Search

Copyright of Journal of Food Safety & Quality is the property of Journal of Food Safety & Quality Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Zhengzhou University (Natural Science Edition) is the property of Journal of Zhengzhou University (Natural Science Edition) Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The treatment of head and neck cancer is dominated by surgery and radiochemotherapy. For elderly patients, however, the rationality of invasive treatment is questioned in clinical practice. Although the proportion of geriatric patients with head and neck cancer is increasing, there are few consistent standards in the literature and guidelines. From the perspective of evidence-based medicine, this paper presents current evidence for treatment so as to provide specific recommendations for treating head and neck cancer in the elderly. Furthermore, based on a systematic literature review, a clinical algorithm is proposed to evaluate the treatment risk of elderly patients with head and neck cancer.

【Objective】 Change in soil water content is not only an indicator of water stresses used for irrigation management but also controls biogeochemical processes in soil. In this paper, we study the feasibility of using thermal infrared imaging to estimate soil moisture dynamics. 【Method】 The experiment was conducted in July-August 2023 in a walnut orchard in Xinjiang. Thermal infrared images of the walnut canopy were measured continuously using a thermal infrared camera. Based on the HSV color space of the images, an improved K-means segmentation algorithm was proposed to analyze the change in canopy temperature. We also measured air temperature and humidity, illuminance, wind speed, atmospheric CO2, and soil water content in the 0-80 cm soil layer, from which we proposed an inversion model to estimate soil water dynamics. 【Result】 The improved K-means algorithm increased the accuracy from 82.34% to 94.55%, and the errors between the canopy temperature acquired from the images and the measured canopy temperature were in the range of 0 to 1.0. The infrared imaging method was most accurate between14:00 pm to 16:00 pm. Our results showed that the walnut roots were most active in taking up water from the 40-60 cm soil layer 50-60 cm away horizons from the tree truck. Canopy temperature, air temperature and relative humidity, and atmospheric CO2 concentration were correlated with soil water content at significant levels; they can thus be used to estimate soil water dynamics, with a coefficient of determination of R2=0.86 and p

Copyright of Journal of Central China Normal University is the property of Huazhong Normal University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Wildland Fire Science is the property of Journal of Wildland Fire Science Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Electric Power is the property of Electric Power Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Strabismus, misalignment of the eyes arising from central nervous system dysregulation and extraocular muscles imbalance, commonly manifests in childhood, leading to amblyopia, binocular vision dysfunction, torticollis and other developmental and psychological disorders. This exerts a negative impact on individuals, families and society. Timely diagnosis and intervention are crucial to prevent permanent damage to vision and stereopsis. Presently, strabismus diagnosis is reliant on the ophthalmologists′ evaluations which results in a lack of efficiency and coverage. However, routine school screening proves inadequate in assessing strabismus degree with low accuracy. Therefore, how to improve the efficiency of strabismus screening is an issue of great importance. This paper delves into the present landscape of strabismus diagnosis and treatment, considering both local and global research advancements. It focuses on the evolution of artificial intelligence technology, illuminating the utilization of artificial intelligence models and algorithms in strabismus. By pinpointing and exploring their strengths and limitations, it offers valuable insights, paving the way for future investigations into artificial intelligence-assisted strabismus diagnosis and treatment.

Object detection of small intestinal villus in pathology images of pigs is of practical use in the agricultural and veterinary fields. However, manual detection cannot fully meet the large-scale production at present, due to the time consuming, and labor-intensive task. In this study, an improved YOLOv5s model was proposed to rapidly and accurately detect the specific location of small intestinal villus in pathology images. The source of experimental data was taken as the pathology images of small intestinal villus in pigs provided by the Institute of Animal Husbandry, Guangdong Academy of Agricultural Sciences. To train the model, 64 high-resolution images of intact small intestinal villus were also collected and then cropped into 626 images with a size of 640×640. According to the ratio of 8:2, these images were divided into the training and validation sets. Firstly, the spatial pyramid was optimized in the backbone network of YOLOv5s using hybrid pooling in the form of tandem. The feature extraction and representation further improved the detection accuracy. This optimization was used to better capture the feature information at different scales. The model was trained to more accurately learn the content. Secondly, a network module called SimAM (A Simple, Parameter-Free Attention Module) was introduced to combine the attention mechanism and residual connectivity of the Bottleneck. A weighted feature representation was then obtained to weight the feature maps in Bottleneck. Thereby the model was enhanced to perceive the target. Compared with the traditional attention module, the SimAM module featured simplicity without the parameter tuning, leading to a more efficient model and easy to implement. The spatial pyramid and the attention mechanism after improvement were better utilized to enhance the detection performance of small intestinal villus in pigs. This improved approach reduced the model parameters and computation with the high detection accuracy, indicating a more lightweight and efficient model. The enhancing ability to perceive the target was used to more accurately locate and identify small intestinal villus in pathology images. More reliable tools and support were provided in the agricultural and veterinary fields. According to the experimental results, the improved model achieved significant performance in detecting the small intestinal villus in pathology images of pigs. The average precision value reached 92.43%, while the detection speed was 40 frames per second. Compared with the original YOLOv5s model, the improved model reduced the inference time by 1.04 ms per frame without increasing the number of parameters. Also, the average precision was improved by 4.62 percentage points. Compared with other common target detection models, significant advantages were found in the detection accuracy, detection speed, and the number of model parameters. There were significant improvements in both speed and accuracy. The network structure was optimized to introduce an attention mechanism, in order to more accurately detect the small bowel villus in pathology images with the higher detection speed. In addition, the improved model was also more efficient and feasible for practical applications, in terms of the number of parameters. [ABSTRACT FROM AUTHOR]

Strabismus, misalignment of the eyes arising from central nervous system dysregulation and extraocular muscles imbalance, commonly manifests in childhood, leading to amblyopia, binocular vision dysfunction, torticollis and other developmental and psychological disorders. This exerts a negative impact on individuals, families and society. Timely diagnosis and intervention are crucial to prevent permanent damage to vision and stereopsis. Presently, strabismus diagnosis is reliant on the ophthalmologists′ evaluations which results in a lack of efficiency and coverage. However, routine school screening proves inadequate in assessing strabismus degree with low accuracy. Therefore, how to improve the efficiency of strabismus screening is an issue of great importance. This paper delves into the present landscape of strabismus diagnosis and treatment, considering both local and global research advancements. It focuses on the evolution of artificial intelligence technology, illuminating the utilization of artificial intelligence models and algorithms in strabismus. By pinpointing and exploring their strengths and limitations, it offers valuable insights, paving the way for future investigations into artificial intelligence-assisted strabismus diagnosis and treatment. [ABSTRACT FROM AUTHOR]

This study summarizes the application of automatic recognition technologies for patient-ventilator asynchrony (PVA) during mechanical ventilation. In the early stages, the method of setting rules and thresholds relied on manual interpretation of ventilator parameters and waveforms. While these methods were intuitive and easy to operate, they were relatively sensitive in threshold setting and rule selection and could not adapt well to minor changes in patient status. Subsequently, machine learning and deep learning technologies began to emerge and develop. These technologies automatically extract and learn data characteristics through algorithms, making PVA detection more robust and universal. Among them, logistic regression, support vector machines, random forest, hidden Markov models, convolutional autoencoders, long short-term memory networks, one-dimensional convolutional neural networks, etc., have all been successfully used for PVA recognition. Despite the significant advancements in feature extraction through deep learning methods, their demand for labelled data is high, potentially consuming significant medical resources. Therefore, the combination of reinforcement learning and self-supervised learning may be a viable solution. In addition, most algorithm validations are based on a single dataset, so the need for cross-dataset validation in the future will be an important and challenging direction for development.

Data fusion technology is the fusion of redundant or complementary information from multiple types of homogeneous or heterogeneous sensors to accurately assess the situation of the observed object, and make correct judgement about things. With the development of medical technology, medical data in different modes are increasing rapidly. Compared with single mode, using data fusion technology to process multi-mode medical data can obtain more abundant and comprehensive information, avoid information silos, and provide strong information support for medical research. In this thesis, we briefly introduced the concept and principle of data fusion technology, summarized the working principle, advantage and disadvantage of different levels and commonly used algorithms in data fusion technology, and finally introduced the application of data fusion technology in the field of disease prevention, diagnosis, treatment and rehabilitation.

Copyright of Journal of Harbin University of Science & Technology is the property of Journal of Harbin University of Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In recent years, with the rapid development of modern information technology, intelligent algorithms have been deeply embedded in all aspects of social life, which not only promotes the continuous development of human economy and society to intelligence, but also promotes the overall pattern of society to intelligence, providing technical guidance for the efficient and orderly operation of national governance and becoming an important driving force for the modernization of national governance level and governance capacity. In this context, how to promote the algorithm technology to be more effectively embedded in government governance at this stage, how to counter the origin of technology while giving full play to technical benefits, and how to better achieve the goal of maximizing public value become an urgent proposition of the times. In this paper, an analytical framework is built from the three dimensions of "technology-power-rules" to study the embedding of algorithms into government governance. Taking the practice of embedding algorithms into different scenes of government governance as the observation window, the operational logic and value implication of enabling government governance by algorithms are explored, and it is proposed that algorithm technology can promote public decision-making to be more rational, algorithm power can promote governance behavior to be more inclusive, and algorithm rules can promote government governance to be more fair. At the same time, it focuses on the risks and challenges that may be brought by the application of algorithms with technical, power and rule attributes in government governance practice, such as the black box of algorithm technology blocking the identification of public responsibility, the power of algorithm weakening the autonomy of government and people, and the preference of algorithm rules leading to the loss of governance justice. Combined with the government governance practice in China, it is proposed that in order to promote the intelligent algorithm to be more effectively embedded in government governance and better realize the goal of maximizing public benefits, efforts should be made to promote the moderate transparency of algorithm technology to clarify the chain of responsibility, standardize the operation of algorithm power and adhere to the humanistic governance concept. Different from the "negative theory of algorithm" and "positive theory of algorithm" held by the existing research results, this paper recognizes that the algorithm has both positive and negative external benefits to government governance from the two dimensions of efficiency and value, and focuses on how to avoid or reduce the possible risks and challenges of the algorithm to government governance from the three levels of technology, power and rules, and amplify the positive benefits of the algorithm to the governance process, so that the intelligent algorithm can be truly and effectively embedded in government governance, and promote the quality and efficiency of social governance. [ABSTRACT FROM AUTHOR]

A steering control system is one of the key steps in the tractor's automatic navigation. Many nonlinear factors (such as hydraulic dead zone, saturation zone, and mechanical backlash) can dominate the performance of steering control, even the accuracy and stability of tractor navigation. In this study, the Lovol Europa M704-2H tractor was taken as the test platform with the electric steering wheel instead of the onboard steering wheel as the actuator, where the angle sensor on the steering wheel axle of the tractor to measure the size of the wheel turning angle. The steering control system included the steering angle calibration and the steering control. The steering angle calibration aimed to obtain the limit angle and median AD value size of the tractor, and then the steering wheel angle size at any angle by linear fitting. The limit steering angle calibration was required for the tractor steering wheel close to the limit through a circle, the satellite positioning of the trajectory point, and the least squares, in order to obtain the minimum turning radius of the tractor. The two-wheel model of the tractor was then utilized to calculate the maximum steering angle. The median steering angle calibration was required for the tractor to drive a section of an approximate straight-line trajectory. The least squares method (LSM) was also used to identify the correction of the zero position for the accurate median AD value. The TZ-QX2A6/5T front wheel angle meter was selected to compare the front wheel angle with the sensor measurement. The results show that the maximum error of calibrated angle measurement was 1.3°, and the average error was 0.11°, fully meeting the demand of steering control. At the same time, the mechanical backlash of the steering system was firstly quantified, in terms of the angular size of steering wheel reversal across the free travel. The accurate size of mechanical backlash was obtained through experimental identification. Then, the fuzzy PD steering control with backlash compensation was designed at the same time. When the electric steering wheel was out of the free travel, the fuzzy PD control was adopted; when the electric steering wheel was in free travel, the steering control was compensated to add the backlash P control into the output of the fuzzy PD control. Simulation results in Simulink show that better performance was achieved in the tracking of the steering angle signals of square wave and sinusoidal wave, fully meeting the requirements. The fuzzy PD steering control with/without the backlash compensation was also used to compare the tracking performance of square wave and sinusoidal steering angle signals. The experimental results show that the response time of the square wave angle signal tracking without backlash compensation was 1.3 s, where the maximum and average steady-state errors were 0.672°, and 0.244°, respectively. The average delay time of the sine wave angle signal tracking without backlash compensation was 0.5 s, where the maximum and average errors were 2.59° and 1.32°, respectively. Compared to the algorithm without backlash compensation, the time of steering angle error within ±0.2° was improved by 71%, the maximum steady state error was reduced by 0.022°, and the average steady state absolute error was reduced by 0.112°. Once the backlash compensation was performed, the response time of the square wave angle signal tracking was 1.1 s, where the maximum and average steadystate errors were 0.65°, and 0.132°, respectively. The average delay time of the sine wave angle signal tracking was 0.5 s, the maximum and average errors were 1.91° and 1.09°, respectively. Compared to the algorithm without backlash compensation, the maximum error was reduced by 0.68°, and the average error was reduced by 0.23°. The experimental results show that the backlash compensation outstandingly improved the stability of steering control for the less control error. The linear navigation test on the leveled field showed that the maximum and absolute average errors of angle tracking were 2.82° and 0.61°, respectively, when the system was in a steady state. Therefore, better performance was achieved with the high accuracy of the steering control system. [ABSTRACT FROM AUTHOR]

A dynamic mechanism model was established for a gold cyanide leaching process, and a nonlinear algorithm based on global orthogonal configuration was proposed to predict the leaching rate. According to the predictive control requirements of the dynamic mechanism model, the interpolation polynomial is constructed at the sampling point to approximate the state variable and the control variable. At the same time, the nonlinear programming problem is obtained through discretization, and then the optimal output and control quantity are obtained by solving it. Finally, the simulation and comparative analysis are performed with the linear prediction algorithm to verify the effectiveness of the algorithm. The results show that the nonlinear predictive control algorithm has more advantages in robustness, tracking and immunity, and the system stability is higher in the control process, which is more suitable for practical production applications. [ABSTRACT FROM AUTHOR]

Copyright of Taiwan Law Review is the property of Angle Publishing Co., Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The application of FPGA is becoming wider because of its advantages of reconfigurability, high performance, low power consumption, and strong real-time performance. However, its shortcomings such as high development threshold, low efficiency, and long development cycle become more prominent as the increase of system size and complexity. This paper uses model-based development methods to study the correlation algorithm of FPGA, including Simulink model building, test validation, code generation and simulation. The results of comparison with traditional manual coding show that the FPGA software development cycle using model-based design methods is only one fifth of the original method and the error in calculation results of the correlation coefficient is also under 0.3%. Although the length of generated code far exceeds that of handwritten code, the resource utilization of logic units, register, storage units, and multiplier units, etc. at runtime are less than those of handwritten code after code synthesis. It can be seen that model-based development methods not only meet the accuracy requirements (not exceeding ±5‰), but also greatly shorten the development cycle and lower the coding threshold.

It is important for regional sustainable development that how to enhance the sewage treatment capacity of cities and improve river water quality. This paper took the Nanming River n Guiyang as the study area, built a one-dimensional coupling simulation modal of water quantity and quality based on MIKE 11 software to study the distribution of ammonia-nitrogen concentration and the causes of water pollution. Also a multi-objective optimization modal of sewage treatment plants was constructed n consideration of cost and water quality for man polluted reaches, setting cost minimizing and water cleaning as optimization goals, and NSGA- II algorithm was adopted to quantitatively analyze the correlation between the investment of sewage treatment plants and ammonia-nitrogen concentration of the river. Then the multi-objective optimization scheme of sewage treatment plants was recommended. The results are shown as follows. 1 There are three areas where the ammonia-nitrogen concentration exceeds the standard, with a total of 5 .23 km of reaches polluted, mainly polluted by Plant 2,Plant 13 and Guancheng River. 2 According to the optimization model, the reasonable investment of the sewage treatment plants should be 628-~850 million yuan, with the maximum ammonia-nitrogen concentration between 1.39 mg/L and 1.50 mg/L. For every 0.01 mg/L decrease of ammonia-nitrogen concentration in the river, the investment of the sewage treatment plants needs to increase by 20 million yuan. 3 The discharge water quality of plant 2 and plant 13 is proposed to improve from the first-level A standard to the quasi-IV class, and four optimization schemes are proposed for the new sewage treatment plant to meet the multi-objective optimization scheme settings of the sewage treatment plant under different needs. [ABSTRACT FROM AUTHOR]

Copyright of Imaging Science & Photochemistry is the property of Imaging Science & Photochemistry Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Tobacco Science & Technology is the property of Tobacco Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Objective To establish, analyze, and screen ideal models based on machine learning algorithms to predict the positive rate of patients who received repeated prostate punctures for guiding clinical decision-making. Methods Clinical data of 281 male patients from multiple centers who underwent at least two consecutive prostate biopsies from January 2008 to December 2022, were retrospectively analyzed, and their first biopsy pathology result was recorded as negative. Key clinical diagnosis and treatment data were recorded for each patient, including BMI, prostate volume, PSA level, fPSA/PSA ratio, PSAD level, complications after the first puncture, pathological results after the first puncture, and strategies used for the two prostate punctures. Statistical analysis was used to analyze the differences in parameters between the final puncture pathology positive group and the negative group. The parameters were trained and fitted through seven machine learning algorithm models such as multivariable logical regression (LR), K-nearest neighbor search (KNN), support vector machine (SVC), decision tree (DT), random forest classifier (RF), naive Bayes classifier (NBC) and gradient enhancement tree (GB). 70% of all data as training set data and 30% of the remaining as verification set data were used for six times cross validation to analyze and compare accuracy and sensitivity, the diagnostic accuracy of each model was evaluated using receiver operating characteristic curve, and its effectiveness in predicting the results of prostate cancer secondary puncture was ultimately evaluated. Results The patients were aged between 32 and 87 years old, with an average of 69. 03 years old. Among the patients, 201 patients were recorded as negative in the final puncture pathology, and 80 patients were recorded as prostate cancer. The inter-group analysis showed that there were significant statistical differences (P <0. 05) in the age of second puncture, volume of first puncture prostate, volume of second puncture prostate, PSA level of second puncture, PSAD of second puncture, and FPSA/PSA ratio of second puncture. All collected data were randomly divided into two parts, such as the training dataset (197 cases, 70% of prostate puncture patients) and the validation dataset (84 cases, the remaining 30%). The results showed that the accuracy rates of LR, KNN, SVC, DT, GNB, RF, and GB in predicting the results of secondary prostate puncture were found to fluctuate between 66. 67% and 74. 88%, the error rates to fluctuate between 21.43% and 34. 29%, the recall rates to fluctuate between 9. 52% and 47. 62%, the specificity to fluctuate between 79. 59 and 97. 96%, the accuracy to fluctuate between 33. 33% and 80. 00%. The specificities of each model (the area under the ROC curve) were found to fluctuate between 0. 568 and 0. 725. Conclusion SVC can more accurately predict the positivity of secondary prostate puncture with fewer parameters. Compared to other algorithm models, it makes the prediction results to have better sensitivity and specificity. Through the collection and training of larger sample size data, it has the potential to become a detection tool with broad compatibility for predicting prostate secondary puncture results. [ABSTRACT FROM AUTHOR]

To address the difficulties of slow convergence and difficulty in finding feasible solutions when solving large-scale constrained multi-objective optimization problems, an adaptive two-stage large-scale constrained multi-objective evolutionary algorithm was proposed. In the first stage, the algorithm adaptively selected some variables for optimization according to the nature of the decision variables, without considering any constraint to make the population quickly cross the infeasible region and approach the unconstrained Pareto front. In the second stage, the algorithm considered all the constraints and optimizes the variables as a whole using the ε constraint-handling technique. At the same time, the feasible and non-dominated solutions obtained in the evolutionary process were saved and updated using archive to continuously improve the convergence and diversity of the population. Finally, the proposed algorithm was experimentally compared with the other six algorithms on 37 test functions, and the results showed that the proposed algorithm could achieved the best results on 25 functions and outperforms the comparison algorithm on at least 31 functions, respectively; meanwhile, the feasibility rate of the proposed algorithm in more than 90% of the functions could reach 100%, which could effectively solve large-scale constrained multi-objective optimization problems. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

With the rapid development and in-depth application of information technology, algorithms are widely used in transportation, medical care, e-commerce, information services, public management and other fields, but their own data dependence, logical black box and general purpose characteristics also bring about data abuse in algorithm applications, algorithm discrimination in public management, algorithm manipulation in information services, algorithm collusion in market competition.In order to deal with the problems caused by the application of algorithms, countries have actively taken measures to strengthen the governance of algorithm applications.To promote the effective use of algorithms and strengthen algorithm governance, we need to further strengthen top-level design, improve governance pattern, enrich governance means, strengthen international coordination, and constantly improve the algorithm governance system.

Abstract： Computer text analysis is an important branch in the field of natural language processing, and it is a computer technology that studies how to extract various types of information from a given corpus from text data. At present, computer text analysis has entered a new historical stage. On the one hand, the keyword extraction algorithm has gradually been completed. On the other hand, with the emergence of the BERT method, the word vector calculation problem has also made great progress. However, there are still some problems to be solved in both keyword extraction and word vector calculation. In addition, many existing studies suitable for using text analysis still use ancient text analysis methods. Therefore, in the future, how to better reduce the model size to promote the integration of disciplines and improve the comprehensive social benefits of text analysis will become an important issue in the development of text analysis algorithms.

Copyright of Journal of Signal Processing is the property of Journal of Signal Processing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Hydrometallurgy of China is the property of Hydrometallurgy of China Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Central China Normal University is the property of Huazhong Normal University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Precision breeding has been a research hotspot in the cattle breeding industry in recent years, with the development of smart livestock farming and large-scale expansion. Among them, facial detection and recognition of cattle can be critical to intelligent farming on ranches. However, the accuracy of facial detection can be severely affected by three common environmental factors: blurriness, backlighting, and occlusion, due to the complexity of the livestock farming environment. In this study, cattle facial detection was proposed using adaptive attention mechanisms in complex scenarios. Firstly, three evaluation indicators were designed for each of the three interfering factors of blurriness, backlighting, and occlusion, respectively, and then normalized the three types of evaluation indicators using fuzzy membership functions. Three indicators were also utilized to comprehensively evaluate the scene information of the input image. The weighting coefficients were adjusted to reflect the complexity of the target scene, according to the changes in the evaluation indicators. Secondly, a new attention mechanism called CDAA (Composite Dual-Branch Adaptive Attention) was introduced into the backbone feature extraction network using YOLOV7-tiny. The parallel structures were incorporated for channel and spatial attention, along with adaptive weighting coefficients to effectively enhance the respective attention branches' importance. Dynamic weighting was realized for the automatic adjustment of channel and spatial attention mechanisms in different scenarios. The network's ability was improved to extract the features in complex scenarios for higher detection accuracy in complex scenarios. The channel attention branch selectively emphasized the information features using global information using a fusion of global average pooling and global max pooling, in order to suppress the redundant features. As such, the edge features of the detection target were selectively highlighted to effectively solve image blurring and occlusion. The spatial attention branch also used a parallel structure of channel max pooling and channel average pooling, in order to arrange the different positions of feature information with different importance. Therefore, the important spatial positions were highlighted to suppress the spatial information redundancy, in order to effectively enhance the regional features under strong background backlighting interference. Finally, the image scene evaluation indicators were introduced into the loss function to adaptively adjust the weight of the large-scale grid loss function. The network was more focused to detect a large number of small targets during training, thereby improving the overall detection accuracy of the network. A series of ablation experiments were conducted on a specific dataset. Various classical detections were compared to verify the effectiveness and real-time performance of the detection. The Jetson Xavier NX platform was adopted to fully meet the transplantation requirements with high detection accuracy. The test results indicate that the improved model was achieved with a detection accuracy of 89.58%. The cattle face detection accuracy was improved by 7.34 percentage points, compared with the original YOLOv7-tiny network. The detection speed was 62 frames per second. Detection performance outperformed both the original and comparative network under the condition of almost no loss in detection speed, particularly for the captured images in complex real-world scenarios. This performance demonstrated excellent robustness and significant practical value with wide-ranging application prospects. [ABSTRACT FROM AUTHOR]

Accurate prediction of regional water evapotranspiration can greatly contribute to the rational utilization of regional water resources for the water resources saving. Crop evapotranspiration can be one of the most important indicators for the water evapotranspiration status of crops, in order to evaluate the soil water balance of farmland and the water management of farmland. However, the calculation of crop evapotranspiration requires a large amount of meteorological factor data. There is often redundant data with the low correlation with the evapotranspiration in meteorological data, which seriously affects the prediction accuracy and efficiency of the model. It is a high demand to extract the core environmental factors for the less-factor prediction model. The classification and regression tree (CART) can be used to make feasible for the large data sources in a short time. Then, Less factors can be extracted for the predictive analysis in a more scientific way. In this study, a less-factor water evapotranspiration model was constructed to efficiently and accurately predict the evapotranspiration data. The core factors were selected from multiple meteorological factors. 23 typical stations were selected in the nine agricultural regions, and then collected data on eight meteorological factors, such as precipitation, and sunshine hours. The meteorological factors were ranked in the order of importance using CART. The top 3-5 meteorological factors were then selected to predict the evapotranspiration using the extreme learning machine (ELM) model. At the same time, the ELM model was optimized using genetic algorithm (GA), particle swarm optimization (PSO), sparrow search algorithm (SSA). Three optimization algorithms (GA-ELM, PSO-ELM, SSA-ELM) were used to construct a less-factor hybrid optimization water evapotranspiration prediction model. Relative root mean square error (RMSE), coefficient of determination (R 2 ), mean absolute error (MAE), and NashSutcliffe coefficient (NSE) were used to evaluate the performance of the ELM and optimization model. The results showed that: 1) The main influencing factors were ranked in the order of the precipitation, sunshine duration, mean station pressure, daily maximum temperature, and average relative humidity using the importance ranking of the CART algorithm. 2) The PSOELM model presented the highest prediction accuracy among the three optimization algorithms. Specifically, the RMSE of evapotranspiration prediction for the 23 stations was ranged from 6.608 to 22.077 mm/d, the NSE of 0.824-0.998, R 2 of 0.908- 0.995, and MAE of 5.075-16.677 mm/d. In addition, the RF and SVR model with the strong generalization performance were selected to compare with the PSO-ELM model. The prediction performance of three models was slightly improved with the increase of the input factors, indicating the slight overall improvement. The three models shared the strong generalization and robustness. The meteorological factors were input into the prediction model, according to the order of importance. The meteorological factors ranked the 4th and 5th were relatively less important, where the prediction accuracy was slightly improved with the increase of the input parameters. The PSO-ELM model presented the highest prediction accuracy among the three models. 3) The ELM model performed better applicability in the Yunnan-Guizhou Plateau region, the Sichuan Basin, and the surrounding areas. The three optimization algorithms showed the better applicability in the South China and the YunnanGuizhou Plateau region, with the highest applicability of the PSO-ELM model. The findings can provide an important reference for the crop water demand calculation in nine major agricultural regions in China. [ABSTRACT FROM AUTHOR]

The Education Metaverse is a brand-new pattern of educational digitization transformation, which opens the possibility of interactive mirrored avatars of life entities, virtual experiences instead of embodied cognition, algorithms customizing to address individual needs, and digital space enabling real-world education. Although the prospect of education is attractive, there are still some hidden problems in education, such as how to confirm the subjectivity of education, how to exert the function of education soul casting, and how to embody the value concern of education to human beings, how to seek the essential basis of educational rationality and other potential risks. By changing our minds, we are rediscovering the children of the Digital Age; by exploiting technology enrichment to create an integrated learning landscape, pursuing the return of virtue through ethical values, making clear our bottom line, following the logic thinking of man-education-social productivity linkage development, the technological advantages inherent from the Education Metaverse can be translated into the effect of educating people. [ABSTRACT FROM AUTHOR]

SMP Negeri 1 Soreang is one of the favorite public junior high schools in the Soreang area, Bandung Regency. To improve the quality of schools, it is done through the distribution of scholarships. Determination of scholarships is determined by using an assessment that has certain criteria. By utilizing classification techniques, one of which is the Decision Tree algorithm model, which is chosen to be able to assist in making decisions about who is eligible to get a scholarship so that it is right on target. Decision Tree Algorithm is a classification method to utilize data in order to find the relationship between the target variable and the input variable. The data contains student scores of 237 people. From the modeling process, the accuracy value is 92%. So it can be concluded that this algorithm can assist in providing selection decisions for junior high school (SMP) scholarship recipients. The Decision Tree algorithm was chosen because it has a high level of accuracy compared to other classification algorithms. In addition, the Decision Tree algorithm is used to make a decision from the information obtained. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Engineering Management / Gongcheng Guanli Xuebao is the property of Journal of Engineering Management Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Dragonfly network,proposed by Kim et al.,is a topology for high-performance computer systems.In dragonfly network,compute nodes are attached to switches,the switches are organized into groups,and the network is organized as a two-level clique.There is a link between any two nodes in a group,and there is a global link between different groups.Completely independent spanning trees(CISTs) play important roles in reliable information transmission,parallel transmission and safe distribution of information.In practical application,with the continuous increase of network scale,the requirements for information transmission efficiency and security are becoming higher and higher.Therefore,it is meaningful to study the construction of completely independent spanning trees in dragonfly network.At present,there are many results on completely independent spanning trees in networks,but completely independent spanning trees in dragonfly network have not been studied.In this paper,construction algorithm for the global link of dragonfly network is proposed,which is divided into completely independent spanning tree under re-lative link,absolute link and circulant link.Based on this division,a construction algorithm for the edge set of completely independent spanning tree is given,and the correctness of the above algorithms is proved.Finally,the time complexity of these algorithms is analyzed.

Objective To develop an automatic and objective quantification algorithm based on corneal topography to evaluate the orthokeratology (Ortho-K) lens fitting. Methods K-means algorithm was used to analyze the defocus ring of corneal topography and fit the most suitable ellipse on the defocusing region of corneal topography combining with weighted least squares method. The decentration distance was defined as the distance between the pupil center and the center of ellipse. Corneal topography images from 200 eyes (100 patients) were used to evaluate the accuracy and repeatability of the algorithm. Results The decentration distance was calculated using this automatic algorithm. One hundred and fourteen eyes (57%) had mild decentration, 75 eyes (37.5%) had moderate decentration and 11 eyes (5.5%) had severe decentration after wearing Ortho-K lenses. Compared with manual evaluation, the automatic algorithm showed higher accuracy and better repeatability. Conclusions Using K-means combining with weighted least squares algorithm, an objective and accurate algorithm was developed to evaluate Ortho-K fitting and could be used as an useful quantitative analysis tool for future clinical practice and research. [ABSTRACT FROM AUTHOR]

Marketization of rural idle homesteads can be promoted for “three rights separation” of rural homesteads. The perceived risk can be one of the most important identification factors in the farmers’ homestead marketization in the reform of the homestead system. In this study, an evaluation system was constructed to identify the risk of homestead marketization from the perspective of farmers’ risk perception, including the risk of the farmers’ equity, cultivated land protection, collective return, social stability, ecological environment, and cultural inheritance risk. Taking Chongqing Dazu, Yongchuan, and other typical homestead system reform pilots as empirical areas, a Variable Fuzzy set was adopted to evaluate the risk level of farmers’ homestead marketization. Subsequently, an efficient machine learning model (called Random Forest, RF) was employed to identify the risk of farmers’ homestead marketization. Finally, risk prevention and control strategies were proposed under different marketization patterns. The results showed that: 1) An inverted “U” shaped fluctuation trend was found in the risk level of farmers’ homestead marketization. Specifically, the risk probability of direct marketization (43.38%) and green indicators marketization (32.14%) were significantly higher than that of marketization of reclamation indicators (13.73%), and marketization of planning adjustment (12.99%). 2) There were some differences in the various risk factors for the four types of marketization patterns. The farmers’ equity risk (46.74%) was higher in the direct marketization, whereas, the cultural inheritance risk (4.76%) was lower. The social stability risk (35.66%) was higher in the marketization of planning adjustment, while the ecological environment risk (5.85%) was lower. The cultivated land protection risk (29.13%) was higher in the marketization of reclamation indicators, while the collective return risk (2.05%) was lower. The farmers’ equity risk (34.24%) was higher in the green indicators marketization, while the ecological environment risk (1.20%) was lower. 3) There were some differences in the risk mechanism. The same risk factor also shared the heterogeneity in the homestead marketization of four types. 4) The differentiated prevention strategies of marketization risks were considered the timing, and the cooperation of various participants, such as farmers, collectives, and governments. The government, the collective, and the farmer were united to focus on the “prevention in advance” in the direct marketization. The collective was supposed to focus on the “prevention in advance” in the marketization of planning adjustment, while the government was on the “prevention afterward”. By contrast, the government was supposed to focus on the “prevention in the event” in the marketization of reclamation indicators, while the collective was on the “prevention afterward”. Only the government was supposed to focus on the “prevention in advance” in the green indicators marketization. In conclusion, the reform of homestead marketization should set the tone of “steady and prudent promotion”. It was different from the complete marketization of state-owned land. But the marketization was limited using “three rights separation”. Meanwhile, future homestead marketization should avoid homogenization patterns. More importantly, it is urgent to establish an income distribution mechanism and risk aversion system, in order to share the benefits and risks by farmers, collectives, and governments. The findings can provide the decisionmaking on rural residential land consolidation. The homestead risk database can be expected to deepen the reform of the homestead system. [ABSTRACT FROM AUTHOR]

With the rapid development and in-depth application of information technology, algorithms are widely used in transportation, medical care, e-commerce, information services, public management and other fields, but their own data dependence, logical black box and general purpose characteristics also bring about data abuse in algorithm applications, algorithm discrimination in public management, algorithm manipulation in information services, algorithm collusion in market competition. In order to deal with the problems caused by the application of algorithms, countries have actively taken measures to strengthen the governance of algorithm applications. To promote the effective use of algorithms and strengthen algorithm governance, we need to further strengthen top-level design, improve governance pattern, enrich governance means, strengthen international coordination, and constantly improve the algorithm governance system. [ABSTRACT FROM AUTHOR]

The update and iteration of high and new technologies such as big data, cloud computing, artificial intelligence and other high technologies, makes the algorithm have a wide range of application fields in the technical level. Ordinary users will encounter negative effects such as algorithm discrimination, algorithm black box and algorithm injustice when using intelligent devices. Therefore, the research of "algorithm colonization" is of significance in two aspects: on the one hand, it analyzes the mechanism behind this phenomenon; on the other hand, it explores the practice path of human society's fair development in the algorithm era. Through the realistic analysis of the algorithms used by Internet companies such as GAFA, this paper points out the basic characteristics of algorithms under the capitalist background, that is, algorithms permeate every corner of human social life with the help of data capital. No single country or region can completely get rid of the dilemma of "algorithmic colonization", which requires us to explore the root causes of "algorithmic colonization" from the perspective of global governance and seek solutions. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Shenzhen University Humanities & Social Sciences is the property of Journal of Shenzhen University (Humanities & Social Sciences) Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Architectural Journal / Jian Zhu Xue Bao is the property of Architectural Journal Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Acta Scientiarum Naturalium Universitatis Sunyatseni / Zhongshan Daxue Xuebao is the property of Sun-Yat-Sen University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The quality of cocoons directly affects the quality of raw silk. Before reeling, the cocoons should be sorted to remove the inferior cocoons that cannot be used for reeling. The double cocoon is the best raw material for making silk quilts. Therefore, it should be separated from other types of inferior cocoons and classified separately in the process of selecting cocoons. The traditional cocoon sorting basically relies on manpower, which involves high labor intensity, low efficiency of cocoon selection and high missing rate, and the accuracy of cocoon selection is easily affected by subjective factors such as the technical level and mental state of cocoon selection personnel. With the rapid development of artificial intelligence, machine vision technology is widely used to replace human eyes for detection and recognition. At present, in machine vision technology, the recognition of double cocoons mainly includes area threshold method and the threshold method of the ratio of long to short axis of ellipse. The disadvantage of the area threshold method is that small double cocoons cannot be detected. The threshold method of the ratio of long to short axis of ellipse is not applicable to the double cocoon of the head-to-tail type and relies too much on the accurate fitting of the cocoon shape. However, due to the slight irregularity of the cocoon shape, the attachment silk and the adhesion between the cocoons, the ellipse fitting is different from the actual shape, which may result in people’s misjudging the cocoon type near the threshold. Therefore, there are some limitations in the identification of double cocoons by only depending on the area threshold or the threshold of the ratio of long to short axis of the fitted ellipse. Due to the dense distribution of cocoons in the cocoon image and the interference of attached silk, the gray scale of adjacent cocoons is similar, so it is difficult to conduct segmentation completely by traditional image segmentation methods. Therefore, the segmentation and counting problem of target cocoons should be solved first for the classification and recognition of cocoons. Aiming at the problems that it is difficult to accurately segment the adhered cocoons and the limitations of the existing double cocoon recognition methods, we proposed a series of algorithms for the segmentation and counting of cocoons, the shape restoration of cocoons and the recognition of double cocoons. Firstly, we proposed an improved image segmentation algorithm based on variable segmentation threshold by combining with morphological operation. Otsu binarization threshold was used as the initial segmentation threshold, and binarization, morphological erosion operation and morphological expansion operation were performed on the image. The pixel area threshold and length-width threshold of single cocoons were used to distinguish the connected region. If a cocoon is a single one, it is marked and counted. If it is determined to be connected to other cocoons, the segmentation threshold is increased again for another segmentation. Then, a cocoon shape restoration method based on edge detection and ellipse fitting was proposed. The pixels of each cocoon that had been segmented were traversed. The Canny operator was used to detect the edge of the original image. The pixel points of the segmented cocoon were used to determine the edge points of the cocoon, and the edge points were elliptically fitted to obtain the image of the cocoon. Finally, the ratio of long to short axis of the cocoon and the area parameters were calculated, and the double-parameter threshold was used to recognize the double cocoon. And then, the serial algorithm proposed in this paper was used to segment cocoon graphics, restore cocoon shape, and distinguish double cocoons in 10 cocoon images. The results show that the 222 cocoons of all the ten pictures are completely segmented, and the counting accuracy is 100%. A total of 46 double cocoons in the pictures are detected, and the classification accuracy of double cocoons and single cocoons is 98.6%. This method detects the small double cocoons which are easy to be missed by the area threshold method, and accurately identifies the cocoon type which is close to the threshold of the ratio of long to short axis. The series algorithm of cocoon target segmentation and double cocoon recognition proposed in this paper can effectively process the machine vision image to get the conclusion whether each cocoon is a double one. In the series algorithm, the pre-experiment is used to determine the area of the normal cocoon and the length of the long axis of the normal cocoon as the prior conditions in the captured image. The cocoon target with slight overlap can be separated, but the cocoons with serious overlap may be mistakenly divided into the same group, thus affecting the classification and recognition of the cocoon. Therefore, this method is suitable for the image processing of cocoons laid flat in the field of vision. [ABSTRACT FROM AUTHOR]

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Central China Normal University is the property of Huazhong Normal University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Automobile Technology is the property of Automobile Technology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Low visibility has significant influences on highways, ferries, civil aviation, and other modes of transportation, and the visibility observation of meteorological departments is not dense enough to meet the monitoring needs of low visibility weather. Using existing video surveillance equipment to extract visibility data can save a significant amount of money on visibility instrument deployment and maintenance, improve data density, and provide finer data support for traffic and urban safety operations. Based on video live image conversion, a simple convolutional neural network classification approach is suggested to extract visibility levels. The algorithm assumes that the video devices are installed horizontally and have an open view, and it creates a new fixed-size image by dividing the original video image into horizontal chunks and extracting the gradient, color saturation, and brightness information from each horizontal chunk. A simple convolutional neural network is used to learn and develop a visibility level recognition model from the converted images. The model is trained by 29668 video images of Yangshan Port Weather Station in Shanghai from September 2019 to December 2020, and then tested with 5757 video images from January to May in 2021. The comparison indicates the recognition model generated with this technique has a greater accuracy than the recognition model built directly with AlexNet network. The model has an overall accuracy of 87.99% during daytime and 81.32% during nighttime when the observed visibility is classified into five levels of fog-free, light fog, fog, dense fog, and thick fog according to the fog forecasting level. The model's identification ability for no fog and light fog is high. However, because the scenery becomes nearly indistinguishable once dense fog appears at night, the model's recognition ability for dense fog level at night is poor, and it is easy to categorize it as a fog level mistakenly. Taking 1000 meters as the criterion of low visibility weather, the algorithm's accuracy is 96.18% during daytime and 96.14% during nighttime. The algorithm features a quick learning rate and ease of application, making it suitable for low visibility video image recognition in most open-field scenarios. The model is applied during a radiation fog in Shanghai on 13 April 2021. The video images of the sparse area of the automatic weather station installation are collected for visibility identification, and the visibility distribution map formed together with the existing automatic weather station visibility meter data is more complete and accurate, which demonstrates that the model established by this algorithm can effectively compensate the problem of insufficient density of the existing automatic station visibility meter data, and has certain application value in meteorological operations.

Copyright of Cyber Security & Data Governance is the property of Editorial Office of Information Technology & Network Security and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)