LLukas22/scidocs huggingface dataset:
Model-Based Control of HCCI Engines Using Exhaust Recompression

Homogeneous charge compression ignition (HCCI) is one of the most promising piston-engine concepts for the future, providing significantly improved efficiency and emissions characteristics relative to current technologies. This paper presents a framework for controlling an HCCI engine with exhaust recompression and direct injection of fuel into the cylinder. A physical model is used to describe the HCCI process, with the model states being closely linked to the thermodynamic state of the cylinder constituents. Separability between the effects of the control inputs on the desired outputs provides an opportunity to develop a simple linear control scheme, where the fuel is used to control the work output and the valve timings are used to control the phasing of combustion. The controller is tested on both a single and multi-cylinder HCCI engine, demonstrating the value of a physical model-based control approach that allows an easy porting of the control structure from one engine to another. Experimental results show good tracking of both the work output and combustion phasing over a wide operating region on both engines. In addition, the controller is able to balance out differences between cylinders on the multi-cylinder engine testbed, and reduce the cycle-to-cycle variability of combustion.
Time Varying Risk Aversion

We use a repeated survey of a large sample of clients of an Italian bank to measure possible changes in investors’ risk aversion following the 2008 financial crisis. We find that both a qualitative and a quantitative measure of risk aversion increase substantially after the crisis. These changes are correlated with changes in portfolio choices, but do not seem to be correlated with “standard” factors that affect risk aversion, such as wealth, consumption habit, and background risk. This opens the possibility that psychological factors might be driving it. To test whether a scary experience (as the financial crisis) can trigger large increases in risk aversion, we conduct a lab experiment. We find that indeed students who watched a scary video have a certainty equivalent that is 27% lower than the ones who did not. Following a sharp drop in stock prices, a fear model predicts that individuals should sell stocks, while the habit model has the opposite implicationspeople should actively buy stock to bring the risky assets to the new optimal level. We show that after the drop in stock prices in 2008 individuals rebalanced their portfolio in a way consistent to a fear model. ∗ We thank John Campbell, James Dow and Ivo Welch and for very and helpful comments. We have benefited from comments from participants at seminars a the University of Chicago Booth, Boston College, University of Minnesota, University of Michigan, London Business School, the 2011 European Financial Association Meetings, UCLA behavioral finance association. Luigi Guiso gratefully acknowledges financial support from PEGGED, Paola Sapienza from the Zell Center for Risk and Research at Kellogg School of Management, and Luigi Zingales from the Stigler Center and the Initiative on Global Markets at the University of Chicago Booth School of Business. We thank Filippo Mezzanotti for excellent research assistantship, and Peggy Eppink for editorial help.
Phonological deficits in specific language impairment and developmental dyslexia: towards a multidimensional model

An on-going debate surrounds the relationship between specific language impairment and developmental dyslexia, in particular with respect to their phonological abilities. Are these distinct disorders? To what extent do they overlap? Which cognitive and linguistic profiles correspond to specific language impairment, dyslexia and comorbid cases? At least three different models have been proposed: the severity model, the additional deficit model and the component model. We address this issue by comparing children with specific language impairment only, those with dyslexia-only, those with specific language impairment and dyslexia and those with no impairment, using a broad test battery of language skills. We find that specific language impairment and dyslexia do not always co-occur, and that some children with specific language impairment do not have a phonological deficit. Using factor analysis, we find that language abilities across the four groups of children have at least three independent sources of variance: one for non-phonological language skills and two for distinct sets of phonological abilities (which we term phonological skills versus phonological representations). Furthermore, children with specific language impairment and dyslexia show partly distinct profiles of phonological deficit along these two dimensions. We conclude that a multiple-component model of language abilities best explains the relationship between specific language impairment and dyslexia and the different profiles of impairment that are observed.
An investigation of seasonal variation in leisure-time physical activity in persons with spinal cord injury

Study design:Secondary cross-sectional analyses of a cohort.Objectives:To examine seasonal variation in total moderate-to-vigorous leisure-time physical activity (MV-LTPA), exercise and sport participation in a cohort of individuals with spinal cord injury (SCI).Setting:Community (Ontario, Canada).Methods:Participants with SCI (n=695) completed telephone interviews regarding their involvement in MV-LTPA along with social cognitive predictors of MV-LTPA. Logistic regression was used to predict the impact of season on participation in MV-LTPA, exercise and sport. Hierarchical linear regression was used to examine seasonal variation in min per day of MV-LTPA in the active sub-cohort (n=342) with a specific focus on exercise (for example, wheeling), and sport (for example, sledge hockey).Results:Logistic regressions revealed that season did not predict whether participants engaged in MV-LTPA, exercise or sport. Linear regressions revealed that individuals in the active sub-cohort who completed the questionnaire during the winter reported engaging in less MV-LTPA than those who were interviewed in summer (βsummer=0.14, P<0.05). This pattern was observed for exercise (βsummer=0.16, P<0.05; R2 change=0.018) but not for sport (βsummer=0.076, P=0.68; R2 change=0.014).Conclusion:Individuals with SCI report less exercise and total LTPA accrued during the winter months; sport was found to be an exception to this case.
Large Margin Multi-Task Metric Learning

Multi-task learning (MTL) improves the prediction performance on multiple, different but related, learning problems through shared parameters or representations. One of the most prominent multi-task learning algorithms is an extension to support vector machines (svm) by Evgeniou et al. [15]. Although very elegant, multi-task svm is inherently restricted by the fact that support vector machines require each class to be addressed explicitly with its own weight vector which, in a multi-task setting, requires the different learning tasks to share the same set of classes. This paper proposes an alternative formulation for multi-task learning by extending the recently published large margin nearest neighbor (lmnn) algorithm to the MTL paradigm. Instead of relying on separating hyperplanes, its decision function is based on the nearest neighbor rule which inherently extends to many classes and becomes a natural fit for multi-task learning. We evaluate the resulting multi-task lmnn on real-world insurance data and speech classification problems and show that it consistently outperforms single-task kNN under several metrics and state-of-the-art MTL classifiers.
Mental health and foster carer training.

AIMS
To evaluate the impact of training foster carers on children's emotional and behavioural functioning.


METHODS
In a randomised controlled trial in 17 Scottish local council areas, with immediate and nine month follow up, 182 children and their foster families were randomly allocated to either standard services alone or standard services plus extra training for foster carers on communication and attachment. Main outcome measures were child psychopathology, attachment disorder, self esteem, and cost of foster care.


RESULTS
Over 60% of children had measurable psychopathology at baseline. The training was perceived as beneficial by participants. Scores for parent reported psychopathology and attachment disorders decreased by around 5%, self esteem increased by 2%, and costs by 22% in the intervention group. Results were non-significant.


CONCLUSIONS
Despite being well received by foster carers, the training was not sufficient to make a useful impact on the high level of psychopathology. This group may warrant more intensive interventions.
Design optimization methods for genomic DNA tiling arrays.

A recent development in microarray research entails the unbiased coverage, or tiling, of genomic DNA for the large-scale identification of transcribed sequences and regulatory elements. A central issue in designing tiling arrays is that of arriving at a single-copy tile path, as significant sequence cross-hybridization can result from the presence of non-unique probes on the array. Due to the fragmentation of genomic DNA caused by the widespread distribution of repetitive elements, the problem of obtaining adequate sequence coverage increases with the sizes of subsequence tiles that are to be included in the design. This becomes increasingly problematic when considering complex eukaryotic genomes that contain many thousands of interspersed repeats. The general problem of sequence tiling can be framed as finding an optimal partitioning of non-repetitive subsequences over a prescribed range of tile sizes, on a DNA sequence comprising repetitive and non-repetitive regions. Exact solutions to the tiling problem become computationally infeasible when applied to large genomes, but successive optimizations are developed that allow their practical implementation. These include an efficient method for determining the degree of similarity of many oligonucleotide sequences over large genomes, and two algorithms for finding an optimal tile path composed of longer sequence tiles. The first algorithm, a dynamic programming approach, finds an optimal tiling in linear time and space; the second applies a heuristic search to reduce the space complexity to a constant requirement. A Web resource has also been developed, accessible at http://tiling.gersteinlab.org, to generate optimal tile paths from user-provided DNA sequences.
Years of Biometric Research : Accomplishments , Challenges , and Opportunities

Biometric recognition refers to the automated recognition of individuals based on their biological and behavioral characteristics such as fingerprint, face, iris, and voice. The first scientific paper on automated fingerprint matching was published by Mitchell Trauring in the journal Nature in 1963. The first objective of this paper is to document the significant progress that has been achieved in the field of biometric recognition in the past 50 years since Trauring’s landmark paper. This progress has enabled current state-of-the-art biometric systems to accurately recognize individuals based on biometric trait(s) acquired under controlled environmental conditions from cooperative users. Despite this progress, a number of challenging issues continue to inhibit the full potential of biometrics to automatically recognize humans. The second objective of this paper is to enlist such challenges, analyze the solutions proposed to overcome them, and highlight the research opportunities in this field. One of the foremost challenges is the design of robust algorithms for representing and matching biometric samples obtained from uncooperative subjects under unconstrained environmental conditions (e.g., recognizing faces in a crowd). In addition, fundamental questions such as the distinctiveness and persistence of biometric traits need greater attention. Problems related to the security of biometric data and robustness of the biometric system against spoofing and obfuscation attacks, also remain unsolved. Finally, larger system-level issues like usability, user privacy concerns, integration with the end application, and return on investment have not been adequately addressed. Unlocking the full potential of biometrics through inter-disciplinary research in the above areas will not only lead to widespread adoption of this promising technology, but will also result in wider user acceptance and societal impact. c © 2016 Elsevier Ltd. All rights reserved.
Obstacle Avoidance of Mobile Robots in Unknown Environments

This paper gives the fuzzy control of a wheeled mobile robot motion in an unknown environment with obstacles. The model of the vehicle has two driving wheels and the angular velocities of the two wheels are independently controlled. When the vehicle is moving towards the target and the sensors detect an obstacle, an avoiding strategy is necessary. We proposed a fuzzy reactive navigation strategy of collision-free motion in an unknown environment with obstacles. First, the kinematic modeling of the autonomous wheeled mobile robots is analyzed. Then the fuzzy control of a wheeled mobile robot motion in an unknown environment with obstacles is proposed. Output of the fuzzy controller are the angular speed difference between the left and right wheels of the vehicle and the vehicle velocity. The simulation results show the effectiveness and the validity of the obstacle avoidance behavior in an unknown environment of the proposed fuzzy control strategy.
Glucose Level Estimation in Diabetes Mellitus By Saliva: A Bloodless Revolution.

BACKGROUND
Diabetes mellitus is a massive, growing, silent epidemic that has the potential to cripple health services in all parts of the world. Currently, a diagnosis of diabetes is achieved by evaluating plasma glucose levels. Saliva offers some distinctive advantages. Whole saliva can be collected non-invasively and by individuals with limited training. The present study was aimed to estimate and correlate the plasma and salivary glucose levels in diabetic and non diabetic subjects, with special reference to age.


METHOD
The study population consisted of three groups: Group I consisted of diabetics with BGL>200mg/dl and Group 2 consisted of diabetics with BGL 130-200mg/dl based on their random plasma glucose levels. Group 3 consisted of healthy population as controls with BGL <130 mg/dl. 2 ml of peripheral blood was collected for the estimation of random plasma glucose levels and unstimulated saliva was collected for the estimation of salivary glucose


RESULTS
The salivary glucose levels were significantly higher in group 1 and group 2 diabetics when compared with controls. The salivary glucose levels show a significant correlation with plasma glucose levels between study populations, suggesting that salivary glucose levels can be used as a monitoring tool for predicting glucose level in diabetic patients.


CONCLUSION
The present study found that estimation of salivary glucose levels can be used as a noninvasive, painless technique for the measurement of diabetic status of a patient in a dental set up.
After international relations : critical realism and the (re)construction of world politics

Introduction I. Overcoming International Relations 1. How it all Started? Can it End? A Genealogy of the International Problematic 2. Learning from Alker: From Quantitative Peace Research to Emancipatory Humanism 3. How to Tell Better Stories About World Politics II. Explicating a Critical Realist Methodology 4. Critical realist Ontology 5. Double Hermeneutics of Iconic Modeling 6. The Normative Logic of Explanatory Emancipation III. Visions of World Politics 7. Modeling Thucydides' Melos Episode 8. A Global Security Community 9. From Security to Emancipatory World Politics: Beyond 'Nordic Nostalgia' Epilogue
A Cascade Model for Externalities in Sponsored Search

One of the most important yet insufficiently studied issues in online advertising is the externality effect among ads: the value of an ad impression on a page is affected not just by the location that the ad is placed in, but also by the set of other ads displayed on the page. For instance, a high quality competing ad can detract users from another ad, while a low quality ad could cause the viewer to abandon the page
Application of honey-bee mating optimization algorithm on clustering

Cluster analysis is one of attractive data mining technique that use in many fields. One popular class of data clustering algorithms is the center based clustering algorithm. K-means used as a popular clustering method due to its simplicity and high speed in clustering large datasets. However, K-means has two shortcomings: dependency on the initial state and convergence to local optima and global solutions of large problems cannot found with reasonable amount of computation effort. In order to overcome local optima problem lots of studies done in clustering. Over the last decade, modeling the behavior of social insects, such as ants and bees, for the purpose of search and problem solving has been the context of the emerging area of swarm intelligence. Honey-bees are among the most closely studied social insects. Honey-bee mating may also be considered as a typical swarm-based approach to optimization, in which the search algorithm is inspired by the process of marriage in real honey-bee. Honey-bee has been used to model agent-based systems. In this paper, we proposed application of honeybee mating optimization in clustering (HBMK-means). We compared HBMK-means with other heuristics algorithm in clustering, such as GA, SA, TS, and ACO, by implementing them on several well-known datasets. Our finding shows that the proposed algorithm works than the best one. 2007 Elsevier Inc. All rights reserved.
Improving Patients' Adherence to Physical Activity in Diabetes Mellitus: A Review

Regular physical activity (PA) is a key element in the prevention and management of type 2 diabetes mellitus (T2DM). Participation in regular PA improves blood glucose control and can prevent or delay T2DM and its complications, along with positively affecting lipids, blood pressure, cardiovascular events, mortality, and quality of life. However, most people with T2DM are not active and show poor adherence. This paper reviews the possible barriers to PA and strategies to improve the adherence to PA. Based on the currently available literature, it is concluded that self-efficacy and social support from family, friends, and health care providers play the important role in adoption and maintenance of regular PA. Here we also highlight some new modern and innovative interventions that facilitate exercise participation and improve the adherence.
The Early Years of Logic Programming

This firsthand recollection of those early days of logic programming traces the shared influences and inspirations that connected Edinburgh, Scotland, and Marseilles, France.
Optimization of laccase production by Pleurotus ostreatus IMI 395545 using the Taguchi DOE methodology.

Production of laccase using a submerged culture of Pleurotus orstreatus IMI 395545 was optimized by the Taguchi orthogonal array (OA) design of experiments (DOE) methodology. This approach facilitates the study of the interactions of a large number of variables spanned by factors and their settings, with a small number of experiments, leading to considerable savings in time and cost for process optimization. This methodology optimizes the number of impact factors and enables to calculate their interaction in the production of industrial enzymes. Eight factors, viz. glucose, yeast extract, malt extract, inoculum, mineral solution, inducer (1 mM CuSO₄) and amino acid (l-asparagine) at three levels and pH at two levels, with an OA layout of L18 (2¹ × 3⁷) were selected for the proposed experimental design. The laccase yield obtained from the 18 sets of fermentation experiments performed with the selected factors and levels was further processed with Qualitek-4 software. The optimized conditions shared an enhanced laccase expression of 86.8% (from 485.0 to 906.3 U). The combination of factors was further validated for laccase production and reactive blue 221 decolorization. The results revealed an enhanced laccase yield of 32.6% and dye decolorization up to 84.6%. This methodology allows the complete evaluation of main and interaction factors.
Developing an interactive mobile phone self-report system for self-management of hypertension. Part 1: Patient and professional perspectives

Low adherence remains a struggle in hypertension management, despite improvement efforts. Presuming that increased patient participation is a possible approach, we collaborated with patients and healthcare professionals to design a self-report system to support self-management. The study aimed to explore and describe relevant aspects of hypertension and hypertension treatment, for use in the development of an interactive mobile phone self-report system. It further aimed to suggest which clinical measures, lifestyle measures, symptoms and side-effects of treatment would be meaningful to include in such a system. Five focus group interviews were performed with 15 patients and 12 healthcare professionals, and data was analysed using thematic analysis. Patients suggested trust, a good relationship with caregivers, and well-being as important aspects of hypertension self-management. Furthermore, they regarded blood pressure, dizziness, stress, headache and tiredness as important outcomes to include. Patients sought to understand interconnections between symptoms and variations in blood pressure, whilst healthcare professionals doubted patients' ability to do so. Healthcare professionals emphasized accessibility, clear and consistent counselling, complication prevention and educational efforts. The study presents aspects of importance for follow-up to understand the interplay between blood pressure and daily life experiences for patients with hypertension.
Space encoding based compressive multiple human tracking with distributed binary pyroelectric infrared sensor networks

This paper presents a distributed, compressive multiple human tracking system based on binary pyroelectric infrared (PIR) sensor networks. The goal of our research is to develop an energy-efficient, low-data-throughput infrared surveillance system for various indoor applications. The compressive measurements are achieved by using techniques of (1) multiplex binary sensing and (2) space encoding. The target positions are reconstructed from the binary compressive measurements through (1) an expectation-maximization (EM) framework for space decoding, (2) representing the prior knowledge of target / sampling geometries with statistical parameters, and (3) hierarchical space encoding / decoding for multiple targets tracking. A wireless networked PIR sensor system is designed to demonstrate the improved sensing efficiency and system scalability of the proposed distributed multiple human tracking system. The proposed compressive tracking framework can be extended to various binary sensing modalities.
On solving norm equations in global function fields

The potential of solving norm equations is crucial for a variety of applications of algebraic number theory, especially in cryptography. In this article we develop general effective methods for that task in global function fields for the first time.