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• Using Sentinel-2 imagery and deep learning, rapeseed fields' spatial distribution can be detected during initial flowering. • PSPNet accurately identified rapeseed fields with high effectiveness (F1 = 88.17 %). • The detection of initial flowering provides valuable data for the management of pollinators. • The PSPNet rapeseed field detection model in the initial flowering period has been validated. In high-intensity farming, swiftly and accurately adjusting the proportion of artificially reared pollinators according to the flowering phenology of crops is vital. This adjustment is essential for sustaining crop yields without disrupting the ecological niche of native pollinators. Although advancements in remote sensing provide practical means to gather data on crop coverage and extent, identifying insect-pollinated crops during their initial flowering to guide the deployment of managed pollinators is an underexamined research area. Here, we tested the capability of utilizing Sentinel-2 satellite images combined with a deep learning model to map crop fields during the initial flowering stage of insect-pollinated crops in Zhaosu County, Xinjiang, China. Specifically, we examined rapeseed fields by employing 12 neural network designs to identify images of the initial flowering stage. Different network combinations of three convolutional neural network (CNN) models (U-Net, PSPNet, and DeepLab V3) and four different backbone networks (ResNet-18, ResNet-34, ResNet-50, and ResNet-101) were explored to determine the most effective model for detecting rapeseed fields at the initial flowering stage. A comparison was conducted with Sentinel-2 images obtained at the peak stage of rapeseed flowering. Our results suggest that the use of a deep learning model in combination with Sentinel-2 image data can successfully identify rapeseed fields at the initial flowering stage, thereby offering preliminary insights for the strategic introduction of managed pollinators. The PSPNet model emerged as the superior choice for the identification of rapeseed fields, exhibiting high accuracy in both detection and boundary recognition, with F1 scores of 88.17 % and an intersection over union (IoU) of 53.11 % during initial flowering and F1 scores of 93.33 % with an IoU of 53.49 % during peak flowering. The planting area of rapeseed fields detected by the model in Zhaosu County was 122.16 km2, distributed within an area of 1068 km2. These findings can provide foundational data for informed decisions regarding artificial pollinator supplementation, helping to sustain the health and stability of agricultural ecosystems. [ABSTRACT FROM AUTHOR]

The rain-wind-induced vibration (RWIV) of stay cables has become a concern in bridge engineering over the past decades. Attaching helical fillets on the surface of stay cables is often used to suppress RWIV. In the critical Reynolds number range, stay cables also experience large amplitude vibration without rainfall, which is known as dry galloping. To investigate the optimal helical fillet parameters that can suppress RWIV and dry galloping, a series of wind tunnel tests were carried out. The results showed that a helical fillet can weaken the Reynolds number effect of the cable in the critical region, and the maximum C L and the decline value of C D are decreased. This effect gradually decreases with the decrease of helical fillet pitch, resulting in the decrease of the maximum C L in the critical region and the increase of C D in the supercritical region. The helical fillet has a good suppression effect on the dry galloping equilibrium position, and the suppression effect of the helical fillet on the dry galloping amplitude depends on the helical fillet parameters. A proper helical fillet setting can effectively suppress both RWIV and dry galloping. Compared with the helical fillet diameter, the suppression effect is more effective when changing its pitch. Finally, the drag coefficients in various Reynolds number regions are summarized to provide a reference for engineering design. • Wind tunnel tests were carried out to reveal the aerodynamics and vibrations for stay cables with helical fillets. • Aerodynamic force coefficients and Strouhal numbers of stay cables with helical fillets were obtained and compared. • The effects of wind speed, rain intensity, inclination, and yaw on the RWIV were studied. • The effects of the helical fillet diameter and distance on RWIV and dry galloping were analyzed. • The drag coefficients of cables at different Reynolds numbers were fitted to facilitate engineering applications. [ABSTRACT FROM AUTHOR]

In order to improve cost competitiveness and the further application of metal-supported cells, it is important to explore cathodes suitable for low-temperature sintering. However, decreasing cathode sintering temperature while maintaining robust interface of cathode/electrolyte and decent electrochemical performances is challenging for solid oxide fuel cells. Herein, by combining the Pechini method with one-pot method, La 0.6 Sr 0.4 CoO 3-δ @ Gd 0.1 Ce 0.9 O 2-δ (LSC@GDC) composite cathodes are prepared and investigated, compared with mechanically mixed LSC-GDC cathodes. At the cathode sintering temperature of 800 °C, the LSC@GDC cathode single cells perform better performances, with deceased resistances, improved power density outputs and better feasibility to wide oxygen partial pressures from 0.21 atm down to 0.03 atm. The optimum composition of 70:30 wt% for LSC@GDC is proposed. The anode supported single cell with LSC@GDC30 cathode exhibits a maximum power density of 0.789 W/cm2 and 0.545 W/cm2 at 750 °C, under cathode oxygen partial pressure of 0.21 atm and 0.03 atm, respectively. Moreover, the low temperature sintered LSC@GDC50 cathode performs decent durability during repeatedly controlled thermal cycling over 10 times. • LSC@GDC cathode is successfully prepared by novel two-step one-pot method. • The cathode sintering activity improves as well as interface contact. • The cell performance in harsh condition is dramatically enhanced. • The cell exhibit decent long-term durability and thermal cycling stability. [ABSTRACT FROM AUTHOR]

Hydrogen-rich gas production from reforming of naphthalene as a typical tar model compound was investigated under catalytic, plasma and plasma-catalytic systems. The effect of Gd promoter and solvent on naphthalene decomposition and gaseous products within the reforming reaction was studied. The catalysts were prepared by the thermal fusion method and characterized by XRD, XPS, SEM, BET, TEM, TG and H 2 -TPR. Compared to catalytic systems and plasma systems, the plasma-catalytic system shows the highest naphthalene conversion, as well as highest yield of hydrogen-rich gas on account of the synergetic effect of plasma and catalyst. Ni–Gd/SiO 2 @Al 2 O 3 catalyst exhibits superior catalytic activity than that on Ni/SiO 2 @Al 2 O 3 , due to higher dispersity of nickel species and reducibility of catalysts, as well as more Ni0 species and more surface oxygen species. No sintering and no coke formed on the fusion used catalyst owing to low content and high dispersion of nickel species. Maximum both naphthalene conversion and H 2 yield were observed on Ni-10Gd catalyst, about 89 % and 0.17 mol/mol-naphthalene, respectively. Furthermore, the presence of OH· radicals originating from methanol and the subsequent methanol reforming reaction play a pivotal role in augmenting naphthalene conversion and facilitating the formation of H 2 , CO, and CH 4. [Display omitted] • Ni-10Gd catalyst showed the highest conversion in plasma reforming of naphthalene owing to synergetic effect. • Gd enhances the dispersity and reducibility of catalyst, Ni0 species and surface oxygen species, as well as the naphthalene disintegrates. • Fusion catalysts exhibited high resistance to carbon deposition and sintering. • More OH· radicals in methanol contribute to the activity in plasma-catalytic system. [ABSTRACT FROM AUTHOR]

Eggshell is one of the most important indicators of egg quality, and due to low shell strength, pimple eggs (PE) are more susceptible to breakage, thus causing huge economic losses to the egg industry. At the current time, the molecular mechanisms that regulate the formation of pimple eggs are poorly understood. In this study, uterine tissues of PE-laying hens (n = 8) and normal egg (NE) -laying hens (n = 8) were analyzed by whole transcriptome sequencing, and a total of 619 differentially expressed mRNAs (DE mRNAs), 122 differentially expressed lncRNAs (DE lncRNAs) and 21 differentially expressed miRNAs (DE miRNAs) were obtained. Based on the targeting relationship among DE mRNAs, DE lncRNAs and DE miRNAs, we constructed a competitive endogenous RNA (ceRNA) network including 12 DE miRNAs, 19 DE lncRNAs, and 128 DE mRNAs. Considering the large amount of information contained in the network, we constructed a smaller ceRNA network to better understand the complex mechanisms of pimple egg formation. The smaller ceRNA network network contains 7 DE lncRNAs (LOC107056551, LOC121109367, LOC121108909, LOC121108862, LOC112530033, LOC121113165, LOC107054145), 5 DE miRNAs (gga-miR-6568-3p, gga-miR-31-5p, gga-miR-18b-3p, gga-miR-1759-3p, gga-miR-12240-3p) and 7 DE mRNAs (CABP1, DNAJC5, HCN3, HPCA, IBSP, KCNT1, OTOP3), and these differentially expressed genes may play key regulatory roles in the formation of pimpled eggs in hens. This study provides the overall expression profiles of mRNAs, lncRNAs and miRNAs in the uterine tissues of hens, which provides a theoretical basis for further research on the molecular mechanisms of pimpled egg formation, and has potential applications in improving eggshell quality. [ABSTRACT FROM AUTHOR]

• Machine learning framework for syngas to C 5+ fuels devised. • Database for Cobalt-based catalysts compiled. • Developed ensemble-tree models for predicting C 5+ fuels and CO conversion. • Model analysis isolates effects of individual descriptor on target variables. Thermocatalytic conversion of the renewable syngas into long chain hydrocarbons fuels was an attractive energy production technology, for combating climate change, energy crisis, and wastes disposal. However, this thermochemical process was very complicated, and target product also highly depended on the feedstock information, catalyst properties, and process condition. At present, it was still challenging to fully understand and optimize this process. To address this gap, we developed a machine learning framework to model Fischer-Tropsch synthesis process of syngas towards C 5+ hydrocarbons fuels from experimental descriptors. A database of Cobalt-based catalyst with 406 datapoints was compiled from literature and subjected to data mining. Accurate ensemble-tree models (R2 > 0.82) were developed to predict the CO conversion and C 5+ hydrocarbons fuels selectivity from 12 descriptors, where the significance of dispersion, pressure, temperature, and metal content was revealed. Casual analysis revealed that C 5+ hydrocarbons fuels selectivity was positively correlated with lower temperature (<481 K) and higher dispersion (>7.72 %). Besides, some interesting findings were also observed, for example, smaller cobalt size, and lower pore size (<9.27 wt%) and cobalt loading (<22 wt%) were positively related to C 5+ hydrocarbons fuels selectivity. The framework was purely data-driven, interpretable, and highlighted the ability of this method to unearth relationships of target variables and descriptors in thermocatalytic conversion of syngas, by isolating effects of individual design parameters in a manner that would be difficult to achieve experimentally. These insights could help design and optimization of subsequent experiments. [ABSTRACT FROM AUTHOR]

The composition of organic carbon molecules (OCMs) impacts microbial diversity and in turn regulates the microbial transformation of elements in soils. However, the potential association of OCMs characteristics with arsenic methylation mediated by arsenite S-adenosylmethionine (SAM) methyltransferase gene (arsM)-harboring microbes in paddy soils remains unclear. Here, we investigated the impacts of OCMs on microbial methylators and arsenic methylation by identifying OCMs with Fourier transform ion cyclotron resonance mass spectrometry. The results showed that arsenic methylation efficiency increased by 57.4–260.1% in pig manure- or rice straw-treated paddy soils compared to the control. Molecules containing carbon, hydrogen, oxygen, and nitrogen (CHONs) with the atomic oxygen to carbon ratio (O/C) ranging from 0.5 to 0.8 had a more significant correlation (p < 0.01) with the abundance of the arsM gene than with that of 16S rRNA. This was confirmed by a significant positive correlation (p < 0.001) between consumed CHONs with O/C of 0.5–0.8 and increased methylated arsenic. CHONs (with O/C of 0.5–0.8) increased the intracellular SAM concentration and methyltransferase activity, stimulating arsenic methylation in vivo. The specialization of arsM -harboring taxa in CHONs with different O/C were identified. These observations provide a basis for predicting the risk of straight-head disease in paddy soils. [Display omitted] • Microbial arsenic methylation in paddy soils is regulated by OCMs. • Priming effects of CHONs with O/C of 0.5–0.8 on arsenic methylation was found. • Increased SAM concentration and ArsM activity in methylators stimulate arsenic methylation. • Specialization of arsM -harboring taxa in CHONs with different O/C were identified. [ABSTRACT FROM AUTHOR]