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Traditional methods for assessing the cleanliness of liquid metal are characterized by prolonged detection times, delays, and susceptibility to variations in sampling conditions. To address these limitations, an online cleanliness-analyzing system grounded in the method of the electrical sensing zone has been developed. This system facilitates real-time, in situ, and quantitative analysis of inclusion size and amount in liquid metal. Comprising pneumatic, embedded, and host computer modules, the system supports the continuous, online evaluation of metal cleanliness across various metallurgical processes in high-temperature environments. Tests conducted with gallium liquid at 90 °C and aluminum melt at 800 °C have validated the system's ability to precisely and quantitatively detect inclusions in molten metal in real time. The detection procedure is stable and reliable, offering immediate data feedback that effectively captures fluctuations in inclusion amount, thereby meeting the metallurgical industry's demand for real-time analyzing and control of inclusion cleanliness in liquid metal. Additionally, the system was used to analyze inclusion size distribution during the hot-dip galvanizing process. At a zinc melt temperature of 500 °C, it achieved a detection limit of 21 μm, simultaneously providing real-time data on the size and amount distribution of inclusions. This represents a novel strategy for the online monitoring and quality control of zinc slag throughout the hot-dip galvanizing process. [ABSTRACT FROM AUTHOR]

Online monitoring and real-time feedback on inclusions in molten metal are essential for metal quality control. However, existing methods for detecting aluminum melt inclusions face challenges, including interference, prolonged processing times, and latency. This paper presents the design and development of an online monitoring system for molten metal inclusions. Initially, the system facilitates real-time adjustment of signal acquisition parameters through a multiplexer. Subsequently, it employs a detection algorithm capable of swiftly extracting pulse peaks, with this task integrated into our proprietary host computer software to ensure timely detection and data visualization. Ultimately, we developed a monitoring device integrated with this online monitoring system, enabling the online monitoring of the aluminum alloy filtration process. Our findings indicate that the system can accurately measure the size and concentration of inclusions during the filtration process in real time, offering enhanced detection speed and stability compared to the industrial LiMCA CM (liquid metal cleanliness analyzer continuous monitoring) standard. Furthermore, our evaluation of the filtration process demonstrates that the effectiveness of filtration significantly improves with the increase in inclusion sizes, and the synergistic effect of combining CFF (ceramic foam filter) and MCF (metallics cartridge filter) filtration methods exceeds the performance of the CFF method alone. This system thus provides valuable technical support for optimizing filtration processes and controlling inclusion quality. [ABSTRACT FROM AUTHOR]

To address the most significant environmental challenges, the quest for high-performance gas sensing materials is crucial. Among numerous two-dimensional materials, this study investigates the gas-sensitive capabilities of monolayer As, Sb, and Bi materials. To compare the gas detection abilities of these three materials, we employ first-principles calculations to comprehensively study the adsorption behavior of NO and NO2 gas molecules on the material surfaces. The results indicate that monolayer Bi material exhibits reasonable adsorption distances, substantial adsorption energies, and significant charge transfer for both NO and NO2 gases. Therefore, among the materials studied, it demonstrates the best gas detection capability. Furthermore, monolayer As and Sb materials exhibit remarkably high capacities for adsorbing NO and NO2 gas molecules, firmly interacting with the gas molecules. Gas adsorption induces changes in the material's work function, suggesting the potential application of these two materials as catalysts. [ABSTRACT FROM AUTHOR]

The approach of dynamic tracking and counting for obscured citrus based on machine vision is a key element to realizing orchard yield measurement and smart orchard production management. In this study, focusing on citrus images and dynamic videos in a modern planting mode, we proposed the citrus detection and dynamic counting method based on the lightweight target detection network YOLOv7-tiny, Kalman filter tracking, and the Hungarian algorithm. The YOLOv7-tiny model was used to detect the citrus in the video, and the Kalman filter algorithm was used for the predictive tracking of the detected fruits. In order to realize optimal matching, the Hungarian algorithm was improved in terms of Euclidean distance and overlap matching and the two stages life filter was added; finally, the drawing lines counting strategy was proposed. ln this study, the detection performance, tracking performance, and counting effect of the algorithms are tested respectively; the results showed that the average detection accuracy of the YOLOv7-tiny model reached 97.23%, the detection accuracy in orchard dynamic detection reached 95.12%, the multi-target tracking accuracy and the precision of the improved dynamic counting algorithm reached 67.14% and 74.65% respectively, which were higher than those of the pre-improvement algorithm, and the average counting accuracy of the improved algorithm reached 81.02%. The method was proposed to effectively help fruit farmers grasp the number of citruses and provide a technical reference for the study of yield measurement in modernized citrus orchards and a scientific decision-making basis for the intelligent management of orchards. [ABSTRACT FROM AUTHOR]

This paper proposes a fundamental investigation of coal gangue and fly ash impact on B. pasteurii to enhance the properties of backfill materials. The goal is to obtain effective microbial mineralization and potential mechanical properties of coal gangue and fly ash as backfill materials and to mitigate the impact of the most common binders used in the backfill material of mines. Micro-scale mineralization was performed with B. pasteurii bacteria using microbially induced carbonate precipitation (MICP) technology to clarify solid waste impact on B. pasteurii and to bind coal gangue and fly ash. Several tests were carried out to analyze the behavior of B. pasteurii, especially when it coexists with these two waste materials separately. In such cases, it was possible to observe a reduction in mineralization initiation time with respect to the natural mineralization of the MICP technology. Moreover, at the macro-scale, the new mineralized backfilling material shows good workability in the fresh state, whereas the strength at 28 days is 5.34 times higher than that obtained with non-mineralized coal gangue and fly ash. [ABSTRACT FROM AUTHOR]

Inspired by periodically aligned micro/nanostructures on biological surfaces, researchers have been fabricating biomimetic structures with superior performance. As a promising and versatile tool, an ultrafast laser combined with other forms of processing technology has been utilized to manufacture functional structures, e.g., the biomimetic subwavelength structures to restrain the surface Fresnel reflectance. In this review paper, we interpret the biomimetic mechanism of antireflective subwavelength structures (ARSSs) for high-transmission windows. Recent advances in the fabrication of ARSSs with an ultrafast laser are summarized and introduced. The limitations and challenges of laser processing technology are discussed, and the future prospects for advancement are outlined, too. [ABSTRACT FROM AUTHOR]

A novel Eu(tta)3([4,4′-(t-bu)2-2,2′-bpy)] complex (tta-thenoyltrifluoroacetone), a ratiometric luminescent-based optical sensor for the quantitative determination of aluminum ion, is synthesized and characterized using XRD and 1H NMR. The XRD data reveal the slightly distorted octahedral structure. The complex displays a bright red emission at 613 nm in methanol which is characteristic of europium (III) complexes. Upon the addition of Al3+ ions, the red emission disappears, and a new blue emission at 398 nm emerges, manifesting the ratiometric nature of the complex. The turn-off of the red emission and turn-on of the blue emission are attributed to Eu-Al trans-metalation, as supported by Raman data that show the emergence of Al-O vibrations at 418, 495, and 608 cm−1 concomitant with the disappearance of Eu-O and Eu-N bond vibrations. Most aluminum sensors are known to suffer from interferences from other metals including Cu2+, Co2+, and Cd2+. However, the sensor reported here is tested for 11 common cations and shows no interference on sensitivity. To the best of our knowledge, this is the first known Eu-based luminescence sensor that successfully exhibited the ability to detect aluminum ions in ppb levels in aqueous environments. The calculated Al3+ binding constant is 2.496 × 103 ± 172. The complex shows a linear relationship in the range of 0–47.6 ppb (1.76 × 10−6 M) Al3+ and the limit of detection (LOD) is 4.79 ppb (1.77 × 10−7 M) in MeOH. ICP-OES is used for validation of the sensor complex in water and then it was used for quantitative detection of Al3+ ions in water as a real-life application. The complex can accurately detect Al3+ ions in the range of 4.97–24.9 ppb (1.84 × 10−7 M–9.2 × 10−7 M) with an LOD of 8.11 ppb (2.99 × 10−7 M). Considering that the aluminum ion serves no recognized function within the human body, its accumulation can lead to severe neurological disorders, including Parkinson's and Alzheimer's diseases. With the LOD value significantly lower than the WHO-recommended maximum permissible level of 200 ppb for aluminum in drinking water, even without high-power laser-aided signal enhancement, the sensor shows promise for detecting trace amounts of aluminum contamination in water. Therefore, it can significantly aid in the monitoring of even the smallest aluminum ion contamination in drinking water, industrial effluents, and natural water bodies. [ABSTRACT FROM AUTHOR]

Aeolian sand (AS) and recycled coarse aggregate (RCA) can be reasonably utilized as green materials for concrete modification. The paucity of natural sand and gravel in the construction industry is anticipated to be remedied by the use of these two eco-friendly concrete ingredients. This is incredibly important for environmental protection. Study on the damage law of self-compacting concrete with the addition of AS and RCA (ARSCC) under severely cold conditions is of great significance for the promotion and implementation of this material. In this study, 12 groups of ARSCC specimens were prepared for freeze–thaw cycle experiments, with AS substitution rates of 0, 20%, 40%, and 60% as well as RCA replacement rates of 0, 25%, and 50%. Then, the degradation mechanism of ARSCC freeze–thaw damage was discussed from both macroscopic and microscopic perspectives via mass loss rate (Wn), relative dynamic modulus of elasticity (Pn), bubble spacing factor, and SEM analysis. Finally, the response surface method was utilized to determine the damage variable. A freeze–thaw damage model for ARSCC was developed based on the Weibull distribution and Grey theories. The results showed that the Pn could reflect the evolution law of the internal structure of ARSCC. Appropriate addition of AS to fill the large, harmful pores in RCA would inhibit freeze–thaw damage of ARSCC. The optimum substitution rates of AS and RCA were determined to be 20–40% and 25–50%, respectively. In addition, the values obtained from theoretical damage modeling and experiments were in good agreement. The acquired damage model had the potential to predict ARSCC damage under freeze–thaw cycles. [ABSTRACT FROM AUTHOR]

(1) Background: Cytoplasmic male sterility (CMS) is important for exploiting heterosis. Soybean (Glycine max L.) has a low outcrossing rate that is detrimental for breeding sterile lines and producing hybrid seeds. Therefore, the molecular mechanism controlling the outcrossing rate should be elucidated to increase the outcrossing rate of soybean CMS lines; (2) Methods: The male–sterile soybean lines JLCMS313A (with a high outcrossing rate; HL) and JLCMS226A (with a low outcrossing rate; LL) were used for a combined analysis of the transcriptome (RNA–seq) and the targeted phenol metabolome; (3) Results: The comparison between HL and LL detected 5946 differentially expressed genes (DEGs) and 81 phenolic metabolites. The analysis of the DEGs and differentially abundant phenolic metabolites identified only one common KEGG pathway related to flavonoid biosynthesis. The qRT–PCR expression for eight DEGs was almost consistent with the transcriptome data. The comparison of the cloned coding sequence (CDS) regions of the SUS, FLS, UGT, and F3H genes between HL and LL revealed seven single nucleotide polymorphisms (SNPs) only in the F3H CDS. Moreover, five significant differentially abundant phenolic metabolites between HL and LL were associated with flavonoid metabolic pathways. Finally, on the basis of the SNPs in the F3H CDS, one derived cleaved amplified polymorphic sequence (dCAPS) marker was developed to distinguish between HL and LL soybean lines; (4) Conclusions: The flavonoid biosynthesis pathway may indirectly affect the outcrossing rate of CMS sterile lines in soybean. [ABSTRACT FROM AUTHOR]

Strain engineering is an effective way to adjust the sensing properties of two-dimensional materials. In this paper, lateral heterojunctions (LHSs) based on arsenic and antimony have been designed along the armchair (AC) or zigzag (ZZ) edges. The adsorption and sensing characteristics of As/Sb LHSs to NO2 before and after applying different types of strain are calculated by first principles. The band gaps of all As/Sb heterostructures are contributed by As-p and Sb-p orbitals. In addition, the adsorption energy of As/Sb ZZ-LHS with −4% compression strain is the largest. Furthermore, its work function changes significantly before and after the adsorption of NO2. Meanwhile, strong orbital hybridizations near the Fermi level are observed and a new state is yielded after applying compressive strain. These results indicate that the As/Sb LHS with ZZ interface under −4% compression strain possesses the best sensing properties to NO2. This work lays the foundation for the fabrication of high-performance NO2 gas sensors. High-performance gas sensors can be used to track and regulate NO2 exposure and emission, as well as to track NO2 concentrations in the atmosphere and support the assessment of air quality. [ABSTRACT FROM AUTHOR]

Electric vehicle battery systems are easily deformed following bottom or side pillar collisions. There is a knowledge gap regarding the fault features of minor mechanical deformation without ISC, which can be used for early warning of mechanical deformation. In this study, the fault features of a lithium-ion battery module under different degrees of mechanical deformation were studied from the perspective of voltage consistency. The results show that the capacity of the battery module declines with an increase in indentation depth, consistent with the capacity degradation of the indented cell. During the charging and discharging processes, the voltage of the indented cell deviates to a lower value compared to the other normal cells. At the end of the discharging process, the voltage sharply declines and exhibits a significant deviation from the other normal cells. The Mean Normalization (MN) method is employed to quantitatively describe the voltage consistency. The results indicate that the MN value of the indented cell's voltage is distributed at the lowest during the charging period and sharply declines below −0.06 at the end of discharging. In the future, a fault detection method for mechanical abuse will be established based on these features. [ABSTRACT FROM AUTHOR]

Pure marine chemical sediments are archives of geochemical proxies for the composition of seawater and may provide information about the ancient hydrosphere–atmosphere system. The early stage of the Emeishan large igneous province (ELIP) was characterized by the subaqueous eruption of mafic igneous rocks around the J. altudaensis zone of the Capitanian Stage that has been proposed to have contributed to the Guadalupian mass extinction. However, detailed mechanisms and the impact of the eruption on the Guadalupian marine environment have yet to be assessed. Here, to examine the Guadalupian marine environment, we studied major and trace element concentrations, particularly rare earth element and yttrium data, along with high-precision Sr-Nd isotope ratios, of three types of Mn ores (i.e., clastic, massive, and oolitic) and siliceous limestones from the Zunyi Mn deposit in South China formed following the early-stage eruption of the ELIP. Our results indicate that the clastic Mn ores contain notable detrital mafic aluminosilicates. In contrast, the massive and oolitic Mn ores and siliceous limestones preserved the pristine geochemical signatures of the Middle–Late Permian seawater characterized by distinctly low (87Sr/86Sr)i and high εNd(t) values. These data indicate a strong impact of the early-stage submarine eruptions of the ELIP on the marine environment in South China and worldwide, likely through intensive seawater–rock interaction. [ABSTRACT FROM AUTHOR]

The gearbox is one of the key components of many large mechanical transmission devices. Due to the complex working environment, the vibration signal stability of the gear box is poor, the fault feature extraction is difficult, and the fault diagnosis accuracy makes it difficult to meet the expected requirements. To solve this problem, this paper proposes a gearbox fault diagnosis method based on an optimized stacked denoising auto encoder (SDAE) and kernel extreme learning machine (KELM). Firstly, the particle swarm optimization algorithm in adaptive weight (SAPSO) was adopted to optimize the SDAE network structure, and the number of hidden layer nodes, learning rate, noise addition ratio and iteration times were adaptively obtained to make SDAE obtain the best network structure. Then, the best SDAE network structure was used to extract the deep feature information of weak faults in the original signal. Finally, the extracted fault features are fed into KELM for fault classification. Experimental results show that the classification accuracy of the proposed method can reach 97.2% under the condition of low signal-to-noise ratio, which shows the effectiveness and robustness of the proposed method compared with other diagnostic methods. [ABSTRACT FROM AUTHOR]

Aspergillus westerdijkiae, known as the major ochratoxin A (OTA) producer, usually occurs on agricultural crops, fruits, and dry-cured meats. Microorganisms produce OTA to adapt to the high osmotic pressure environment that is generated during food processing and storage. To investigate the relationship between OTA biosynthesis and the high osmolarity glycerol (HOG) pathway, the transcription factor AwHog1 gene in A. westerdijkiae was functionally characterised by means of a loss-of-function mutant. Our findings demonstrated that the growth and OTA production of a mutant lacking AwHog1 decreased significantly and was more sensitive to high osmotic media. The ΔAwHog1 mutant displayed a lower growth rate and a 73.16% reduction in OTA production in the wheat medium compared to the wild type. After three days of culture, the growth rate of the ΔAwHog1 mutant in medium with 60 g/L NaCl and 150 g/L glucose was slowed down 19.57% and 13.21%, respectively. Additionally, the expression of OTA biosynthesis genes was significantly reduced by the deletion of the AwHog1 gene. The infection ability of the ΔAwHog1 mutant was decreased, and the scab diameter of the pear was 6% smaller than that of the wild type. These data revealed that transcription factor AwHog1 plays a key role in the osmotic response, growth, OTA production, and pathogenicity in A. westerdijkiae. [ABSTRACT FROM AUTHOR]

To combat the impacts of uncertain noise on the estimation of vehicle state parameters and the high cost of sensors, a state-observer design with an adaptive unscented Kalman filter (AUKF) is developed. The design equation of the state observer is derived by establishing the vehicle's three degrees-of-freedom (DOF) model. On this basis, the Sage–Husa algorithm and unscented Kalman filter (UKF) are combined to form the AUKF algorithm to adaptively update the statistical feature estimation of measurement noise. Finally, a co-simulation using Carsim and Matlab/Simulink confirms the algorithm is effective and reasonable. The simulation results demonstrate that the proposed algorithm, compared with the UKF algorithm, increases estimation accuracy by 19.13%, 32.8%, and 39.46% in yaw rate, side-slip angle, and longitudinal velocity, respectively. This is because the proposed algorithm adaptively adjusts the measurement noise covariance matrix, which can estimate the state parameters of the vehicle more accurately. [ABSTRACT FROM AUTHOR]

In the ultrasonic welding system, the ultrasonic power supply drives the piezoelectric transducer to work in the resonant state to realize the conversion of electrical energy into mechanical energy. In order to obtain stable ultrasonic energy and ensure welding quality, this paper designs a driving power supply based on an improved LC matching network with two functions, frequency tracking and power regulation. First, in order to analyze the dynamic branch of the piezoelectric transducer, we propose an improved LC matching network, in which three voltage RMS values are used to analyze the dynamic branch and discriminate the series resonant frequency. Further, the driving power system is designed using the three RMS voltage values as feedback. A fuzzy control method is used for frequency tracking. The double closed-loop control method of the power outer loop and the current inner loop is used for power regulation. Through MATLAB software simulation and experimental testing, it is verified that the power supply can effectively track the series resonant frequency and control the power while being continuously adjustable. This study has promising applications in ultrasonic welding technology with complex loads. [ABSTRACT FROM AUTHOR]

There is a huge amount of solid waste generated in coal resource exploitation and utilization, leading to effects on the environment and hindering sustainable development. Aiming at the difficulty of disposal of coal-based solid waste from high-yield, high-efficiency mines, this research proposed a technique of gangue grouting filling in subsequent space (GGFSS) for coal mining and explored the movement of key strata backfilled using GGFSS technique and surface deformation characteristics using theoretical analysis and a field measurement method. The results indicate that the equivalent backfilled ratio (EBR) when using GGFSS is defined as the ratio of the grouting volume to available void volume, which is mainly affected by bed separation void volume, grouting volume, and the void volume of the caving zone. Using an EBR ground control mechanical model, the changes in the displacement and bending moment of key strata with the variation of the EBR were investigated. The critical EBR for the rupture of key strata was determined to be 74.44%. On this basis, the calculation methods of equivalent mining height (EMH) and the subsidence factor of GGFSS were proposed and then a surface deformation prediction model of GGFSS was established based on the principle of the probability integration method. The predictions implied that, at an EBR greater than 75%, the surface deformation could be significantly reduced. Engineering applications verified the effect of GGFSS on the control of ground movement. In-situ measurements of concentration, density, and particle size of the filling body were all close to the those designed values: the GGFSS technique shows longer grouting duration and a larger amount of grouting and its filling body can support impervious key strata above the working face in a mine, significantly decreasing surface subsidence. [ABSTRACT FROM AUTHOR]

In response to the problems of impedance variation and rich background harmonics in the weak grid environment of traditional LCL grid-connected inverters, which cause distortion of the grid-connected current and reduce the stability of the entire grid-connected system, this paper proposes a passive fuzzy-active disturbance rejection control strategy. Firstly, passivity-based control (PBC) is introduced into the grid-connected inverter system to ensure the global stability of the entire system. Secondly, in order to solve the problem of difficult selection of inverter-side current, active disturbance rejection control (ADRC) is used to directly track the grid-connected current, avoiding the tracking error of AC signal and suppressing the interference of background harmonics of grid voltage. The combination of fuzzy control and ADRC simplifies the system parameter tuning process, and the FUZZY-ADRC grid-connected current outer loop and PBC inductance current inner loop are combined to form a multi-loop control system. Finally, simulation comparison verifies that the proposed control strategy can effectively suppress the harmonics of grid-connected current, improve the quality of grid-connected current, and enhance the stability of the entire grid-connected system in a weak grid environment. [ABSTRACT FROM AUTHOR]

The R294K mutation in neuraminidase (NA) causes resistance to oseltamivir in the avian influenza virus H7N9. Reverse transcription droplet digital polymerase chain reaction (RT-dd PCR) is a novel technique for detecting single-nucleotide polymorphisms. This study aimed to develop an RT-dd PCR method for detecting the R294K mutation in H7N9. Primers and dual probes were designed using the H7N9 NA gene and the annealing temperature was optimized at 58.0 °C. The sensitivity of our RT-dd PCR method was not significantly different from that of RT-qPCR (p = 0.625), but it could specifically detect R294 and 294K in H7N9. Among 89 clinical samples, 2 showed the R294K mutation. These two strains were evaluated using a neuraminidase inhibition test, which revealed that their sensitivity to oseltamivir was greatly reduced. The sensitivity and specificity of RT-dd PCR were similar to those of RT-qPCR and its accuracy was comparable to that of NGS. The RT-dd PCR method had the advantages of absolute quantitation, eliminating the need for a calibration standard curve, and being simpler in both experimental operation and result interpretation than NGS. Therefore, this RT-dd PCR method can be used to quantitatively detect the R294K mutation in H7N9. [ABSTRACT FROM AUTHOR]

Solution-processed devices with thermally activated delayed fluorescence (TADF) compounds have gained great attention due to their low cost and high performance. Here, two solution-processable TADF emitters named ACCz-DPyM and POxCz-DPyM were synthesized by coupled 9,10-dihydro-9,9-dimethylacridine or phenoxazine modified carbazole as donor with di(pyridin-3-yl)methanone as acceptor. Both TADF compounds show same small ΔΕST of 0.04 eV and high PLQY of 66.2% and 58.2%. The devices fabricated by ACCz-DPyM and POxCz-DPyM as emitters show excellent performance as solution-processed with low turn-on voltage of 4.0 and 3.4 V, high luminance of 6209 and 3248 cd m−2 at 8 V, the maximum current efficiency of 9.9 and 15.9 cd A−1, the maximum external quantum efficiency of 6.6% and 6.5% and low efficiency roll-off. The solution-processed device based on ACCz-DPyM shows bluish-green emission. These results show that ACCz-DPyM and POxCz-DPyM are suitable for solution processing devices. [ABSTRACT FROM AUTHOR]

Using coal gangue as subgrade filler can not only solve the environmental problems of coal mine waste accumulation but also decrease the subgrade cost, which has important theoretical and practical significance. A series of cyclic triaxial tests was carried out using the large-scale dynamic and static triaxial apparatus (LSDSTA) to investigate the permanent deformation (ε) of coal gangue subgrade filler (CGSF) under cyclic loading. Experimental grading was designed by using the fractal model grading equation (FMGE), and then well-grading limits of CGSF were captured. The relationship curve between ε and the numbers of cyclic loading (N) can be divided into three stages, i.e., the rapid growth phase, the deceleration growth phase, and the approaching stability phase. N = 1000 can be used as the criterion for reaching the stable stage of CGSF. The effect of confining pressure ( σ 3 ′ ) on ε is related to the level of σ 3 ′ . The effect of σ 3 ′ on ε is significant when σ 3 ′ is smaller, whereas the influence of σ 3 ′ on ε is smaller when σ 3 ′ is larger. Furthermore, the influence of grading ( D f ) on ε of coal gangue samples is significant. With the increase of D f , ε first increases and then decreases, reflecting that there is an obvious optimal grading for coal gangue samples under cyclic loading. Moreover, the effect of compaction degree ( D c ) on ε of CGSF depends on the level of D c . ε is hardly affected when D c is smaller, whereas increasing D c has a significant effect on restraining ε when D c is bigger. In addition, according to the analysis of the permanent deformation curve for CGSF, the unified calculation model of permanent deformation for CGSF under cyclic loading is established. Compared with the existing permanent deformation models, the proposed model in this paper can better describe the permanent deformation of CGSF under cyclic loading. Finally, the model parameters are analyzed, and the model is verified. [ABSTRACT FROM AUTHOR]

Unmanned air vehicle (UAV) systems for performing forestry applications have expanded in recent decades and have great economic benefits. They are validated to be more appealing than traditional platforms in various aspects, such as repeat rate, spatial resolution, and accuracy. This paper consolidates the state-of-the-art unmanned systems in the forestry field with a major focus on UAV systems and heterogeneous platforms, which are applied in a variety of forestry applications, such as wood production, tree quantification, disease control, wildfire management, wildlife conservation, species classification, etc. This review also studies practical applications under multiple forestry environments, including wild and managed forests, grassland, urban green parks, and stockyards. Special forest environments and terrains present customized demands for unmanned systems. The challenges of unmanned systems deployment are analyzed from environmental characterization, maneuverability and mobility improvement, and global regulatory interpretation. To better apply UAV systems into forestry, future directions are analyzed in terms of mobility enhancement and customized sensory adaption, which need to be further developed for synchronizing all possible agents into automatic functioning systems for forestry exploration. [ABSTRACT FROM AUTHOR]

Speech reflects people's mental state and using a microphone sensor is a potential method for human–computer interaction. Speech recognition using this sensor is conducive to the diagnosis of mental illnesses. The gender difference of speakers affects the process of speech emotion recognition based on specific acoustic features, resulting in the decline of emotion recognition accuracy. Therefore, we believe that the accuracy of speech emotion recognition can be effectively improved by selecting different features of speech for emotion recognition based on the speech representations of different genders. In this paper, we propose a speech emotion recognition method based on gender classification. First, we use MLP to classify the original speech by gender. Second, based on the different acoustic features of male and female speech, we analyze the influence weights of multiple speech emotion features in male and female speech, and establish the optimal feature sets for male and female emotion recognition, respectively. Finally, we train and test CNN and BiLSTM, respectively, by using the male and the female speech emotion feature sets. The results show that the proposed emotion recognition models have an advantage in terms of average recognition accuracy compared with gender-mixed recognition models. [ABSTRACT FROM AUTHOR]

The complex operating environment of gearboxes and the easy interference of early fault feature information make fault identification difficult. This paper proposes a fault diagnosis method based on a combination of whale optimization algorithm (WOA), variational mode decomposition (VMD), and deep transfer learning. First, the VMD is optimized by using the WOA, and the minimum sample entropy is used as the fitness function to solve for the K value and penalty parameter α corresponding to the optimal decomposition of the VMD, and the correlation coefficient is used to reconstruct the signal. Second, the reconstructed signal after reducing noise is used to generate a two-dimensional image using the continuous wavelet transform method as the transfer learning target domain data. Finally, the AlexNet model is used as the transfer object, which is pretrained and fine-tuned with model parameters to make it suitable for early crack fault diagnosis in gearboxes. The experimental results show that the method proposed in this paper can effectively reduce the noise of gearbox vibration signals under a complex working environment, and the fault diagnosis method of using transfer learning is effective and achieves high accuracy of fault diagnosis. [ABSTRACT FROM AUTHOR]

Millimeter-wave SAR (Synthetic Aperture Radar) imaging is widely studied as a common means of RF (Radio Frequency) imaging, but there are problems of the ghost image in Sparsely-Sampled cases and the projection of multiple targets at different distances. Therefore, a robust imaging algorithm based on the Analytic Fourier Transform is proposed, which is named mmSight. First, the original data are windowed with Blackman window to take multiple distance planes into account; then, the Analytic Fourier Transform that can effectively suppress the ghost image under Sparsely-Sampled is used for imaging; finally, the results are filtered using a Mean Filter to remove spatial noise. The experimental results show that the proposed imaging algorithm in this paper, relative to other algorithms, can image common Fully-Sampled single target, hidden target, and multiple targets at the same distance, and solve the ghost image problem of single target in the case of Sparsely-Sampled, as well as the projection problem of multiple targets at different distances; the Image Entropy of the mmSight is 4.6157 and is on average 0.3372 lower than that of other algorithms. Compared with other algorithms, the sidelobe and noise of the Point Spread Function are suppressed, so the quality of the image obtained from imaging is better than that of other algorithms. [ABSTRACT FROM AUTHOR]

China's total coal production in 2021 exceeded 4.13 billion tons, 52% of the world's total. Coal gangue, a solid waste of coal mining accounts for 15–20% of coal production, when directly discharged on the ground surface as waste heaps, it occupies large areas of land and cause environmental pollution. This paper summarizes the existing gangue backfilling methods, their working principles, efficiency, and application status. The methods that are meeting Middle and Western China's mining demands are discussed in detail. The state-of-the-art technologies that can realize high-efficiency, centralized, and large-scale underground backfilling of coal gangue are analyzed. This paper shows that the industrial implementation of these technologies can increase the current maximum disposal capacity of coal gangue by three times, reaching five million tons per year. The equipment innovation and automation are analyzed, and the environmental effect of coal gangue backfilling is discussed. This review offers inspirations and guidelines for coal gangue disposal and the environmental hazard reduction of coal mining. [ABSTRACT FROM AUTHOR]

With the increasing popularity of smart devices, users can control their mobile phones, TVs, cars, and smart furniture by using voice assistants, but voice assistants are susceptible to intrusion by outsider speakers or playback attacks. In order to address this security issue, a millimeter-wave radar-based voice security authentication system is proposed in this paper. First, the speaker's fine-grained vocal cord vibration signal is extracted by eliminating static object clutter and motion effects; second, the weighted Mel Frequency Cepstrum Coefficients (MFCCs) are obtained as biometric features; and finally, text-independent security authentication is performed by the WMHS (Weighted MFCCs and Hog-based SVM) method. This system is highly adaptable and can authenticate designated speakers, resist intrusion by other unspecified speakers as well as playback attacks, and is secure for smart devices. Extensive experiments have verified that the system achieves a 93.4% speaker verification accuracy and a 5.8% miss detection rate for playback attacks. [ABSTRACT FROM AUTHOR]

The extended state observer (ESO) has been widely used in the state and perturbation estimation of the electro-hydraulic servo system. It was found that there was a controlled quantity in the transfer function between the perturbation estimation value and the disturbance. This indicates that the traditional linear ESO's estimation of the disturbance is affected by the change in the control input. To solve this problem, a new structure ESO for a hydraulic system (LHYESO) was designed by introducing the hydraulic system's load pressure and system model. The corresponding frequency domain analysis results show that it eliminates the control input in the transfer function and reduces the dependence of the high-frequency domain range of the perturbation estimation on the significant observer gain. To improve the estimation speed, a finite-time convergent ESO for hydraulic systems (FTHYESO) was proposed based on the structure of LHYESO, and it was proved that the observation error converged to a sufficiently small value during a finite time. Moreover, a finite-time backstepping controller has been designed by using the Lyapunov method to guarantee the rapidity and precise response of the hydraulic servo system. Finally, the experiment results show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

An experimental study was carried out on the ignition characteristics of the HAN/(Emim)(EtSO4) (hydroxylammonium nitrate and 1-ethyl-3-methyl-imidazolium ethyl sulfate) dual-mode ionic liquid monopropellant in chemical propulsion mode in model thrusters. Firstly, a model thruster with a detachable convergent nozzle was designed and fabricated. Secondly, catalytic ignition experiments at different flow rates were carried out in atmosphere and in high chamber pressure environment, respectively, using a model thruster, with and without the convergent nozzle. During the catalytic ignition process, measurement methods such as thermocouple, pressure sensor, and flue gas analyzer were employed to obtain the temperature at different depths of the catalytic bed, the pressure of the combustion chamber, and the concentration variations of gaseous products CO, CO2, CH4, SO2, NO, and NO2. Then the three characteristic stages of water evaporation, HAN decomposition, and (Emim)(EtSO4) combustion were analyzed at the initiation time, and the reaction characteristics in the process of the catalytic ignition were analyzed. In addition, the composition and concentration of the combustion products at equilibrium were theoretically calculated. The effects of temperature and pressure on the concentrations of five main gaseous products were studied. Finally, the exhaust gas of the three groups of catalytic ignition experiments under different pressure environments was separately collected and measured with gas chromatography (GC) when the experiments approached equilibrium, the result of which roughly agrees with the theoretical calculations. These results are of great significance for exploring the chemical propulsion of the dual-mode ionic liquid propellant and understanding its physical catalytic combustion mechanisms. [ABSTRACT FROM AUTHOR]

Diesel engines have a wide range of functions in the industrial and military fields. An urgent problem to be solved is how to diagnose and identify their faults effectively and timely. In this paper, a diesel engine acoustic fault diagnosis method based on variational modal decomposition mapping Mel frequency cepstral coefficients (MFCC) and long-short-term memory network is proposed. Variational mode decomposition (VMD) is used to remove noise from the original signal and differentiate the signal into multiple modes. The sound pressure signals of different modes are mapped to the Mel filter bank in the frequency domain, and then the Mel frequency cepstral coefficients of the respective mode signals are calculated in the mapping range of frequency domain, and the optimized Mel frequency cepstral coefficients are used as the input of long and short time memory network (LSTM) which is trained and verified, and the fault diagnosis model of the diesel engine is obtained. The experimental part compares the fault diagnosis effects of different feature extraction methods, different modal decomposition methods and different classifiers, finally verifying the feasibility and effectiveness of the method proposed in this paper, and providing solutions to the problem of how to realise fault diagnosis using acoustic signals. [ABSTRACT FROM AUTHOR]

The safe operation of diesel engines performs a vital function in industrial production and life. Because diesel engines often work in harsh environmental conditions, they are prone to failure. Therefore, this paper proposes a fault analysis method based on a combination of optimized variational mode decomposition (VMD) and improved convolutional neural networks (CNN) to address the necessary need for preventive maintenance of diesel engines. The authentic vibration sign is first decomposed by using the (VMD) algorithm, then the greatest range of decomposition layers is decided by using scattering entropy and the useful components are preferentially chosen for reconstruction. The continuous wavelet transform (CWT) records preprocessing method is then delivered to radically change the noise-reduced vibration sign into a time-frequency map, which is fed into the CNN for model coaching and extraction of fault features. Finally, fault classification is realized by support vector machine (SVM) with excellent classification performance. Through preset fault experiments on diesel engines, it is established that the technique proposed in this paper can successfully identify fault states, and the classification accuracy is higher than alternative methods. [ABSTRACT FROM AUTHOR]

Previously conducted studies have established that surface subsidence is typically avoided by filling coal mined-out areas with solid waste. Backfilling hydraulic supports are critically important devices in solid backfill mining, whose operating performance can directly affect backfill mining efficiency. To accurately evaluate the operating performance, this paper proposes hybrid machine learning models for the operating states. An analysis of the factors that influence operating performance provides eight indices for evaluating backfilling hydraulic supports. Based on the data obtained from the Creo simulation model and field measurement, six hybrid models were constructed by combining swarm intelligent algorithms and support vector machines (SVM). Models of the SVM optimized by the modified sparrow search algorithm have shown improved convergence performance. The results show that the modified model has a prediction accuracy of 95.52%. The related evaluation results fit well with the actual support intervals of the backfilling hydraulic support. [ABSTRACT FROM AUTHOR]

Algebraic persistent fault analysis (APFA), which combines algebraic analysis with persistent fault attacks, brings new challenges to the security of lightweight block ciphers and has received widespread attention since its introduction. Threshold Implementation (TI) is one of the most widely used countermeasures for side channel attacks. Inspired by this method, the SKINNY block cipher adopts the S_box decomposition to reduce the number of variables in the set of algebraic equations and the number of Conjunctive Normal Form (CNF) equations in this paper, thus speeding up the algebraic persistent fault analysis and reducing the number of fault ciphertexts. In our study, we firstly establish algebraic equations for full-round faulty encryption, and then analyze the relationship between the number of fault ciphertexts required and the solving time in different scenarios (decomposed S_boxes and original S_box). By comparing the two sets of experimental results, the success rate and the efficiency of the attack are greatly improved by using S_box decomposition. In this paper, We can recover the master key in a minimum of 2000s using 11 pairs of plaintext and fault ciphertext, while the key recovery cannot be done in effective time using the original S_box expression equations. At the same time, we apply S_box decomposition to another kind of algebraic persistent fault analysis, and the experimental results show that using S_box decomposition can effectively reduce the solving time and solving success rate under the same conditions. [ABSTRACT FROM AUTHOR]

Although it possesses the capabilities of both anti-contamination and rapid response, the deflector jet servo valve is still confronted with the issue of weak performance robustness, usually manifesting as considerable uncertainty of the product pressure indices. To elaborate on the complex internal mechanism of deflector jet valves and figure out the origins of performance fluctuations, a three-dimensional mathematical model of the deflector jet pilot valve is constructed, in which a series of assumptions are presented to apply specific theorems to different regions of the flow field. Numerical simulations and experiments show that this theoretical system can provide a distinct and logical explanation for both the internal flow distribution and the external performance of the servo valve. On this basis, the causes of performance fluctuations are discussed, concerning the installation error of the deflector and the machining error of the shunt wedge. Calculations show that the latter can bring about remarkable performance variation. Quantificationally, a 10 micron width error of the shunt wedge will induce 7.4% and 3.6% drifts of the receiver pressure and the pressure gain, respectively. However, further analyses confirm that a decrease in the deflector jet distance will lead to dramatic deterioration of the valve's susceptibility to errors. Hence, it is concluded that to enhance the performance robustness of servo valve products, the machining accuracy of the shunt wedge and non-negative errors of the deflector jet distance should both be guaranteed. [ABSTRACT FROM AUTHOR]

The SiCf/SiC composite manufactured by chemical vapour infiltration (CVI) is a kind of porous material. Liquid molten salt in a Molten Salt Reactor (MSR) may enter into the porous composites and affect their performance. Through the study of the internal pores in the material, the permeability behaviour of the material can be investigated, which is of great significance to the analysis of the properties of the material itself. However, there is less investigation on effects of molten salt infiltration on the internal pore structure of SiCf/SiC composites. In this paper, a molten salt infiltration experiment of 2D woven SiCf/SiC composites was implemented at 650 °C, 3 atm. SEM, CT and XRD were used to characterize it. The results indicated that the microstructure could be affected by partial molten salt infiltration and temperature change. The distribution of porosity of the composite showed an obvious transformation. The lattice spacing of SiC showed an increased tendancy of stress relaxation. [ABSTRACT FROM AUTHOR]

Evodiamine (EVO) and rutaecarpine (RUT) are the main active compounds of the traditional Chinese medicinal herb Evodia rutaecarpa. Here, we fully optimized the molecular geometries of EVO and RUT at the B3LYP/6-311++G (d, p) level of density functional theory. The natural population analysis (NPA) charges, frontier molecular orbitals, molecular electrostatic potentials, and the chemical reactivity descriptors for EVO and RUT were also investigated. Furthermore, molecular docking, molecular dynamics simulations, and the analysis of the binding free energies of EVO and RUT were carried out against the anticancer target topoisomerase 1 (TOP1) to clarify their anticancer mechanisms. The docking results indicated that they could inhibit TOP1 by intercalating into the cleaved DNA-binding site to form a TOP1–DNA–ligand ternary complex, suggesting that they may be potential TOP1 inhibitors. Molecular dynamics (MD) simulations evaluated the binding stability of the TOP1–DNA–ligand ternary complex. The calculation of binding free energy showed that the binding ability of EVO with TOP1 was stronger than that of RUT. These results elucidated the structure–activity relationship and the antitumor mechanism of EVO and RUT at the molecular level. It is suggested that EVO and RUT may be potential compounds for the development of new anticancer drugs. [ABSTRACT FROM AUTHOR]

A vital component that is frequently employed in the industrial powder conveying sector is the fluidized bed. In the light of powder unloading with a fluidized bed as the research object, an orthogonal experiment with two factors and four levels was established for the structural parameters of the fluidized bed. In the case of different noise factors, 16 schemes are designed and all schemes via computational fluid dynamics numerical simulation. The Taguchi method and regression analysis are used to analyze the response. Finally, the accuracy of the optimization results is tested. The results show that gas velocity decreases sharply at the airway's entrance and, then, gas flows to the second half of the airway and velocity decreases steadily and uniformly. Airway arc length L exerts a greater effect on the signal-to-noise ratio (SNR) than airway height H. The parameter combination of 180 mm L and 17 mm H for obtaining the optimal velocity distribution uniformity is determined. The test results indicate that the overall fluidization effect of the fluidized bed with the optimal parameters is better. The linked research findings can be used as a guide when designing a fluidized bed system for transporting comparable powder. [ABSTRACT FROM AUTHOR]

In order to improve the mixture ratio accuracy of the bipropellant thruster and prolong the service life of a satellite on orbit, a variety of different liquid-flow tests and the final firing test are critical in the production testing process. However, after comparison with a large number of tests data, it was found that the liquid-flow test results were far away from matching the firing-test data without clear regularity, resulting in a qualified rate of mixture ratio of less than 40%. This study developed a BP-RNN (back propagation—recurrent neural network) chain method based on machine learning, which uses multi-dimensional nonlinear parameters to construct the specific dataset after data enhancement. Then, the mapping characteristic of the neural network was used to fit the historical data for the weight analysis and mixture ratio prediction, and effectively improved the qualified rate of the mixture ratio. The back propagation neural network was used to learn the association rules of the 10-dimension characteristic data and the firing test results generated in the historical process of thruster production. Then, the features with high influencing weight were extracted and sorted, so the "many-to-one" mixture ratio prediction was conducted through the subsequent recurrent neural network. The accepted prediction accuracy could reach around 75% after the test data verification. By using this method, most of bipropellant thrusters could directly reach the qualified mixture ratio in the firing test after adjusting the throttle orifice size in the liquid-flow tests. This chain method first bridges the data between the liquid-flow test and the firing test. [ABSTRACT FROM AUTHOR]

The positioning of indoor electronic devices is an essential part of human–computer interaction, and the accuracy of positioning affects the level of user experience. Most existing methods for RF-based device localization choose to ignore or remove the impact of multipath effects. However, exploiting the multipath effect caused by the complex indoor environment helps to improve the model's localization accuracy. In response to this question, this paper proposes a multipath-assisted localization (MAL) model based on millimeter-wave radar to achieve the localization of indoor electronic devices. The model fully considers the help of the multipath effect when describing the characteristics of the reflected signal and precisely locates the target position by using the MAL area formed by the reflected signal. At the same time, for the situation where the radar in the traditional Single-Input Single-Output (SISO) mode cannot obtain the 3D spatial position information of the target, the advantage of the MAL model is that the 3D information of the target can be obtained after the mining process of the multipath effect. Furthermore, based on the original hardware, it can achieve a breakthrough in angular resolution. Experiments show that our proposed MAL model enables the millimeter-wave multipath positioning model to achieve a 3D positioning error within 15 cm. [ABSTRACT FROM AUTHOR]

Due to the relative insufficiencies of conventional time-domain waveform and spectrum analysis in fault diagnosis research, a diesel engine fault diagnosis method based on the Stacked Sparse Autoencoder and the Support Vector Machine is proposed in this study. The method consists of two main steps. The first step is to utilize the Stacked Sparse Autoencoder (SSAE) to reduce the feature dimension of the multi-sensor vibration information; when compared with other dimension reduction methods, this approach can better capture nonlinear features, so as to better cope with dimension reduction. The second step consists of diagnosing faults, implementing the grid search, and K-fold cross-validation to optimize the hyperparameters of the SVM method, which effectively improves the fault classification effect. By conducting a preset failure experiment for the diesel engine, the proposed method achieves an accuracy rate of more than 98%, better engineering application, and promising outcomes. [ABSTRACT FROM AUTHOR]

Extracting features manually and employing preeminent knowledge is overly utilized in methods to conduct fault diagnosis. A diagnosis approach utilizing intelligent methods of the optimized variational mode decomposition and deep transfer learning is proposed in this manuscript to deal with fault diagnosis. Firstly, the variational mode decomposition is optimized by K values of the dispersion entropy to realize an adaptive decomposition and reduce the noise of the signal. Secondly, an image with two dimensions is generated by a vibration signal with one dimension utilizing a short-time Fourier transform, after conducting noise reduction. Then, the ResNet18 network model is used to pre-train the model. Finally, the model transfer method is used to detect faults of a diesel engine. The results show that the proposed method outperforms the deep learning methods available in the literature. Besides, better noise reduction ability and higher diagnostic accuracy are attained. [ABSTRACT FROM AUTHOR]

In order to enhance the fluid mixing in the stirred tank and reduce the power consumption under the condition of full baffle, a sinusoidal sawtooth baffle was established in the present study. Based on the Eulerian–Eulerian method, a numerical simulation of the mixed flow in the stirred tank was conducted, and the reliability of the simulation method was verified by means of PIV experiments. The different structural characteristics of a standard baffle and the sine baffle were compared, to explore the effect of the modified baffle on flow mixing and power consumption in the tank. The outcomes indicate that the sinusoidal sawtooth structure had the effect of reducing drag and shunting, which could lessen the backflow on the backside of the baffle, strengthen the intensity of fluid turbulence and strain rate, improve the uniformity of particle distribution, and significantly lower the power consumption. When the relative tooth height was 0.333 and the relative tooth width was 0.028, the power consumption was reduced by 11.7%. [ABSTRACT FROM AUTHOR]

This paper suggests a novel method for diagnosing planetary gearbox faults. It addresses the issue of network bandwidth limitation during wireless data transmission and the problem of relying on expert experience and insufficient training samples in traditional fault diagnosis. The continuous wavelet transform was combined with the AlexNet convolutional neural network using transfer learning and the compressed theory of sense. The original vibration signal was compressed and reconstructed using the compressed sampling orthogonal matching pursuit reconstruction algorithm. A continuous wavelet transform was used to convert the compressed signal into a time–frequency image. The pretrained AlexNet model was selected as the migration object, the network model was fine-tuned and retrained, and the trained AlexNet model was used to diagnose the fault using the model-based migration method. It was demonstrated by the experimental results when the compression ratio CR = 0.5. Compared to other network models, the classification accuracy rate is 97.78%. This method has specific reference value and application prospects and good feature extraction and fault classification capabilities. [ABSTRACT FROM AUTHOR]

The flow field structure in the pilot stage of the electro-hydraulic servo valve is small and complex, and the extreme temperature environment will aggravate the self-excited oscillation, resulting in a decrease in the control accuracy of the servo valve. With the increase in temperature, the size of the orifice, the temperature characteristics of the fluid and the pressure loss in the flow pipe will influence the characteristics of the pilot stage. Considering the influence of temperature and pressure loss, a theoretical mathematical model is established to describe the flow force in the pilot stage. To verify the accuracy of the theoretical model, CFD simulations of the flow force at different inlet pressures and deflection positions and temperatures are analyzed in this paper. As the temperature rises, the oil viscosity rapidly decreases, which results in the flow force acting on the flapper increasing with the temperature. When the temperature exceeds 50 °C, the effect of oil viscosity is small, and the flow force tends to decrease slightly with the combined effect. As the supply oil pressure increases and the flapper moves toward the nozzle, the flow force acting on the flapper increases, and the trend is consistent with the CFD simulation results. An experimental device is designed, including establishing the experimental conditions and measuring the flow force to validate the theoretical model and to observe the cavitation phenomenon of the pilot stage. [ABSTRACT FROM AUTHOR]

The cytoplasmic male sterility/restorer-of-fertility (CMS/Rf) system plays a vital role in high-efficiency hybrid seed production in crops, including soybean (Glycine max (L.) Merr.). The markers linked to fertility restoration and the restorer-of-fertility (Rf) genes are essential because they can facilitate the breeding of new CMS lines and production of commercial hybrid soybean seeds. To date, several soybean Rf genes have been mapped to various genetic loci in diverse genetic populations. However, the mapping range of restorer genes remains narrow, with relatively limited practical applicability. Therefore, in the present study, F2 and F3 segregating populations derived from the CMS line JLCMS5A crossed with the restorer line JLR2 were developed and used for Rf3 gene fine mapping. Genetic investigation indicated that the restorer line JLR2 was controlled by a single dominant gene, Rf3. By integrating bulk-segregant analysis and next-generation sequencing, a 4 Mb region on chromosome 9 was identified, which was most likely the target region harboring the candidate gene responsible for fertility restoration. This region was further narrowed down to 86.44 Kb via fine mapping in F2 and F3 populations using SSR, InDel, and dCAPS markers. This region contained 10 putative genes (Glyma.09G171100–Glyma.09G172000). Finally, Glyma.09G171200, which encodes a mitochondria-targeted pentatricopeptide repeat protein, was proposed as the potential candidate for Rf3 using sequence alignment and expression analysis in restorer and CMS lines. Based on single-nucleotide polymorphisms in Glyma.09G171200, a CAPS marker co-segregated with Rf3 named CAPS1712 was developed. Our results will be fundamental in the assisted selection and creation of potent lines for the production and rapid selection of novel restorer lines. [ABSTRACT FROM AUTHOR]

The integration of variable stiffness materials and structures into soft robots is a popular trend, allowing soft robots to switch between soft and rigid states in different situations. This concept combines the advantages of rigid mechanisms and soft robots, resulting in not only excellent flexibility but also tunable stiffness for high load capacity and fast and precise operation. Here, a stiffness-tunable soft actuator based on wire/fiber jamming structure is proposed, where the fiber-reinforced soft actuator is responsible for the bending motion, and the jamming structure acts as a stiffness-tunable layer controlled by vacuum pressure. The primary design objective of this study is to fabricate a jamming structure with wide-range stiffness, universal adaptability and high dexterity. Thus, the behaviors of wire/fiber jamming structures with different layouts, materials and wire arrangements are analyzed, and a theoretical model is developed to predict the effect of geometric parameters. Experimental characterizations show that the stiffness can be significantly enhanced in the bending direction, while the stiffness is smaller in the torsion direction. Additionally, by integrating Velcro strips into the design, a quick and detachable scheme for the stiffness-tunable soft actuator is achieved. Application examples exhibit high load capacity and good shape adaptability. [ABSTRACT FROM AUTHOR]

Resistant starch (RS) shows several health benefits. Enhancing the RS content of wheat is of major commercial importance. However, knowledge regarding the RS synthesis mechanism in wheat remains limited. In this study, the dynamic accumulation of RS during the filling process in two wheat (Triticum aestivum L.) genotypes with contrasting RS contents (H242, high RS content; H189, low RS content) were investigated. The results demonstrate that beyond 25 days after pollination (DAP), the RS content of H189 was relatively stable, but that of H242 continued to increase. Secondary accumulation was observed in the high-RS-content wheat genotype. A comparative transcriptome analysis between H242 and H189 at 20 DAP and 35 DAP showed that the differentially expressed genes were mainly involved in glycerolipid metabolism, glycerophospholipid metabolism and glucuronate interconversions. Furthermore, weighted gene coexpression network analysis suggested that lipid metabolic pathways such as the glycerophospholipid metabolism pathway might be involved in RS synthesis, and lipid-related genes upregulated beyond 25 DAP resulted in RS secondary accumulation. This work provides insight into the characteristics and mechanisms of RS synthesis. [ABSTRACT FROM AUTHOR]

Gas–liquid flow in a pipeline is a very common. Slug two-phase flow is dominated in the case of slightly upward flow (+0.25°) and considered to be the comprehensive flow configuration, and can be in close contact with all the other flow patterns. The models of different flow patterns can be unified. Precise prediction of the slug flow is crucial for proper design and operation. In this paper, we develop hydrodynamics unified modeling for gas–liquid two-phase slug flow, and the bubble and droplet entrainment is optimized. For the important parameters (wall and interfacial friction factors, slug translational velocity and average slug length), the correlations of these parameters are optimized. Furthermore, the related parameters for liquid droplet and gas bubble entrainment are given. Accounting for the gas–liquid interface shape, hydrodynamics models, i.e., the flat interface model (FIM) and the double interface model (DIM), of liquid film in the slug body are applied and compared with the experimental data. The calculated results show that the predictions for the liquid holdup and pressure gradient of the DIM agree with experimental data better than those of the FIM. A comparison between the available experimental results and Zhang's model calculations shows that the DIM model correctly describes the slug dynamics in gas–liquid pipe flow. [ABSTRACT FROM AUTHOR]