scholarly journals Cigarette smoking and waiting time to pregnancy: results of a pilot study

Medicina ◽  
2007 ◽  
Vol 43 (12) ◽  
pp. 959 ◽  
Author(s):  
Birutė Žilaitienė ◽  
Marius Diržauskas ◽  
Romualdas Preikša ◽  
Valentinas Matulevičius
Keyword(s):  
Pilot Study ◽  
Cigarette Smoking ◽  
Waiting Time ◽  
Medical Records ◽  
Contraceptive Use ◽  
Time To Pregnancy ◽  
Mean Waiting Time ◽  
Human Reproductive ◽  
The Mean ◽  
Paternal Smoking

Waiting time to pregnancy is an important characteristic of human reproductive health, which has not been investigated in Lithuania until now. Data on waiting time to pregnancy have been collected from medical records of 111 women admitted to the Department of Obstetrics, Klaipėda Hospital. Seven women in whom pregnancy was the result of infertility treatment were excluded from the analysis, and the rest 104 cases were analyzed. We evaluated waiting time to pregnancy in respect to the age of couples, contraceptive use, cigarette smoking of both partners, and some other features of obstetric history. The mean waiting time to pregnancy in the cohort was 5.21±7.03 months. If both partners smoked, the mean waiting time to pregnancy was significantly longer than in nonsmoking couples (7.68±9.41 vs. 4.30±5.73, P<0.05). Risk to have waiting time to pregnancy longer than 6 months was significantly higher if both partners smoked as compared to nonsmoking couples (OR 3.32, 95% CI 1.07–10.30, P=0.03), while paternal smoking and smoking of any partner did not increase this risk significantly. The other possible factors – age, living place (rural or city), previous contraceptive use, regularity of menstrual cycle, and frequency of intercourse – did not influence waiting time to pregnancy significantly. Results obtained from this pilot study enable us to plan and implement a larger-scale study of waiting time to pregnancy in Lithuanian population.

Results of Traumatic Canalicular Laceration Repair Performed by Trainees in a Tertiary Eye Care Center

2020 ◽  
Vol 103 (5) ◽  
pp. 492-496
Keyword(s):  
Success Rate ◽  
Waiting Time ◽  
Medical Records ◽  
Care Center ◽  
Common Condition ◽  
Eye Care ◽  
Post Operative Period ◽  
Mean Waiting Time ◽  
The Mean

Background: Lacrimal canalicular laceration is a common condition in emergency medicine. Many eyelid lacerations involve the lacrimal canalicular system. In the authors’ institution, nearly all cases of lacrimal canalicular laceration are treated by ophthalmology trainees, and are followed-up periodically in the post-operative period. Objective: To analyze the success rate of the cases of lacrimal canalicular laceration treated by trainees in a tertiary eye care center in Thailand. Materials and Methods: Thirty patients with canalicular laceration were treated between January 2005 and December 2017. Data gathered from ICD10, operation records, and in-patient and out-patient medical records were retrospectively analyzed. Results: There were 20 males (66%) and 10 females (34%) with a mean age of 30.86 years (range 1 to 78 years). Seventy percent of patients presented outside of normal office hours. Most injuries were caused by non-vehicular accidents. Twentyfive of 30 cases (83.33%) were repaired by trainees, and 63% of cases were repaired after office hours. The mean waiting time from presentation to repair was 9 hours 56 minutes (range: 3 hours 35 minutes to 22 hours 10 minutes). The follow-up rate at six months postoperative was 53.33%. The success rate of repairs performed by trainees was 93.75%. Conclusion: The success rate of lacrimal canalicular laceration repair performed by trainees was very satisfactory. The average waiting time for surgery was acceptable. Only half of the patients were followed-up for six months post-operative. Improvement is required in the recording of pertinent details in the medical records, which is an important issue to train the trainees. Keywords: Tear canaliculus, Lacrimal canalicular laceration, Repair, Residents, Trainees, Oculoplastic surgeon

scholarly journals Estimation of the mean waiting time of a customer subject to balking: a simulation study

2007 ◽  
Vol 19 (1) ◽  
pp. 63-63
Author(s):  
Jaejin Jang ◽  
Jaewoo Chung ◽  
Jungdae Suh ◽  
Jongtae Rhee
Keyword(s):  
Waiting Time ◽  
Simulation Study ◽  
Mean Waiting Time ◽  
The Mean

scholarly journals Waiting Time Problems for Patterns in a Sequence of Multi-State Trials

Mathematics ◽  
2020 ◽  
Vol 8 (11) ◽  
pp. 1893
Author(s):  
Bara Kim ◽  
Jeongsim Kim ◽  
Jerim Kim
Keyword(s):  
Waiting Time ◽  
Time Distribution ◽  
Probability Generating Function ◽  
Finite Collection ◽  
Numerical Inversion ◽  
Analytic Method ◽  
Waiting Time Distribution ◽  
Mean Waiting Time ◽  
The Matrix ◽  
The Mean

In this paper, we investigate waiting time problems for a finite collection of patterns in a sequence of independent multi-state trials. By constructing a finite GI/M/1-type Markov chain with a disaster and then using the matrix analytic method, we can obtain the probability generating function of the waiting time. From this, we can obtain the stopping probabilities and the mean waiting time, but it also enables us to compute the waiting time distribution by a numerical inversion.

scholarly journals Processor-sharing and random-service queues with semi-Markovian arrivals

2005 ◽  
Vol 42 (02) ◽  
pp. 478-490
Author(s):  
De-An Wu ◽  
Hideaki Takagi
Keyword(s):  
Waiting Time ◽  
Sojourn Time ◽  
Time Distribution ◽  
Arrival Process ◽  
Interarrival Time ◽  
Size Distributions ◽  
Processor Sharing ◽  
Queue Size ◽  
Mean Waiting Time ◽  
The Mean

We consider single-server queues with exponentially distributed service times, in which the arrival process is governed by a semi-Markov process (SMP). Two service disciplines, processor sharing (PS) and random service (RS), are investigated. We note that the sojourn time distribution of a type-lcustomer who, upon his arrival, meetskcustomers already present in the SMP/M/1/PS queue is identical to the waiting time distribution of a type-lcustomer who, upon his arrival, meetsk+1 customers already present in the SMP/M/1/RS queue. Two sets of system equations, one for the joint transform of the sojourn time and queue size distributions in the SMP/M/1/PS queue, and the other for the joint transform of the waiting time and queue size distributions in the SMP/M/1/RS queue, are derived. Using these equations, the mean sojourn time in the SMP/M/1/PS queue and the mean waiting time in the SMP/M/1/RS queue are obtained. We also consider a special case of the SMP in which the interarrival time distribution is determined only by the type of the customer who has most recently arrived. Numerical examples are also presented.

scholarly journals The mean waiting time to a repetition

1983 ◽  
Vol 15 (01) ◽  
pp. 216-218
Author(s):  
Gunnar Blom
Keyword(s):  
Waiting Time ◽  
Mild Condition ◽  
Random Variables ◽  
Stationary Sequence ◽  
Mean Waiting Time ◽  
The Mean

Let X 1, X2, · ·· be a stationary sequence of random variables and E 1 , E 2 , · ··, EN mutually exclusive events defined on k consecutive X's such that the probabilities of the events have the sum unity. In the sequence E j1 , E j2 , · ·· generated by the X's, the mean waiting time from an event, say E j1 , to a repetition of that event is equal to N (under a mild condition of ergodicity). Applications are given.

How many random digits are required until given sequences are obtained?

1982 ◽  
Vol 19 (03) ◽  
pp. 518-531 ◽  
Author(s):  
Gunnar Blom ◽  
Daniel Thorburn
Keyword(s):  
Generating Function ◽  
Waiting Time ◽  
Probability Generating Function ◽  
Recursive Formula ◽  
Fixed Number ◽  
Digit Sequence ◽  
Mean Waiting Time ◽  
The Mean ◽  
The Given

Random digits are collected one at a time until a given k -digit sequence is obtained, or, more generally, until one of several k -digit sequences is obtained. In the former case, a recursive formula is given, which determines the distribution of the waiting time until the sequence is obtained and leads to an expression for the probability generating function. In the latter case, the mean waiting time is given until one of the given sequences is obtained, or, more generally, until a fixed number of sequences have been obtained, either different sequences or not necessarily different ones. Several results are known before, but the methods of proof seem to be new.

Limit theorem for continuous-time random walks with two time scales

2004 ◽  
Vol 41 (2) ◽  
pp. 455-466 ◽  
Author(s):  
Peter Becker-Kern ◽  
Mark M. Meerschaert ◽  
Hans-Peter Scheffler
Keyword(s):  
Random Walks ◽  
Time Scales ◽  
Waiting Time ◽  
Continuous Time ◽  
Fractional Partial Differential Equations ◽  
Finite Dimensional ◽  
Mean Waiting Time ◽  
Continuous Time Random Walks ◽  
Random Jumps ◽  
The Mean

Continuous-time random walks incorporate a random waiting time between random jumps. They are used in physics to model particle motion. A physically realistic rescaling uses two different time scales for the mean waiting time and the deviation from the mean. This paper derives the scaling limits for such processes. These limit processes are governed by fractional partial differential equations that may be useful in physics. A transfer theorem for weak convergence of finite-dimensional distributions of stochastic processes is also obtained.

A Unified Theory for the Glass Transition Dynamics and its Singularities

1996 ◽  
Vol 455 ◽  
Author(s):  
T. Odagaki ◽  
J. Matsui ◽  
M. Fujisaki ◽  
M. Higuchi
Keyword(s):  
Glass Transition ◽  
Waiting Time ◽  
Excess Entropy ◽  
Freezing Process ◽  
Slow Process ◽  
Mean Waiting Time ◽  
Long Time ◽  
Power Law Function ◽  
Unified View ◽  
The Mean

ABSTRACTVitrification is a gradual freezing process of supercooled liquids, during which a slow process is separated from the fast diffusive and microscopic motions. The slow process is identified as a non-trapped jump motion and can be characterized by the waiting time distribution (WTD) of the elementary relaxation process. We first show that the WTD can be expressed as a power law function in the long time limit in general with modest assumptions. Defining the glass transition temperature by vanishing diffusivity or the divergence of the mean waiting time, we relate the exponent to the Adam-Gibbs parameter Tsc(T) where T is the temperature and sc(T) is the excess entropy. We also show that the divergence of the fluctuation of WTD leads to a cross over in the non-Gaussianity and present a unified view of the dynamics in the vitrification process.

Limit theorem for continuous-time random walks with two time scales

2004 ◽  
Vol 41 (02) ◽  
pp. 455-466 ◽  
Author(s):  
Peter Becker-Kern ◽  
Mark M. Meerschaert ◽  
Hans-Peter Scheffler
Keyword(s):  
Random Walks ◽  
Time Scales ◽  
Waiting Time ◽  
Continuous Time ◽  
Fractional Partial Differential Equations ◽  
Finite Dimensional ◽  
Mean Waiting Time ◽  
Continuous Time Random Walks ◽  
Random Jumps ◽  
The Mean

Continuous-time random walks incorporate a random waiting time between random jumps. They are used in physics to model particle motion. A physically realistic rescaling uses two different time scales for the mean waiting time and the deviation from the mean. This paper derives the scaling limits for such processes. These limit processes are governed by fractional partial differential equations that may be useful in physics. A transfer theorem for weak convergence of finite-dimensional distributions of stochastic processes is also obtained.

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