Irrigation Water Demand Model as a Comparative Tool for Assessing Effects of Land Use Changes for Agricultural Crops in Fraser Valley, Canada

Available water for human needs and agriculture is a growing global concern. Agriculture uses approximately 70% of global freshwater, mainly for irrigation. The Lower Fraser Valley (LFV), British Columbia, is one of the most productive agricultural regions in Canada, supporting livestock production and a wide variety of crops. Water scarcity is a growing concern that threatens the long-term productivity, sustainability, and economic viability of the LFV’s agriculture. We used the BC Agriculture Water Demand Model as a tool to determine how crop choice, irrigation system, and land-use changes can affect predicted water requirements under these different conditions, which can aid stakeholders to formulate better management decisions. We conducted a comparative assessment of the irrigation water demand of seven major commercial crops, by distinct soil management groups, at nineteen representative sites, that use both sprinkler vs drip irrigation. Drip irrigation was consistently more water-efficient than sprinkler irrigation for all crops. Of the major commercial crops assessed, raspberries were the most efficient in irrigation water demand, while forage and pasture had the highest calculated irrigation water demand. Significant reductions in total irrigation water demand (up to 57%) can be made by switching irrigation systems and/or crops. This assessment can aid LFV growers in their land-use choices and could contribute to the selection of water management decisions and agricultural policies.

An Induced Demand Model for High Speed 1 in UK

Induced travel is an important component of travel demand and increasing attention has been paid to building analytical model to get more precise travel demand forecasting. In general, induced demand can be defined in terms of additional trips that would be made if travel conditions improved (less congested, lower vehicle costs or tolls). In this paper the induced demand resulting from higher design speeds and, therefore by less travel time, for the High Speed 1 in UK will be modelled on the basis of the relationship between existing High Speed Rail demand (dependent variable) to existing High Speed Rail travel times and costs. The covariates include socioeconomic variables related to population and employment in the zones connected by the High Speed Rail services. This model has been calibrated by mean of a before and after study carried on the corridor, when the new High Speed Rail services was introduced. Elasticities of induced travel (trips and VMT) have been computed with respect to fares, travel time and service frequency.

Seismic fragility analysis of bridges by relevance vector machine based demand prediction model

A relevance vector machine(RVM)based demand prediction model is explored for efficient seismic fragility analysis(SFA)of a bridge structure.The proposed RVM model integrates both record-to-record variations of ground motions and uncertainties of parameters characterizing the bridge model.For efficient fragility computation,ground motion intensity is included as an added dimension to the demand prediction model.To incorporate different sources of uncertainty,random realizations of different structural parameters are generated using Latin hypercube sampling technique.Mean fragility,along with its dispersions,is estimated based on the log-normal fragility model for different critical components of a bridge.The effectiveness of the proposed RVM model-based SFA of a bridge structure is elucidated numerically by comparing it with fragility results obtained by the commonly used SFA approaches,while considering the most accurate direct Monte Carlo simulation-based fragility estimates as the benchmark.The proposed RVM model provides a more accurate estimate of fragility than conventional approaches,with significantly less computational effort.In addition,the proposed model provides a measure of uncertainty in fragility estimates by constructing confidence intervals for the fragility curves.

Forecast for the Cameroon’s Residential Electricity Demand Based on the Multilinear Regression Model

The electricity needs of populations in Cameroon are increasing and are still very inadequate. Companies, public buildings and households are facing frequent blackout which constrain development and social well-being. Therefore, the present work tried to forecast the electricity demand in the residential sector in Cameroon, in order to contribute significantly to the mastery of electricity consumption and highlight decision-makers in this sector. Six macroeconomics parameters covering the period 1994-2014 are used for these issues. Stationarity tests within gross domestic product, gross domestic product per capita, electricity consumption, population and numbers of subscribers and households respectively;reveal that all the series are I(1). Thus, the VAR (Vector Autoregressive) model has been retained to forecast the electricity demand until 2020. The cusum test and the cusum of squared test attest the stability of that model with a margin of error of 0.02%. Previsions are then more reliable and show that the electric request will skip from 1721 GWh in 2014 to more than 2481 GWh in 2020 approximatively, following a growing yearly rate of 5.36%. In order to reach its emergence, Cameroon ought to speed up its production in the domain of hydroelectric and thermal grid in order to meet the requirements in electric power in short and long term.

Finite element modeling assumptions impact on seismic response demands of MRF-buildings

Recent seismic events have raised concerns over the safety and vulnerability of reinforced concrete moment resisting frame ＂RC-MRF＂ buildings. The seismic response of such buildings is greatly dependent on the computational tools used and the inherent assumptions in the modelling process. Thus, it is essential to investigate the sensitivity of the response demands to the corresponding modelling assumption. Many parameters and assumptions are justified to generate effective structural finite element（FE） models of buildings to simulate lateral behaviour and evaluate seismic design demands. As such, the present study focuses on the development of reliable FE models with various levels of refinement. The effects of the FE modelling assumptions on the seismic response demands on the design of buildings are investigated. the predictive ability of a FE model is tied to the accuracy of numerical analysis; a numerical analysis is performed for a series of symmetric buildings in active seismic zones. The results of the seismic response demands are presented in a comparative format to confirm drift and strength limits requirements. A proposed model is formulated based on a simplified modeling approach, where the most refined model is used to calibrate the simplified model.

A Study on an Extensive Hierarchical Model for Demand Forecasting of Automobile Components

Demand forecasting and big data analytics in supply chain management are gaining interest.This is attributed to the wide range of big data analytics in supply chain management,in addition to demand forecasting,and behavioral analysis.In this article,we studied the application of big data analytics forecasting in supply chain demand forecasting in the automotive parts industry to propose classifications of these applications,identify gaps,and provide ideas for future research.Algorithms will then be classified and then applied in supply chain management such as neural networks,k-nearest neighbors,time series forecasting,clustering,regression analysis,support vector regression and support vector machines.An extensive hierarchical model for short-term auto parts demand assess-ment was employed to avoid the shortcomings of the earlier models and to close the gap that regarded mainly a single time series.The concept of extensive relevance assessment was proposed,and subsequently methods to reflect the relevance of automotive demand factors were discussed.Using a wide range of skills,the factors and co-factors are expressed in the form of a correlation characteristic matrix to ensure the degree of influence of each factor on the demand for automotive components.Then,it is compared with the existing data and predicted the short-term historical data.The result proved the predictive error is less than 6%,which supports the validity of the prediction method.This research offers the basis for the macroeconomic regulation of the government and the production of auto parts manufacturers.

Demand Elasticity for China's Major Imported Agriculture Textile Material Based on Restricted Version of Source Differentiated Almost Ideal Demand System Model

By comparing China's import of major imported agriculture textile material( cotton and wool),the characteristics of import are concluded. On this basis,a restricted version of source differentiated almost ideal demand system( RSDAIDS) is used to estimate the income and price elasticity of major imported agriculture textile material from the major sources based on the data from 1992 to 2015. The results are shown as follows.( 1) Although the dependency on imported cotton is lower than wool, the fluctuation of cotton import is much more drastic; China's demand for cotton is relatively price elastic with higher expenditure elasticity compared with wool; besides,the existence of complementarity is proved between imported cotton and wool.( 2) According to the import elasticity of cotton,demand for cotton imported from India shows priority over cotton from other sources; demand for cotton imported from America is the most price-sensitive one; substitution among cotton from different sources is weak.( 3) According to the import elasticity of wool,wool imported from Uruguay has bright market prospects. In addition,wool imported from Australia has irreplaceable advantage than that from New Zealand.

Prediction of primary energy demand in China based on AGAEDE optimal model

In this article,we present an application of Adaptive Genetic Algorithm Energy Demand Estimation(AGAEDE) optimal model to improve the efficiency of energy demand prediction.The coefficients of the two forms of the model(both linear and quadratic) are optimized by AGA using factors,such as GDP,population,urbanization rate,and R&D inputs together with energy consumption structure,that affect demand.Since the spurious regression phenomenon occurs for a wide range of time series analysis in econometrics,we also discuss this problem for the current artificial intelligence model.The simulation results show that the proposed model is more accurate and reliable compared with other existing methods and the China's energy demand will be 5.23 billion TCE in 2020 according to the average results of the AGAEDE optimal model.Further discussion illustrates that there will be great pressure for China to fulfill the planned goal of controlling energy demand set in the National Energy Demand Project(2014—2020).

Optimization Model for School Transportation Based on Supply-Demand Analyses

This paper presents a new conception model of school transportation supply-demand ratio (STSDR) in order to define the number of school buses needed in a limited area and to describe the conditions of school transport system. For this purpose, a mathematical equation was elaborated to simulate the real system based on the school transport conditions and on the estimated results of STSDR from 15 zones of Cuenca city in Ecuador. The data used in our model was collected from several diverse sources (i.e. administrative data and survey data). The estimated results have shown that our equation has described efficiently the school transport system by reaching an accuracy of 96%. Therefore, our model is suitable for statistical estimation given adequate data and will be useful in school transport planning policy. Given that, it is a support model for making decisions which seek efficiency in supply and demand balance.

The 3D simulation and optimized management model of groundwater systems based on eco-environmental water demand

Through the study of mutual process between groundwater systems and eco-environmental water demand, the eco-environmental water demand is brought into groundwater systems model as the important water consumption item and unification of groundwater抯 economic, environmental and ecological functions were taken into account. Based on eco-environmental water demand at Da抋n in Jilin province, a three-dimensional simulation and optimized management model of groundwater systems was established. All water balance components of groundwater systems in 1998 and 1999 were simulated with this model and the best optimal exploitation scheme of groundwater systems in 2000 was determined, so that groundwater resource was efficiently utilized and good economic, ecologic and social benefits were obtained.

A New Perspective on the Demand of the Cross-cultural Audiences:Lacuna and Universal Model

Lacuna and Universal Model provides a new terminology and classification for the factors behind the success and failure of cross-cultural media content,and thus forms an analysis framework for the study of the cross-cultural audiences'need.According to this model,the audience will dislike or not select foreign media content under these circumstances:(1)audiences find that the content is irrelevant or unsuitable;(2)audiences cannot comprehend the content;3)they do not like the style of such content.This model also argues that cross-cultural media content is successfully spread under these circumstances:(1)the media content shows attractive attribute to cross-cultural audience;(2)the media content is open to alternative readings.

融合时频空间特征的土石坝地震易损性分析改进MLP模型研究

针对现有地震易损性分析中采用的峰值加速度、峰值速度等地震动指标未能充分反映地震动复杂的时频空间特征,且现有基于对数空间线性函数关系假设的地震需求模型难以揭示地震动指标与地震响应间复杂非线性关系的问题,提出一种融合时频空间特征的土石坝地震易损性改进多层感知机(Multi Layer Perceptron,MLP)模型。该模型利用胶囊网络(Capsule Network,CapsNet)能够充分捕捉和表征目标特征空间位置分布的优势,从地震动小波时频图中提取反映地震动时频空间分布的深层特征,并以特征拼接的方式与既有特征进行融合,构建地震动融合指标;进一步地,采用树形Parzen优化算法(Tree structured Parzen Estimator,TPE)对MLP的神经元数量、学习率等超参数进行优化,提出基于TPE-MLP的土石坝地震需求模型,以反映地震动融合指标与地震响应间的复杂非线性关系,进而实现土石坝地震易损性的可靠分析。案例分析表明,相比于既有地震动指标,基于地震动时频空间特征融合指标的土石坝地震需求模型的MAE降低了40.5%,表明了所提模型的可靠性和优越性。

绿色设计理念下皮革材料在现代窑洞民居室内设计中的创新应用

窑洞是我国西北部地区极具地域性特征的建筑。以绿色设计理念为核心,对皮革材料在现代窑洞民居设计中的创新性应用进行分析,着重强调了皮革材料在建筑装饰领域的应用优势,对窑洞这种中国传统民居与皮革材料的融合思路进行总结,认为皮革材料与现代窑洞民居室内设计需要着重强调发挥皮革材料的视觉与材质特征,将皮革材料与窑洞造型、民居需求等进行充分结合。

基于Kano模型的三级中医院住院患者中医护理服务需求属性研究

[目的]分析三级中医院住院患者的中医护理服务需求属性,为提升三级中医院中医护理服务提出策略。[方法]基于Kano模型设计三级中医院住院患者中医护理服务需求调查问卷,对天津市某三级中医院469例住院患者进行调查,采用定量与定性的方法确定三级中医院住院患者中医护理服务需求属性分类及优先级排序。[结果]通过Kano模型和Better-Worse二维矩阵图进行需求分析,最终获得三级中医院住院患者中医护理服务必备属性需求14个,魅力属性需求9个,期望属性需求6个。[结论]三级中医院医院管理者应依据住院患者中医护理服务需求优先级顺序精准施策,保障必备型需求项目供应,加大期望型需求投入,同时发展创新魅力型需求,确保中医护理服务需求与供给高效匹配。

A-Kano模型下全过程工程咨询业主需求实证研究

我国全过程工程咨询实践正处于多种模式并存、咨询内容差异较大的探索性阶段。以业主需求为导向,合理确定咨询服务内容,提升全过程工程咨询企业服务能力,是推进全过程工程咨询健康发展的关键。通过梳理相关文献,识别出12种全过程工程咨询业主需求,进而借助A-Kano模型开展正、负向满意度与重要度问卷调查,并计算出各类需求的优先级。实证分析结果表明:工程监理和造价咨询属于基本型需求;项目管理、设计及勘察属于期望型需求;招标代理、前期策划和BIM咨询为兴奋型需求;后期运营、建筑节能与绿色建筑咨询、工程检测服务以及工程保险咨询为无差异需求。基于上述结果为工程咨询企业提出转型建议,使其更好地满足业主集成化、个性化的咨询需求。

中澳两国免疫规划疫苗需求预测和管理比较

疫苗需求量预测是影响疫苗可及性的关键,对疫苗需求量进行合理预测是保证疫苗可持续性的基础;疫苗流通和接种过程的管理也会在一定程度上影响地区免疫规划的实施与效果。该研究通过分析、比较中国和澳大利亚疫苗需求估算方法和相关影响因素,以及两国国家免疫计划管理的异同,为完善我国免疫规划管理提供合理借鉴。

Application of a Bayesian Network Complex System Model Examining the Importance of Customer-Industry Engagement to Peak Electricity Demand Reduction

This paper explores the importance of customer-industry engagement (CIE) to peak energy demand by means of a newly developed Bayesian Network (BN) complex systems model entitled the Residential Electricity Peak Demand Model (REPDM). The REPDM is based on a multi-disciplinary perspective designed to solve the complex problem of residential peak energy demand. The model provides a way to conceptualise and understand the factors that shift and reduce consumer demand in peak times. To gain insight into the importance of customer-industry engagement in affecting residential peak demand, this research investigates intervention impacts and major influences through testing five scenarios using different levels of customer-industry engagement activities. Scenario testing of the model outlines the dependencies between the customer-industry engagement interventions and the probabilities that are estimated to govern the dependencies that influence peak demand. The output from the model shows that there can be a strong interaction between the level of CIE activities and interventions. The influence of CIE activity can increase public and householder support for peak reduction and the model shows how the economic, technical and social interventions can achieve greater peak demand reductions when well-designed with appropriate levels of CIE activities.

基于卡诺模型的城市居民居家社区养老服务需求分析

《“十四五”国家老龄事业发展和养老服务体系规划》明确了“十四五”期间养老服务体系建设目标。准确了解居家社区养老人员服务需求,可以更精准地实现规划目标。首先设计城市居民居家社区养老服务需求指标体系和卡诺问卷,以江门市10个社区居家养老人员为调查对象,收集有效问卷249份。其次运用卡诺模型对问卷数据进行需求属性频数统计、Better-Worse矩阵分析、需求指标敏感性分析,得到魅力属性、期望属性、必备属性和无差异属性分类结果。最后提出总体需求改善策略和针对4类需求属性的需求改善策略,以及4类需求属性和各需求属性中需求指标的优先改善次序。

基于Logit模型的技术供需交易影响因素分析

将技术供方和专利视为整体,将供需双方是否通过某专利交易视为被解释变量,系统研究技术特征、组织实力和供需方多维邻近关系等对技术交易的影响。基于Logit模型对燃料电池领域专利数据实证研究发现:(1)考虑三维特征建立Logit模型,对交易形成预测的精度高达83.3%;(2)技术特征、组织实力特征、多维邻近特征对技术交易均有显著正向影响;(3)多维邻近特征对技术交易的促进程度远高于其他维度特征,供需双方信任是交易形成的关键因素。最后,从建立高质量技术供需数据库、技术交易信任体系、技术交易网络及交易推荐机制等方面提出对策思考。