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中国石油天然气股份有限公司新疆油田分公司主要从事原油、天然气开采业务，2010年7月发生下列业务： (1)采用分期收款方式销售自行开采的原油1200吨，不含税销售额504万元，合同规定，货款分两个月支付，本月支付60%，其余贷款于8月15日前支付。由于购货方资金紧张，7月支付货款200万元。 (2)采用预收货款方式向甲企业销售原油1000吨，不含税销售额420万元，当月收取20%定金，不含税金额为84万元，合同规定8月5日发货，并收回剩余货款。 (3)销售天然气1000千立方米，不含税销售额300万元。 (4)在油田范围内运输稠油的过程中，用于加热的原油为50吨，天然气为30千立方米。 要求：根据上述资料，按照下列序号计算回答问题，每问需计算出合计数。 业务(1)、(2)销售原油应纳资源税；
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Questions 28-33Complete the summary with the list of words A-H below.Write your answers in boxes 28-33 on your answer sheet.In re-branding, Nice’s Stop’s objective was to attract (28) with more money to spend. In the mid-2000s Nice’s Stop began to lose its market value as it failed to get along well with changing customer (29) . During the time, young people were increasingly interested in buying (30) goods. Workshops showed that Nice’s Stop needed to modify the brand image other than its (31) . The new advertising campaign was intended to give the Nice’s Stop brand a (32) image. The new tagline was intended to encourage consumers to buy (33) that they hadn’t tried before. 32（）
电流互感器的选择和校验。 电流互感器一次电流变化的大小对误差的影响，说法正确的是( )。
In the wake of 11 September, Visionics, a leading manufacturer, issued a fact sheet explaining how its technology could enhance airport security. They called it "protecting civilization from the faces of terror". The company’s share price skyrocketed, as did the stocks of other face-recognition companies, and airports across the globe began installing the software and running trials. As the results start to come in, however, the gloss (光滑表面) is wearing off. No matter what you might have heard about face-recognition software, Big Brother it ain’t. The concern was based largely on an independent assessment of face-recognition systems carried out in 2000 in the U. S. by the Department of Defense. These tests found that to catch 90 per cent of suspects at an airport, face-recognition software would have to raise a huge number of false alarms. One in three people would end up being dragged out of the line and that’s assuming everyone looks straight at the camera and makes no effort to disguise himself. Results from the recent airport trials would seem to justify that concern. Most face-recognition systems use some kind of geometric technique to translate a picture of a face into a set of numbers that capture its characteristics. Once it has identified these boundaries, the software calculates their relative sizes and positions and converts this geometry into what Visionics calls a "faceprint". Feed the software a series of mug-shots (通缉犯), and it’ll calculate their faceprints. Then it can monitor live CCTV images for the faces of known suspects. When it finds a match, it raises an alarm. Even if the system does manage to capture a face, the problems aren’t over. The trouble is that a suspect’s faceprint taken from live CCTV is unlikely to match the one in the database in every detail. To give themselves the best chance of picking up suspects, operators can set the software so that it doesn’t have to make an exact match before it raises the alarm. But there’s a price to pay: the more potential suspects you pick up, the more false alarms you get. You have to get the balance just right. Despite the disappointing tests, some people insist that face-recognition technology is good enough to put terrorists off. After all the claims and counter-claims, with no one able to discern (洞察) the truth, the industry may soon have to face up to reality. What makes it so hard for the face-recognition systems to work effectively
In the wake of 11 September, Visionics, a leading manufacturer, issued a fact sheet explaining how its technology could enhance airport security. They called it "protecting civilization from the faces of terror". The company’s share price skyrocketed, as did the stocks of other face-recognition companies, and airports across the globe began installing the software and running trials. As the results start to come in, however, the gloss (光滑表面) is wearing off. No matter what you might have heard about face-recognition software, Big Brother it ain’t. The concern was based largely on an independent assessment of face-recognition systems carried out in 2000 in the U. S. by the Department of Defense. These tests found that to catch 90 per cent of suspects at an airport, face-recognition software would have to raise a huge number of false alarms. One in three people would end up being dragged out of the line and that’s assuming everyone looks straight at the camera and makes no effort to disguise himself. Results from the recent airport trials would seem to justify that concern. Most face-recognition systems use some kind of geometric technique to translate a picture of a face into a set of numbers that capture its characteristics. Once it has identified these boundaries, the software calculates their relative sizes and positions and converts this geometry into what Visionics calls a "faceprint". Feed the software a series of mug-shots (通缉犯), and it’ll calculate their faceprints. Then it can monitor live CCTV images for the faces of known suspects. When it finds a match, it raises an alarm. Even if the system does manage to capture a face, the problems aren’t over. The trouble is that a suspect’s faceprint taken from live CCTV is unlikely to match the one in the database in every detail. To give themselves the best chance of picking up suspects, operators can set the software so that it doesn’t have to make an exact match before it raises the alarm. But there’s a price to pay: the more potential suspects you pick up, the more false alarms you get. You have to get the balance just right. Despite the disappointing tests, some people insist that face-recognition technology is good enough to put terrorists off. After all the claims and counter-claims, with no one able to discern (洞察) the truth, the industry may soon have to face up to reality. What is the fatal defect of the system according to the U. S. Department of Defense
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