Aquifer Water Abundance Evaluation Based on a Variable Weight Model

To indicate health status of bridges and help stakeholders make decision on maintenance, a comprehensive framework has been proposed to evaluate structural efficiency of suspension bridges using analytic hierarchy process. First, the analytical hierarchy model (i.e. hierarchical network together with data aggregation algorithms) has been constructed using multi-source data, including visual inspection, non-destructive testing and structural health monitoring information. Age-dependent variable weight theory is developed to account for the service history of elements ensuring the alignment of variation trend of index weights with the objective law in bridge maintenance and management activities. To overcome the limitations of factor-based variable weight model for weight adjustment, the factor- and age-based variable weight model has been adopted for data aggregation. Finally, four cases are used to test the effectiveness of the three models (i.e. constant weight model, factor-based variable weight model and factor- and age-based variable weight model). By comparing the performance of the three models, the recommended maintenance strategy derived from factor- and age-based variable weight model aligns more with the actual strategy than the other two. The factor- and age-based variable weight model outperforms both the factor-based variable weight model and constant weight model in helping bridge owners make maintenance decisions.

A variable-length shipments-orders mechanism, with application to industries in Canada. A model of the shipments-orders relation is developed which combines a fixed work lag and a variable wait lag. The wait lag is represented by a Pois- son distribution and the work lag by a polynomial function. This model allows the shape and length of the distributed lag relation to change each period. The model, along with a fixed weight shipments-orders model and a variable weight model developed by Tinsley, is estimated. The fixed shipments-orders model is rejected as a satisfactory specification. Although the two variable weight models perform equally well the variable length model satisfies the a priori constraints more often.

This paper uses assessment method of vacuum circuit breaker (VCB) conditions based on variable weight and cloud model. The state indicators are determined by the test data and relevant literature. The improved analytic hierarchy process method is used to optimize the subjectivity of the weights, and the variable weight is introduced to correct the constant weight, which according to the actual data. The cloud model is used to dividing the condition level of vacuum circuit breaker and calculating the membership degree of each grade cloud. The condition of the vacuum circuit breaker is determined by the fuzzy synthesis of the weights and the condition matrix. Taking the ZN63A-12 vacuum circuit breaker as an example, the results based on the method are closer to the actual condition, which can provide a reference to the condition maintenance.

This study proposes a variable weight model prediction control strategy based on road surface recognition (VW-MPC-RSR), aiming to address the problems of road recognition timeliness and model prediction control (MPC) weight adjustment under multi-working conditions. The road surface recognition is achieved by utilizing the VIT-B/16 network model. A test set and a continuous frame video are used to verify the effectiveness of the road recognition strategy. At the same time, a 2-DOF suspension dynamics model is established, and the variable weight MPC control strategy based on road surface recognition is designed. The optimal weights of six road types are determined using a cross-comparison method, and the corresponding weight-switching strategy is formulated. The performance of VW-MPC-RSR active suspension, MPC active suspension, and passive suspension is compared by simulation. The results show that the road surface recognition strategy based on the VIT network can recognize various road states efficiently and accurately. In addition, the suspension control strategy designed in this paper can adjust the control parameters in real time according to road conditions and significantly improve the overall performance of suspensions.

In this paper, an evaluation model based on entropy method, cloud model and evidence theory is established to solve the problem that the evaluation result of the elevator operation status is too subjective. By analyzing the primary features associated with the operation status of the elevator, a layered evaluation index system for elevator condition assessment is built. Firstly, the entropy method is used to acquire the constant weights of each indicator, and the variable weight model based on balance function is introduced, in which the constant weights is modified to get the variable weights according to the deviation of actual data and normal value. Then the cloud theory is applied to achieve the conversion between quantitative index and qualitative concept, in which the membership degree is obtained so that index is mapped to each status. Lastly, based on the evidence theory, the index information is fused, and the evaluation results are obtained by the principle of maximum membership degree and reliability criterion. By applying the model we proposed, the example analysis shows that, the model is correct and effective, which could reflect the operation status of the elevator accurately.

In view of the automotive comfort under high-speed following conditions, a semi-active suspension control method based on model predictive control is proposed. The performance function of the controller consists of the predicted output of the vehicle, the output force of the actuator, and their respective weights. Usually, the selection of the weights requires sufficient engineering experience and a large number of experiments to determine. There are mainly three working conditions for constant speed, acceleration and braking conditions, and different weight groups are proposed for different driving conditions. Therefore, the Particle swarm algorithm (PSO) is used to optimize the parameters of the MPC controller, and in order to obtain three groups of weights under different driving conditions. The simulation results show that compared with the passive suspension system, the MPC controller based on the particle swarm algorithm reduces the RMS value of the vertical acceleration of the vehicle under the three working conditions, so the suspension control strategy effectively improves the ride comfort of passengers.

The water resource is an important guarantee of social and economic sustainable development. The improvement of water’s ecological carbon sequestration ability is a direct response to the goal of “double carbon”. Water quality directly affects its carbon sequestration capacity. So it is necessary to understand the water quality of rivers. In view of the fuzziness and uncertainty in water quality evaluation, this paper uses the cloud model to realize the qualitative to quantitative transformation of water quality in Wenyu River. By combining moment estimation theory with critic weight, AHM weight, and variable weight theory. A water quality evaluation method integrating a variable weight cloud model is constructed. And the temporal and spatial changes in water quality in Wenyu River are studied. The results show that the combined weights balance the influence of each index while retaining the advantages of subjective and objective weights. The results of the water quality evaluation are consistent with the practice, which verifies the feasibility and applicability of the method.

In order to accurately predict the macroscopic material flow, aiming at the limitations of the existing medium and long-term macro material flow forecasting models, we propose a multi-objective variable weight combination prediction mode (MOVWCP) based on the parallel cell coordinates system Adaptive Multi-Objective Particle Swarm optimizer algorithm (pccsAMOPSO) to analyze and predict macro material flows. In order to improve the stability of MOVWCP, the concept of error entropy is proposed, at the same time, MOVWCP uses mean absolute percentage error (MAPE) and error entropy to build the objective functions. An intelligent heuristic algorithm based on pccsAMOPSO is designed to solve the Pareto front of variable weights during the fitting period and the variable weight Pareto solution was selected by using the sensitivity difference. A series of numerical experimental results verify the superiority of MOVWCP and its algorithm.

Based on systematic collection of the hydrogeological data of coal mine, we analyzed the factors affecting water inrush from coal floor and determined the major factors. We used the fuzzy clustering method to evaluate the water inrush risk from coal floor in order to solve the evaluating limitation of water inrush risk from coal floor by the water inrush coefficient method. The fuzzy clustering method gave a comprehensive consideration of many factors affecting water inrush from coal floor and it solved the problems of evaluating critical values by the water inrush coefficient method. To rapidly predict the water inrush risk from coal floor and improve the prediction accuracy, we developed the prediction software for water inrush risk from coal floor. On the theoretical basis of water inrush coefficient method and fuzzy clustering method, we used Visual Basic 6.0 design language as development platform, combining Access database and Matlab software, to develop prediction software, which had a zoning evaluation of water inrush risk. Take the Xinglongzhuang coal mine for example, the major factors affecting water inrush risk from coal floor were head pressure of Ordovician limestone aquifer, aquifuge thickness, geological structure and water abundance of Ordovician limestone aquifer. And we had a zoning evaluation of its water inrush risk. The evaluation results provide scientific basis for working out measures to prevent and control the water of Ordovician limestone aquifer to Xinglongzhuang coal mine. It also has important guiding significance to exploitation safety of coal mining. Because the prediction software has common applicability, it provides powerful support for rapidly correct evaluation of water inrush risk to other coal mines.

Due to the high uncertainties in temperature changes, traditional regression analysis and time series prediction methods fail to provide accurate temperature forecasts to reduce the impact of extreme weather on human society. Considering the spatiotemporal features of temperature changes, this paper proposes a variable weight combination model based on a temporal graph convolutional network (T-GCN), Luong attention network (LUA) and gated recurrent unit (GRU) network, which fully utilizes spatiotemporal information to predict future temperature changes more accurately. The model uses the T-GCN model to capture spatiotemporal features while introducing Luong attention to weight the inputs at different time steps to improve the prediction accuracy and further reduce the prediction error by fusing the outputs of the T-GCN-Luong attention and GRU models through the variable weight combination method. The results revealed that (1) the inclusion of spatial information significantly improved the effectiveness of the temperature predictions. (2) The Luong attention mechanism weights different time steps and improves the prediction accuracy of the T-GCN model. (3) The TGLAG combination model constructed via the variable weight method exhibited good predictive performance at 15 sites. Compared with that of the simple GRU model, the accuracy of the proposed model is improved by approximately 31.949% in terms of the root mean square error (RMSE) and 26.913% in terms of the mean absolute error (MAE). Compared with the second-best model, T-GCN-Luong attention, the TGLAG model yields a 5.946% lower RMSE and 9.535% lower MAE, which indicates that TGLAG has good application prospects in the field of temperature prediction.

In view of the complex application environment, channel instability and link disturbance of self-organizing, self-configuring, multi-hop and multi-channel wireless network design a cross-layer transmission variable routing measurement model. Qualitative expression of link available bandwidth, transmission delay and link reliability converted into quantitative data. Adjust routing parameters and use the weighted average method, normalized algorithm and artificial swarm algorithm in order to obtain the mathematical model of wireless node search path and an integrated routing measurement algorithm with variable weight coefficients. Variable-weighted integrate routing metric algorithm selects the link with relatively idle nodes and achieves the global optimal solution. Simulation results show that the optimized variable routing measurement model improves the packet delivery rate and reduces the end-to-end average delay. At the same time, artificial swarm algorithm is used to change the disadvantage of traditional selection of the optimal path and overcomes the shortcoming of premature stagnation. Algorithm meets the requirements of complex application environment.

Thermodynamic models of aqueous solutions have indicated that the mixing of seawater and calcite‐saturated fresh groundwater can produce a water that is undersaturated with respect to calcite. Mixing of such waters in coastal carbonate aquifers could lead to significant amounts of limestone dissolution. The potential for such dissolution in coastal saltwater mixing zones is analyzed by coupling the results from a reaction simulation model (PHREEQE) with a variable density groundwater flow and solute transport model. Idealized cross sections of coastal carbonate aquifers are simulated to estimate the potential for calcite dissolution under a variety of hydrologic and geochemical conditions. Results show that limestone dissolution in mixing zones is strongly dependent on groundwater flux and nearly independent of the dissolution kinetics of calcite. The amount of dissolution varies within a mixing zone, depending on the properties, physical dimensions, and boundary conditions of the aquifer system. Nearly all of the dissolution occurs in the fresher side of the mixing zone, with the maximum dissolution occurring in water that is fresher than that predicted solely by geochemical reaction models. The greatest porosity and permeability development occur at the toe and at the top of the mixing zone. If permeability increases as porosity increases, asymmetry in the dissolution causes the mixing zone to migrate landward over time. Dissolution rates indicated by the model show that this mechanism can produce significant increases in porosity and permeability over time spans on the order of tens of thousands of years. Given the comparatively long span of geologic time, this process may be largely responsible for porosity and permeability development observed in those carbonate rocks through which a freshwater‐saltwater mixing zone had at one time migrated.

Radial velocities measured from near-infrared spectra are a potentially powerful tool to search for planets around cool stars and sub-stellar objects. However, no technique currently exists that yields near-infrared radial velocity precision comparable to that routinely obtained in the visible. We describe a method for measuring high-precision relative radial velocities of these stars from K-band spectra. The method makes use of a glass cell filled with ammonia gas to calibrate the spectrograph response similar to the "iodine cell" technique that has been used very successfully in the visible. Stellar spectra are obtained through the ammonia cell and modeled as the product of a Doppler-shifted template spectrum of the object and a spectrum of the cell, convolved with a variable instrumental profile model. A complicating factor is that a significant number of telluric absorption lines are present in the spectral regions containing useful stellar and ammonia lines. The telluric lines are modeled simultaneously as well using spectrum synthesis with a time-resolved model of the atmosphere over the observatory. The free parameters in the complete model are the wavelength scale of the spectrum, the instrumental profile, adjustments to the water and methane abundances in the atmospheric model, telluric spectrum Doppler shift, and stellar Doppler shift. Tests of the method based on the analysis of hundreds of spectra obtained for late M dwarfs over six months demonstrate that precisions of ~5 m/s are obtainable over long timescales, and precisions of better than 3 m/s can be obtained over timescales up to a week. The obtained precision is comparable to the predicted photon-limited errors, but primarily limited over long timescales by the imperfect modeling of the telluric lines.

Chronic respiratory disease is an important public health problem in the United States and globally. Diet, an important part of a healthy lifestyle, is also relevant to chronic respiratory health. We aimed to explore the relationship between overall dietary quality and the risk of chronic respiratory disease (CRD), include chronic bronchitis (CB), emphysema and asthma. A total of 4,499 United States adults were extracted from the National Health and Nutrition Examination Survey (NHANES) in 2017-2018. Diet quality was assessed using 2 day, 24 h dietary recall data and quantified as the Healthy Diet Index (HEI)-2020 score. Binary logistic regression models, restricted cubic splines (RCS) and generalized additive modeling (GAM), the weighted quartile sum (WQS) and qgcom models were used to assess the relationship between HEI-2020 scores and risk of CB, emphysema and asthma. High HEI-2020 scores are associated with low risk of chronic respiratory disease (CB: 0.98, 0.97-0.99; emphysema: 0.98, 0.97-0.99; asthma: 0.98, 0.97-0.99) and consistent results across different dietary variable categorization (Tertile: CB: 0.58, 0.42-0.81; asthma: 0.51, 0.35-0.74; Quartile: CB: 0.57, 0.34-0.97; asthma: 0.56, 0.36-0.86) and different weighting models. Negative dose-response relationship between dietary quality and risk of chronic respiratory disease also shown in RCS and GAM models. The WQS and qgcom models also showed a healthy mixing effect of dietary components on respiratory disease, with high-quality proteins, vegetables, and fruits making the heaviest contributions. Higher HEI-2020 scores were associated with lower risk of CB, emphysema, and asthma. Following Dietary Guidelines for Americans 2020-2025 could support enhanced respiratory health.

Research on the simulation of groundwater flow fields disturbed by coal mining based on multi-scale coupled decision-making.