Extended Reaction Rate Integral as Solutions of Some General Differential Equations

Here an extended form of the reaction rate probability integral, in the case of nonresonant thermonuclear reactions with the depleted tail and the right tail cut off, is considered. The reaction rate integral then can be looked upon as the inverse of the convolution of the Mellin transforms of Tsallis type statistics of nonextensive statistical mechanics and stretched exponential as well as that of superstatistics and stretched exponentials. The differential equations satisfied by the extended probability integrals are derived. The idea used is a novel one of evaluating the extended integrals in terms of some special functions and then by invoking the differential equations satisfied by these special functions. Some special cases of limiting situations are also discussed.Comment: 9 pages, LaTe

Energy Consumption Prediction with Big Data: Balancing Prediction Accuracy and Computational Resources

In recent years, advances in sensor technologies and expansion of smart meters have resulted in massive growth of energy data sets. These Big Data have created new opportunities for energy prediction, but at the same time, they impose new challenges for traditional technologies. On the other hand, new approaches for handling and processing these Big Data have emerged, such as MapReduce, Spark, Storm, and Oxdata H2O. This paper explores how findings from machine learning with Big Data can benefit energy consumption prediction. An approach based on local learning with support vector regression (SVR) is presented. Although local learning itself is not a novel concept, it has great potential in the Big Data domain because it reduces computational complexity. The local SVR approach presented here is compared to traditional SVR and to deep neural networks with an H2O machine learning platform for Big Data. Local SVR outperformed both SVR and H2O deep learning in terms of prediction accuracy and computation time. Especially significant was the reduction in training time; local SVR training was an order of magnitude faster than SVR or H2O deep learning

Energy Cost Forecasting for Event Venues

Electricity price, consumption, and demand forecasting has been a topic of research interest for a long time. The proliferation of smart meters has created new opportunities in energy prediction. This paper investigates energy cost forecasting in the context of entertainment event-organizing venues, which poses significant difficulty due to fluctuations in energy demand and wholesale electricity prices. The objective is to predict the overall cost of energy consumed during an entertainment event. Predictions are carried out separately for each event category and feature selection is used to select the most effective combination of event attributes for each category. Three machine learning approaches are considered: k-nearest neighbor (KNN) regression, support vector regression (SVR) and neural networks (NN). These approaches are evaluated on a case study involving a large event venue in Southern Ontario. In terms of prediction accuracy, KNN regression achieved the lowest average error. Error rates varied greatly among different event categories

Energy Forecasting for Event Venues: Big Data and Prediction Accuracy

Advances in sensor technologies and the proliferation of smart meters have resulted in an explosion of energy-related data sets. These Big Data have created opportunities for development of new energy services and a promise of better energy management and conservation. Sensor-based energy forecasting has been researched in the context of office buildings, schools, and residential buildings. This paper investigates sensor-based forecasting in the context of event-organizing venues, which present an especially difficult scenario due to large variations in consumption caused by the hosted events. Moreover, the significance of the data set size, specifically the impact of temporal granularity, on energy prediction accuracy is explored. Two machine-learning approaches, neural networks (NN) and support vector regression (SVR), were considered together with three data granularities: daily, hourly, and 15 minutes. The approach has been applied to a large entertainment venue located in Ontario, Canada. Daily data intervals resulted in higher consumption prediction accuracy than hourly or 15-min readings, which can be explained by the inability of the hourly and 15-min models to capture random variations. With daily data, the NN model achieved better accuracy than the SVR; however, with hourly and 15-min data, there was no definitive dominance of one approach over another. Accuracy of daily peak demand prediction was significantly higher than accuracy of consumption prediction

Energy Slices: Benchmarking with Time Slicing

Benchmarking makes it possible to identify low-performing buildings, establishes a baseline for measuring performance improvements, enables setting of energy conservation targets, and encourages energy savings by creating a competitive environment. Statistical approaches evaluate building energy efficiency by comparing measured energy consumption to other similar buildings typically using annual measurements. However, it is important to consider different time periods in benchmarking because of differences in their consumption patterns. For example, an office can be efficient during the night, but inefficient during operating hours due to occupants’ wasteful behavior. Moreover, benchmarking studies often use a single regression model for different building categories. Selecting the regression model based on actual data would ensure that the model fits the data well. Consequently, this paper proposes Energy Slices, an energy benchmarking approach with time slicing for existing buildings. Time slicing enables separation of time periods with different consumption patterns. The regression model suited for the specific scenario is selected using cross validation, which ensures that the model performs well on previously unseen data. The evaluation is carried out on a case study involving two sports arenas; event energy efficiency is benchmarked to identify low-performing events. The case study demonstrates the Energy Slice procedure and shows the importance of model selection

Male Infertility and Future Cardiometabolic Health: Does the Association Vary by Sociodemographic Factors?

Objective: To determine whether the association between male infertility and incident cardiometabolic disease is modified by socioeconomics, race, or geographic region. / Materials and Method: Retrospective review of data from insurance claims from Optum's de-identified Clinformatics Data Mart Database. Subjects were men, 18-50 years old, with an associated diagnosis of infertility in the United States between 2003 and 2016. Analytical sample were men captured by the Optum's de-identified Clinformatics Data Mart Database with an associated diagnosis of infertility. Men were classified as either infertile, or not, based on diagnosis or procedural codes. Cardiometabolic health outcomes were then assessed using current procedural terminology codes for diabetes, hypertension, hyperlipidemia, and heart disease. Confounding factors were controlled for such as race, education, socioecomonic status, and region. The main outcomes were development of diabetes, hypertension, hyperlipidemia, and heart disease. / Results: A total of 76,343 males were diagnosed with male factor infertility, 60,072 males who underwent fertility testing, and 183,742 males that underwent vasectomy (control population). For all men, infertile men had a higher risk of incident hypertension, diabetes, hyperlipidemia, and heart disease when compared to those undergoing vasectomy. Identical associations were found across all education, income, racial, and geographic strata. / Conclusion: Our study suggests that men with infertility have a higher risk of cardiometabolic disease in the years following a fertility evaluation regardless of race, region, or socioeconomic status

Biology of advanced uveal melanoma and next steps for clinical therapeutics

Uveal melanoma is the most common intraocular malignancy although it is a rare subset of all melanomas. Uveal melanoma has distinct biology relative to cutaneous melanoma, with widely divergent patient outcomes. Patients diagnosed with a primary uveal melanoma can be stratified for risk of metastasis by cytogenetics or gene expression profiling, with approximately half of patients developing metastatic disease, predominately hepatic in location, over a 15-yr period. Historically, no systemic therapy has been associated with a clear clinical benefit for patients with advanced disease, and median survival remains poor. Here, as a joint effort between the Melanoma Research Foundation's ocular melanoma initiative, CURE OM and the National Cancer Institute, the current understanding of the molecular and immunobiology of uveal melanoma is reviewed, and on-going laboratory research into the disease is highlighted. Finally, recent investigations relevant to clinical management via targeted and immunotherpies are reviewed, and next steps in the development of clinical therapeutics are discussed

Structural Health Monitoring for Performance Assessment of Bridges under Flooding and Seismic Actions

Bridges can be subjected to damaging environmental actions due to flooding and seismic hazards. Flood actions that result in scour are a leading cause of bridge failure, while seismic actions that induce lateral forces may lead to high ductility demand that exceeds pier capacity. When combined, seismic actions and scour can lead to effects that depend on the governing scour condition affecting a bridge. Loss of stiffness under scour can reduce the ductility capacity of a bridge but can also lead to an increase in flexibility that may reduce seismic inertial forces. Conversely, increased flexibility can lead to deck collapse due to support loss, so there exists some uncertainty about the combined effect of both phenomena. A necessary step towards the performance assessment of bridges under flooding and seismic actions is to calibrate numerical models that can reproduce structural responses under different actions. A further step is verifying the achievement of performance goals defined by codes. Structural health monitoring (SHM) techniques allow the computation of performance parameters that are useful for calibrating numerical models and performing direct checks of performance goal compliance. In this paper, various strategies employed to monitor bridge health against scour and seismic actions are discussed, with a particular focus on vibration-based damage identification methods

Aspiration of parenteral nutrition – a previously unreported complication of central venous access in an infant: a case report

Introduction: The insertion of percutaneous central venous catheters is a common procedure in neonatal intensive care nurseries. Placement of the catheter tip in a large central vein is most desirable. Occasionally, due to difficult venous access, catheter tips are left in places that are less than ideal. Case presentation: A female infant with a complicated gastroschisis developed signs of short bowel syndrome post surgery. She was treated with a combination of parenteral nutrition and enteral feeds. A central venous line was inserted through a scalp vein. The tip was noted to be in a vessel at the level of the mandible. She subsequently became unwell with large milky pharyngeal aspirates and episodes of bradycardia. Chest radiography revealed aspiration. The central venous line was removed because of presumed extravasation. This is the first reported case of parenteral nutrition extravasation into the pharynx causing aspiration in an infant. Conclusion: This complication may have been prevented by recognising that the tip of the catheter was not correctly placed. When catheters are in unusual positions it may be useful to obtain a second radiograph from a different angle or an ultrasound scan to confirm the positioning of the catheter tip

Aspiration of parenteral nutrition – a previously unreported complication of central venous access in an infant: a case report

Introduction: The insertion of percutaneous central venous catheters is a common procedure in neonatal intensive care nurseries. Placement of the catheter tip in a large central vein is most desirable. Occasionally, due to difficult venous access, catheter tips are left in places that are less than ideal. Case presentation: A female infant with a complicated gastroschisis developed signs of short bowel syndrome post surgery. She was treated with a combination of parenteral nutrition and enteral feeds. A central venous line was inserted through a scalp vein. The tip was noted to be in a vessel at the level of the mandible. She subsequently became unwell with large milky pharyngeal aspirates and episodes of bradycardia. Chest radiography revealed aspiration. The central venous line was removed because of presumed extravasation. This is the first reported case of parenteral nutrition extravasation into the pharynx causing aspiration in an infant. Conclusion: This complication may have been prevented by recognising that the tip of the catheter was not correctly placed. When catheters are in unusual positions it may be useful to obtain a second radiograph from a different angle or an ultrasound scan to confirm the positioning of the catheter tip