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AbstractDue to strong increase of solar power generation, the predictions of incoming solar energy are acquiring more importance. Photovoltaic and solar thermal are the main sources of electricity generation from solar energy. In the case of solar thermal energy plants with storage energy system, its management and operation need reliable predictions of solar irradiance with the same temporal resolution as the temporal capacity of the back-up system. These plants can work like a conventional power plant and compete in the energy stock market avoiding intermittence in electricity production. This work presents a comparisons of statistical models based on time series applied to predict half daily values of global solar irradiance with a temporal horizon of 3 days. Half daily values consist of accumulated hourly global solar irradiance from solar raise to solar noon and from noon until dawn for each day. The dataset of ground solar radiation used belongs to stations of Spanish National Weather Service (AEMet). The models tested are autoregressive, neural networks and fuzzy logic models. Due to the fact that half daily solar irradiance time series is non-stationary, it has been necessary to transform it to two new stationary variables (clearness index and lost component) which are used as input of the predictive models. Improvement in terms of RMSD of the models essayed is compared against the model based on persistence. The validation process shows that all models essayed improve persistence. The best approach to forecast half daily values of solar irradiance is neural network models with lost component as input, except Lerida station where models based on clearness index have less uncertainty because this magnitude has a linear behaviour and it is easier to simulate by models.Article Outline1. Introduction 1.1. Solar thermal energy with storage energy system1.2. Solar irradiance predictions1.3. Solar irradiance predictions with applications in solar thermal power plants2. Solar radiation dataset3. Methodology 3.1. Autoregressive models (AR)3.2. Neural networks (NN)3.3. Adaptative-network-based fuzzy inference system (ANFIS)4. Results and discussions 4.1. Models results4.2. Final model selection5. ConclusionAcknowledgementsReferences95A hybrid optimization approach for distribution capacitor allocation considering varying load conditionsOriginal Research ArticleInternational Journal of Electrical Power & Energy Systems, Volume 31, Issue 10, November-December 2009, Pages 589-595Alireza Seifi, Mohammad Reza HesamzadehClose preview| Purchase PDF (357 K) | Related articles|Related reference work articles AbstractAbstract | Figures/TablesFigures/Tables | ReferencesReferences AbstractThis work presents a new algorithm based on a combination of fuzzy (FUZ), Forward Update (FWD), and Genetic Algorithm (GA) approaches for capacitor allocation in distribution feeders. The problem formulation considers three distinct objectives related to total cost of energy loss and total cost of capacitors including the purchase and installation costs and one term related to total cost of produced power under peak load condition. The novel formulation is a multi-objective and non-differentiable optimization problem. The proposed methodology of this article uses an iterative optimization technique based on Forward Update approach which is embedded in a Genetic Algorithm framework. The fuzzy reasoning supported by the fuzzy set theory is used for sitting of capacitors and the GA is employed for finding the optimum shape of membership functions. The proposed method has been implemented in a software package and its effectiveness has been verified through a 9-bus radial distribution feeder along with a 34-bus radial distribution feeder for the sake of conclusions supports. A comparison has been done among the proposed method of this paper and similar methods in other research works that shows the effectiveness of the proposed method of this paper for solving optimum capacitor planning problem.Article Outline1. Introduction2. Mathematical model of the problem3. Proposed hybrid optimization approach for capacitor allocation in radial distribution networks 3.1. Fuzzy modelling3.2. Constant load condition3.3. Varying load condition 3.3.1. Method 1 fuzzy product approach3.3.2. Method 2 effective load modelling approach4. Case study 4.1. Constant load condition (9-bus system)4.2. Varying load condition (34-bus system) 4.2.1. Fuzzy product method4.2.2. The effective load model5. ConclusionReferences96The urbanization of wildlife management: Social science, conflict, and decision makingOriginal Research ArticleUrban Forestry & Urban Greening, Volume 1, Issue 3, 2003, Pages 171-183Michael E. Patterson, Jessica M. Montag, Daniel R. WilliamsShow preview| Purchase PDF (183 K) | Related articles|Related reference work articles 97Offshoring manufacturing: Implications for engineering jobs and education: A survey and case studyOriginal Research ArticleRobotics and Computer-Integrated Manufacturing, Volume 22, Issues 5-6, October-December 2006, Pages 576-587Bopaya Bidanda, Ozlem Arisoy, Larry J. Shuman早就看完实习生（/topic.php?tid=145679），一直没和大家分享。其实这本书，确实写得蛮好的，我也认真的阅读了。具体内容相信大家也都大概知道了，我就不一一描述了。 如果拍成电视剧，我觉得会比“杜拉拉升职记”更好看的，毕竟这本“实习生”更贴近生活，贴近我们的现实。 为了表示我的诚意，特此奉上我的第一份工作经历，相对来说和“实习生”里面的主角，我这也算是“实习生”了 有点长（其实还可以更长），大家要有耐心哦！ 一、怀揣500块独闯大上海=有份工作真好 2009年元宵节那天，我怀揣500元现金来到了上海，人生地不熟，什么都不懂，什么都没有，什么都害怕，只能凭着自己从小的独立以及那么一丁点的暑期兼职经验去找工作。 每次我去网吧投完简历，下班高峰期的时候，看见马路上忙碌的人，顶着风雨骑车或者是拼命挤公交的上班族，我很羡慕，真的很羡慕。我突然觉得不管人生将会怎样，能有份工作还是蛮好的，至少不会没安全感，不会失去自信。 二、让人心生恐惧的电话销售，我们像是进了“特务训练营” 大半个月过去了，我终于上班了。我做的是证券投资咨询公司的电话销售，我知道这种工作只有刚毕业的人才会去做，但我还是很高兴。因为不仅可以解决我的工作问题，在这茫茫人海的人群中交到朋友，还能安抚我远在家乡担心我的父母，又能让自己在这个城市生存下去。即使只有单休；即使底薪很低；即使心里没底，我还是很高兴。于是，我满怀憧憬地去上班了。 刚去的第一个礼拜，老总亲自给我们培训，这是我毕业第一次面对职场，虽然很紧张，但还是很有自信。心里鼓励自己一定要好好做，争取给自己一份满意的答复。 我对股票什么都不懂，我从基础学，遇到不会的问题就积极地询问，如果同事的讲解我还是不会，那就再问老总。老总和我们讲了很多，刚开始听得糊里糊涂，慢慢的也上路了。第二个礼拜就去办公室，分配给各个老员工带领，试用期间有什么问题包括销售方面不懂以及不会的东西全部由老员工指导，老员工忙不过来可以直接找经理（经理和我们同一个办公司，就坐在员工旁边），过两天就开始自己打电话，然后一个礼拜之内要出单。 可能刚开始是因为我们刚毕业不懂职场的缘故，后来时间一长我们慢慢的就是发现职场的