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Hydraulic fracturing, especially pulse hydraulic fracturing, is an important method for extracting oil and gas from low-permeability reservoirs, improving recovery rates significantly. Pulse hydraulic fracturing, which involves varying injection rates to create pressure waves, outperforms traditional constant-flow fracturing methods significantly. However, during pulse hydraulic fracturing operations, the flow properties of the fluid in the column change from moment to moment. Furthermore, current research on pulse hydraulic fracturing primarily focuses on vertical wells, while horizontal wells have become a common operational strategy. Therefore, a transient flow model of fluid within a horizontal well, considering variable-flow injection and unsteady friction conditions, is established in this paper. The model is solved using both the characteristic line method and the finite difference method. The hydrodynamic properties of the fracturing fluid were analyzed, and the propagation mechanisms of pressure waves within horizontal wells under various fluid injection schemes and well depths are analyzed to provide a reference for selecting appropriate fluid injection schemes in engineering practice. The study highlights the impact of fluid viscosity and injection flow amplitude on bottomhole pressure fluctuations, advancing the efficient development of low-permeability oilfields.

Unique metabolites contribute to the performance of meat flavor and potential function. In this study, UHPLC-Q Exactive HF-X-based metabolomics and multivariate analysis were applied to explore the characteristic metabolites in the breast meat of Beijing-You chicken (BYC) aged 150, 300, and 450 days (D150, D300, and D450). Based on the criteria of variable importance in the projection (VIP) > 1 and p < 0.05, a total of 154 and 97 differential metabolites (DMs) were screened out compared with D450 (D450 vs. D150, D450 vs. D300), respectively. In general, the relative content of carnosine, L-L-homoglutathione, demethyloleuropein, neohesperidin dihydrochalcone, 7-chloro-2-(3,4-dimethoxyphenyl)-3,5-dihydroxy-6,8-dimethoxy-4H-chromen-4-one, glycerophospholipids, exhibited the highest abundance at D450, while balenine, anserine, L-beta-aspartyl-L-leucine, glutathione, oxidized glutathione, stearoylcarnitine, ganoderic acid alpha, oleuroside, Lysoglycerophospholipid species (LGP) presented a downward trend with age. These 210 DMs were involved in 10 significantly enriched pathways related to the synthesis and metabolism of amino acids, peptides, and glycerophospholipid, such as glutathione metabolism, histidine metabolism, glycerophospholipid metabolism, arginine biosynthesis, tyrosine metabolism, and lysine degradation. In conclusion, this work could not only facilitate a better understanding of the differences of chicken flavor and benefit properties with age, but also provide potential valuable bioactive compounds for further research.

Plant stomata regulate transpiration (T) and CO2 assimilation, essential for the water–carbon cycle. Quantifying how environmental factors influence stomatal conductance will provide a scientific basis for understanding the vegetation–atmosphere water–carbon exchange process and water use strategies. Based on eddy covariance and hydro-metrological observations from FLUXNET sites with four plant functional types and using three widely applied methods to estimate ecosystem T from eddy covariance data, namely uWUE, Perez-Priego, and TEA, we quantified the regulation effect of environmental factors on canopy stomatal conductance (Gs). The environmental factors considered here include radiation (net radiation and solar radiation), water (soil moisture, relative air humidity, and vapor pressure deficit), temperature (air temperature), and atmospheric conditions (CO2 concentration and wind speed). Our findings reveal variation in the influence of these factors on Gs across biomes, with air temperature, relative humidity, soil water content, and net radiation being consistently significant. Wind speed had the least influence. Incorporating the leaf area index into a Random Forest model to account for vegetation phenology significantly improved model accuracy (R2 increased from 0.663 to 0.799). These insights enhance our understanding of the primary factors influencing stomatal conductance, contributing to a broader knowledge of vegetation physiology and ecosystem functioning.

Soybean is an important source of high-quality vegetable protein with various health-improving properties, and its main bioactive substances are small peptides produced by in vitro enzymatic hydrolytic processes. In traditional layer breeding, the nutritional health of roosters is frequently neglected, ultimately affecting the quality and quantity of offspring. This study investigated the effects of various quantities (0%, 0.15%, 0.30%, 0.45%, and 0.60%) of soybean bioactive peptide (SBP) feed additives on immunological and antioxidant functions, gut health, and reproductive performance of roosters. SBP supplementation significantly improved male growth and reproductive performance, including growth rate, feed conversion ratio, reproductive organ development, and semen quality. SBP also increased immune and antioxidant levels, boosted the integrity of the small intestinal physiological structure and barrier function, and diversity of cecal microbes, and decreased the apoptotic ratio of small intestinal epithelial cells. The effects of SBP on various functions of males showed a quadratic trend, with the optimal concentration determined to be 0.45%.

Given the constraints on the invariant distribution in chaotic systems, flexibly setting the probability distribution of chaotic signals poses a significant challenge. To tackle this issue, this paper proposes a strategy that transforms the task into solving and modifying the probability density function of the chaotic intrinsic signal. Initially, kernel density estimation algorithms are employed to address the issue of obtaining smooth probability density functions for high-dimensional chaotic signals. Any chaotic signal can serve as the intrinsic signal source, with its probability density function and distribution function being solvable using this algorithm. Subsequently, a graph-based transformation algorithm is introduced for the flexible adjustment of chaotic signal probability distribution. This algorithm can convert the intrinsic signal into a chaotic signal with the desired distribution type based on the characteristics of the target distribution, providing an analytical expression for the transformation relationship. Finally, the effectiveness of this strategy is validated by generating uniform distribution chaotic signals using a Chua chaotic signal as the intrinsic source. The outstanding performance of this signal in suppressing common-mode conducted electromagnetic interference in high-frequency converters is highlighted. The experimental results demonstrate this strategy’s ability to flexibly configure probability distribution types of chaotic signals. Additionally, chaotic signals with a uniform distribution can achieve uniform power spectrum shaping, with a suppression effect on maximum common-mode conducted electromagnetic interference reaching 16.56 dB.

Non-isolated structures have strong destructive effects and poor isolation effects when encountering earthquakes. Setting isolation bearings can prolong the natural vibration period of the structure, reduce the horizontal seismic response of the structure under the influence of variables such as acceleration, base reaction, and inter story displacement, and enhance the overall seismic performance of the structure. The new material—epoxy plate thick layer rubber isolation bearing—has unique advantages compared to other bearings, such as effective energy absorption, simple construction, and low cost. This study establishes a three-dimensional isolated nuclear power plant containment structure based on the principle of similarity ratio, and compares and analyzes the acceleration, base reaction, and displacement responses of non-isolated and isolated structures. At the same time, the incremental dynamic analysis method (IDA) is used to analyze the seismic vulnerability of the structure, and the isolation performance of the nuclear containment structure using epoxy plate thick layer rubber isolation bearings is comprehensively and deeply explored. The results show that the epoxy plate thick layer rubber isolation bearing effectively prolongs the natural vibration period of the structure, reduces the horizontal seismic response of the structure, reduces the dome acceleration response by 66.55%, and reduces the base horizontal shear force by 55.51%. Therefore, setting epoxy plate thick layer rubber isolation bearings in the isolation layer can effectively enhance the seismic performance of the structure, thereby improving the redundancy of the nuclear power plant containment structure.

This paper presents a wideband dual-polarized dipole antenna structure operating at 1.7–3.8 GHz (76.4%). For a traditional 4G dipole antenna that covers the band 1.71–2.69 GHz, it is difficult to maintain the satisfactory impedance matching and normal stable radiation patterns within the 5G sub-6 GHz band 3.3–3.8 GHz, mainly due to the fixed antenna height no longer being a quarter-wavelength. To solve this, a connected-ring-shaped metasurface structure is proposed and deployed to operate as an artificial magnetic conductor (AMC). As a result, stable antenna radiation patterns are obtained within the whole band 1.7–3.8 GHz. For verification, this wideband dipole antenna using AMC is implemented and tested. The measured results show that the proposed antenna has an impedance bandwidth of 80.7% (1.7–4.0 GHz). It has an average measured in-band realized gain of 7.0±1.0 dBi and a stable 70±5 half power beam width (HPBW) within the 4G/5G-sub 6GHz bands 1.71–2.69 GHz and 3.3–3.8 GHz.

This paper presents a compact wideband circularly polarized cross-dipole antenna with a low-pass filter response. It consists of two pairs of folded cross-dipole arms printed separately on both sides of the top substrate, and the two dipole arms on the same surface are connected by an annular phase-shifting delay line to generate circular polarization. A bent metal square ring and four small metal square rings around the cross-dipoles are employed to introduce new resonant frequencies, effectively extending the impedance and axial-ratio bandwidth. Four square patches printed on the middle substrate are connected to the ground plane by the vertical metal plates in order to reduce the antenna height. Thus, a compact wideband circularly polarized antenna is realized. In addition, a transmission zero can be introduced at the upper frequency stopband by the bent metal square rings, without using extra filter circuits. For verification, the proposed model is implemented and tested. The overall size of the model is 90mm×90mm×33mm (0.37λ0×0.37λ0×0.14λ0; λ0 denotes the center operating frequency). The measured impedance bandwidth and 3 dB axial-ratio (AR) bandwidth are 53.3% and 41%, respectively. An upper-band radiation suppression level greater than 15 dB is realized, indicating a good low-pass filter response.

The present study aimed to explore the clinical applicability of ultrasound super-resolution imaging (US SRI) for assessing renal microcirculation in patients with acute kidney injury (AKI). A total of 62 patients with sepsis were enrolled in the present study—38 with AKI and 24 control patients—from whom renal ultrasounds and clinical data were obtained. SonoVue contrast (1.5 mL) was administered through the elbow vein and contrast-enhanced ultrasound (CEUS) images were obtained on a Mindray Resona A20 ultrasound unit for 2 min. The renal perfusion time-intensity curve (TIC) was analyzed and, after 15 min, additional images were obtained to create a microscopic blood flow map. Microvascular density (MVD) was calculated and its correlation with serum creatinine (Scr) levels was analyzed. There were significant differences in heart rate, Scr, blood urea nitrogen, urine volume at 24 h, and glomerular filtration rate between the two groups (p < 0.01), whereas other characteristics, such as renal morphology, did not differ significantly between the AKI group and control group (p > 0.05). The time to peak and mean transit times of the renal cortex in the AKI group were prolonged compared to those in the control group (p < 0.01), while the peak intensity and area under the TIC were lower than those in the control group (p < 0.05). The MVD of the renal cortex in the AKI group was lower than that in the control group (18.46 ± 5.90% vs. 44.93 ± 11.65%; p < 0.01) and the MVD in the AKI group showed a negative correlation with Scr (R = −0.84; p < 0.01). Based on the aforementioned results, US SRI can effectively assess renal microcirculation in patients with AKI and is a noninvasive technique for the diagnosis of AKI and quantitative evaluation of renal microcirculation.

Background: A gut-microbial metabolite, trimethylamine N-oxide (TMAO), has been associated with type 2 diabetes mellitus (T2DM). Few previous prospective studies have addressed associations between the changes in TMAO and T2DM incidence. Methods: Data were derived from a longitudinal cohort conducted from 2019 to 2021 in rural areas of Fuxin County, Liaoning Province, China, and 1515 diabetes-free participants aged above 35 years were included. The concentrations of serum TMAO and its precursors were measured at two time points, namely in 2019 and 2021. TMAO and TMAO changes (ΔTMAO) were separately tested in a logistic regression model. For further examination, the odds ratios (ORs) for T2DM were calculated according to a combination of TMAO levels and ΔTMAO levels. Results: During a median follow-up of 1.85 years, 81 incident cases of T2DM (5.35%) were identified. Baseline TMAO levels exhibited a nonlinear relationship, first decreasing and then increasing, and only at the highest quartile was it associated with the risk of T2DM. The OR for T2DM in the highest quartile of serum TMAO was 3.35 (95%CI: 1.55–7.26, p = 0.002), compared with the lowest quartile. As for its precursors, only choline level was associated with T2DM risk and the OR for T2DM in the Q3 and Q4 of serum choline was 3.37 (95%CI: 1.41–8.05, p = 0.006) and 4.72 (95%CI: 1.47–15.13, p = 0.009), respectively. When considering both baseline TMAO levels and ΔTMAO over time, participants with sustained high TMAO levels demonstrated a significantly increased risk of T2DM, with a multivariable-adjusted OR of 8.68 (95%CI: 1.97, 38.34). Conclusion: Both initial serum TMAO levels and long-term serum TMAO changes were collectively and significantly associated with the occurrence of subsequent T2DM events. Interventions aimed at normalizing TMAO levels, such as adopting a healthy dietary pattern, may be particularly beneficial in T2DM prevention.

High-sensitivity fluorescence monitoring has been widely used in agriculture and environmental science. However, the active fluorescence detection information of leaf segments mainly focuses on total chlorophyll, and the fluorescence information of chlorophyll a, chlorophyll b, and some other pigments has not been explored. This only considers the fluorescence spectrum characteristics at a single wavelength or the fluorescence integral from a range of wavelength regions and does not completely consider the linkage relation between the excitation, emission, and interference information. In this paper, the three-dimensional fluorescence spectrum, containing the excitation and emission fluorescence spectra, and the corresponding multiple pigment characteristics from the upgraded LOPEX_ZJU database were collected. The linkages of excitation and emission of the three-dimensional fluorescence spectra of these pigments were analyzed for the newly built multiple pigment 3-D fluorescence spectral indices (3-D FSIs), including those of chlorophyll a, chlorophyll b, carotenoid, anthocyanin, and flavonoid 3-D FSIs. Then, these pigment inversion models were established and validated. The results show that the 3-D FSIs performances for the photosynthetic pigment content inversion (including chlorophyll a and b, and carotenoids) were much better than those for the photo-protective pigments (including anthocyanins and flavonoids) from the 3-D fluorescence spectra of these plant leaves. Here, the 3-D fluorescence normalization index (FNI ((F430,690 − F430,763)/(F430,690 + F430,763))) for the chlorophyll a inversion model has a high accuracy, the RMSE is 2.96 μg/cm2, and the 3-D fluorescence reciprocal difference index (FRI (F650,704/F650,668) for the chlorophyll b model has an encouraging RMSE (2.01 μg/cm2). The RMSE of the 3-D fluorescence ratio index (FRI (F500,748/F500,717)) for the carotenoid inversion is 3.77 μg/cm2 RMSE. Only FRI (F370,615/F370,438) was selected for the modeling and validating evaluation of the leaf Flas content inversion, but the evaluation metrics were not good, with an RMSE (151.13 μg/cm2). For Ants, although there was a 3-D FSI (FRDI (1/F540,679 − 1/F540,557)), and its evaluation metrics, with an RMSE (2.8 μg/cm2), were at or over 0.05 level, the validating evaluation metric VC (98.3577%) was not encouraging. These results showed that fluorescence, as a nondestructive and efficient detection method, could determine the contents of chlorophyll a, chlorophyll b, and carotenoid in plant leaves, providing a new method to detect plant information. It can also provide a potential chance for the fluorescence images of fine photo-protective pigments, especially chlorophyll a and b, using the special active fluorescence excitation light source and a few fluorescence imaging channels from the optimal FSIs.

Moisture significantly affects the mechanical behavior of gas shale and further determines the hydraulic fracturing performance, as it is more attractive. Nevertheless, batch experiments have usually involved variable methodologies regarding the preparation of moisture-contained shale specimens in the sequence (and/or frequency) of drying and soaking treatments. Accordingly, this work investigates how the preparation methodology influences the test results of moisture-contained shale samples. This study compares three commonly used shale sample preparation strategies for acquiring different moisture contents, that is, “dry-wet”, “dry-wet-dry”, and “wet-dry-wet” strategies, followed by a Brazilian splitting test for the mechanical parameters. The results show that under the same saturation conditions, the longer the soaking time during sample preparation, the higher the degradation degree of shale tensile strength. Meanwhile, prolonged soaking can lead to a more discrete distribution of strength values, and the failure mode may deviate from the Brazilian splitting theory model. Under the combined influence of moisture content and soaking time, the tensile strength of shale decreases approximately linearly with increasing saturation, while the degradation degree increases nonlinearly with increasing saturation, and the degradation rate changes from slow to fast. According to the observation of the microstructure of hydrated shale, prolonged soaking can lead to an increase in the expansion of clay minerals in shale by hydration, resulting in looser and more fragmented internal structure, and further degradation in shale strength. In order to weaken the interference of hydration when studying the effect of moisture content on the tensile strength of shale, the soaking time should be minimized as much as possible during the preparation process.

This paper presents an enhanced ground vehicle localization method designed to address the challenges associated with state estimation for autonomous vehicles operating in diverse environments. The focus is specifically on the precise localization of position and orientation in both local and global coordinate systems. The proposed approach integrates local estimates generated by existing visual–inertial odometry (VIO) methods into global position information obtained from the Global Navigation Satellite System (GNSS). This integration is achieved through optimizing fusion in a pose graph, ensuring precise local estimation and drift-free global position estimation. Considering the inherent complexities in autonomous driving scenarios, such as the potential failures of a visual–inertial navigation system (VINS) and restrictions on GNSS signals in urban canyons, leading to disruptions in localization outcomes, we introduce an adaptive fusion mechanism. This mechanism allows seamless switching between three modes: utilizing only VINS, using only GNSS, and normal fusion. The effectiveness of the proposed algorithm is demonstrated through rigorous testing in the Carla simulation environment and challenging UrbanNav scenarios. The evaluation includes both qualitative and quantitative analyses, revealing that the method exhibits robustness and accuracy.

For the complex structure of fibrous network materials, it is a challenge to analyze the network strength and deformation mechanism. Here, we identify a failure mode transition within the network material comprising brittle fibers and bonds, which is related to the strength ratio of the bond to the fiber. A failure criterion for this type of fibrous network is proposed to quantitatively characterize this transition between bond damage and fiber damage. Additionally, tensile experiments on carbon and ceramic fibrous network materials were conducted, and the experimental results show that the failure modes of these network materials satisfy the theoretical prediction. The relationship between the failure mode, the relative density of network and strength of the components is established based on finite element analysis of the 3D network model. The failure mode transforms from bond damage to fiber damage as increasing of bond strength. According to the transition of the failure modes in the brittle fibrous network, it is possible to tailor the mechanical properties of fibrous network material by balancing the competition between bond and fiber properties, which is significant for optimizing material design and engineering applications.

Against the backdrop of increasingly severe global climate change, the risk of rainstorm-induced waterlogging has become the primary threat to the safety of historic and cultural districts worldwide. This paper focuses on the historic and cultural districts of Beijing, China, and explores techniques and methods for identifying extreme rainstorm warnings in cultural heritage areas. Refined warning and forecasting have become important non-engineering measures to enhance these districts’ waterlogging prevention control and emergency management capabilities. This paper constructs a rainstorm-induced waterlogging risk warning model tailored for Beijing’s historical and cultural districts. This model system encompasses three sets of models: a building waterlogging early-warning model, a road waterlogging early-warning model, and a public evacuation early-warning model. During the construction of the model, the core concepts and determination methods of “1 h rainfall intensity water logging index” and “the waterlogging risk index in historical and cultural districts” were proposed. The construction and application of the three models take into full account the correlation between rainfall intensity and rainwater accumulation, while incorporating the characteristics of flood resilience in buildings, roads, and the society in districts. This allows for a precise grading of warning levels, leading to the formulation of corresponding warning response measures. Empirical tests have shown that the construction method proposed in this paper is reliable. The innovative results not only provide a new perspective and method for the early-warning of rainstorm-induced waterlogging, but also offer scientific support for emergency planning and response in historical and cultural districts.

In the context of green and low-carbon development, energy saving, and emission reduction, hot recycling technology (RT) has been researched, which is divided into hot central plant RT and hot in-place RT. However, due to the aged asphalt binders, the shortcomings of hot recycled asphalt mixtures have become apparent, as in comparison to new asphalt mixtures, their resistance to cracking was inferior and the cracking resistance deteriorated more rapidly. Therefore, it was very necessary to focus on the improvement of crack resistance of hot recycled asphalt mixtures. Basalt fiber has been proved to be able to effectively improve the comprehensive road performance of new asphalt mixtures. Therefore, this paper introduced basalt fiber to hot central plant recycled and hot in-place recycled asphalt mixtures, in order to improve the crack resistance of asphalt as a new type of fiber stabilizer. Firstly, six types of SMA-13 fiber asphalt mixtures were designed and prepared, i.e., hot mixtures with basalt fiber or lignin fiber, hot central plant recycled mixtures with basalt fiber or lignin fiber, and hot in-place recycled mixtures with basalt fiber or lignin fiber. Secondly, the trabecular bending test, low-temperature creep test, semi-circular bending test, and IDEAL-CT were used to comparatively study the changing patterns of low and intermediate temperature cracking resistance of hot recycled mixtures with conventional lignin fibers or basalt fibers. Finally, Pearson’s correlation coefficient was used to analyze the correlation of the different cracking resistance indicators. The results show that the low and intermediate temperature cracking resistance of hot central plant recycled mixtures increased by 45.6% (dissipative energy ratio, Wd/Ws) and 74.8% (flexibility index, FI), respectively. And the corresponding cracking resistance of hot in-place recycled mixture increased by 105.4% (Wd/Ws) and 55.7% (FI). The trabecular bending test was more suitable for testing the low-temperature cracking resistance of hot recycled asphalt mixtures, while the IDEAL-CT was more suitable for testing the intermediate-temperature cracking resistance. The results can provide useful references for the utilization of basalt fiber in the hot recycling of SMA-13 asphalt mixtures.

Pupil size is a significant biosignal for human behavior monitoring and can reveal much underlying information. This study explored the effects of task load, task familiarity, and gaze position on pupil response during learning a visual tracking task. We hypothesized that pupil size would increase with task load, up to a certain level before decreasing, decrease with task familiarity, and increase more when focusing on areas preceding the target than other areas. Fifteen participants were recruited for an arrow tracking learning task with incremental task load. Pupil size data were collected using a Tobii Pro Nano eye tracker. A 2 × 3 × 5 three-way factorial repeated measures ANOVA was conducted using R (version 4.2.1) to evaluate the main and interactive effects of key variables on adjusted pupil size. The association between individuals’ cognitive load, assessed by NASA-TLX, and pupil size was further analyzed using a linear mixed-effect model. We found that task repetition resulted in a reduction in pupil size; however, this effect was found to diminish as the task load increased. The main effect of task load approached statistical significance, but different trends were observed in trial 1 and trial 2. No significant difference in pupil size was detected among the three gaze positions. The relationship between pupil size and cognitive load overall followed an inverted U curve. Our study showed how pupil size changes as a function of task load, task familiarity, and gaze scanning. This finding provides sensory evidence that could improve educational outcomes.

In urban areas where populations commonly reside, particle mass concentrations in street canyons can pose significant risks to human health. This study aimed to investigate the diffusion mechanism of particle mass concentrations in urban street canyons by developing and applying a prediction model based on the mathematical modeling of physical processes. The prediction model considered factors such as the influence of traffic wind, natural wind, traffic flow, and other relevant variables influencing particle mass concentrations in street canyons. Field measurements were conducted in Dalian, China, to verify the feasibility of the model. Particle mass concentrations, traffic flow, temperature, relative humidity, and wind speed were measured on Shichang Street (a two-lane one-way road), Tangshan Street (a four-lane two-way road), and Shengli Road (a six-lane two-way road). The results indicated that the majority of traffic peaks occurred around 19:00 on all road types. The PM1.0 mass concentration was well diluted on the four-lane two-way road, with the least dilution observed on the two-lane one-way road. A strong correlation between the particle mass concentrations and traffic flow was discovered. Furthermore, a prediction model was established, accurately predicting the particle mass concentrations when the prediction step was from 5 to 15 s. The coefficient of determination (R2) between the predicted and measured values on the two-lane one-way road, four-lane two-way road, and six-lane two-way road was 0.9319, 0.6582, and 0.9238, respectively. Additionally, the prediction model allowed for a detailed analysis of traffic flow limitations, corresponding to the recommended World Health Organization (WHO) PM2.5 values. Overall, the findings of this study offer valuable insights for forecasting particle exposure levels in street canyons.

To enhance the slagging efficiency of the lime-based slag system during the pre-treatment stage of hot metal, a composite calcium ferrite flux based on aluminum industry solid waste was developed in this study. The melting characteristics of the flux and its application in the pre-treatment of hot metal were investigated. The results indicated that the main phases of the composite calcium ferrite were CaFe2O4, Ca2Fe2O5, and Ca2(Fe,Al)2O4. It exhibited high oxidation, high alkalinity, and a low melting point, thereby achieving excellent melting performance. Simulations of various dephosphorization fluxes in the pre-treatment of high-phosphorus hot metal, ordinary hot metal, and kilogram-scale dephosphorization experiment processes were conducted. Under the same experimental conditions, the composite calcium ferrite flux was able to achieve a dephosphorization rate of over 90% and a final phosphorus content of less than 0.02 wt% under high carbon content ([%C] = 3.2 wt%). In the application of hot metal pre-dephosphorization, this flux was able to achieve efficient melting and rapid slagging of lime at a lower temperature, and its slagging time was 50% faster than that of calcium ferrite flux. In addition, this flux enhanced the utilization efficiency of lime during the steelmaking process, effectively prevented the agglomeration of slag, and achieved efficient slag–metal separation. These characteristics were significantly better than the application effect of calcium ferrite flux. This flux has significant implications for the industrial application of deep dephosphorization in the pre-treatment stage of hot metal or the early stage of converter steelmaking.

Climate is the most important environmental factor influencing yield during rice growth and development. To investigate the relationships between climate and yield under different crop rotation patterns and planting methods, three typical rotation patterns (vegetable–rice (V), rape–rice (R), and wheat–rice (W)) and two mechanical planting methods (mechanical transplanting (T1) and mechanical direct seeding (T2)) were established. The results showed that compared to the V rotation pattern, the average daily temperature (ADT) during the sowing to heading stage increased under both R and W rotation patterns, which significantly shortened the growth period. Thus, the effective accumulated temperature (EAT), photosynthetic capacity, effective panicle (EP), and spikelet per panicle (SP) under R and W rotation patterns significantly decreased, leading to reductions in grain yield (GY). VT2 had a higher ratio of productive tillers (RPT), relative chlorophyll content (SPAD), leaf area index (LAI), and net photosynthetic rate (Pn) than those of VT1, which significantly increased panicle dry matter accumulation (DMA), resulting in an increase in GY. Although RT2 and WT2 had a higher RPT than those of RT1 and WT1, the GY of RT1 and WT1 decreased due to the significant reductions in EAT and photosynthetic capacity. Principal component analysis (PCA) showed that the comprehensive score for different rotation patterns followed the order of V > R > T with VT2 ranking first. The structural equation model (SEM) showed that EAT and ADT were the most important climate factors affecting yield, with total effects of 0.520 and −0.446, respectively. In conclusion, mechanical direct seeding under vegetable–rice rotation pattern and mechanical transplanting under rape–rice or wheat–rice rotation pattern were the rice-planting methods that optimized the climate resources in southwest China.

CULLIN (CUL) protein is a subtype of E3 ubiquitin ligase that is involved in a variety of biological processes and responses to stress in plants. In Uncaria rhynchophylla, the CUL gene family has not been identified and its role in plant development, stress response and secondary metabolite synthesis has not been studied. In this study, 12 UrCUL gene members all contained the typical N-terminal domain and C-terminal domain identified from the U. rhynchophylla genome and were classified into four subfamilies based on the phylogenetic relationship with CULs in Arabidopsis thaliana. They were unevenly distributed on eight chromosomes but had a similar structural composition in the same subfamily, indicating that they were relatively conserved and potentially had similar gene functions. An interspecific and intraspecific collinearity analysis showed that fragment duplication played an important role in the evolution of the CUL gene family. The analysis of the cis-acting elements suggests that the UrCULs may play an important role in various biological processes, including the abscisic acid (ABA) response. To investigate this hypothesis, we treated the roots of U. rhynchophylla tissue-cultured seedlings with ABA. The expression pattern analysis showed that all the UrCUL genes were widely expressed in roots with various expression patterns. The co-expression association analysis of the UrCULs and key enzyme genes in the terpenoid indole alkaloid (TIA) synthesis pathway revealed the complex expression patterns of 12 UrCUL genes and some key TIA enzyme genes, especially UrCUL1, UrCUL1-likeA, UrCUL2-likeA and UrCUL2-likeB, which might be involved in the biosynthesis of TIAs. The results showed that the UrCULs were involved in the response to ABA hormones, providing important information for elucidating the function of UrCULs in U. rhynchophylla. The mining of UrCULs in the whole genome of U. rhynchophylla provided new information for understanding the CUL gene and its function in plant secondary metabolites, growth and development.

Shale gas is considered a crucial global energy source. Hydraulic fracturing with multiple fractures in horizontal wells has been a crucial method for stimulating shale gas. During multi-stage fracturing, the fracture propagation is non-uniform, and fractures cannot be induced in some clusters due to the influence of stress shadow. To improve the multi-fracture propagation performance, technologies such as limited-entry fracturing are employed. However, perforation erosion limits the effect of the application of these technologies. In this paper, a two-dimensional numerical model that considers perforation erosion is established based on the finite element method. Then, the multi-fracture propagation, taking into account the impact of perforation erosion, is studied under different parameters. The results suggest that perforation erosion leads to a reduction in the perforation friction and exacerbates the uneven propagation of the fractures. The effects of erosion on multi-fracture propagation are heightened with a small perforation diameter and perforation number. However, reducing the perforation number and perforation diameter remains an effective method for promoting uniform fracture propagation. As the cluster spacing is increased, the effects of erosion on multi-fracture propagation are aggravated because of the weakened stress shadow effect. Furthermore, for a given volume of fracturing fluid, although a higher injection rate is associated with a shorter injection time, the effects of erosion on the multi-fracture propagation are more severe at a high injection rate.

In recent years, bi-layer pipes, composed of an inner layer and an outer layer, have been widely used in offshore engineering. In this study, the governing equation for a bi-layer pipe subjected to axisymmetric loadings is derived based on classical shell theory. Then, the equation is used to develop stress concentration factor formulations for girth welds in bi-layer pipes with fabrication tolerances and thickness transitions. Axisymmetric finite element analysis is carried out to verify the accuracy of the proposed formulations. It is noted that these formulations can be well suited for determining the stress concentration factors for a wide range of thickness ratios (ratio of the inner layer thickness to the total thickness in a bi-layer pipe) varying from 0.0 to 1.0. They can also obtain accurate stress concentration factors whether the elastic modulus of the inner layer is smaller or larger than that of the outer layer.

Vibrio parahaemolyticus is a halophilic and heat-labile gram-negative bacterium and is the most prevalent foodborne bacterium in seafood. In order to develop a rapid and sensitive method for detecting the foodborne pathogenic bacterium Vibrio parahaemolyticus, an aptamer-modified magnetic nanoparticle and an aptamer-modified upconversion nanoparticle were synthesised and used as a capture probe and a signal probe, respectively. The aptamer-modified magnetic nanoparticle, V. parahaemolyticus cell, and aptamer-modified upconversion nanoparticle formed a sandwich-like complex, which was rapidly separated from a complex matrix using a magnetic force, and the bacterial concentration was determined by fluorescence intensity analysis. The results showed that the fluorescence intensity signal correlated positively with the concentration of V. parahaemolyticus in the range of 3.2 × 102 to 3.2 × 105 CFU/mL, with a linear equation of y = 296.40x − 217.67 and a correlation coefficient of R2 = 0.9610. The detection limit of the developed method was 4.4 CFU/mL. There was no cross-reactivity with other tested foodborne pathogens. This method is highly specific and sensitive for the detection of V. parahaemolyticus, and can achieve the qualitative detection of this bacterium in a complex matrix.

Reclaimed asphalt pavement (RAP) has been widely utilized because it is an environmentally friendly and economical material. The performance of recycled asphalt mixtures will deteriorate gradually with the secondary aging process of asphalt, including the self-healing property. To further understand the self-healing characteristics of asphalt after secondary aging, taking 70# petroleum asphalt, SBS-modified asphalt, and extracted old asphalt mastics as objects, the fatigue self-healing test and fracture self-healing test were conducted to simulate the intermediate-and low-temperature healing behaviors of different asphalt mastics. The impact of healing time, healing temperature, and aging degree of mastics on the healing performance was systematically investigated. The results show that the original unaged asphalt mastics present excellent fatigue healing properties with an index of 0.796 and 0.888 for 70# petroleum and SBS-modified asphalt mastics, respectively. The secondary aging process causes significant impact on the healing properties, leading to a great drop in the corresponding index, which decreased to 47.5% and 72.5% of that of the unaged ones. The fracture healing ability of all mastics was much inferior to the fatigue healing. After secondary aging, the fracture healing index values of 70# petroleum asphalt, SBS-modified asphalt, and extracted old asphalt mastics were all as low as around 0.3, indicating similar performance can be found in the secondary aged SBS-modified asphalt mastics and 70# asphalt mastics. Overall, after secondary aging, the fatigue damage of SBS-modified asphalt mastics can be cured effectively by self-healing, but the fatigue and fracture self-healing properties of 70# asphalt mastics are difficult to recover. These results could provide an innovative view to understand the fatigue and fracture healing characteristics of recycled asphalt pavement after secondary aging.

The digitalization of information is crucial for the upgrading of the bayberry digital agriculture industry, while the low-cost information detection sensing equipment for bayberry are a bottleneck for the digital development of the industry. The existing rapid and non-destructive detection devices for fruit acidity and sugar content mainly use near-infrared and mid-infrared spectral characteristic for detection. These devices use expensive InGaAs sensor, which are difficult to promote and apply in the bayberry digital industry. This study is based on the high-spectral range of 454–998 nm in bayberry fruit to study the mechanism of fruit sugar and acidity content detection and to develop a portable bayberry fruit sugar and acidity detection device using Si-sensor in order to achieve low-cost quality parameter detection of bayberry fruit. The research results show that: Based on the hyperspectral of bayberry fruit, the sensitive wavelength for sugar content inversion is 610 nm, and the inversion accuracy (RMSE) is 1.399Brix; the sensitive wavelength for pH inversion is 570 nm, and the inversion accuracy (RMSE) is 0.1329. Based on the above spectroscopic detection mechanism and spectral dimension reduction methods, combined with low-cost Si-sensor (400–1000 nm), a low-cost non-destructive portable bayberry fruit sugar and acidity detection device has been developed, with detection accuracies of 94.74% and 97.14%, respectively. This bayberry fruit sugar and acidity detector provides a low-cost portable non-destructive quality detection instrument of bayberry, which is in line with the industrial group of low consumption in which the bayberry is mainly cultivated on a small scale, accelerating the digitalization process of the bayberry industry.

The complexity inherent in navigating intricate traffic environments poses substantial hurdles for intelligent driving technology. The continual progress in mapping and sensor technologies has equipped vehicles with the capability to intricately perceive their exact position and the intricate interplay among surrounding traffic elements. Building upon this foundation, this paper introduces a deep reinforcement learning method to solve the decision-making and trajectory planning problem of intelligent vehicles. The method employs a deep learning framework for feature extraction, utilizing a grid map generated from a blend of static environmental markers such as road centerlines and lane demarcations, in addition to dynamic environmental cues including vehicle positions across varied lanes, all harmonized within the Frenet coordinate system. The grid map serves as the input for the state space, and the input for the action space comprises a vector encompassing lane change timing, velocity, and vertical displacement at the lane change endpoint. To optimize the action strategy, a reinforcement learning approach is employed. The feasibility, stability, and efficiency of the proposed method are substantiated via experiments conducted in the CARLA simulator across diverse driving scenarios, and the proposed method can increase the average success rate of lane change by 6.8% and 13.1% compared with the traditional planning control algorithm and the simple reinforcement learning method.

Despite the effective antivirals and vaccines, COVID-19 remains a public health concern. The mutations that occurred during the early stage of the pandemic can be valuable in assessing the viral fitness and evolutionary trajectory. In this study, we analyzed a panel of 2969 spike sequences deposited in GISAID before April 2020 and characterized nine representative spike single-point mutants in detail. Compared with the WA01/2020, most (8 out of 9) mutants demonstrated an equivalent or diminished protein expression or processing, pseudovirus infectivity, and cell–cell fusion. Interestingly, most of the mutants in native form elicited minimum antibody responses in mice despite unaltered CD4+ and CD8+ T cell responses. The mutants remained sensitive to the antisera and the type I interferon. Taken together, these data suggest that the early emerging mutants are virologically divergent, and some of which showed transmission fitness. Our findings have important implications for the retrospective tracing of the early SARS-CoV-2 transmission and future pandemic preparedness.

In a dynamic environment, autonomous driving vehicles require accurate decision-making and trajectory planning. To achieve this, autonomous vehicles need to understand their surrounding environment and predict the behavior and future trajectories of other traffic participants. In recent years, vectorization methods have dominated the field of motion prediction due to their ability to capture complex interactions in traffic scenes. However, existing research using vectorization methods for scene encoding often overlooks important physical information about vehicles, such as speed and heading angle, relying solely on displacement to represent the physical attributes of agents. This approach is insufficient for accurate trajectory prediction models. Additionally, agents’ future trajectories can be diverse, such as proceeding straight or making left or right turns at intersections. Therefore, the output of trajectory prediction models should be multimodal to account for these variations. Existing research has used multiple regression heads to output future trajectories and confidence, but the results have been suboptimal. To address these issues, we propose QINET, a method for accurate multimodal trajectory prediction for all agents in a scene. In the scene encoding part, we enhance the feature attributes of agent vehicles to better represent the physical information of agents in the scene. Our scene representation also possesses rotational and spatial invariance. In the decoder part, we use cross-attention and induce the generation of multimodal future trajectories by employing a self-learned query matrix. Experimental results demonstrate that QINET achieves state-of-the-art performance on the Argoverse motion prediction benchmark and is capable of fast multimodal trajectory prediction for multiple agents.

The physical properties, free amino acids, and metabolites of Beijing-You chicken (BYC) breast meat aged 90, 120, and 150 days were analyzed to investigate the flavor changes with age. The shear force and intramuscular fat increased from 90 to 120 days significantly. The contents of total free amino acids and essential amino acids decreased from 90 to 120 days significantly. No significant differences were detected between 120 and 150 days. The contents of sweet amino acids, bitter amino acids, and umami amino acids showed no significant differences between different ages. In addition, GC-MS and LC-MS were integrated for metabolite detection in breast meat. A total of 128, 142, and 88 differential metabolites were identified in the comparison groups of 120 d vs. 90 d, 150 d vs. 90 d, and 150 d vs. 120 d. Amino acids and lipids were the main differential metabolites. The pathway analysis showed that arginine biosynthesis, histidine metabolism, purine metabolism, and cysteine and methionine metabolism were the main pathways involved in flavor formation during BYC development. It was also found that the metabolites associated with flavor, such as methionine, cysteine, glucose, anserine, arachidonic acid, and glycerol 1-phosphate, were significantly affected by age.

Reverse transcription quantitative polymerase chain reaction (RT-qPCR) is an accurate method for quantifying gene expression levels. Choosing appropriate reference genes to normalize the data is essential for reducing errors. Gelsemium elegans is a highly poisonous but important medicinal plant used for analgesic and anti-swelling purposes. Gelsenicine is one of the vital active ingredients, and its biosynthesis pathway remains to be determined. In this study, G. elegans leaf tissue with and without the application of one of four hormones (SA, MeJA, ETH, and ABA) known to affect gelsenicine synthesis, was analyzed using ten candidate reference genes. The gene stability was evaluated using GeNorm, NormFinder, BestKeeper, ∆CT, and RefFinder. The results showed that the optimal stable reference genes varied among the different treatments and that at least two reference genes were required for accurate quantification. The expression patterns of 15 genes related to the gelsenicine upstream biosynthesis pathway was determined by RT-qPCR using the relevant reference genes identified. Three genes 8-HGO, LAMT, and STR, were found to have a strong correlation with the amount of gelsenicine measured in the different samples. This research is the first study to examine the reference genes of G. elegans under different hormone treatments and will be useful for future molecular analyses of this medically important plant species.

Instance segmentation in remote sensing (RS) imagery aims to predict the locations of instances and represent them with pixel-level masks. Thanks to the more accurate pixel-level information for each instance, instance segmentation has enormous potential applications in resource planning, urban surveillance, and military reconnaissance. However, current RS imagery instance segmentation methods mostly follow the fully supervised paradigm, relying on expensive pixel-level labels. Moreover, remote sensing imagery suffers from cluttered backgrounds and significant variations in target scales, making segmentation challenging. To accommodate these limitations, we propose a semantic attention enhancement and structured model-guided multi-scale weakly supervised instance segmentation network (SASM-Net). Building upon the modeling of spatial relationships for weakly supervised instance segmentation, we further design the multi-scale feature extraction module (MSFE module), semantic attention enhancement module (SAE module), and structured model guidance module (SMG module) for SASM-Net to enable a balance between label production costs and visual processing. The MSFE module adopts a hierarchical approach similar to the residual structure to establish equivalent feature scales and to adapt to the significant scale variations of instances in RS imagery. The SAE module is a dual-stream structure with semantic information prediction and attention enhancement streams. It can enhance the network’s activation of instances in the images and reduce cluttered backgrounds’ interference. The SMG module can assist the SAE module in the training process to construct supervision with edge information, which can implicitly lead the model to a representation with structured inductive bias, reducing the impact of the low sensitivity of the model to edge information caused by the lack of fine-grained pixel-level labeling. Experimental results indicate that the proposed SASM-Net is adaptable to optical and synthetic aperture radar (SAR) RS imagery instance segmentation tasks. It accurately predicts instance masks without relying on pixel-level labels, surpassing the segmentation accuracy of all weakly supervised methods. It also shows competitiveness when compared to hybrid and fully supervised paradigms. This research provides a low-cost, high-quality solution for the instance segmentation task in optical and SAR RS imagery.

Uncaria rhynchophylla (Miq.) Miq. ex Havil, a traditional medicinal herb, is enriched with several pharmacologically active terpenoid indole alkaloids (TIAs). At present, no method has been reported that can comprehensively select and evaluate the appropriate reference genes for gene expression analysis, especially the transcription factors and key enzyme genes involved in the biosynthesis pathway of TIAs in U. rhynchophylla. Reverse transcription quantitative PCR (RT-qPCR) is currently the most common method for detecting gene expression levels due to its high sensitivity, specificity, reproducibility, and ease of use. However, this methodology is dependent on selecting an optimal reference gene to accurately normalize the RT-qPCR results. Ten candidate reference genes, which are homologues of genes used in other plant species and are common reference genes, were used to evaluate the expression stability under three stress-related experimental treatments (methyl jasmonate, ethylene, and low temperature) using multiple stability analysis methodologies. The results showed that, among the candidate reference genes, S-adenosylmethionine decarboxylase (SAM) exhibited a higher expression stability under the experimental conditions tested. Using SAM as a reference gene, the expression profiles of 14 genes for key TIA enzymes and a WRKY1 transcription factor were examined under three experimental stress treatments that affect the accumulation of TIAs in U. rhynchophylla. The expression pattern of WRKY1 was similar to that of tryptophan decarboxylase (TDC) under ETH treatment. This research is the first to report the stability of reference genes in U. rhynchophylla and provides an important foundation for future gene expression analyses in U. rhynchophylla. The RT-qPCR results indicate that the expression of WRKY1 is similar to that of TDC under ETH treatment. It may coordinate the expression of TDC, providing a possible method to enhance alkaloid production in the future through synthetic biology.

Nowadays, the intelligent unmanned agricultural unmanned aerial vehicle (UAV) swarm collaboration network (AUSCN) has fully demonstrated its advantages in agricultural monitoring and management. By using an AUSCN, multi-machine cooperation can be realized to expand the detection range, and more complex tasks can be completed without human participation, so as to improve work efficiency and reduce the consumption of manpower and material resources. In AUSCNs, clustering is a key method to lower energy consumption. However, there is a challenge to select cluster heads in AUSCNs because of the limitation of transmission distances and the complexity of network topological structures. In addition, this problem has been confirmed as NP-hard. In this paper, a new niche adaptive elite evolutionary algorithm (NAEEA) is proposed to solve this problem. NAEEAs can search within various complicated stochastic situations at high speeds with characterized high precision and fast convergence. This algorithm integrates the merits of elite selection and adaptive adjusting to achieve high performance. In NAEEAs, a new adaptive operator is designed to speed up the convergence rate, while a novel elite operator is proposed to avoid local optima and raise the exploration ability. Furthermore, a new niche operator is also proposed to increase the population diversity. The simulation results show that, compared with an evolutionary algorithm (EA), a simulated annealing algorithm (SA) and a leapfrog algorithm (SFLA), clustering energy consumption based on an NAEEA is significantly reduced, and the network energy consumption of the AUSCN is up to 21.43%, 25.00% and 25.76% lower than the other three algorithms, respectively.

China’s approach to addressing rural poverty has evolved from a thorough resolution of absolute poverty to a focus on providing essential support for vulnerable individuals and improving the income and welfare conditions of those who are relatively poor, taking into account multiple dimensions. This study utilizes a dataset consisting of 526 research sets collected from the central region of Shaanxi Province. The research employs structural equation modeling to examine the fractal multidimensional relative poverty trap experienced by farm households. Additionally, the study investigates the behavior motivation mechanism that can potentially alleviate the multidimensional relative poverty trap at the farm household level. The study found that (1) farm households in the central Shaanxi region are caught in a multidimensional relative poverty trap, with education poverty and health poverty having a conduction and amplification effect; health poverty and education poverty amplify employment poverty; and consumption poverty amplifies education poverty and health poverty, and education poverty further amplifies information poverty. (2) Multidimensional relative poverty in farming households creates a self-reinforcing poverty trap, and community relative poverty amplifies the multidimensional poverty trap in farming households. (3) Farmers can overcome the multidimensional relative poverty trap through the behavior motivation mechanism.

Considering Inner Mongolia as the study area, the ecological theory of climate change, and human activities affecting a wide range of biodiversity patterns, MODIS multi-timeseries remote sensing image data were used and the interannual variation index was obtained by the method of fitting the curve to obtain the annual phenological and seasonal indicators. At the same time, the Landsat 8 standard deviation image was calculated to obtain the spatial variation index and generate spatial–temporal remote sensing indices to quantify the threat of climate change to biodiversity. In addition, the impact of human activities on biodiversity was quantified by generating a map of the human footprint in Inner Mongolia. The spatial–temporal remote sensing index and the human footprint index were integrated to identify areas protected from climate change and human activities, respectively. Eventually, the hotspot areas of biodiversity conservation in Inner Mongolia were obtained and priority protected area planning was based on the hotspot identification results. In this study, remote sensing technology was used to identify biodiversity conservation hotspots, which can overcome the limitations of insufficient species data from the past, improve the reliability of large-scale biodiversity conservation analyses, and be used for targeted management actions that have practical significance for biodiversity conservation planning.

Plenty of freshwater species, especially macroinvertebrates that are essential to the provision of numerous ecosystem functions, encounter higher mortality due to acute hypoxia. However, within the family Chironomidae, a wide range of tolerance to hypoxia/anoxia is displayed. Propsilocerus akamusi depends on this great tolerance to become a dominant species in eutrophic lakes. To further understand how P. akamusi responds to acute hypoxic stress, we used multi-omics analysis in combination with histomorphological characteristics and physiological indicators. Thus, we set up two groups—a control group (DO 8.4 mg/L) and a hypoxic group (DO 0.39 mg/L)—to evaluate enzyme activity and the transcriptome, metabolome, and histomorphological characteristics. With blue–black chromatin, cell tightness, cell membrane invagination, and the production of apoptotic vesicles, tissue cells displayed typical apoptotic features in the hypoxic group. Although lactate dehydrogenase (LDH), alcohol dehydrogenase (ADH), catalase (CAT), and Na+/K+ -ATPase (NKA) activities were dramatically enhanced under hypoxic stress, glycogen content, and superoxide dismutase (SOD) activities were significantly reduced compared to the control group. The combined analysis of the transcriptome and metabolome, which further demonstrated, in addition to carbohydrates, including glycogen, the involvement of energy metabolism pathways, including fatty acid, protein, trehalose, and glyoxylate cycles, provided additional support for the aforementioned findings. Lactate is the end product of glycogen degradation, and HIF-1 plays an important role in promoting glycogenolysis in acute hypoxic conditions. However, we discovered that the ethanol tested under hypoxic stress likely originates from the symbiodinium of P. akamusi. These results imply that some parameters related to energy metabolism, antioxidant enzyme activities, and histomorphological features may be used as biomarkers of eutrophic lakes in Chironomus riparius larvae. The study also provides a scientific reference for assessing toxicity and favoring policies to reduce their impact on the environment.

In this study, basalt fiber having two types of diameters (16 μm and 25 μm) was selected and added to asphalt mastic and asphalt mixtures using different fiber proportions. The influences of fiber diameters and proportions on the properties of asphalt mastic and mixtures were studied. The adhesion behavior of the fiber-asphalt mastic (FAM) interface was evaluated by a monofilament pullout test, and the rheological properties of FAM were evaluated by temperature sweep, linear amplitude sweep, and bending beam rheological tests. In addition, the high-temperature stability, intermediate and low-temperature cracking resistance, and water stability of fiber-modified mixtures were studied by wheel tracking, ideal cracking, a low-temperature bending beam, and a water-immersed Marshall test. The results showed that the interface adhesion behavior between 16 μm fiber and asphalt mastic was more likely in the fiber failure mode at both −12 °C and 25 °C. Adding basalt fiber can significantly improve the high-temperature and fatigue properties of asphalt mastics. Moreover, 16 μm fiber had a better modifying effect on asphalt mastic than 25 μm fiber. The same enhancement trend can be observed in asphalt mixtures. Basalt fibers with 16 μm diameters can improve the high-temperature performance of asphalt mixtures more significantly. In addition, 16 μm fiber could sharply enhance the cracking performance of the mixtures at intermediate and low temperatures, while the enhancing effect of 25 μm fiber on the mixture is insignificant, though both diameters of the fibers have a minor effect on the water stability.

The insulin-like peptide (ILP) family is well known for regulating reproduction in invertebrates, while its role in mollusks remains largely unknown. In this study, we first isolated and characterized the ILP gene in the cuttlefish Sepiella japonica. The full-length SjILP cDNA obtained was 926 bp and encoded a precursor protein of 161 amino acids. The precursor protein consisted of a signal peptide, a B chain, a C-peptide, and an A chain. It possessed the typical features of ILP proteins, including two cleavage sites (KR) and eight conserved cysteines. To define the function of SjILP, the expression of SjILP in different tissues and ovarian development stages were analyzed using qRT-PCR. SjILP was mainly expressed in the ovary, and its gene expression correlated with ovarian development. Furthermore, silencing SjILP using RNA interference (RNAi) dramatically decreased the expression levels of four ovarian-development-related genes (vitellogenin1, vitellogenin2, cathepsin L1-like, and follistatin). These data suggest the critical role of SjILP in the regulation of ovarian development in S. japonica.

Solid pancreatic lesions (SPLs) encompass a variety of benign and malignant diseases and accurate diagnosis is crucial for guiding appropriate treatment decisions. Endoscopic ultrasonography-guided fine-needle aspiration/biopsy (EUS-FNA/B) serves as a front-line diagnostic tool for pancreatic mass lesions and is widely used in clinical practice. Artificial intelligence (AI) is a mathematical technique that automates the learning and recognition of data patterns. Its strong self-learning ability and unbiased nature have led to its gradual adoption in the medical field. In this paper, we describe the fundamentals of AI and provide a summary of reports on AI in EUS-FNA/B to help endoscopists understand and realize its potential in improving pathological diagnosis and guiding targeted EUS-FNA/B. However, AI models have limitations and shortages that need to be addressed before clinical use. Furthermore, as most AI studies are retrospective, large-scale prospective clinical trials are necessary to evaluate their clinical usefulness accurately. Although AI in EUS-FNA/B is still in its infancy, the constant input of clinical data and the advancements in computer technology are expected to make computer-aided diagnosis and treatment more feasible.

With the gradual improvement in coal mining efficiency, the disturbance of groundwater systems caused by high-intensity mining also increases, leading to challenges in maintaining mine safety and protecting water resources in mining areas. How to accurately describe the dynamic changes in the groundwater system under mining and quantitatively predict mine water inflow are currently major problems to be addressed. Based on a full analysis of the response characteristics of a groundwater system to the extraction disturbance, this paper presents a new method to establish a mine hydrogeological conceptual model that can accurately represent the water inrush process. The unstructured-grid package of MODFLOW is used to accurately characterize the formation structure and finally make accurate water inflow predictions. Taking the Caojiatan coal mine in Shaanxi Province, China, as an example, a numerical model of unstructured water inflow is established, and the changes in the water inflow source and intensity are quantitatively evaluated. Compared with the traditional water inflow prediction method, the prediction accuracy of the new model is improved by 12–17%, which is achieved by detailing the response of the complex groundwater system under high-intensity mining conditions. The method presented in this paper has great significance and applicatory value for obtaining a comprehensive understanding of the disturbance characteristics of human underground engineering activities (e.g., coal mining) on groundwater systems, as well as accurately predicting water inflow.

The color of sweet cherry fruits is an important indicator of their appearance and quality. That influences the purchasing desires of consumers. We performed a multi-omics analysis of two different colors of sweet cherry fruits (yellow “Bing Hu” and dark-red “Hong Deng” fruits). A total of 12 flavonoid differential metabolites, including hesperetin, rutin, and quercetin, and 18 differential structural genes, including PAL, CHS, FLS, and DFR, were identified. Possible key regulatory genes for the second stage of color change (from green to yellow) of “Bing Hu” sweet cherry fruits were identified as SBP, bHLH, WD40, and bZIP, which regulated the accumulation of flavonoids, including hesperetin and naringenin. In addition, the possible important roles of transcription factors, which were mainly MYB, bHLH, AP2, and WRKY, in the third stage of color change in both fruits were also identified. This study offers new insights into the changes in fruit coloration between yellow and dark-red sweet cherries, while the analysis of key metabolites and differential genes lays a molecular foundation for future color improvement and breeding programs.

The aim of this study was to further research the mechanical properties of epoxy thick-layer rubber isolation bearings, adopting orthogonal design and efficacy coefficient methods in order to optimize the geometric dimensions and material parameters of the bearing, summarizing the influence of various factors on the overall performance indicators of the bearing, and determining the optimal plan through parameter adjustment. Through a combination of experiments and simulations, the fundamental characteristics of epoxy thick-layer rubber isolation bearings are studied to determine the influence law of vertical pressure on their horizontal stiffness, vertical stiffness, and damping ratio. The analysis results suggest that epoxy plate thick-layer rubber isolation bearing exhibits stable deformation ability and possesses distinctive damping characteristics. Furthermore, it is observed that the horizontal stiffness of these bearings gradually diminishes as the vertical pressure increases. When the shear displacement reaches 80 mm, there is a notable strengthening effect observed in the horizontal stiffness of the bearing. This strengthening phenomenon proves advantageous in preventing damage to the bearing due to excessive displacement; furthermore, it is noteworthy that the vertical stiffness and damping ratio of the bearing increased with the rise in vertical pressure.

In order to solve the problems of performance degradation in energy tower heat pump (ETHP) systems under low temperature conditions and soil heat imbalances in ground source heat pump (GSHP) systems in cold regions, a new coupled system of ETHP and GSHP systems (the ET–GSHP system) and its operating mode were proposed. The mathematical model of the system was constructed along with the system’s form and operation scheme. The COP (coefficient of performance) and total energy consumption of the coupled system were then simulated and studied under a number of common operating situations. The heating season is divided into four periods based on varying outdoor ambient temperatures: the first period operates in series mode and has an average outdoor temperature of 2.38 °C; the second period operates in parallel mode and has an average outdoor temperature of −8.56 °C; the third period uses soil source heat pumps to operate separately; and the fourth period operates in series mode and has an average outdoor temperature of −11.32 °C. Operation of the coupled system in four periods was simulated and analyzed, and the operational efficiency and energy saving of the system were analyzed using an actual commercial building in a cold region as an example. The results demonstrate that the ET–GSHP system’s overall energy consumption during the heating period is reduced by 4.34% when compared to the traditional GSHP systems; the system’s COP can maintain a high level throughout the heating period, with an average COP of 3.315; and the soil temperature at the conclusion of the heating period is 25 °C, which is 8.89 °C higher than that of the traditional GSHP system, providing a guarantee of summer heat return. The new ET–GSHP system significantly boosts the efficiency of the system’s operation, achieves effective coupling between various heat sources through multi-stage control, and offers improved energy-saving advantages.

The rapid economic growth of China has caused significant adverse effects on the environment. Meanwhile, technological innovation, a fundamental driver of economic development and social progress, plays a pivotal role in mitigating haze pollution. This study comprehensively examines the influence of technological innovation on haze pollution in the Yangtze River urban agglomeration, using the STIRPAT model and analyzing research data from 2004 to 2020. Additionally, the study investigates the specific moderating role of urban spatial structure in the relationship between technological innovation and haze pollution. The findings reveal that technological innovation in the middle reaches of the Yangtze River urban agglomeration can effectively curtail haze pollution, and its impact extends to surrounding areas through spillover effect. The polycentric urban spatial structure significantly enhances the haze-reducing effect of technological innovation in the region. Notably, in cities with a strong level of environmental regulation, the urban spatial structure plays a substantial role in augmenting the haze-reducing impact of technological innovation. The policy implications of this research underscore the importance of continuous improvement in technological innovation within the middle reaches of the Yangtze River urban agglomeration. To address haze pollution in future development, the adoption of a polycentric development strategy and the establishment of sound environmental governance policies are recommended.

E. rutaecarpa var. officinalis is a traditional Chinese medicinal plant known for its therapeutic effects, which encompass the promotion of digestion, the dispelling of cold, the alleviation of pain, and the exhibition of anti-inflammatory and antibacterial properties. The principal active component of this plant, limonin, is a potent triterpene compound with notable pharmacological activities. Despite its significance, the complete biosynthesis pathway of limonin in E. rutaecarpa var. officinalis remains incompletely understood, and the underlying molecular mechanisms remain unexplored. The main purpose of this study was to screen the reference genes suitable for expression analysis in E. rutaecarpa var. officinalis, calculate the expression patterns of the genes in the limonin biosynthesis pathway, and identify the relevant enzyme genes related to limonin biosynthesis. The reference genes play a pivotal role in establishing reliable reference standards for normalizing the gene expression data, thereby ensuring precision and credibility in the biological research outcomes. In order to identify the optimal reference genes and gene expression patterns across the diverse tissues (e.g., roots, stems, leaves, and flower buds) and developmental stages (i.e., 17 July, 24 August, 1 September, and 24 October) of E. rutaecarpa var. officinalis, LC-MS was used to analyze the limonin contents in distinct tissue samples and developmental stages, and qRT-PCR technology was employed to investigate the expression patterns of the ten reference genes and eighteen genes involved in limonin biosynthesis. Utilizing a comprehensive analysis that integrated three software tools (GeNorm ver. 3.5, NormFinder ver. 0.953 and BestKeeper ver. 1.0) and Delta Ct method alongside the RefFinder website, the best reference genes were selected. Through the research, we determined that Act1 and UBQ served as the preferred reference genes for normalizing gene expression during various fruit developmental stages, while Act1 and His3 were optimal for different tissues. Using Act1 and UBQ as the reference genes, and based on the different fruit developmental stages, qRT-PCR analysis was performed on the pathway genes selected from the “full-length transcriptome + expression profile + metabolome” data in the limonin biosynthesis pathway of E. rutaecarpa var. officinalis. The findings indicated that there were consistent expression patterns of HMGCR, SQE, and CYP450 with fluctuations in the limonin contents, suggesting their potential involvement in the limonin biosynthesis of E. rutaecarpa var. officinalis. This study lays the foundation for further research on the metabolic pathway of limonin in E. rutaecarpa var. officinalis and provides reliable reference genes for other researchers to use for conducting expression analyses.

The GNSS-S radar utilizes the signals of a global navigation satellite system (GNSS) to carry out target detection. Due to the very low power of GNSS signals, long-term accumulation is needed to improve the gain of the echo signals. However, when it is used for moving object detection, the random movement of the target will cause residual Doppler frequency after the echoes are correlated and compressed through the direct signal. The residual Doppler frequency will cause two problems: on the one hand, the signal coherence will deteriorate, affecting the coherent accumulation gain; on the other hand, the amplitude of the signal after compression will decrease due to the sensitivity of GNSS signals to Doppler frequency. Therefore, how to increase the signal amplitude and eliminate the phase fluctuation caused by the Doppler frequency shift in the GNSS echoes of moving targets is an important issue for GNSS-S radar to detect moving targets. This paper proposes a dual-frequency GNSS echo enhancement method that uses the dual-frequency signals transmitted by the GNSS satellites to enhance and regularize the target echo. First, the phase relationship model of the GNSS dual-frequency echo is constructed, and the phase difference is made to the compressed dual-frequency echo signal to obtain the differential phase without fluctuation; then, the amplitudes of the dual-frequency echo signals are added together; and finally, a new signal with enhanced amplitude and consistent phase is constructed by using the dual-frequency additive amplitude and differential phase, and the long-term coherent accumulation of the signal is carried out, which can improve the processing gain of the weak echo signal of the moving target. The simulation and field experiments show that this method makes full use of the energy of the GNSS dual-frequency signal and eliminates the phase fluctuation in the echo signal of the moving target so that the compressed signal energy remains consistent in the slow-time dimension. After long-term coherent accumulation, the echo SNR was greatly improved, which enabled the detection of two high-speed cars by GNSS-S radar in the experiment.

The goal of this paper is to analyze the aging behavior and the mechanism evolution of nano-Al2O3 (NA)-reinforced styrene-butadiene-styrene (SBS) asphalt under different thermal-oxidative aging conditions. First, NA/SBS-modified asphalt and SBS-modified asphalt with different aging levels were prepared. Second, the viscosity and high temperature rheological performance of the specimens were tested and the property-related aging indexes were calculated and compared. Third, a Fourier transform infrared (FTIR) test of the specimen was conducted and the chemical group-related aging indexes were calculated and analyzed. Fourth, gel permeation chromatography (GPC) was used to analyze the molecular weight of the specimens under different aging levels. Then, an atomic force microscope (AFM) was adopted to analyze the microsurface morphology of different specimens. Finally, correlation analysis between property-related indexes and chemical group indexes was conducted. The results show that NA can enhance the thermal-oxidative aging resistance of SBS asphalt. NA can inhibit the increase in sulfoxide groups and the degradation of the SBS polymer with the increase in aging. NA can slow down the formation of large molecule during the aging process. The degree of change in both the bee structures and micromorphological roughness of NA/SBS asphalt is lower than that of SBS asphalt under different aging levels.

This paper focuses on the problems of inaccurate extraction of winter wheat edges from high-resolution images, misclassification and omission due to intraclass differences as well as the large number of network parameters and long training time of existing classical semantic segmentation models. This paper proposes a lightweight winter wheat planting area extraction model that combines the DeepLabv3+ model and a dual-attention mechanism. The model uses the lightweight network MobileNetv2 to replace the backbone network Xception of DeepLabv3+ to reduce the number of parameters and improve the training speed. It also introduces the lightweight Convolutional Block Attention Module (CBAM) dual-attention mechanism to extract winter wheat feature information more accurately and efficiently. Finally, the model is used to complete the dataset creation, model training, winter wheat plantation extraction, and accuracy evaluation. The results show that the improved lightweight DeepLabv3+ model in this paper has high reliability in the recognition extraction of winter wheat, and its recognition results of OA, mPA, and mIoU reach 95.28%, 94.40%, and 89.79%, respectively, which are 1.52%, 1.51%, and 2.99% higher than those for the original DeepLabv3+ model. Meanwhile, the model’s recognition accuracy was much higher than that of the three classical semantic segmentation models of UNet, ResUNet and PSPNet. The improved lightweight DeepLabv3+ also has far fewer model parameters and training time than the other four models. The model has been tested in other regions, and the results show that it has good generalization ability. The model in general ensures the extraction accuracy while significantly reducing the number of parameters and satisfying the timeliness, which can achieve the fast and accurate extraction of winter wheat planting sites and has good application prospects.

To study the heating performance of a solar-assisted cold-water phase-change-energy heat pump system, its heating performance under series and parallel connections is simulated for a community in Harbin, the influence of ice thickness on the different operation modes is analyzed, and the economy of the system is calculated for series and parallel connections in this paper. The results show that the water supply temperature is higher and more uniform in the parallel operation, and more terminal heat is supplied; the ice thickness has more of an influence on the series connection compared to the parallel connection; and the dynamic payback period is 6.72 years for the series connection and 7.28 years for the parallel connection. This case study can serve as a guide for practical engineering application projects and act as a reference for heating and economic data for the promotion of this heat pump system.