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Abstract A low‐complexity anti‐multipath interference spread spectrum communication system is proposed to address shortcomings in traditional anti‐multipath technology, such as complex structures and high resource consumption. The system utilizes a fast Fourier transform to capture pseudo‐noise code signals and simultaneously detect multipath signals. Moreover, the signal‐to‐noise ratio (SNR) of each path signal is calculated, and the multipath signal is synthesized through the designed optimal SNR synthesis method to improve the SNR of the demodulated signal to reduce the bit error rate. MATLAB is used to perform functional simulations of the system to verify its feasibility. The simulation results show that the system can successfully detect and synthesize multipath signals and achieve lower communication error rates than traditional code division multiple access (CDMA) systems.

The histone lysine methyltransferase SET (Suppressor of variegation, Enhancer of Zeste, Trithorax) and MYND (Myeloid-Nervy-DEAF1) domain-containing protein (SMYD2) plays a role in the tumorigenesis of cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC). However, the prognostic significance of SMYD2 in CESC and the link between SMYD2 and tumor-infiltrating immune cells are unknown. The prognostic value of SMYD2 in CESC was obtained from The Cancer Genome Atlas (TCGA). SMYD2 mRNA and protein were both highly expressed in CESC compared with normal tissues. The high expression of SMYD2 was associated with advanced tumor status and poor prognosis in CESC patients. SMYD2 was an independent prognostic factor for overall survival. In vitro experiments with knockdown of SMYD2 suppressed CESC cell migration and invasion. The online tumor immune estimation resource (TIMER) and Kaplan-Meier analysis results revealed that the infiltration of CD4+ T and CD8+ T cells was related to poor prognosis. In TIMER-based multivariate Cox regression analysis, CD8+ T cells and SMYD2 were demonstrated as independent prognostic factors of CESC. In conclusion, our data suggest that high SMYD2 expression is a predictor of poor prognosis in CESC patients; SMYD2 could serve as a prognostic biomarker and molecular therapeutic target for CESC.

In recent years, the low Earth orbit microsatellite network technology has experienced rapid development due to its advantages of low latency, low cost, and short development cycles. However, building an efficient and reliable satellite network routing system faces many challenges due to the characteristics of satellite networks, such as fast‐changing topology, large variations in link latency, higher probabilities of node and link failures, and limited resources. Routing algorithms have a significant impact on intersatellite communication and have become a research hotspot. The current mainstream algorithms focus on reducing information propagation delays between satellites to enable faster transmission. However, many satellite networks, such as meteorological observation satellite networks, are not sensitive to propagation delays but emphasize reducing hardware costs, especially for the receiving and transmitting signal systems of satellites. This means minimizing the single‐step signal transmission distance of satellites. This article proposes a routing algorithm based on time slot planning and the shortest path step length. Experimental simulation results demonstrate that this algorithm significantly reduces the step length of signal transmission and lowers hardware costs. [ABSTRACT FROM AUTHOR]

College student education and management can be enhanced through a data‐driven approach involving student surveys, academic records, and text analysis to understand student interests and concerns. Effective categorization of relevant topics enables universities to provide tailored support and educational content, thus improving the quality of education and fostering student success and well‐being by adapting to evolving student needs and aspirations. The primary contribution of this work is demonstrating the effectiveness of semisupervised learning methods in educational content classification, providing a robust solution for enhancing college student education and management with limited labeled data. The objective of this study was to evaluate the feasibility of using semisupervised learning methods in educational content classification using the Yahoo Answers dataset. For the Yahoo_500 dataset, the supervised neural network achieved a best evaluation accuracy of 0.6565, an average precision of 0.6539, an average recall of 0.6565, and an average F1 score of 0.6547. In contrast, semisupervised approaches, Dash, FixMatch, and FreeMatch, consistently demonstrated superior performance. Among the evaluated semisupervised architectures, FreeMatch achieved the highest best evaluation accuracy (0.6759), average precision (0.6739), average recall (0.6759), and average F1 score (0.6744). The Yahoo_2000 dataset, which benefited from an increased labeled data pool, exhibited a similar trend with semisupervised approaches consistently surpassing the supervised approach. FreeMatch maintained its top performing position in several categories, including computers and Internet, consumer electronics, and business and finance, with impressive F1 score of ≥0.753. Overall, the semisupervised approaches prove highly effective in improving model performance, highlighting its practical advantages. These results underscore the robustness of semisupervised approaches and their capability to improve classification performance even with limited labeled data. Employing semisupervised learning on the Yahoo Answers dataset and additional data sources for college student management and education can be a powerful tool for gaining insights into students' interests and concerns. [ABSTRACT FROM AUTHOR]

In warehouses with vast quantities of heavy goods, heavy-duty forklift Automated Guided Vehicles (AGVs) play a key role in facilitating efficient warehouse automation. Due to their large load capacity and high inertia, heavy-duty forklift AGVs struggle to automatically navigate optimized routes. Additionally, rapid acceleration and deceleration can pose safety hazards. This paper proposes a velocity-adaptive model predictive control (MPC)-based path tracking method for heavy-duty forklift AGVs. The movement of heavy-duty forklift-type AGVs is categorized into straight-line and curve-turning motions, corresponding to the straight and curved sections of the reference path, respectively. These sections are segmented based on their curvature. The best driving speeds for straight and curved sections were 1.5 m/s and 0.3 m/s, respectively, while the optimal acceleration rates were 0.2 m/s2 for acceleration and −0.2 m/s2 for deceleration in straight paths and 0.3 m/s2 for acceleration with −0.15 m/s2 for deceleration in curves. Moreover, preferred sampling times, prediction domain, and control domain were determined through simulations at various speeds. Four path tracking methods, including pure tracking, Linear Quadratic Regulator (LQR), MPC, and the velocity-adaptive MPC, were simulated and evaluated under straight-line, turning, and complex double lane change conditions. Field experiments conducted in a warehouse environment demonstrated the effectiveness of the proposed path tracking method. Findings have implications for advancing path tracking control in narrow aisles. [ABSTRACT FROM AUTHOR]

BACKGROUND: Accurate detection of weeds and estimation of their coverage is crucial for implementing precision herbicide applications. Deep learning (DL) techniques are typically used for weed detection and coverage estimation by analyzing information at the pixel or individual plant level, which requires a substantial amount of annotated data for training. This study aims to evaluate the effectiveness of using image‐classification neural networks (NNs) for detecting and estimating weed coverage in bermudagrass turf. RESULTS: Weed‐detection NNs, including DenseNet, GoogLeNet and ResNet, exhibited high overall accuracy and F1 scores (≥0.971) throughout the k‐fold cross‐validation. DenseNet outperformed GoogLeNet and ResNet with the highest overall accuracy and F1 scores (0.977). Among the evaluated NNs, DenseNet showed the highest overall accuracy and F1 scores (0.996) in the validation and testing data sets for estimating weed coverage. The inference speed of ResNet was similar to that of GoogLeNet but noticeably faster than DenseNet. ResNet was the most efficient and accurate deep convolution neural network for weed detection and coverage estimation. CONCLUSION: These results demonstrated that the developed NNs could effectively detect weeds and estimate their coverage in bermudagrass turf, allowing calculation of the herbicide requirements for variable‐rate herbicide applications. The proposed method can be employed in a machine vision‐based autonomous site‐specific spraying system of smart sprayers. © 2024 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

Three-dimensional profilometry tends to be less effective at measuring discontinuous surfaces. To overcome this problem, an optimized profilometry based on fringe projection is proposed in this paper. Due to the limitation of the shooting angle, there are projection blind spots on the surface of discontinuous objects. Since the noises and unwrapping errors are always localized at the projection blind spots, an algorithm is designed to determine the blind spots automatically with the light intensity difference information. Besides, in order to improve the measurement accuracy, a processing scheme is introduced to deal with the local height distortion introduced by the dual-frequency grating profilometry. Lots of measurement tests on various surfaces are carried out to assess the optimized profilometry, and experimental results indicate that the modified profilometry system works more robust with high reliability and accuracy in measuring different kinds of surfaces, especially discontinuous ones.

Wastewater treatment using microbial fuel cells (MFCs) is a potentially useful technology due to its low cost, environmental friendliness, and low sludge production. In this study, a single-chambered air cathode MFC (SCMFC) was developed and investigated regarding its performance and microbial community evolution following nitrate exposure. During long-term operation, diverse denitrifiers accumulated on the electrodes to form a denitrifying MFC (DNMFC) with stable activity for nitrate reduction. The DNMFC presented considerably higher electroactivity, stability, and denitrification rates than the SCMFC. Though energy recovery decreased in the DNMFC by partial organics utilized for heterotrophic denitrification, the electron transfer efficiency increased. Geobacter as the absolutely dominant genus in the SCMFC anode was eliminated and replaced by Azonexus and Pseudomonas in the DNMFC. Furthermore, the biomass of Pseudomonas (151.0 ng/μL) in the DNMFC cathode was five-fold higher than that in the SCMFC, although the bacterial community compositions were quite similar. The DNMFC with highly abundant Pseudomonas exhibited much better performance in terms of electrochemical activity and nitrate removal. The evolution process of functional bacteria from the SCMFC to the DNMFC comprehensively reveals the significant role of denitrifying electroactive bacteria in a bioelectrochemical system for nitrogen-containing wastewater treatment. [ABSTRACT FROM AUTHOR]

The genetic information of plasma total‐exosomes originating from tissues have already proven useful to assess the severity of coronary artery diseases (CAD). However, plasma total‐exosomes include multiple sub‐populations secreted by various tissues. Only analysing the genetic information of plasma total‐exosomes is perturbed by exosomes derived from other organs except the heart. We aim to detect early‐warning biomarkers associated with heart‐exosome genetic‐signatures for acute myocardial infarction (AMI) by a source‐tracking analysis of plasma exosome. The source‐tracking of AMI plasma total‐exosomes was implemented by deconvolution algorithm. The final early‐warning biomarkers associated with heart‐exosome genetic‐signatures for AMI was identified by integration with single‐cell sequencing, weighted gene correction network and machine learning analyses. The correlation between biomarkers and clinical indicators was validated in impatient cohort. A nomogram was generated using early‐warning biomarkers for predicting the CAD progression. The molecular subtypes landscape of AMI was detected by consensus clustering. A higher fraction of exosomes derived from spleen and blood cells was revealed in plasma exosomes, while a lower fraction of heart‐exosomes was detected. The gene ontology revealed that heart‐exosomes genetic‐signatures was associated with the heart development, cardiac function and cardiac response to stress. We ultimately identified three genes associated with heart‐exosomes defining early‐warning biomarkers for AMI. The early‐warning biomarkers mediated molecular clusters presented heterogeneous metabolism preference in AMI. Our study introduced three early‐warning biomarkers associated with heart‐exosome genetic‐signatures, which reflected the genetic information of heart‐exosomes carrying AMI signals and provided new insights for exosomes research in CAD progression and prevention. [ABSTRACT FROM AUTHOR]

Hepatitis B virus (HBV) has been classified into ten genotypes (A-J). Genotype B (HBV/B) is divided into nine subgenotypes (B1-B9), each with specific geographical predominance. Some reference sequence of HBV/B subgenotypes are currently in use, but these sequences have defects, being insufficient to represent the reference of individual subgenotype. The aim of this study was to establish a more representative reference of HBV/B subgenotypes in different regions. Full genomic sequences of HBV/B were obtained from GenBank and compartmentalized into genomic subtypes. The homology between our established HBV/B subgenotype references was evaluated at the nucleotide level. We established reference strains for B1-Japan, B2-China, B3-Indonesia, B4-Vietnam, B6-North America, B7-Indonesia and B9-Southeast Asia. Fractional significant mutation sites of the strains that were established were observed in the BCP/Pre-C regions. We calculated the genetic divergence time from the most recent common ancestor of HBV/B pedigree. The reference sequences established in the study provide reference standards for studies on molecular characterization, virology and pathogenesis of HBV/B.

Background: Cornichon homolog 4 (CNIH4) belongs to the CNIH family. It functions as an oncogene in many tumors. However, CNIH4's significance in the immune landscape and its predictive potential in cervical cancer (CESC) is unexplored. Methods: CNIH4 levels and its effect on the survival of patients with CESC were evaluated using data retrieved from The Cancer Genome Atlas (TCGA). The oncogenic effect of CNIH4 in CESC was determined using small interfering RNA‐mediated transfected cell lines and tumorigenesis experiments in animal models. Results: Higher expression of CNIH4 was found in advanced tumor and pathological stages, as well as lymph node metastasis. CNIH4 expression correlated positively with the infiltration of macrophages M2 and resting dendritic cells into the affected tissue. Additionally, functional enrichment of RNA‐sequencing of CNIH4‐knocked down CESC cell lines showed the association of CNIH4 to the PI3K‐Akt signaling pathway. Single‐sample gene set enrichment analysis highlighted several immune pathways that were elevated in the CESC samples with enhanced levels of CNIH4, including Type‐I and Type‐II IFN‐response pathways. The impact of CNIH4 on drug sensitivity was further assessed using the GDSC database. As CNIH4 is linked to the immune landscape in CESC, this study determined a four‐gene risk prediction signature utilizing CNIH4‐related immunomodulators. The risk score quantified from the prediction signature was an independent predictive indicator in CESC. Receiver operating characteristic curve analysis verified the good predictive ability of the four‐gene signature in TCGA‐CESC cohort. Thus, the CNIH4‐related model showed potential as an auxiliary TNM staging system tool. Conclusion: CNIH4 may be an effective predictive biomarker for patients with cervical cancer, thus providing new ideas and research directions for CESC. [ABSTRACT FROM AUTHOR]

The rapid development of multi-satellite formations requires inter-satellite radio frequency (RF) measurement to be both precise and scalable. The navigation estimation of multi-satellite formations using a unified time reference demands the simultaneous RF measurement of the inter-satellite range and time difference. However, high-precision inter-satellite RF ranging and time difference measurements are investigated separately in existing studies. Different from the conventional two-way ranging (TWR) method, which is limited by its reliance on a high-performance atomic clock and navigation ephemeris, asymmetric double-sided two-way ranging (ADS-TWR)-based inter-satellite measurement schemes can eliminate such reliance while ensuring measurement precision and scalability. However, ADS-TWR was originally proposed for ranging-only applications. In this study, by fully exploiting the time-division non-coherent measurement characteristic of ADS-TWR, a joint RF measurement method is proposed to obtain the inter-satellite range and time difference simultaneously. Moreover, a multi-satellite clock synchronization scheme is proposed based on the joint measurement method. The experimental results show that when inter-satellite ranges are hundreds of kilometers, the joint measurement system has a centimeter-level accuracy for ranging and a hundred-picosecond-level accuracy for time difference measurement, and the maximum clock synchronization error was only about 1 ns. [ABSTRACT FROM AUTHOR]

This study aimed to establish a complement tolerance test (CTT) as a marker of protein fragility and discuss its clinical significance. Total complement activity (TCA) of serum was measured using a self-hemolysis colorimetric method. Human O-erythrocytes and rabbit anti-human O-erythrocyte antibodies were used to replace sheep erythrocytes and the corresponding hemolysin for the hemolysis test, respectively. The antigen-antibody specific binding activated the classical pathway of complement, generating a membrane attack complex, and the red blood cells rupture. A CTT was established to measure complement heat tolerance according to the sensitivity of complement proteins to temperature, which was calculated according to differences in TCA at different temperatures. The smaller the CTT the stronger the complement resistance to heat. The method was applied to the detection of diabetic patients and healthy controls. The mean value of CTT (mean) = 0.063 ± 0.003 with a coefficient of variation of 4.8% for the same specimen tested for complementary thermal resistance on 5 consecutive days, which is a good stability of the assay. Application of CTT on samples from patients with different ages revealed significantly higher mean CTT values for elderly patients (≥60-years old) relative to those for younger patients (20–40-years old) (p < 0.05). In addition, the mean CTT values for diabetic patients were significantly higher than those for healthy patients (p < 0.001). We successfully established a method that uses complement thermal resistance as a marker of protein fragility, with the results demonstrating the ability of the CTT identify age- and disease-related variations in patient samples and its potential efficacy for clinical application. [ABSTRACT FROM AUTHOR]

Machine vision‐based weed detection relies on features such as plant colour, leaf texture, shape, and patterns. Drought stress in plants can alter leaf colour and morphological features, which may in turn affect the reliability of machine vision‐based weed detection. The objective of this research was to evaluate the feasibility of using deep convolutional neural networks for the detection of Florida pusley (Richardia scabra L.) growing in drought stressed and unstressed bahiagrass (Paspalum natatum Flugge). The object detection neural networks you only look once (YOLO)v3, faster region‐based convolutional network (Faster R‐CNN), and variable filter net (VFNet) failed to effectively detect Florida pusley growing in drought stressed or unstressed bahiagrass, with F1 scores ≤0.54 in the testing dataset. Nevertheless, the use of the image classification neural networks AlexNet, GoogLeNet, and Visual Geometry Group‐Network (VGGNet) was highly effective and achieved high (≥0.97) F1 scores and recall values (≥0.98) in detecting images containing Florida pusley growing in drought stressed or unstressed bahiagrass. Overall, these results demonstrated the effectiveness of using an image classification convolutional neural network for detecting Florida pusley in drought stressed or unstressed bahiagrass. These findings illustrate the broad applicability of these neural networks for weed detection. [ABSTRACT FROM AUTHOR]

Precision spraying can significantly reduce herbicide input for turf weed management. A major challenge for autonomous precision herbicide spraying is to accurately and reliably detect weeds growing in turf. Deep convolutional neural networks (DCNNs), an important artificial intelligent tool, demonstrated extraordinary capability to learn complex features from images. The feasibility of using DCNNs, including various image classification or object detection neural networks, has been investigated to detect weeds growing in turf. Due to the high level of performance of weed detection, DCNNs are suitable for the ground-based detection and discrimination of weeds growing in turf. However, reliable weed detection may be subject to the influence of weeds (e.g., biotypes, species, densities, and growth stages) and turf factors (e.g., turf quality, mowing height, and dormancy vs. non-dormancy). The present review article summarizes the previous research findings using DCNNs as the machine vision decision system of smart sprayers for precision herbicide spraying, with the aim of providing insights into future research. [ABSTRACT FROM AUTHOR]

BACKGROUND: Precision spraying of synthetic herbicides can reduce herbicide input. Previous research demonstrated the effectiveness of using image classification neural networks for detecting weeds growing in turfgrass, but did not attempt to discriminate weed species and locate the weeds on the input images. The objectives of this research were to: (i) investigate the feasibility of training deep learning models using grid cells (subimages) to detect the location of weeds on the image by identifying whether or not the grid cells contain weeds; and (ii) evaluate DenseNet, EfficientNetV2, ResNet, RegNet and VGGNet to detect and discriminate multiple weed species growing in turfgrass (multi‐classifier) and detect and discriminate weeds (regardless of weed species) and turfgrass (two‐classifier). RESULTS: The VGGNet multi‐classifier exhibited an F1 score of 0.950 when used to detect common dandelion and achieved high F1 scores of ≥0.983 to detect and discriminate the subimages containing dallisgrass, purple nutsedge and white clover growing in bermudagrass turf. DenseNet, EfficientNetV2 and RegNet multi‐classifiers exhibited high F1 scores of ≥0.984 for detecting dallisgrass and purple nutsedge. Among the evaluated neural networks, EfficientNetV2 two‐classifier exhibited the highest F1 scores (≥0.981) for exclusively detecting and discriminating subimages containing weeds and turfgrass. CONCLUSION: The proposed method can accurately identify the grid cells containing weeds and thus precisely locate the weeds on the input images. Overall, we conclude that the proposed method can be used in the machine vision subsystem of smart sprayers to locate weeds and make the decision for precision spraying herbicides onto the individual map cells. © 2022 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

Low-orbit micro-satellite technology has developed rapidly in recent years due to its advantages of low time delay, low cost and short research period. However, among the existing inter-satellite routing algorithms, the classical flooding and greedy algorithms and their derivatives also have some limitations. The path delay calculated by the flooding algorithm is small but the calculation is large, while the greedy algorithm is the opposite. In this paper, a balanced inter-satellite routing algorithm based on partition routing is proposed. This paper presents the simulation experiments for the following indexes of the classic inter-satellite routing algorithms and the balanced partition routing algorithm: computation complexity, single-node computation pressure, routing path delay, path delay variance (data in Topo table satisfy μ   = 5 , σ 2 = 10 ). The results reveal that the balanced partition routing algorithm achieves better performance. In this paper, two optimization directions of the balanced partition routing algorithm are simulated under conditions that the data in the Topo table satisfy μ   = 5 , σ 2 = 6, σ 2 = 10 and σ 2 = 15 , respectively, when comparing their performance indicators. The experiments show that these two optimization methods can be adapted to various application scenarios and can further reduce the hardware cost of satellite nodes. [ABSTRACT FROM AUTHOR]

The large amounts ofheavy metal from landscape wastewater have become serious problems of environmental pollution and risks for human health. The development of efficient novel adsorbent is a very important for treatment of heavy metal. The functionalized porous nanoscale Fe3O4 particles supported biochar from peanut shell (PS-Fe3O4) for removal of Pb(II) ions from aqueous solution was investigated. The characterization of PS-Fe3O4 composites showed that biochar was successfully coated with porous nanoscale Fe3O4 particles. The pseudo second-order kinetic model and Langmuir model were more fitted for describing the adsorption process of Pb(II) ions in solution. The adsorption process of Pb(II) ions removal by PS-Fe3O4 composites was a spontaneous and endothermic process. The adsorption mechanisms of Pb(II) ions by PS-Fe3O4 composites were mainly controlled by the chemical adsorption process. The maximum adsorption capacity of Pb(II) ions removal in solution by PS-Fe3O4 composites reached 188.68 mg/g. The removal mechanism included Fe–O coordination reaction, co-precipitation, complexation reaction, and ion exchange. PS-Fe3O4 composites were thought as a low-cost, good regeneration performance, and high efficiency adsorption material for removal of Pb(II) ions in solution. [ABSTRACT FROM AUTHOR]

BACKGROUND Precision weed control in vegetable fields can substantially reduce the required weed control inputs. Rapid and accurate weed detection in vegetable fields is a challenging task due to the presence of a wide variety of weed species at various growth stages and densities. This paper presents a novel deep‐learning‐based method for weed detection that recognizes vegetable crops and classifies all other green objects as weeds. RESULTS: The optimal confidence threshold values for YOLO‐v3, CenterNet, and Faster R‐CNN were 0.4, 0.6, and 0.4/0.5, respectively. These deep‐learning models had average precision (AP) above 97% in the testing dataset. YOLO‐v3 was the most accurate model for detection of vegetables and yielded the highest F1 score of 0.971, along with high precision and recall values of 0.971 and 0.970, respectively. The inference time of YOLO‐v3 was similar to CenterNet, but significantly shorter than that of Faster R‐CNN. Overall, YOLO‐v3 showed the highest accuracy and computational efficiency among the deep‐learning architectures evaluated in this study. CONCLUSION: These results demonstrate that deep‐learning‐based methods can reliably detect weeds in vegetable crops. The proposed method avoids dealing with various weed species, and thus greatly reduces the overall complexity of weed detection in vegetable fields. Findings have implications for advancing site‐specific robotic weed control in vegetable crops. [ABSTRACT FROM AUTHOR]

The current tumor-node-metastasis (TNM) system is limited in predicting the survival and guiding the treatment of hepatocellular carcinoma (HCC) patients since the TNM system only focuses on the anatomical factors, regardless of the intratumoral molecule heterogeneity. Besides, the landscape of intratumoral immune genes has emerged as a prognostic indicator. The mediator complex subunit 8 (MED8) is a major polymerase regulator and has been described as an oncogene in renal cell carcinoma, but its pathophysiological significance of HCC and its contribution to the prognosis of HCC remain unclear. Here, we aimed to discuss the expression profile and clinical correlation of MED8 in HCC and construct a predictive model based on MED8-related immunomodulators as a supplement to the TNM system. According to our analyses, MED8 was overexpressed in HCC tissues and increased expression of MED8 was an indicator of poor outcome in HCC. The knockdown of MED8 weakened the proliferation, colony forming, and migration of HepG2 and Huh7 cells. Subsequently, a predictive model was identified based on a panel of three MED8-related immunomodulators using The Cancer Genome Atlas (TCGA) database and further validated in International Cancer Genome Consortium (ICGC) database. The combination of the predictive model and the TNM system could improve the performance in predicting the survival of HCC patients. High-risk patients had poor overall survival in TCGA and ICGC databases, as well as in subgroup analysis with early clinicopathology classification. It was also found that high-risk patients had a higher probability of recurrence in TCGA cohort. Furthermore, low-risk score indicated a better response to immunotherapy and drug therapy. This predictive model can be served as a supplement to the TNM system and may have implications in prognosis stratification and therapeutic guidance for HCC. [ABSTRACT FROM AUTHOR]

BACKGROUND: In‐field weed detection in wheat (Triticum aestivum L.) is challenging due to the occurrence of weeds in close proximity with the crop. The objective of this research was to evaluate the feasibility of using deep convolutional neural networks for detecting broadleaf weed seedlings growing in wheat. RESULTS: The object detection neural networks, including CenterNet, Faster R‐CNN, TridenNet, VFNet, and You Only Look Once Version 3 (YOLOv3) were insufficient for weed detection in wheat because the recall never exceeded 0.58 in the testing dataset. The image classification neural networks including AlexNet, DenseNet, ResNet, and VGGNet were trained with small (5500 negative and 5500 positive images) or large training datasets (11 000 negative and 11 000 positive images) and three training image sizes (200 × 200, 300 × 300, and 400 × 400 pixels). For the small training dataset, increasing image sizes decreased the F1 scores of AlexNet and VGGNet but generally increased the F1 scores of DenseNet and ResNet. For the large training dataset, no obvious difference was detected between the training image sizes since all neural networks exhibited remarkable classification accuracies with high F1 scores (≥0.96). All image classification neural networks exhibited high F1 scores (≥0.99) when trained with the large training dataset and the training images of 200 × 200 pixels. CONCLUSION: CenterNet, Faster R‐CNN, TridentNet, VFNet, and YOLOv3 were insufficient, while AlexNet, DenseNet, ResNet, and VGGNet trained with a large training dataset were highly effective for detection of broadleaf weed seedlings in wheat. © 2021 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

Almost all existing studies on inter-satellite radio-frequency (RF) measurement have focused on two-satellite formations. Although some frequency division multiple access and code division multiple access multisatellite RF measurement schemes have been proposed, their poor scalability does not satisfy the inter-satellite measurement requirements of multisatellite formations, especially large-scale formations. Two-way ranging (TWR), which is based on a time division mechanism, is an effective solution that has been used for inter-satellite links in the global positioning system and Beidou navigation constellations. However, the high measurement accuracy achieved with TWR in these navigation constellations is heavily reliant on high-performance atomic clocks and the assistance of navigation ephemeris, which are not available on microsatellite platforms. This work focuses on a scalable multisatellite measurement scheme that adopts a distributed broadcast-based time division multiple access mechanism as the media access control layer and uses an asymmetric double-side TWR method as the physical layer. The measurement performance of the proposed scheme is evaluated through in-depth theoretical modeling, simulation verification, and experimental validation, along with a comprehensive comparison with the conventional TWR method. The experimental results show that centimeter-level measurement accuracy can be achieved with the proposed scheme when only a common miniaturized frequency source is used. This accuracy level is two orders of magnitude better than that of the TWR method, and thus satisfies the application requirements of general large-scale microsatellite formations. [ABSTRACT FROM AUTHOR]

This paper investigates the pull-in dynamics of single-sided and double-sided parallel-plate (SSPP and DSPP) structures employed in MEMS devices for acceleration measurement. A lumped electromechanical model considering a variable damping coefficient and external acceleration is developed for studying their pull-in dynamical behavior. The critical pull-in displacement and pull-in voltage for both parallel-plate structures operated with external acceleration are derived based on the static and dynamical analysis of the proposed model, which are in good agreement with the experimental results. The derived closed-form solutions are shown to be capable of predicting the pull-in behavior of both parallel-plate structures in an overdamped system. Two MEMS parallel-plate structures exhibit distinct pull-in characteristics due to their distinct configurations. The commonly used SSPP structure for MEMS devices is shown to have a relatively linear response to the external acceleration in terms of the critical pull-in displacement and pull-in voltage. However, the DSPP structure exhibits much larger yet nonlinear sensitivity of both the critical pull-in displacement and the critical pull-in voltage to a small range of external acceleration, which has the potential of developing high-sensitivity accelerometers whenever the sensitivity nonlinearity is compensated. Moreover, the effects of pressure are characterized for both DSPP and SSPP devices, which affects the difference between the critical static and dynamic pull-in voltages. The SSPP structure is shown to have the critical pull-in voltage proportional to the logarithmic magnitude of the pressure, while that of the DSPP structure do not vary linearly with respect to the logarithmic magnitude of the pressure. [ABSTRACT FROM AUTHOR]

Existing high‐precision inter‐satellite radio frequency (RF) measurement systems were mostly based on two‐satellite formations, and there lacks further research on measurement schemes for multi‐satellite formations. The frequency division multiple access (FDMA) and the code division multiple access based schemes are widely used, but not applicable for distributed applications due to their poor scalability. The time division multiple access (TDMA) based scheme can overcome this weakness, and has been applied in the global positioning system (GPS) IIR/IIF inter‐satellite link. However, the atomic clock used in GPS is not suitable for microsatellites. If a miniaturised frequency source instead of the atomic clock is utilised, the two way ranging (TWR) method adopted in this system would encounter a sharp decrease of measurement accuracy. To this end, this Letter aims to propose a novel TDMA based distributed RF measurement scheme for multi‐microsatellite formations. A TDMA based distributed broadcast protocol is employed in the media access control layer, closely integrated with the asymmetric double‐sided two‐way ranging method adopted in the physical layer. Numerical and simulation results demonstrate the superiority of the proposed scheme over the conventional TDMA scheme. The proposed scheme can be recommended for future multi‐microsatellite formation missions. [ABSTRACT FROM AUTHOR]

Amino alcohol alkaloids are the active components in the lateral root of Aconitum carmichaelii Debx. (Fuzi), and they have a variety of pharmacological activities. However, the chemical fingerprints of the ester alkaloids reported to date were mainly obtained from high‐performance liquid chromatography coupled with ultraviolet detection, and it is difficult to obtain information about amino alcohol alkaloids in Fuzi from such chromatograms. In this paper, a comprehensive fingerprinting method was established using high‐performance liquid chromatography coupled with an evaporative light‐scattering detector for the simultaneous quantitative analysis of both the amino alcohol alkaloids and ester alkaloids. A total of 42 samples of Fuzi from four production areas were analyzed by constructing high‐performance liquid chromatography fingerprints. Then, the quantitative results of the chemical fingerprints combined with chemometrics methods were employed to reveal the factors affecting the geo‐authentic Fuzi and to determine characteristic components that can be used to identify these samples. The results indicated distinct differences in the alkaloid contents among samples from the four regions; the geographical origin may be the primary factor affecting the geo‐authentic Fuzi, and 15 major components (including songorine, neoline, and hypaconitine, which were quantitatively determined) were found to be characteristic components for the discrimination of Fuzi samples from various regions. Neoline might be a critical component for identifying geo‐authentic Fuzi. This approach is convenient, reproducible and provides a promising method for the quality evaluation of Fuzi. [ABSTRACT FROM AUTHOR]

Precision application of specific herbicides to susceptible weeds can significantly save herbicide. This is the first study evaluating the performances of precision sprayer for weed control in turf based on the herbicide weed control spectrum in field conditions. The results showed that EfficientNet-v2 and ResNet never fall below 0.992 for discriminating and detecting the grid cells encompassing weeds susceptible to ACCase-inhibiting and synthetic auxin herbicides. MCPA, a synthetic auxin herbicide, is used to evaluate the performance of the developed smart sprayer for precision control of broadleaf weeds in dormant bermudagrass turf. The developed smart sprayer prototype detected and sprayed every grid cell containing broadleaf weeds in field experiments. Compared to the broadcast application, precision spraying of MCPA provided the same level of control of broadleaf weeds. By 18 days after treatment (DAT), the nontreated control had 13 weeds no. m−2, while the plots that received broadcast and precision spraying had 0 and 1 broadleaf weed plant no. m−2, respectively. Precision herbicide application according to the herbicide weed control spectrum (HWCS) with the developed smart sprayer provided the same level of broadleaf weed control and could save more herbicides compared to an approach without discriminating weed species. Overall, these findings clearly indicated that the developed smart sprayer prototype could effectively detect, discriminate, and spray herbicides onto the grid cells containing target weeds based on the HWCS. • Discriminating weed species based on their susceptibility to herbicides allows targeted herbicide application. • A smart sprayer prototype was designed and developed for precision herbicide application on turf. • Precision herbicide application with the smart sprayer achieved weed control equivalent to broadcast application. [ABSTRACT FROM AUTHOR]

Increasingly high bandwidths are desired in modern navigation and autonomous RF ranging signal design to achieve better pseudo-noise (PN) ranging accuracy. However, fulfilling the Nyquist criterion for such high-bandwidth signals would be a challenging issue for system design. Considering that the Nyquist criterion is not a necessary condition for PN code tracking, sub-Nyquist sampling can be adopted as a feasible solution to this issue. Nevertheless, it is at the expense of an increase in thermal noise caused by tracking error. This problem is particularly prominent for tracking weak signals such as navigation signals, which limits the applicability of this sampling scenario. As far as high-precision autonomous PN ranging is concerned, however, the tracking error due to digital implementation is not negligible and even becomes the dominating factor instead of thermal noise. This means that the performance loss incurred by sub-Nyquist sampling can be eliminated under high-signal-to-noise ratio conditions. Exploiting this fact, this Letter proposes a novel PN code tracking approach based on sub-Nyquist and non-commensurate sampling, providing a solution to achieving high-ranging accuracy while breaking the bounds of the Nyquist sampling theorem without performance degradation. The approach is very attractive for precise PN ranging applications demanding miniaturised implementation. [ABSTRACT FROM AUTHOR]

This paper proposes two methods to mitigate the zero bias of noncommensurate sampling based code-tracking loops to significantly improve the pseudonoise ranging accuracy. For the compensation-based method, a set of algorithms is developed to directly calculate the zero bias which is then removed from the range measurement. For the compensation-free method, a special category of sampling ratios are selected such that the zero bias is self-cancelled. Simulations validate the effectiveness of the proposed methods. [ABSTRACT FROM PUBLISHER]

The n-Si/i-p-i SiGe/n-Si structure, which is the typical npn SiGe heterojunction bipolar transistor (HBT) layer structure, has been investigated. The structure was grown by ultra-high-vacuum chemical molecular epitaxy, and analyzed by cross-sectional transmission electron microscopy and secondary ion mass spectroscopy. The structure was also used to fabricate the microwave power SiGe HBT.

Owing to membrane penetration, a novel route of nitrogen removal was proposed in a dual-chamber microbial fuel cell with a proton exchange membrane (PEM). The results showed that NH 4 +-N rapidly migrated across PEM with a mass transfer coefficient (K A) of 1.79 ± 0.51 × 10−4 cm s−1, 50% of which was oxidized to NO 3 −-N in the cathode chamber, then the remainder being eliminated by short-cut nitrification/denitrification. Meanwhile, NO 3 −-N went across the PEM again with a low K A of 5.50 ± 0.24 × 10−6 cm s−1, and was subsequently reduced via anodic denitrification. In the anode, the functional microorganisms were divided into exoelectrogenic bacteria (46.2%) and denitrifying bacteria (37.3%), while the dominated bacteria were mainly affiliated with nitrifying bacteria (19.6%) and aerobic denitrifying bacteria (52.9%) in the cathode. These findings provide a new insight into nitrogen removal during bioelectrochemical treatment of actual wastewater. [Display omitted] • All forms of nitrogen can migrate across PEM in dual-chamber MFCs. • NH 4 + is removed by complete nitrification and short nitrification/denitrification. • Simultaneous nitrification and denitrification occur aerobically in the cathode. • Anodic denitrification is the main route of nitrate reduction. [ABSTRACT FROM AUTHOR]