Relationship between Project Consultants’ Performance and Project Success in the Rwandan Construction Industry

As discovered, today’s Rwandan construction industry is developing day by day, which plays a very important role in the country’s economic growth. In the construction industry, project consultants play a very significant role in providing services for such projects. The project consultants provide services from the beginning of the project to the completion of the project. Using project consultants is very useful for construction projects because it can ameliorate project efficiency and effectiveness. The main purpose of this paper is to investigate the relationship between project consultants’ performance and project success in the Rwandan construction industry. The data used in this study were obtained from primary and secondary sources. The secondary data was attained through an elaborated literature review of various books, articles, and papers related to this research to outlining and describing the chief ideas of this research title. The primary data was compiled through a questionnaire survey that was directed to 110 selected professionals in the construction projects in Rwanda to collect data from the site for statistical analysis of the research to test the hypothesis. However, a total of 90 usable responses were received within the scheduled period representing the response rate of 81.82%, which is likely to be representative and acceptable. Data collected from the questionnaire surveys were analyzed using the Statistical Package for Social Scientists (SPSS), excel spreadsheets, and Relative Importance Index (RII), which provide more merit presentations. The survey results show that even if there are many obstacles in the use of project consultants in the Rwandan construction industry such as lack of knowledge and practice in project consulting, lack of well-trained project consulting professionals, lack of training opportunities in project consulting, lack of knowledge and experience in addition to the senior management opposition, and lack of local project consulting guidelines and information;they are needed in the construction project to make it more successful through reducing and saving the overall project’s life cycle cost according to client’s wishes, keeping time of construction project, improving quality of the project products in the present and future, removing major variations that affect construction project with its attendant cost overrun, and advising on construction project process. Therefore, it is more important to remark that the good performance of the project consultants in any industry especially in the construction industry will contribute to the successful implementation of the project. From the results of the study, the performance of project consultants is closely related to the success of the construction industry in Rwanda. The project consultants’ leading skills and knowledge automatically guide the project to complete with accurate time, budget, and quality to make the project successful. In this paper, we will also consider the project success criteria;the role and responsibility of project consultants;the factors affecting the performance of project consultants;and the reasons that hinder the implementation of project consulting in the Rwandan construction industry.

The Relationship between Financial Reporting System and Project Success: A Case of Construction Projects in UAE

The purpose of this paper is to deliver a better perception of the project success,and how reporting system might increase the probability of project success in construction projects in UAE.Further,it aims at exploring the relationship between effective reporting system in terms of the characteristic of its outputs(mainly effectiveness of financial reporting system)and project success.Semi-structured interviews with a number of interior auditors,accountants,and chief financial officers(CFOs)from different corporations in construction sector in UAE in order to recognize how effective reporting system in terms of the characteristic of its outputs(broad scope,timeliness,aggregation,and integration)might contribute to increase the probability of project success.There are several success factors in construction projects in UAE,thus it is very hard to capture all these factors in one paper.Hence,this paper is not considering all project success factors rather it focuses on the characteristic of outputs of the reporting system generally and the financial reporting system particularly(broad scope,timeliness,aggregation,and integration),as most of the studies in the literature considered the reporting system as a key factor of project success.This paper adds to both project management and accounting research by evidencing results from an exploration how effective reporting might impact the project success,with valuable implications for standard officials,customers,investors,stakeholders,sponsors,shareholders,CFOs,project developer,consultants,internal auditors,and accounting academics.The effective reporting system in United Arab Emirates(UAE)construction projects enables possessors,customers,and contractors of projects to get timely information about the progress of project in a brief and significant format which in turn improve the decision-making process and contribute to project success.This paper implies a contribution for both project management literature and accounting research by investigative the effectiveness of reporting system in project successes from historical point of view and contemporary point of view.

A Suggested Theoretical Framework for Software Project Success

Current literature on software project management indicates that the majority of software projects either are considered a failure or challenged. These projects are characterized by exceeding budget, exceeding time, and failing to meet customer expectations. The fact that most projects fail highlights the need for research regarding the factors that lead to software project success or failure. This paper presents a theoretical framework for software project success. Based on deep analysis of current literature on software success factors, a theoretical framework is formulating. The suggested framework shows factors that have to be carefully considered in order to achieve software project success. The factors included in the framework are dividing into four categories: organizational factors, technical factors, people factors, and culture factors. The suggested framework is new in the sense that it includes many factors that are not founding together in any of the previous similar frameworks.

Ordered successive noise projection cancellation algorithm for dual lattice-reduction-aided MIMO detection

A novel nonlinear multi-input multi-output MIMO detection algorithm is proposed which is referred to as an ordered successive noise projection cancellation OSNPC algorithm. It is capable of improving the computation performance of the MIMO detector with the conventional ordered successive interference cancellation OSIC algorithm. In contrast to the OSIC in which the known interferences in the input signal vector are successively cancelled the OSNPC successively cancels the known noise projections from the decision statistic vector. Analysis indicates that the OSNPC is equivalent to the OSIC in error performance but it has significantly less complexity in computation.Furthermore when the OSNPC is applied to the MIMO detection with the preprocessing of dual lattice reduction DLR the computational complexity of the proposed OSNPC-based DLR-aided detector is further reduced due to the avoidance of the inverse of the reduced basis of the dual lattice in computation compared to that of the OSIC-based one. Simulation results validate the theoretical conclusions with regard to both the performance and complexity of the proposed MIMO detection scheme.

A Successful Integrated Project

In recent years,the Juxian County Family Planning Association of Shandong Province has vigorously initiated various income-generating activities and achieved encouraging results in integrating the family plan-ning programme with the development of the rural cconomny,improving living standards and building up the mental civilization of the people.

Sino-Japanese Cooperation Project in Tarim Basin Successfully Wound up

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Successful Completion of AI-Ahmadi Refinery Restoration Project in Kuwait

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The continuous wavelet projections algorithm: A practical spectral-feature-mining approach for crop detection

Spectroscopy can be used for detecting crop characteristics. A goal of crop spectrum analysis is to extract effective features from spectral data for establishing a detection model. An ideal spectral feature set should have high sensitivity to target parameters but low information redundancy among features.However, feature-selection methods that satisfy both requirements are lacking. To address this issue,in this study, a novel method, the continuous wavelet projections algorithm(CWPA), was developed,which has advantages of both continuous wavelet analysis(CWA) and the successive projections algorithm(SPA) for generating optimal spectral feature set for crop detection. Three datasets collected for crop stress detection and retrieval of biochemical properties were used to validate the CWPA under both classification and regression scenarios. The CWPA generated a feature set with fewer features yet achieving accuracy comparable to or even higher than those of CWA and SPA. With only two to three features identified by CWPA, an overall accuracy of 98% in classifying tea plant stresses was achieved, and high coefficients of determination were obtained in retrieving corn leaf chlorophyll content(R^(2)= 0.8521)and equivalent water thickness(R^(2)= 0.9508). The mechanism of the CWPA ensures that the novel algorithm discovers the most sensitive features while retaining complementarity among features. Its ability to reduce the data dimension suggests its potential for crop monitoring and phenotyping with hyperspectral data.

Design-Stage Prediction of Project Correctness or Incorrectness

The success of a software development project requires the early objective determination of the project’s correctness or incorrectness and the identification of the most effective solution for project management. However, few studies have been conducted on the reliable quantitative early judgment of correctness or incorrectness. In recent years, the collection and accumulation of actual attribute data from Japanese domestic software development projects have been conducted by the Software Engineering Centre of the Information-Technology Promotion Agency of Japan. In a previous article, we proposed a precise definition of project correctness or incorrectness and identified the important factors in successful projects;we also proposed a quantitative decision-making method for judging project correctness or incorrectness objectively and quantitatively on the basis of discriminant analysis using project completion attribute data. On the basis of the previous results, we propose a quantitative decision-making technique for the early judging of project correctness or incorrectness based on the attribute data of design stage as early stage of development.

基于SPA-GA-SVR模型的土壤水分及温度预测

土壤湿度和温度是影响水文循环和气候变化的重要参数,在农业实践活动和生态平衡中起着重要作用。为及时、准确地监测土壤含水量(Soil Moisture Content,SMC)及温度,提出了一种基于高光谱数据的预测方法。实验数据集来自为期5天的实地测量,所获得的高光谱数据包含大量的噪声及冗余信息,因此首先用Savitzky-Golay卷积平滑对光谱数据进行降噪处理,利用连续投影算法(Successive Projection Algorithm,SPA)提取数据特征波长,然后通过遗传算法(Genetic Algorithm,GA)对支持向量机回归(Support Vector Regression,SVR)的超参数权值和偏置进行优化,构建SPA-GASVR混合算法模型对土壤水分和温度进行预测,并与BP神经网络(Back Propagation Neural Network,BPNN)、SPA-BP、SVR、SPA-SVR、GA-SVR这5种模型的预测性能进行比较。实验结果表明:各模型在土壤湿度低于30%的情况下,表现出的预测能力差异并不显著。但整体上,复合模型相比于单一的神经网络或机器学习模型具有明显的优势,且经过连续投影算法优化的模型进一步的提高其预测能力,最终SPA-GA-SVR算法在各项指标上均优于其他模型,土壤水分预测模型的R^(2)=0.981、RMSE=0.473%,土壤温度预测模型R^(2)=0.963、RMSE=0.883℃。实验证明基于高光谱数据,经过SPA和GA优化的SVR模型能实现对土壤湿度和温度精准的预测。该方法具有一定的应用价值和现实意义,可应用于便携式高光谱仪和无人机上,实现对土壤水分和温度的实时监测,为今后的播种及灌溉提供理论参考。

“一村一名大学生计划”对农村创业成功的影响研究

兴大业,必先用人。人才振兴是推进乡村振兴的重要引擎力量。本文基于教育部“一村一名大学生计划”评选出的69个案例,运用扎根理论,通过开放式编码、主轴式编码以及选择式编码,分析出“一村一名大学生计划”对农村创业成功的核心影响因素,并结合社会资本理论与自我效能理论,探究了“一村一名大学生计划”实现农村创业成功的路径。结果发现:“一村一名大学生计划”的开展,具有增加农村创业者社会资本和专业知识、提高创业者个人能力的功能,从而帮助农村创业者有效地识别创业机会,获取更优质的创业资源,做出科学正确的创业行为,最终实现创业成功。同时,基于所得结果,归纳、构建出“一村一名大学生计划”影响农村创业成功的机理模型,据此提出未来助力“乡村振兴”的相关建议。

紫外吸收光谱结合SPA-ELM算法的水体磷酸根离子检测研究

在工业锅炉中随着水蒸气蒸发,大量的钙镁离子留在炉水中,如果不加处理,在水冷管中会形成水垢,造成爆管停炉。为了保障设备的安全运行,消除安全隐患,工业上通过维持水中一定含量的磷酸根离子来去除锅炉中的钙、镁水垢。传统的磷酸根离子检测技术主要有比色法、分光光度法、色谱法、电位法等,这些方法前期处理步骤较繁琐且耗时较长。光谱法是通过测定物质的吸收光谱并建立和浓度关系的数学模型,对物质浓度定量的一种分析方法。为了满足磷酸根离子快速、实时测量的要求,提出了一种基于紫外吸收光谱结合SPA-ELM算法快速测量磷酸根离子的方法。按照我国《工业锅炉水质GB/T 1576—2018》中所规定的进入热水锅炉前的水质参数要求,配置37份浓度范围在5~100 mg·L^(-1)磷酸根溶液,使用搭建的实验装置,采集紫外吸收光谱。使用SPXY将样品按照7∶3的比例随机划分训练集、测试集;使用Savitzky-Golay(S-G)滤波对数据预处理以提高光谱的信噪比;通过连续投影算法(SPA)压缩光谱数据,筛选出5个与磷酸根强相关的特征波长;使用极限学习机(ELM)将特征波长处的吸光度与样本浓度进行拟合,以决定系数R2和均方根误差RMSE作为模型评价指标,建立磷酸根离子的回归模型。采用所提出的建模方法所建立的模型训练集的R2与RMSE分别为0.9972和1.3015 mg·L^(-1),测试集的R^(2)与RMSE分别为0.9995和0.5174 mg·L^(-1)。为了验证所提出的SPA-ELM预测模型效果,另外建立了LASSO-ELM、PCA-ELM、SPA-PLS和SPA-SVR四种预测模型进行对比。实验结果表明,SPA-ELM建立的预测模型的R2和RMSE均优于其他四种预测模型,说明采用的特征选择方法和回归方法均为最优,能够对磷酸根浓度范围为5~100 mg·L^(-1)的水体进行准确预测,为水中磷酸根离子的检测提供了一种新的解决方法。

ZY1-02DAHSI影像归一化阴影植被指数NSVI的波段选择及其构建

高光谱影像具有连续的地物光谱信息,在阴影检测方面具有巨大的潜力,而波段冗余度高需进行波段优选。归一化阴影植被指数(NSVI)能够扩大光谱差异,在高光谱影像中应用NSVI将更有效地识别阴影。资源一号02D卫星是我国首颗自主研发并成功运行的高光谱业务卫星,数据信噪比大、覆盖能力强,对该高光谱影像进行准确的阴影检测具有重要意义。以ZY1-02DAHSI影像为试验数据,提取并分析明亮区植被、阴影区植被及水体的光谱反射率;结合竞争自适应重加权采样(CARS)和连续投影算法(SPA)筛选能够有效区分典型地物的主要波段,综合考虑算法的特性进一步选出特征波段构建NSVI;通过步长法确定最佳阈值对影像进行分类,从像元值分布情况、分类精度和光谱增强效果等对比出构建NSVI的最佳波段,并结合不同的阴影指数、波段和影像进行综合评价,验证该方法的意义及普适性。结果表明:波段32和波段73是构建NSVI的最佳波段,分别对应红光波段和近红外波段;不同波段构建的NSVI分类精度均高于90%,由最佳波段构建的NSVI分类精度为94.33%,Kappa系数为0.8328,分类效果最优;NSVI能够增强典型地物间的光谱差异并缓解归一化植被指数的“易饱和”现象,在该影像中因水体累积产生的小波峰有助于提取水体;在ZY1-02DAHSI影像中NSVI的分类效果优于归一化阴影指数和阴影指数,于另一景影像的分类精度也达到93.55%,Kappa系数为0.8167。由算法筛选出的波段具有一定的代表性,最佳波段构建的NSVI在ZY1-02DAHSI影像中具有较好的阴影检测能力,对高光谱影像阴影检测及构建植被指数具有一定的借鉴和参考意义。

Influence of Farmer Capacity Building in Institutional Linkages on Performance of Smallholder Irrigation Projects in Migori County, Kenya

The smallholder irrigation sector in Kenya is considered inadequately developed. The study views unawareness of critical success factors in the smallholder irrigation sector as contributing to poor performance of smallholder irrigation projects. Consequently, the study investigated influence of farmer capacity building in institutional linkages on performance of smallholder irrigation projects in Migori County, Kenya. The study embraced a pragmatic view of philosophy, and used cross sectional and correlation research design. The target population was 2815, and comprised farmers drawn from fifteen smallholder irrigation projects that receive water from River Kuja through Lower Kuja Project. The sample size was 341. The study used systematic random sampling procedure to draw the sample, and collected data using questionnaire. Data was analysed using descriptive and inferential statistics. The study established that farmer capacity building in institutional linkages has a significant influence on performance of smallholder irrigation projects (r = 0.803, R2 = 0.645, F (5, 331) = 120.254 and p < 0.000 < 0.05). Thus, the study recommends that Migori County Government come up with policies that link smallholder irrigation projects in Lower Kuja Project to institutions that provide land ploughing services, certified seeds and fertilisers, control of crop pests and diseases, transportation of farm produce and marketing of farm produce.

基于 SPA-PSO-BP 的花生高光谱图像分类方法研究

为了提高可见-近红外(VNIR)高光谱花生图像分类的准确率和减少分类检测的运算时间,提出了基于连续投影算法(SPA)融合粒子群算法优化后向传播神经网络(PSO-BP)的分类检测模型。利用高光谱成像系统采集了7个花生品种样本的VNIR光谱数据,并进行了背景分割和光谱信息的提取,去除受噪声和杂散光影响大的波段后,运用Savitzky-Golay卷积平滑对400 nm~900 nm范围的波长进行预处理;采用SPA降维及均方根误差值选择了25个特征波长,同时利用PSO-BP神经网络的初始权重和阈值,构建PSO-BP模型作为分类器进行了实验,取得了测试集识别准确率为98.7%、kappa系数为0.98及遗漏误差为3的数据。结果表明,相较4个对比方法构建的分类模型,该模型的准确率分别提高了2.1%、8.6%、3.9%和4.3%。该方法在基于高光谱成像的花生品种分类技术中具有很好的应用前景,为花生品种的高精度、快速无损分类提供了新思路。

基于高光谱成像的烟丝中梗签分类识别研究

针对烟丝中梗签分类识别检测问题,利用高光谱成像技术,结合机器学习方法对烟丝中掺杂的梗签进行分类快速识别。首先,基于短波近红外高光谱成像技术,采用标准正态变化(SNV)对烟丝和梗签光谱数据进行预处理,消除光谱散射和漫反射的影响,减少检测干扰信息。然后,利用连续投影算法(SPA)进行特征波长选择,融合极端梯度提升(XGBoost)算法,提出了1种基于XGboost算法的烟丝中梗签分类模型。最后,采用Mean-shift均值漂移算法和形态学梯度算法相结合的方式作为后处理方法,对模型的分类结果进行后处理,实现烟丝中梗签的快速智能检测。结果表明:建立的SNV-SPA-XGBoost分类模型,训练集和测试集准确率分别达到100%和99.32%,经后处理后对A_(1)(1.0~1.5 cm)、A_(2)(0.5~1.0 cm)、A_(3)(<0.5 cm)梗签检测准确率分别达100%、95.50%和86%。

基于高光谱和连续投影算法的猕猴桃叶片氮平衡指数的估测

通过研究猕猴桃叶片氮平衡指数(Nitrogen balance index,NBI)与高光谱反射率之间的关系,建立合适的遥感估算模型,以期为指导陕西咸阳地区猕猴桃生长监测及田间精准施氮奠定理论基础。以陕西省咸阳市杨凌区的徐香猕猴桃为主要研究对象,测定其高光谱反射率、叶片氮平衡指数,通过一阶导数、二阶导数、连续统去除和标准正态分布光谱变换,分析包含原始光谱在内的5种不同光谱与叶片氮平衡指数之间的关系;进一步通过连续投影算法,剔除冗余信息,筛选出特征波长,并基于不同光谱的特征波长,使用单因素回归模型、随机森林回归(Random forest regression,RF)模型、支持向量机回归(Support vector regression,SVR)模型和偏最小二乘回归(Partial least square regression,PLSR)模型进行建模,比较模型精度。结果表明,当NBI值不同时,猕猴桃叶片相关指标的变化趋势类似:可见光波段的反射率随NBI值的增加呈现下降的趋势,而近红外波段反射率的变化趋势则与之相反,表现出随NBI值的增加而上升的趋势;部分光谱变换可以增加通过0.01水平显著性检验的波段数,提升与NBI值的相关性,其中连续统去除光谱的敏感波段数增加得最多,增加了190个,一阶导数光谱相关系数的绝对值最大值为0.77;连续投影算法可最大限度地减少数据的冗余,最高降维比达99%,在大幅提高计算效率的同时提高了模型的精度;与单因素回归模型相比,多因素机器学习模型对猕猴桃氮平衡指数的估算能力较高,其中SNV-SVR的表现最好,决定系数(R 2)为0.82,相对百分比差异(RPD)为2.34。在今后对猕猴桃氮平衡指数的估测中,可优先考虑本研究模型。

基于变量筛选与机器学习算法的渭-库绿洲土壤有机质含量估测研究

选择合适的变量筛选方法和模型可有效提升土壤有机质含量的估测精度。本研究以新疆渭干河-库车河绿洲为研究区,基于哨兵2号(Sentinel-2)卫星影像和实测土壤有机质,通过对土壤有机质与遥感影像波段及多种光谱指数进行相关分析,结合Boruta算法和连续投影算法(Successive Projections Algorithm,SPA)进行变量筛选,构建随机森林(Random Forest,RF)模型和BP神经网络(Back Propagation Neural Network,BPNN)模型进行表层土壤有机质含量的估测。结果表明:(1)波段B3、B4、B5、B7和B8A以及转换植被指数(Transformed Vegetation Index,TVI)、颜色指数(Color Index,CI)对土壤有机质含量的估测具有重要作用;(2)单独使用Boruta算法和SPA算法筛选的变量集建模效果要优于全变量集以及结合算法筛选的变量集,Boruta算法优于SPA算法;(3)RF模型的估测能力优于BPNN模型,最优估测模型训练集和验证集的决定系数(R^(2))均大于0.74,模型拟合效果较好,均方根误差(RMSE)小于2.0 g/kg,相对分析误差(RPD)大于1.6,能够较好地进行土壤有机质含量的估测。采用Boruta算法结合随机森林模型可较好地反演绿洲表层土壤有机质的空间分布,为该区域土壤养分评价提供参考。

基于CARS-SPA特征提取的黄水淀粉近红外光谱定量模型优化

为提高白酒固态发酵的副产物黄水中淀粉含量预测模型精度和建模效率。采用傅里叶变换近红外光谱仪采集黄水光谱信息,利用一阶导数对光谱进行预处理,并结合偏最小二乘回归(partial least squares regression,PLSR)建立黄水淀粉定量预测模型。使用决定系数(R^(2))和预测均方误差(root mean square error of prediction,RMSEP)评价模型性能。光谱中含有大量冗余信息,为有效提升黄水淀粉含量检测精度和优化模型效率,将不同特征提取方法的优点结合,发现使用竞争性自适应重加权算法(competitive adaptive reweighted sampling,CARS)结合连续投影算法(successive projections algorithm,SPA)提取的光谱特征所建立的PLSR模型,相较于未使用特征提取或仅使用单一特征提取所建立的模型均有明显提升。在单一使用CARS时,模型的R^(2)为0.9654,RMSEP为0.2012%,而结合SPA后,R2为0.9738,RMSEP为0.1748%。此外,光谱维度从2203个减少到了126个,不仅提高了预测精度,也提升了建模效率。本研究提出的方法可作为黄水近红外定量模型优化的有效途径。