THE REVITALIZATION OF THE WEST PADEMAWU VILLAGE MARKET, BECOME A THEMATIC MARKET

The new village market that was built in Pademawu Barat Village, Pademawu District, is not yet fully able to compete with the traditional markets that were running earlier. The inability to compete, includes, the level of visits to the market is low, the income of market management is low, so that the people don't want to visit the village market. The Village Government prefers the important thing to build a physical market, rather than presentations on the future of the Village market. From these problems, researchers want to encourage the realization of thematic markets. As a result, the average community agreed that the market should be used as a thematic market. With the hope, the number of visitors can increase. The method in this research is to use a qualitative descriptive research method

Molecular Screening for Erythromycin Resistance Genes in Streptococcus pyogenes Isolated from Iraqi Patients with Tonsilo-Pharyngites

Streptococcus pyogenes is the leading cause of uncomplicated bacterial pharyngitis and tonsillitis commonly referred to as strep throat. Erythromycin is administered for patients allergy to penicillin. In this study, 125 throat swab samples were collected from children between 2-12 years old with tonsillo-pharyngitis attended to at the AL-Imammain AL-Kadhimain Medical City-Baghdad-Iraq and Pediatric Caring Hospital-Baghdad-Iraq from February 2014 to February 2015. Only 72 throat swab samples showed bacterial growth. The isolates were identified using Vitek 2 Compact system for Gram-Positive. Antibiotics susceptibility was examined using the BioMérieux Vitek2 compact system AST card. For direct molecular identification of S. pyogenes, 16S rRNA and 16S-23S rRNA gene amplification were used. Molecular screening for erythromycin resistance genes erm(A), erm(B) and mef(A) were done using PCR. The results of identification using Vitek2 GP show that 21 (29.2%) samples were S. pyogenes-positive while 51(70.8%) of samples were due to other causes of tonsillo-pharyngitis. The results of molecular identification of S. pyogenes strains using 16S rRNA and 16S-23S rRNA amplification showed that only four strains were positive for 16S-23S rRNA, while two strains out of four were also positive for 16S rRNA. According to the results of antibiotic sensitivity, there were seven strains resistant to erythromycin. The results of molecular screening for erythromycin resistant genes showed that all these resistant strains did not contain the resistant genes erm(A), erm(B) or mef (A). We conclude that, maybe there was a specific sequence variations in genes used for identification of S. pyogenes. Also, resistance to erythromycin could be attributed to causes other than the studied mutations, such as structural modification of erythromycin by phosphorylation, glycosylation or lactone ring cleavage by erythromycin esterase. Keywords: Streptococcus pyogenes, Molecular Identification, Erythromycin Resistance Gene

Enhanced Forensic Speaker Verification Using a Combination of DWT and MFCC Feature Warping in the Presence of Noise and Reverberation Conditions

© 2013 IEEE. Environmental noise and reverberation conditions severely degrade the performance of forensic speaker verification. Robust feature extraction plays an important role in improving forensic speaker verification performance. This paper investigates the effectiveness of combining features, mel frequency cepstral coefficients (MFCCs), and MFCC extracted from the discrete wavelet transform (DWT) of the speech, with and without feature warping for improving modern identity-vector (i-vector)-based speaker verification performance in the presence of noise and reverberation. The performance of i-vector speaker verification was evaluated using different feature extraction techniques: MFCC, feature-warped MFCC, DWT-MFCC, feature-warped DWT-MFCC, a fusion of DWT-MFCC and MFCC features, and fusion feature-warped DWT-MFCC and feature-warped MFCC features. We evaluated the performance of i-vector speaker verification using the Australian Forensic Voice Comparison and QUT-NOISE databases in the presence of noise, reverberation, and noisy and reverberation conditions. Our results indicate that the fusion of feature-warped DWT-MFCC and feature-warped MFCC is superior to other feature extraction techniques in the presence of environmental noise under the majority of signal-to-noise ratios (SNRs), reverberation, and noisy and reverberation conditions. At 0-dB SNR, the performance of the fusion of feature-warped DWT-MFCC and feature-warped MFCC approach achieves a reduction in average equal error rate of 21.33%, 20.00%, and 13.28% over feature-warped MFCC, respectively, in the presence of various types of environmental noises only, reverberation, and noisy and reverberation environments. The approach can be used for improving the performance of forensic speaker verification and it may be utilized for preparing legal evidence in court

Enhanced forensic speaker verification using multi-run ICA in the presence of environmental noise and reverberation conditions

© 2017 IEEE. The performance of forensic speaker verification degrades severely in the presence of high levels of environmental noise and reverberation conditions. Multiple channel speech enhancement algorithms are a possible solution to reduce the effect of environmental noise from the noisy speech signals. Although multiple speech enhancement algorithms such as multi-run independent component analysis (ICA) were used in previous studies to improve the performance of recognition in biosignal applications, the effectiveness of multi-run ICA algorithm to improve the performance of noisy forensic speaker verification under reverberation conditions has not been investigated yet. In this paper, the multi-run ICA algorithm is used to enhance the noisy speech signals by choosing the highest signal to interference ratio (SIR) of the mixing matrix from different mixing matrices generated by iterating the fast ICA algorithm for several times. Wavelet-based mel frequency cepstral coefficients (MFCCs) feature warping approach is applied to the enhanced speech signals to extract the robust features to environmental noise and reverberation conditions. The state-of-The-Art intermediate vector (i-vector) and probabilistic linear discriminant analysis (PLDA) are used as a classifier in our approach. Experimental results show that forensic speaker verification based on the multi-run ICA algorithm achieves significant improvements in equal error rate (EER) of 60.88%, 51.84%, 66.15% over the baseline noisy speaker verification when enrolment speech signals reverberated at 0.15 sec and the test speech signals were mixed with STREET, CAR and HOME noises respectively at-10 dB signal to noise ratio (SNR)

UINSA emas menuju world class university

Menapaki perjalanan sejarah yang dilalui, Universitas Islam Negeri Sunan Ampel Surabaya (UINSA) yang dulunya berbentuk Institut (IAIN) sedikit banyak telah berkiprah nyata dalam ikut serta mencerdaskan bangsa, terutama dalam mengembangkan dan menyebarkan ilmu keagamaan Islam di bumi Indonesia. Berbagai situasi dan kondisi telah dialami. Beragam tantangan dalam berbagai dimensinya juga terus dilalui. Demikian pula beragam mahasiswa dari sisi etnis, latar belakang sosial, dan lainnya diantarkan untuk meraih cita-cita mereka, sebagaimana pula banyak alumni yang dilahirkan dengan aneka profesi dan jabatan. Semua itu merupakan pengalaman berharga yang menjadikan salah satu perguruan tinggi Islam negeri tertua di Indonesia ini terus berupaya mengukuhkan diri sebagai lembaga pendidikan tinggi keagamaan dalam arti senyatanya. Untuk itu, pembenahan dalam berbagai aspeknya dilakukan secara berkelanjutan. Manajemen pengelolaan diperkuat sejalan dengan pengembangan sumber daya manusia. Sarana dan parasarana ditambah dan diupayakan disesuaikan dengan tuntutan dan keperluan pembelajaran dan pendidikan. Di atas semua itu, aspek akademik yang bertumpu pada tridharma perguruan tinggi diperkuat –baik dari sisi kualitas maupun dari sisi karakteristik –dari saat ke saat