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Online since: September 2013
Authors: Phillipa Marsh, Dai Zhong Su, Abdelsalam Saad, Zhong Min Wu
A qualitative approach using semi-structured interviews was adopted to collect the data with general, operation, and quality managers in LFI.
Such approaches as zero defects, waste reduction, life-cycle assessment and employee involvement and training are commonly used [5].
Method In this paper a qualitative approach (interviews) was adopted as the most appropriate method, to collect the data, for studying the current situation relating to quality techniques and machinery.
Management Level Companies Top ¹ Middle ² Low 3 Total Company (A) Al-Rayhan Drinks 2 2 2 6 Company (B) Bou Attane Drinks 1 2 2 5 Company (C) National Mills and Fodders 1 2 2 5 Total 4 6 6 16 1Top Management Mangers, 2Middle Managers of Quality and Operation Department, 3Low Managers of Quality and Operation Department Analysing qualitative data started by writing down each interviewee’s response on a separate sheet of paper.
The three highest motivations were ranked by the respondents; saving cost, increased production efficiency, and reduction of waste and pollution to the environment.
Such approaches as zero defects, waste reduction, life-cycle assessment and employee involvement and training are commonly used [5].
Method In this paper a qualitative approach (interviews) was adopted as the most appropriate method, to collect the data, for studying the current situation relating to quality techniques and machinery.
Management Level Companies Top ¹ Middle ² Low 3 Total Company (A) Al-Rayhan Drinks 2 2 2 6 Company (B) Bou Attane Drinks 1 2 2 5 Company (C) National Mills and Fodders 1 2 2 5 Total 4 6 6 16 1Top Management Mangers, 2Middle Managers of Quality and Operation Department, 3Low Managers of Quality and Operation Department Analysing qualitative data started by writing down each interviewee’s response on a separate sheet of paper.
The three highest motivations were ranked by the respondents; saving cost, increased production efficiency, and reduction of waste and pollution to the environment.
Online since: April 2010
Authors: Simon C. Hopkins, Bartek A. Glowacki, Ludovic Thilly, Marco Di Michiel, Ian Pong, Christian Scheuerlein, Carmine Senatore, Alexandre Gerardin, Luc-Rene Oberli, Guillaume Geandier, Luca Bottura
The formation of Cu-Ti causes a delayed reduction of the residual
resistivity ratio (RRR) and adversely affects the deformation behaviour of the strands.
The sample was heated in a dedicated furnace in flowing He + 4% H2, and rotated during data acquisition.
Temperature and strain effects were taken into account, especially when interpreting high-temperature data.
Data were acquired from 90°C to after 15 minutes at 900°C, at five-minute intervals.
Data were logged at ten-second intervals.
The sample was heated in a dedicated furnace in flowing He + 4% H2, and rotated during data acquisition.
Temperature and strain effects were taken into account, especially when interpreting high-temperature data.
Data were acquired from 90°C to after 15 minutes at 900°C, at five-minute intervals.
Data were logged at ten-second intervals.
Online since: September 2013
Authors: Ke Qing Li, Qiang Zhang, Feng Wu
In contrast, the second and third valuation method is generally considered when the data can not satisfy the application requirement of the first kind of method.
(3) Measurement of yield reduction caused by farmland destruction Since a variety of crops can be cultivated in a certain area of land, it is necessary to calculate yield reduction of each crop [13].
(4) Measurement of yield reduction caused by environmental pollution Environmental factor that leads to yield reduction mainly includes air pollution and water pollution.
(4) Where, V4 is yield reduction caused by environmental pollution; Pi is price of i crop; Qi is yield per unit area of i crop; S' is the polluted farmland area; K is number of crop type; R is yield reduction rate (in China, yield reduction rate caused by air pollution is in commonly 5 - 10%)
To measure the cost, we need to know the data such as operating, monitoring, repairing and daily managing costs per unit of mineral product in cleaner production process.
(3) Measurement of yield reduction caused by farmland destruction Since a variety of crops can be cultivated in a certain area of land, it is necessary to calculate yield reduction of each crop [13].
(4) Measurement of yield reduction caused by environmental pollution Environmental factor that leads to yield reduction mainly includes air pollution and water pollution.
(4) Where, V4 is yield reduction caused by environmental pollution; Pi is price of i crop; Qi is yield per unit area of i crop; S' is the polluted farmland area; K is number of crop type; R is yield reduction rate (in China, yield reduction rate caused by air pollution is in commonly 5 - 10%)
To measure the cost, we need to know the data such as operating, monitoring, repairing and daily managing costs per unit of mineral product in cleaner production process.
Online since: December 2012
Authors: Ji Ning Yan, Ke Fa Zhou, Li Sun, Yan Fang Qin, Shu Guang Zhou, Nan Xiang
Therefore, the method could provide necessary support for quantitative use of HJ_1A HSI data.
Since the quality of HSI data products is poor and the random additive noise disturbance is serious.
The greater its value is, the more sparse of the wavelet representation in the noise reduction model.
Data processing and results analysis The experimental data is HJ_1A satellite HSI secondary product obtained on October 30, 2010, which is after radiometric calibration and system geometric correction.
Therefore, the method could provide necessary support for quantitative use of HJ_1A HSI data.
Since the quality of HSI data products is poor and the random additive noise disturbance is serious.
The greater its value is, the more sparse of the wavelet representation in the noise reduction model.
Data processing and results analysis The experimental data is HJ_1A satellite HSI secondary product obtained on October 30, 2010, which is after radiometric calibration and system geometric correction.
Therefore, the method could provide necessary support for quantitative use of HJ_1A HSI data.
Online since: January 2013
Authors: Jiao Sun, Qing Qing Chen
Data represent mean ± SD, n=3, * p <0.05 when compared with control, **p<0.01 when compared with control.
Data represent mean ± SD, n=3, * p <0.05 when compared with control, **p<0.01 when compared with control.
Liver is the largest detoxification organ, and for some nanoparticles, previous data have showed that hepatic deposition is the highest among all the other tissues [5,6].
Taken together, these data suggested that exposure to SiO2 NPs caused a dose-dependent cytotoxicity in BRL cells.
Thus the viability reduction and cell apoptosis of BRL cells initiated by SiO2 NPs may be the result of increased ROS production and antioxidant depletion.
Data represent mean ± SD, n=3, * p <0.05 when compared with control, **p<0.01 when compared with control.
Liver is the largest detoxification organ, and for some nanoparticles, previous data have showed that hepatic deposition is the highest among all the other tissues [5,6].
Taken together, these data suggested that exposure to SiO2 NPs caused a dose-dependent cytotoxicity in BRL cells.
Thus the viability reduction and cell apoptosis of BRL cells initiated by SiO2 NPs may be the result of increased ROS production and antioxidant depletion.
Online since: February 2012
Authors: Hong Bo Jiang, Xin Yu Feng
Data is reduced and processing is increased when cryptography is realized using short key length.
Architecture of Ecc An m (163, 233 and 283) bits data could be encrypted using this ECC core.
It squares an m bits element in F2m and mode reduction polynomials.
Testing data input ECC directly.
Fig.9 Testbench of ECC Where, Generator is a testing data generator for ECC, Testmodel is verification model for encryption and DUT is made of encrypt and decrypt module.
Architecture of Ecc An m (163, 233 and 283) bits data could be encrypted using this ECC core.
It squares an m bits element in F2m and mode reduction polynomials.
Testing data input ECC directly.
Fig.9 Testbench of ECC Where, Generator is a testing data generator for ECC, Testmodel is verification model for encryption and DUT is made of encrypt and decrypt module.
Online since: December 2012
Authors: Yong Guo, Zhi Yong Liu
Kim and Smith (2001) proposed a two-stage synthesis method based on the pinch analysis technology to design distributed cooling systems for effluent temperature reduction.
Then calculate the heat load of the cooling tower according to the limited data, and put them in ascending order.
Q ( MAX ) = ( TMAX-TIN )× OSU (1) Table 1 Cooling water supply information Source T(n) (℃) OSU (kW/℃) Tmax Q(KW) T1 20 40 50 1200 T2 22 67 50 1876 T3 25 87 55 2610 The limiting data for cooling water using operations are given in Table 2.
Table 2 Limiting cooling water data Operation Tin (℃) T out (℃) Fin (kW/℃) Q (kW) OP1 30 45 40 600 OP2 40 60 15 300 OP3 25 50 32 800 OP4 45 60 40 600 OP5 40 55 20 300 OP6 30 45 46.7 700 Put the heat transfer process in ascending order according to the heat load for better observation.
Table 3 Limiting cooling water data for ascending order according to the heat load Operation Tin (℃) T out (℃) Fin (kW/℃) Q (kW) OP2 40 60 15 300 OP5 40 55 20 300 OP1 30 45 40 600 OP4 45 60 40 600 OP6 30 45 46.7 700 OP3 25 50 32 800 The total heat load of the cooling water is Q=3300kw , So T1 and T3 be selected for the total heat load of T1 and T3 just meet the requirements of the heat load of the cooling water.
Then calculate the heat load of the cooling tower according to the limited data, and put them in ascending order.
Q ( MAX ) = ( TMAX-TIN )× OSU (1) Table 1 Cooling water supply information Source T(n) (℃) OSU (kW/℃) Tmax Q(KW) T1 20 40 50 1200 T2 22 67 50 1876 T3 25 87 55 2610 The limiting data for cooling water using operations are given in Table 2.
Table 2 Limiting cooling water data Operation Tin (℃) T out (℃) Fin (kW/℃) Q (kW) OP1 30 45 40 600 OP2 40 60 15 300 OP3 25 50 32 800 OP4 45 60 40 600 OP5 40 55 20 300 OP6 30 45 46.7 700 Put the heat transfer process in ascending order according to the heat load for better observation.
Table 3 Limiting cooling water data for ascending order according to the heat load Operation Tin (℃) T out (℃) Fin (kW/℃) Q (kW) OP2 40 60 15 300 OP5 40 55 20 300 OP1 30 45 40 600 OP4 45 60 40 600 OP6 30 45 46.7 700 OP3 25 50 32 800 The total heat load of the cooling water is Q=3300kw , So T1 and T3 be selected for the total heat load of T1 and T3 just meet the requirements of the heat load of the cooling water.
Online since: October 2014
Authors: Zhi Ping Yang, Peng Fu, Ning Ling Wang, Long Fei Zhu
Wang[3] proposed energy-consumption benchmark state concept under the varying operation conditions and determine it with data mining method, such as fuzzy rough set (FRS)-based decision table reduction and fuzzy C mean(FCM)-based clustering.
A large mass of information which reflects the systems and its related equipment performance is hidden in the historical operating data of thermal power units.
Based on the actual data of the unit, is achieved and shown in Eq (9): (9) Determination of Energy-consumption Benchmark State Figure 2 shows a simplified system diagram.
Table 1 ASFC of All States state M P T Tre P1 … P8 Pc bpipe bH bI bL bc bboiler b 1 473.6 23777 561 547.7 6064 … 11.88 5.54 0.85 11.69 1.141 4.96 14.97 131 290.3 2 473 23719 558.3 543.3 6033 … 15.57 5.5 0.84 11.67 1.141 4.65 14.83 130.5 289.2 3 474.6 23745 558 541.9 6043 … 18.45 5.49 0.85 11.7 1.146 4.45 14.81 130.8 289.3 4 478.7 23557 560 541 6128 … 19.11 5.35 0.86 11.86 1.152 4.46 14.67 131.6 290.2 5 480.9 23758 560 543.6 6170 … 19.19 5.31 0.86 11.92 1.156 4.48 14.66 132.1 290.8 … … … … … … … … … … … … … … … … n 460.7 23581 561.9 547 5989 … 12.83 5.12 0.82 11.26 1.111 4.85 13.99 127.7 285.4 … … … … … … … … … … … … … … … … 48 466.2 23946 560.4 541.1 6124 … 18.85 4.77 0.83 11.72 1.078 4.89 13.44 128.1 285.6 49 483.3 23942 558.3 539.4 6333 … 20.14 5.03 0.86 12.22 1.112 4.88 14.17 131.5 290.5 50 485.2 24495 563.1 545.2 6345 … 19.98 5.11 0.86 12.34 1.119 4.91 14.4 132.5 291.9 THA 466 24200 566 566 5977 … 18.74 5.88 0.84 11.11 1.023 4.68 14.89 130.5 288.7 Fifty sets of actual data under
Adjusting the unit based on benchmark state, FSC would be reduced by 3.3 g/(kW·h), 3.5 g/(kW·h) and 4.3 g/(kW·h) and the percentage of reduction are 1.14%, 1.19% and 1.44%.
A large mass of information which reflects the systems and its related equipment performance is hidden in the historical operating data of thermal power units.
Based on the actual data of the unit, is achieved and shown in Eq (9): (9) Determination of Energy-consumption Benchmark State Figure 2 shows a simplified system diagram.
Table 1 ASFC of All States state M P T Tre P1 … P8 Pc bpipe bH bI bL bc bboiler b 1 473.6 23777 561 547.7 6064 … 11.88 5.54 0.85 11.69 1.141 4.96 14.97 131 290.3 2 473 23719 558.3 543.3 6033 … 15.57 5.5 0.84 11.67 1.141 4.65 14.83 130.5 289.2 3 474.6 23745 558 541.9 6043 … 18.45 5.49 0.85 11.7 1.146 4.45 14.81 130.8 289.3 4 478.7 23557 560 541 6128 … 19.11 5.35 0.86 11.86 1.152 4.46 14.67 131.6 290.2 5 480.9 23758 560 543.6 6170 … 19.19 5.31 0.86 11.92 1.156 4.48 14.66 132.1 290.8 … … … … … … … … … … … … … … … … n 460.7 23581 561.9 547 5989 … 12.83 5.12 0.82 11.26 1.111 4.85 13.99 127.7 285.4 … … … … … … … … … … … … … … … … 48 466.2 23946 560.4 541.1 6124 … 18.85 4.77 0.83 11.72 1.078 4.89 13.44 128.1 285.6 49 483.3 23942 558.3 539.4 6333 … 20.14 5.03 0.86 12.22 1.112 4.88 14.17 131.5 290.5 50 485.2 24495 563.1 545.2 6345 … 19.98 5.11 0.86 12.34 1.119 4.91 14.4 132.5 291.9 THA 466 24200 566 566 5977 … 18.74 5.88 0.84 11.11 1.023 4.68 14.89 130.5 288.7 Fifty sets of actual data under
Adjusting the unit based on benchmark state, FSC would be reduced by 3.3 g/(kW·h), 3.5 g/(kW·h) and 4.3 g/(kW·h) and the percentage of reduction are 1.14%, 1.19% and 1.44%.
Online since: December 2013
Authors: Jarin Paphangkorakit, Sucharat Limsitthichaikoon, Supanigar Ruangsri Sermswatsri, Waranuch Pitiphat, Preeyarat Thaiprasong, Panupong Patchadee, Aroonsri Priprem
The present study confirmed the use of the ultrasonic dental scaler in enhancing the efficacy of lidocaine in pain reduction from palatal injection.
Finally the pain reduction capability of LN was compared with the conventional topical anesthetic, 18% benzocaine/2% tetraciane gel (BZ).
Since the data from the split-mouth comparison were not normally distributed (Shapiro-Wilk test), medians and ranges were described and a Wilcoxon signed rank test was used to analyze the VAS difference between LN and BZ.
In the split-mouth experiment, LN showed a significant pain reduction compared to 18% benzocaine/2% tetracaine gel.
However, the lower VAS in the LN group indicated a more effective pain reduction of the LN compared to the previous 2% lidocaine liposomes.
Finally the pain reduction capability of LN was compared with the conventional topical anesthetic, 18% benzocaine/2% tetraciane gel (BZ).
Since the data from the split-mouth comparison were not normally distributed (Shapiro-Wilk test), medians and ranges were described and a Wilcoxon signed rank test was used to analyze the VAS difference between LN and BZ.
In the split-mouth experiment, LN showed a significant pain reduction compared to 18% benzocaine/2% tetracaine gel.
However, the lower VAS in the LN group indicated a more effective pain reduction of the LN compared to the previous 2% lidocaine liposomes.
Online since: April 2012
Authors: Jacek Tarasiuk, Krzysztof Wierzbanowski, P. Gerber, Sebastian Wroński, Brigitte Bacroix
The deformed state and fully recrystallized state have been analyzed and compared with data obtained eight years ago.
Introduction A sample of cold rolled copper was deformed to 70% reduction and a part of it was recrystallized in the year 2000 [1].
SE reduction for S and Goss (G) components is smaller but visible.
The most important reduction of SE value is observed for rotated cube (Rwd) component.
Modeling of Recrystallization Using Monte Carlo Method Based on EBSD Data, Materials Science Forum, vol. 408-412 (2002), p. 395 [3] K.
Introduction A sample of cold rolled copper was deformed to 70% reduction and a part of it was recrystallized in the year 2000 [1].
SE reduction for S and Goss (G) components is smaller but visible.
The most important reduction of SE value is observed for rotated cube (Rwd) component.
Modeling of Recrystallization Using Monte Carlo Method Based on EBSD Data, Materials Science Forum, vol. 408-412 (2002), p. 395 [3] K.