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For the purpose of establishing vertical knowledge graph and auxiliary applications in the agricultural field, a set of agricultural knowledge graph construction methods, calculation frameworks and practical application systems are proposed. Firstly, the existing storage form and knowledge representation of knowledge in the agricultural field are integrated and regularized. On the basis of this data processing, the intelligent construction method of automatic and manual dual mode of knowledge graph in the agricultural field is proposed, and the key technology of entity relationship joint model to extract entity relationship and intelligent retrieval of irregular data. Then, similarity calculation will be used to perform entity knowledge fusion on knowledge graph in the agricultural field, making the graph more standardized, accurate and complete. A good graph is visualized and applied to the mainstream functions of intelligent question answering, which makes the whole system sort out the messy agricultural knowledge and apply it better to better assist learning and research.

Based on the engineering background of the diversion auxiliary tunnel at the Jinping II Hydropower Station, the discrete element method and the particle flow code theory are used to construct a jointed rock mass model. This model fully reflects the joint distribution characteristics and considers the effects of micro-fracturing. The generation and evolution of the zonal disintegration phenomenon are discussed in detail. The numerical results show the same fracture characteristics as the on-site ultrasonic tests. It is indicated that the type of micro-fracture is mainly tensile, but the direction of the extension of the fracture zones is dominated by shear micro-fractures. The root cause of the zonal disintegration of the rock surrounding the tunnel is that the existence of joints makes the internal strength distribution of the rock mass uneven, and the expansion direction of the fracture zone around the tunnel then becomes circumferential. The trace length and spacing of the joints have an influence on the shape and size of the fracture zones. For unbonded joints, the friction coefficient of the smooth-joint model affects the number and locations of the fracture zones. For bonded joints, the cohesion strength of the smooth-joint model has a significant effect on the zonal disintegration of the rock. The results in this paper provide a benchmark for further investigations of the zonal disintegration phenomenon.

Non-negative continuous outcomes with a substantial number of zero values and incomplete longitudinal follow-up are quite common in medical costs data. It is thus critical to incorporate the potential dependence of survival status and longitudinal medical costs in joint modeling, where censorship is death-related. Despite the wide use of conventional two-part joint models (CTJMs) to capture zero-inflation, they are limited to conditional interpretations of the regression coefficients in the model’s continuous part. In this paper, we propose a marginalized two-part joint model (MTJM) to jointly analyze semi-continuous longitudinal costs data and survival data. We compare it to the conventional two-part joint model (CTJM) for handling marginal inferences about covariate effects on average costs. We conducted a series of simulation studies to evaluate the superior performance of the proposed MTJM over the CTJM. To illustrate the applicability of the MTJM, we applied the model to a set of real electronic health record (EHR) data recently collected in Iran. We found that the MTJM yielded a smaller standard error, root-mean-square error of estimates, and AIC value, with unbiased parameter estimates. With this MTJM, we identified a significant positive correlation between costs and survival, which was consistent with the simulation results.

Assisted ultrasonic vibration technique can significantly improve the efficiency of hard rock drilling in petroleum and mineral engineering. In this study, to determine the fragmentation mechanism of rocks under ultrasonic vibration, numerical simulations using the discrete element method (DEM) were performed. A novel flat‐joint model (FJM), combined with an ultra‐high‐frequency loading boundary condition, was used to model the damage process of the hard rock under ultrasonic vibration loading. The numerical results demonstrated that the evolution of local strain and fragmentation were in good agreement with the experimental results. Based on the established model, the influence of loading parameters was investigated. Furthermore, by analyzing the development of the full strain field, crack orientations, and crack distribution, the fragmentation mechanism was revealed for the rock under ultrasonic vibration. Under ultra‐high‐frequency loading, the rock deformed in a heterogeneous manner and produced both compressive and tensile strain zones. The compressive zones were mainly distributed in the fringe and tensile zones in the top center. The generated tensile cracks caused by compression and tension in these two strain zones led to the rock failure. In this work, we determined the fragmentation mechanism of rocks under ultrasonic vibration based on the numerical simulations using the discrete element method (DEM). A novel flat‐joint model (FJM), combined with an ultra‐high‐frequency loading boundary condition, was used to model the damage process of the hard rock under ultrasonic vibration loading. In order to accelerate the simulation process, the “amplitude enlargement” assumption has been employed based on the relationship between the failure cycle number and loading amplitude.

Clinical studies often collect longitudinal and time-to-event data for each subject. Joint modeling is a powerful methodology for evaluating the association between these data. The existing models, however, have not sufficiently addressed the problem of missing data, which are commonly encountered in longitudinal studies. In this paper, we introduce a novel joint model with shared random effects for incomplete longitudinal data and time-to-event data. Our proposed joint model consists of three submodels: a linear mixed model for the longitudinal data, a Cox proportional hazard model for the time-to-event data, and a Cox proportional hazard model for the time-to-dropout from the study. By simultaneously estimating the parameters included in these submodels, the biases of estimators are expected to decrease under two missing scenarios. We estimated the proposed model by Bayesian approach, and the performance of our method was evaluated through Monte Carlo simulation studies.

Probabilistic forecasts of species distribution and abundance require models that accommodate the range of ecological data, including a joint distribution of multiple species based on combinations of continuous and discrete observations, mostly zeros. We develop a generalized joint attribute model (GJAM), a probabilistic framework that readily applies to data that are combinations of presence-absence, ordinal, continuous, discrete, composition, zero-inflated, and censored. It does so as a joint distribution over all species providing inference on sensitivity to input variables, correlations between species on the data scale, prediction, sensitivity analysis, definition of community structure, and missing data imputation. GJAM applications illustrate flexibility to the range of species-abundance data. Applications to forest inventories demonstrate species relationships responding as a community to environmental variables. It shows that the environment can be inverse predicted from the joint distribution of species. Application to microbiome data demonstrates how inverse prediction in the GJAM framework accelerates variable selection, by isolating effects of each input variable's influence across all species.

The Distinct Element Method (DEM) has gained recent attention to study geotechnical designs with rock-concrete or rock–rock interfaces, such as rock-socketed piles. In this work, 3D DEM models with non-standard contacts laws (the Smooth-Joint and Flat-Joint contact models) are proposed to analyze the response of axially loaded rock-socketed piles with different sockets roughness, since socket roughness is a key factor affecting their side shear resistance that is not usually considered for pile design. DEM models are calibrated using experimental data, and the consequences of applying 2D models for calibration, to be subsequently used in a 3D analysis, are studied. Numerical results suggest that such DEM models can be employed to reproduce key aspects of the behavior of rock-socketed piles, such as their load and global stiffness-settlement response, their side shear resistance, and the damage at the rock-pile interface. Finally, an empirical factor αRF,1%D is proposed to estimate the side shear resistance of rock-socketed piles considering the socket roughness and the uniaxial compressive strength (UCS) of the weaker material (rock or pile) at the interface.

Frailty is a dynamic health state that changes over time. Our hypothesis was that there are identifiable subgroups of the older population that have specific patterns of deterioration. The objective of this study was to evaluate the application of joint latent class model in identifying trajectories of frailty progression over time and their group-specific risk of death in older people. The primary care records of UK patients, aged over 65 as of January 1, 2010, included in the Clinical Practice Research Datalink: GOLD and AURUM databases, were analyzed and linked to mortality data. The electronic frailty index (eFI) scores were calculated at baseline and annually in subsequent years (2010-2013). Joint latent class model was used to divide the population into clusters with different trajectories and associated mortality hazard ratios. The model was built in GOLD and validated in AURUM. Five trajectory clusters were identified and characterized based on baseline and speed of progression: low–slow, low–moderate, low–rapid, high–slow, and high–rapid. The high–rapid cluster had the highest average starting eFI score; 7.9, while the low–rapid cluster had the steepest rate of eFI progression; 1.7. Taking the low–slow cluster as reference, low–rapid and high–rapid had the highest hazard ratios: 3.73 (95% CI 3.71, 3.76) and 3.63 (3.57-3.69), respectively. Good validation was found in the AURUM population. Our research found that there are vulnerable subgroups of the older population who are currently frail or have rapid frailty progression. Such groups may be targeted for greater healthcare monitoring.

Infantile colic viewed as a non-dangerous prevalent issue could lead to stress in parents and long-term negative consequences in ex-colicky children. Researchers have not been successful in finding a certain treatment for colic symptoms. Studies suggest completely different approaches as its treatment. Massage therapy as an alternative method in reducing colic symptoms has been recommended in several studies. A total of 100 colicky infants in a single blind study were randomly specified to two equal groups of intervention and control. Infants in the intervention group received massage for 15-20 minutes once during the day and once at night before sleep, while infants in the control group were rocked for 15-25 minutes when the symptoms of colic appeared. Parents recorded the details of the colic symptoms in a diary every day. All these outcomes were modeled simultaneously via a random-effects joint model. Among 100 infants included in the analysis, 48% were female; 91% of all infants were breastfed and 54% of them were born via normal vaginal delivery. In general, the effect of massage therapy on colic symptoms was assessed using the joint model. Our findings illustrated that massaging colicky infants would substantially reduce colic symptoms and increase the sleep duration in babies compared with the rocking group ( <0.001). Massage therapy could be considered as an effective method in reducing colic symptoms. Mean of the symptoms dropped significantly in the intervention group compared with that in the rocking group. Our study also represents that a relevant and correct statistical model could result in more reliable findings.

Background Extracting biomedical entities and their relations from text has important applications on biomedical research. Previous work primarily utilized feature-based pipeline models to process this task. Many efforts need to be made on feature engineering when feature-based models are employed. Moreover, pipeline models may suffer error propagation and are not able to utilize the interactions between subtasks. Therefore, we propose a neural joint model to extract biomedical entities as well as their relations simultaneously, and it can alleviate the problems above. Results Our model was evaluated on two tasks, i.e., the task of extracting adverse drug events between drug and disease entities, and the task of extracting resident relations between bacteria and location entities. Compared with the state-of-the-art systems in these tasks, our model improved the F1 scores of the first task by 5.1% in entity recognition and 8.0% in relation extraction, and that of the second task by 9.2% in relation extraction. Conclusions The proposed model achieves competitive performances with less work on feature engineering. We demonstrate that the model based on neural networks is effective for biomedical entity and relation extraction. In addition, parameter sharing is an alternative method for neural models to jointly process this task. Our work can facilitate the research on biomedical text mining.