Processing Tincal Ores using Ultrasonic Waves

ABSTRACT: In this study, effects of ultrasonic sound waves have been studied on the processing of tincal ores using ultrasonic baths producing 35 kHz frequency and 1.24 W/cm 2 intensity. Tincal ores (24.37 90B2O3) were processed in saturated solutions for 15 minutes and a concentrate containing 35.29 % B2O1 was obtained with 95.69 % recovery. During ultrasonic treatment boron minerals were found to corrode to a small extent. These findings showed that efficiency of dispersion process was proportional to directly treatment time and indirectly to the sample quantity, solids density and particle size

Mutation analysis of CBP and PCAF reveals rare inactivating mutations in cancer cell lines but not in primary tumours

In this study we screened the histone acetyltransferases CBP and PCAF for mutations in human epithelial cancer cell lines and primary tumours. We identified two CBP truncations (both in cell lines), seven PCAF missense variants and four CBP intronic microdeletions. These data suggest that neither gene is commonly inactivated in human epithelial cancers

VULNERABILITY INDEX ASSESSMENT USING NEURAL NETWORKS (VIANN): A CASE STUDY OF NICOSIA, CYPRUS

Many scholars have used microtremor applications to evaluate the vulnerability index. In order to reach fast and reliable results, microtremor measurement is preferred as it is a cost-effective method. In this paper, the vulnerability index will be reviewed by utilization of microtremor measurement results in Nicosia city. 100 measurement stations have been used to collect microtremor data and the data were analysed by using Nakamura's method. The value of vulnerability index (K-g) has been evaluated by using the fundamental frequency and amplification factor. The results obtained by the artificial neural network (ANN) will be compared with microtremor measurements. Vulnerability Index Assessment using Neural Networks (VIANN) is a backpropagation neural network, which uses the original input microtremor Horizontal Vertical Spectrum Ratio (HVSR) spectrum set. A 3-layer back propagation neural network which contains 4096 input, 28 hidden and 3 output neurons are used in this suggested system. The output neurons are classified according to acceleration sensitivity zone, velocity zones, or displacement zones. The sites are classified by their vulnerability index values using binary coding: [1 0 0] for the acceleration sensitive zone, [0 1 0] for the velocity sensitive zone, and [0 0 1] for the displacement sensitive zone

VULNERABILITY INDEX ASSESSMENT USING NEURAL NETWORKS (VIANN): A CASE STUDY OF NICOSIA, CYPRUS

Many scholars have used microtremor applications to evaluate the vulnerability index. In order to reach fast and reliable results, microtremor measurement is preferred as it is a cost-effective method. In this paper, the vulnerability index will be reviewed by utilization of microtremor measurement results in Nicosia city. 100 measurement stations have been used to collect microtremor data and the data were analysed by using Nakamura's method. The value of vulnerability index (K-g) has been evaluated by using the fundamental frequency and amplification factor. The results obtained by the artificial neural network (ANN) will be compared with microtremor measurements. Vulnerability Index Assessment using Neural Networks (VIANN) is a backpropagation neural network, which uses the original input microtremor Horizontal Vertical Spectrum Ratio (HVSR) spectrum set. A 3-layer back propagation neural network which contains 4096 input, 28 hidden and 3 output neurons are used in this suggested system. The output neurons are classified according to acceleration sensitivity zone, velocity zones, or displacement zones. The sites are classified by their vulnerability index values using binary coding: [1 0 0] for the acceleration sensitive zone, [0 1 0] for the velocity sensitive zone, and [0 0 1] for the displacement sensitive zone

Two dimensional determination of peak acceleration distributions during earthquakes

© Published under licence by IOP Publishing Ltd.In this study, the two dimensional peak acceleration values between the bedrock and the ground surface is examined by using the soil bedrock model formed for a profile in the Bomova Plain in the east of Izmir Bay. For this purpose, two dimensional soil bedrock model was created for profile using microgravity and Extended Spatial Autocorrelation (ESPAC) method results. The S-wave velocities obtained from the ESPAC method were used to determine the density for microgravity modelling. After the density determination process, microgravity data on the profile were obtained by forward Talwani modelling. The obtained model is divided with gridding the data at regular intervals. In the study area, a recorded earthquake data used for deconvolution by soil-bedrock model known strong ground motion station BYN, and then transfer to the interface of bedrock in addition to obtain required bedrock data. Then, the calculated site- specific bedrock earthquake data was transferred from the bedrock surface of the grid model to the soil surface and layer boundaries. By combining these peak acceleration values for the profile, the two dimensional peak acceleration distribution for the profile was calculated

VULNERABILITY INDEX ASSESSMENT USING NEURAL NETWORKS (VIANN): A CASE STUDY OF NICOSIA, CYPRUS

Many scholars have used microtremor applications to evaluate the vulnerability index. In order to reach fast and reliable results, microtremor measurement is preferred as it is a cost-effective method. In this paper, the vulnerability index will be reviewed by utilization of microtremor measurement results in Nicosia city. 100 measurement stations have been used to collect microtremor data and the data were analysed by using Nakamura’s method. The value of vulnerability index (Kg) has been evaluated by using the fundamental frequency and amplification factor. The results obtained by the artificial neural network (ANN) will be compared with microtremor measurements. Vulnerability Index Assessment using Neural Networks (VIANN) is a backpropagation neural network, which uses the original input microtremor Horizontal Vertical Spectrum Ratio (HVSR) spectrum set. A 3-layer back propagation neural network which contains 4096 input, 28 hidden and 3 output neurons are used in this suggested system. The output neurons are classified according to acceleration sensitivity zone, velocity zones, or displacement zones. The sites are classified by their vulnerability index values using binary coding: [1 0 0] for the acceleration sensitive zone, [0 1 0] for the velocity sensitive zone, and [0 0 1] for the displacement sensitive zone

A Truncating Mutation in SERPINB6 Is Associated with Autosomal-Recessive Nonsyndromic Sensorineural Hearing Loss

More than 270 million people worldwide have hearing loss that affects normal communication. Although astonishing progress has been made in the identification of more than 50 genes for deafness during the past decade, the majority of deafness genes are yet to be identified. In this study, we mapped a previously unknown autosomal-recessive nonsyndromic sensorineural hearing loss locus (DFNB91) to chromosome 6p25 in a consanguineous Turkish family. The degree of hearing loss was moderate to severe in affected individuals. We subsequently identified a nonsense mutation (p.E245X) in SERPINB6, which is located within the linkage interval for DFNB91 and encodes for an intracellular protease inhibitor. The p.E245X mutation cosegregated in the family as a completely penetrant autosomal-recessive trait and was absent in 300 Turkish controls. The mRNA expression of SERPINB6 was reduced and production of protein was absent in the peripheral leukocytes of homozygotes, suggesting that the hearing loss is due to loss of function of SERPINB6. We also demonstrated that SERPINB6 was expressed primarily in the inner ear hair cells. We propose that SERPINB6 plays an important role in the inner ear in the protection against leakage of lysosomal content during stress and that loss of this protection results in cell death and sensorineural hearing loss

Kültürel Mirasın İzinde Ankara

Kentlerin belleği vardır. Sokakların, meydanların, binaların, kaldırımların, parkların, taşların ve ağaçların belleği... Cansız bina yığınından ve koşturan insanlardan ibaret gibi görünen kentlerin belleği... Elinizde tuttuğunuz bu kitap, o belleğin taşıyıcısı ve sürdürücüsü olan herkese ve herşeye adanmıştır. Bizi büyüten güzel Ankara'ya vefa borcumuzu ödeme çabasıdır bu derlem. Unutmaya ve unutturmaya karşı direnç, ayrıntılara ve belleğe bir övgüdür... Romanların, şiirlerin, mektupların, binaların, toprağın, heykellerin, yolların ve o yollarda yürüyenlerin içinde sakladığı, unutmadığı her ayrıntıya bir güzellemedir. Bin yılların kentine, Cumhuriyet'in başkentine sevgiyle kaleme alınmış bilimsel yazılardan ve tanıklıklardan meydana gelmiştir. Hacettepe Üniversitesi Tarihi ve Kültürel Mirası Araştırma Merkezi çatısı altında, Ankara'nın belleğine katkıda bulunmaktan mutluluk ve gurur duyuyoruz. Bu derlemin oluşmasına katkıda bulunan yazarlarımıza içtenlikle teşekkür ederiz