foster power tong 54 93 made in china

*(RPM based on published requirements of motor manufacturer 30 GPM to tong motor)(See through clutch and final drive covers for display only. Actual tong comes with metal cover.)

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MOT"s Hydraulic Power Tongs are used for running or pulling tubular strings during well repair, workover, snubbing, drilling and casing operations to extract oil and gas from wells. MOT’s

Our TEDA Style power tongs apply correct, uniform torque, reducing costly rod and tubing failures, delivering ample power to make up and break out rods and tubulars from 5/8” rod to 5-1/2” casing. Our series of XQ power tongs come with Bi-Directional jaw systems that save time and enhance safety, and go from make to break with the flip of a knob.

Tongs offer a variety of upgrades from our standard XQ series of TEDA Style models featuring Eaton hydraulic motors for extra torque and reliability, two or three spool directional control valves for more efficient hydraulic operations, and modified structures for higher strength, durability and adaptability to specific job requirements.

Are available from 5-1/2” to 20” pipe and are able to handle both API Standard and PREMIUM (Proprietary Steel Grade) casing connections which require higher torque. Available in a variety of models with hydraulic back-up tongs optional: “KMA", “KHT”, “TQ", “KJD”, “KD” and SE 16-25 with torques ranging from 15,000 ft-lbs up to 55,000 ft-lbs.

Emperor Wu of Han (156 – 29 March 87BC), formally enshrined as Emperor Wu the Filial (Chinese: 孝武皇帝), born Liu Che (劉徹) and courtesy name Tong (通), was the seventh emperor of the Han dynasty of ancient China, ruling from 141 to 87 BC.Kangxi Emperor more than 1,800 years later and remains the record for ethnic Chinese emperors. His reign resulted in a vast expansion of geopolitical influence for the Chinese civilization, and the development of a strong centralized state via governmental policies, economical reorganization and promotion of a hybrid Legalist–Confucian doctrine. In the field of historical social and cultural studies, Emperor Wu is known for his religious innovations and patronage of the poetic and musical arts, including development of the Imperial Music Bureau into a prestigious entity. It was also during his reign that cultural contact with western Eurasia was greatly increased, directly and indirectly.

Emperor Wu is considered one of the greatest emperors in Chinese history due to his strong leadership and effective governance, which made the Han dynasty one of the most powerful nations in the world.Michael Loewe called the reign of Emperor Wu the "high point" of "Modernist" (classically justified Legalist) policies, looking back to "adapt ideas from the pre-Han period."Shang Yang.Confucianism as the state philosophy and code of ethics for his empire and started a school to teach future administrators the Confucian classics. These reforms had an enduring effect throughout the existence of imperial China and an enormous influence on neighbouring civilizations.

Now sealed in the marriage alliance with Consort Wang, Princess Guantao began incessantly criticising Lady Li in front of Emperor Jing. Over time, Emperor Jing started to believe his sister"s words, so he decided to test out Lady Li. One day he asked Lady Li whether she would happily foster-care the rest of his children if he were to pass away, only to have her rudely refuse to comply. This made Emperor Jing angry and worried that if Liu Rong were to inherit the throne and Lady Li to become empress dowager, many of his concubines might suffer the tragic fate of Consort Qi in the hands of Empress Lü. Princess Guantao then began to openly praise her son-in-law-to-be to her royal brother, further convincing Emperor Jing that Liu Che was a far better choice for heir apparent than Liu Rong.

The Han dynasty up to this point was run according to a Taoist economic freedom and government decentralization. With regard to foreign policy-wise, periodic Xiongnu confederacy to the north. These policies were important in stimulating economic recovery following the post-Qin dynasty civil war, but had their drawbacks. The non-interventionist policies resulted in loss of monetary regulation and political control by the central government, allowing the feudal vassal states to become powerful and unruly, culminating in the Rebellion of the Seven States during Emperor Jing"s reign. Nepotism among the ruling class also stagnated social mobility and encouraged nobles" rampant disregard of laws, leading to the rise of local despots who bullied and oppressed the population. The heqin policy also failed to protect the Han borders against nomadic raids, with Xiongnu cavalries invading as close as 300 li (100 miles, 160 km) from the capital during Emperor Wen"s reign, and over 10,000 border residents abducted or enslaved during Emperor Jing"s reign. Prominent politicians like Jia Yi and Chao Cuo had both previously advised on the necessity of important policy reforms, but neither Emperor Wen nor Emperor Jing was willing to risk implementing such changes.

However, Emperor Wu"s reforms threatened the interests of the nobles and were swiftly defeated by his powerful grandmother Grand Empress Dowager Dou, who held real political power in the Han court and supported the conservative factions. Most of the reformists were punished: Emperor Wu"s two noble supporters Dou Ying (窦婴) and Tian Fen (田蚡, Empress Dowager Wang"s half-brother and Emperor"s uncle) lost their positions, and his two mentors Wang Zang (王臧) and Zhao Wan (赵绾) were impeached, arrested and forced to commit suicide in prison.

Emperor Wu, deprived of any allies, was now the subject of conspiracies designed to have him removed from the throne. For example, his first wife Empress Chen Jiao was unable to become pregnant. In an attempt to remain his first love, she had prohibited him from having other concubines. Emperor Wu"s political enemies used his childlessness as an argument to seek to depose him, as the inability of an emperor to propagate a royal bloodline was a serious matter. These enemies of Emperor Wu wished to replace him with his uncle Liu An, the King of Huainan, who was renowned for his expertise in Taoist ideology. Even Emperor Wu"s own maternal uncle Tian Fen switched camps and sought Liu An"s favor, as he predicted the young emperor would not be in power for long. Emperor Wu"s political survival now relied heavily on the lobbying of his influential aunt / mother-in-law, Princess Guantao (Liu Piao), who served as a mediator in seeking the Emperor"s reconciliation with his powerful grandmother. Princess Guantao took every opportunity to influence the Grand Empress and also constantly made demands on behalf of her nephew / son-in-law.

Knowing that the conservative noble classes occupied every level of the Han court, Emperor Wu changed his strategy. He secretly recruited a circle of young loyal supporters from ordinary backgrounds and promoted them to middle-level positions in order to infiltrate executive ranks in the government. These newly established officials, known as the "insider court" (内朝), took orders and reported directly to Emperor Wu. They had real influence over the operation of government affairs though lower in rank. They became a powerful counter against the "outsider court" (外朝) made up of the Three Lords and Nine Ministers that, at the time, were mostly composed of anti-reformists. Furthermore, Emperor Wu sent out nationwide edicts appealing to grassroots scholars such as Gongsun Hong to enrol in government services in an attempt to break the stranglehold that the older-generation noble class had on the nation"s levers of power.

Around the same time, perhaps as a sign of what would come to be, Emperor Wu began to trust governing officials who were harsh in their punishment, believing that such harshness would be the most effective method to maintain social order and so placing these officials in power. For example, one such official, Yi Zong (义纵), became the governor of the Commandery of Dingxiang (part of modern Hohhot, Inner Mongolia) and executed 200 prisoners, even though they had not committed capital crimes; he then executed their friends who happened to have been visiting. In 122 BC, Liu An, the Prince of Huainan (a previously trusted adviser of Emperor Wu, and closely enough related to have imperial pretensions) and his brother Liu Ci (刘赐), the Prince of Hengshan, were accused of plotting treason. They committed suicide; their families and many alleged co-conspirators were executed. Similar action was taken against the other vassal Princes, and by the end of the reign, all the vassal kingdoms had been political and militarily disabled.

Starting about 113 BC, Emperor Wu began to display further signs of abusing his power. He began to incessantly tour the commanderies, initially nearby Chang"an, but later extending to much farther places, worshipping the various gods on the way, perhaps again in search of immortality. He also had a succession of magicians whom he honoured with great things. In one case, he even made one a marquess and married his daughter, the Eldest Princess Wei, to him; that magician, Luan Da, was later exposed as a fraud and executed. Emperor Wu"s expenditures on these tours and magical adventures put a great strain on the national treasury and caused difficulties on the locales that he visited, twice causing the governors of commanderies to commit suicide after they were unable to supply the emperor"s entire train.

Emperor Wu"s political reform resulted in the strengthening of the Emperor"s power at expense of the prime minister"s authority. The post of Shangshu (court secretaries) was elevated from merely managing documents to that of the Emperor"s close advisor, and it stayed this way until the end of the imperial era.

Cosmo, Nicola Di (2002). Ancient China and its Enemies: The Rise of Nomadic Power in East Asian History (Updated ed.). Cambridge: Cambridge University Press. ISBN 9780521770644.

Pollard, Elizabeth; Rosenberg, Clifford; Tignor, Robert (2015). Worlds Together Worlds Apart. Volume One - Beginning Through the 15th Century (concise ed.). New York City: W.W. Norton & Company. ISBN 9780393918472.

jisi day of the 4th month of the 4th year of Emperor Jing"s reign, per vol.16 of Zizhi Tongjian. This was the same day Liu Rong was made Crown Prince.

B.C., Sima, Qian, approximately 145 B.C.-approximately 86 (1993). Records of the grand historian. Columbia University Press. ISBN 0-231-08164-2. OCLC 904733341.

Yap, Joseph P. (2009). Wars With The Xiongnu, A Translation from Zizhi tongjian. AuthorHouse, Bloomington, Indiana, U.S.A. ISBN 978-1-4490-0604-4. Chapters 3–7.

China, the European Union, and the United States are now emerging as the main competitors for global leadership in AI. Indeed, China, which achieved success in the Internet economy in part by shutting out U.S. firms, has clearly stated its ambition of achieving dominance in AI—both to increase its competitiveness in industries that have traditionally been vital to the U.S. and EU economies, and to expand its military power.[1] Moreover, the EU’s coordinated plan on AI states that its “ambition is for Europe to become the world-leading region for developing and deploying cutting-edge, ethical and secure AI.”[2] The outcome of this race to become the global leader in AI will affect the trio’s future economic output and competitiveness, as well as military superiority.

The United States leads for several reasons. First, it has the most AI start-ups, with its AI start-up ecosystem having received the most private equity and venture capital funding.[3] Second, it leads in the development of both traditional semiconductors and the computer chips that power AI systems.[4] Third, while it produces fewer AI scholarly papers than the EU or China, it produces the highest-quality papers on average.[5] Finally, while the United States has less overall AI talent than the European Union, its talent is more elite.[6]

As this report demonstrates, China, the European Union, and the United States each have different areas they can improve to become more competitive in the AI economy. For example, China should expand its capacity to teach AI-related subjects at the university level, encourage research quality over quantity, and foster a stronger culture of promoting open data. Meanwhile, the EU should focus on developing policies that incentivize talent to remain in the EU, help transfer research successes into business applications, encourage the development of larger firms that can better compete in a global market, and reform regulations to better enable use of data for AI. Finally, for the United States to maximize its lead, it should focus on policies that grow its domestic talent base, enable foreign AI talent to immigrate, and increase incentives for research and development (R&D). More detailed recommendations are enumerated at the end of this report.

First, China is investing in AI education. In 2017, the State Council, the chief administrative body in China, released a plan calling for the creation of an AI academic discipline.[48] In 2018, the Ministry of Education launched multiple initiatives to boost education, and the combined initiatives include plans to develop 50 AI research centers, world-class online courses, and a 5-year plan to train more than 500 instructors and 5,000 students.[49] Three of China’s top universities—Tsinghua University, USTC, and Shanghai Jiao Tong University—have already significantly increased the number of students enrolled in AI and machine learning courses since 2016. For example, between 2016 and 2018, USTC increased its AI and machine learning course enrollment from 1,745 to 3,286 students.[50] Second, Chinese researchers can and do quickly replicate advanced algorithms developed by other nations because AI researchers frequently detail the architecture of their AI model, and how they implemented and trained it, on openly available prepublications websites.[51] Anecdotal evidence also suggests Chinese researchers translate English AI publications significantly more often and faster than Western nations translate papers in Chinese, thereby creating an information asymmetry.[52] Third, AI researcher and venture capitalist Kai-Fu Lee has argued that China’s lack of top-end talent is not a significant barrier to it leading in AI, stating “[T]he current age of implementation [AI application commercialization] appears well-suited to China’s strengths in research: large quantities of highly-skilled, though not necessarily best-of-best, AI researchers and practitioners.”[53] Lee believes breakthroughs such as deep learning occur once every several decades, and AI has entered an age in which data will be the decisive factor that determines the ability of AI systems.[54]

Countries need organizations to perform research in order to sustain innovation. In the past decade, algorithmic innovations, along with greater computing power, have increased the functionality of AI systems and drastically reduced the time it takes to train them.[55] But AI is far from a mature technology; more research and more advances are needed.

While the EU’s top organizations are producing higher-quality research on average than the best Chinese organizations, the EU is nonetheless experiencing relative stagnation in paper output and quality. Since 1998, the European Union’s FWCI has grown only 11 percent, compared with 24 percent for the United States and 154 percent for China.[78] Maintaining the same rapid increase in FWCI as it experienced between 2012 and 2016, China may have surpassed the EU in FWCI by 2018 (data was only available through 2016).[79] In addition, five nations—the United Kingdom, Germany, France, Spain, and Italy—primarily drive AI research in the EU, but their annual AI publication output has actually contracted since 2014.[80]

Our overall findings align with research done by multiple groups, including the Organization for Economic and Cooperative Development (OECD), which also found the United States received the most funding over the last three years.[92] In addition, data from PitchBook, a private-capital market-data provider, confirmed that the United States, followed by China and the European Union, led in private equity and venture capital funding.[93]

Number of AI Start-ups (2017): Similar to other technology-based start-ups, AI start-ups can be an important driver of a nation’s economic growth and competitiveness.[100] Roland Berger, a global consultancy, and Asgard, a Berlin-based investment firm, categorized AI start-ups as firms that produce a primary product or service that utilizes AI, excluding hardware. The firms’ research found that the United States was home to 1,393 AI start-ups in 2017, ahead of the European Union (726 start-ups) and China (383 start-ups).[101] Per one million workers, the United States led (8), followed by the European (3) and China (0.5).[102]

Chinese firms are doing a better job than U.S. firms of conveying the importance of AI to their employees. For example, 43 percent of U.S. individuals say their employers present the development of AI and the digital transformation of the organization as being strategically important, compared with 85 percent of Chinese individuals.[160] Regarding the development of AI and digital transformation, Chinese individuals are also more likely to expect their manager to make statements about the subject, hire new individuals because of it, increase the number of training courses for the topic, and launch new projects because of it.[161] Thus, it is unsurprising that the same survey found 54 percent of U.S. individuals responded that their workplace had no plan to deploy AI tools, versus 22 percent of Chinese individuals.[162]

Yet, in 2015, only 30 percent of U.S. hospitals could find, send, and receive EHRs to and from other providers.[190] Qualitative evidence suggests interoperability is even lower in China and the European Union. In China, hospitals frequently use EHR systems that are not interoperable, forcing patients to bring printed health records when seeing doctors in different hospitals.[191] In the European Union, the ability to access and share medical data across borders varies greatly, limiting the ability to train AI systems on cross-border data.[192] Indeed, many European citizens have no access to EHRs.[193]

AI systems rely on semiconductor devices, such as integrated circuits, that can perform large numbers of operations per second. Indeed, graphics processing units (GPUs), which are circuits that perform mathematical operations in parallel, have catalyzed recent AI developments.[226] In addition, technologies such as supercomputers, which combine processing units such as GPUs and central processing units, can expand the capabilities of AI systems through massive computational power. For example, researchers have combined supercomputers and machine learning techniques to model climate change as well as the merging of blackholes.[227]

In addition, several leading Chinese technology firms, including Baidu, Tencent, Alibaba, and Huawei, are developing AI-optimized integrated circuits, which large U.S. technology firms are also doing. Huawei—which in particular has demonstrated some design prowess—and Apple were the first firms to create a smartphone processor that uses 7 nanometer (nm) process technology, which refers to the size of the transistors in a processor. Smaller transistors more efficiently use power than larger ones and increase the potential number of transistors in a processor, thereby making it potentially more powerful.[254]

China has made significant progress in AI, but still lags behind, especially on a per capita basis. China however, produces more AI research papers and has an advantage in data. While China is on a path toward challenging the United States for global AI leadership, several of its policies limit its immense potential in AI.[262] For example, policies such as “civil-military” integration make it harder for its firms to succeed in the global market because such policies foster distrust in other societies. A lack of trust will hinder Chinese firms’ ability to acquire significant global market share outside of nations that are taking part in China’s subsidized Digital Silk Road initiatives.

The Chinese government should encourage the development of joint programs that allow students to major in both AI and another field to foster creativity.[264]

Several Chinese AI chip start-ups have recently received hundreds of millions of dollars in funding and firms such as Huawei have developed impressive chip designs.[276] Nonetheless, the complexity of developing chips, China’s shortage of talent, and the lack of multiple Chinese semiconductor firms being in the top 15 globally for sales indicate China still needs to make significant progress in order to match the United States in semiconductors. China should also stop unfair trade practices such as forced technology transfers and intellectual property theft, which have led to increased scrutiny of its firms by the Committee on Foreign Investment in the United States (CFIUS) and put its ability to use Western semiconductors and designs in peril. Computing power used to be a commodity China could easily procure.[277]

The European Data Portal has a wide variance of participation from EU member states.[290] The EU should foster the further development of open data policies in its member states to increase the number of datasets available to the public.

In terms of the percentage of firms adopting AI, the European Union is behind China and the United States. Thus, the EU should focus on both initiatives that decrease the barriers to public-sector adoption and those that foster private-sector adoption.

Every EU member state should appoint a chief digital officer to not only champion data innovation domestically, but also serve on an EU-wide advisory panel charged with counseling the European Commission on development of a cohesive vision and strategy for capturing the full benefits of data-driven innovation.[293]

The Department of Commerce (DOC) should establish organizations designed to advance the development of innovative AI applications in various sectors. For example, Manufacturing USA, which is overseen by federal agencies, including DOC and DOE, is a network of research institutes focused on fostering innovation and collaboration in the manufacturing sector. Among them is the Advanced Robotics for Manufacturing, a public-private partnership in Pittsburgh that focuses on AI and automation. Using this model, agencies should support similar institutes that include industry, academia, and government agency resources to advance AI in other sectors such as city management and precision medicine.[318]

[11]. European Commission, “Artificial Intelligence: Commission Takes Forward Its Work on Ethics Guidelines,” news release, April 8, 2019, http://europa.eu/rapid/press-release_IP-19-1893_en.htm.

[19]. Corinne Abrams, “Google’s Effort to Prevent Blindness Shows AI Challenges,” The Wall Street Journal, January 26, 2019, https://www.wsj.com/articles/googles-effort-to-prevent-blindness-hits-roadblock-11548504004.

[25]. Maintaining American Leadership in Artificial Intelligence, Executive Order 13859, Executive Office of the President (2019), https://www.federalregister.gov/documents/2019/02/14/2019-02544/maintaining-american-leadership-in-artificial-intelligence.

[47]. Jeffrey Ding, “Deciphering China’s AI Dream: The Context, Components, Capabilities, and Consequences of China’s Strategy to Lead the World in AI” (Future of Humanity Institute, March 2018), https://www.fhi.ox.ac.uk/wp-content/uploads/Deciphering_Chinas_AI-Dream.pdf; Qinqqing Yang, “Ceiling Hit by Search Model, Baidu Uplifts AI Strategy with New CEO,” 21st Century Business Herald, January 18, 2017, http://epaper.21jingji.com/html/2017-01/18/content_54928.htm.

[93]. Pitchbook provided us with data on AI venture capital and private equity funding for 2016–18 for China, the European Union, and the United States. Pitchbook defines AI firms as those “developing technologies that enable computers to autonomously learn, deduce and act, through utilization of large datasets. The technology enables development of systems that collect and store massive amounts of data, and analyze that content to make decisions based on probability and statistical analysis. Applications for Artificial Intelligence & Machine Learning include speech recognition, computer vision, robotic control and accelerating processes in the empirical sciences where large datasets are essential.” Pitchbook’s data demonstrates the United States received 57 percent of venture capital funding going to AI firms in 2018, compared with 29 percent of Chinese firms and 7 percent for EU firms. Pitchbook accessed the data on June 7, 2019.

[146]. Maintaining American Leadership in Artificial Intelligence, Executive Order 13859, Executive Office of the President (2019), https://www.federalregister.gov/documents/2019/02/14/2019-02544/maintaining-american-leadership-in-artificial-intelligence.

[190]. A Jay Holmgreen, Vaishali Patel, and Julia Adler-Milstein, “Progress In Interoperability: Measuring US Hospitals’ Engagement In Sharing Patient Data,” Health Affairs vol. 26, no. 10 (2017), https://www.healthaffairs.org/doi/abs/10.1377/hlthaff.2017.0546.

[254]. Gregory C. Allen, “Understanding China’s AI Strategy: Clues to Chinese Strategic Thinking on Artificial Intelligence and National Security” (Center for New American Security, February 2019), https://www.cnas.org/publications/reports/understanding-chinas-ai-strategy.

[266]. “China Launches First University Program to Train International AI Talents,” China Daily, April 4, 2018, http://www.chinadaily.com.cn/m/beijing/zhongguancun/2018-04/04/content_35979394.htm.

[299]. Joy Dantong Ma, “The AI Race Is Wide Open, If America Remains Open,” Macro Polo, April 15, 2019, https://macropolo.org/us-china-ai-race-talent/.

[328]. Joshua New and Daniel Castro, “How Policymakers Can Foster Algorithmic Accountability” (Center for Data Innovation, May 2017), http://www2.datainnovation.org/2018-algorithmic-accountability.pdf.