Predicting vehicular travel times by modeling heterogeneous influences between arterial roads

Predicting travel times of vehicles in urban settings is a useful and tangible quantity of interest in the context of intelligent transportation systems. We address the problem of travel time prediction in arterial roads using data sampled from probe vehicles. There is only a limited literature on methods using data input from probe vehicles. The spatio-temporal dependencies captured by existing data driven approaches are either too detailed or very simplistic. We strike a balance of the existing data driven approaches to account for varying degrees of influence a given road may experience from its neighbors, while controlling the number of parameters to be learnt. Specifically, we use a NoisyOR conditional probability distribution (CPD) in conjunction with a dynamic bayesian network (DBN) to model state transitions of various roads. We propose an efficient algorithm to learn model parameters. We propose an algorithm for predicting travel times on trips of arbitrary durations. Using synthetic and real world data traces we demonstrate the superior performance of the proposed method under different traffic conditions.Comment: 13 pages, conferenc

Corneal Dermoid

A 20 years old boy presented with left corneal mass. The mass involved entire cornea extending to the sclera. The mass had a skin like surface and protruded outside the palpebral aperture. The eye with the mass was excised .The histopathology report confirmed the diagnosis of corneal dermoid. This late presentation of huge corneal dermoid extending to sclera is first such report in the literature

Over the counter ophthalmic drug misuse, are we aware?

Aim: To investigate the misuse of ‘over the counter’ ophthalmic medications in our city. Method: Responses of a structured questionnaire covering various aspects of over the counter drug use was obtained from pharmacy workers in and around our city. Results: Eighty nine pharmacy workers took part in this cross–sectional study. An average number of seven patients per day with ophthalmic complaints were seen by the pharmacy workers. Dispensing over the counter was practiced by 89.9% of the pharmacists. The most common complaint of the patients visiting the pharmacy, was redness and itching (86.5%). Antibiotics (96.6%) were the most common eye drops dispensed over the counter, followed by steroids (55.1%), decongestants (54.1%), antibiotic-steroid combination eye drops (29.2%) and lubricants (16.8%). Awareness regarding complications of steroid use was seen in 40.6% of pharmacists. 6.7% pharmacists had seen patients with complications following use of over the counter medications. In our study, majority of the eye drops dispensed were prescription drugs. Conclusion: Availability of prescription eye drops over the counter is an immense public threat. Educating the pharmacist and the population can decrease ocular morbidity. Research into methods to effectively deal with over-the-counter drug misuse is required and law can be enforced based on the findings

Stability, Causality, and Passivity in Electrical Interconnect Models

Modern packaging design requires extensive signal integrity simulations in order to assess the electrical performance of the system. The feasibility of such simulations is granted only when accurate and efficient models are available for all system parts and components having a significant influence on the signals. Unfortunately, model derivation is still a challenging task, despite the extensive research that has been devoted to this topic. In fact, it is a common experience that modeling or simulation tasks sometimes fail, often without a clear understanding of the main reason. This paper presents the fundamental properties of causality, stability, and passivity that electrical interconnect models must satisfy in order to be physically consistent. All basic definitions are reviewed in time domain, Laplace domain, and frequency domain, and all significant interrelations between these properties are outlined. This background material is used to interpret several common situations where either model derivation or model use in a computer-aided design environment fails dramatically.We show that the root cause for these difficulties can always be traced back to the lack of stability, causality, or passivity in the data providing the structure characterization and/or in the model itsel