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Steel containment vessels for nuclear power plants can experience gas leakage due to minute defects such as cracks, corrosion, and aging, leading to gas leakage. A gas leakage model for microchannels is established to elucidate the mechanism underlying gas leakage within microchannels caused by these defects, specifically addressing the issue of unidirectional gas flow. Computational Fluid Dynamics (CFD) and the UK R6 method are employed to calculate the gas leakage rate within microchannels. Furthermore, the characteristics of gas flow within microchannels are explored, including the factors affecting the gas leakage rate. Validation of the calculation results is verified experimentally. The results indicate that the gas mass flow rate exhibits a linear decrease with decreasing internal pressure and a non-linear decline as temperature increases. Additionally, the gas mass flow rate demonstrates a negative correlation with the microchannel length but a positive association to its hydraulic diameter. The primary influencing factors on gas leakage rates are hierarchically ranked as follows: pressure difference, microchannel cross-sectional area, temperature, microchannel length, and microchannel hydraulic diameter.

Summary: Piezoelectric atomization is becoming mainstream in the field of inhalation therapy due to its significant advantages. With the rapid development of high-viscosity gene therapy drugs, the demand for piezoelectric atomization devices is increasing. However, conventional piezoelectric atomizers with a single-dimensional energy supply are unable to provide the energy required to atomize high-viscosity liquids. To address this problem, our team has designed a flow tube internal cavitation atomizer (FTICA). This study focuses on dissecting the atomization mechanism of FTICA. In contrast to the widely supported capillary wave hypothesis, our study provides evidence in favor of the cavitation hypothesis, proving that cavitation is the key to atomizing high-viscosity liquids with FTICA. In order to prove that the cavitation is the key to atomizing in the structure of FTICA, the performance of atomization is experimented after changing the cavitation conditions by heating and stirring of the liquids.

Impact toughness is essential for evaluating the mechanical properties of ship hull steels. This study focused on understanding the embrittlement mechanism of NiAl precipitation-strengthened HSLA steels by integrating advanced characterization and atomic-scale calculations. The factors causing the deterioration of the impact toughness of NiAl precipitation-strengthened steels, which have been contentious, were identified. The embrittlement mechanism and crack propagation mode were revealed using first-principles calculations and 3D impact fracture morphology, respectively. The results suggested that the numerous homogeneous NiAl nanoparticles within the bcc-Fe matrix reduced the impact toughness of the HSLA steels. As the precipitate interparticle spacing (L) decreased, the impact toughness decreased until it attained a critical value (∼27 nm). This is interpreted effectively by the calculations indicating that the shear modulus (75 GPa) and fracture energy (4.5 J/m2) of the NiAl phase, particularly the NiAlMn phase (46 GPa, 4 J/m2), are significantly lower than those of bcc-Fe (83 GPa, 5 J/m2). This induces the fracture of nanoparticles under rapid impact loading, which functions as numerous crack initiations before plastic deformation of the matrix. The small L achieved after peak-hardening aging can result in the interconnection of these crack sources and cause instantaneous cleavage fractures, similar to brittle materials.

Wildfires significantly affect climate and environmental changes, closely tied to extreme weather responses. Vegetation combustion emits greenhouse gases (CO2, CH4, CO), warming the climate. Climate shifts, in turn, impact vegetation growth, altering combustible material types and quantities, thus affecting wildfire intensity, duration, and frequency. Wildfires profoundly affect ecosystems, influenced by factors like atmospheric oxygen and climate. Their combustion gases impact climate and vegetation growth. Recent advancements in studying ancient wildfires include analyzing nanoparticles as key indicators. This study discovered six types of nanoparticles in ancient wildfire remains, with sizes ranging from 50 nm to 500 nm and diverse compositions including elements such as C, O, Mg, Al, Ti, Fe, S, Ca, and P. These findings indicate that wildfires generate a variety of nanoparticles, offering new insights into ancient fire events. Elemental analysis revealed low magnesium but high calcium and aluminum levels, suggesting a warm, humid paleoclimate during these fires. The presence of high Ti-O ratios and carbon-rich nanoparticles points to ground fires with incomplete combustion. This research underscores the significance of nanoparticles in understanding the history and characteristics of ancient wildfires.

This study focuses on energy conservation, reducing the amount of energy consumed to heat a room, and decreasing the intensity of carbon emissions. The research object is a room heated by a floor with a built-in finned Trombe wall (TW) located in Lanzhou, Gansu Province. ANSYS software was employed to conduct a simulation study on parameters such as fin height, transverse spacing, longitudinal spacing, arrangement mode, and fin apex angle. The simulation results were used to determine the fin parameters’ thermal impact on the TW’s thermal performance, including with respect to a room’s thermal environment (TE). The results show that the heat transfer performance of a TW with respect to the thermal environment of a room is the greatest when the height of the heat-absorbing surface is 20 mm, the transverse spacing is 0.20 m, the longitudinal spacing is 0.533 m, and in-line 90° top-angle fins, that is, isosceles right triangle fins, are used. The average Nu number of the fin-type TW is 154.75. Compared with the average Nu number of the finless TW, which is 141.43, the average Nu number increases by 13.32 due to the addition of fins. The optimized fin-type TW has 7.77% higher convective heat supply efficiency than the finless TW. Although the PMV-PPD results of the two TW-type rooms are not very different, the comfort period of the fin-type TW room is longer. At the same time, the LPD3 of the non-finned TW and the finned TW rooms is less than 10%, the wind speed at the head and ankle is less than 0.12 m/s, the air gust sensation is not strong, and the thermal comfort is good, indicating that the addition of fins is beneficial to the improvement of indoor thermal comfort. Compared to standard rooms, finless TW rooms and fin-type TW rooms have energy-saving rates of 36.38% and 44.63%, respectively. Thus, fin-type TW rooms’ energy saving rate is 8.25% higher, resulting in effective savings in heating energy consumption. Therefore, the indoor TE and auxiliary heating conditions are improved, and the integration of solar building technology can be facilitated, which offers significant reference value for energy transformation.

Piezoelectric pumps play an important role in modern medical technology. To improve the flow rate of valveless piezoelectric pumps with flow tube structures and promote the miniaturization and integration of their designs, a cardioid flow tube valveless piezoelectric pump (CFTVPP) is proposed in this study. The symmetric dual-bend tube design of CFTVPP holds great potential in applications such as fluid mixing and heat dissipation systems. The structure and working principle of the CFTVPP are analyzed, and flow resistance and velocity equations are established. Furthermore, the flow characteristics of the cardioid flow tube (CFT) are investigated through computational fluid dynamics, and the output performance of valveless piezoelectric pumps with different bend radii is studied. Experimental results demonstrate that CFTVPP exhibits the pumping effect, with a maximum vibration amplitude of 182.5 μm (at 22 Hz, 100 V) and a maximum output flow rate of 5.69 mL/min (at 25 Hz, 100 V). The results indicate that a smaller bend radius of the converging bend leads to a higher output flow rate, while the performance of valveless piezoelectric pumps with different diverging bends shows insignificant differences. The CFTVPP offers advantages such as a high output flow rate, low cost, small size for easy integration, and ease of manufacturing.

Oxyhalides, containing oxygen and halogen atoms and combining the advantages of oxides and halides in geometry and optical response, have great potential in optical materials. In this study, the electronic structures and the optical properties of the Pb3O2X2 (X = Cl, Br, I) compounds have been investigated using the first principles method. The results show that these compounds have birefringence at 0.076, 0.078, and 0.059 @ 1064 nm, respectively. And, the asymmetric stereochemical active lone pair electrons were found around lead atoms, which were confirmed by the projected density of states, the electronic localization functions, and the crystal orbitals. The contribution of atoms and polyhedra to birefringence was further evaluated using the Born effective charge. The results show that halogen atoms give negative contribution, and lead—oxygen polyhedra give positive contribution. The spin—orbit coupling effect is also investigated, and the downshift of the conduction band and variation in the valence band are found after relevant spin—orbit coupling (SOC), which leads to a reduction in the band gap and birefringence.

It is attractive to fabricate molds and dies using additive manufacturing (AM) technology because the increased freedom of design makes higher cooling efficiency possible. However, little is known so far about the thermal properties of the tool steels fabricated by AM, as well as how to balance between the thermal and mechanical properties. In this work, an H13 tool steel was processed by selective laser melting (SLM) followed by heat treatments and the thermal and mechanical properties in relation to microstructure were investigated. The as-built H13 showed lower thermal conductivities compared with conventional counterparts and exhibited anisotropy with a lower thermal conductivity along the building direction. Heat treatments generally increased the values of thermal conductivities and largely reduced anisotropy, but the final thermal conductivity differed when using different heat-treating schedules. It was found that porosity, retained austenite, the melt-pool structure and the ultra-fine cellular-columnar microstructure, which were functions of SLM and heat-treating parameters, were factors determining the thermal conductivity of H13. Based on the present study of SLMed H13, it is proposed that a trade-off is necessary between thermal and mechanical properties when designing the processing route.

Accurate and efficient short-term power load forecasting is crucial for ensuring the stable operation of power systems and rational planning of electricity resources. However, power load data are often characterized by nonlinearity and instability due to external factors such as meteorological conditions and day types, making accurate load forecasting challenging. While some hybrid models can effectively capture the spatiotemporal features of power load data, they often overlook the multi-periodicity of load data, leading to suboptimal feature extraction and efficiency. In this paper, a novel hybrid framework for short-term load forecasting based on TimesNet and temporal convolutional network (TCN) is proposed. Firstly, the original load data are preprocessed to reconstruct a feature matrix. Secondly, the TimesNet transforms the one-dimensional time series into a set of two-dimensional tensors based on multiple periods, capturing dependencies within different time scales and the relationships between different time scales in power load data. Then, the temporal convolutional network is employed to further extract the temporal features and long-term dependencies of the load data, enabling a more global pattern to be obtained for temporal information. Finally, the results of load forecasting can be achieved from the fully connected layer based on the extracted features. To verify the effectiveness and generalization of the proposed model, experiments have been conducted based on the ISO-NE and Southern China datasets. Experimental results show that the proposed model greatly outperforms the long short-term memory (LSTM), TCN, TimesNet, TCN-LSTM, and TimesNet-LSTM models. The proposed model reduces the mean absolute percentage error by 20% to 43% for the ISO-NE dataset and by 10% to 31% for the Southern China dataset, respectively.

The intelligent fault diagnosis of main circulation pumps is crucial for ensuring their safe and stable operation. However, limited research has been conducted on this topic, and applying existing fault diagnosis methods designed for other equipment may not yield optimal results when directly used for main circulation pump fault diagnosis. To address this issue, we propose a novel ensemble fault diagnosis model for the main circulation pumps of converter valves in voltage source converter-based high voltage direct current transmission (VSG-HVDC) systems. The proposed model employs a set of base learners already able to achieve satisfying fault diagnosis performance and a weighting model based on deep reinforcement learning that synthesizes the outputs of these base learners and assigns different weights to obtain the final fault diagnosis results. The experimental results demonstrate that the proposed model outperforms alternative approaches, achieving an accuracy of 95.00% and an F1 score of 90.48%. Compared to the widely used long and short-term memory artificial neural network (LSTM), the proposed model exhibits improvements of 4.06% in accuracy and 7.85% in F1 score. Furthermore, it surpasses the latest existing ensemble model based on the improved sparrow algorithm, with enhancements of 1.56% in accuracy and 2.91% in F1 score. This work presents a data-driven tool with high accuracy for the fault diagnosis of main circulation pumps, which plays a critical role in maintaining the operational stability of VSG-HVDC systems and satisfying the unmanned requirements of offshore flexible platform cooling systems.

Three continent-scale ductile shear zones trending N-S are distributed in the southeast margin of the Qinghai–Tibet Plateau. Their tectonic deformation and exhumation histories are of great significance to understanding the orogenic processes within the continent and the growth as well as expansion mechanism of the plateau. The Gaoligong shear zone (GLGSZ) is the westernmost zone near the boundary of the Indian subduction plate and is less well studied than the Ailaoshan–Red River shear zone (ASRRSZ) in the east. In this study, low-temperature thermochronological methods including apatite (U-Th)/He (AHe), zircon (U-Th)/He (ZHe), and apatite fission track (AFT) were used to date the vertical profile samples from the Gaoligong Mountain to understand the exhumation processes that occurred in the late Cenozoic. Our results show that the GLGSZ has experienced two stages of rapid exhumation events since the late Cenozoic: in the middle Miocene (∼14.5 Ma) and early Pleistocene (∼2.9 Ma). Based on our data, we divided the late Cenozoic tectonic deformation and exhumation processes in the Gaoligong Mountain area into two stages: 1) From the middle to the late Miocene, large-scale regional dextral strike-slip movements and lateral compressions controlled the ductile shear zone and continuously denuded the mountain surface; 2) in the Pleistocene, rapid river erosion and undercutting caused by fluctuations of the monsoon system, together with the continuous activity of brittle faults, drove the latest rapid exhumations. A comparison of the thermochronological data of the different areas along the Gaoligong Mountain shows that the exhumation rate in its northern transect is significantly higher and the time of the onset of exhumation is earlier than that in the southern transect. These results indicate that the deformation processes began in the north and continued southwards and controlled the geomorphological characteristics of the Gaoligong Mountain, whose elevation is higher in the northern part than in the southern part.

Purpose. To investigate the relative position of femur fixed screws using intramedullary systems for intertrochanteric fractures and to improve the accurate measurement method of the tip-to-apex distance (TAD) while providing a theoretical basis for the clinical treatment of such fractures. Methods. In the anteroposterior (AP) radiographs of the hip joint, the femoral neck axis through the femoral head geometry point was designated as the X-axis, while the line perpendicular to the X-axis passing through the femoral head geometry point was designated as the Y-axis. In the lateral radiographs of the hip joint, the line perpendicular to the X-axis passing through the femoral head geometry point was identified as the Z-axis. The head of the nail tip’s location projected on the three axes was described as AAP, B in the AP radiographs; and ALAT, C in the lateral radiograph. The TAD was described as XAP and XLAT. The radius of the femoral head was D. All distance units were expressed in mm. Results. When the lateral projection angle was standardized, the AAP was equal to the ALAT, while the XAP2=B2+D−AAP2 and XLAT2=C2+D−ALAT2. When the lateral projection angle was not standardized, the value of C had no significant change; however, the D−ALAT value changed. Conclusions. The measurement value did not match the actual values of TAD when the lateral projection angle was not standardized, possibly leading to a misinterpretation during clinical work. The XLAT should be amended using the formula XLAT2=C2+D−AAP2.

A novel electrochemical biosensing method for protein kinase (PKA) activity was demonstrated by using a reduced graphene oxide-polydopamine-silver nanoparticle-Ti4+ (rGO-PDA-AgNPs-Ti4+) nanocomposite. The obtained nanocomposite possessed an integral capability for phosphopeptide recognition and signal readout. The polydopamine modified reduced graphene oxide (rGO-PDA) was firstly prepared based on a self-polymerization method of dopamine. The silver ions were adsorbed onto polydopamine (PDA) layer and directly reduced into silver nanoparticles (AgNPs), which was used for electrochemical signal reporting. Then, the Ti4+ cations were attached onto the PDA layer for phosphopeptide recognition according to the strong coordination ability of PDA with Ti4+ and phosphate group. The prepared rGO-PDA-AgNPs-Ti4+ nanocomposites were characterized with different methods. The developed rGO-PDA-AgNPs-Ti4+ nanocomposites were then employed for electrochemical analysis of PKA-catalyzed kemptide phosphorylation. The sensitive detection toward PKA activity was realized with an experimental detection limit of about 0.01 U/mL. It may be also extended for the inhibitor evaluation. Thus, it provided a facile and sensitive means for electrochemical analysis of PKA activity and inhibitor screening.

A spatial co-location pattern denotes a subset of spatial features whose instances frequently appear nearby. High influence co-location pattern mining is used to find co-location patterns with high influence in specific aspects. Studies of such pattern mining usually rely on spatial distance for measuring nearness between instances, a method that cannot be applied to an influence propagation process concluded from epidemic dispersal scenarios. To discover meaningful patterns by using fruitful results in this field, we extend existing approaches and propose a mining framework. We first defined a new concept of proximity to depict semantic nearness between instances of distinct features, thus applying a star-shaped materialized model to mine influencing patterns. Then, we designed attribute descriptors to perceive attributes of instances and edges from time series data, and we calculated the attribute weights via an analytic hierarchy process, thereby computing the influence between instances and the influence of features in influencing patterns. Next, we constructed influencing metrics and set a threshold to discover high influencing patterns. Since the metrics do not satisfy the downward closure property, we propose two improved algorithms to boost efficiency. Extensive experiments conducted on real and synthetic datasets verified the effectiveness, efficiency, and scalability of our method.

A few-layer graphene (FLG) composite material was synthesized using a rich reservoir and low-cost coal under the microwave-assisted catalytic graphitization process. X-ray diffraction, Raman spectroscopy, transmission electron microscopy, and X-ray photoelectron spectroscopy were used to evaluate the properties of the FLG sample. A well-developed microstructure and higher graphitization degree were achieved under microwave heating at 1300 °C using the S5% dual (Fe-Ni) catalyst for 20 min. In addition, the synthesized FLG sample encompassed the Raman spectrum 2D band at 2700 cm−1, which showed the existence of a few-layer graphene structure. The high-resolution TEM (transmission electron microscopy) image investigation of the S5% Fe-Ni sample confirmed that the fabricated FLG material consisted of two to seven graphitic layers, promoting the fast lithium-ion diffusion into the inner surface. The S5% Fe-Ni composite material delivered a high reversible capacity of 287.91 mAhg−1 at 0.1 C with a higher Coulombic efficiency of 99.9%. In contrast, the single catalyst of S10% Fe contained a reversible capacity of 260.13 mAhg−1 at 0.1 C with 97.96% Coulombic efficiency. Furthermore, the dual catalyst-loaded FLG sample demonstrated a high capacity—up to 95% of the initial reversible capacity retention—after 100 cycles. This study revealed the potential feasibility of producing FLG materials from bituminous coal used in a broad range as anode materials for lithium-ion batteries (LIBs).

Intricate health problems, such as insulin resistance (IR) and its associated diseases, call for multi-targeted therapies with few side effects. Based on traditional Chinese medicine (TCM), Dai-Zong-Fang (DZF) is an herbal formula mainly composed of Rhizoma Coptidis (Huanglian) and Fructus Aurantii Immaturus (Zhishi), of which berberine and naringin are the main constituents. Though DZF has been clinically used for treatment of IR and metabolic syndrome for decades, its mechanism in vivo remains unknown. In the present study, we observed that both DZF and metformin, the first-line drug for type 2 diabetes, ameliorated insulin resistance with significant improvement of oral glucose tolerance test (OGTT) and homeostasis model assessment of IR (HOMA-IR) level in diabetic C57BL/Ksj-Lepr db−/− (db/db) mice. Low-density lipoprotein cholesterol (LDL-C) and fatty acids (FAs) also decreased in the blood. Higher dose of DZF (1 g·kg−1), but not metformin (0.25 g·kg−1), alleviated hepatic steatosis with reduced liver weight and hepatic lipid accumulation and provided protection from hepatic injury with lower alanine aminotransferase and aspartate aminotransferase and increased hepatic superoxide dismutase activity in db/db mice. Quantitative reverse transcription polymerase chain reaction (qRT-PCR) showed a decrease in FA synthase gene (Fasn) and an increase in FA oxidation gene Ppara expression. Western blot demonstrated that both DZF and metformin activated 5′ AMP-activated protein kinase (AMPK) but inhibited Notch intracellular domain (NICD) and Hairy/enhancer-of-split 1 (Hes1) of Notch signaling pathway in the liver. DZF also dramatically improved the ultrastructure of skeletal muscles, AMPK phosphorylation, and GLUT4 translocation. DZF also promoted FA transport and oxidation with Cd36 and Cpt1b up-regulation in the skeletal muscle. In conclusion, DZF improves insulin sensitivity by reducing hepatic lipids through AMPK activation and Notch signal pathway inhibition and enhancing energy metabolism in the skeletal muscle via AMPK. This study provides insights into the treatment of complex conditions, such as IR, where TCM herbal formulas exert multipronged effects through correlating pathways.

Aiming to solve the problem of accurate diagnosis of the size and location of rolling bearing faults, a novel quantitative and localization fault diagnosis method of the rolling bearing is proposed based on the quantitative mapping model (QMM). The fault size and location of the rolling bearing affect the impulse type and the modulation degree of the vibration signal, which subsequently changes the complexity and randomness of the time-domain distribution of the vibration signal. According to the relationship between the multiscale permutation entropy (MPE) of the vibration signal and rolling bearing fault size, an average MPE (A-MPE) index is proposed to establish linear and nonlinear QMMs through the regression function. The proper QMM is selected through the error rate of fault size prediction to achieve a quantitative fault diagnosis of the rolling bearing. Due to the mathematical characteristics of the QMM, the localization fault diagnosis is realized. The multiscale morphological filtering (MMF) method is also introduced to extract the time-domain geometric feature of the fault bearing vibration signal and to improve the QMM accuracy of the fault size prediction. The results show that the QMM has a great effect on the quantitative fault size prediction and localization diagnosis of the rolling bearing.

Background: We assumed that the pharmacokinetics of lovastatin could be changed by the induction effect of berberine. Methods: An UPLC-MS/MS method was developed and validated for the pharmacokinetics tudy of lovastatin to investigate the in vivo drug-drug interactions between lovastatin and berberine. SD male rats were random divided into lovastatin group and berberine induced prior to lovastatin group for the in vivo pharmacokinetic studies. Meanwhile HepG2 cells were induced by berberine for three days to study the metabolism of lovastatin. Results: The AUC (p < 0.01) and Cmax (p < 0.01) could be significantly decreased in the berberine-induced group in vivo, and the metabolic activity of HepG2 cell ccould be increased by berberine induction in vitro. The metabolism parameters of lovastatin such as CL, Vmax and Km were increased after the induction of berberine. From the pharmacokinetic study of lovastatin induced with berberine, we obtained pharmacokinetic parameters which are compliance with the metabolic parameters of lovastatin in HepG2 cells with berberine induction in vitro. Conclusions: From the in vivo pharmacokinetics study and the HepG2 cell metabolism study in vitro, berberine could be an inducer for the metabolism of lovastatin according to our previous research on berberine induction effects on HepG2 cells, which may be relevant to the fact that berberine possesses induction effects through the CYP 450 3A4 enzyme.

Two new selenites, K2Zn3Se4O12 (compound 1) and K4Zn3V4Se2O19 (compound 2), have been successfully synthesized by solid-state reactions in vacuum tubes. Compound 1 consists of a three-dimensional (3D) framework with [SeO3] triangular pyramids and [ZnO4] tetrahedra in the monoclinic space group P21/c (No. 14). Compound 1′s cut-off edge is below 344 nm, based on its UV-Vis-NIR diffuse reflectance studies, and theoretical calculations indicate a birefringence of around 0.043 at 1064 nm. The two-dimensional layer of compound 2, in contrast, is made up of [SeO3] triangular pyramids, [ZnO4] tetrahedra, and [V4O13] tetrahedra. It crystallizes in the monoclinic space group C2/c (No. 15). Its UV-Vis-NIR diffuse reflectance studies demonstrate that the compound's cut-off edge is lower than 330 nm. [ABSTRACT FROM AUTHOR]