On the Distribution of Traffic Volumes in the Internet and its Implication

Getting good statistical models of traffic on network links is a well-known, often-studied problem. A lot of attention has been given to correlation patterns and flow duration. The distribution of the amount of traffic per unit time is an equally important but less studied problem. We study a large number of traffic traces from many different networks including academic, commercial and residential networks using state-of-the-art statistical techniques. We show that the log-normal distribution is a better fit than the Gaussian distribution commonly claimed in the literature. We also investigate a second heavy-tailed distribution (the Weibull) and show that its performance is better than Gaussian but worse than log-normal. We examine anomalous traces which are a poor fit for all distributions tried and show that this is often due to traffic outages or links that hit maximum capacity. We demonstrate the utility of the log-normal distribution in two contexts: predicting the proportion of time traffic will exceed a given level (for service level agreement or link capacity estimation) and predicting 95th percentile pricing. We also show the log-normal distribution is a better predictor than Gaussian or Weibull distributions

Menopause Awareness, Symptoms Assessment and Menqol Among Bahrain Women

Menopause is a normal physiological process of the permanent cessation of ovarian hormone reproduction, thereby offending the regular menstrual cycle. Critical period in women’s life occurs 40 to 60 years. Frequently reported symptoms are physiological disturbances, psychological complaints including mood swings and& other changes that may impair the overall quality of life. Menopausal symptoms significantly reduce the Quality of Life (QOL) and further worsens with more severity of the condition. The aim of this study was to assess the menopausal awareness and related symptoms that affects the quality of life. Methods: A cross-sectional community survey design was adopted among 128 women through convenience sampling by using Menopause awareness scale and MenQol. Results: The domain-wise prevalence of symptoms score on vasomotor was 51.5%(66), psychosocial 35.2%(45), 44.5%(57), sexual 25.8%(33) and others 44.5%(57). Overall score was 40.6%(52). In relation to menopause awareness, low 7.8 % (10), Moderate 82.0% (105) and high 10.2%(13).Conclusion: Menopause awareness programs need to be initiated at the community level for better health and the Quality of Life. Keywords: Menopause, Bahraini women, Menopause related QOL, Awarenes

Unconventional Reservoir Characterization and Formation Evaluation: A Case Study of a Tight Sandstone Reservoir in West Africa

Unconventional reservoirs, including gas shales and tight gas sands, have gained prominence in the energy sector due to technological advancements and escalating energy demands. The oil industry is eagerly refining techniques to decipher these reservoirs, aiming to reduce data collection costs and uncertainties in reserve estimations. Characteristically, tight reservoirs exhibit low matrix porosity and ultra-low permeability, necessitating artificial stimulation for enhanced production. The efficacy of the stimulation hinges on the organic material distribution, the rock’s mechanical attributes, and the prevailing stress field. Comprehensive petrophysical analysis, integrating standard and specialized logs, core analyses, and dynamic data, is pivotal for a nuanced understanding of these reservoirs. This ensures a reduction in prediction uncertainties, with parameters like shale volume, porosity, and permeability being vital. This article delves into an intricate petrophysical evaluation of the Nene field, a West African unconventional reservoir. It underscores the geological intricacies of the field, the pivotal role of data acquisition, and introduces avant-garde methodologies for depth matching, rock typing, and the estimation of permeability. This research highlights the significance of unconventional reservoir exploration in today’s energy milieu, offering a granular understanding of the Nene field’s geological challenges and proffering a blueprint for analogous future endeavours in unconventional reservoirs

On the Distribution of Traffic Volumes in the Internet and its Implications

In this edition of the Voice, the College’s Career Planning Placement Service offers a variety or workshops include one on life planning. Wooster Chief of Security and Dr. Startzman of the campus wellness center, speak to students on the topic of rape and safety at the College. The Wooster Board of Trustees begins the process to select a new president of the College of Wooster. The Art Center offers classes on quilting, plants, printmaking, drawing, and other artistic mediums, to students for eight weeks. Additionally, an article discusses the, then up and coming, Bicentennial of the United States.https://openworks.wooster.edu/voice1971-1980/1131/thumbnail.jp

Athletes’ Relationships with Training Scale (ART)

The Athletes’ Relationships with Training Scale (ART)* is a self-report measure of unhealthy training behaviors and beliefs in athletes. The ART was designed for use by clinicians and athletic trainers to help identify athletes who are engaging in unhealthy training practices which could be associated with an eating disorder. The ART may also be helpful for tracking clinical outcomes in athletes with eating disorders who are receiving treatment. This record contains the 15-item ART as well as scoring instructions and guidelines for interpreting total scores

The Athletes\u27 Relationships with Training Scale (ART): A Self-Report Measure of Unhealthy Training Behaviors Associated with Eating Disorders

Objective: Several studies indicate that eating-disorder (ED) psychopathology is elevated in athletes compared to non-athletes. The assessment of excessive exercise among athletes is a challenge because, compared to non-athletes, athletes are required to train at higher intensities and for longer periods of time. However, individuals participating in competitive sports are still susceptible to unhealthy physical-activity patterns. Most ED assessments were developed and normed in non-athlete samples and, therefore, do not capture the nuances of athletes\u27 training experiences. The purpose of the current study was to develop and validate a clinically useful, self-report measure of unhealthy training behaviors and beliefs in athletes, the Athletes\u27 Relationships with Training Scale (ART). Method: The initial item pool was administered to N = 267 women collegiate athletes who were participating in an ED prevention program study and N = 65 women athletes who were in ED treatment. Results: Factor analyses indicated the ART had a four-factor structure. Factorial and construct validity of the ART were demonstrated. ART scores significantly predicted health care utilization and differed between athletes with an ED versus athletes without an ED. For athletes in ED treatment, ART scores significantly decreased from treatment admission to discharge. Discussion: The ART showed evidence of strong psychometric properties and clinical utility. The ART could be helpful for clinicians and athletic trainers to help gauge whether athletes are engaging in unhealthy training practices that may warrant clinical attention and for tracking clinical outcomes in athletes with EDs who are receiving treatment

Postoperative outcomes in oesophagectomy with trainee involvement

BACKGROUND: The complexity of oesophageal surgery and the significant risk of morbidity necessitates that oesophagectomy is predominantly performed by a consultant surgeon, or a senior trainee under their supervision. The aim of this study was to determine the impact of trainee involvement in oesophagectomy on postoperative outcomes in an international multicentre setting. METHODS: Data from the multicentre Oesophago-Gastric Anastomosis Study Group (OGAA) cohort study were analysed, which comprised prospectively collected data from patients undergoing oesophagectomy for oesophageal cancer between April 2018 and December 2018. Procedures were grouped by the level of trainee involvement, and univariable and multivariable analyses were performed to compare patient outcomes across groups. RESULTS: Of 2232 oesophagectomies from 137 centres in 41 countries, trainees were involved in 29.1 per cent of them (n = 650), performing only the abdominal phase in 230, only the chest and/or neck phases in 130, and all phases in 315 procedures. For procedures with a chest anastomosis, those with trainee involvement had similar 90-day mortality, complication and reoperation rates to consultant-performed oesophagectomies (P = 0.451, P = 0.318, and P = 0.382, respectively), while anastomotic leak rates were significantly lower in the trainee groups (P = 0.030). Procedures with a neck anastomosis had equivalent complication, anastomotic leak, and reoperation rates (P = 0.150, P = 0.430, and P = 0.632, respectively) in trainee-involved versus consultant-performed oesophagectomies, with significantly lower 90-day mortality in the trainee groups (P = 0.005). CONCLUSION: Trainee involvement was not found to be associated with significantly inferior postoperative outcomes for selected patients undergoing oesophagectomy. The results support continued supervised trainee involvement in oesophageal cancer surgery

التنقيب عن الاراء العربية باستخدام الانتولوجيا بالاعتماد على المستوى

With the rapid increase in the volume of Arabic reviews that use applications such as online review sites, blogs, forums, social networking, and so forth, comes at an increasing demand for Arabic opinion mining techniques. In Arabic language, researchs in this area is progressing at a very slow pace compared to that being carried out in English and other languages. In this thesis, we highlight two problems for Arabic opinion mining technique: firstly, when analyzing review having different features with diverse opinion strengths. It considers all features extracted from the reviews to be equally important in failing to determine the proper polarity of the review and makes the review’s sentiment classification less accurate. Secondly, the opinion summary for each feature doesn’t consider the sub-features that represented it and makes the featurebased summary is incomplete. This research presents a technique using ontology that work at feature level classification to classify Arabic user generated reviews by identifying the important features from the review based on level of these features on the ontology tree and to generate an opinion summary for each feature in the whole corpus by identifiying the opinion of its sub-feature terms in the ontology. To evaluate our work, we use public datasets which are hotels and books datasets. We use accuracy, recall, precision, f-measure metrics to evaluate the performance and compare the results with other supervised or unsupervised techniques. Also, subjective evaluation is used in our method to demonstrate the effectiveness of feature and opinion extraction process and summarization. We show that our method improves the performance compared with other opinion mining classification techniques, obtaining 78.83% f-measure in hotel domain and 79.18% in book domain. Furthermore, subjective evaluation shows the effectiveness of our method by obtaining an average f-measure of 84.62% in hotel domain and 86.31% in book domain

Internet Traffic Volumes Are Not Gaussian - They Are Log-Normal: An 18-Year Longitudinal Study With Implications for Modelling and Prediction

Getting good statistical models of traffic on network links is a well-known, often-studied problem. A lot of attention has been given to correlation patterns and flow duration. The distribution of the amount of traffic per unit time is an equally important but less studied problem. We study a large number of traffic traces from many different networks including academic, commercial and residential networks using state-of-the-art statistical techniques. We show that traffic obeys the log-normal distribution which is a better fit than the Gaussian distribution commonly claimed in the literature. We also investigate an alternative heavy-tailed distribution (the Weibull) and show that its performance is better than Gaussian but worse than log-normal. We examine anomalous traces which exhibit a poor fit for all distributions tried and show that this is often due to traffic outages or links that hit maximum capacity. We demonstrate that the data we look at is stationary if we consider samples of 15- minute long or even 1-hour long. This gives confidence that we can use the distributions for estimation and modelling purposes. We demonstrate the utility of our findings in two contexts: predicting that the proportion of time traffic will exceed a given level (for service level agreement or link capacity estimation) and predicting 95th percentile pricing. We also show that the log-normal distribution is a better predictor than Gaussian or Weibull distributions in both contexts