Experimental results show that the proposed method was effective for the retrieval and classification tasks of Chinese roses, and the average classification accuracy was approximately 87%, which can meet the basic retrieval requirements of 3D flower models. The proposed method promotes the classification of Chinese roses from 2D space to 3D space, which broadens the research method of flower classification.Flower classification is of great importance to the research fields of plants, food, and medicine. Due to more abundant information on three-dimensional (3D) flower models than two-dimensional 2D images, it makes the 3D models more suitable for flower classification tasks. In this study, a feature extraction and classification method were proposed based on the 3D models of Chinese roses. Firstly, the shape distribution method was used to extract the sharpness and contour features of 3D flower models, and the color features were obtained from the Red-Green-Blue (RGB) color space. Then, the RF-OOB method was employed to rank the extracted flower features. A shape descriptor based on the unique attributes of Chinese roses was constructed, χ The Chinese rose is of great variety with significant differences in shape, such as peach, cone, egg, etc., and in color, such as red, blue, yellow, green, white, etc. It requires rich professional knowledge and personal experience to accurately classify them . With thThe 3D models of flowers contain all domain information of flower morphology, which is an omnidirectional and multi-level information set of 2D images of flowers. With the increasing maturity of 3D modeling technology in the plant field, it is an inevitable trend to improve research methods from the traditional 2D space to the 3D space. Therefore, the 3D model could significantly improve the classification accuracy when identifying flowers with high similarity . The sigThe morphological structure of the 3D Chinese rose model is complex and diverse, and the single form of shape features cannot comprehensively represent the Chinese roses. The feature points generated by the traditional shape distribution method are incompatible with the mesh area on the surface of the 3D model. The morphological changes of the 3D model structure are mainly represented by a small-area mesh. Therefore, the feature points extracted by the shape distribution method cannot accurately describe the detail of the Chinese roses. Aiming at applying feature extraction technology to 3D Chinese rose model, in this study, a new method based on rose color and shape features was proposed to improve the classification accuracy of Chinese rose species.The contributions of this study are summarized as follows(1) Inspired by 3D CAD model retrieval methods, the feature extraction method was successfully applied to the 3D Chinese rose models, which promotes the classification of Chinese roses from 2D space to 3D space.(2) The proposed method combines color and shape features, which helps to meet the basic retrieval needs of 3D Chinese rose models. The constructed shape-feature descriptor for the unique attributes of Chinese rose flowers can be applied to various search applications to improve plant classification accuracy. Besides, the research results could improve the 3D data’s practicability of Chinese roses.The rest of this study is organized as follows: First, the processing steps in the analysis and construction of the descriptor with color and shape features are described in section 2. In section 3, the experimental results are presented and discussed from different aspects. Finally, conclusions are given in section 4.The Chinese roses used in this study are kindly provided by the College of Horticulture, China Agricultural University. The experiment is carried out at the College of Horticulture, China Agricultural University. The specific experimental equipment and methods are described in the following sections.Shape distribution is one of the statistical probability methods, which takes distance, angle, length, and area of feature points as samples; it can transform the similarity measurement problem between complex 3D models into probability distribution matching . Among tThe main factor affecting the feature extraction is the position’s direction. The rotation normalization caused by the change of position and size is the most complex. The six shape distribution functions shown in The shape distribution feature is a general geometric feature; however, 3D models of plant flowers have specific morphological attributes. The attribute features of plant flowers were included to improve the effectiveness of the Chinese rose’s features. According to the extraction method of the 2D image, a special morphological feature descriptor for Chinese roses was constructed.Sharpness is the quantitative indicator of plant visible defects, which can be used to describe the incomplete degree of the 3D Chinese rose models . Firstly(1)nx, ny, and nz represent the X, Y, and Z coordinates of the vertex of the n-th node.where the sum represents all mesh vertices of the 3D Chinese rose model; Then, the Euclidean distance from the mesh vertex to the center point is defined as:(2)S7 of 3D Chinese rose models:The ratio of the maximum to the minimum from the boundary point to the center point is then defined as the sharpness feature (3)P8 is defined as follows:The sharpness feature represents flower fragility, while the contour feature represents the openness and fullness. Referring to the quantitative contour feature of 2D images, the 3D flower contour feature (4)flowerV is the volume of 3D Chinese rose model; sphereV is the smallest volume of the ball that can surround the 3D Chinese rose model; the closer the P8 value is to 1, the fuller the rose flowers are.where Chinese roses’ color can be divided into red, blue, yellow, green, white, etc. The corresponding relationship between rose color and species is shown in Commonly used color spaces include HSB, Lab, RGB, and CMYK. Each space represents a different range of color gamuts, and the specific calculation method is slightly different. Because the RGB color space is the most used and has a high matching degree in optical and physical equipment, it has the advantages of simple calculation and easy storage . In the n△E between nP  and green RGB  is calculated as follows:(1) Taking 50 color points randomly in the 3D Chinese rose model. Because green leaves could may interfere with the 3D reconstruction, the color difference (5)n△E are less than the threshold N, the Chinese roses’ color indicator C9 = 6, which means that the rose flower is green. If the color difference of some sampling points is less than the threshold N and some is greater than the threshold N, it indicates that the color difference may be due to the interference of green leaves. Thus, the sampling points with color differences less than the threshold N will be eliminated, and the remaining color sampling points retained.If all (2) Calculating the color difference between the selected sampling points of the eight kinds of Chinese roses separately. The RGB values and the color indicator of rose flowers with different colors are shown in Similarly, the Chinese rose’s centroid was taken as the center of the 3D Chinese rose model. After determining the model center, the Chinese rose’s center point was translated to the coordinate origin to realize translation normalization. Next, the maximum distance between each vertex on the flower surface and the flower centroid was determined, and the maximum radius scaling method was applied to process the model to complete the scale normalization of the mesh model.(6)F represents the unnormalized rose model; sF is the normalized rose model; dis is the distance between the flower surface vertex iv and center point mV.where According to the calculation formula of sphere volume, the sphere volume was divided into three parts. When the distance between the surface-point coordinates and the center point of the rose flower was , the flowers were divided into an outer layer (Out layer) representing the outer contour of petals. When the distance between the surface-point coordinates of rose flowers and the center point was , the flowers were divided into a middle layer (Mid layer) representing the internal morphology of petals. When the distance between them was , the flowers were divided into an inner layer (In layer) representing the stamen structure. The inner and middle layers represent the main form of the model, and the outer layer presents the detailed features of the model. The sampling points of shape distribution were crossed in turn, and points were taken from three different layers, which cannot only improve the efficiency of sampling points, but also fully extracts the inner, middle, and outer external structural features of 3D Chinese rose model, to characterize the structural attributes of rose flowers to the greatest extent.The key to fuse the features is to determine the number and weights of features . A totalte of features was tested without any weight when classifying rose flowers. The classification error rate tht feature in the sample. The difference between the error rate before and after the replacement was the important indicator of feature variables, the calculation formula is defined as follows:The training dataset is composed of different kinds of Chinese roses with various colors. The nine features were combined in the order D1, D2, D3, A3, D4, C1, S7, P8, and C9. The error rate (7)RF-OOB is one of the ensemble learning approaches with high robustness; it can calculate the importance of output features completely, which has high applicability for rose feature extraction . Conside2 distance is used herein to measure rose feature similarity.χ (8)Ahist(i) and Bhist (i) are the frequencies of the ith characteristic component of the Ahist  and Bhist  shape-distribution histograms of the rose models AF and BF, respectively; S is the sum of the number of histogram feature components and sharpness, contour, and color feature components.where The similarity calculation formula for the 3D Chinese rose model is:(9)Dis) is the maximum distance between the rose model AF and the rose model with the largest shape difference; min (Dis) is the minimum one.where max , Cp (0.19), P8 (0.14), C1 (0.11), A3 (0.08), S7 (0.07), D1 (0.05), D2 (0.04), D4 (0.02), and D3 (0.02). The experimental results with exponential distribution weight method and average distribution weight method are shown in It can be seen that the classification accuracy of the average distribution weight method is greater than 70%, the average classification accuracy is 87%, the lowest classification accuracy of the exponential distribution weight method is 82%, and its average classification accuracy is 90%. The average classification accuracy of the exponential distribution weight method is 3% higher than that of the average distribution weight method, which reflects the advantages of RF-OOB, indicating that the main features should be highlighted in the process of Chinese roses’ feature classification; it is necessary to expand the weight of main feature components. Further comparative analysis shows that, on the premise of the high classification accuracy of the average weight method, the improvement of the exponential distribution weight method is not obvious, and the improvement amount is approximately 2%, while in rose categories with a low classification accuracy of average weight, the improvement effect of the index distribution weight method is more significant, with an increase of approximately 7%. This means that when the color and shape of rose flowers are greatly different, the exponential weight method basically does not work, while when the shape of rose flowers is relatively similar, the advantage of the exponential weighting method is more prominent. RF-OOB combined with the exponential distribution weight method is more suitable for 3D Chinese rose models. The morphology of the Golden Phoenix species is similar to that of the Golden Jade species, resulting in a low classification accuracy, while the classification accuracy of Lady Figaro, Star, Venus, and other species is high due to their particularity.Precision ratio and recall ratio were applied to verify further the proposed shape feature extraction and retrieval method for 3D Chinese rose models. They are essential indicators representing the effectiveness of information retrieval. In this study, precision ratio means the accuracy of the same kind and similar shape of rose flowers in the samples, while recall ratio means the comprehensiveness of the same kind and similar shape of rose flowers in the samples. The two indicators verify the accuracy and comprehensiveness of retrieving rose flowers based on 3D Chinese rose models. Generally, the retrieval test is conducted with a graph with the recall ratio as the abscissa and the precision ratio as the ordinate. The ideal precision-recall curve should be a line graph with an area of 1. As seen from According to the similarity with the input model, the models are arranged in sequence in 4This paper proposes a feature extraction and classification method based on 3D Chinese rose models, which can effectively classify different Chinese rose varieties. The sharpness and contour features combined with the color information were extracted from the 3D Chinese rose models. The RF-OOB method was adopted to rank the importance of the extracted features. A shape-feature descriptor based on the unique attributes of Chinese rose flowers was constructed. The experimental results prove that the sharpness and contour features are the typical features in 3D rose models, and the color information is an important reference feature to improve the classification accuracy. The fused features based on the exponential distribution weight method have an effective average classification accuracy of approximately 87%, which can meet the basic retrieval and classification needs of 3D Chinese rose models. In the future, the authors will continue optimizing and expanding the proposed retrieving and classification method for the other 3D flower models; and explore the application of AI and VR technology to the visualization and evaluation of 3D flower models.The datasets presented in this article are not readily available because the dataset also forms part of an ongoing study. Requests to access the datasets should be directed to the corresponding author.All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.This research was funded by the National Key R&D Plan Project (2018YFD1000400) and the Natural Science Foundation of Shandong Province (ZR2021QE217).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


This research work is devoted to collecting a high-quality dataset of BODIPYs in a series of 10–30 solvents. In total, 115 individual compounds in 71 solvents are represented by 1698 arrays of the spectral and photophysical properties of the fluorophore. Each dye for a series of solvents is characterized by a calculated value of solvatochromic sensitivity according to a semiempirical approach applied to a series of solvents. The whole dataset is classified into 6 and 24 clusters of solvatochromic sensitivity, from high negative to high positive solvatochromism. The results of the analysis are visualized by the polarity mapping plots depicting, in terms of wavenumbers, the absorption versus emission, stokes shift versus − (absorption maxima + emission maxima), and quantum yield versus stokes shift. An analysis of the clusters combining several dyes in an individual series of solvents shows that dyes of a high solvatochromic sensitivity demonstrate regular behaviour of the corresponding plots suitable for polarity and viscosity mapping. The fluorophores collected in this study represent a high quality dataset of pattern dyes for analytical and bioanalytical applications. The developed tools could be applied for the analysis of the applicability domain of the fluorescent sensors.  Currently, the solvatochromic properties of BODIPY sensors and other efficient probes are being extensively explored in analytical ,2, bio, Routine information regarding the spectral and photophysical properties of dyes important for the further development of BODIPY fluorophores and discussions regarding their structure–property relationships are dispersed in numerous papers and reviews that have been published within the last two decades. In the original research, several libraries of BODIPY for intracellular cell imaging have been reported for individual solvents, namely: dimethylsulfoxide ,38,39,40One of the key strategies for the design and optimization of fluorescent sensors is focused on the polarity mapping of biological tissue. Several structures based on BODIPY scaffolds exemplify selected polarity probes developed in numerous research works ,52,53,54The above libraries present useful data for analysis and for seeking target molecules satisfying the requirements of the spectral and photophysical properties, sensitivity to surrounding media, selectivity for adsorption on biological tissue, etc. An examination of the above libraries shows a lack of data for selecting BODIPY fluorophores, which could serve as analytical templates for polarity.Recent progress in the quantification of the solvatochromism of BODIPY probes ,57 is prThe list of fluorescent dyes selected for the research is shown in A general overview of the spectral properties, Stokes shift, and quantum yield for the whole dataset is demonstrated by the frequency diagrams in The total dataset of 115 BODIPY dyes and 1698 combinations of dye and a solvent was divided into six clusters according to the solvatochromic sensitivity parameter A. The solvatochromic sensitivity (or polarity) of each compound in the whole dataset of BODIPY sensors varied in the range of −1.3 to +1.2. Each cluster combined several series of dyes, and a series of solvents for each dye appeared only once, according to the arrangements of the data. Parameter A, representing solvatochromic sensitivity, was defined according to a method described in The chemical structures of the BODIPY sensors classified according to the above procedure of clustering are provided in the six chats below .The spectral and photophysical properties of the dyes in the clusters are presented below in several diagrams. The clustering of the dyes according to solvatochromic sensitivity parameter A helps understand the general concept of the solvatochromic classification of the dyes. For instance, the plot for absorption maxima versus emission maxima for cluster 1 in The frequency diagram shown in More detailed clustering of the dataset up to count 24 helps to identify the more regular behavior of the dyes in several clusters. The first one, the absorption maxima versus the emission maxima, is shown in The diagrams in It is noteworthy that the diagram for quantum yield versus Stokes shift in The diagrams in The individual dyes covering a wide range of quantum yields are exemplified by the following compounds shown in −1, 1270–1400 cm−1, and 2190–2800 cm−1. The above-mentioned members of clusters 7, 8, 9, 10, and 19 formed groups of dyes with quantum yields within 0.02–1 and Stokes shifts within 170–860 cmSeveral dyes mentioned above in this section exhibited molecular rotor effects, for example, those in clusters 7a, 9a, 9b, and 19. The dye in cluster 7 is known to exhibit a high quantum yield in solvents with a high polarity. This observation demonstrates the applicability of the developed tool for selecting efficient fluorophores demonstrating a low spectral sensitivity and high sensitivity of quantum yield to the polar solvents.A new tool for selecting the fluorescent probes proposed above is convenient for the analysis of deviation of experimental data for identical compounds. In essence, the problem of duplicates in chemoinformatics is solved by special algorithms based on, for example, SMILES or IUPAC InChI formats . NormallThe chemical structures of the duplicates of the dataset collected in the current research are represented in The analysis of the solvatochromic sensitivity parameter A for all duplicates is a compact form of comparison of the data for solvatochromic behaviour compared with the whole spectral dataset. The solvatochromic sensitivity of the compounds of the groups G, M, N, O, P, Q, and R lies within the limits of the experimental error shown in the dataset represented in files of SI.csv and SI.sdf. However, several other compounds depict a high deviation, for example, a parent compound 2009 Arroyo-1 in group A, 2015 Waddell-1, 2015 Waddell-2, and the compounds in groups I and J. It is noteworthy that several duplicate compounds have been studied in a number of experiments for quite different solvents. For example, the dyes in group F demonstrate a similar solvatochromic sensitivity with a positive solvatochromism (A > 0) for a series of 16, 22, and 6 solvents. According to the data in the diagram shown in The above results could be clarified by a comparison of the spectral and photophysical data reported by different authors for each solvent. A few examples are shown for groups A, B, C, and D, respectively, in the figures, namely: The list of dyes included a total dataset of 115 BODIPYs and the corresponding references are shown above in The chemical structures of the fluorophores are depicted in The dispersion of spectral data was evaluated through the use of duplicates in the dataset. For this purpose, additional data for spectral behaviour of BODIPYs covering six to nine solvents in each series, shown in A, B are the solvatochromic sensitivity coefficients, evaluated as regression coefficients of the linear approximation µ(g) and µ(e) are the vectors of the dipole moments of the fluorophore in the ground and excited states. For simplicity, the vector symbol is omitted.In previous research, a novel methodology was reported for studying the solvatochromism of BODIPYs ,57. ThisA, described by Equation (5), is written in a more general form than in previous research [Parameter research ,57 , (2a), and (3a).Coefficient The form of Equations (1), (2a), and (3a) is convenient for the simultaneous analysis of the behaviour of the three spectral functions, plot see  below.C and E, respectively:A pairwise comparison of Equations (2) and (2a), and (3) and (3a) yields slopes A is estimated directly as the regression coefficient of Equation (1) for the Stokes shift or as the difference between C and E:Solvatochromic parameter C and E, defined by Equation (6), are suitable for estimating the relationship On the other hand, parameters A > 0, A < 0, while the relation E/C, defined by Equation (8), is useful for characterising the near-zero solvatochromic sensitivity, A ≈ 0, E/C ≈ 1.Expression (7) is useful for the measurement of the solvatochromic sensitivity of the relatively high positive and negative values, µ(g) and µ(e), they can be used as scalar parameters, thus Expressions (5) and (8) are simplified for a clear understanding of the physicochemical meaning of parameter A defining the solvatochromic sensitivity:In the case of coplanarity of the vectors of the dipole moments An illustration of the calculation method is provided in the papers ,57. SomeThe experimentally evaluated parameters of Equations (1), (2a), (3a), (7), and (8), as well as their statistical characteristics for a dataset of 115 BODIPYs, are provided in the The clustering of the experimental data for a whole family of BODIPYs was performed by the parameters of solvatochromic sensitivity parameter A. This method of presentation helps to highlight each dye in a series of solvents, as well as to compare different series with a similar solvatochromic sensitivity. A graphical analysis of the spectral and photophysical properties of the collected BODIPYs was performed with the software Scimago Graphica .Based on published results, a dataset of 115 BODIPY sensors in a series of 10–30 solvents with correspondent spectral and photophysical characteristics was collected. The solvatochromic sensitivity of each dye was evaluated by a recently developed semiempirical approach for the analysis of the solvatochromism. The polarity of the dyes was divided into 6 and 24 clusters from high negative to high positive levels, and was applied for the classification of fluorophores and an analysis of their properties.−1, 1270–1400 cm−1, and 2190–2800 cm−1.The analysis of clusters combining several dyes in an individual series of solvents shows that dyes with a high polarity, with either a positive or negative solvatochromism, demonstrated regular changes in the absorption maxima versus emission maxima, Stokes shift versus − (absorption maxima + emission maxima), and quantum yield versus Stokes shift in a wide range of properties. Non-polar probes were arranged into characteristic clusters with quantum yields of 0.02–1 and Stokes shifts of 170–860 cmThe developed tool based on the classification of solvatochromic sensitivity is suitable for the efficient selection of fluorescent probes, for both the polarity and viscosity sensing of biological tissue through the use of individual dyes and a set of dyes. The selected BODIPY probes could serve as pattern samples for the testing and design of the fluorophores.


To explore the hydration characteristics and early strength evolution of classified fine tailings cemented backfill (CFTCB), a nuclear magnetic resonance (NMR) analysis and a volume resistivity test were performed on classified fine tailings filling slurry (CFTFS). The early hydration products of CFTCB were studied by scanning electron microscopy (SEM) and X-ray diffraction (XRD) phase analysis. Uniaxial compressive strength (UCS) test was carried out, and the microscopic characteristics and strength rules of the hydration reaction of CFTCB were analyzed. Based on the experiment, we found the law of water content change and porosity evolution. The early hydration reaction can be divided into the dissolution, setting, and hardening stages. The volume resistivity test results show that the volume resistance of filling slurry increases slowly at first then decreases, and finally increases rapidly. The variation trend of volume resistivity is related to the degree of hydration reaction. When combined with the hydration characteristics of backfill materials, the hydration reaction rate determines the growth rate of early strength of backfill, and the formation of hydration products is the reason for the early strength increase in backfill. The research conclusion has an important theoretical guiding value and engineering significance in mine filling production organization and filling ratio parameter optimization.  At present, underground gold mines usually adopt the classified coarse tailing cemented filling approach in goaf treatment . Classif2 is rarely generated in the hydration process of ultra-fine tailings, the calcium silicate hydrate (C-S-H) gel content is high, and the hydration products and morphology of the two are different. The results of previous research [2, and the addition of fiber enhances the ability of the hydration products to resist external deformation, enabling the filling body to maintain good mechanical properties. Li Wenchen [The use of classified fine tailings as a filling aggregate is a relatively advanced attempt and practice at home and abroad, and classified fine tailings’ full hydration reaction with cementing materials is a crucial link in strength formation. A previous study  shows thresearch  show tharesearch  found thresearch  used blaresearch  found thresearch  and S.K.research  found thresearch  found th Wenchen  and Benz Wenchen  found th Wenchen  show tha Wenchen  found thHowever, the research on the law of water transformation and evolution in the hydration and hardening process of filling slurry and its internal influence on the early strength formation of backfills is limited. Through NMR, UCS, and SEM experiments, this paper studies the early-stage strength evolution law and stage characteristics of classified fine tailings backfill from the perspective of the hydration process and products of filling slurry, which has an important theoretical guiding value and an engineering significance in mine filling production organization and the optimization of the filling ratio parameter.2 and Al2O3, are a good inert filling material suitable for downhole filling raw materials.Classified fine tailings were selected for the experiment, and the experimental material was derived from the ore tailing of a gold mine in Shandong Province, China. The classified fine tailings are dried before configuring the filling slurry. The physical parameters are presented in 2, and Al2O3. As shown in 2(C3S), tricalcium aluminate 3CaO-Al2O3(C3A), and gypsum CaSO4·2H2O, etc. This material, which contains small amounts of MgO, Fe2O3, and TiO2, is a kind of active cementitious material suitable for underground filling raw materials. In addition, the existence of slag in “filling C material” reduces the preparation cost.Cementitious material, “filling C material”, which was independently developed by a laboratory, was used in this experiment. The mineral composition of the material is mainly slag and gypsum. The chemical composition of the filling C material is shown in The porosity of the sample and the content and type of water in the sample was determined by an NMR analytical system . A slurry with a slurry concentration of 64%  was prepared at CTRs of 1:4, 1:6, and 1:8. Three samples were prepared for each CTR, and the CFTFS was evenly stirred into a 30 mL lidded cylinder glass bottle. After curing for a certain number of hours  at a constant temperature in a humidity-curing box, NMR analysis was performed.A resistance tester (ATSIO) was used to measure the resistivity of the CFTFS. The slurry concentration was 64%, and the CTR was 1:6. Three groups of samples were configured to test the volume resistivity of the samples according to the AC impedance method.A Rigaku D/Max2500 X-ray diffractometer was used to determine the mineral composition of the cemented backfill samples. A sample with a concentration of 64% and a CTR of 1:6 was prepared, and the sample was measured at 6, 12, 24, and 48 h after preparation.A JSM-6700F cold field emission scanning electron microscope was used to determine the microstructure of the cemented backfill samples. For backfill samples with a concentration of 64% and a CTR of 1:6, the phase characteristics of the hydration products at different early ages (3 and 7 days) were tested.UCS test was performed on the CFTCB with a slurry concentration of 64% and a CTR of 1:6. Three groups of samples were prepared, and each group was prepared in three 50 mm × 50 mm × 100 mm cylinders. After curing for 2, 3, and 7 days at a constant temperature in a humidity curing box, the strength of the CFTCB was measured after curing the test block. The pore size distribution of the CFTFS formed after 72 h of hydration reaction at different CTRs is shown in The variation in porosity in the hydration process of the CFTFS with different CTRs is shown in The variation trend of the T2 spectrum distribution of the CFTFS with a CTR of 1:6 within 0–10,000 ms is shown in To obtain the hydration characteristics of the CFTFS, the porosity of the CFTFS (CTR of 1:6) was combined with the variation curves of free water and adsorbed water, as shown in As shown in (1)Dissolution stage I2O3, CaO, Fe2O3, and other dissolved substances. Then, many positive and negative charged ions are released into the water. Slag is composed of the active vitreous body of slag and a protective layer of silicon–oxygen network structure on its surface. In the alkaline environment, the OH− ions in the solution react with the protective layer on the surface of the slag glass. After the protective layer is destroyed, OH− ions enter the slag glass body and react with active CaO to form Ca(OH)2. Given the high activity of tricalcium aluminate (C3A) in the filling C material, C3A first reacts with gypsum under alkaline conditions. In the dissolution stage, the molar ratio of gypsum to C3A is generally greater than 3, and needle-like ettringite (Aft) is mainly formed after the reaction. After mixing the filling material with water and stirring evenly, the main components of the cementing material dissolve quickly. The surface of the classified fine tailings and other particles also show Al3A, thus decelerating the hydrolysis rate of C3A. At the late dissolution stage, the content of ions in the solution stabilizes temporarily, and the hydration reaction then proceeds to the setting stage.The generation of Aft at this stage produces a wrapping film on the surface of C(2)Setting stage II2 reacts with Al2O3 and SiO2 to form C-S-H gel. This stage is mainly a hydration reaction process of the filling C material. The effect of the hydration of the filling C material on the classified fine tailings is generally regarded as the passing of hydration product C-H through the phase interface to form a hydration film on the surface of the tailing particles, as shown in With the continuous dissolution of each component in the filling C material and the active component in the classified fine tailings, at this time, the slag glass body disintegrates, and Ca(OH)(3)Hardening stage IIIAt the end of the setting period of the CFTFS, the hydration reaction rate decelerates until it stops, and the hydration products produced  increase the hydration compounds on the surface and surrounding of the classified fine tailings particles, and the particle spacing continuously narrows, thereby overcoming the repulsion between the suspended solids in the slurry. Under the action of the Van der Waals and chemical bonding forces, particles of different particle sizes polymerize with each other, or the crystal nuclei of particles connect with each other, thus forming a hardened crystalline structure and gradually condensing the backfill into a whole.The change characteristics of the volume resistivity of the CFTFS with a CTR of 1:6 were divided into three stages, namely, slow growth stage I, decline stage II, and rapid growth stage III, as shown in 2 and Aft, formed after the condensation of the classified fine tailings filling material. After curing for 6 h, Aft and Ca(OH)2 formed in small amounts. After 12 h, the relative contents of Aft and Ca(OH)2 increased, indicating that more hydration products were generated with the progress of the reaction. Ca(OH)2, which belongs to the tripartite crystal system, is the product of the hydration reaction, and the crystals mostly exhibit a flake-like structure. Aft is the product formed by the reaction of C3A in the silicate filling c material and the gypsum. It is worth noting that after the hydration reaction, mullite was detected by XRD. Mullite may come from tailings or filling C material, and it is not a hydration product. As the C-S-H gel is an amorphous phase, it cannot be detected by XRD, so SEM can be used for observation.When combined with the division of the hydration reaction stages, the hydration reaction went through the dissolution and setting stages within 12 h, and numerous hydration products were generated. After 18 h, the hydration reaction entered the hardening stage and then stopped. The XRD results show that nearly no new hydration products were generated, and the backfill structure gradually stabilized.The CEMHYD 3D hydration model was used to numerically simulate the microstructure evolution in the hydration process. The changes in hydration degree, hydration products, and porosity with hydration time were obtained. The calculated results of the analyzed hydration model were basically consistent with the experimental data. As shown in (1)Strength formation stage I2+ in the filling slurry increases, resulting in the saturation of Ca(OH)2 in the slurry and rapid crystallization. Furthermore, the C-S-H gel is continuously increasing. At this stage, the hydration reaction has reached the hardening stage, and the generation of hydration products is the main reason for the formation and rapid growth of the backfill strength.At this stage, after stirring the filling material with water, the main components in the filling material dissolve quickly, and the concentration of Ca(2)Strength growth blocked stage IIAt this stage, the hydration reaction decelerates, and the hydration products covering the particles of the filling material block the dissolution of ions, resulting in a reduction in the concentration of ions that can participate in the hydration reaction in the slurry. Consequently, the growth rate of the filling body’s strength decelerates.(3)Strength stable stage IIIAt this stage, the hydration reaction stops, and all components involved in the hydration reaction in the cementitious material no longer dissolve. Combined with the analysis of the morphology of hydration products, the result indicates that many hydration products crystallize on the particles of the filling materials, forming a hardened crystalline structure, which leads to the condensation and solidification of filling materials as a whole and stabilizes the strength of the filling body.On the basis of the hydration reaction characteristics of the CFTFS, this paper studied the early strength rule and reached the following conclusions. Notably, this study did not consider the influence of the hydration exothermic in the division of the hydration reaction stage, which has certain limitations. Future work with exothermic hydration should be considered to further study the early strength evolution rule of classified fine tailings backfills.According to the NMR experiment, the porosity of the CFTFS presents a trend of a rapid increase to a slow decrease, and the pore size of the CFTFS, the number of large pores, and the porosity increase with the CTR. In the CFTFS, water exists in the form of adsorbent water; from the perspective of water content, the adsorbed water and free water are converted to each other, the pore water eventually decreases, and the final reduced water content is transformed into chemically bound water that cannot be detected by NMR experiment. The hydration reaction of the CFTFS is divided into three stages, namely, dissolution stage I, setting stage II, and hardening stage III, which explain the characteristics of each hydration process of the CFTFS.The variation trend of volume resistivity is related to the degree of hydration reaction. In the initial stage of the hydration reaction, the cementation of the sample is dominant, and the volume resistivity of the sample increases slowly. In the middle stage, the resistance and the volume resistivity decrease due to the increase in ion concentration. In the latter stage, the sample slurry gradually condensed with the progress of the hydration reaction, resulting in the gradual increase in the internal resistance of the material, indicating that the resistivity increased with time.2), a layered C-S-H, an acicular Aft, and other hydration products with the hydration reaction, increasing the density of the backfill structure and explaining the formation of the early backfill strength.According to the results of the phase analysis and the SEM experiment, the CFTCB condenses and forms a new flake (Ca(OH)On the basis of the hydration characteristics of the backfill, hydration products increase the early strength of the backfill, while the reduction in the concentration of ions involved in the hydration reaction will lead to the blockage of the strength growth. Finally, the strength of the backfill tends to stabilize with the completion of the hydration reaction.


The timely and accurate detection of ewe estrus behavior in precision animal husbandry is an important research topic. The timely detection of estrus ewes in mutton sheep breeding can not only protect the welfare of ewes themselves, but also improve the interests of breeding enterprises. With the continuous increase in the scale of mutton sheep breeding and the gradual intensification of breeding methods, the traditional manual detection methods require high labor intensity, and the contact sensor detection methods will cause stress reaction problems in ewes. In recent years, the rapid development of deep learning technology has brought on new possibilities. We propose a method for the recognition ewe estrus based on a multi-target detection layer neural network. The results show that the method can meet the requirements of timely and accurate detection of ewe estrus behavior in large-scale mutton sheep breeding.There are some problems with estrus detection in ewes in large-scale meat sheep farming: mainly, the manual detection method is labor-intensive and the contact sensor detection method causes stress reactions in ewes. To solve the abovementioned problems, we proposed a multi-objective detection layer neural network-based method for ewe estrus crawling behavior recognition. The approach we proposed has four main parts. Firstly, to address the problem of mismatch between our constructed ewe estrus dataset and the YOLO v3 anchor box size, we propose to obtain a new anchor box size by clustering the ewe estrus dataset using the K-means++ algorithm. Secondly, to address the problem of low model recognition precision caused by small imaging of distant ewes in the dataset, we added a 104 × 104 target detection layer, making the total target detection layer reach four layers, strengthening the model’s ability to learn shallow information and improving the model’s ability to detect small targets. Then, we added residual units to the residual structure of the model, so that the deep feature information of the model is not easily lost and further fused with the shallow feature information to speed up the training of the model. Finally, we maintain the aspect ratio of the images in the data-loading module of the model to reduce the distortion of the image information and increase the precision of the model. The experimental results show that our proposed model has 98.56% recognition precision, while recall was 98.04%, F1 value was 98%, mAP was 99.78%, FPS was 41 f/s, and model size was 276 M, which can meet the accurate and real-time recognition of ewe estrus behavior in large-scale meat sheep farming. At present, the feeding mode of mutton sheep mainly based on small-scale scattered breeding of farmers is the main factor restricting the growth of mutton production. The large-scale and intensive breeding mode proposed by precision animal husbandry is an important measure and guarantee for increasing mutton production. Artificial insemination can be carried out at the most appropriate time through the timely and accurate detection of ewe estrus in large-scale and intensive farming, thereby reducing the interval between birth and fertilization costs ,2,3. TheResearchers have proposed a sensor-based technique for recognizing animal estrus behavior based on the physiological changes and behavioral characteristics of animals during estrus; along with the properties of sensors ,17,18,19In recent years, with the proposed video-image-processing algorithms in the field of computer vision, researchers have used a method based on video image processing to identify animal estrus behavior ,21,22,23In summary, we can see that deep learning technology can not only automatically obtain multi-dimensional and multi-level feature information of the target in the image, but also effectively solve the problem of manually extracting the target feature vector, which makes deep learning technology have a broad application prospect in the field of precision animal husbandry. Among the deep learning techniques, the YOLO v3 model is a representative of target detection methods. YOLO v3 has the advantages of fast recognition speed, high recognition accuracy, and strong model resistance to interference, and has the potential to recognize ewe estrus crawling behavior in complex environments. Therefore, we chose YOLO v3 for the detection of ewe estrus.To address the shortcomings of manual detection methods and sensor detection methods in the detection of ewe estrus, we proposed a multi-objective detection layer neural network-based method for ewe estrus crawling behavior recognition. Firstly, we decomposed the ewe estrus videos obtained from large-scale meat sheep farms into pictures using the video keyframe decomposition technique. Secondly, we used LabelImg to manually annotate the images containing ewe estrus crawling behavior to obtain the sample dataset. Then, we improved the model based on the dataset and YOLO v3 model structure characteristics. We mainly improved the model in terms of target detection layer, residual structural unit, anchor box size, and image resolution-keeping aspect ratio. Finally, we train, validate, and test the improved model using the PyTorch deep learning framework, the PyCharm development tool, and a high-performance computing platform.Briefly, the main contributions of this work are summarized as follows:(1) We established an ewe estrus dataset, along with the thorough analysis and findings on our dataset.(2) We propose to use the YOLO v3 model to detect the estrus crawling behavior of ewes, and improve the YOLO v3 model.(3) The advantages and disadvantages of our proposed model are compared with other models.The rest of this paper is organized as follows. In The video of the estrus crawling behavior of ewes in this study was collected from the Xinao animal husbandry and mutton sheep breeding base in Lanzhou Bay Town, Manas County, Xinjiang Uygur Autonomous Region. Through a field investigation, we found that the estrus crawling behavior of ewes mainly occurred in the shaded area; therefore, this study chose to be in the shaded area for video capture. The shaded area is 33.75 m long and 13 m wide. Two surveillance cameras with a resolution of 4 million pixels (Hikvision DS-2CD2346FWD-IS dome network cameras)  are installed in the shaded area, and the camera installation height is 2.7 m.The installed camera and field of view are shown in When we watched the video image of ewe estrus, we found that the video image taken by camera 2 contained more unrelated areas. In the process of ewe estrus detection, these more unrelated regions will increase the amount of calculations required and error recognition rate. On the one hand, the video shooting position and camera rotation angle are fixed; on the other hand, the ewes’ activity range is within the shaded area; therefore, there is no ewe estrus crawling behavior occurring outside the shade area. In summary, in order to reduce the computation and detect interference, we removed the redundant irrelevant regions in the video screen using a masking method, as shown in After watching the ewe activity monitoring video, the breeding staff selected 300 video clips with estrus climbing behavior from the video, with each clip ranging from 4 to 20 s in length. We used Python script to decompose video key frames of video clips, and take one frame every three frames to obtain 3640 original images. In these original images, we found that each image contains not only the crawling behavior of ewes in estrus, but also other behaviors of ewes not in estrus, such as feeding, standing, walking, lying down, etc.In order to expand the sample size of the dataset and improve the generalization ability of the model, we performed data enhancement processing on the dataset. Firstly, in view of the uncertainty of the location of ewe’s climbing behavior in the shaded area, we performed horizontal flipping, horizontal moving, and vertical moving of the samples in the dataset. Then, according to the complex and variable illumination characteristics of the shaded area, brightness enhancement and brightness reduction were carried out. Data enhancement processing is shown in Dataset label production is the first step before model training. Whether the production of dataset labels is standardized will directly affect the model training, verification, and test results. When labeling, the label box should only select the entire climbing behavior as much as possible. If the label box is too big, it will include too much noise, such as dust and backdrop, reducing the model identification’s precision.We used the open-source labeling tool LabelImg to label the dataset and generate the corresponding xml file manually. After randomly dividing the obtained 5600 images according to the ratio of training, validation, and testing of 7:2:1 , there wThe YOLO v3 model modifies the YOLO v2 model presented by Redmon  in 2018.YOLO v3 produces feature layers of three sizes from input images for feature extraction. If the input image size is 416 × 416 × 3, the feature layer sizes are 52 × 52, 26 × 26, and 13 × 13, respectively. The prediction target mechanism of multi-scale feature fusion was added in the target detection layer. Three feature fusion techniques were employed to gather the target’s location and semantic information; the detection is then performed on three distinct object detection layers. The picture was divided into grids of various sizes. Each grid was allocated three anchor boxes of varying sizes for bounding box prediction. The bounding box of the retrieved object has five predictions: x, y, w, h, and c. Where x, y is the target detection frame’s center coordinates, c is the confidence level, w, h is the detection frame’s width and height. Finally, the output tensors of the three target detection layers were merged, and the output is as shown in Equation (1):According to YOLO v3, target detection is performed on the COCO dataset, and the number of categories in the dataset is 80. The output is shown in Because the camera position was fixed, we found that the ewe imaging occupies a smaller percentage in the video pictures of ewe estrus crawling behavior when the ewe is far away from the camera. The ewe target with a smaller imaging share in the picture will cause part of the relative position information to be lost after multi-layer convolution calculation, which affects the detection accuracy of the model for ewe estrus crawling behavior recognition.When we directly used the original YOLO v3 model to detect distant ewe targets in estrus, the detection results did not reach a satisfactory level. Therefore, we need to improve the YOLO v3 model to improve the precision of the model for detecting distant ewes in video pictures.Based on the three target detection layers of the original YOLO v3 model, we added a target detection layer with a size of 104 × 104. Our improved model has four target detection layers, where the 104 × 104 target detection layer is mainly responsible for detecting small targets in video pictures.First, we obtained a 104 × 104 feature layer by convolving and upsampling the 52 × 52 feature layer after the operation. Then, we spliced the obtained 104 × 104 feature layer with the 104 × 104 feature layer in the feature extraction network using a concat function, and thus obtain the 104 × 104 target detection layer.Our improved method helps the model extract more features from the shallow network and improves the model’s ability to detect distant estrus ewes in images. At the same time, there was some improvement in the wrong detection, missed detection and low confidence level of ewe estrus behavior identification. The model improvement locations are shown as Improvement 1 in The improved model has four target detection layers. Finally, the output tensors of the four target detection layers should be merged. For the input image, the output of the model is as shown in Equation (2):In Equations (1) and (2), N is the number of input images and M denotes the total number of categories in the dataset. The number of dataset categories in this paper is 1, so M is 1. The improved model output is shown in The number of residual units in the residual structure of the original YOLO v3 model is 1, 2, 8, 8, and 4, respectively.Since we added a 104 × 104 target detection layer, the improved model may experienced a loss of shallow feature information during the forward propagation training process, which in turn leads to a decrease in the fitting ability of the model. At the same time, the model may suffer from gradient dispersion and gradient explosion during the backpropagation training process, resulting in the inability to transfer deep feature information to shallow layers, which in turn leads to poor detection of the model.Therefore, we add two residual unit to the last residual structure in the model, which is used to help the model extract more information about the deeper features of ewe estrus climbing behavior, thus increasing the model’s detection precision. The number of residual units in the residual structure of our improved model is 1, 2, 8, 8, and 6, respectively.The optimized location of the residual structure unit is shown in The resolution size of the dataset image we created is 2560 pixels × 1440 pixels, and the data-loading module of the model will adjust the resolution of the input image to 416 pixels × 416 pixels. Therefore, the resolution of the image will be adjusted during the training, verification, and testing of the model.If the resolution of the image is directly adjusted, it will not only cause serious distortion due to the stretching of the image, but also lead the model to extract the wrong features.In order to reduce the adverse effect of picture distortion on the model and enable the model to extract effective features, we redefine the adjusted picture resolution function in the data-loading module and adopt the method of maintaining the horizontal and vertical ratio of the picture, as shown in Equations (3)–(6).1 is the width of the image resolution required by the model, h1 is the height of the image resolution required by the model, w2 is the width of the image resolution of the dataset, h2 is the height of the image resolution of the dataset, w3 is the width of the picture adjusted to maintain the aspect ratio, h3 is the height of the picture adjusted to maintain the aspect ratio, dw is the size of the pixels that need to be filled on the periphery of the image’s width, dh is the size of the pixels that need to be filled on the periphery of the image’s height. When we carry out the picture fill, the fill pixel size is gray RGB . The dataset picture is shown in In Equations (3)–(6), wThe YOLO v3 model obtains nine anchor boxes in the COCO dataset using the K-means algorithm. Anchor boxes are a set of values with fixed width and height values. Among the anchor boxes, the three sizes of 10 × 13, 16 × 30 and 33 × 23 correspond to the 52 × 52 target detection layer and are responsible for detecting small targets. Among them, the three sizes of anchor boxes, 30 × 61, 62 × 45 and 59 × 119, correspond to the 26 × 26 target detection layer and are responsible for detecting medium-sized targets. Among them, the three sizes of anchor boxes, 116 × 90, 156 × 198 and 373 × 326, correspond to the 13 × 13 target detection layer and are responsible for detecting large targets.During model training, validation and detection, three anchor boxes are assigned to each target detection layer. Each anchor box is computed in turn with the intersection and merge ratio  with the sample box containing the target center. Back propagation through the loss function adjusts the model parameters to continuously bring the prediction box close to the true box. Therefore, the size of the anchor box will directly affect the accuracy of the model detection. Due to the target size in the COCO dataset not matching the target size in this dataset, we need to re-cluster the anchor box size to improve the precision of model detection.The K-means algorithm is an unsupervised learning method that uses distance as a similarity indicator. First, the width and height of the anchor boxes are selected as the clustering objects. Then, the distance between each sample box and the clustering center is calculated to assign the sample boxes. Finally, constant iterations are performed to update the clustering centers.Since the K-means algorithm has a large randomness in the selection of the initial clustering centroids, this randomness will have a certain impact on the clustering results, leading to different results for each clustering, meaning the algorithm cannot determine the global optimal solution. Therefore, we used the K-means++ algorithm instead of K-means algorithm, and the selection of initial clustering centroids is improved to make the clustering better. The goal of the clustering algorithm is to make the intersection ratio (IOU) between the anchor box and the labeled box as large as possible, and the objective function uses IOU as the measure. The objective function is shown in Equation (7).In Equation (7), p is the cluster center, k is the number of cluster centroids, q is the target frame of the sample labels, and n is the number of samples in the dataset. The relationship between the number of clustering centers and the average intersection ratio of the K-means++ algorithm on the dataset of this paper is shown in We found that as the value of K increases, the value of IOU also increases. When K = 12, the value of the average cross-merge ratio IOU tends to level off, while too many values of K affect the speed of detection. Therefore, we finally determined the number of anchor boxes to be 12. The distribution of the dataset label boxes is shown in The values of the cluster center coordinates we obtained are the normalized data. Therefore, the cluster center coordinate values need to be multiplied by the size of the model input image to get the final anchor box. The input image size of the model in this paper is 416 × 416 pixels. Therefore, the clustering effect is shown in We rounded the obtained anchor box sizes and arranged them by size. Among them, the three sizes of anchor point boxes, 19 × 34, 19 × 59, and 31 × 49, correspond to the 104 × 104 target detection layer. The three sizes of anchor boxes, 40 × 59, 28 × 81, and 51 × 81, correspond to the 52 × 52 target detection layer. The anchor boxes of 31 × 118, 41 × 118, and 62 × 103 correspond to the 26 × 26 target detection layer. The anchor box sizes of 53 × 131, 72 × 140, and 92 × 122 correspond to the 13 × 13 target detection layer. The anchor box sizes are shown in In order to evaluate the performance of the ewe estrus crawling behavior recognition model proposed in this study, the following evaluation indexes were selected: precision, recall, average precision, mean average precision F1, FPS and model size, as shown in Equations (8)–(12).P represents the number of positive samples identified in the positive samples, FP represents the number of positive samples identified in negative samples, FN represents the number of positive samples identified as negative, n represents the number of categories, and P is precision, R is recall, F1 is the comprehensive evaluation index of precision and recall, AP is average precision, and mAP is the mean of average precision.In Equations (8)–(12), TIn the evaluation process of the network model, each category can draw the P–R curve according to P and R, and the average precision AP is the area between the P–R curve and the coordinate axis. The average value of AP of all categories is the value of mAP. Since category n in this paper is 1, AP is also equal to the mAP.Our model is on the same computer during training, validation, and testing. The computer was configured with an operating system of Windows 10, a GPU of GeForceRTX2080, a processor of Inter(R) Core(TM) i7-9700k CPU@3.2 GHz, and a running memory of 32 G. Our model was built using the PyTorch deep learning framework , PyCharm development tool , and implemented through the Python language. The computational framework uses CUDA version 10.0 .The relevant parameters of the model are set as follows: the training sample batch size is 10, the initial learning rate is set to 0.001, the weight decay is set to 0.0005, the momentum decay is set to 0.9, the optimizer is SGD, and the total number of training iterations is 100.Our model loss value variation curves during training are shown in The total loss value of the model during the training process is the superposition of the loss values of all target detection layers. Our proposed model not only has four target detection layers but also changes the residual structure, which causes the model to have such high loss values in the initial phase of training.Although our model has a high initial loss value, the loss value decreases rapidly as the training proceeds. This is because the network model updates the node weights during the back-propagation process of training, and the updated weights make the model’s fitting ability gradually improve.When the number of training iterations of the network model exceeds 80, the loss value gradually stabilizes between 2 and 2.5. The loss values of the subsequent model smooth out overall with only slight oscillations, indicating the completion of the network model training.When we train the model, a model weight is saved after one training iteration is completed. After the model training was completed, we obtained a total of 100 model weights. For the subsequent identification of ewe estrus climbing behavior, we need to evaluate the obtained models using evaluation metrics to find the optimal detection model. We can see from the figure that the overall trend of the model is growing during the first 60 training iterations, although the variation of each metric is large. After 80 training iterations, the overall metrics of the models gradually stabilized. The mAP of several of the models reached above 99%. After considering the F1 value and other three indexes, we finally chose the model saved after 96 training sessions as the ewe estrus climbing behavior recognition model in this paper.We also need to compare the values of P, R, F1, and AP of this model under different confidence thresholds after selecting the optimal model.From When the confidence threshold is set larger, our model will exhibit higher precision, lower recall, and higher ewe estrus misidentification. When the confidence threshold is set smaller, our model will exhibit lower precision, higher recall, and lower ewe estrus misidentification.This phenomenon leads us to set the confidence threshold of the model as neither too large nor too small.Therefore, we mainly refer to the F1 value of the combined evaluation index of precision and recall in the selection of confidence threshold.From To analyze the variability of our model with other models, we used faster R-CNN (Resnet50), faster R-CNN (VGG16), YOLO v3 and YOLO v4 to compare the performance with our model. In the comparative analysis, we used P, R, F1, mAP, FPS, and model size as evaluation metrics, which were validated on the same test set. The results are shown in We can see the advantages and disadvantages of our model with other models from the data in the Compared with YOLO v3, our model F1 was increased by 1%, the mAP is not much different, the P was increased by 3.7%, the R was decreased by 0.89%, the FPS was decreased by 4 f/s, and the model size was increased by 41 MB. Compared with YOLO V4, our proposed model F1 was increased by 2%, mAP is not much different, P was increased by 0.44%, R was increased by 4.83%, FPS was increased by 4.77 f/s, and model size was increased by 32 MB.Based on the original YOLO v3 model, our model adds a 104 × 104 target detection layer to detect distant ewes in the video image, enhancing the model’s ability to detect small targets. At the same time, we increase the number of cells in the residual structure of the model. On the one hand, it prevents the loss of shallow feature information that occurs during the forward-propagation training of the model. On the other hand, we prevent the problem of gradient dispersion and gradient explosion during the backward-propagation training of the model. The fitting capability of the model is enhanced.In summary, our model can obtain more deep and shallow feature information, and the deep and shallow feature data are utilized with each other, which increases the learning ability and performance of the model. It enables our model to improve both P and F1 values for ewe estrus climbing across behavior recognition, and the detection speed FPS was 41 f/s, which can meet the requirements of real-time recognition.In order to further validate the performance of the model in the recognition of estrus climbing behavior of ewes, we perform a comparative analysis in terms of false recognition, occlusion, and long distances.In conclusion, our proposed model has lower false identification results than the original model in detecting ewes in estrus and can correctly identify the climbing behavior of ewes in estrus.We can see from From the recognition results in We can see from We can see the advantages of our model from the recognition results, which are mainly due to our model’s additional layer of target detection and increased number of residual units compared to the original model.We know that the shallow feature layer is rich in detail information such as color, texture, edge and target location, while the deep feature layer has complex and abstract semantic information.Our improved method allows the model to further integrate shallow and deep feature information, thus improving the model’s ability to identify estrus crawling behavior in distant ewes.The timely and accurate detection of estrus behavior in ewes is an important research topic in precision animal husbandry. Our research work has provided some assistance in the detection of ewe estrus crawling behavior in large-scale meat sheep farming and has led to the following conclusions:(1) We have made some improvements based on the YOLO v3 model structure. We improve the ability of the model to detect distant estrus ewes in video images by increasing the target detection layer of the model. By increasing the number of cells in the residual structure of the model, we improve the model’s ability to fuse shallow feature information with deep feature information. We reduce the impact of image distortion on the model by performing an image resolution maintenance aspect ratio operation on the model data-loading module. We made some improvements to the model’s structure through the abovementioned operation to improve the ability of the model to detect estrus crawling behavior of ewes.(2) We re-clustered the anchor box size of the model to further improve its performance, and finally obtained the ewe estrus crawling behavior detection model. Our model shows better performance in comparison tests with models such as faster R-CNN (Resnet50), faster R-CNN (VGG16), YOLO v3 and YOLO v4.In conclusion, we very much hope that our proposed model/method can provide some help and reference for estrus behavior detection of ewes in large-scale meat sheep farming.


Background and aim: In the past few years, the number of studies conducted on the designs of abutments and related materials has increased. The restoration is performed over the implant using prefabricated standard abutments. Due to the problems encountered in relation to bone resorption following restoration, other types of abutment designs have been introduced in the dental field such as molded abutments usually fabricated by the lost-wax technique and computer-designed abutments produced by three-dimensional (3D) printing technology. The aim of this study was to compare the clinical performance of molded abutments and computer-designed 3D printed abutments in terms of the bone loss around the implant and the depth of periodontal pockets in a one-year observation period.Methods: The research sample consisted of 32 dental implants for patients who required two adjacent implants. Patients were randomly allocated into two groups: in the first group, the patients received molded abutments, whereas, in the second group, the patients received 3D printed abutments with an allocation ratio of 1:1. In the molded abutment group, the plastic abutments were waxed and poured using the Ni-Cr mixture in the lost-wax technique, while in the printed abutment group, the abutments were designed by a computer program and printed using the Cr-Co mixture employing a 3D laser printer. The bone level and pocket depth around the implant were evaluated at 3, 6, and 12 months following cementation.Results: After one year, there were significant differences in the bone resorption mean values between the 3D laser-printed abutment group (0.43±0.11) and molded abutment group (0.54±0.11). In addition, there were significant differences in the mean values of probing depth between the 3D laser-printed abutment group (3.39±0.12) and molded abutment group (3.53±0.08). Therefore, the 3D laser-printed abutment was slightly better and had a lower bone loss degree than the molded abutment.Conclusion: Within the limitations of the current work, the mean bone resorption for both types of abutments was within the normal limits. However, the implants that were restored using the printed abutments had less bone resorption than those restored using the molded abutments. Dental implants are an established treatment modality in dental practice and are used to restore the functional and aesthetic aspects of the edentulous patient . To achiThe platform-switching concept is the use of an abutment smaller in diameter than the implant platform; the internal gap between the neck of the implant and the neck of the abutment is among other factors that determine implant performance and bone loss after restoration, including the dimensions of the loss area and the form of contact between the implant and the abutment -6.Microbial leakage at the implant-abutment interface has been associated with inflammatory reactions that may jeopardize peri-implant bone stability . The resRecently, three-dimensional (3D) printing technology has become more important in the world of dentistry, especially selective laser-melting metal printing technology ,12. WithMost of the published articles that evaluated 3D printed abutments were only confined to laboratory investigations or short-term clinical studies . It is rStudy design and settingsThis study was a parallel-group randomized controlled trial conducted at the Department of Fixed Prosthodontics, Faculty of Dentistry, Damascus University, Damascus, Syria. It was ethically approved by the Local Research Ethics Committee of the Faculty of Dentistry, Damascus University (UDDS-5455-02092020/SRC-5791). The project was self-funded and it was registered in the Clinical Trials database with the following registration number: NCT05350293.Sample size calculationG*Power software, version 3.1.3  was used to calculate the sample size. Crestal bone loss was the variable of interest in this study. The following assumptions were used: (1) the smallest difference requiring detection of the crestal bone loss after one year was 1.5 mm; (2) a significance level of 0.05, (3) a power of 95%, (4) the standard deviation (SD) of the change in the crestal bone level was 0.33 mm, as taken from a previous study , (5) thePatient recruitment and follow-upParticipants were derived from patients attending the Department of Fixed Prosthodontics of the University of Damascus Dental School, who required two adjacent or opposite implants (single posterior prosthesis) on each side of the jaw, whether upper or lower, and wished to have a prosthetic solution with implants. After the clinical, dental, and radiographic examination, 32 patients accurately met the following inclusion criteria as specified by the American Society of Anesthesiologists . The incExclusion criteria were as follows: (1) the presence of non-functional habits such as stridor, (2) acute periodontitis, (3) previous loss of implants, (4) poor general health conditions, (5) previous radiotherapy in the head and neck area, (6) mental incompetence, and (7) orthodontic treatment.RandomizationRandomization was performed using a computer-generated random list  with an allocation ratio of 1:1. The patients were randomly allocated into the two groups. In the first group, the patients received molded abutments, whereas, in the second group, the patients received 3D printed abutments. Using sealed and sequentially numbered envelopes, the allocation procedure was concealed from the researcher and was conducted by one of the academic staff not involved in this research project. Blindness was only applied during data analysis.Implantation proceduresThe first surgical stage included inserting the implants  in the place of loss after conducting a radiological study using cone-beam computed tomography (CBCT) imaging that allowed knowing the type of bone and determining the diameter and length of the implant that was appropriate to the place of loss Figures , 2. The Prosthetic stageThe impression was taken for the implants in the open-tray technique using the appropriate transfer and an alternative to the laboratory implant. The gingival mask was injected similarly to placing the gum inside the mouth. The impression was poured using phosphate-bonded gypsum powder .First group: plastic molded abutmentsThe plastic molded abutment, which was available at the implant manufacture, was placed over the analog on the cast to ensure the position of the implant, the abutment, and its relationship with the rest of the teeth. Any necessary modifications to the shape of the plastic abutment to make it suit the location of the implant and its relationship with the adjacent teeth were made, if needed, using wax. The waxed plastic abutment was dismantled, wedged, and poured using a Ni-Cr mixture. Then it was trimmed, making it ready to make the permanent restoration over it. The prostheses were cemented above the abutment using dual-cure resin cement, and then, the final restoration (crown and abutment) was fixed in a screw-retained manner Figures -5H.Second group: 3D printed abutmentsA marker or scan body appropriate for the type of implant was placed on the laboratory implant substitute. A digital scan of the marker was done in the lab. The scanned image was transferred to a computer designing software where the prosthesis was designed . ThereafIn the printing phase, the final prosthesis was manufactured using a stepwise metal powder supply and a laser fusion process. Finally, heat treatment was performed to relieve the internal stress caused by thermal gradients during the whole 3D printing process, making the abutment ready to make the permanent restoration over it . The proMaking final prosthesis over both types of abutmentsAfter making the abutments, an experiment was conducted for the abutment inside the mouth to ensure its correct position. The abutments were radiographed using the intra-oral sensor . The abutment was placed on the implant analog and photographed using the digital scanner. The metal caps were designed on top of them using exocad DentalCAD software . Then, they were printed using a 3D laser printer. The porcelain was built on top of the metal caps to give them the shape of the final restoration.Outcome measuresRadiographic VariablesPreapical radiographs were taken for both groups after fixing the final restoration, using a digital intra-oral sensor (EzSensor Classic), and a special holder in a parallel way, with the same head orientation. The preapical radiographs were used to evaluate the level of the bone around the implant, depending on the marginal bone loss score (MBLS) concept by taking two referring points as described by Koutouzis and Lundgren, using a special computer program  .The measurements were taken immediately following fixation and after 3, 6, and 12 months Figures -7D.Clinical VariablesThe depth of the pocket around the implants was measured from the middle of the mesial and distal surfaces of both types of abutments individually using a special probe  immediately following the fixation of the prosthesis and after 3, 6, and 12 months, noting the changes that occurred in the depth of the pocket around the implant in both groups.Statistical analysisData analysis was performed using IBM SPSS Statistics, version 20 , and data were transferred to an Excel spreadsheet (Microsoft). Because of normal distribution, two-sample t-tests were employed to examine intergroup differences. Statistical significance was set at the 0.05 level.Baseline sample characteristicsThe sample consisted of 32 abutments of 32 implants implanted in 16 patients with the age ranging from 33 to 50 years. The average age of the recruited sample was 45.6 years, with the average age being 44.4 years for males and 46.2 for females (Table Radiological outcome: change in the bone level around the implantsThere were no significant differences in bone resorption mean values between the 3D laser-printed abutment group and the molded abutment group after three- and six-month periods (p>0.05; Table Periodontal outcome: change in the probing depth around the implantsAfter one year of observation, there were significant differences in mean values of the probing depth between the 3D laser-printed abutment group and the molded abutment group (p<0.05; Table The amount of bone loss and depth of probing that occurred in both types of abutments after one year can be considered normal and within the normal limits, since the mean bone loss was 0.54 mm and 0.43 mm in the molded abutment and the laser-printed abutment groups, respectively. These mean values are less than 2 mm. At the same time, the probing depth did not exceed the threshold of 4 mm that also showed good periodontal health around the inserted implants .The regression of bone that occurred in the molded abutments was more than that in the printed abutments, despite standardizing the implant measurements and the area of the jaw in which it was implanted. However, the shape and fit of the abutment contributed to an increase in bone resorption. This is because the cast abutments had less diameter in width, and the neckline was simple in contrast to the diameter and shape of the neckline found in the printed abutments. Therefore, the transmission and distribution of forces were better in the printed abutment than in the molded one. The findings of the current study agree with those of another study that indicated that a good adaptation between mechanical components of the implant was essential to minimize biological and prosthetic complications .The risk of bacterial penetration into the implant‐abutment interface has been investigated both in vitro and in vivo. In vitro studies have shown that implant‐abutment connection design characteristics do have an impact. In vivo studies have also confirmed the findings of in vitro studies, and an association of bacterial penetration into the implant‐abutment interface with crestal bone loss has also been shown. We agree with the study of Koutouzis and Lundgren who found that the shape of the junction of the implant and the abutment had an effect on the marginal bone resorption, and it must be designed to prevent the microorganisms from leaking into the contact surface between them and causing bony absorption .In the current study, the depth of probing in the printed abutments was less than that of the molded abutments, and the depth of the probing was related to the level of the bone around the implant. Therefore, any change in the bone would lead to a change in the probing depth; this may be because the better fit of the printed abutments within the implants allowed the transfer of the forces applied to the implant perpendicularly, which allowed maintaining the least degree of bone loss, which is within the international standard natural limits.The findings of the current work agree with those of a study conducted by Kuo et al. in which the prostheses made using a laser printer achieved better fit and sealing of edges than the prostheses made using the lost-wax technology . AnotherLimitations of the current studyOne of the limitations of the current work is the short follow-up period that could have been extended to 18 or 24 months. Another limitation could be the relatively small sample size. Other outcome measures should have been included in the current trial such as the assessment of patient-reported outcomes and possible periodontal complications.Within the limitations of the current work, the following conclusions can be made: the amount of bone loss for both types of abutments was within the normal limits. However, the implants that were restored using the 3D printed abutments were better with less bone loss and less probing depth than the implants that were restored using molded abutments.