The area covered by Chinese-style solar greenhouses (CSGs) has been increasing rapidly. However, only a few pyranometers, which are fundamental for solar radiation sensing, have been installed inside CSGs. The lack of solar radiation sensing will bring negative effects in greenhouse cultivation such as over irrigation or under irrigation, and unnecessary power consumption. We aim to provide accurate and low-cost solar radiation estimation methods that are urgently needed. In this paper, a method of estimation of solar radiation inside CSGs based on a least mean squares (LMS) filter is proposed. The water required for tomato growth was also calculated based on the estimated solar radiation. Then, we compared the accuracy of this method to methods based on knowledge of astronomy and geometry for both solar radiation estimation and tomato water requirement. The results showed that the fitting function of estimation data based on the LMS filter and data collected from sensors inside the greenhouse was y = 0.7634x + 50.58, with the evaluation parameters of R2 = 0.8384, rRMSE = 23.1%, RMSE = 37.6 Wm−2, and MAE = 25.4 Wm−2. The fitting function of the water requirement calculated according to the proposed method and data collected from sensors inside the greenhouse was y = 0.8550x + 99.10 with the evaluation parameters of R2 = 0.9123, rRMSE = 8.8%, RMSE = 40.4 mL plant−1, and MAE = 31.5 mL plant−1. The results also indicate that this method is more effective. Additionally, its accuracy decreases as cloud cover increases. The performance is due to the LMS filter’s low pass characteristic that smooth the fluctuations. Furthermore, the LMS filter can be easily implemented on low cost processors. Therefore, the adoption of the proposed method is useful to improve the solar radiation sensing in CSGs with more accuracy and less expense.

Natural ventilation as part of a greenhouse environment control system can save energy, reduce pollution, and cut the costs of production. Many studies have used the energy balance method to model greenhouse environments and calculate the rates of ventilation needed to control them. However, the efficacy of this method is influenced by many factors. There are often many parameters in the energy balance model, related to the greenhouse structure, crop growth, and climate. Thus, the effectiveness and applicability of greenhouse ventilation systems based on energy balance can be compromised. Here, we study a hierarchical fuzzy control method and use simple fuzzy logic controllers to control the coordination and opening angles of a new energy-saving solar greenhouse roof and sidewall ventilation. This design reduces the complexity of the fuzzy rule base and the fuzzy subsystem related to the physical model, therefore making it easy to design and build. The system uses the fuzzy tool in the Matlab environment, enabling a quick design, and the fuzzy inference engine fis.c file (Fuzzy Inference System) to load the design results into the fuzzy control system, thus making the modification and maintenance of the system easier. The experimental data showed that the new hierarchical fuzzy control reduced temperature fluctuations and maintained temperature closer to desired temperature than a non-fuzzy control method. Moreover, this method can also be easily used to control other equipment in the greenhouse.

目的:探索屈光参差性弱视患者静息状态下脑网络内及网络间功能连接的变化,为进一步了解弱视对脑功能的影响提供实验依据.方法:采集14名屈光参差性弱视儿童(AAC)和9名正常视力儿童(NSC)的静息态功能磁共振图像,提取其静息态功能连接(rsFC)作为特征,采用多变量模式分析进行分类,并使用单变量分析进行组间比较.结果:多变量模式分析对弱视儿童和正常视力儿童分类的正确率显著较高;弱视儿童组的默认网络内,默认网络与额顶控制网络及背侧注意网络之间的rsFC减弱.结论:基于rsFC的多变量模式分析可以将弱视儿童识别出来;弱视儿童组减弱的rsFC提示弱视儿童在调节外部和内部目标导向认知之间的动态平衡及相互转换以适应任务需求的能力减弱.

In order to promote the modern management of greenhouse in North China, and to make the facility horticulture develop towards high yield and high efficiency, the intelligent control system was established based on ZigBee.To meet the needs of the control of agricultural greenhouse facilities, the system was produced by Jennic wireless microcontroller JN5139 as the control core, and the wireless sensor network was composed of sensor nodes and ZigBee wireless intelligent terminal.The system realized the real-time collection, monitoring, display, warning and control of the greenhouse environmental factors (including air temperature, humidity, light intensity, CO2 concentration and soil pH value), and could provide the historical data of greenhouse environmental factors.In order to make the results more accurate, the sensor data sequence was smoothed three times on the nodes, then the smoothed data was sent to the gateway, and the linear regression analysis of data were carried out.The system basically met the needs of wireless, intelligent and precise modern facilities horticulture.The results showed that the greenhouse intelligent control system had the characteristics of stable and simple operation, and the results were accurate, which could effectively improve the management efficiency of the solar greenhouse, and had a good application prospect.

Simulation of disease lesion on the surface of plant organ is a tough task due to the difficulty of the dynamic lesion appearance data acquisition. A method for three-dimensional visualization of plant disease appearance is presented only using lesion images. Given a single lesion image, the framework first extracts static appearance properties (e.g., lesion shape, diffuse reflectance, and texture), and deduces time-varying dynamic transition processes of these properties based on intrinsic decomposition. To simulate the spatial-varying appearance of disease on the plant organ surface, the user indicates to the framework the lesion distribution information(e.g., position, size, and direction) by simple interactions. A time-varying appearance model is also proposed for simulating the weathering appearance of the plant disease lesion. The experimental results demonstrate that the proposed method can realistically render appearances of various disease lesion types, and it has significant potential to be used as an effective visualization tool for decision making and education training in the field of agriculture.

Assemble language programming is a course with combination of software and hardware. Theory is as important as practice in this course. Assemble language is a low-level language which is hardware oriented. This course is more difficult to master compared with high-level language for it is uninteresting. This paper analyzes the position of the course in computer teaching system, the effect of the course and the existing problems of the course. Some reform measures to the problems existed in the course are suggested in the paper.

目前大学毕业生就业越来越难,尤其高等农业院校工科专业大学毕业生就业形势越来越严峻.计算机专业大学生对实践动手能力要求较高,校企联合人才培养模式是提高大学生实践能力、解决就业难的主要途径.从优化人才培养方案、强化实践技能、改革实验课程考试制度、借助校企合作培养双师型教师队伍等方面构建校企联合人才培养机制.实践证明,开展校企联合办学,实现高等学校和用人企业无缝对接,促进双师型教师队伍建设,有效地提高大学生的动手实践能力,增加大学毕业生在人才市场上的就业竞争能力.

Amblyopia is a neurological disorder of vision that follows abnormal binocular interaction or visual deprivation during early life. Previous studies have reported multiple functional or structural cortical alterations. Although white matter was also studied, it still cannot be clarified clearly which fasciculus was affected by amblyopia. In the present study, tract-based spatial statistics analysis was applied to diffusion tensor imaging (DTI) to investigate potential diffusion changes of neural tracts in anisometropic amblyopia. Fractional anisotropy (FA) value was calculated and compared between 20 amblyopic children and 18 healthy age-matched controls. In contrast to the controls, significant decreases in FA values were found in right optic radiation (OR), left inferior longitudinal fasciculus/inferior fronto-occipital fasciculus (ILF/IFO) and right superior longitudinal fasciculus (SLF) in the amblyopia. Furthermore, FA values of these identified tracts showed positive correlation with visual acuity. It can be inferred that abnormal visual input not only hinders OR from well developed, but also impairs fasciculi associated with dorsal and ventral visual pathways, which may be responsible for the amblyopic deficiency in object discrimination and stereopsis. Increased FA was detected in right posterior part of corpus callosum (CC) with a medium effect size, which may be due to compensation effect. DTI with subsequent measurement of FA is a useful tool for investigating neuronal tract involvement in amblyopia.

Insect pestilence is one of the main defects of the apple industry, which could be caused by pest entrance during apple tree growth stages. Insect pest detection in apples is important for an automatic apple quality inspection and sorting system. In this study, we intended to determine the feature vectors that can be used for nondestructive detection of apple fruit insect pests and utilized hyperspectral imaging technology to carry out an effective method for rapid, non-invasive detection of the intact apples and insect pests. There were 160 samples of 80 intact and 80 insect infected ‘red Fuji’ apples to be investigated from an apple planting demonstration garden in the Shenbei New Area in Shenyang city. A hyperspectral imaging collection system with the wavelength range of 400-1 000 nm was established to acquire the hyperspectral images of these apple samples. Via the analysis of spectral reflectance of apple pest parts and the normal region, there were obvious differences in spectral reflectance at the 646 nm wavelength. So, the image of the 646nm wavelength was named the feature image. Then, the feature image was manipulated by threshold segmentation, dilation, and erosion operation, to obtain a mask image. The mask image was used for image analysis to mask and carried on principal component analysis. The optimum PC1 image was chosen and handled by the maximum entropy threshold segmentation to extract the pest region. Later, a comparative analysis of the texture feature of the insect infested region and the normal region on apples of the PC1 image, a region of interest (ROI) with 80 pixels×65 pixels of the PC1 image of each sample, was obtained. The texture features of the gray level co-occurrence matrix (of energy, entropy, moment of inertia and correlation) in four directions, which were 0, 45, 90, and 135 deg, respectively, were extracted. In addition, the spectral relative reflectance of the apple surface pests and normal regions, whether it was visible or near infrared region, had obvious difference. So the two spectral features of the spectrum relative reflectivity at 646 and 824 nm wavelength were acquired, which had larger relative reflectance differences between the apple surface pests and normal regions in the visible region and near infrared region, respectively. Feature vector selection was one of the key steps in detecting apple insect pests. For faster and more accurate detection of the apple insect pests, in this study, the optimization and integration of the texture features and the spectral feature vectors was analyzed. Four feature vector groups were posed respectively as the input vector of the BP neural network. The validation set of 30 normal apples and 30 insect infested apples was detected by using the BP neural network. The recognition rate was the highest when there was a fusion of the texture features of energy, entropy, moment of inertia, the correlation of 0 deg direction, and the spectral features of relative spectral reflectance with two feature wavelengths of 646 and 824 nm. A recognition rate of the normal apples and insect infested apples was 100 percent. Besides, in this case, the speed of detection is the fastest, and the MSE error is the smallest. Results show that the obtained feature vectors based on hyperspectral imaging technology can identify insect infestation effectively and provide a reference for apples quality detection and grading system using multispectral imaging.

Amblyopia is a developmental disorder resulting from anomalous binocular visual input in early life. Task-based neuroimaging studies have widely investigated cortical functional impairments in amblyopia, but changes in spontaneous neuronal functional activities in amblyopia remain largely unknown. In the present study, functional connectivity density (FCD) mapping, an ultrafast data-driven method based on fMRI, was applied for the first time to investigate changes in cortical functional connectivities in amblyopia during the resting-state. We quantified and compared both short- and long-range FCD in both the brains of children with anisometropic amblyopia (AAC) and normal sighted children (NSC). In contrast to the NSC, the AAC showed significantly decreased short-range FCD in the inferior temporal/fusiform gyri, parieto-occipital and rostrolateral prefrontal cortices, as well as decreased long-range FCD in the premotor cortex, dorsal inferior parietal lobule, frontal-insular and dorsal prefrontal cortices. Furthermore, most regions with reduced long-range FCD in the AAC showed decreased functional connectivity with occipital and posterior parietal cortices in the AAC. The results suggest that chronically poor visual input in amblyopia not only impairs the brain's short-range functional connections in visual pathways and in the frontal cortex, which is important for cognitive control, but also affects long-range functional connections among the visual areas, posterior parietal and frontal cortices that subserve visuomotor and visual-guided actions, visuospatial attention modulation and the integration of salient information. This study provides evidence for abnormal spontaneous brain activities in amblyopia.

In light of the better mobility and flexibility of the mobile terminal, this paper proposes a design of Android-based power se-curity risk on site monitoring system. With this software, users can view and operate the instructions during the operation in real-time, monitor and record the information of the job sites, to achieve the assessment, warning and containment of the full state of standardized job security risk. And it comprehensively improves the level of standardization of operating risk containment.

Aims To investigate the potential morphological alterations of grey and white matter in monocular amblyopic children using voxel-based morphometry (VBM) and diffusion tensor imaging (DTI). Methods A total of 20 monocular amblyopic children and 20 age-matched controls were recruited. Whole-brain MRI scans were performed after a series of ophthalmologic exams. The imaging data were processed and two-sample t-tests were employed to identify group differences in grey matter volume (GMV), white matter volume (WMV) and fractional anisotropy (FA). Results After image screening, there were 12 amblyopic participants and 15 normal controls qualified for the VBM analyses. For DTI analysis, 14 amblyopes and 14 controls were included. Compared to the normal controls, reduced GMVs were observed in the left inferior occipital gyrus, the bilateral parahippocampal gyrus and the left supramarginal/postcentral gyrus in the monocular amblyopic group, with the lingual gyrus presenting augmented GMV. Meanwhile, WMVs reduced in the left calcarine, the bilateral inferior frontal and the right precuneus areas, and growth in the WMVs was seen in the right cuneus, right middle occipital and left orbital frontal areas. Diminished FA values in optic radiation and increased FA in the left middle occipital area and right precuneus were detected in amblyopic patients. Conclusions In monocular amblyopia, cortices related to spatial vision underwent volume loss, which provided neuroanatomical evidence of stereoscopic defects. Additionally, white matter development was also hindered due to visual defects in amblyopes. Growth in the GMVs, WMVs and FA in the occipital lobe and precuneus may reflect a compensation effect by the unaffected eye in monocular amblyopia.

The production practice indicated that temperature was one of the important factors affecting the growth and development of rice.Appropriate water temperature could promote the growth and development of rice.This paper presented the design philosophy of an automatic control irrigation system which was heated by solar,and introduced the working principle of this system and design methodology of software and hardware.The practice demonsrated that this system had high research value based on the advantages of saving energy and improving the quality and output of rice.

Objective To investigate white matter volume changes in children with anisometropic or ametropic amblyopia.Methods A total of fourteen anisometropic amblyopic children,eight ametropic amblyopia and twenty matched normal controls participated in this study.After high resolution T1WI images of the brain were acquired,the data were processed using voxel-based morphometry (VBM).Two sample t-test were employed to analyze the morphological changes of white matter in amblyopic groups.Results Compared to healthy controls,anisometropic amblyopic group showed decreased white matter volume in left calarine sulcus and superior parietal lobule,whereas increased white matter volume was detected in right cuneus.In ametropic amblyopia group,only reduced white matter volumes were found,and the regions predominantly located in right occipital lobe and frontal lobe.Conclusions Both anisometropic and ametropic amblyopic children undergo white matter morphologic changes in vision associated regions.These findings suggest that besides gray matter,developmental abnormalities also exist in white matter,and such morphologic changes also may contribute to visual impairment in amblyopic children as well.

This paper presents a short-term electric load forecasting method based on Autoregressive Tree Algorithm and Rough Set Theory. Firstly, Rough Set Theory was used to reduce the testing properties of Autoregressive Tree. It can optimize the Autoregressive Tree Algorithm. Then, Autoregressive Tree Model of Short-term electric load forecasting is set up. Using Rough Set Theory, the attributes will be reduced off; whose dependence is zero, through knowledge reduction method. It not only avoids the complexity and long training time of the model, but also considers various factors comprehensively. At the same time, this algorithm has improved the prediction rate greatly by using automatic Data Mining Algorithms. Practical examples show that it can improve the load forecast accuracy effectively, and reduce the prediction time.

Operation risk is the primary risk to rural power company, and it is also an important part of security risk management. The paper establishes a model to assessed risks of standard operation in rural power network, based on Job Risk Analysis (LEC). The example and application of the model in rural power network security risk management system shows that it can realize the real-time assessment of standard operation risk, the model use is simple and the assessment result is accurate. This study has important reference value to practical application of rural power security risk assessment.

An intelligent standardization operating management system was developed, which can fit the status of power development in China's rural areas and combine the modern information technology. The object-oriented idea was applied in the design of the system. The.NET Framework system was used to develop the system. The system was based on a C/S and B/S mixed mode and three-layer structure. The workload of staff was lower and the risk of field operations was reduced because of the automatic generation, the unified management of the operation guidance books the work bills and operation bills. The system provided support decision-making for the overall statistics, analysis and mining of the information.

On the basis of the newest version of Standards of Power Supply Enterprise Security Risk Assessment and Prevention Manual of Power Supply Enterprise Security Risk Identification compiled by State Grid Corporation,Security Risk Assessment Management System was designed and developed. The system combined the past experience of hazard identification and management and daily production safety practice,to assess the security risk in daily standard operation,and to scientifically manage the information of enterprise security risk. The article analyzes the management process of security risk assessment,formulates the overall target of the system and ac-complishes overall system architecture design. The application of the system shows that the system can effectively assess the risk and improves the level of standardization of the assessments.

In order to achieve favorable greenhouse environmental conditions for plant growth, modeling and simulation of greenhouse environments is very essential. In this paper, a flexile tool which called ANFIS is used to model the greenhouse climate in the North of China. A set of published data was used to train and test this model. The values predicted by the ANFIS model were compared with the measured data. The accuracy of the model is checked using several statistical and graphical criterions. The presented method in this paper is a new approach in estimating environmental factors and faster and simpler solutions can be obtained.

The paper analyzed and designed a rural power network standardization operation management system with UML technology based on C/S and B/S composited structure. The paper also analyzed the management process of standardization operation, described the development environment, functions and structure of the system. The system use case was designed. In particular, the design of the operation guidance book was presented. Through the application of standardization operation management system, the scientific and normal management of standardization operation can be realized.