performance and operational indicators of air defense weapon systems. The training of air defense systems requires simulated targets; however, the traditional targets cannot simulate the radar cross-section (RCS) distribution characteristics of fifth-generation fighter aircrafts. In addition, the existing target aircrafts are expensive and cannot be mass-produced. Therefore, in this paper, a corner reflector and a Luneburg ball reflector with RCS distribution characteristics of a fifth-generation fighter in a certain spatial area are designed for target simulation. Several corner reflectors and Luneburg balls are used to form an array to realize the simulations. The RCS value and distribution characteristics of the target can be combined with fuzzy clustering and a single-chip microcomputer to design an intelligent switching system, which improves the practicability of the intelligent target design proposed in this paper.

Piezoelectric materials, especially Pb-based piezoelectric materials, are widely used in the key components of sensors, actuators, and transducers. Due to the rising concern of the toxicity of Pb, global legislation has been adopted to restrict the use of Pb. Given that the available Pb-free piezoelectric materials cannot replace the Pb-based ones for various reasons, we designed and fabricated a low-Pb piezoelectric solid-solution ceramic system, (1–x)Ba(Zr0.18Ti0.82)O3–x(Ba0.78Pb0.22)TiO3 (denoted as BZ0.18T–xBP0.22T herein). The crystal structure, ferroelectric, dielectric, and piezoelectric properties of the BZ0.18T–xBP0.22T system were systematically studied. With the increase in BP0.22T content, the structure of the samples changed from a rhombohedral phase to a tetragonal phase; the intermediate composition x = 0.5 was located at the morphotropic phase boundary of the system and corresponded to the state with the coexistence of the rhombohedral and tetragonal phases. Moreover, x = 0.5 exhibited the optimum comprehensive properties among all the samples, with a piezoelectric coefficient d33 of 240 pC/N, a maximum dielectric temperature Tm of 121.1°C, and a maximum polarization Pm of 15 μC/cm2. Our work verifies the validity of the route to design low-Pb high-piezoelectric materials and may stimulate the interests for exploring new low-Pb high-performance ferroelectric and piezoelectric materials.

In order to discover pesticidal lead compounds with high activity and low toxicity, a series of novel benzamides substituted with pyrazole-linked 1,2,4-oxadiazole were designed via bioisosterism. The chemical structures of the target compounds were confirmed via 1H NMR, 13C NMR and HRMS analysis. The preliminary bioassay showed that most compounds exhibited good lethal activities against Mythimna separate, Helicoverpa armigera, Ostrinia nubilalis and Spodoptera frugiperda at 500 mg/L. Particularly in the case of Mythimna separate, compound 14q (70%) exhibited obvious insecticidal activity. In addition, compound 14h demonstrated good fungicidal activity against Pyricularia oryae with an inhibition rate of 77.8%, and compounds 14e, 14k, 14n and 14r also showed certain antifungal activities (55.6–66.7%). The zebrafish toxicity test showed that the LC50 of compound 14h was 14.01 mg/L, which indicated that it may be used as a potential leading compound for further structural optimization.

Electrowetting is a widely used and effective method to tune the wettability of ionic liquids at solid-liquid interfaces, but it usually requires an external electric field. Here, we proposed a strategy for conveniently tuning ionic liquid wettability by adopting ferroelectric LiNbO3 single crystals as functional substrates. A heating pretreatment process was applied to modulate the surface charge characteristics of LiNbO3 substrates, leading to an improved wettability of [EMIM][BF4] and [EMIM][NTf2] on the LiNbO3 substrates with both positively poled (+Z) and negatively poled (−Z) surfaces. This work may be of great interest in the field of ferroelectric-based microelectronics.

To develop new compounds with high activity, broad spectrum and low-toxicity, 17 benzamides substituted with quinoline-linked 1,2,4-oxadiazole were designed using the splicing principle of active substructures and were synthesized. The biological activities were evaluated against 10 fungi, indicating that some of the synthetic compounds showed excellent fungicidal activities. For example, at 50 mg/L, the inhibitory activity of 13p (3-Cl-4-Cl substituted, 86.1%) against Sclerotinia sclerotiorum was superior to that of quinoxyfen (77.8%), and the inhibitory activity of 13f (3-CF3 substituted, 77.8%) was comparable to that of quinoxyfen. The fungicidal activities of 13f and 13p to Sclerotinia sclerotiorum were better than that of quinoxyfen (14.19 mg/L), with EC50 of 6.67 mg/L and 5.17 mg/L, respectively. Furthermore, the acute toxicity of 13p was 19.42 mg/L, classifying it as a low-toxic compound.

The single batch normalization (BN) method is commonly used in the instance segmentation algorithms. The batch size is concerned with some drawbacks. A too small sample batch size leads to a sharp drop in accuracy, but a too large batch may result in the memory overflow of graphic processing units (GPU). These problems make BN not feasible to some instance segmentation tasks with inappropriate batch sizes. The self-adaptive normalization (SN) method, with an adaptive weight loss layer, shows good performance in instance segmentation algorithms, such as the YOLACT. However, the parameter averaging mechanism in the SN method is prone to problems in the weight learning and assignment process. In response to such a problem, the paper proposes to replace the single BN with an adaptive weight loss layer in SN models, based on which a weight learning method is developed. The proposed method increases the input feature expression ability of the subsequent layers. By building a Pytorch deep learning framework, the proposed method is validated in the MS-COCO data set and Autonomous Driving Cityscapes data set. The experimental results prove that the proposed method is effective in processing samples independent from the batch size. The stable accuracy for all kinds of target segmentation is achieved, and the overall loss value is significantly reduced at the same time. The convergence speed of the network is also improved.