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With the rapid development of the social economy, habitat fragmentation is becoming more and more obvious, which seriously threatens regional ecological security and sustainable development. Understanding the ecological health status of mountainous cities is of great significance for improving the efficiency of ecological protection. However, the research on the ecosystem health and ecological network construction of mountain cities is still limited. Given the shortcomings of the research, this study constructed an ecosystem health assessment framework in Qujing City, identifying ecological sources with the help of health diagnosis results, simulating biological walking paths with circuit theory, and identifying ecological “nodes” and barrier points. The results showed that: (1) From 2000 to 2020, the ecosystem health level of Qujing City increased first and then decreased. The ecological risk spillover phenomenon existed in the urban center area, which was strongly affected by human activities. (2) Based on VORE model and landscape connectivity software consensus, 27 ecological sources were mainly composed of forest and grassland, and the total area was 7068.24 km2. (3) 53 ecological corridors were identified by the circuit theory, with a length of 734.33 km, 83 ecological sandwichs, a total area of 58.59 km2, and 49 ecological barrier points, among which the ecological sandwichs mostly coincided with the river corridor. The barrier points were concentrated in the north and southeast of the study area. This study can provide a scientific reference for the ecological restoration of mountainous cities.

ObjectiveTo identify factors associated with visual acuity regression following successful treatment of anisometropic amblyopia.Design and methodThis was a retrospective cohort study. Database records for 100 and 61 children with anisometropic amblyopia who met at least one criterion for successful treatment proposed by the Pediatric Eye Disease Investigator Group (PEDIG) and had at least 1 year of follow-up data available after the criterion was met were analyzed. The study sample was split into two groups, those who regressed within 1 year of successful treatment (no longer met any of the PEDIG criteria for successful treatment) and those who did not. A two-step analysis involving a least absolute shrinkage and selection operator (LASSO) regression and a logistic regression were used to identify predictor variables for increased risk of regression. A broad range of clinical, perceptual, and demographic variables were included in the analyses.ResultsSixty-eight (42.5%) children regressed within 1 year of successful treatment. Among the 27 predictor variables considered within the statistical modeling process, the three most important for predicting treatment regression were the extent of amblyopic eye visual acuity improvement, age at first hospital visit and sex. Specifically, lower risk of regression was associated with larger amblyopic eye visual acuity improvement with treatment, younger age at initiation of treatment and female sex.ConclusionPatients who received treatment at a younger age and responded well to treatment had a lower risk of treatment regression. This pattern of results suggests that early detection of amblyopia and strategies that enhance treatment adherence may reduce the risk of treatment regression. The higher risk of regression in boys than girls that we observed may reflect known sex differences in brain development and /or sex differences in environment within our sample of children from South China.

The traditional lateral flow immunoassay (LFIA) detection method suffers from issues such as unstable detection results and low quantitative accuracy. In this study, we propose a novel multi-test line lateral flow immunoassay quantitative detection method using smartphone-based SAA immunoassay strips. Following the utilization of image processing techniques to extract and analyze the pigments on the immunoassay strips, quantitative analysis of the detection results was conducted. Experimental setups with controlled lighting conditions in a dark box were designed to capture samples using smartphones with different specifications for analysis. The algorithm’s sensitivity and robustness were validated by introducing noise to the samples, and the detection performance on immunoassay strips using different algorithms was determined. The experimental results demonstrate that the proposed lateral flow immunoassay quantitative detection method based on image processing techniques achieves an accuracy rate of 94.23% on 260 samples, which is comparable to the traditional methods but with higher stability and lower algorithm complexity.

Node localization is one of the key technologies in the wireless sensor network research field, which is crucial to the high-accuracy localization of mobile nodes, but the positioning error of traditional algorithms such as received signal strength indicator and angle of arrival is more than 4 m, which has almost no practical value. For example, the localization accuracy of the localization algorithm based on received signal strength indicator will be reduced sharply when affected by signal reflection, multipath propagation, and other interference factors. To solve the problem, a three-dimensional localization algorithm of mobile nodes was proposed in this article based on received signal strength indicator–angle of arrival and least-squares support-vector regression, which fused the ranging information of received signal strength indicator algorithm and the angle of arrival algorithm and optimized the estimated distance of unknown nodes. Next, the mobile node model and least-squares support-vector regression modeling mechanism were built according to the hop count of the shortest distance between nodes. Finally, the unknown mobile nodes were localized based on least-squares support-vector regression modeling. The experimental results showed that compared with the localization algorithms without optimized ranging information or least-squares support-vector regression modeling, the algorithm proposed in this study exhibited significantly improved stability, a reduced mean localization error by more than 50%, and increased localization accuracy.

Considering the low indoor positioning accuracy and poor positioning stability of traditional machine-learning algorithms, an indoor-fingerprint-positioning algorithm based on weighted k-nearest neighbors (WKNN) and extreme gradient boosting (XGBoost) was proposed in this study. Firstly, the outliers in the dataset of established fingerprints were removed by Gaussian filtering to enhance the data reliability. Secondly, the sample set was divided into a training set and a test set, followed by modeling using the XGBoost algorithm with the received signal strength data at each access point (AP) in the training set as the feature, and the coordinates as the label. Meanwhile, such parameters as the learning rate in the XGBoost algorithm were dynamically adjusted via the genetic algorithm (GA), and the optimal value was searched based on a fitness function. Then, the nearest neighbor set searched by the WKNN algorithm was introduced into the XGBoost model, and the final predicted coordinates were acquired after weighted fusion. As indicated in the experimental results, the average positioning error of the proposed algorithm is 1.22 m, which is 20.26–45.58% lower than that of traditional indoor positioning algorithms. In addition, the cumulative distribution function (CDF) curve can converge faster, reflecting better positioning performance.

To reduce the influence of received signal strength indication (RSSI) on ranging error, as well as the influence of least squares support vector regression (LSSVR) on localization algorithm, a node three-dimensional localization algorithm based on RSSI and LSSVR parameter optimization is proposed. First, the RSSI average values at 1–25m in four different directions are collected by experiments and the weighted recursive mean optimization method is used to optimize the values of RF factor and propagation factor. Then, the parameters of RBF kernel function and grid width of LSSVR are optimized. Finally, the RSSI range values are used as the input of LSSVR localization model, and the LSSVR regression model is used to solve, in this way, the location estimation of unknown WSN nodes is realized. The simulation results show that the average localization error of the algorithm without parameter optimization is 21.82%, and the localization error of the algorithm after parameter optimization is 11.70%, which has higher localization accuracy. At the same time, a node three dimensional localization experiment platform was built to verify the proposed algorithm in the actual environment, and the test results verified the effectiveness and superiority of the proposed algorithm.

Contrast sensitivity (CS) is important when assessing functional vision. However, current techniques for assessing CS are not suitable for young children or non-verbal individuals because they require reliable, subjective perceptual reports. This study explored the feasibility of applying eye tracking technology to quantify CS as a first step toward developing a testing paradigm that will not rely on observers’ behavioral or language abilities. Using a within-subject design, 27 healthy young adults completed CS measures for three spatial frequencies with best-corrected vision and lens-induced optical blur. Monocular CS was estimated using a five-alternative, forced-choice grating detection task. Thresholds were measured using eye movement responses and conventional key-press responses. CS measured using eye movements compared well with results obtained using key-press responses [Pearson’s rbest–corrected = 0.966, P < 0.001]. Good test–retest variability was evident for the eye-movement-based measures (Pearson’s r = 0.916, P < 0.001) with a coefficient of repeatability of 0.377 log CS across different days. This study provides a proof of concept that eye tracking can be used to automatically record eye gaze positions and accurately quantify human spatial vision. Future work will update this paradigm by incorporating the preferential looking technique into the eye tracking methods, optimizing the CS sampling algorithm and adapting the methodology to broaden its use on infants and non-verbal individuals.

Polo-like kinase 4 (PLK4) involves in tumor progression via regulating centriole duplication. This study aimed to investigate correlations of PLK4 with tumor characteristics and survival in cutaneous melanoma patients undergoing surgical resection. Tumor specimens of 43 patients were retrieved for PLK4 determination by immunohistochemistry (IHC). The IHC score was a multiplication of staining intensity and percentage of staining-positive cells. This study found the median and mean tumor PLK4 IHC score was 0.0 (interquartile range: 0.0-6.0) and 3.5 ± 3.2 (mean ± SD), respectively. Elevated tumor PLK4 IHC score correlated with lymph node metastasis (P = 0.028), higher tumor node metastasis (TNM) stage (P = 0.004), and adjuvant therapy (P =0.029). Tumor PLK4 IHC score > 0 did not relate to disease-free survival (DFS) or overall survival (OS) (both P > 0.050). Tumor PLK4 IHC score > 3 associated with decreased DFS (P = 0.027), but not OS (P = 0.098). Five-year DFS rate of patients with tumor PLK4 IHC score = 0 and > 0 was 75.0% and 53.9%, correspondingly; while the rate of patients with the score ≤ 3 and > 3 was 81.0% and 37.5%, respectively. Five-year OS rate of patients with the score = 0 and > 0 was 100.0% and 66.3%, accordingly; whereas the rate of patients with the score ≤ 3 and > 3 was 85.7% and 61.5%, correspondingly. According to forward-step multivariate analysis, neither the score > 0 nor > 3 independently related to worse DFS and OS (all P > 0.050). Further validation via THE HUMAN PROTEIN ATLAS database showed high PLK4 RNA expression associated with shortened OS in melanoma patients (P = 0.001). PLK4 correlates with lymph node metastasis, increased TNM stage, and poor DFS in cutaneous melanoma patients undergoing surgical resection. [ABSTRACT FROM AUTHOR]

Self-regulated learning (SRL) is especially important in blended/flipped learning environments. This study explored and described the use of SRL in a specific language learning context. It investigated what problems Chinese-as-a-foreign-language learners (CFL) had with their Chinese language learning, whether and how they solved the problems, what SRL strategies they adopted, and what support they needed to gain the maximum benefits from the blended/flipped learning environment. Nineteen CFL learners who were in their second semester studying Chinese in a blended/flipped environment participated in the descriptive study. Self-reported data were collected using WeChat in an individualized exchange format, which was designed to help with data richness, completeness, and accuracy. The results show that students had different problems in learning, and they would benefit if they had the opportunity to learn and incorporate SRL strategies. The areas in which they needed the most help were knowing how to set a goal, how to create a plan to achieve the goal, and how to use different strategies to manage their learning and manage their time. Implications for teaching are also discussed in this study.

A Cuckoo Search (CS) algorithm based on ant colony algorithm is proposed for scheduling problem in permutation flow shop scheduling problem (PFSP). When the raised CS algorithm obtains the position of the bird nest to be updated, it is used as a set of initial solution of the ant colony optimization algorithm (ACO), and ACO algorithm search optimization is performed in a very small range. After that, the solution obtained by the ACO search is taken as a new candidate solution, compared with the candidate bird nest according to the fitness degree. When the candidate solution of the ACO search optimization is better than the one generated by the Lévy flight, the latter is replaced. Finally, the CS algorithm is selected, changing the new bird nest position according to the abandonment probability. The updated position tends to be more optimal, which improves the quality of the solution as well as the convergence speed and accuracy of the algorithm. Comparing the performance of the proposed algorithm with the standard Cuckoo one, by testing function, the optimized performance was verified. Finally, the Car benchmark test served as test data, and the performance in the PFSP was compared. The effectiveness and superiority in the algorithm in solving problem were confirmed.

To reduce the influence of non-line-of-sight (NLOS) errors in the ultra-wideband (UWB) positioning process, a UWB positioning algorithm based on fuzzy inference and adaptive anti-NLOS Kalman filtering (KF) was proposed in this paper. First of all, the NLOS errors of the channel impulse response (CIR) signal characteristics were estimated by the fuzzy inference algorithm and then initially mitigated. Next, an adaptive anti-NLOS KF algorithm was developed to perform a second mitigation on the ranging errors after mitigation of the NLOS errors with the fuzzy inference, thereby further raising the range estimation accuracy. At last, the range estimation information after error mitigation was taken as the ranging information of the LS positioning algorithm for target localization. In the static positioning experiment, the probability of producing an error range of less than 19.1 cm with the positioning algorithm combining fuzzy inference with adaptive anti-NLOS KF was 0.93, which was much better than the positioning algorithm based on fuzzy inference and the adaptive anti-NLOS KF positioning algorithm. In the dynamic positioning experiment, compared with the adaptive anti-NLOS KF positioning algorithm, the RMSE was reduced by 43.31% in the overall positioning. Furthermore, compared with those of the positioning algorithm based on fuzzy inference, the RMSEs in overall positioning were lowered by 12.89%. The positioning accuracy was improved significantly.

Given that the tracking accuracy and real-time performance of the particle filter (PF) target tracking algorithm are greatly affected by the number of sampled particles, a PF target tracking algorithm based on particle number optimization under the single-station environment was proposed in this study. First, a single-station target tracking model was established, and the corresponding PF algorithm was designed. Next, a tracking simulation experiment was carried out on the PF target tracking algorithm under different numbers of particles with the root mean square error (RMSE) and filtering time as the evaluation indexes. On this basis, the optimal number of particles, which could meet the accuracy and real-time performance requirements, was determined and taken as the number of particles of the proposed algorithm. The MATLAB simulation results revealed that compared with the unscented Kalman filter (UKF), the single-station PF target tracking algorithm based on particle number optimization not only was of high tracking accuracy but also could meet the real-time performance requirement.

The designer network is the carrier for designers to carry out product design tasks, and the study of its evolution helps to accurately identify the designer team. The uncertainty of whether the designer chooses to keep in touch with other members of the network, its nonlinearity on the overall performance of the network and the external interference of the network, etc., increase the complexity of the designer network, which is not conducive to the development of product design tasks. Therefore, it is very interesting and important to study the co-evolution mechanism of the designer’s network structure and the designer’s opinion. This paper analyzes the Deffuant opinion dynamics model, considers the asymmetry of nodes in the network caused by the designer’s unequal relationship (the number of node links is different), and the difference in the degree of acceptance of other people’s opinions by different individuals, and improves the Deffuant model. Then combining the improved Deffuant model with the BA (Barabasi–Albert) model, a DBA (Deffuant and BA Adaptive) model was proposed that integrates opinion update, broken edge and reconnection, and opinion changes. On this basis, this paper designs a virtual network through the crowdsourcing of a certain product. The adjacency matrix of this network is symmetric, and corresponding comparative experiments are carried out on this network. The analysis of test results shows that under six different deliberate attacks, the DBA model is more robust than the BA model. In addition, the average shortest path of the DBA network will vary with the parameters. The proposed integrated DBA model has important guiding significance for building a robust designer network.

Aiming at the problem that the standard cuckoo search algorithm relies on Levy flights, which leads to the step-size randomness of the search process, a self-adaptive step cuckoo search algorithm based on dynamic balance factor is proposed in our paper. First, two parameters are introduced in our paper, which were iteration number ratio parameter and adaptability ratio parameter. Then, a dynamic balance factor parameter is introduced to adjust the weight number of iteration number ratio parameter and adaptability ratio parameter. Finally, parameter skewness value calculation method and self-adaptive step strategy were proposed combined with the dynamic balance factor. Six typical test functions are used to test the performance of the proposed algorithm, the standard cuckoo search algorithm and the self-adaptive step cuckoo search algorithm which relies only on the iterative times. The test results show that the proposed algorithm had good convergence speed and accuracy. Meanwhile, taking the permutation flow shop scheduling problem as an example, eight operators of Car benchmark class are used as the test data to compare the performance of three algorithms, the effectiveness and superiority of the algorithm in solving the permutation flow shop scheduling problem are verified.

Six machine-learning approaches, including multivariate linear regression (MLR), gradient boosting decision tree, k-nearest neighbors, random forest, extreme gradient boosting (XGB), and deep neural network (DNN), were compared for near-surface air-temperature (Tair) estimation from the new generation of Chinese geostationary meteorological satellite Fengyun-4A (FY-4A) observations. The brightness temperatures in split-window channels from the Advanced Geostationary Radiation Imager (AGRI) of FY-4A and numerical weather prediction data from the global forecast system were used as the predictor variables for Tair estimation. The performance of each model and the temporal and spatial distribution of the estimated Tair errors were analyzed. The results showed that the XGB model had better overall performance, with R2 of 0.902, bias of −0.087°C, and root-mean-square error of 1.946°C. The spatial variation characteristics of the Tair error of the XGB method were less obvious than those of the other methods. The XGB model can provide more stable and high-precision Tair for a large-scale Tair estimation over China and can serve as a reference for Tair estimation based on machine-learning models.

Cell division cycle 42 (CDC42) mediates immune escape in cancers. This study aimed to investigate linkages of CDC42 with tumor features, treatment response, and survival in advanced melanoma patients receiving programmed death-1 (PD-1) inhibitors. Pre-treatment and post-treatment (after 2 cycles) serum CDC42 of 35 advanced melanoma patients receiving PD-1 inhibitor was assessed by enzyme-linked immunosorbent assay. Patients with tumor-node-metastasis (TNM) stage IV (vs. III) (P = 0.050) and abnormal (vs. normal) lactate dehydrogenase (LDH) (P = 0.022) had higher pre-treatment CDC42. After 2-cycle therapy, CDC42 was declined (P < 0.001). Objective response and disease control rates were 34.3% and 62.9%, respectively. Additionally, pre-treatment and post-treatment CDC42 was reduced in patients with objective response and disease control than those without (all P < 0.050). Concerning survival, pre-treatment with CDC42 > 700 pg/mL was associated with shorter progression-free survival (PFS) (P = 0.013), but not overall survival (OS) (P = 0.060). Specifically, the 12-month PFS rate was 26.7% and 66.2%, and the 12-month OS rate was 61.1% and 82.5% in patients with pre-treatment with CDC42 > 700 pg/mL and = 700 pg/mL, respectively. Post-treatment with CDC42 > 700 pg/mL was correlated with shortened PFS (P = 0.010) and OS (P = 0.006). The 12-month PFS rate was 12.5% and 62.0%, and the 12-month OS rate was 42.3% and 88.0% in patients with post-treatment with CDC42 > 700 pg/mL and = 700 pg/mL, accordingly. Furthermore, post-treatment with CDC42 > 700 pg/mL was independently related to PFS [hazard ratio (HR): 2.704, P = 0.029 and OS (HR: 7.749, P = 0.005)]. Elevated CDC42 correlates with advanced TNM, abnormal LDH, worse clinical response, and dismal survival in advanced melanoma patients receiving PD-1 inhibitors. [ABSTRACT FROM AUTHOR]

Directing at various problems of the traditional Q-Learning algorithm, such as heavy repetition and disequilibrium of explorations, the reinforcement-exploration strategy was used to replace the decayed ε-greedy strategy in the traditional Q-Learning algorithm, and thus a novel self-adaptive reinforcement-exploration Q-Learning (SARE-Q) algorithm was proposed. First, the concept of behavior utility trace was introduced in the proposed algorithm, and the probability for each action to be chosen was adjusted according to the behavior utility trace, so as to improve the efficiency of exploration. Second, the attenuation process of exploration factor ε was designed into two phases, where the first phase centered on the exploration and the second one transited the focus from the exploration into utilization, and the exploration rate was dynamically adjusted according to the success rate. Finally, by establishing a list of state access times, the exploration factor of the current state is adaptively adjusted according to the number of times the state is accessed. The symmetric grid map environment was established via OpenAI Gym platform to carry out the symmetrical simulation experiments on the Q-Learning algorithm, self-adaptive Q-Learning (SA-Q) algorithm and SARE-Q algorithm. The experimental results show that the proposed algorithm has obvious advantages over the first two algorithms in the average number of turning times, average inside success rate, and number of times with the shortest planned route.

Aimed at the shortcomings of low localization accuracy of the fixed multianchor method, a three-dimensional localization algorithm for wireless sensor network nodes is proposed in this paper, which combines received signal strength indicator (RSSI) and time of arrival (TOA) ranging information and single mobile anchor node. A mobile anchor node was introduced in the proposed three-dimensional localization algorithm for wireless sensor networks firstly, and the mobile anchor node moves according to the Gauss–Markov three-dimensional mobility model. Then, based on the idea of using RSSI ranging in the near end and TOA ranging in the far end, a ranging method combining RSSI and TOA ranging information is proposed to obtain the precise distance between the anchor node and the unknown node. Finally, the maximum-likelihood estimation method is used to estimate the position of unknown nodes based on the obtained ranging values. The MATLAB simulation results show that the proposed algorithm had a higher localization accuracy and lower localization energy consumption compared with the traditional RSSI localization method or TOA localization method.

The spatial and temporal distribution characteristics of high clouds over the Tibetan Plateau (TP) were studied using the Atmospheric Infrared Sounder (AIRS) and the GCM-Oriented CALIPSO Cloud Product (CALIPSO-GOCCP) monthly mean cloud products from 2007 to 2017. The high clouds over the TP are dominated by cirrus and show seasonal variation characteristics. There were two distinct areas with the high occurrence of cirrus clouds in the area (0°–60°N, 75°–103°E). One was located in the regions from the equator to 25°N, and the other was within the latitude belt from 30° to 40°N. From January to May, cirrus clouds mainly occurred in the central and northern parts of the TP (30° to 40°N), and the cirrus cloud fraction increased from January and reached its maximum (∼0.51) in April. From June to August, cirrus clouds mainly occurred in the southern part of the TP during summer. The cirrus clouds over the southern TP were relatively high (located in 10–17 km) and manifested northward and southward movements. The ice clouds in the southern TP are associated with plateau deep convection activities and abundant vapor transmitted by the Asian monsoon. Cirrus clouds over the northern and central TP may be relevant to the atmospheric lift by an approaching cold front and topographic lifting. Moreover, the high clouds below 11 km are dominated by opaque clouds, while the nonopaque (or thin) and opaque (or thick) clouds above 11 km are comparable.

This study proposed an instantaneous summer air temperature (Tair) estimation model using the Himawari-8 Advanced Himawari Imager (AHI) brightness temperatures (BTs) in split-window channels and other auxiliary data. Correlation analysis and stepwise linear regression were used to select the predictors for Tair estimation. Nine predictors such as AHI BTs in channels 14 and 15, elevation, precipitable water vapor (PWV), and relative humidity (RH) were finally selected. Stepwise linear regression and neural network (NN) methods were applied to construct summer Tair estimation models over China, respectively. The estimated Tair by linear and NN models was evaluated using the observed Tair from 272 meteorological stations over China. The results showed that AHI BTs in channels 14 and 15, elevation, PWV, and RH were more important for Tair estimation than other predictors. The accuracy of the NN models was better than the linear models. The correlation coefficient (R), root mean square error (RMSE), and bias were 0.97, 1.72°C, and 0.04°C, respectively, for the NN model and were 0.89, 3.28°C, and 0.07°C, respectively, for the linear model. About 75.6% of the Tair differences by the NN model were within 2.0°C, and even 45.8% were within 1.0°C. The performance of the Tair estimation model for each site was also investigated, and the accuracy of Tair estimation in southeast China is better than northwest China.

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A photovoltaic power supply with a simple structure and high tracking efficiency is needed in self-powered, wireless sensor networks. First, a maximum power point tracking (MPPT) algorithm, including the load current maximization-perturbation and observation (LCM-P&O) methods, with a fixed step size, is proposed by integrating the traditional load current maximization (LCM) method and perturbation and observation (P&O) method. By sampling the changes of load current and photovoltaic cell input current once the disturbance is applied, the pulse width modulation (PWM) regulation mode, i.e., increasing or reducing, can be determined in the next process. Then, the above algorithm is improved by using the variable step size strategy. By comparing the difference between the absolute value of the observed current value and the theoretical current value at the maximum power point of the photovoltaic cell with the set threshold value, the variable step size for perturbation is determined. MATLAB simulation results show that the LCM-P&O method, with a variable step size, has faster convergence speed and higher tracking accuracy. Finally, the two MPPT algorithms are tested and analyzed under constant voltage source input and indoor fluorescent lamp illumination through an actual circuit, respectively. The experimental results show that the LCM-P&O method with variable step size has a higher tracking efficiency, about 90%−92%, and has higher stability and lower power consumption.