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Central precocious puberty (CPP) is an endocrine disease in children, characterized by rapid genital development and secondary sexual characteristics before the age of eight in girls and nine in boys. The premature activation of the hypothalamic-pituitary-gonadal axis (HPGA) limits the height of patients in adulthood and is associated with a higher risk of breast cancer. How to prevent and improve the prognosis of CPP is an important problem. Vitamin D receptor (VDR) is widely expressed in the reproductive system, participates in the synthesis and function of regulatory sex hormones, and affects the development and function of gonads. In addition, gut microbiota plays an important role in human health by mainly regulating metabolites, energy homeostasis, and hormone regulation. This review aims to clarify the effect of vitamin D deficiency on the occurrence and development of CPP and explore the role of gut microbiota in it. Although evidence on the interaction between vitamin D deficiency, gut microbiota, and sexual development remains limited, vitamin D supplementation and gut microbiota interventions offer a promising, non-invasive strategy for managing CPP. [ABSTRACT FROM AUTHOR]

Concepts learning is the most fundamental unit in the process of human cognition in philosophy. Granularity is one of the fundamental concepts of human cognition. The combination of granular computing and concept learning is critical in the cognitive process. Meanwhile, efficiently and accurately using the information collected from different sources is the focus of data mining in the contemporary. Hence, how to sufficiently learn concepts under a multi-sources context is an essential concern in the field of cognition. This paper offers a new thought for two-way concept-cognitive learning based on granular computing in multi-source fuzzy decision tables. Firstly, based on the best possible guarantee of the classification ability, original information from different sources is fused by conditional entropy, which is the kind of multi-source fusion method (i.e., CE-fusion). Secondly, we learn concepts from a given object set, attribute set, or pair of object and attribute sets in the fused information table, and these three types of concept learning algorithms are designed. This analysis shows that two-way concept learning based on multi-source information fusion is a suitable method of multi-source concept learning. Some examples are valuable for applying these theories to deal with practical issues. Our work will provide a convenient novel tool for researching concept-cognitive learning methods with multi-source fuzzy context. [ABSTRACT FROM AUTHOR]

In the shallow-water waveguide environment, the tonal signals radiated by moving targets carry modal interference and Doppler shift information. The modal interference can be used to obtain the time of the closest point of approach ( t C P A ) and the ratio of the range at the closest point of approach to the velocity of the source ( r C P A / v ). However, parameters r C P A and v cannot be solved separately. When t C P A is known, the r C P A and the v of the target can be obtained theoretically by using the Doppler information. However, when the Doppler frequency shift is small or at a low signal-to-noise ratio, there will be a strong parametric coupling between r C P A and v. In order to solve the above parameter coupling problem, a target motion parameter estimation method from tonal signals with a single hydrophone is proposed in this paper. The method uses the Doppler and modal interference information carried by the tonal signals to obtain two different parametric coupling curves. Then, the parametric coupling curves can be used to estimate the two motion parameters. Simulation experiments verified the rationality of this method. The proposed method was applied to the SWellEx-96 and speedboat experiments, and the estimation errors of the motion parameters were within 10%, which shows the method is effective in its practical applications. [ABSTRACT FROM AUTHOR]

The trisecting–acting–outcome model of a three-way decision is a practical approach to solving complex problems, consisting of three components: trisecting, acting, and outcome. Trisecting means dividing a complex problem into three parts based on an evaluation function, and acting means designing the corresponding action to apply to these three parts. The outcome denotes evaluation effectiveness based on the first two steps. Measuring this outcome is a challenging and moving question after trisecting-and-acting (T &A). This paper proposes a method to measure outcome based on object changes before and after T &A based on top-down and bottom-up perspectives, depending on the decision needs. First, evaluation functions are constructed that extend the traditional evaluation functions to measure changes. Then, based on the introduced evaluation functions, qualitative and quantitative of three-way changes are introduced and analyzed. Furthermore, we present the processes that determine the optimal strategy with the quantitative analysis. The analysis of several examples shows the effectiveness and practicality of our proposed method. [ABSTRACT FROM AUTHOR]

The trisecting–acting–outcome (TAO) model of three-way decision plays a crucial role in solving complex problems. It comprises three components: trisecting, acting, and outcome. Trisecting means dividing an entire set into three relatively small parts based on an evaluation function, and acting means designing the corresponding action to apply to these three parts. The outcome denotes evaluation effectiveness based on the first two steps. Several previous investigations have measured effects from a coarse-grained perspective. Still, they have not considered the impact of fine-grained changes in objects and have not devised the method of selecting a strategy-constructed tripartition for a given expected outcome. The present study aimed to explore the outcome measure from the view of fine-grained changes in objects named change-based three-way decision. It also provides a novel approach to quantifying outcomes using utility theory. Specifically, we conducted the study using two perspectives: top-down and bottom-up, depending on the decision demands. The object is first divided into three regions based on changes corresponding to different utilities from the top-down view. Then, the overall utility is aggregated based on the particular utility metrics representing different satisfaction levels in the three regions. From the bottom-up perspective, we derive a pair of thresholds based on the desired utility of the decision-maker and thus obtain three parts whose changes can make by some action to achieve these effects. The principal conclusions of this exploration are that outcomes can be better ranked using objects' changes using utility theory and that strategies can be better acting to achieve corresponding variations based on the expected utility of the decision-maker. We describe the specific steps and procedures of the proposed method through several examples. The findings presented in this paper add to our understanding of the outcome measure for the three-way decision. [ABSTRACT FROM AUTHOR]

The trisecting-acting-outcome (TAO) model of three-way decisions includes trisecting a universal set into three separate and closely connected regions, devising, and applying efficient strategies on the three regions, furthermore evaluating the outcome. This paper introduces the proportional utility function (PUF), representing the ratio between an object's initial and final quantity, to measure the outcomes from two different perspectives for movement-based three-way decision. The first perspective, is that if each object produces the same benefits or costs when they have the same movement (call region-independent), we sum up the three regions' utility as the overall outcome. The second scenario, is that each object generates a different cost or benefit, even if they have the same movements (call region-dependent). Here, finding an optimal investment plan is an essential matter based on their equivalent classification according to their specific characteristics. For a single equivalence class, we design a strategy to reach the goal under a specific investment, although this investment may be conservative. For all equivalence classes, we give one-time optimal investment plans using PUF based on invested or reserved resources in the movement-based three-way decision. Based on the program, we adopt a series of strategies in action under these investment budgets. The experimental results show that our strategy choices have practical significance and are in line with expected results. [ABSTRACT FROM AUTHOR]

The basic idea of the three-way decisions (3WD) is 'thinking in threes.' The TAO (trisecting-acting-outcome) model of 3WD includes three components, trisect a whole into three reasonable regions, devise a corresponding strategy on the trisection, and measure the effectiveness of the outcome. By reviewing existing studies, we found that only a few papers touch upon the third component, i.e., measure the effect. This paper's principal aim is to present an effectiveness measure framework consisting of three parts: a specific TAO model - Change-based TAO model, interval sets, and utility functions with unique characteristics. Specifically, the change-based TAO model provides a method to measure effectiveness based on the difference before and after applying a strategy or an action. First, we use interval sets to represent these changes when a strategy or an action is applied. These changes correspond to three different intervals. Second, we use the utility measurement method to figure out three change intervals. Namely, different utility measures correspond to the different intervals, concave utility metric, direct utility metric, and convex utility metric, respectively. Third, it aggregates the toll utility through the joint of the three utilities mentioned above. The weights among these three are adjusted by a dual expected utility function that conveys the decision-makers' preferences. We give an example and experiment highlighting the validity and practicability of the utility measure method in the change-based TAO model of three-way decisions. [ABSTRACT FROM AUTHOR]

Purpose: Calcareous soils are characterized by high pH and phosphorus (P) fixation capacity. Increasing application of P fertilizer recently has significantly improved soil P concentration, especially available P (Olsen-P) and inorganic phosphate (Pi) fractions. However, there are few data available on the ability of soils with different initial Olsen-P levels to continuously supply P (i.e., P desorption capacity) to crops without additional P fertilization and on which Pi fraction exerts the greatest influence on P desorption capacity. Materials and methods: Five soils with different initial Olsen-P levels (0.5, 14.3, 38.4, 55.4, 72.3 mg kg−1, hereafter refer as OP1, OP2, OP3, OP4, and OP5) but similar other soil properties were selected to evaluate the capacity of P desorption and its relationship with Pi fractions. Soil P was sequentially extracted once daily for 16 consecutive days using Olsen solution. Results and discussion: The content and proportions of dicalcium phosphate fraction (Ca2-P), octacalcium phosphate fraction (Ca8-P), aluminum phosphorus fraction (Al-P), and iron phosphorus fraction (Fe-P) in Pi increased significantly with the increase of initial Olsen-P (P < 0.01). Applied P fertilizer was mostly stored as Ca8-P in the soil. Soil P desorbed reached an equilibrium after 16 extractions for all soils, and P desorption capacity (12–358 mg kg−1) showed a significant linear relationship with initial Olsen-P (P < 0.01), with an increase of 4.2 mg kg−1 desorbed P per 1 mg kg−1 increase of initial Olsen-P. Ca2-P exerted the conclusive effect on P desorption in the first four extractions, but Ca8-P played a more important role in the 16 extractions. Conclusions: Ca8-P was the greatest potential pool for P desorption after Ca2-P was depleted. P desorption capacity was significantly linearly related to initial Olsen-P (P < 0.01). Different fertilizer use strategies were developed based on P desorption capacity for soils with different initial Olsen-P levels. The present study provided basic data on how to reduce effectively the application amount of chemical P fertilizer. [ABSTRACT FROM AUTHOR]

Purpose: Phosphorus (P) fertilizer has been widely applied to improve crop yields in the North China Plain; however, most of applied P fertilizer is disabled due to the strong sorption/fixing processes in the soil. This study evaluates the change characteristics of soil properties on different textural soils and their relationships with the P adsorption and desorption under long-term fertilization to do a favor to make strategies for improving the P availability of the soil.Materials and methods: Five textural fluvo-aquic soils including gritty sand, silt sand, sandy loam, medium loam, and light clay were investigated in a 27-year (1990-2017) long-term fertilization experiment at Zhengzhou (Henan province) of China. Soil samples were collected in 1990, 1999, and 2017 at 0-20 cm to measure soil properties (i.e., soil mechanical composition) and P adsorption and desorption characteristics.Results and discussion: The clay and silt contents decreased and sand content increased in the light clay soil with the cultivation years, the opposite situation for the gritty sand and silt sand soils. The contents of total P, Olsen-P, and organic matter (SOM) in the five textural soils were increased over time. There were stronger P absorption capacity and weaker desorption ability for the light clay soil relative to others, which was reflected in the largest P maximum buffer capacity (MBC) and the lowest β value; however, the P adsorption capacity tended to decrease over time for the light clay soil. Clay and silt fractions were positively correlated with MBC, while negatively correlated with β (P < 0.01); similar disciplines were found in the contents of SOM, total iron (Fe), and free Fe oxides (FeDCB).Conclusions: The mechanical composition of five textural fluvo-aquic soils changed to some extent under the long-term cultivation, thereby improved P absorption and desorption capacity. The clay and silt fractions as well as SOM and FeDCB contents were the major factors for the P adsorption, but sand content was the major factor for the P desorption. [ABSTRACT FROM AUTHOR]

To find an electrical conductivity (EC) in the nutrient solution used for pakchoi (Brassica campestris L. ssp. Chinensis) cultivation that optimizes the plant’s physiology, growth, and quality, we conducted an experiment with eight EC treatments (from EC0 to EC9.6) in a hydroponic production system (i.e. soilless culture) under greenhouse condition in Shanghai, China. Plants biomass production, leaf photosynthesis, vegetable quality variables, tissue nitrate and nitrite contents, and antioxidant enzyme activities were measured. The results showed that very high (EC9.6) or low EC (EC0-0.6) treatments clearly decreased plants fresh weight (FW) and dry weight (DW), leaf size, leaf water content, leaf net photosynthetic rate (Pn), stomatal conductance (Gs), transpiration rate (Tr), and taste score. Nitrite content, and antioxidant enzyme activities were low in medium EC treatments (EC1.8 and EC2.4), but high in very high or low EC treatments. Leaf relative chlorophyll, ascorbic acid, and nitrate contents increased gradually from low EC to high EC treatments, while crude fiber and soluble sugar contents decreased. Based on growth and quality criteria, the optimal EC treatment would be EC1.8 or EC2.4 for pakchoi in the hydroponic production system. Too high or too low EC would induce nutrient stress, enhance plant antioxidant enzyme activities, and suppress pakchoi growth and quality. [ABSTRACT FROM AUTHOR]

BACKGROUND The carbon sequestration potential is affected by cropping system and management practices, but soil organic carbon ( SOC) sequestration potential under fertilizations remains unclear in north China. This study examined SOC change, total C input to soil and, via integration of these estimates over years, carbon sequestration efficiency ( CSE, the ratio of SOC change over C input) under no fertilization (control), chemical nitrogen fertilizer alone (N) or combined with phosphorus and potassium fertilizers ( NP, NK, PK and NPK), or chemical fertilizers combined with low or high (1.5×) manure input ( NPKM and 1. 5NPKM). RESULTS Results showed that, as compared with the initial condition, SOC content increased by 0.03, 0.06, 0.05, 0.09, 0.16, 0.26, 0.47 and 0.68 Mg C ha−1 year−1 under control, N, NK, PK, NP, NPK, NPKM and 1. 5NPKM treatments respectively. Correspondingly, the C inputs of wheat and maize were 1.24, 1.34, 1.55, 1.33, 2.72, 2.96, 2.97 and 3.15 Mg ha−1 year−1 respectively. The long-term fertilization-induced CSE showed that about 11% of the gross C input was transformed into SOC pool. CONCLUSION Overall, this study demonstrated that decade-long manure input combined with chemical fertilizers can maintain high crop yield and lead to SOC sequestration in north China. © 2016 Society of Chemical Industry [ABSTRACT FROM AUTHOR]

An inexact multi-constraint programming model under uncertainty was developed by incorporating a production plan algorithm into the crop production optimization framework under the multi-plant collaborative cultivation system. In the production plan, orders from the customers are assigned to a suitable plant under the constraints of plant capabilities and uncertainty parameters to maximize profit and achieve customer satisfaction. The developed model and solution method were applied to a case study of a multi-plant collaborative cultivation system to verify its applicability. As determined in the case analysis involving different orders from customers, the period of plant production planning and the interval between orders can significantly affect system benefits. Through the analysis of uncertain parameters, reliable and practical decisions can be generated using the suggested model of a multi-plant collaborative cultivation system. [ABSTRACT FROM AUTHOR]

With the continuous development and wide application of multivariate statistical analysis methods, more and more spectral pre-processing and modeling methods are used to analyze the spectral data in order to establish high-precision hyperspectral prediction models. This study selected soil samples from National Long-term (more than 20 years) Monitoring Station of Fluvo-aquic Soil Fertility and Fertilizer Effects. The soil can represent Huang-Huai-Hai Fluvo-aquic soil type and fertilization models. A total of 83 soil samples were collected from the depth of 0-20 cm from treatments with different ratio of fertilization. Reflectance measurements from 350 nm to 2 500 nm were obtained using FieldSpec 3 Hi Spectroradiometer (Analytical Spectral Devices Inc.) in laboratory after soils were air-dried and sieved (0.18 mm). Twenty five pre-processing methods including 15 single pre-processing methods (standard normal variate transformation?normalization? multiple scatter correction?derivative method with different smoothing points and operational parameters) and 10 pre-processing methods adding operations of spectral data and three multivariate techniques (stepwise multiple linear regression, SMLR,partial least-squares regression, PLSR,support vector machine regression, SVMR) were compared with the aim of identifying the best combination to predict fluvo-aquic soil organic matter content. The coefficient of determination?the root mean square error (RMSEv) and relative prediction deviation (RPD) of validation set were used to evaluate the models. The result showed that the best multivariate technique was PLSR, which associated with variety pre-processing methods could resulted in high accuracy and reliability of models. The averaged coefficient of determination?RMSEv and RPD of 25 prediction methods were 0.913?1.264 g/kg and 3.299 respectively. The optimal pre-processing method varied with the multivariate technique used. Compared with the single pre-processing methods, pre-processing methods add operations were better for data preparation among the 3 multivariate techniques, of which average coefficient of determination was higher 0.049?0.033and 0.071 than the single ones, respectively, and the average RPD was higher 0.530?0.307 and 1.144 than the single ones, respectively, but the average RMSEv lower 0.318?0.204 and 0.528 g/kg than the single ones , respectively. The optimal pre-processing method was multiple scatter correction added Savitzky-Golay 1st derivative with a search window of 5 measurements(MSC-SGF5-2) since it performed best among the 3 multivariate techniques, with the average coefficient of determination=0.934, RMSEv=1.17 g/kg and RPD=3.59. This pre-processing method probably can be used as a common spectral data preparation method for fluvo-aquic soil organic matter content prediction model. Among the tested models,the best prediction model for fluvo-aquic soil organic matter was PLSR multivariate techniques associated with normalization by the maximum value pre-processing method (coefficient of determination=0.948, RMSEv=0.972 g/kg, RPD=4.276), and it has high accuracy, reliability and was easy to operate. [ABSTRACT FROM AUTHOR]

Monitoring plant nitrogen (N) in a timely way and accurately is critical for precision fertilization. The imaging technology based on visible light is relatively inexpensive and ubiquitous, and open-source analysis tools have proliferated. In this study, texture- and geometry-related phenotyping combined with color properties were investigated for their potential use in evaluating N in pakchoi (Brassica campestris ssp. chinensis L.). Potted pakchoi treated with four levels of N were cultivated in a greenhouse. Their top-view images were acquired using a camera at six growth stages. The corresponding plant N concentration was determined destructively. The quantitative relationships between the nitrogen nutrition index (NNI) and the image-based phenotyping features were established using the following algorithms: random forest (RF), support vector regression (SVR), and neural network (NN). The results showed the full model based on the color, texture, and geometry-related features outperforms the model based on only the color-related feature in predicting the NNI. The RF full model exhibited the most robust performance in both the seedling and harvest stages, reaching prediction accuracies of 0.823 and 0.943, respectively. The high prediction accuracy of the model allows for a low-cost, non-destructive monitoring of N in the field of precision crop management. [ABSTRACT FROM AUTHOR]