Phase change materials (PCM) are widely applied in battery thermal management due to their superior latent heat storage capacity. However, their intrinsic low thermal conductivity and leakage risks during phase transition restrict their practical application. Therefore, it is imperative to develop shape-stabilized composite materials with enhanced heat transfer performance. In this work, a composite phase change material was prepared using paraffin wax (PW) as the matrix, styrene-ethylene-propylene-styrene (SEPS) as the flexible support, and multi-walled carbon nanotubes (MWCNT) as the thermal conductive filler to enhance the temperature control performance of the battery thermal management system. Firstly, leakage experiments determined that the optimal mass ratio of PW/SEPS is 85/15. The material exhibited a leakage rate of only 3.8% after heating at 60 degrees C for 4 h, demonstrating excellent shape stability. Subsequently, the effects of the MWCNT content and encapsulation thickness of the material on the battery surface temperature were analyzed. The results indicated that the introduction of MWCNT forms a continuous thermal conductive network within the material, significantly enhancing thermal conductivity. The cooling effect was optimal at an MWCNT mass fraction of 4 wt%. The maximum peak surface temperature was effectively reduced, and the maximum temperature difference was controlled within 5 degrees C. Increasing the encapsulation thickness effectively prolonged the temperature control duration, with a 6 mm thickness achieving the best balance between cooling and heat dissipation. This study provides a reference basis for the performance improvement of composite phase change materials applied in battery thermal management.

The presence of remanent magnetization introduces uncertainties in the processing and interpretation of magnetic data. In the literature, a variety of methods have been proposed to extract the intensity and direction of remanent magnetization. However, the existing methods still have some limitations, such as biases in results due to the use of inaccurate prior information and the complex computational process of extracting remanent magnetization information, especially from superimposed anomalies by multiple field sources. In this study, we develop an effective method to extract the intensity and direction of the remanent magnetization based on deep learning. We first use an improved U-Net as the backbone network to obtain the feature of spatial location and remanent magnetization parameters of anomalies and fuse the extracted multiscale feature information. At the same time, residual connections are added between the convolution layers to alleviate the loss of information and reduce gradient disappearance. The network, through continuous training, can directly learn the nonlinear mapping relationship between anomalies and the remanent magnetization intensity and direction, without the need for a prior information and complex calculations. Subsequently, we test the proposed method on synthetic examples and field data example in Yeshan region. All the outcomes demonstrate the capability in accurately extracting intensity and direction of remanent magnetization.

Shipping emission inventory is the basis of regional air pollution assessments and pollution prevention and control policies. The accuracy depends to a large extent on the calculation results of every single ship based on AIS. This paper establishes a regional stratified sampling emission model based on the emission calculation of every single ship to improve the efficiency and precision of regional shipping emission inventory preparation. It performs calculations according to waterway characteristics, ship types and main engine power. The key findings of this paper include (1) the fast algorithm calculates exhaust emissions using complete ship information, helping reduce the uncertainty caused by the absence of single ship parameters during estimation, (2) when the ship sampling ratio is not lower than 1/3, the relative error of emission results is less than 3% and (3) the preparation of shipping emission inventory using the fast algorithm significantly improves the calculation efficiency and accuracy.

To investigate the differences and similarities in vortex and cavity characteristics between translational and rotational domain hydraulic machines, the NACA0009 hydrofoil and a self-designed impeller blade were selected as representative cases in the translational and rotational domains. The STAR-CCM+ software was utilized to simulate the multi-phase flow, and the experimental results of NACA0009 hydrofoil from EPFL were employed to validate the accuracy of the simulation. The following conclusions were drawn from the analysis of the simulation results: Firstly, both types of hydraulic machinery generate similar vortex types, including the tip leakage vortex, tip separation vortex, and secondary tip leakage vortex. However, each type also exhibits unique vortices, such as the perpendicular vortex in the translational domain and the trailing edge vortex in the rotational domain. Secondly, the tip leakage vortex is initially weak but continuously absorbs other vortices, thereby strengthening itself as it develops. Additionally, the blades in the rotational domain must achieve a higher speed to produce the same level of cavitation as those in the translational domain. Finally, the attached cavitation on the blade surface is repelled by the spin of the tip leakage vortex, which cannot promote the generation of tip leakage vortex cavitation. The primary source of tip leakage vortex cavitation is the tip separation vortex cavitation. The strengths of cavitation and vortices differ between the rotational and translational domains, leading to varying effects on the equipment.

The structural constraint is widely used to establish joint inversion of multiple geophysical data. To implement the above method, weighting parameters for different items (such as data misfit term, regularization term, and structural coupling term) should be pre-set, and global structural consistency assumption should also be made. However, these prerequisites are not easy to set very reasonable. To address the above issues, this article presents a novel deep learning (DL) network method based on a multitask learning strategy to realize joint inversion of gravity and magnetic data. Due to the automatic processing of the DL technique, this method does not consider the weighting parameters. The issue of global structural consistency assumption is addressed using a multitask learning strategy. The proposed network with a multitask learning strategy consists of five tasks. In the overall network, two tasks are used for the independent inversion, one task constructed by a gated network extracts the structural similarity information from the shared information of the independent inversion networks and generates the structural similarity model, and two tasks use the structural similarity information to constrain the independent inversion to achieve the accurate joint inversion. A synthetic example and an application of the real data from the Galinge iron-ore deposit in Qinghai Province demonstrate the effectiveness of the proposed method.

The presence of remanent magnetization brings uncertainty to the processing and interpretation of magnetic data. Therefore, separating the contributions of the remanent magnetization from the total magnetic data is always the research hotspot. In the literature, numerous methods have been introduced to handle this issue. However, most of the existing methods are complex to calculate, have strict requirements on magnetic sources, and need prior information. In this study, a new method for automatically separating the total magnetic anomalies into the components due to induced and remanent magnetization based on deep learning has been presented. The presented method designs an end-to-end network structure based on the U-Net network structure and then performs continuous training and parameter optimization to determine the optimal network structure. Afterward, the presented method is tested on synthetic examples and actual magnetic data in Yeshan Region (Eastern China). The results demonstrate that the presented method can separate anomalies by induced and remanent magnetization.

Phytochemical research on an extract of Notopterygium incisum yielded fifteen compounds ( 1 - 15 ), including four previously undescribed compounds ( 10-13 ). The structures of the unreported compounds were elucidated by spectroscopic and spectrometric data analysis such as 1D and 2D NMR, IR and HR -ESI -MS. Compounds 1 -5 and 10 -14 were isolated from N. incisum for the first time. 7 S * ,8 R * -Phenethyl-(7-methoxy-8-isoeugenol)-ferulate ( 10 ), 7 S * ,8 R * - p -hydroxyphenethyl-(7-methoxy-8-isoeugenol)-ferulate ( 11 ), 7 S * ,8 R * -benzyl-(7-methoxy-8-iso- eugenol)-ferulate ( 12 ) and p -hydroxyphenethyl-(4-benzoy-3-methoxy)-cinnamate ( 13 ) are the undescribed ferulic acid derivatives. Additionly, the anti-neuroinflammatory effects of compounds were evaluated in lipopolysaccharide (LPS)-induced BV2 cells. The pharmacological results showed that 6 beta ,10 beta -epoxy-4 alpha -hydroxy- guaiane ( 6 ), teuclatriol ( 7 ) and 7 S * ,8 R * - p -hydroxyphenethyl-(7-methoxy-8-isoeugenol)-ferulate ( 11 ) inhibited the production and expression of nitric oxide (NO) in the LPS-induced BV2 cells in a concentration -dependent manner. Acorusnol ( 4 ), teucladiol ( 9 ), 7 S * ,8 R * -benzyl-(7-methoxy-8-isoeugenol)-ferulate ( 12 ) and p -hydrox- yphenethyl-(4-benzoy-3-methoxy)-cinnamate ( 13 ) only inhibited the release of NO at concentration of 20 mu M. Moreover, 7 S * ,8 R * - p -hydroxyphenethyl-(7-methoxy-8-isoeugenol)-ferulate ( 11 ) reduced the level of tumor necrosis factor- alpha (TNF- alpha ) and interleukin-6 (IL -6) in LPS-stimulated BV2 cells. The results demonstrated 7 S * ,8 R * - p - hydroxyphenethyl-(7-methoxy-8-isoeugenol)-ferulate ( 11 ) could be a potential anti-neuroinflammatory agent and is worthy of further study.

Shipping emissions have a direct negative impact on the ecological environment, residents' health and social economy of port cities. However, the relevant literature seldom focuses on the quantitative analysis of economic losses. A Regional CGE (RCGE) model with shipping emissions and diffusion is constructed to evaluate the comprehensive economic impact on Shanghai in this paper. It is found that: (1) the direct economic loss caused by shipping emissions to Shanghai is about RMB 1.017 billion, of which SO2, NOx, PM2.5 and PM10 are 0.2587 billion, 0.2776 billion, 0.2057 billion and 0.2304 billion respectively. (2) the GDP changes under three scenarios of economic loss, environmental protection investment and pollution taxes are -0.07 %, -0.02 %, and - 0.014 % respectively in 2019. (3) some factors, such as innovation of shipping emission technology and optimization of energy structure, will not only reduce shipping pollutant emissions but also promote the coordinated development of environment, economy and society.

Qing-Chengzi (QCZ) is an important silver-gold mining area in the eastern part of the Northeast China Craton. The shallow minerals in this area are almost completely depleted, leading to a demand for exploration to find deeper, concealed deposits. However, due to the rugged terrain, few high-precision ground surveys have been executed in this area, resulting in an insufficient understanding of the unexposed ores. To address this issue, this study implemented a high-precision ground magnetic survey to identify faults and potential rocks in this area. To achieve these goals, remanence was analyzed to reduce its adverse effect on processing. Then, lineament enhancement with directional derivatives was conducted on the pre-processed magnetic anomalies to highlight structural features. Based on the results, eight major and twenty-one minor faults were identified, among which three major faults correspond well to the known faults. Most of the major faults run N–S, and the others run NW/NE. Furthermore, 3D inversion was conducted to locate potential rocks. Our inversion results indicate that there are six hidden rocks in the underground, extending from a depth of a few hundred meters to no more than three km. Two of the rocks correspond well to the already mined areas. This study provides support for subsequent exploration in the QCZ area.

As one of the largest gold deposits in the Liaodong area, northeast of China, the Wulong gold field (WGF) has suffered a severe decline in resource reserves after decades of mining, so it is urgent to explore the deeper unknown orebodies. However, the complex terrain and a large reservoir make it challenging to carry out a complete ground survey in the study area, leading to a lack of first-hand data for geological surveys. Therefore, in this study, aeromagnetic data covering the whole WGF and its adjacent regions are used to depict the distribution of various plutons, distinguish the fault structures, and predict possible orebodies in several gold deposits with their guide for further gold mineral exploration. Fifteen new faults are identified from the lineament enhancement results of the anomalies, and the high magnetic susceptibility of some faults is demonstrated to be due to the shear deformation and dike intrusion of the Wulong pluton. The 3D magnetic inversion results show the spatial location of the Sanguliu pluton and verify that the genesis of the Wulong gold deposit is related to Sanguliu Pluton from the perspective of geophysical models. Further, the inversion results suggest that the depth of concealed orebodies in the Wulong gold deposit, Yangjia gold deposit, and Chengshan gold deposit could be 900, 1500, and 1300 m, respectively. This study deepens the understanding of the mineralization of the WGF, verifies the great exploration potential of the WGF, and provides a geophysical basis for the subsequent prospecting.

Clustering constraint is one of the effective ways to improve reliability of the inversion results by incorporating petrophysical information based on some mathematical techniques, such as the Fuzzy C-means clustering (FCM) algorithm. However, for most practical geophysical works, enough reliable petrophysical information is not available. In this way, the FCM inversion method is difficult to be performed. In order to solve this problem to some extent, we proposed an adaptive FCM inversion method (AFCM) in this study, with which the cluster information contained in the gravity data can be extracted adaptively and automatically, and then the FCM inversion can be executed without prior petrophysical information. We presented the detailed steps of the proposed method and illustrated its effectiveness on synthetic and field example tests. The obtained inversion results demonstrate that the AFCM method yields much better results compared with the conventional inversion methods without clustering information, as well as those obtained by using the FCM methods with wrong petrophysical information.

In the study, annular fins are used to improve the thermal management performance of the phase change material (PCM) towards a cylindrical heat source. A two-dimensional axisymmetric model is established and the influence of fins on the temperature of the heat source is studied in conjunction with the melting process of PCM. The effects of the positions of a single annular fin are investigated. Results show that the lower the individual fin is in the PCM, the faster the melting rate and the lower the temperature, which is because the PCM above the fins can absorb more heat from the upper surface of the fin through natural convection. Maintaining a constant total volume of fins, the effect of multiple annular fins is studied. The results show that multiple annular fins have larger heat transfer areas and more uniform fin distribution, resulting in lower temperature and temperature difference of the heat source surface. Furthermore, the use of multiple fins of unequal height, with bottom fins being higher, is found to be more effective. The best improvement in the thermal management performance of the system is achieved when the height of the fins is 4, 8, and 11 mm from top to bottom, respectively.

In mineral exploration, the ores with different magnetic susceptibility can be well delineated by physical property inversion of magnetic data. However, further wide applications of the magnetic inversion method have been seriously restricted by the existence of strong remanence and the computation of large-scale datasets. To solve these two problems, this study innovatively presents an efficient magnetic inversion algorithm in the presence of strong remanence. The weighted data misfit term and regularization term were used to establish the objective function. The detailed related formulas were derived and presented. For numerical computation, the Taylor series approximation was introduced in the objective function to linearize the relationship between the transformed magnetic data and magnetic susceptibility. The objective function was converted to a general Tikhonov form by several matrix transformations. To realize computational efficiency, a fast randomized algorithm was utilized to efficiently solve the Tikhonov problem and to efficiently determine the suitable regularization parameter along with the generalized cross-validation (GCV) method. Comparative tests on synthetic example show the effectiveness and efficiency of the proposed algorithm. The proposed efficient algorithm is successfully applied to two sets of real magnetic data, and the inversion results are verified.

Despite mounting evidence for dietary protease benefits, the mechanisms beyond enhanced protein degradation are poorly understood. This study aims to thoroughly investigate the impact of protease addition on the growth performance, intestinal function, and microbial composition of weaned piglets. Ninety 28-day-old weaned pigs were randomly assigned to the following three experimental diets based on their initial body weight for a 28-day experiment: (1) control (CC), a basic diet with composite enzymes without protease; (2) negative control (NC), a diet with no enzymes; and (3) dietary protease (PR), a control diet with protease. The results show that dietary proteases significantly enhanced growth performance and boosted antioxidant capacity, increasing the total antioxidant capacity (T-AOC) levels (p < 0.05) while reducing malonaldehyde levels (p < 0.05). Additionally, protease addition reduced serum levels of inflammatory markers TNF-α, IL-1β, and IL-6 (p < 0.05), suppressed mRNA expression of pro-inflammatory factors in the jejunum (p < 0.01), and inhibited MAPK and NF-κB signaling pathways. Moreover, protease-supplemented diets improved intestinal morphology and barrier integrity, including zonula occludens protein 1(ZO-1), Occludin, and Claudin-1 (p < 0.05). Microbiota compositions were also significantly altered by protease addition with increased abundance of beneficial bacteria (Lachnospiraceae_AC2044_group and Prevotellaceae_UCG-001) (p < 0.05) and reduced harmful Terrisporobacter (p < 0.05). Further correlation analysis revealed a positive link between beneficial bacteria and growth performance and a negative association with inflammatory factors and intestinal permeability. In summary, dietary protease addition enhanced growth performance in weaned piglets, beneficial effects which were associated with improved intestinal barrier integrity, immunological response, and microbiota composition.

The thickness of the Antarctic ice sheet is a crucial parameter for inferring glacier mass and its evolution process. In the literature, the gravity method has been proven to be one of the effective means for estimating ice sheet thickness. And it is a preferred approach when direct measurements are not available. However, few gravity inversion methods are valid in rugged terrain areas with undulating observation surfaces (UOSs). To solve this problem, this paper proposes an improved high-precision 3D density interface inversion method considering terrain and UOSs simultaneously. The proposed method utilizes airborne gravity data at their flight altitudes, instead of the continued data yield from the unstable downward continuation procedure. In addition, based on the undulating right rectangular prism model, the large reliefs of the terrain are included in the iterative inversion. The proposed method is verified on two synthetic examples and is successfully applied to real data in East Antarctica.

The presence of undulating terrain introduces uncertainty in the inversion and interpretation of gravity data. Therefore, the operation of continuation from undulating surface to a horizontal plane for gravity data is crucial for subsequent studies. Several methods have been proposed in the literature to deal with this problem. However, most of the existing methods yield unsatisfactory in the case of highly undulating terrain and large continuation distances. When large-scale undulating surface data is involved, the processing of continuation from undulating surface to a horizontal plane is inefficient. To address above issues, this study proposes a novel method for gravity data continuation from undulating surface to a horizontal plane based on deep learning. The proposed method utilizes the U-Net network architecture to design an end-to-end framework, followed by continuous training and parameter optimization to determine the optimal network structure. Afterward, the presented method is tested on a synthetic example. The results indicate that the proposed method is effective for gravity data continuation from an undulating surface to a horizontal plane without suffering from above-mentioned problems arisen from conventional methods.

This study aims to investigate the impact of dietary supplementation with selenium yeast (SeY) and glycerol monolaurate (GML) on the transfer of antioxidative capacity between the mother and fetus during pregnancy and its underlying mechanisms. A total of 160 sows with similar body weight and parity of 3–6 parity sows were randomly and uniformly allocated to four groups (n = 40) as follows: CON group, SeY group, GML group, and SG (SeY + GML) group. Animal feeding started from the 85th day of gestation and continued to the day of delivery. The supplementation of SeY and GML resulted in increased placental weight and reduced lipopolysaccharide (LPS) levels in sow plasma, placental tissues, and piglet plasma. Furthermore, the redox balance and inflammatory markers exhibited significant improvements in the plasma of sows fed with either SeY or GML, as well as in their offspring. Moreover, the addition of SeY and GML activated the Nrf2 signaling pathway, while downregulating the expression of pro-inflammatory genes and proteins associated with inflammatory pathways (MAPK and NF-κB). Vascular angiogenesis and nutrient transportation (amino acids, fatty acids, and glucose) were upregulated, whereas apoptosis signaling pathways within the placenta were downregulated with the supplementation of SeY and GML. The integrity of the intestinal and placental barriers significantly improved, as indicated by the increased expression of ZO-1, occludin, and claudin-1, along with reduced levels of DLA and DAO with dietary treatment. Moreover, supplementation of SeY and GML increased the abundance of Christensenellaceae_R-7_group, Clostridium_sensus_stricto_1, and Bacteroidota, while decreasing levels of gut microbiota metabolites LPS and trimethylamine N-oxide. Correlation analysis demonstrated a significant negative relationship between plasma LPS levels and placental weight, oxidative stress, and inflammation. In summary, dietary supplementation of SeY and GML enhanced the transfer of antioxidative capacity between maternal-fetal during pregnancy via gut–placenta axis through modulating sow microbiota composition.

IntroductionGlobal warming augments the risk of adverse pregnancy outcomes in vulnerable expectant mothers. Pioneering investigations into heat stress (HS) have predominantly centered on its direct impact on reproductive functions, while the potential roles of gut microbiota, despite its significant influence on distant tissues, remain largely unexplored. Our understanding of deleterious mechanisms of HS and the development of effective intervention strategies to mitigate the detrimental impacts are still limited.ObjectivesIn this study, we aimed to explore the mechanisms by which melatonin targets gut microbes to alleviate HS-induced reproductive impairment.MethodsWe firstly evaluated the alleviating effects of melatonin supplementation on HS-induced reproductive disorder in pregnant mice. Microbial elimination and fecal microbiota transplantation (FMT) experiments were then conducted to confirm the efficacy of melatonin through regulating gut microbiota. Finally, a lipopolysaccharide (LPS)-challenged experiment was performed to verify the mechanism by which melatonin alleviates HS-induced reproductive impairment.ResultsMelatonin supplementation reinstated gut microbiota in heat stressed pregnant mice, reducing LPS-producing bacteria (Aliivibrio) and increasing beneficial butyrate-producing microflora (Butyricimonas). This restoration corresponded to decreased LPS along the maternal gut-placenta-fetus axis, accompanied by enhanced intestinal and placental barrier integrity, safeguarding fetuses from oxidative stress and inflammation, and ultimately improving fetal weight. Further pseudo-sterile and fecal microbiota transplantation trials confirmed that the protective effect of melatonin on fetal intrauterine growth under HS was partially dependent on gut microbiota. In LPS-challenged pregnant mice, melatonin administration mitigated placental barrier injury and abnormal angiogenesis via the inactivation of the TLR4/MAPK/VEGF signaling pathway, ultimately leading to enhanced nutrient transportation in the placenta and thereby improving the fetal weight.ConclusionMelatonin alleviates HS-induced low fetal weight during pregnancy via the gut-placenta-fetus axis, the first time highlighting the gut microbiota as a novel intervention target to mitigate the detrimental impact of global temperature rise on vulnerable populations.

Delineation of the basement relief structure is of great significance for oil and gas exploration in sedimentary basins. In literature, a number of methods have been presented to depict the basement relief with gravity anomaly. However, existing methods still have some limitations, such as local morphological distortion caused by improper use of known depth points, and imprecise solutions yield from inaccurate determination of the underground density contrasts. To address the above two issues, this study proposes an improved high-precision method to characterize the basement relief. For the proposed method, a new prior depth soft constraint, which is based on a cosine attenuation function, is introduced to utilize the known depth at a few points more rationally. Moreover, a physical property constraint, which is based on the least square theory, is employed to constrain the fitting between the recovered gravity anomaly and the observations better. The effectiveness of the proposed method is tested and validated on synthetic data with noise and real data in Sydney, NSW, Australia.