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Dual-frequency and moderate thermosonication (TS, 300 + 300 W, 20 + 40 kHz, 25~60 °C) was employed to inactivate Pichia membranifaciens in soybean paste. The aim was to evaluate the effect of TS on the inactivation of P. membranaefaciens and on the quality of soybean paste. The Weibull model fitted the survival data of P. membranaefaciens in thermosonicated soybean paste well and a decrease of 5 log of P. membranaefaciens in soybean paste was obtained at TS50°C, TS55°C, TS60°C, and T65°C for 15.41, 7.49, 2.27, and 18.61 min. Scanning electron microscope observation revealed TS50°C damaged the cell structure, leading to the leakage of intracellular contents. The physicochemical properties of soybean paste treated by TS were more retained than in paste treated by heat. The GC-MS analysis indicated that the flavor components had increased after TS treatment, especially at TS50°C. In conclusion, TS can inactive P. membranaefaciens in soybean paste without causing significant changes in its physicochemical and flavor qualities.

This study developed a novel dual-wavelength confocal laser speckle imaging platform. The system includes both visible and near-infrared lasers and two imaging modes: confocal and wide-field laser speckle contrast imaging. The experimental results confirm that the proposed system can be used to measure not only blood flow but also blood oxygen saturation. Additionally, we proposed a blood flow perfusion imaging method called BlingNet (a blood flow imaging CNN) based on the laser speckle contrast imaging technique and deep learning approach. Compared to the traditional nonlinear fitting method, this method has superior accuracy and robustness with higher imaging speed, making real-time blood flow imaging possible.

The TCP gene family encodes plant transcription factors crucial for regulating growth and development. While TCP genes have been identified in various species, they have not been studied in Phoebe bournei (Hemsl.). This study identified 29 TCP genes in the P. bournei genome, categorizing them into Class I (PCF) and Class II (CYC/TB1 and CIN). We conducted analyses on the PbTCP gene at both the protein level (physicochemical properties) and the gene sequence level (subcellular localization, chromosomal distribution, phylogenetic relationships, conserved motifs, and gene structure). Most P. bournei TCP genes are localized in the nucleus, except PbTCP9 in the mitochondria and PbTCP8 in both the chloroplast and nucleus. Chromosomal mapping showed 29 TCP genes unevenly distributed across 10 chromosomes, except chromosome 8 and 9. We also analyzed the promoter cis-regulatory elements, which are mainly involved in plant growth and development and hormone responses. Notably, most PbTCP transcription factors respond highly to light. Further analysis revealed three subfamily genes expressed in five P. bournei tissues: leaves, root bark, root xylem, stem xylem, and stem bark, with predominant PCF genes. Using qRT-PCR, we examined six representative genes—PbTCP16, PbTCP23, PbTCP7, PbTCP29, PbTCP14, and PbTCP15—under stress conditions such as high temperature, drought, light exposure, and dark. PbTCP14 and PbTCP15 showed significantly higher expression under heat, drought, light and dark stress. We hypothesize that TCP transcription factors play a key role in growth under varying light conditions, possibly mediated by auxin hormones. This work provides insights into the TCP gene family’s functional characteristics and stress resistance regulation in P. bournei.

In the era of the digital economy (DE), technology factors and data factors, like a two-wheel drive, have not only redefined the mode of production but also innovatively reshaped production relations. To examine how the DE can ensure food security (FS) in China, this study explores the negative impacts of land resource mismatch (LRM) on FS, the mechanisms of the impacts, and the critical role played by the DE in mitigating its negative impacts, based on China’s provincial-level panel data from 2011 to 2022. This study finds that, first, LRM leads to a reduction in food production, which, in turn, threatens FS, and this conclusion remains robust after a series of robustness tests. Second, the heterogeneity analysis finds that LRM has a greater negative impact on FS in regions with high urbanization levels, regions with a short tenure of officials, and regions that are not major food-producing regions. Finally, in a further analysis, the specific channels and solution paths of the negative impact of LRM on FS are explored in depth. LRM negatively affects the material base and production capacity of food production, including reducing the supply of land, labor, and capital factors for food production; the DE reduces the negative impact of LRM on FS through the use of digital technology and open government data.

Abstract: Urban greenspaces have been essential in supporting residents’ well-being during the COVID-19 pandemic, particularly under strict lockdown measures. However, the impact of changing containment policies on residents’ perceived greenspace quality remains insufficiently explored. This study utilized online survey data collected between 11 October and 29 December 2022, in Shanghai, coinciding with the major policy shift on 5 December 2022. A probability proportionate to size sampling was adopted to survey residents aged 18 and above who had lived in the city for at least six months, yielding a total of 577 valid responses. We assessed residents’ perceived greenspace quality using 20 park- and community-level variables, focusing on both overall quality and specific features of greenspaces. A regression discontinuity design (RDD) was applied to evaluate how the lifting of the COVID-19 policies influenced residents’ perceptions of parks and community greenspaces. Our RDD estimation indicates no statistically significant change in residents’ overall perceived quality of parks after the policy shift, except for increased satisfaction with specific features such as plant diversity, maintenance, seating areas, trails, and large open spaces. In contrast, residents who responded after the policy shift reported a significantly higher perceived quality of community greenspaces compared to those who completed the survey before the shift (0.609 score difference, p < 0.01), with notable increases in satisfaction regarding plant diversity, maintenance, and seating areas. Perception of plant quantity remained unchanged in both types of greenspaces. Residents expressed greater satisfaction with sports facilities in parks, while community greenspaces were preferred for their water features and esthetic qualities. By adopting an RDD with a unique dataset, this study contributes empirical evidence to the current ongoing debate on the role of urban greenspace during the later stages of COVID-19. Specifically, it examines how changes in public health policy and the resulting increase in mobility might affect residents’ perceived greenspace quality. The findings can assist decision-makers and urban planners in developing more adaptive strategies to address the diverse needs of residents for greenspaces during the transitional period of a public health crisis.

The rapid development of artificial intelligence has greatly ensured maritime safety and made outstanding contributions to the protection of the marine environment. However, improving maritime safety still faces many challenges. In this paper, the development background and industry needs of smart ships are first studied. Then, it analyzes the development of smart ships for navigation from various fields such as the technology industry and regulation. Then, the importance of navigation technology is analyzed, and the current status of key technologies of navigation systems is deeply analyzed. Meanwhile, this paper also focuses on single perception technology and integrated perception technology based on single perception technology. As the development of artificial intelligence means that intelligent shipping is inevitably the trend for future shipping, this paper analyzes the future development trend of smart ships and visual navigation systems, providing a clear perspective on the future direction of visual navigation technology for smart ships.

Objective: Contactless monitoring of instantaneous heart rate and respiration rate has a significant clinical relevance. This work aims to use Speckle Vibrometry (i.e., based on the secondary laser speckle effect) to contactlessly measure these two vital signs in an intensive care unit. Methods: In this work, we propose an algorithm for the estimation of instantaneous heart rate and respiration rate from mechanically ventilated patients. The algorithm uses multiple regions, principal component analysis, and dominant angle analysis. A semi-automated peak detection method is implemented to precisely label the aortic valve opening peak within the cardiac waveform. Results: Compared with electrocardiography, the present work achieves limits of agreement of [−2.19, 1.73] beats per minute of instantaneous heart rate. The measurement spot is on the chest covered with two to three layers of duvet blankets. Compared with the airway flow signal measured by the mechanical ventilator, the present work achieves limits of agreement of [−0.68, 0.46] respirations per minute of instantaneous respiration rate. Conclusions: These results showcased Speckle Vibrometry’s potential in vital sign monitoring in a clinical setting. Significance: This is the first human clinical study for Speckle Vibrometry.

To mitigate accidents in submarine cable construction within the rapidly expanding offshore wind power sector, this study employed the analytic hierarchy process (AHP) and risk matrix method (LS) to assess the risks associated with identified factors. Based on project research and expert consultations, five primary and twenty-two secondary risk factors were identified. AHP was utilized to rank the primary risk factors by severity, probability, and detection difficulty, with the highest risk being the environmental impact, followed by third-party destruction and worker error. LS was applied to rank the secondary risk factors by likelihood and severity, with the highest risks being complex submarine topography, low underwater visibility, and fishing operations. The study proposes risk reduction measures based on these evaluations and offers methodological guidance for improving construction safety in similar enterprises.

Climate change is having important effects on the migration routes and seasonal–spatial distribution patterns of aquatic animals, including the cephalopods Sepiella maindroni de Rochebrune (Hoyle, 1886) and Sepia kobiensis (Hoyle, 1885) in the East China Sea region. We conducted bottom trawling surveys from 2018 to 2019 in the East China Sea region to identify the seasonal–spatial distribution patterns, including the locations of spawning and nursery grounds of both species, and to determine how they are related to environmental variables. We used random forests and boosted regression trees to identify the distribution patterns of both species from spring to winter to estimate the annual mean situations. We also predicted the habitat distribution variations in 2050 and 2100 under the SSP1-2.6 and SSP5-8.5 climate change scenarios. From our survey data, we detected increasing biomass densities of S. maindroni from 29.50° N to 28.50° N, where the largest value of 213.92 g·ind−1 occurred. In spring, juvenile groups were present in coastal areas and larger individuals were found in offshore areas. We identified potential spawning grounds at 29.50°–33.00° N 122.50°–123.00° E adjacent to the Zhejiang coastline, and larger individuals and higher biomass densities in south of the 29.50° N line in summer. In autumn, the average individual weight increased in the 28.00° N 122.00° E→124.00° E area. We located potential S. kobiensis spawning grounds at 27.00° N 122.00°–123.50° E in spring. Growing overwintering juveniles migrated to the area of 29.50°–30.50° N 125.00°–127.00° E in winter. The sea surface temperature of the areas inhabited by both species showed obvious seasonal variation. The SSP1-2.6 and SSP5-8.5 scenarios indicated that the habitat of S. maindroni would shift to the south first and then to the north of the study area with the intensification of CO2 emissions, and it would first expand and then greatly decrease. However, the habitat area of S. kobiensis would increase. Our results will contribute to a better understanding of the life history traits of both species and the changes in their distribution patterns under different climate scenarios to ensure sustainable exploitation and fisheries management.

In some scenarios, electric heavy-duty trucks with battery swapping mode (ETBSm) are more cost-effective than battery charging mode. The viability of battery swapping stations is contingent upon the operational requirements and range capabilities of the ETBSm. Low temperatures have the effect of reducing the range of the ETBSm, thereby creating difficulties for battery swapping. This article proposes the use of motor waste heat recovery (MWHR) to heat batteries, which would improve range. A number of subsystem models have been established, including the ETBSm, battery, motor, and thermal management system (TMS). The calibration of battery temperature and motor efficiency is achieved with a model error of less than 5%. Comparison of performance, such as temperature, energy consumption, and range, when using only positive temperature coefficient (PTC) heating and when using both PTC heating and motor waste heat. The results indicate a 15% increase in the rate of rise in battery temperature and a 10.64 kW·h reduction in energy consumption under Chinese heavy-duty vehicle commercial vehicle test cycle (CHTC) conditions. Then, the motor waste heat percentage, energy consumption, and range are analyzed at different ambient temperatures. At an ambient temperature of −20 °C, −10 °C, and 0 °C, the percentage of the motor waste heat is 32.1%, 35%, and 40.5%; when 75% of the state of charge (SOC) is consumed, the range is improved by 6.55%, 4.37%, and 4.49%. Additionally, the effect of the PTC heater on temperature characteristics and power consumption is investigated by changing the target temperature of the coolant at the battery inlet. In accordance with the stipulated conditions of an ambient temperature of −20 °C and a target coolant temperature of 40 °C at the battery inlet, the simulation results indicated a battery temperature rise rate of 0.85 °C/min, accompanied by a PTC power consumption of 15.6 kW·h. This study demonstrates that as the ambient temperature increases, the utilization of motor waste heat becomes more effective in reducing PTC heating power consumption. At the lowest ambient temperature tested, the greatest improvement in driving range is observed. It is important to note that while an increase in the target heating temperature of the PTC helps to raise the battery temperature more rapidly, this is accompanied by a higher energy consumption. This article provides a reference for the ETBSm with MWHR.

The growth status of winter wheat seedlings during the greening period is called the seedling situation. Timely and accurate determinations of the seedling situation type are important for subsequent field management measures and yield estimation. To solve the problems of low-efficiency artificial classification, subjective doping, inaccurate classification, and overfitting in transfer learning in classifying the seedling condition of winter wheat seedlings during the greening period, we propose an improved ConvNeXt winter wheat seedling status classification and identification network based on the pre-training–fine-tuning model addressing over-fitting in transfer learning. Based on ConvNeXt, a SETFL-ConvNeXt network (Squeeze and Excitation attention-tanh ConvNeXt using focal loss), a winter wheat seedling identification and grading network was designed by adding an improved SET attention module (Squeeze and Excitation attention-tanh) and replacing the Focal Loss function. The accuracy of the SETFL-ConvNeXt reached 96.68%. Compared with the classic ConvNeXt model, the accuracy of the Strong class, First class, and Third class increased by 1.188%, 2.199%, and 0.132%, respectively. With the model, we also compared the effects of different optimization strategies, five pre-training-fine-tuning models, and the degree of change in the pre-trained model. The accuracy of the fine-tuning models trained in the remaining layers increased by 0.19–6.19% using the last three frozen blocks, and the accuracy of the pre-trained model increased by 3.1–8.56% with the least degree of change method compared with the other methods. The SETFL-ConvNeXt network proposed in this study has high accuracy and can effectively address overfitting, providing theoretical and technical support for classifying winter wheat seedlings during the greening period. It also provides solutions and ideas for researchers who encounter overfitting.

Currently, tomato plant lowering is performed manually, which is both inefficient and costly. The manual process presents challenges in terms of efficiency and cost, creating a need for automated solutions in greenhouse environments. This paper addresses this issue by presenting the design and development of a tomato-plant-lowering robot utilizing machine vision and deep learning techniques. The study includes the design of an end effector optimized for plant-lowering operations based on the physical characteristics of tomato vines and roller hooks; precise positioning of roller hooks achieved through kinematic analysis and a custom dataset; integration of the RepC3 module from RT-DETR with YOLOv5s for enhanced object detection and positioning; and real-time camera feed display through an integrated application. Performance evaluation through experimental tests shows improvements in recognition accuracy, positioning precision, and operational efficiency, although the robot’s success rate in leaf removal needs further enhancement. This research provides a solid foundation for future developments in plant-lowering robots and offers practical insights and technical guidance.

GNSS precise point positioning (PPP) plays an important role in retrieving atmospheric water vapor values and performing numerical weather prediction. However, traditional PPP relies on real-time orbits and clocks, which require continuous internet or satellite communication. Improved broadcast ephemerides of GNSSs offer new opportunities for PPP with broadcast ephemerides (BE-PPP) instead of using precise ephemeride products. Therefore, we investigated the feasibility of utilizing BE-PPP for retrieving zenith tropospheric delay (ZTD) and precipitable water vapor (PWV) data. We processed the GPS/Galileo observations from 80 IGS stations during a 30-day experiment to retrieve ZTD values using both real-time PPP (RT-PPP) and BE-PPP solutions. Then, we processed observations from 20 EUREF Permanent GNSS Network (EPN) stations to retrieve PWV data. The IGS final tropospheric products were used to validate the ZTD, and radiosonde (RDS) and ERA5 data were used to validate the PWV. The results show that the real-time ZTD from BE-PPP agrees well with that from RT-PPP: the standard deviation (STD) of the ZTD is 1.07 cm when using BE-PPP and 0.6 cm when using RT-PPP. Furthermore, the STD of the PWV is 1.69 mm when using BE-PPP, and 0.96 mm when using RT-PPP, compared to the ERA5-PWV. Compared to the RDS-PWV, the STD is 3.09 mm when using BE-PPP and 1.39 mm when using RT-PPP. In conclusion, the real-time ZTD/PWV products obtained using the BE-PPP solution are consistent with those of RT-PPP and meet the requirements of NWP, so this method can be used as an effective complement to RT-PPP to expand its application potential.

The complete characterization of the almost-entropic region yields rate regions for network coding problems. However, this characterization is difficult and open. In this paper, we propose a novel algorithm to determine whether an arbitrary vector in the entropy space is entropic or not, by parameterizing and generating probability mass functions by neural networks. Given a target vector, the algorithm minimizes the normalized distance between the target vector and the generated entropic vector by training the neural network. The algorithm reveals the entropic nature of the target vector, and obtains the underlying distribution, accordingly. The proposed algorithm was further implemented with convolutional neural networks, which naturally fit the structure of joint probability mass functions, and accelerate the algorithm with GPUs. Empirical results demonstrate improved normalized distances and convergence performances compared with prior works. We also conducted optimizations of the Ingleton score and Ingleton violation index, where a new lower bound of the Ingleton violation index was obtained. An inner bound of the almost-entropic region with four random variables was constructed with the proposed method, presenting the current best inner bound measured by the volume ratio. The potential of a computer-aided approach to construct achievable schemes for network coding problems using the proposed method is discussed.

Rehmannia glutinosa, a crucial medicinal plant native to China, is extensively cultivated across East Asia. We used high-throughput sequencing to identify viruses infecting R. glutinosa with mosaic, leaf yellowing, and necrotic symptoms. A novel Torradovirus, which we tentatively named “Rehmannia torradovirus virus” (ReTV), was identified. The complete sequences were obtained through reverse-transcription polymerase chain reaction (RT-PCR), 5′ and 3′ rapid amplification of cDNA ends, and Sanger sequencing. The amino acid sequence alignment between the ReTV-52 isolate and known Torradovirus species in the Pro-Pol and coat protein regions were 51.3–73.3% and 37.1–68.1%, respectively. Meanwhile, the amino acid sequence alignment between the ReTV-8 isolate and known Torradovirus species in the Pro-Pol and coat protein regions were 52.7–72.8% and 36.8–67.5%, respectively. The sequence analysis classified ten ReTV strains into two variants. The ReTV-52 genome has two RNA segments of 6939 and 4569 nucleotides, while that of ReTV-8 consists of two RNA segments containing 6889 and 4662 nucleotides. Sequence comparisons and phylogenetic analysis showed ReTV strains clustered within the Torradovirus, exhibiting the closet relation to the squash chlorotic leaf spot virus. The RT-PCR results showed a 100% ReTV detection rate in all 60 R. glutinosa samples. Therefore, ReTV should be classified as a novel Torradovirus species. ReTV is potentially dangerous to R. glutinosa, and necessitating monitoring this virus in the field.

Foxtail millet prolamin, one of the major protein constituents of foxtail millet, has garnered attention due to its unique amino acid composition and function. Foxtail millet prolamin exhibits specific physicochemical and functional characteristics, such as solubility, surface hydrophobicity, emulsifying, and foaming properties. These characteristics have been exploited in the preparation and development of products, including plant-based alternative products, nutritional supplements, and gluten-free foods. Additionally, because of the favorable biocompatibility and biodegradability, foxtail millet prolamin is frequently used as a carrier for encapsulation and targeted delivery of bioactive substances. Moreover, studies have shown that foxtail millet prolamin is highly nutritious and displays various biological activities like antioxidant effects, anti-inflammatory properties, and anti-diabetic potential, making it a valuable ingredient in medicinal products and contributing to its potential role in therapeutic diets. This review summarizes the current knowledge of the amino acid composition and structural characteristics of foxtail millet prolamin, as well as the functional properties, biological activities, and applications in functional food formulation and drug delivery strategy. Challenges and future perspectives for the utilization of foxtail millet prolamin are also pointed out. This review aims to provide novel ideas and broad prospects for the effective use of foxtail millet prolamin.

A wideband Doppler Effect is a significant challenge for underwater acoustic communications (UAC). This paper proposes a new two-stage structure of direct adaptive multi-resampling turbo equalizer (DAM-TEQ) for solving the problem of large timescale errors in time-varying channels, which uses an innovative adaptive time-domain resampling method for Doppler estimation and compensation. In this equalizer, the received signal is first fed into the first-stage structure, in which an adaptive resampling is performed using equalization coefficient detection to achieve a Doppler rough estimation. After the processing is completed, it is fed into the second-stage structure for joint equalization and decoding, effectively reducing the error of information transmission. Compared with the conventional turbo equalizer (TEQ) based on timescale estimation, the proposed equalizer can avoid the problem of the Doppler Effect not being accurately estimated in time-varying channels, with only a slight increase in complexity. Simulations and lake trails show that the equalizer can effectively perform a Doppler estimation and compensation in time-varying channels, and has a better bit error rate (BER) performance than the traditional timescale-based TEQ.

In high-resolution remote sensing images, there are areas with weak textures such as large building roofs, which occupy a large number of pixels in the image. These areas pose a challenge for traditional semantic segmentation networks to obtain ideal results. Common strategies like downsampling, patch cropping, and cascade models often sacrifice fine details or global context, resulting in limited accuracy. To address these issues, a novel semantic segmentation framework has been designed specifically for large-format high-resolution remote sensing images by aggregating global and local features in this paper. The framework consists of two branches: one branch deals with low-resolution downsampled images to capture global features, while the other branch focuses on cropped patches to extract high-resolution local details. Also, this paper introduces a feature aggregation module based on the Transformer structure, which effectively aggregates global and local information. Additionally, to save GPU memory usage, a novel three-step training method has been developed. Extensive experiments on two public datasets demonstrate the effectiveness of the proposed approach, with an IoU of 90.83% on the AIDS dataset and 90.30% on the WBDS dataset, surpassing state-of-the-art methods such as DANet, DeepLab v3+, U-Net, ViT, TransUNet, CMTFNet, and UANet.

Personalized news recommendation can alleviate the information overload problem, and accurate modeling of user interests is the core of personalized news recommendation. Existing news recommendation methods integrate the titles and contents of news articles that users have historically browsed to construct user interest models. However, this method ignores the phenomenon of “title–content mismatching” in news articles, which leads to the lack of precision in user interest modeling. Therefore, a hybrid news recommendation method based on title–content matching is proposed in this paper: (1) An interactive attention network is employed to model the correlation between title and content contexts, thereby enhancing the feature representation of both; (2) The degree of title–content matching is computed using a Siamese neural network, constructing a user interest model based on title–content matching; and (3) neural collaborative filtering (NCF) based on factorization machines (FM) is integrated, taking into account the perspective of the potential relationships between users for recommendation, leveraging the insensitivity of neural collaboration to news content to alleviate the impact of title–content mismatching on user feature modeling. The proposed model was evaluated on a real-world dataset, achieving an nDCG of 83.03%, MRR of 81.88%, AUC of 85.22%, and F1 Score of 35.10%. Compared to state-of-the-art news recommendation methods, our model demonstrated an average improvement of 0.65% in nDCG and 3% in MRR. These experimental results indicate that our approach effectively enhances the performance of news recommendation systems.

This study examined the impact of tea polyphenols (TPs) on the intestinal flora of loaches (Paramisgurnus dabryanus) under chronic ammonia nitrogen stress using high-throughput sequencing. Two groups of 600 loaches were studied over one month, and they were separated into a control group and tea polyphenol group. Alpha and beta diversity analyses showed diverse bacterial communities, with significant differences in the abundance and uniformity observed initially but not between sampling time points. Cluster analyses revealed distinct differences in microbial communities between groups. A predictive function analysis indicated enrichment in pathways related to amino acid and nucleotide biosynthesis. These findings offer initial insights into how tea polyphenols may affect intestinal microbial communities in loaches under ammonia nitrogen stress.

Rehmannia glutinosa is one of the most important medicinal plants in China and is affected by viral diseases. In this study, a new virus tentatively named Rehmannia Allexivirus virus (ReAV) was identified through high-throughput sequencing, reverse-transcription polymerase chain reaction (RT-PCR), and Sanger sequencing. The complete genome length was 7297 nt and it contained five open reading frames (ORFs) encoding replicase, triple gene block 1(TGB1), TGB2, TGB3, and coat protein (CP). The replicase and CP presented nucleotide homology ranges of 59.9–65.2% and 47.5–55.5% between the nine ReAV isolates and the other 12 species of the genus Allexivirus. In the nine isolates, ReAV-20 and ReAV-31 isolates showed breakpoints in the replicase and CP regions, respectively. The other isolates shared 87.2–96.5% nt with the whole genome nucleotide identity. The phylogenetic tree showed that seven ReAV isolates based on replicase, CP, and whole genome sequences were clustered in the same branch and were related to the genus Allexivirus. The ReAV detection rates for 60 R. glutinosa samples were 73.3–81.7% through RT-PCR using primers targeting the replicase or CP genes. These results demonstrate that ReAV is the dominant virus in R. glutinosa. This study provides important evidence for understanding viruses infecting R. glutinosa and for establishing efficient strategies to prevent viral spread.

Based on morphological, chemical and molecular studies, two new species of the lichen genus Letrouitia are newly described from China. Letrouitia arcuata is distinguished by its arcuate ascospores [8–10(–12)-locular, (28–)33–50(–62.5) × (8–)10–14.5 µm] and L. sinuosa by its ascomata with wavy margins and ascospores with lens-shaped locules [6–8-locular, (18–)19.5–32(–34) × (6.5–)8–14 μm]. In addition, L. magenta is reported for the first time as a new record in China, characterized by small and round ascomata. The descriptions, distribution and phylogenetic analysis of the respective species have been actualized and a key to the Letrouitia species known from China is provided.

The wavefront reconstruction is a crucial step in determining the performance of wavefront detection instruments. The wavefront reconstruction algorithm is primarily evaluated in three dimensions: accuracy, speed, and noise immunity. In this paper, we propose a hybrid zonal reconstruction algorithm that introduces slope and curvature information in the diagonal, anti-diagonal, horizontal, and vertical directions by dividing the neighbor sampling points into subregions in groups of four. By canceling the same parameters in integration equations, an algorithm using multi-directional slope–curvature information is achieved with only two sets of integration equations in each subregion, reducing the processing time. Simulation experiments show that the relative root-mean-square reconstruction error of this algorithm is improved by about 4 orders of magnitude compared with existing algorithms that use multi-directional slope information or slope–curvature information alone. Compared with the hybrid multi-directional slope–curvature algorithm, the proposed algorithm can reduce computation time by about 50% as well as provide better noise immunity and reconstruction accuracy. Finally, the validity of the proposed algorithm is verified by the null test experiment.

The detection of the moisture content of wheat is an important index used to measure the quality and preservation of wheat. In order to rapidly and non-destructively detect the moisture content of wheat, in this study, we designed a stripline detection device that measures 151 frequency points in the 50–200 MHz frequency range with a vector network analyzer. Random forest (RF), extreme learning machine (ELM), and BP neural network prediction models were established, using the frequency, temperature, volume density and dielectric constant as input and the water content as output. It was shown that, in the frequency range 50–200 MHz, the permittivity of wheat decreases as the frequency increases, and that this is negatively correlated. The dielectric constant of wheat increases as the moisture content, temperature, and bulk density increase, and these are positively correlated. The random forest (RF) prediction model, which uses the frequency, temperature, effective dielectric constant εeff. and volume density as inputs and the wheat moisture content as the output, demonstrates the best performance. The determination coefficient (R2) = 0.99977, the mean absolute error (MAE) = 0.044368, the mean square error (MAE) = 0.0053011, and the root mean square error (RMSE) = 0.072809. This study provides a new device and method for the detection of the moisture content of wheat. The device is small and is not easily disturbed by the external environment. It can be measured in a variety of conditions and is important for the development of low-cost, high-precision, and portable devices for the detection of the moisture content of wheat.

Controlling the collective behavior of micro/nanomotors with ultrasound may enable new functionality in robotics, medicine, and other engineering disciplines. Currently, various collective behaviors of nanomotors, such as assembly, reconfiguration, and disassembly, have been explored by using acoustic fields with a fixed frequency, while regulating their collective behaviors by varying the ultrasound frequency still remains challenging. In this work, we designed an ultrasound manipulation methodology that allows nanomotors to exhibit different collective behaviors by regulating the applied ultrasound frequency. The experimental results and FEM simulations demonstrate that the secondary ultrasonic waves produced from the edge of the sample cell lead to the formation of complex acoustic pressure fields and microfluidic patterns, which causes these collective behaviors. This work has important implications for the design of artificial actuated nanomotors and optimize their performances.

Cucumber (Cucumis sativus L.) lateral branch elongation is influenced by a variety of environmental signals, including light [e.g., far-red (FR) light] and hormones. In this experiment, the effect of FR light on the lateral branch elongation of cucumber (‘Zhongnong No. 26’) seedlings was investigated. The results showed that FR light significantly inhibited the lateral branch elongation of cucumber seedlings. In addition, FR light significantly increased the auxin (indole-3-acetic acid, IAA) content, decreased the cytokinin (CTK; Zeatin) content, and suppressed the expression of most CTK synthetic-related genes, such as IPTs, in cucumber seedlings. The lateral branch elongation of cucumber seedlings was assessed in response to decapitation and exogenous 6-BA treatment to further investigate the relationship between IAA and CTK on the lateral branch elongation of cucumber seedlings under FR light. Both decapitation and exogenous 6-BA treatment eliminated the inhibitory effect of FR light on the lateral branch elongation of cucumber seedlings. In conclusion, these results indicated that IAA and CTK were involved in the regulatory effects of FR light on cucumber seedling lateral branch elongation.

Superoxide dismutase (SOD) is an important antioxidant metalloenzyme present in plants that plays a vital role in plant growth and development, but studies on the SOD gene family in Pakchoi are lacking. In this study, we identified 13 SOD genes from pakchoi, including three MnSODs, five Cu/ZnSODs, and five FeSODs. Through structural analysis, the gene structures and motif patterns in the three subfamilies showed a high degree of conservation. From an evolutionary point of view, gene duplication is an important pathway driving the evolutionary development of the SOD gene family. In addition, by analyzing the structure and function of BchSOD proteins, most of the genes were shown to be involved in different developmental stages of pakchoi, and their expression was shown to be regulated by external conditions such as light, phytohormones, and abiotic stress. qPCR results revealed that BchSODs were expressed in different parts of pakchoi, and most of the genes were expressed in response to abiotic stresses (salt and drought) and hormones (GA and MeJA). In addition, the BchFSD2 gene was studied in depth, and subcellular localization confirmed that the BchFSD2 gene was expressed in plant chloroplasts. Overexpression of BchFSD2 promoted salt tolerance, limited superoxide anion and MDA production, and increased antioxidant enzyme activities in Arabidopsis. In summary, the BchSOD gene family was comprehensively analyzed in this study to provide new insights for a better understanding of BchSOD function and to improve salt tolerance in pakchoi.

Cytokinins (CKs) are among the hormones that regulate plants’ growth and development, and the CKX and IPT genes, which are CK degradation and biosynthesis genes, respectively, play important roles in fine-tuning plants’ cytokinin levels. However, the current research on the function of IPT and CKX in cucumber’s growth, development, and response to abiotic stress is not specific enough, and their regulatory mechanisms are still unclear. In this study, we focused on the IPT and CKX genes in cucumber, analyzed the physiological and biochemical properties of their encoded proteins, and explored their expression patterns in different tissue parts and under low light, salt stress, and drought stress. Eight CsCKX and eight CsIPT genes were identified from the cucumber genome. We constructed a phylogenetic tree from the amino acid sequences and performed prediction analyses of the cis-acting elements of the CsCKX and CsIPT promoters to determine whether CsCKXs and CsIPTs are responsive to light, abiotic stress, and different hormones. We also performed expression analysis of these genes in different tissues, and we found that CsCKXs and CsIPTs were highly expressed in roots and male flowers. Thus, they are involved in the whole growth and development process of the plant. This paper provides a reference for further research on the biological functions of CsIPT and CsCKX in regulating the growth and development of cucumber and its response to abiotic stress.

Non-heading Chinese cabbage (cabbage) is an essential green leafy vegetable, and bolting and flowering are necessary for reproduction. However, further research is needed to study the effect of photoperiod on the bolting and flowering of cabbage, particularly on the development of the stem. In this study, we performed phenotypic analysis and measured endogenous gibberellin levels in the cabbage. We carried out these experiments under four different photoperiodic treatments, 12 h (light)/12 h (dark), 14 h (light)/10 h (dark), 16 h (light)/8 h (dark), and 18 h (light)/6 h (dark). The results showed that the time of bolting and flowering gradually decreased with increasing light duration. The development of stems was optimal under the 16 h (light)/8 h (dark) photoperiod treatment, and the same result was obtained via cytological observation. In addition, the changes in the endogenous gibberellin3 (GA3) content under different photoperiodic treatments were consistent with the development of stems and peaked at 16 h (light)/8 h (dark). At the same time, qRT-PCR analysis showed that the relative expression of the key gibberellin synthase genes, BcGA3ox2 and BcGA20ox2, exhibited upregulation. When treated with exogenous GA3 and its synthesis inhibitor, paclobutrazol (PAC), exogenous gibberellins significantly promoted bolting; conversely, gibberellin inhibitors suppressed the bolting, flowering, and stem elongation of cabbage. Therefore, the photoperiod may regulate cabbage bolting by regulating endogenous GA3.

Screening and identifying the active compounds in foods are important for the development and utilization of functional foods. In this study, the anti-enteritis activity of ethanol extract from Camellia oleifera oil (PECS) was quickly evaluated using a Smurf Drosophila model and the metabolomics approach, combined with molecular docking techniques, were performed to rapidly screen and identify compounds with potential anti-enteritis activity in PECS. PECS showed good anti-enteritis activity and inhibited the activity of 5-lipoxygenase (LOX), cyclooxygenase 2 (COX-2) and inducible nitric oxide synthase (iNOS). In particular, wighteone and p-octopamine were newly identified in C. oleifera oil and were proven to have good anti-enteritis activity. The inhibitory activity of kaempferitrin (IC50 = 0.365 mmol L−1) was higher than that of wighteone (IC50 = 0.424 mmol L−1) and p-octopamine (IC50 = 0.402 mmol L−1). Of note, the IC50 value of salazosulfapyridine was 0.810 mmol L−1. Inhibition of LOX activity is likely one of the anti-enteritis mechanisms of PECS. These new findings lay the foundation for further investigations into the underlying mechanisms of anti-enteritis activity in C. oleifera oil.

Immobilized enzymes are currently being rapidly developed and are widely used in juice clarification. Immobilized enzymes have many advantages, and they show great advantages in juice clarification. The commonly used methods for immobilizing enzymes include adsorption, entrapment, covalent bonding, and cross-linking. Different immobilization methods are adopted for different enzymes to accommodate their different characteristics. This article systematically reviews the methods of enzyme immobilization and the use of immobilized supports in juice clarification. In addition, the mechanisms and effects of clarification with immobilized pectinase, immobilized laccase, and immobilized xylanase in fruit juice are elaborated upon. Furthermore, suggestions and prospects are provided for future studies in this area.

The semantic segmentation of point clouds is a crucial undertaking in 3D reconstruction and holds great importance. However, achieving precise semantic segmentation represents a significant hurdle. In this paper, we present BEMF-Net, an efficient method for large-scale environments. It starts with an effective feature extraction method. Unlike images, 3D data comprise not only geometric relations but also texture information. To accurately depict the scene, it is crucial to take into account the impacts of texture and geometry on the task, and incorporate modifications to improve feature description. Additionally, we present a multi-scale feature fusion technique that effectively promotes the interaction between features at different resolutions. The approach mitigates the problem of the smoothing of detailed information caused by downsampling mechanisms, while ensuring the integrity of features across different layers, allowing a more comprehensive representation of the point cloud. We confirmed the effectiveness of this method by testing it on benchmark datasets such as S3DIS, SensatUrban, and Toronto3D.

Subsequent commutation failure in high-voltage DC transmission systems seriously emphasizes the safe and stable operation of power systems. Via analyzing the mechanism of commutation failure and the principle of voltage-dependent current order limiter (VDCOL), this paper proposes a dynamic nonlinear VDCOL control strategy based on the Taylor expansion of DC voltage for suppressing subsequent commutation failure. To solve the problem of fluctuating DC current command value caused by a large drop in DC voltage, this paper constructs a nonlinear VDCOL control that can be dynamically adjusted according to the AC bus voltage level, and Taylor expansion of DC voltage is used to obtain its first-order and second-order differential components. Different scales of differential elements are chosen to predict the DC voltage compensation value while balancing sensitivity and accuracy. The compensated DC voltage, used as the starting voltage of VDCOL, is input to the VDCOL control constructed in this paper to suppress the subsequent commutation failure of the transmission system by reducing the fluctuation of the current command value. Finally, the standard test model of HVDC is established based on the actual parameters, and the simulation results show that the Method proposed in this paper has an effective suppressing effect in the case of single-phase or three-phase faults of different severity and is conducive to the restoration of the system power transmission.

Haptic information is essential in everyday activities, especially for visually impaired people in terms of real-world navigation. Since human haptic sensory processing is nonlinear, asymmetric vibrations have been widely studied to create a pulling sensation for the delivery of directional haptic cues. However, the design of an input control signal that generates asymmetric vibrations has not yet been parameterised. In particular, it is unclear how to quantify the asymmetry of the output vibrations to create a better pulling sensation. To better understand the design of an input control signal that generates haptic directional cues, we evaluated the effect of the pulling sensations corresponding to the three adjustable parameters (i.e., delay time, ramp-down step length, and cut-off voltage) in a commonly applied step-ramp input signal. The results of a displacement measurement and a psychophysical experiment demonstrate that when the quantified asymmetry ratio is in a range of 0.3430–0.3508 with an optimised cut-off voltage for our hand-held device, the haptic directional cues are better perceived by participants. Additionally, the results also showed a superior performance in haptic delivery by shear forces than normal forces.

The traveling wave protection of high voltage direct current (HVDC) transmission lines is susceptible to the influence of transition resistance. As a backup protection, current differential protection has absolute selectivity, but usually requires an increase in delay to avoid misoperation caused by distributed capacitance on the line, resulting in a longer action time. Based on this, a two-terminal directional protection method for HVDC transmission lines is proposed based on Sparrow Search Algorithm (SSA)-Variational Mode Decomposition (VMD) and Hilbert phase difference. On the basis of analyzing the directional characteristics of the current fault component at both ends of the rectifier and inverter sides under different faults, SSA is first used to optimize the parameters of VMD. The residual components representing the direction of the current fault component at both ends are extracted through VMD, and then the Hilbert phase difference of the residual components at both ends is calculated to identify faults inside and outside the line area. In addition, fault pole selection can be achieved based on the ratio of the sum of multi-band Hilbert energy of single-terminal voltage fault components at the positive and negative poles. Simulation experiments have shown that the proposed protection scheme can quickly and effectively identify fault and has good tolerance to transition resistance and noise interference.

Compound heat–precipitation events exert significant impacts on severe weather occurrences. Intense vertical air movement, driving vigorous convection, primarily contributes to the formation of extreme precipitation. Nevertheless, such compound events’ temporal and spatial variation patterns at convection-permitting resolutions remain inadequately explored. This study assesses the performance of the Convection-Permitting Model (CPM) against a model of convection parameterization while investigating the spatial dynamics of compound heat–precipitation events in Guangdong, China. Our findings indicate that the CPM exhibits heightened reliability and precision in simulating temperature and precipitation patterns, especially in extreme precipitation simulation, which would be highly underestimated without a convection-permitting process. Projections from the CPM reveal that, across historical and future periods, the occurrence frequency and fraction of T-P events (instances of extreme heat followed by extreme precipitation) surpass those of P-T events (occurrences of extreme precipitation followed by extreme heat). For T-P events, the CPM exhibits better capability in capturing high-frequency occurrence areas, whereas the results of the relatively low-resolution model show less distinct spatial variations. Both types of events exhibit noticeable upward trends yearly within each period. By the close of this century, the provincial average frequency of P-T events is anticipated to decrease from 20.32 times to 14.55 times. In contrast, the frequency of T-P events is projected to increase from 87.7 times to 101.38 times. These projected changes underscore the shifting dynamics of compound heat–precipitation events in the study region.

Understanding genetic architectures of yield and yield-related traits of wheat (Triticum aestivum L.) grown under dryland or irrigated conditions is pivotal for developing modern high-yielding germplasm and cultivars. The objectives of this study were to detect quantitative trait loci (QTL) linked to yield and yield components using a mapping population derived from ‘TAM 113’/‘Gallagher’, including 191 recombinant inbred lines (RILs). The population was grown in McGregor, College Station, and Bushland, Texas, for three consecutive years from 2019 to 2021. A high-density genetic map covering all 21 chromosomes was constructed using a set of 8,075 single nucleotide polymorphisms (SNPs) obtained using genotyping-by-sequencing (GBS). A total of 147 QTLs for 16 yield-related traits were identified, which included 16 QTLs consistently detected in multiple experiments and 8 QTLs that showed pleiotropic effects. Of them, five pleiotropic QTLs overlapped with the consistent QTL. They increased grain yield (YLD) up to 37.64 g m−2, thousand kernel weight (TKW) up to 1.33 g, harvest (HI) up to 0.97%, kernel length up to 0.08 mm, and kernel width up to 0.04 mm with Gallagher alleles and increased YLD up to 22.21 g m−2, kernels spike−1 up to 1.77, TKW up to 1.14 g, and HI up to 3.72% with TAM 113 alleles. One major and consistent QTL on chromosome 2D at 34.4 Mbp overlapped with the major photoperiod gene Ppd-D1 and was affected by multiple traits, including kernel diameter (DIAM), TKW, kernel hardness index (KHI), heading date (HD), and plant height (PH). Another QTL cluster region on 7D between 52 and 66 Mbp, encompassing one consistent and three pleiotropic QTLs. One of the pleiotropic QTLs at 52 Mbp increased YLD up to 24.16 g m−2, HI up to 1%, and DIAM up to 0.03 mm. This study dissected genetic loci associated with yield and yield-related traits, providing valuable information on wheat improvement using marker-assisted selection (MAS).

This paper presents a study on bioinspired rigid-flexible coupling adaptive compliant motion control of a robot gecko with hybrid actuation for space stations. The biomimetic robot gecko is made of a rigid trunk, four motor-driven active legs with dual-degree-of-freedom shoulder joints, and four pneumatic flexible pleated active attachment–detachment feet. The adaptive impedance model consists of four input parameters: the inertia coefficient, stiffness coefficient, damping coefficient, and segmented expected plantar force. The robot gecko is equipped with four force sensors mounted on its four feet, from which the normal force of each foot can be sensed in real-time. Based on the sensor signal, the variable stiffness characteristics of the feet in different states are analyzed. Furthermore, an adaptive active compliance control strategy with whole-body rigidity–flexibility-force feedback coupling is proposed for the robot gecko. Four sets of experiments are presented, including open-loop motion control, static anti-interference experiment, segmented variable stiffness experiment, and adaptative compliant motion control, both in a microgravity environment. The experiment results indicated that the presented control strategy worked well and the robot gecko demonstrates the capability of stable attachment and compliant detachment, thereby normal impact and microgravity instability are avoided. It achieves position tracking and force tracking while exhibiting strong robustness for external disturbances.

China and Brazil established the “Comprehensive Strategic Partnership” in 2012. Since the establishment of the partnership, China and Brazil have continued to deepen economic and trade cooperation, and cooperation in various fields has achieved remarkable results. China has long been consistent in maintaining its status as Brazil’s largest export destination and source of imports. The total volume of bilateral trade between China and Brazil has significantly increased, and the trade structure in goods is becoming increasingly diversified. The total volume of bilateral trade between China and Brazil has grown considerably, and the trade structure in goods has become increasingly diversified. On 20 April 2023, when Brazilian President Lula visited China, China and Brazil issued a joint statement on deepening the “Comprehensive Strategic Partnership”, further deepening the cooperation in trade in goods has become a consensus. From the perspective of goods trade under the comprehensive strategic partnership, the article analyses the complementarities and competitiveness of goods trade between China and Brazil with the help of relevant formulas to form a complete and transparent study of the goods trade between the two countries.

The rapid pace of urbanization and the emergence of social challenges, including an aging population and increased labor costs resulting from the COVID-19 pandemic, have underscored the urgency to explore smart city solutions. Within these technologically advanced urban environments, zoos have assumed a pivotal role that extends beyond their recreational functions. They face labor cost challenges and ecological considerations while actively contributing to wildlife conservation, environmental education, and scientific research. Zoos foster a connection with nature, promote biodiversity awareness, and offer a valuable space for citizens, thereby directly supporting the pillars of sustainability, public engagement, and technological innovation in smart cities. This study employs a quantitative analysis to assess the alignment between smart projects and the distinctive characteristics of Kyoto Zoo. Through questionnaires, we collected feedback on performance and importance, and subsequently employed the analytic hierarchy process and the fuzzy integrated evaluation method to obtain quantitative results. The findings reveal the high level of intelligence exhibited by Kyoto Zoo, and the analysis provides insightful guidance that can be applied to other urban facilities. At the same time, we compared Kyoto Zoo with Ueno Zoo to see the difference in intellectualization achievements in different contexts in terms of data and systems.

Long human ultra-conserved non-coding elements (UCNEs) do not have any sequence similarity to each other or other characteristics that make them unalterable during vertebrate evolution. We hypothesized that UCNEs have unique dinucleotide (DN) composition and arrangements compared to the rest of the genome. A total of 4272 human UCNE sequences were analyzed computationally and compared with the whole genomes of human, chicken, zebrafish, and fly. Statistical analysis was performed to assess the non-randomness in DN spacing arrangements within the entire human genome and within UCNEs. Significant non-randomness in DN spacing arrangements was observed in the entire human genome. Additionally, UCNEs exhibited distinct patterns in DN arrangements compared to the rest of the genome. Approximately 83% of all DN pairs within UCNEs showed significant (>10%) non-random genomic arrangements at short distances (2–6 nucleotides) relative to each other. At the extremes, non-randomness in DN spacing distances deviated up to 40% from expected values and were frequently associated with GpC, CpG, ApT, and GpG/CpC dinucleotides. The described peculiarities in DN arrangements have persisted for hundreds of millions of years in vertebrates. These distinctive patterns may suggest that UCNEs have specific DNA conformations.

Cancer immunotherapy is an innovative treatment strategy to enhance the ability of the immune system to recognize and eliminate cancer cells. However, dose limitations, low response rates, and adverse immune events pose significant challenges. To address these limitations, gold nanoparticles (AuNPs) have been explored as immunotherapeutic drug carriers owing to their stability, surface versatility, and excellent optical properties. This review provides an overview of the advanced synthesis routes for AuNPs and their utilization as drug carriers to improve precision therapies. The review also emphasises various aspects of AuNP-based immunotherapy, including drug loading, targeting strategies, and drug release mechanisms. The application of AuNPs combined with cancer immunotherapy and their therapeutic efficacy are briefly discussed. Overall, we aimed to provide a recent understanding of the advances, challenges, and prospects of AuNPs for anticancer applications.

Instantaneous heart rate (IHR) has been investigated for sleep applications, such as sleep apnea detection and sleep staging. To ensure the comfort of the patient during sleep, it is desirable for IHR to be measured in a contact-free fashion. In this work, we use speckle vibrometry (SV) to perform on-skin and on-textile IHR monitoring in a sleep setting. Minute motions on the laser-illuminated surface can be captured by a defocused camera, enabling the detection of cardiac motions even on textiles. We investigate supine, lateral, and prone sleeping positions. Based on Bland–Altman analysis between SV cardiac measurements and electrocardiogram (ECG), with respect to each position, we achieve the best limits of agreement with ECG values of [−8.65, 7.79] bpm, [−9.79, 9.25] bpm, and [−10.81, 10.23] bpm, respectively. The results indicate the potential of using speckle vibrometry as a contact-free monitoring method for instantaneous heart rate in a setting where the participant is allowed to rest in a spontaneous position while covered by textile layers.

We employed publicly available user-generated content (UGC) data from the website Tripadvisor and developed an autoregressive integrated moving average (ARIMA) model using the R language to analyze the seasonality of the use of urban green space (UGS) in Okinawa under normal conditions and during the COVID-19 pandemic. The seasonality of the use of ocean-area UGS is primarily influenced by climatic factors, with the peak season occurring from April to October and the off-peak season from November to March. Conversely, the seasonality of the use of non-ocean-area UGS remains fairly stable throughout the year, with a relatively high number of visitors in January and May. The outbreak of the COVID-19 pandemic greatly impacted visitor enthusiasm for travel, resulting in significantly fewer actual postings compared with predictions. During the outbreak, use of ocean-area UGS was severely restricted, resulting in even fewer postings and a negative correlation with the number of new cases. In contrast, for non-ocean-area UGS, a positive correlation was observed between the change in postings and the number of new cases. We offer several suggestions to develop UGS management in Okinawa, considering the opportunity for a period of recovery for the tourism industry.

To overcome the limitations of the Flamingo Search Algorithm (FSA), such as a tendency to converge on local optima and improve solution accuracy, we present an improved algorithm known as the Multi-Strategy Improved Flamingo Search Algorithm (IFSA). The IFSA utilizes a cube chaotic mapping strategy to generate initial populations, which enhances the quality of the initial solution set. Moreover, the information feedback model strategy is improved to dynamically adjust the model based on the current fitness value, which enhances the information exchange between populations and the search capability of the algorithm itself. In addition, we introduce the Random Opposition Learning and Elite Position Greedy Selection strategies to constantly retain superior individuals while also reducing the probability of the algorithm falling into a local optimum, thereby further enhancing the convergence of the algorithm. We evaluate the performance of the IFSA using 23 benchmark functions and verify its optimization using the Wilcoxon rank-sum test. The compared experiment results indicate that the proposed IFSA can obtain higher convergence accuracy and better exploration abilities. It also provides a new optimization algorithm for solving complex optimization problems.

Understanding the accumulation characteristics of heavy metals in the growth process of American ginseng can provide theoretical support for its safe production. In this study, the content of Cu, Mn, As, Pb, Cd, Cr, and Ni in American ginseng (annual, biennial, and triennial) and planting soil were determined using inductively coupled plasma mass spectrometry (LCP-MS). In addition, the change in the content of these heavy metals in American ginseng was evaluated after soaking the plant for various time periods. The results indicated that the content of some heavy metals in American ginseng was correlated with soil heavy metal contents. For example, Ni, Cd, and Mn content in American ginseng was significantly negatively correlated with Ni content in soil. American ginseng exhibited distinct heavy metal accumulation characteristics in different parts at different growth stages. For example, in annual American ginseng, Mn and As are mainly enriched in lateral roots and taproots, while in biennial and triennial American ginseng, they are mainly enriched in reed heads. When American ginseng plant was soaked for various time intervals, its heavy metal content changed to varying degrees. In general, after soaking American ginseng for 30 min, the content of most heavy metals decreased.