Murtaza Haider

Bio: Murtaza Haider is an academic researcher from Ryerson University. The author has contributed to research in topics: Mode choice & Poison control. The author has an hindex of 14, co-authored 35 publications receiving 3131 citations. Previous affiliations of Murtaza Haider include University of Toronto & Creighton University.

Effects of transportation infrastructure and location on residential real estate values: application of spatial autoregressive techniques

Abstract: Proximity to transportation infrastructure (highways and public transit) influences residential real estate values. Housing values also are influenced by propinquity to a shopping facility or a recreational amenity. Spatial autoregressive (SAR) models were used to estimate the impact of locational elements on the price of residential properties sold during 1995 in the Greater Toronto Area. A large data set consisting of 27,400 freehold sales was used in the study. Moran's I was estimated to determine the effects of spatial autocorrelation that existed in housing values. SAR models, using a combination of locational influences, neighborhood characteristics, and structural attributes, explained 83 percent variance in housing values. Using the "comparable sales approach," a spatiotemporal lag variable was estimated for every property in the database. This research discovered that SAR models offered a better fit than nonspatial models. This study also discovered that in the presence of other explanatory varia...

An analysis of public bus transit performance in Indian cities

Abstract: Maintaining and enhancing public transit service in Indian cities is important, to meet rapidly growing mass mobility needs, and curb personal motor vehicle activity and its impacts at low cost. Indian cities rely predominantly on buses for public transport, and are likely to continue to do so for years. However, the public bus transit service is inadequate, and unaffordable for the urban poor. The paper explores the factors that contribute to and affect efforts to improve this situation, based on an analysis of the financial and operational performance of the public bus transit service in the four metropolitan centres and four secondary cities during the 1990s. Overall, there were persistent losses, owing to increasing input costs and declining productivity. The losses occurred despite rapidly increasing fares, and ridership declined. The situation, and the ability to address it, is worse in the secondary cities than the metropolitan centres. We suggest a disaggregated approach based on the needs and motivations of different groups in relation to public transit, along with improved operating conditions and policies to internalize costs of personal motor vehicle use, to address the challenge of providing financially viable and affordable public bus transit service.

Laparoscopic re-operation for failed Heller myotomy

Abstract: SUMMARY. Laparoscopic Heller myotomy for achalasia has a 10–20% failure rate and may require re-operation to control persistent or recurrent symptoms. We report follow-up of 15 patients who underwent laparoscopic re-operation for failed Heller myotomy. Between 1993 and 2004, 15 patients underwent laparoscopic re-operation for failed Heller myotomy at our center. The mean duration between procedures was 23 months. Follow-up was completed at a mean duration of 30 months in 14 patients (93%) via a telephone questionnaire. Our overall failure rate for primary surgery (n = 106) was 5.6%. The mechanisms of failure were incomplete myotomy (33%), myotomy fibrosis (27%), fundoplication disruption (13%), too tight fundoplication (7%) and a combination of myotomy fibrosis and incomplete myotomy (20%). Significant symptom improvement was observed with postoperative symptom resolution seen in 71% of patients with dysphagia, 89% for regurgitation, 58% for heartburn and 40% for chest pain. Fifty percent reported excellent results and 79% would recommend the procedure to a friend. Subsequent dilations were performed in four patients (29%). Two patients required conversion to open surgery (13%). Three patients (20%) failed the re-operation and required further revisional surgery. Complications included intraoperative perforation in three (none of which resulted in postoperative morbidity) and a pneumothorax in one patient. Prior endoscopic therapies (pneumatic dilation or Botulinum toxin) were not associated with poor results. Laparoscopic re-operation for failed Heller myotomy is feasible and results are encouraging.

Technique and follow-up of minimally invasive Heller myotomy for achalasia

Abstract: Laparoscopic Heller myotomy has been proven effective. Reliable predictive factors for outcome and the true benefit of the da Vinci robotic system, however, remain unknown. Seventy patients underwent laparoscopic Heller myotomy. The number of intraoperative perforations and the symptom-predictive value of postoperative esophagogram width measurement at the gastroesophageal junction were analyzed. The overall complication rate was 11%. Four patients experienced intraoperative perforation during the laparoscopic technique. No perforations were experienced with the da Vinci robotic system (n = 19). Of the total, 82% of patients had resolution of dysphagia, 91% of regurgitation, 91% of heartburn and 82% of chest pain. Immediate postoperative esophagogram gastroesophageal junction width demonstrated a positive predictive trend from 0 to 10 mm for dysphagia. Laparoscopic Heller myotomy is an effective treatment for achalasia. Immediate postoperative esophagogram gastroesophageal junction width measurement as a predictor for symptom resolution requires further study.

A New Convolutional Neural Network-Based Data-Driven Fault Diagnosis Method

Abstract: Fault diagnosis is vital in manufacturing system, since early detections on the emerging problem can save invaluable time and cost. With the development of smart manufacturing, the data-driven fault diagnosis becomes a hot topic. However, the traditional data-driven fault diagnosis methods rely on the features extracted by experts. The feature extraction process is an exhausted work and greatly impacts the final result. Deep learning (DL) provides an effective way to extract the features of raw data automatically. Convolutional neural network (CNN) is an effective DL method. In this study, a new CNN based on LeNet-5 is proposed for fault diagnosis. Through a conversion method converting signals into two-dimensional (2-D) images, the proposed method can extract the features of the converted 2-D images and eliminate the effect of handcrafted features. The proposed method which is tested on three famous datasets, including motor bearing dataset, self-priming centrifugal pump dataset, and axial piston hydraulic pump dataset, has achieved prediction accuracy of 99.79%, 99.481%, and 100%, respectively. The results have been compared with other DL and traditional methods, including adaptive deep CNN, sparse filter, deep belief network, and support vector machine. The comparisons show that the proposed CNN-based data-driven fault diagnosis method has achieved significant improvements.

Digital Twin and Big Data Towards Smart Manufacturing and Industry 4.0: 360 Degree Comparison

Abstract: With the advances in new-generation information technologies, especially big data and digital twin, smart manufacturing is becoming the focus of global manufacturing transformation and upgrading. Intelligence comes from data. Integrated analysis for the manufacturing big data is beneficial to all aspects of manufacturing. Besides, the digital twin paves a way for the cyber-physical integration of manufacturing, which is an important bottleneck to achieve smart manufacturing. In this paper, the big data and digital twin in manufacturing are reviewed, including their concept as well as their applications in product design, production planning, manufacturing, and predictive maintenance. On this basis, the similarities and differences between big data and digital twin are compared from the general and data perspectives. Since the big data and digital twin can be complementary, how they can be integrated to promote smart manufacturing are discussed.