nils-herrmann/clean_scirepeval huggingface dataset:
The biological activities, chemical stability, metabolism and delivery systems of quercetin: A review

Quercetin, one of the most well known flavonoids, has been included in human diet for a long history. The use of quercetin has been widely associated with a great number of health benefits, including antioxidant, anti inflammatory, antiviral and anticancer as well as the function to ease some cardiovascular diseases (i.e. heart disease, hypertension, and high blood cholesterol) However, poor water solubility, chemical instability and low bioavailability of quercetin greatly limit its applications. Utilization of delivery systems can improve its stability, efficacy and bioavailability. Scope and approach In this review, biological activities, chemical stability, metabolism and toxicity of quercetin and different delivery systems for quercetin were discussed.  Quercetin digested in human body (e.g. mouth, small intestine, liver, kidneys) undergoes glucuronidation, sulfation or methylation. During the food processing and storage, many factors such as heat, pH, metal ions, could affect the chemical stability (including oxidation and degradation) of quercetin. Utilization of delivery systems including lipid based carriers, nanoparticles, inclusion complexes, micelles and conjugates based encapsulation has the potential to improve both the stability and bioavailability and thus health benefits of quercetin. Each delivery system has its unique advantages and shortcomings, and the specific selection should be based on the application domains. Moreover, the exploration of natural food grade ingredients as main compositions of delivery systems for quercetin might be required in the future.
Soft computing techniques for congestion management in power systems
Venue: 2016 IEEE 1st International Conference on Power Electronics, Intelligent Control and Energy Systems (ICPEICES)

During last few eras, restructuring has surpassed all possible areas comprising the electric power supply industry. Restructuring has carried out significant advantages such that an electricity is nowadays a commodity. Competitive market also paved way for numerous participants. This leads to congestion in transmission lines. Furthermore, transmission network in open access has strengthened the congestion problem. Therefore, power system congestion management is most significant to the power industry. A review on different techniques is carried out in this paper to consolidate all the periodicals in congestion management.
Predicting Negative Emotions Based on Mobile Phone Usage Patterns: An Exploratory Study
Venue: JMIR research protocols

Prompt recognition and intervention of negative emotions is crucial for patients with depression. Mobile phones and mobile apps are suitable technologies that can be used to recognize negative emotions and intervene if necessary.  Mobile phone usage patterns can be associated with concurrent emotional states. The objective of this study is to adapt machine learning methods to analyze such patterns for the prediction of negative emotion.  We developed an Android based app to capture emotional states and mobile phone usage patterns, which included call logs (and use of apps) Visual analog scales (VASs) were used to report negative emotions in dimensions of depression, anxiety, and stress. In the system training phase, participants were requested to tag their emotions for 14 consecutive days. Five feature selection methods were used to determine individual usage patterns and four machine learning methods were tested. Finally, rank product scoring was used to select the best combination to construct the prediction model. In the system evaluation phase, participants were then requested to verify the predicted negative emotions for at least 5 days.  Out of 40 enrolled healthy participants, we analyzed data from 28 participants, including 30% (9/28) women with a mean (SD) age of 29.2 (5.1) years with sufficient emotion tags. The combination of time slots of 2 hours, greedy forward selection, and Naive Bayes method was chosen for the prediction model. We further validated the personalized models in 18 participants who performed at least 5 days of model evaluation. Overall, the predictive accuracy for negative emotions was 86.17%  We developed a system capable of predicting negative emotions based on mobile phone usage patterns. This system has potential for ecological momentary intervention (EMI) for depressive disorders by automatically recognizing negative emotions and providing people with preventive treatments before it escalates to clinical depression.
A hybrid vision system for detecting use of mobile phones while driving
Venue: 2016 International Joint Conference on Neural Networks (IJCNN)

In this work, a vision system has been developed using a frontal camera to monitor the driver, enabling to recognize the use of a cell phone while driving. It is estimated that 80% of car crashes and 65% of near collisions involved drivers who were inattentive in traffic for three seconds before the event. Five videos in real environments were generated to test the proposed system. The solution is a hybrid system and that uses a pattern recognition system (PR) for classification and a movement detection system (MD) for choosing the PR parameters at the end of each period of 3 seconds. The PR parameters are the threshold (frames identified as a cell phone use) and classifier selection. The classifiers are based on ANN, furthermore, the value of constants in neuron activation function and network training parameters were adopted with a genetic algorithm. Experimentally, it was established that when the movement indicates a possible use of the cell phone, the threshold 60% and an MLP/Gaussian classifier with seven neurons in intermediate layer are suitable; otherwise, a threshold of 85% and MLP/Gaussian with two neurons in intermediate layer for classification are used. The average accuracy achieved was 91.68% in real environment scenes.
Looking for Metacognition A Knowledge Taxonomy for Psychotherapeutic Games
Venue: GALA

Most of scientific literature on computer games aimed at offering or aiding in psychotherapy has little information on how the game exactly relates to the relatively recent development of the 'third wave' of behavioural psychotherapy, which includes metacognition. This paper first introduces metacognition and subsequently studies five cases of psychotherapeutic games (Personal Investigator, Treasure Hunt, Ricky and the Spider, Moodbot and SuperBetter) by looking at them through the lens of Blooms' Revised Taxonomy of Knowledge. The paper offers design recommendations for future (metacognitive) psychotherapeutic games.
Ageing, metabolism and cardiovascular disease
Venue: The Journal of physiology

Age is one of the major risk factors associated with cardiovascular disease (CVD) About one fifth of the world population will be aged 65 or older by 2030, with an exponential increase in CVD prevalence. It is well established that environmental factors (overnutrition, smoking, pollution, sedentary lifestyles) may lead to premature defects in mitochondrial functionality, insulin signalling, endothelial homeostasis and redox balance, fostering early senescent features. Over the last few years, molecular investigations have unveiled common signalling networks which may link the ageing process with deterioration of cardiovascular homeostasis and metabolic disturbances, namely insulin resistance. These different processes seem to be highly interconnected and their interplay may favour adverse vascular and cardiac phenotypes responsible for myocardial infarction, stroke and heart failure. In the present review, we carefully describe novel molecular cues underpinning ageing, metabolism and CVD. In particular, we describe a dynamic interplay between emerging pathways such as FOXOs, AMPK, SIRT1, p66Shc, JunD and NF kB. This overview will provide the background for attractive molecular targets to prevent age driven pathology in the vasculature and the heart.
Mathematically modelling the dynamics of cholesterol metabolism and ageing
Venue: Biosyst.

Cardiovascular disease (CVD) is the leading cause of morbidity and mortality in the UK. This condition becomes increasingly prevalent during ageing; 34.1% and 29.8% of males and females respectively, over 75 years of age have an underlying cardiovascular problem. The dysregulation of cholesterol metabolism is inextricably correlated with cardiovascular health and for this reason low density lipoprotein cholesterol (LDL C) and high density lipoprotein cholesterol (HDL C) are routinely used as biomarkers of CVD risk. The aim of this work was to use mathematical modelling to explore how cholesterol metabolism is affected by the ageing process. To do this we updated a previously published whole body mathematical model of cholesterol metabolism to include an additional 96 mechanisms that are fundamental to this biological system. Additional mechanisms were added to cholesterol absorption, cholesterol synthesis, reverse cholesterol transport (RCT) bile acid synthesis, and their enterohepatic circulation. The sensitivity of the model was explored by the use of both local and global parameter scans. In addition, acute cholesterol feeding was used to explore the effectiveness of the regulatory mechanisms which are responsible for maintaining whole body cholesterol balance. It was found that our model behaves as a hypo responder to cholesterol feeding, while both the hepatic and intestinal pools of cholesterol increased significantly. The model was also used to explore the effects of ageing in tandem with three different cholesterol ester transfer protein (CETP) genotypes. Ageing in the presence of an atheroprotective CETP genotype, conferring low CETP activity, resulted in a 0.6% increase in LDL C. In comparison, ageing with a genotype reflective of high CETP activity, resulted in a 1.6% increase in LDL C. Thus, the model has illustrated the importance of CETP genotypes such as I405V, and their potential role in healthy ageing.
A multivariate single index model for longitudinal data

Index measures are commonly used in medical research and clinical practice, primarily for quantification of health risks in individual subjects or patients. The utility of an index measure is ultimately contingent on its ability to predict health outcomes. Construction of medical indices has largely been based on heuristic arguments, although the acceptance of a new index typically requires objective validation, preferably with multiple outcomes. In this article, we propose an analytical tool for index development and validation. We use a multivariate single index model to ascertain the best functional form for risk index construction. Methodologically, the proposed model represents a multivariate extension of the traditional single index models. Such an extension is important because it assures that the resultant index simultaneously works for multiple outcomes. The model is developed in the general framework of longitudinal data analysis. We use penalized cubic splines to characterize the index components while leaving the other subject characteristics as additive components. The splines are estimated directly by penalizing nonlinear least squares, and we show that the model can be implemented using existing software. To illustrate, we examine the formation of an adiposity index for prediction of systolic and diastolic blood pressure in children. We assess the performance of the method through a simulation study.
Connecting Automatic Generation Control and Economic Dispatch From an Optimization View
Venue: IEEE Transactions on Control of Network Systems

Automatic generation control (AGC) regulates mechanical power generation in response to load changes through local measurements. Its main objective is to maintain system frequency and keep energy balanced within each control area in order to maintain the scheduled net interchanges between control areas. The scheduled interchanges as well as some other factors of AGC are determined at a slower time scale by considering a centralized economic dispatch (ED) problem among different generators. However, how to make AGC more economically efficient is less studied. In this paper, we study the connections between AGC and ED by reverse engineering AGC from an optimization view, and then we propose a distributed approach to slightly modify the conventional AGC to improve its economic efficiency by incorporating ED into the AGC automatically and dynamically.
Phosphorus recovery from municipal wastewater: An integrated comparative technological, environmental and economic assessment of P recovery technologies.
Venue: The Science of the total environment

Phosphorus (P) is an essential and limited resource. Municipal wastewater is a promising source of P via reuse and could be used to replace P derived from phosphate rocks. The agricultural use of sewage sludge is restricted by legislation or is not practiced in several European countries due to environmental risks posed by organic micropollutants and pathogens. Several technologies have been developed in recent years to recover wastewater P. However, these technologies target different P containing flows in wastewater treatment plants (effluent, digester supernatant, sewage sludge, and sewage sludge ash) use diverse engineering approaches and differ greatly with respect to P recycling rate, potential of removing or destroying pollutants, product quality, environmental impact and cost. This work compares 19 relevant P recovery technologies by considering their relationships with existing wastewater and sludge treatment systems. A combination of different methods, such as material flow analysis, damage units, reference soil method, annuity method, integrated cost calculation and a literature study on solubility, fertilizing effects and handling of recovered materials, is used to evaluate the different technologies with respect to technical, ecological and economic aspects. With regard to the manifold origins of data an uncertainty concept considering validity of data sources is applied. This analysis revealed that recovery from flows with dissolved P produces clean and plant available materials. These techniques may even be beneficial from economic and technical perspectives under specific circumstances. However, the recovery rates (a maximum of 25% relative to the wastewater treatment plant influent are relatively low. The approaches that recover P from sewage sludge apply complex technologies and generally achieve effective removal of heavy metals at moderate recovery rates ~40 50% relative to the WWTP input) and comparatively high costs. Sewage sludge ash is the most promising P source, with recovery rates of 60 90% relative to the wastewater P. The costs highly depend on the purity requirements of the recycled products but can be kept comparatively low, especially if synergies with existing industrial processes are exploited.
Design Of Water Resource Systems New Techniques For Relating Economic Objectives Engineering Analysis And Governmental Planning

Thank you for reading design of water resource systems new techniques for relating economic objectives engineering analysis and governmental planning. As you may know, people have search hundreds times for their favorite books like this design of water resource systems new techniques for relating economic objectives engineering analysis and governmental planning, but end up in malicious downloads. Rather than enjoying a good book with a cup of coffee in the afternoon, instead they are facing with some infectious bugs inside their desktop computer.
Engineering Economic Analysis

Thank you very much for downloading engineering economic analysis. Maybe you have knowledge that, people have look hundreds times for their chosen readings like this engineering economic analysis, but end up in infectious downloads. Rather than reading a good book with a cup of coffee in the afternoon, instead they cope with some infectious bugs inside their computer. engineering economic analysis is available in our digital library an online access to it is set as public so you can download it instantly. Our digital library hosts in multiple locations, allowing you to get the most less latency time to download any of our books like this one. Kindly say, the engineering economic analysis is universally compatible with any devices to read.
Online approximate solution of HJI equation for unknown constrained input nonlinear continuous time systems
Venue: Inf. Sci.

This paper is concerned with the approximate solution of Hamilton Jacobi Isaacs (HJI) equation for constrained input nonlinear continuous time systems with unknown dynamics. We develop a novel online adaptive dynamic programming based algorithm to learn the solution of the HJI equation. The present algorithm is implemented via an identifier critic architecture, which consists of two neural networks (NNs) an identifier NN is applied to estimate the unknown system dynamics and a critic NN is constructed to obtain the approximate solution of the HJI equation. An advantage of the proposed architecture is that the identifier NN and the critic NN are tuned simultaneously. With introducing two additional terms, namely, the stabilizing term and the robustifying term to update the critic NN, the initial stabilizing control is no longer required. Meanwhile, the developed critic tuning rule not only ensures convergence of the critic to the optimal saddle point but also guarantees stability of the closed loop system. Moreover, the uniform ultimate boundedness of the weights of the identifier NN and the critic NN are proved by using Lyapunov's direct method. Finally, to illustrate the effectiveness and applicability of the developed approach, two simulation examples are provided.
Energy compensation based approach for solving the HJI equation in the nonlinear H synthesis

The H framework provides an efficient and systemic method for the design of controllers for both linear and nonlinear systems. In the nonlinear controller synthesis, however, the limitation of this method is usually associated with the existence of a solution to the Hamilton Jacobi Isaac (HJI) equation. In this paper, an innovative energy compensation based approach to the solution of the HJI equations is presented and compared with the existing methods relying on Taylor series expansion. This new approach provides an efficient methodology that ensures the existence of a solution to the HJI equation. Numerical application to spacecraft attitude control is presented to validate the developments.
Recursive Regression with Neural Networks: Approximating the HJI PDE Solution
Venue: ArXiv

Most machine learning applications using neural networks seek to approximate some function g(x) by minimizing some cost criterion. In the simplest case, if one has access to pairs of the form (x, y) where y g(x) the problem can be framed as a regression problem. Beyond this family of problems, we find many cases where the unavailability of data pairs makes this approach unfeasible. However, similar to what we find in the reinforcement learning literature, if we have some known properties of the function we are seeking to approximate, there is still hope to frame the problem as a regression problem. In this context, we present an algorithm that approximates the solution to a partial differential equation known as the Hamilton Jacobi Isaacs PDE and compare it to current state of the art tools. This PDE, which is found in the fields of control theory and robotics, is of particular importance in safety critical systems where guarantees of performance are a must.
Adaptive HJI sliding mode control of three dimensional overhead cranes
Venue: 2016 Chinese Control and Decision Conference (CCDC)

This paper develops an adaptive version of HJI sliding mode controller for the overhead cranes. It deals with the effects of actuator saturation and the presence of disturbances based on RBF neural networks. The sufficient condition under which the overhead crane system is robustly dissipative is proposed using Hamilton Jacobi Issacs inequality(HJI) approach. The controller can simultaneously move the trolley and the bridge to their destinations, reduce cargo vibrations during transfer and eliminate cargo swings at the trolley and bridge destinations. The simulation results indicate that the proposed controller asymptotically stabilize all system responses.
How does the OH reactivity affect the ozone production efficiency: case studies in Beijing and Heshan

19 Total OH reactivity measurements have been conducted in August 2013 on the 20 Peking University campus, Beijing and from October to November 2014 in Heshan, 21 Guangdong Province. The daily median result for OH reactivity was 19.98 11.03 s 22 in Beijing and 30.62 19.76 s in Heshan. Beijing presented a significant diurnal 23 variation with maxima over 27 s in the early morning and minima below 16 s in the 24 afternoon. Measurements in Heshan gave a much flatter diurnal pattern. Missing 25 reactivity was observed at both sites, with 21% missing in Beijing and 32% missing in 26 Heshan. Unmeasured primary species, such as branched alkenes could contribute to 27 missing reactivity in Beijing, especially in morning rush hour. An observation based 28 model with the Regional Atmospheric Chemical Mechanism 2 was used to understand 29 the daytime missing reactivity in Beijing by adding unmeasured oxygenated volatile 30 organic compounds and simulated intermediates of primary VOCs degradation. 31 However, the model failed to explain the missing reactivity in Heshan, where the 32 ambient air was found to be more aged, and the missing reactivity was presumably to 33 Atmos.  2016 Manuscript under review for journal Atmos.  15 July 2016 c (c) Author(s) 2016. CC BY 3.0 License.
A 4D Light Field Dataset and CNN Architectures for Material Recognition
Venue: ECCV

We introduce a new light field dataset of materials, and take advantage of the recent success of deep learning to perform material recognition on the 4D light field. Our dataset contains 12 material categories, each with 100 images taken with a Lytro Illum, from which we extract about 30,000 patches in total. To the best of our knowledge, this is the first mid size dataset for light field images. Our main goal is to investigate whether the additional information in a light field (such as multiple sub aperture views and view dependent reflectance effects) can aid material recognition. Since recognition networks have not been trained on 4D images before, we propose and compare several novel CNN architectures to train on light field images. In our experiments, the best performing CNN architecture achieves a 7 boost compared with 2D image classification (70\\rightarrow 77\ These results constitute important baselines that can spur further research in the use of CNNs for light field applications. Upon publication, our dataset also enables other novel applications of light fields, including object detection, image segmentation and view interpolation.