Search

Background. Head and neck squamous cell carcinoma (HNSC) is one of the most common malignancies, and identification of HNSC biomarkers is critical. LIM Domain And Actin Binding 1 (LIMA1) is involved in actin cytoskeleton regulation and dynamics. The role of LIMA1 in HNSC is unclear. This is the first study to investigate the expression of LIMA1 in HNSC patients and its prognostic value, potential biological functions, and impact on the immune system. Methods. Gene expression and clinicopathological analysis, enrichment analysis, and immune infiltration analysis were all based on data from The Cancer Genome Atlas (TCGA) with additional bioinformatics analysis. Statistical analysis was performed using TIMER and ssGSEA to analyze the immune response to LIMA1 expression in HNSCs. In addition, Gene Expression Omnibus (GEO), Kaplan–Meier(K-M) survival analysis, and data from the Human Protein Atlas (HPA) were used to validate the results. Results. LIMA1 played a key role as an independent prognostic factor in HNSC patients. GSEA found that LIMA1 is associated with promoting cell adhesion and suppressing immune function. LIMA1 expression was significantly correlated with infiltration of B cells, CD8+ T cells, CD4+ T cells, dendritic cells, and neutrophils and was coexpressed with immune-related genes and immune checkpoints. Conclusion. The expression of LIMA1 is increased in HNSC, and the high expression of LIMA1 is associated with poor prognosis. LIMA1 may affect tumor development by regulating tumor-infiltrating cells in the tumor microenvironment (TME). LIMA1 may be a potential target for immunotherapy.

Background The incidence of sinonasal adenocarcinoma is low, and there are few studies on survival and prognosis. Therefore, we aim to develop and validate a prognostic model for predicting the overall survival of sinonasal adenocarcinoma and provide guidance for clinical management. Methods Patients who were diagnosed as sinonasal adenocarcinoma through Surveillance, Epidemiology, and End Results database between 1975 and 2015 were randomly divided into a training group and validation group. Univariate, multivariate survival analysis was performed to screen independent survival factors. A nomogram was established to predict the overall survival rate of sinonasal adenocarcinoma. Receiver operating characteristic curve and calibration plot were performed to verify the discrimination and accuracy of the model. A decision curve analysis was performed to verify the clinical applicability of the model. Results A total of 423 patients with sinonasal adenocarcinoma were randomly divided into training group (n = 299) and verification group (n = 124). We established and verified the Nomo map including age, marriage, grade, surgery and tumour size. The c-index of Surveillance, Epidemiology, and End Results stage, T stage and this model are 0.635, 0.626 and 0.803, respectively. The survival rate of the high-risk group scored by this model was lower than that of the low-risk group (P Conclusion Our model is considered to be a useful tool for predicting the overall survival of sinonasal adenocarcinoma, with good discrimination and clinical applicability. We hope that this model will help rhinologists to make clinical decisions and manage patients diagnosed with sinonasal adenocarcinoma.

Swift, non-destructive detection approaches should address the problem of insufficient sensitivity when attempting to obtain and perceive live crab information in low-light environments caused by the crab’s phototaxis. We propose a learning-based low-illumination image enhancer (LigED) for effective enhanced lighting and elimination of darkness in images. The camera response function was combined with the reflectance ground-truth mechanism of image decomposition. Self-attention units were then introduced in the reflectance restoration network to adjust the illumination to avoid visual defects, thus jointly strengthening the adaptability of dark-light enhancement and ability to perceive crab information. Convolutional neural network (CNN)-based detection methods can further enhance the algorithm’s robustness to light and adaptability to different environments, which motivated the development of a scalable lightweight live crab detector (EfficientNet-Det0) utilizing the two-stage compound scaling CNN approach. The lightness order error and natural image quality evaluator based on the proposed methods were 251.26 and 11.60, respectively. The quality of average precision detection increased by 13.84–95.40%. The fastest detection speed of a single image was 91.74/28.41 f·s−1 using a common GPU/CPU, requiring only 15.1 MB of storage, which advocates for the utilization of LigED and EfficientNet-Det0 for the efficient detection of underwater live crabs.

Objective To explore the survival value of lymph node dissection (LND) in elderly patients with T3-T4 laryngeal cancer, analyze the risk factors of lymph node metastasis, and construct a preoperative prediction model. Materials and Methods The study included 996 patients aged ≥65 years with laryngectomy confirmed T3-T4 laryngeal cancer queried from Surveillance, Epidemiology and End Results (SEER) database between 2010 and 2017. Propensity score matching (PSM) was applied to balance the effects of confounding factors. Kaplan–Meier (K–M) analysis and competitive risk model were used to compare the overall survival (OS) and cancer-specific survival (CSS) between LND and no-LND (N-LND) group. Combined with risk factors of multivariate logistic regression, a nomogram was built to predict lymph node metastasis preoperatively. The performance was assessed in the training set and the validation set, and internal validation was assessed. Results Among the cohort, 822 patients underwent LND and 410 patients had positive lymph nodes. The OS and CSS of patients who underwent LND were not better than that of N-LND patients (P>0.05). The prognosis of patients with lymph node metastases was significantly worse than that of negative patients (P 5cm and grade 3–4 classification were associated with significantly greater odds of lymph node metastasis. The nomogram showed favorable predictive efficacy and good calibration (in the training cohort C-index=0.700; in the validation cohort C-index=0.721). Conclusion For elderly patients with T3-T4 laryngeal cancer, LND did not bring significant survival values. Supraglottis cancer, tumor size >5cm and grade 3–4 classification were independent risk factors of lymph node metastasis, which means poor prognosis. The nomogram developed was an easy-to-use tool for lymph node prediction.

This study focuses on establishing apple viscoelastic finite element complex model to estimate apple stress variation during grasping with its own characteristics of constant velocity and continuous energy input. The deformation resistance of apple is calculated by MATLAB based on apple viscoelastic Burgers model and stage dynamic characteristics of grasping process, and used for defining the load function in ANSYS. Then, the layered solid model consisted by skin, flesh, core of apple is established in ANSYS and simulation carries out. Solution results of designed model show that maximum equivalent stress and maximum deformation of apple are 0.42 MPa and 0.72 mm respectively at grasping velocity of 3 mm/s, after that apple skin starts to have grasping damage prior to the flesh and core of apple due to plastic deformation occurring. Lastly, the actual experiment to verify the reliability of designed model was carried out. This study proposes an effective complex model to estimate apple stress from the perspective of grasping velocity by general finite element software.

Aiming at the small sample with high-feature dimension and few numbers will cause a serious problem if simply using the traditional Elman neural network to deal with the small sample; these problems include poor learning ability, the redundancy structure, and incomplete training; these defects will result in lower operating efficiency and poor recognition precision. In this paper, combining the theory of partial least squares (PLS) and genetic algorithm (GA), as well as the nature of Elman neural network, and an optimized Elman neural network classification algorithm based on PLS and GA (PLS-GA-Elman) is established. The new algorithm reduces the feature dimension of small samples by PLS, the relatively ideal low-dimensional data is obtained, and the purpose reduces the neural network’s inputs and simplifies its structure. Using GA to optimize the connection weights, threshold values, and the number of hidden neurons and adopting the optimized way of encoding respectively and evolving simultaneously can improve the neural network incomplete training condition, leading to fewer number of samples and improvement in training speed and generalization ability; this ensures the optimal Elman neural network algorithm. A new algorithm based on twice consecutive optimization was the basis for a precise classification model. The results of experimental analysis illustrate that operating efficiency and classification precision of the new algorithm have been improved.

In the green pepper harvesting robot, the color of green pepper is similar to that of leaves, which makes it difficult to recognize the green pepper target. In order to solve this problem, a green pepper recognition method based on least-squares support vector machine optimized by the improved particle swarm optimization (IPSO-LSSVM) is proposed in this paper. Firstly, the green pepper images are segmented by K-Means method under the Lab color space, and the segmentation images of the target and background are obtained. The processed green pepper image was divided into training and testing samples. Then, the shape and texture features of green pepper targets are extracted separately from the training sample using the hu invariant moment and Tamura texture feature. Meanwhile, in order to reduce the complexity of data calculations and improve the efficiency, the extracted feature vectors are normalized. The feature vector is used as the input eigenvector of the least-squares support vector machine (LSSVM). The particle swarm optimization algorithm is used to obtain the optimal regularization parameter and the kernel function width. In order to maintain the particle activity, the mutation strategy is introduced to improve the particle swarm optimization algorithm. The experimental results show that the recognition rate of IPSO-LSSVM is higher than that of other methods, and the recognition accuracy is 89.04%. It could meet the requirements of green pepper identification.

The bearingless induction motor (BIM) system has the characteristics of non-linear, strongly coupled, and highly accurate. To achieve the stable and accurate control of BIM, the methods of direct torque control (DTC) and direct suspension force control (DSFC) based on space vector modulation (SVM) are introduced first. However, the dynamic performance and uncertain anti-jamming robustness of the system need to be further researched. Based on the BIM control system, the active disturbance rejection control (ADRC) technology is selected in this paper. The total disturbance of the system is estimated in real time by using the extended state observer (ESO). According to the estimated value of the ESO, the disturbance can be compensated by the ADRC in time to eliminate the steady-state tracking error to achieve good anti-interference performance. The ADRC-based direct torque and suspension force control system for the BIM is designed and simulated by the MATLAB software. The simulation and experiment results show that the ADRC technology can effectively suppress the impact of load disturbance, reduce the overshoot, and have strong robustness.

The skins of most mature apple fruits are incompletely red and also include green and pale yellow color, which increases the difficulty of fruit detection by machine vision. A detection method based on color and shape features is proposed for this kind of apple fruits. Simple linear iterative clustering (SLIC) is adapted to segment images taken in orchards into super-pixel blocks. The color feature extracted from blocks is used to determine candidate regions, which can filter a large proportion of non-fruit blocks and improve detection precision. Next, the histogram of oriented gradient (HOG) is adopted to describe the shape of fruits, which is applied to detect fruits in candidate regions and locate the position of fruits further. The proposed method was tested by images taken under different illuminations. The average values of recall, precision, and F1 reach 89.80%, 95.12%, and 92.38% respectively. The performance of detecting fruits covered at different levels is also tested. The values of the recall are all more than 85%, which indicates that proposed method can detect a great part of covered fruits. Compared with pedestrian detection method and faster region-based convolutional neural network (RCNN), the proposed method has the best performance and higher than faster RCNN slightly. However, the proposed method is not robust to noise and its elapsed time of one image is 1.94 s and less than faster RCNN.

Under the complex agricultural operation environment, reliable navigation system is the basic guarantee to realize the agricultural robot automated operation. This study focuses on improving navigation accuracy and control accuracy and conducts related research on autonomous navigation control of agricultural robots. This article discusses the advantages of using strict convergence criteria and combining Sage–Husa adaptive filtering with strong tracking Kalman filtering and then proposes an improved adaptive Kalman filter algorithm. The new algorithm can effectively suppress the filter divergence, improve the dynamic performance of the filter, and ensure its better filtering accuracy and strong adaptive ability to improve navigation accuracy of GPS. Further variable structure switching method is used to prevent proportional integral differential (PID) controller integral saturation phenomenon, which effectively solves the controller over-saturation problem. And combining this method with an improved adaptive filtering algorithm not only can effectively inhibit control interference but also achieve the anti-saturation effect, thereby enhancing the stability and accuracy of the control system. Finally, the simulation and experiment of the new method show that the proposed method greatly improves the ability of the filter to suppress divergence and control precision.

Feeding is an important and also a heavy work in river crab farming, uniform feeding is necessary for raising river crab on the whole pond. The overall scheme of automatic bait casting system carried by paddlewheel catamaran is proposed based on RTK-GPS navigation positioning device. The feeding device equipped with the controllable flow velocity of bait feeder, distance adjustable thrower and bait weight measurable device which could perform variable feeding is designed to solve the problem of nonuniform distribution of the feed. The hardware and software system of automatic feeding device are designed. Then the calculation and simulation for the modeling of the feeding device are pefromed. The test results show that the system linear track positioning accuracy is less than 30 cm, and automatic continuous feeding speed is up to 240 m2/min. The results of this study provide technology reference for further study of automatic uniform feeding on the whole pond in river crab aquaculture.

In order to improve the harvesting efficiency of apple harvesting robot, this article presents an apple recognition method based on pulse coupled neural network and genetic Elman neural network (GA-Elman). Firstly, we use pulse coupled neural network to segment the captured 150 images, respectively, and extract six color features of R, G, B, H, S, and I and 10 shape features of circular variance, density, the ratio of perimeter square to area, and Hu invariant moments of segmented images, and these 16 features are considered as the inputs of Elman neural network. In order to overcome some defects of Elman neural network, such as, trapping local minimum easily and determining the number of hidden neurons difficultly; in this article, genetic algorithm is introduced to optimize it, and new optimization way is designed, that is, the connection weights and number of hidden neurons separate encoding and evolving simultaneously, in the process of structural evolution at the same time the learning of connection weights is completed, and then the operating efficiency and recognition precision of Elman model are improved. In order to get more precision neural network, and avoid the influence of fruit recognition caused by branches or leaves shadow, apple along with branches and leaves is allowed to train. The results of experiments show that compared with the traditional back-propagation, Elman neural network, and other two recognition algorithms of obscured fruit. the genetic Elman neural network algorithm is the optimal method which successful training rate can reach to 100%, recognition rate of overlapping fruit and obscured fruit can reach to 88.67% and 93.64%, respectively, and the total recognition rate reaches to 94.88%.

Cabled intelligent systems bring with them the complexities of structures, the complications of data measurements and transmission, and a limited scale of application. A wireless sensor network is used to eliminate these disadvantages, however reliability of data transmission and energy saving in a wireless sensor network are two challenges that still need to be addressed. The design information on three types of nodes in a wireless sensor network is described in detail. Tree topology for WSN is adopted to decrease the packet loss rate and improve reliability of data transmission. Allowing sensor nodes to sleep and reorganising the data frames are the two approaches used to achieve energy-saving. The experimental results demonstrate the usefulness of these approaches in solving the challenges.

In order to address the issues of nonuniform feeding and high labor cost plaguing the process of farming Chinese river crabs, the present study proposes a multifunctional automatic river crabs feeding boat based on Advanced RISC Machine (ARM) and Global Positioning System / Inertial Navigation System (GPS/INS) integrated navigation. This paper proposes a new calculation method based on real time point insertion. This method calculates the current target position of the boat in the real time according to the position of the boat and the turning points of the current route. A new turning and route-switching strategy is also presented in this paper to improve the ship's operational efficiency and prevent the ship from veering off the target route due to its high speed. Considering the boat's unique movement characteristics including non-linearity, large delay and underdamped nature, a route-speed dual-loop control algorithm is designed based on fuzzy Proportion Integration Differentiation (PID) method. Through analyzing the bait distribution associated with the feeding machine, the present study proposes an inner-spiral-based full coverage traversal method and a travel distance optimization model so as to improve the uniformity of the automatic feeding. Results show that the speed overshoot is no more than 5% and the steady-state error can be kept within 3%. Compared with the finite point method, the real time point insertion method decreases the peak route deviation errors by 82.82% and 84.14% while turning and going straight.

Linear orbit tracking algorithm of navigation for aquaculture paddle boat. Based on the motion coordinate system, after the establishment of the paddle boat hydrodynamic model, the transfer function and state equation of the paddle boat yaw angle system, the sliding mode control method is adopted to achieving high-precision track tracking for the paddle boat. In order to save the energy, reduce the active frequency of the control system caused by the disturbance, and extend the duration of sailing of the paddle boat, the Kalman filter is proposed to reduce the influence of the disturbance on the track tracking. The simulation and experimental verification show that the sliding mode control method based on Kalman filter can realize high-precision linear track tracking, and Kalman filter can effectively reduce the influence of disturbance.