关于目标和计划英文演讲稿

关于目标和计划英文演讲稿（精选6篇）

篇1：关于目标和计划英文演讲稿

My name is __, from __. I scored unsatisfactory and did their desired universities. I think, either in the country on a first-clacolleges, or to go abroad. If on a domestic university, it is a waste of time. So I chose to study abroad.

So I understand that New Zealand students through the Internet has more advantages. First of all, New Zealand is a multicultural country, a time-advanced level. Second, the students lower costs than other countries, more reasonable. Third, the weather and almost southern China. Therefore, I propose to the parents to New Zealand to study the wishes of the parents get the support.

From 06 to 07, I CIBT School of Beijing University of Technology to study for two years, I am learning is that I am interested in businemanagement. Through this two-year study, as in the commercial, I later laid a foundation for learning.

Many schools in New Zealand, I chose the UNITEC Institute. UNITEC is unique in that the traditional university academic standards and polytechnics and technical advantages of combining vocational education. School in combining theory and practice for school concept, exploring the creativity of the students to continue to adapt to social change.

This year, China will host the Olympic Games in Beijing. , The eyes of the world gathered here. In particular, some businessmen in China, a huge potential market in search of busineopportunities. Therefore, I choose to UNITEC Institute study marketing, I believe that this right after I returned employment development will be of great help. UNITEC particularly to the integration of theory and practical teaching methods, more determined my choice. Reading through UNITEC of Chinese students in the exchange, I understand MARKETING study very hard, I need to put more time and energy on top. Maybe I can not be like the other students on edge as the Edge Reading. But my parents understood my idea, I am willing to fully support the school.

篇2：关于目标和计划英文演讲稿

If you are going to wear your everyday clothes, like pants and a t-shirt, be very careful to wear what matches your look. If you have red hair, unless it is Valentine’s Day,Additional Details avoid anything related to red such as pink. You will look like a walking valentine. Aside from matching you, your clothes must match each other. If you wanted to wear a pink shirt with white on it, an adorable choice for a sweater would be a white jacket. Suppose, you wore a white shirt with pink on it, you shouldn’t wear the white sweater. Instead, wear a pink sweater. Also, if you wear this shirt and wanted to wear a headband that was pink but a slightly different shade of pink with it- that is a fashion don’t. Never wear denim on denim. That is to wear a denim jacket with a denim skirt or jeans. Especially if the denim is two different shades. You may think similarity is a good thing, but in some cases its not. Dark green with light green is fine-light blue with dark blue is fine too. But, they must be correct shades. A pink with a slight orange tinge is not good with plain pink. Never wear brown and black together. Black looks good with lots of things-just not brown. Be in touch with your seasons and holidays. Unless it is the Fourth of July, or some patriotic holiday, avoid wearing red, white, and blue. Unless it is around Christmas time, don’t wear red and green. During winter, light blue looks good, and so does white and beige. During spring, green, yellow and cheerful colors look best. Fall is the time for solemn colors like dark red, brown, and black. Summer is the cheery red, orange and plain cool and crisp white.

Types of clothes are also important. A long-sleeved shirt with shorts is never ok, but a short-sleeved shirt with shorts or pants is ok. No socks with sandals even if its toe socks. Clothes are fabulous things for girls to fuss over and spend hours deciding what to wear. Let’s try our best to make them look great.

篇3：关于目标和计划英文演讲稿

Synthetic aperture radar sensors can produce range imagery of high spatial resolution under differen conditions.However,because the image data are formed by coherent interaction of the transmitted microwave with the targets,they suffer from the effects of speckle noise which arises from coherent summation of the signals scattered from scatterers.To sea SAR image,point target detection usually becomes difficult because of the intensive sea-clutter speckles.To solve this problem,traditional concepts can be classified into two aspects:one is threshold segmentation based on intensity difference[1],for example,based on histogram information;the other is target extraction based on speckle reduction,for which sparse code shrinkage[2]taking advantage of independent component analysis(ICA)[3]serves a good case by applying nonlinear operator on each component.However,both of them appear to be not effective enough especially when sea clutter is strong.As to the former,intensive sea clutter usually has the same grey value with point target as a result of which no considerable difference of intensity can be obtained;as to the latter,de-noising is usually tentative because of much uncertainty about the speckle information.In this paper,an efficient method is proposed with both intensity and de-noising taken into consideration.Briefly,two kernel procedures,Pre-K and Post-K,are involved.

Pre-K:To avoid intensity confusion between sea clutter and point target,we calculate the Hölder exponent[4]of original SAR image as the first step of proposed‘Pre-K’.Because Hölder exponent characterizes the loca singularity without feature structure loss,an improved intensity differentiation can be achieved as a matter of fact In addition,‘Pre-K’also employs binary-fuzzy processing as a further step,in which,Hölder exponents of the image is binarized according to a criterion and next fuzzed with average filtering.Then,the processed image becomes more compact and predigested so that it can be inputted to ICA technique for superior convergence performance.

Post-K:As the second kernel procedure focusing on the outputting data from ICA technique,an efficient de-noising theory is proposed based on signal separation,avoiding huge computation complexity by other traditional method,like sparse code shrinkage.As a result,sea clutter speckles are sharply removed and poin target detection is successfully obtained due to the clean image.

2 Overview on Independent Component Analysis

The Independent Component Analysis(ICA)is a statistical method for transforming an observed multidimensional random vector into components that are mutually independent.Denote by X the observed matrix:X=[x1,x2,…,xm]T,by S the independent components matrix(or sparse code matrix):S=[s1,s2,…,sn]T,and by A(m×n,m>n)the mixed matrix,the linear representation can be given by:

Where:xi(i=1,2,…,m)is the observed signal and si(i=1,2,…,n)is the independent component.W namely de-mixed matrix or transformation matrix is the pseudo inverse of A,that is W=A+.Because S has a“sparse structure”which means any image can be represented by a relatively small number of descriptors out of a much larger set to choose from.Thus,an image I(x,y)can be modeled as a linear superposition of basisφk,which can be defined as follows:

Where,sk,b is the element of code matrix S at row k,column b.Andφk is the kth column vector of mixed matrix.A=[φ1,…φk,…,φn].

If we use symbol‘↔’to define the mapping association between a basis vector and the corresponding independent component,we obtain sk↔φk,k=1,……,n.

3 Pre-K Procedure

3.1 Hölder Exponent

Hölder exponent is an instrumental tool for image segmentation and de-noising,also a powerful parameter for studying the structure of singular signals[4].

Letα>0,and x0∈K⊂R.A functionf:K→Ris inCxα0(or has a pointwise Hölder exponentαat x0),if for all in a neighborhood x of x0:

Where,c is a constant independent of x0 andα.The Hölder exponentαis computed as:

Clearly,a function that is differentiable at x=x0 has a Hölder exponentα≥1.Geometrically,this means that the magnitude of oscillations of the function near x0 decreases faster than the distance to x.In general,if this inequality holds forα0,then it will hold for allα<α0.Thus,the Hölder exponent of a function is the upper bound ofα.For images,this exponent characterizes the edges and the features.

3.2 Binary-fuzzy Processing

Hölder exponent contains substantive information of the image,but usually weakens the‘target’feature unavoidably.To enhance target information of the image of Hölder exponent,we propose a scheme namely binary-fuzzy processing and apply it on Hölder exponent image.

According to multifractal theory[5],small targets can be regarded as points with strong singularity because of higher intensity than neighbouring points.Strong singularity corresponds to small value of Hölder exponent since Hölder exponent is a tool for singularity measurement.If we extract the strong singularity ones from Hölder exponent image,a more‘centralized’points set can be roughly obtained.Here,a simple criterion is proposed for this extraction,which can be defined as follows:

Where:µis a scale coefficient with interval(0,1).H(x,y)is the grey value of each pixel in Hölder exponent image.Then,we obtain binary Hölder image modified by this criterion.To overcome convergence failure resulting from applying ICA algorithm on binary image,we further employ some fuzzy processing.Here,we use average filtering with a template 3×3 to obtain this fuzzy version.The template can be given as below:

4 Post-K Procedure:Space Separation

Suppose we have obtained X=AS using ICA technique.From the view of signal separation,we believe that‘clutter pattern’in an image comes from another kind of signal source which is different from the‘target’signal source.(Note here,we denote‘target’by‘clean’,and‘clutter’by‘noise’).Therefore,an image space can be separated into two subspaces.If we can separate the original one into these two sub-ones using some criterion,we can effectively obtain the‘target’version extracted from the original image by reconstruction on the‘target’subspace.Let us denote the original image space by Sorigin,‘target’subspace by Sclean and‘clutter’subspace by Snoise,we obtain:Sorigin=Sclean+Snoise,which is equivalent to Xorigin=Xclean+Xnoise.Because each space comprises basis images and corresponding independent components.Separating basis implies separation of space.

The proposed principle on separation and reconstruction is shown in Figure 1 for clear illustration.

Suppose the number of‘clean basis’is h,and the number of‘noise basis’is g:Clearly,the central is how to separate two subspaces from the original one.We define the separation rule as follows:

For more clarity,the formula(7)can be written as:

Where:i=1…n,n is the number of components,si is the i th component,φi is the i th basis with relationφi↔si.N is the number of sample points of each component.θ⋅(||S||∞)/nis the separation threshold withθ∈(0,1),which can be easily set in particular experiment.

5 Steps Involved

1)The pointwise Hölder exponent(H image for short)of original SAR image is computed.

2)Use formula(5)withµ=0.3 as a criterion for H image binarisation.

3)Apply proposed fuzzy processing using a template mentioned in formula(6)to obtain binary H image Then the modified version of H image is obtained.

4)Use a sliding window(size=16×16)sampling the modified H image regularly,ensuring that each block sampled has an interval step of 8 pixels both in row and column.Then,we obtain the observed matrix X with each row-vector as an observed signal.For example,if an image is 256×256,then the observed matrix X should be256×961 by this kind of sampling.

5)To avoid huge computation complexity,we subtract the mean of each signal and then apply PCA(principal component analysis)[6]to reduce dimension of the vectors to 64,which implies 64 basis images we shall have.

6)The preprocessed X is used as the input to Fast ICA algorithm[7],with‘tanh’non-linearity.

7)The 64 basis images from A and correspondingly independent components from S are obtained after convergence of Fast ICA algorithm.

8)Apply proposed space separation theory(or Post-K procedure)on outputting data computed from ICA technique.As a result,the original space of modified H image can be separated into two sub-ones:‘target’(clean)and‘clutter’(noise).

9)According to Xclean=AcleanSclean,the detection result with target strikingly intensified is obtained.Then,a clear target extraction can be achieved with a simple morphological erosion[8].

6 Results and Discussions

An original SIR-C SAR image under intensive sea clutter speckles(256×256,acquired on October 9,1994on the Arabic Sea,150km west of Bombay(India))with point target at the bottom is shown in Figure 2.We apply the proposed method to this image.For performance comparison,other two traditional approaches:threshold segmentation based on histogram information and ICA sparse code shrinkage,are also employed.

1)Results with two traditional methods

(1)Threshold segmentation based on histogram information

The histogram of the original image is shown in Fig.3,the detection result is shown in Fig.4 with a selected threshold 240.Clearly,the performance is not desirable because of the low difference of intensity between target and sea clutter,which means clutter speckles have greatly impacted the target to be differentiated from them.

(2)ICA sparse code shrinkage

The de-noising result is shown in Fig.5 and detection result is shown in Fig.6 with a binarisation threshold which is 0.9 times of maximal gray-value in de-noising image.Obviously,the performance is also unqualified with much clutter imposed.

2)Results with the proposed method

H image computed from original SAR image is shown in Fig.7.Modified H image after applying binary-fuzzy processing is shown in Fig.8,from which,we can see that point target has been obviously enhanced compared to original image.With a separation threshold set to 0.65 in proposed Post-K procedure,the basis(64block images)of the modified H image,shown in Fig.9,is separated into two subsets,‘clean’basis(35 block images)shown in Fig.10 and‘noise’basis(29 block images)in Fig.11.Detection result with construction on‘clean’subspace is shown in Fig.12 and binary result of target extraction using morphological erosion is shown in Fig.13.

7 Conclusions

Based on independent component analysis,we propose a novel approach for point target detection for sea SAR image under intensive clutter speckles.With a combination of both fractal and signal separation theory,the proposed method modifies H exponent image on one hand,avoiding intensity confusion,and on the other hand separates‘target’pattern from original space,resulting effective speckle reduction.Experimental results demonstrate that point target detection can be efficiently achieved with proposed method,which is superior to traditional approaches.Nevertheless,model establishment correlating separation threshold with statistical property of a SAR image needs further research.

参考文献

[1] Tonje Nanette Arnesen,Richard B Olsen. Literature review on vessel detection[R]. FFI/Rapport-2004/02619,NorwegianDefence Research Establishment,Kjeller,Norway,2004.

[2] Aapo Hyv-rinen,Patrik Hoyer,Erkki Oja. Sparse Code Shrinkage:Denoising by Nonlinear Maximum Likelihood Estimation[C]//Proceedings of the 1998 conference on Advances in neural information processing systems II table of contents.Cambridge,MA,USA:MIT Press,1999:473-479.

[3] Pierre Comon. Independent component analysis--a new concept-[J]. Signal Processing,1994,36(3):287-314.

[4] Jacques Levy-Vehel. Introduction to the Multifractal Analysis of Images[C]//Fractal Image Encoding and Analysis:A NATOASI Series Book[M]. Yuval Fisher. New York:Springer Verlag,1998:331-401.

[5] 赵 健,宋祖勋,俞卞章. 基于多重分形分析的SAR图像消噪增强研究[J]. 西北工业大学学报,2003,21(1):30-33.ZHAO Jian,SONG Zu-xun,YU Bian-zhang. On Denoising SAR Image by Processing Based on Multifractal Analysis[J].Journal of Northwestern Polytechnical University,2003,21(1):30-33.

[6] 李玉珍,王宜怀. 主成分分析及算法[J]. 苏州大学学报(自然科学版),2005,21(1):32-36.LI Yu-zhen,WANG Yi-huai. PCA and algorithm analysis[J]. Journal of Suzhou University(Natural Science Edition),2005, 21(1):32-36.

[7] Aapo Hyv-rinen,Erkki Oja. A Fast Fixed-Point Algorithm for Independent Component Analysis[J]. Neural Computation, 1997,9(7):1483-1492.

篇4：如何协调公司短期目标和长期计划

索尼克连锁餐厅的故事

如果要说明执行短期计划比制定长期计划更重要，没有比“索尼克免下车连锁餐厅”（以下简称“索尼克”）更好的例子了。该餐厅在美国30个州有3000家分店，在快餐业中具有最高的顾客回头率。在过去的10年中，其平均营业收入增长率是惊人的23.09%。

按照过去10年的财务业绩，索尼克是文化、创新和效率的典范。我们追问该公司的CEO克利夫·哈德森：为什么不在每个州都开设分店？为什么开设3000家分店而不是10000家？

“我们不缺钱，可以发展得更快些，”他说，“但是如果我们没有注意把每天的事情都做好，那么长期的发展远景就没有任何意义。对我们来说，更重要的是以能够盈利的方式发展，保证所有的合伙人都干得不错并尊重我们的品牌，同时培养适当类型的团队成员。从而我们可以继续发展，并且明智地在那些最赚钱的地方发展新分店。”

然而，索尼克的前40年，像许多公司一样面临着错综复杂的问题：由于公司发展太快，公司合伙人有无数“宏伟的计划”，与此同时，一个个好的想法和创新却被埋没；人们在想象明天的回报方面所花的时间，比在思考今天的具体情况方面所花的时间多得多。到上世纪80年代中期，无休止的内讧和权力斗争使得公司面临破产的危险，甚至有几个合伙人被踢出了公司。几乎每个人都有自己不为人知的小算盘，都建立了自己的地盘并极力加以保护。由于没有系统和流程，导致品牌缺乏明确的定位；公司营销已经陷入停顿，没有了共用品的集中采购。最终有400家加盟餐馆离开了索尼克或者被关闭了。剩下的董事会成员为了扭转乾坤，引进了年轻的律师克利夫·哈德森任CEO。

“我必须改革公司的文化，”哈德森说，“营销部门不按照运营官的指导测试新的营销创意，采购部门也一样无法控制。整个公司里，谁都不和其他人合作。”

终于，哈德森受不了了。“有一天，”他回忆道，“我直截了当地说，谁要是不接受各自为政的日子已经结束的这个事实，那么他就应当离开公司。大家都要一起合作，否则在索尼克就没有你的一席之地。”看到他不愿意妥协，所有的高级管理者只有一个留了下来，其余的最终都离开了公司。最后留下来的那个高管帕迪·摩尔最终被任命为公司总裁。

哈德森和摩尔知道他们的公司必须代表某种东西，于是制定了4种核心价值指导自己的改革征程——“索尼克免下车连锁餐厅的马路规则”：1、提供令顾客惊喜的特色产品；2、提高对所有接触过我们品牌的人的尊重；3、重视关系作为一种生活方式的重要性；4、反映企业精神和个人的力量。

此前，多年来，在索尼克，特许经营分销商和加盟商一直具有最终决定权，因此许多公司总部的高管想以一种独裁或者专制的方式使用权力，迫使分销商就范。“马路规则”改变了这一切，哈德森选择了让分销商和加盟商参与有关菜单、店面翻新以及广告的决策并且倾听他们的意见。

哈德森和摩尔的倾听没有白费。“我们在卡罗来纳州的一个分销商对制定统一菜单的前景感到心烦意乱，”哈德森说，“他担心我们会取消冰淇淋。当他告诉我们说他最好的分店30%的收入都来自冰淇淋时，我们没有对他置之不理，相反，我们决定最好听听他的意见。由此产生的直接结果就是，在1996年我们推出了冷饮和冷冻产品并且开始促销。”

“销售额，”哈德森说，“确实是直上云霄。我们在公司历史上从来没有见过这种景象。下午、晚上和周末总是应接不暇。”许多餐馆因为从冰淇淋销售中获得了极高的利润，开始接受哈德森和摩尔的计划。

下面是哈德森的功劳：公司总部文化的转变；整个连锁体系内统一菜单的制定；所有分店的翻新；目前每年超过1亿美元的营销预算。其中每个分店创造的年收入为100万，利润为15万美元。如果考虑到有些加盟商拥有30家到200家分店的话，其经济效益就非常诱人。

哈德森和摩尔并没有依靠神奇的战术或者强迫自己遵守一份有关5年后他们将有多少家分店的时间表，相反他们实施了一些最基本的措施：要求所有的餐馆集中采购以保证实现显著的成本节约和质量控制；实施了营销合作，对媒体广告提供财务保障；不断更新所有分店的外观；以及在整个连锁系统内提供标准的菜单。哈德森知道，通过提高同一家分店的年销售额，耐心地以每年一两百家的速度增加分店的数量，保证每个供应商都成为有价值的合作伙伴和各自为政的情况不再出现，以及一如既往、坚定不移地执行和实现短期目标，那么他们公司就离实现全部经济潜力这个长期目标不远了。

僵化的长期计划的危害

实际上，索尼克这样的公司每年能够实现两位数的收入增长，其中一个主要原因就是他们没有固定的长期计划。公司在不断地把他们共同的目光投向长期远景的同时，还必须懂得如果没有正确地执行和实现短期计划，固定不变的长期计划就是浪费时间。如果组织用僵化的长期计划约束自己，就会产生严重后果：

1、资源分配：如果一个管理者认为自己企业未来的规模将比现在大几倍，那么他就可能把资源分配给许多职能部门、设施以及人员，而这些部门、设施和人员可能永远也用不到。在网络泡沫破裂很多年以后，仍然有成千上万的公司在千方百计地出租自己建造的，或者转租原先按照错误的长期计划租赁的、面积达几百万平方英尺的办公空间。

2、未来决定一切：如果公司采取长期计划，他们往往满脑子想的都是要实现这种计划，于是不再关心那些关键业务每天的具体情况，在许多情况下，他们的计划甚至比自己的顾客还重要。

3、被贪婪所控制：如果管理者开始想象自己的计划得以实现，出售股票有了很多钱以后，应该买什么样的海边别墅、私人飞机和游艇，那么他们就把自己的精力放在了错误的事情上。

4、预期管理不当：如果未来并没有像原先预想的那样发展，员工就会对那些向他们宣传长期计划的人失去信心，然后就会变得玩世不恭或者离去。

5、投资者失去信心：如果一个企业向外部投资者及债权人宣扬自己的长期计划，那么这种计划就成为他们评价这个企业的实际准则。任何偏离计划的行为都被认为是管理失职的表现。

6、改变路线很困难：如果已经把员工召集在一起并且朝着一个方向进发，那么要让他们改变路线几乎是不可能的。

7、僵化的长期计划会拖累各个方面：如果公司把自己牢牢地固定在一个计划中，他们往往不愿意去想任何不符合他们计划的事情。因此就会变得不如以前敏捷，就会失去按照市场状况的要求灵活处理的能力。

坚持扎实有效地工作

1982年，达特食品公司在哥伦布骑士礼堂的一间会议室里举行了首次全国销售会议，参加会议的一共有6人。“我们连一个会议室都没有，”CEO帕特·特雷西说，“所以在会议开始时，我们没有其他地方可以举行会议。”他回忆到，“我在一个活动挂图上写下了100万美元这个数字，而在整个会议期间没有提到这个数字。最后，在会议结束时，有人问，‘那个数字是什么意思？’我告诉大家如果认真经营并且干好，将来有一天我们会达到那个数目。”

达特食品公司在6年时间内就突破了100万，然后大家问特雷西下一个数目是多少，他又说出了另一个数目：10亿。这家公司12年后在2000年实现了那个目标，并且早就开始朝着收入20亿美元这个目标迈进。“当然我们还想达到其他更大的数目，”特雷西说，“如果我们管好自己的事情并且干好的话，我们将来会有一天实现这些目标的。但是我们从来没有真正说过我们的目标是这个数目或者那个数目。”他把有关这个话题最重要的看法留在了最后：“数量是虚荣，利润是理智，而我们对理智的兴趣要远远大于对虚荣的兴趣。”

你可能永远也感觉不到特雷西和他的兄弟姐妹为了控制他们有一天可能会达到的收入而分配资源。如果距离下一个目标很远，他们会等待而且会以最高的效率运转，不会为了控制可能永远也不会实现的销售量而投入资源建设有关设施。达特食品公司是要以能够盈利的方式发展，不会采取任何可能会危及长期盈利能力的措施。

赛仕公司的总裁吉姆·古德奈特从来不制定5年计划：“在技术行业，一切都变化的非常快。如果你固守5年计划，那么等到计划完成时，你的产品早就过时了。试想一下，如果我们对万维网（the World Wide Web）或者无线技术置之不理，那就等于自杀。”

赛仕公司的另一位高管柯林斯也有同感：“我们的公司之所以发展得不错，其中的原因之一就是我们没有死抱着计划不放，而是坚持做好自己的事情并且竭尽全力干好。我们从来没有为了获得市场份额而去做疯狂的事情或做些勉强的事情。我们首先搞好自己的核心业务，”他说，“然后再慢慢探索其他领域。如果我们受到束缚，必须要实现一个固定的5年计划，那么我们就丧失了创新意识、对顾客的承诺以及我们的竞争优势。”

篇5：关于目标和计划英文演讲稿

关于全省2008耕地保护和土地利用计划目标责任考核情况的通报

(青政〔2009〕8号)

西宁市、各自治州人民政府，海东行署，省政府各委、办、厅、局：

根据2008年初省政府与各州（地、市）政府签订的《青海省2008年耕地保护和土地利用计划目标责任制领导责任书》（以下简称《责任书》），受省政府委托，省国土资源厅会同省农牧厅、省统计局对全省各地区2008耕地保护和土地利用计划目标责任完成情况进行了全面考核。根据考核结果，为进一步推进全省耕地保护工作，省政府决定对2008耕地保护和土地利用计划目标责任考核获得第一名的海南州政府，第二名海北州政府和海西州政府，第三名海东行署给予通报表彰，并分别给予海南州政府、海北州政府和海西州政府1万元，海东行署2万元的奖励。

希望受表彰的地区保持荣誉、戒骄戒躁，发扬成绩，再接再厉，以更加出色的工作，在进一步加强耕地保护工作中做出更大的贡献。全省各地区要从学习实践科学发展观的战略高度出发，以先进地区为榜样，进一步增强责任感和使命感，按照基本农田总量不减少，用途不改变，质量要提高的要求，抓紧将省级土地利用总体规划分解下达的耕地保有量和基本农田保护面积的任务，落实到各州（地、市）土地利用规划的修编工作之中，研究分解和确定县、市（区）行政区域内的耕地保有量、基本农田保护面积和保护率等约束性考核指标，把耕地保护工作真正抓紧、抓实、抓好，确保国家下达的810万亩耕地保有量和651万亩基本农田保护任务的落实。

青海省人民政府

二○○九年二月三日

全省州(地、市)政府2008耕地保护和土地利用计划目标责任考核结果

受省政府的委托，省国土资源厅会同省农牧厅、省统计局于2008年底对全省州(地、市)政府2008耕地保护和土地利用计划目标责任制落实情况进行了考核，具体考核结果如下:

西宁市政府

西宁市政府在保障当地经济快速发展的同时，高度重视耕地保护工作。注重宣传，强化管理，落实责任，制定了《西宁市2008年耕地保护目标制度领导考核办法》，健全了基本农田保护监督检查责任体系，不定期进行巡回检查，有效遏制了在基本农田内滥采沙石、畜牧养殖、植树等行为；明确各区县政府主要负责人为辖区内耕地保护第一责任人。市、县（区）、乡(镇)层层签订了耕地保护目标责任书，将耕地保护目标责任落到实处；积极争取和实施土地开发整理项目，加大了耕地占补平衡工作的力度。严格实行土地利用计划台帐管理，新增建设用地总量没有突破省政府分解下达的计划指标；基本农田保护图、表、册等档案材料齐全，重新设立刷新标志牌490个。确保了全市17．85万公顷的耕地保有量和13．566万公顷的基本农田保护面积。考核评定成绩为90分。

海东行署

海东行署主要领导负总责、分管领导具体抓，把耕地保护和基本农田保护责任量化到县、乡级政府，进一步加强耕地保护措施，层层分解目标任务，明确责任；从严控制非农建设项目，实行了土地利用计划台帐管理。全区各类建设占用耕地120．4公顷，收取开垦费375万元，足额上缴省财政，由省国土资源厅选择与耕地占补平衡挂钩的土地开发整理项目统一实施，保证了全区耕地保护面积不减少，积极落实全省土地开发整理项目，全区实施了4个项目，总规模8483亩；基本农田保护县、乡（镇）、村签订目标责任书2．1万份，设立基本农田标志512块，图、表、册等档案资料齐全，较好地完成了与省政府签订的《责任书》目标任务。考核评定成绩为94分。

海北州政府

海北州政府切实加强全州耕地、特别是基本农田保护工作，成立了由政府主管领导为组长，国土、农牧、水利和林业等相关部门领导为成员的耕地保护领导小组。层层签订了耕地保护目标责任书，严把建设用地审批关，全年报批建设项目占用耕地13．8公顷，耕地占补平衡按规定足额缴纳了耕地开垦费73．57万元，委托省国土资源厅实施耕地占补平衡，确保耕地总量动态平衡;严格实行土地利用计划台帐管理，新增建设用地总量没有突破省政府分解下达的计划指标；基本农田保护图、表、册等档案材料齐全，全州共划定基本农田869块。确保了全州6．84万公顷的耕地保有量和5．78万公顷的基本农田面积，保护率达到84%，较好地完成了与省政府签订的《责任书》目标任务。考核评定成绩为95．5分。

海西州政府

海西州政府认真贯彻落实耕地保护工作的相关政策，加强领导，落实责任，抓督促、抓落实，制定了《海西州耕地保护目标责任考核办法》，州、县、乡、村层层签订了耕地保护目标责任书，将耕地保护的措施和目标管理责任落到了实处;加大耕地保护宣传和动态巡查工作，进一步规范基本农田管理;从严控制新增建设用地，实行土地利用计划台帐管理；全年报批建设项目占用耕地75．6公顷，耕地占补平衡按规定足额缴纳了耕地开垦费130万元，委托省国土资源厅实施耕地占补平衡，确保耕地总量动态平衡。基本农田保护图、表、册等档案材料齐全;确保了全州4．74万公顷的耕地保有量和4．14万公顷的基本农田面积，保护率达到87%，较好地完成了与省政府签订的《责任书》目标任务。考核评定成绩为95．2分。

海南州政府

海南州政府认真履行职责，切实加强领导，把保护耕地和基本农田工作作为全年工作的重中之重，州、县政府分别成立了由相关部门组成的基本农田工作领导小组。将耕地和基本农田保护纳入政府目标考核内容，层层签订了目标责任书，耕地保护目标责任落到实处;年初州政府下发了《关于进一步规范基本农田保护工作的通知》，明确要求，广泛宣传，细化措施，强化监督管理；建立健全基本农田保护的六项管理制度，基本农田保护图、表、册等档案材料齐全，设立永久性保护标志牌89块；全年报批建设项目用地44．74公顷，占用耕地14．8公顷。耕地占补平衡按规定足额缴纳了耕地开垦费71．8万元，委托省国土资源厅实施耕地占补平衡，确保耕地总量动态平衡。严格实行土地利用计划台帐管理，新增建设用地总量没有突破省政府分解下达的计划指标；积极实施国家投资的两个土地开发整理项目，建设总规模1535公顷，可新增耕地面积200公顷。确保了全州10．38万公顷的耕地保有量和8．95万公顷的基本农田面积，保护率为86%，较好地完成了与省政府签订的《责任书》目标任务。考核评定成绩为96．4分。

黄南州政府

黄南州政府明确职责，认真落实耕地保护制度，将耕地保护目标纳入州政府考核体系，层层签订了目标责任书。严格实行土地利用计划管理，新增建设用地总量没有突破省政府分解下达的计划指标；认真落实省国土资源厅选择的与耕地占补平衡挂钩的土地开发整理项目，确保新增建设用地与当地耕地总量平衡；基本农田保护图、表、册等档案材料齐全，确保了全州2．45万公顷的耕地保有量和2．02万公顷的基本农田面积，保护率达到82%，完成了与省政府签订的《责任书》目标任务。考核评定成绩为93．5分。

玉树州政府

玉树州政府明确耕地保护责任，年初制定下发了《玉树州2008年耕地保护目标责任考核办法》，层层签订了耕地保护目标责任书，将耕地保护的措施和目标管理责任落到实处;基本农田图、表、册等基础资料基本齐全。确保了全州1．77万公顷的耕地保有量和1．35万公顷的基本农田面积，保护率达到76%，完成了与省政府签订的《责任书》目标任务。考核评定成绩为84．5分。

果洛州政府

果洛州政府主管领导与各县政府签订了耕地保护目标责任书，将耕地保护的措施和目标管理责任落到实处;规范基本农田图、表、册等基础资料，确保了全州0．17万公顷的耕地保有量和0．12万公顷的基本农田面积，保护率达到71%，完成了与省政府签订的《责任书》目标任务。考核评定成绩为84．6分。

篇6：我的人生目标(中英文)演讲稿

每个人应该清楚自己的人生目标是什么。每个人在每个阶段的目标又不一样，例如:儿时的梦想总以伟人当榜样，是积极的；学生时代，梦想变的富有色彩，充满幻想；走出校门后，梦想开始关注生活，希望借着年轻的冲动大干一翻事业；

有了目标，人生就变的充满意义，—切似乎清晰、明朗地摆在你的面前。什么是应当去做的，什么是不应当去做的，为什么而做，为谁而做，所有的要素都是那么明显而清晰。于是生活便会添加更多的活力与激情。

目标可以再分解，人生目标可分为长远目标和短期目标，我短期目标是学好专业知识技能，长期目标是能拼出自己的一片天地！

“千里之行，始于足下”。即使有了目标，实现它也需要—个过程。成功的人是最有理想、最明智，也是最有毅力、最坚定。他们懂得—切的成功都不是—蹴而就的，都需要通过艰苦卓绝的努力，不断地改进和提高；成功的人绝不会只以事情做完为满足，而会要求自己不断地做得更好，以获取更大的成功。我觉得我也会是个成功的人！

希望我们每—个人从现在开始就制定人生目标，从点滴做起，落实人生目标。抛弃那种无聊地重复着自己平庸的生活，努力去挖掘自己内在的潜力，激发自己的闪光点，相信是金子不论在哪里迟早都会发光的道理，不管遇到什么艰难险阻，终究会取得成功。

谢谢！

The ancients cloud: "where there is, a will have become..Everyone should know what is your goal.Everyone at every stage of goals and different, for example: a childhood dream always by great man as a model, is active;Student, dreams become rich color, fantasy;After getting out of school, dream begin to pay close attention to life, hope to take over the impulse of young career;rock