Miracle-dz/UniFAR-FTUs huggingface dataset:
A Local Area Analysis of Racial, Ethnic, and Neighborhood Disparities in Breast Cancer Staging

Few studies have examined the role of neighborhood socioeconomic condition in shaping breast cancer disparities in defined local areas. We tested associations between three measures of neighborhood socioeconomic condition (poverty, median income, and a composite neighborhood score) on breast cancer staging in two urban counties of the state of New Jersey. Data for these counties were obtained from the New Jersey Surveillance, Epidemiology, and End Results tumor registry and were selected because of their large racial/ethnic and socioeconomic diversity and pilot prevention efforts taking place in these areas. Our study population included Black, Latina, and White women (N = 4,589) diagnosed with breast cancer from 1999 to 2004. Each cancer case was geocoded and linked to socioeconomic data obtained from the 2000 U.S. census. Census tracts served as proxies for neighborhoods. Logistic regression models accounting for clustering of individuals within neighborhoods were fitted with Generalized Estimating Equations. Women living in neighborhoods with lower versus higher neighborhood scores were significantly more likely to have advanced-stage disease (odds ratio, 1.6; confidence intervals, 1.1-2.3), after adjusting for age at diagnosis and race/ethnicity. In analyses stratified by race/ethnicity, results remained significant for all neighborhood measures for White and Black women, but not for Latinas. Moreover, neighborhood poverty showed a weaker socioeconomic gradient in breast cancer staging among White women. Our study findings support the use of a multidimensional neighborhood index to better capture differences in cancer staging risk across racial/ethnic groups and provides evidence that population-based cancer data could be used to identify local needs specific to local populations. (Cancer Epidemiol Biomarkers Prev 2009;18(11):3024–9)
Developmental origins of early antisocial behavior

Abstract Early antisocial behavior has its origins in childhood behavior problems, particularly those characterized by aggressive and destructive behavior. Deficits in self-regulation across multiple domains of functioning, from the physiological to the cognitive, are associated with early behavior problems, and may place children at greater risk for the development of later antisocial behavior. Data are presented from a longitudinal study of early self-regulation and behavior problems, the RIGHT Track Research Project, demonstrating that children at greatest risk for early and persistent problem behavior display patterns of physiological and emotional regulation deficits early in life. Parenting behavior and functioning have also been examined as predictors of trajectories of early problem behavior, and some data support the interaction of parenting and self-regulation as significant predictors of patterns of problematic behavior and ongoing problems with the regulation of affect. Peer relationships also affect and are affected by early self-regulation skills, and both may play a role in academic performance and subsequent school success. These data provide evidence that the social contexts of early family and peer relationships are important moderators of the more proximal mechanism of self-regulation, and both types of processes, social and biobehavioral, are likely implicated in early antisocial tendencies. Implications of these findings on self-regulation and early behavior problems are discussed in terms of future research and treatment approaches.
A comparative study of practitioner perceptions of selection methods in the United Kingdom

In this study, 476 participants, divided into occupational psychology (OP)-, Chartered Institute of Personnel and Development (CIPD)-, human resource management (HRM)-qualified, and layperson subgroups, provided their perceptions of the validity, fairness, and frequency of use of employee selection methods. Results of a mixed-effects analysis of covariance revealed that respondent qualification background predicted the degree to which participant validity perceptions were aligned with research-based estimates of validity, F [3, 29.39] = 20.06, p < . 001, η2 = . 67. Corrected pairwise comparisons suggested that perceptions of participants with CIPD and HRM backgrounds were not significantly more aligned with research estimates of validity than were the perceptions of laypeople. OP participant validity perceptions were significantly more aligned with research estimates than all other subgroups, (p < .03). Evidence was also found for some between-group consistency regarding frequency-of-use perceptions, but less between-group consistency was found vis-a-vis perceptions of fairness. Implications for decision-making in employee selection are discussed. Practitioner points Knowledge about employee selection measures might not be effectively shared between those with CIPD- and HRM-related qualifications versus those with OP-related qualifications. Laypeople and respondents with CIPD- and HRM-related qualifications were found to deviate similarly from up-to-date research findings about the validity of selection measures. Respondents with OP-related qualifications were more closely aligned with up-to-date findings about the validity of selection measures than were other comparison groups.
Energy Demand Analysis and Forecast

Sustainable energy systems are necessary to save the natural resources avoiding environmental impacts which would compromise the development of future generations. Delivering sustainable energy will require an increased efficiency of the generation process including the demand side. The architecture of the future energy supply can be characterized by a combination of conventional centralized power plants with an increasing number of distributed energy resources, including cogeneration and renewable energy systems. Thus efficient forecast tools are necessary predicting the energy demand for the operation and planning of power systems. The role of forecasting in deregulated energy markets is essential in key decision making, such as purchasing and generating electric power, load switching, and demand side management. This chapter describes the energy data analysis and the basics of the mathematical modeling of the energy demand. The forecast problem will be discussed in the context of energy management systems. Because of the large number of influence factors and their uncertainty it is impossible to build up an ‘exact’ physical model for the energy demand. Therefore the energy demand is calculated on the basis of statistical models describing the influence of climate factors and of operating conditions on the energy consumption. Additionally artificial intelligence tools are used. A large variety of mathematical methods and ideas have been used for energy demand forecasting (see Hahn et al., 2009, or Fischer, 2008). The quality of the demand forecast methods depends significantly on the availability of historical consumption data as well as on the knowledge about the main influence parameters on the energy consumption. These factors also determine the selection of the best suitable forecast tool. Generally there is no 'best' method. Therefore it is very important to proof the available energy data basis and the exact conditions for the application of the tool. Within this chapter the algorithm of the model building process will be discussed including the energy data treatment and the selection of suitable forecast methods. The modeling results will be interpreted by statistical tests. The focus of the investigation lies in the application of regression methods and of neural networks for the forecast of the power and heat demand for cogeneration systems. It will be shown that similar methods can be applied to both forecast tasks. The application of the described methods will be demonstrated by the heat and power demand forecast for a real district heating system containing different cogeneration units.
Visualizing Temporal Patterns in Large Multivariate Data using Textual Pattern Matching

Extracting and visualizing temporal patterns in large scientific data is an open problem in visualization research. First, there are few proven methods to flexibly and concisely define general temporal patterns for visualization. Second, with large timedependent data sets, as typical with today’s large-scale simulations, scalable and general solutions for handling the data are still not widely available. In this work, we have developed a textual pattern matching approach for specifying and identifying general temporal patterns. Besides defining the formalism of the language, we also provide a working implementation with sufficient efficiency and scalability to handle large data sets. Using recent large-scale simulation data from multiple application domains, we demonstrate that our visualization approach is one of the first to empower a concept driven exploration of large-scale time-varying multivariate data.
COUPLED INDUCTOR BASED DC-DC CONVERTER FOR HIGH STEP- UP APPLICATION

In this paper, a coupled inductor based high step-up dc–dc converter for high step-up applications is proposed. The concept is to utilize two capacitors and one coupled inductor. The two capacitors are charged in parallel during the switch-off period and are discharged in series during the switch-on period by the energy stored in the coupled inductor to achieve a high step-up voltage gain. In addition, the energy stored in the coupled inductor is recycled; the voltage stress of the main switch is reduced. The switch with low resistance RDS(ON) can be adopted to reduce the conduction loss and the reverse-recovery problem of the diodes is alleviated. Not only lower conduction losses but also higher power conversion efficiency is benefited from lower turns ratios. The operating principle and steady-state analyses are discussed in detail. Finally, A 200W Converter Operating at 50KHZ with 12V input and 120V output simulation is presented to demonstrate the performance. The results are verified through MATLAB Software.
Grazing incidence reflection and scattering of MeV protons

Treating protons as de Broglie waves shows that up to a few MeV energies protons experience total external reflection using the index of refraction concept for the target earlier applied to electrons. Angular distributions can be explained by random surface scattering as known for X-rays. Applied ot the Chandra and XMM-Newton X-ray telescopes the calculated reflection efficiencies can explain the observed degradation of the X-ray CCDs for both missions. Some discussion about the possibility of realizing imaging sub-MeV proton optics is presented.
Nonlinear Dynamic Modeling of Langevin-Type Piezoelectric Transducers

Langevin transducers are employed in several applications, such as power ultrasound systems, naval hydrophones, and high-displacement actuators. Nonlinear effects can influence their performance, especially at high vibration amplitude levels. These nonlinear effects produce variations in the resonant frequency, harmonics of the excitation frequency, in addition to loss of symmetry in the frequency response and “frequency domain hysteresis”. In this context, this paper presents a simplified nonlinear dynamic model of power ultrasound transducers requiring only two parameters for simulating the most relevant nonlinear effects. One parameter reproduces the changes in the resonance frequency and the other introduces the dependence of the frequency response on the history of the system. The piezoelectric constitutive equations are extended by a linear dependence of the elastic constant on the mechanical displacement amplitude. For introducing the frequency hysteresis, the elastic constant is computed by combining the current value of the mechanical amplitude with the previous state amplitude. The model developed in this work is applied for predicting the dynamic responses of a 26 kHz ultrasonic transducer. The comparison of theoretical and experimental responses, obtained at several input voltages around the tuned frequency, shows a good agreement, indicating that the model can accurately describe the transducer nonlinear behavior.
Women in leader roles within higher education in Bangladesh

Women have long been under-represented in organization leader roles within higher education. Research has identified, mapped and examined the data, with recommendations for change. The research reported in this article adds to current knowledge, and raises methodological questions by focusing on senior female leaders in higher education in Bangladesh. Using a narrative approach to the construction of professional biographies, the study seeks to explore and understand the complex situations that Bangladeshi women in leader roles encounter and negotiate in their work and careers. This project makes a contribution to the field through not only adding more evidence, but also providing new insights into the contextual context in which women are aspiring to and securing senior roles. In addition, we help to inform the research design agenda by identifying a lack of information regarding the composition of the higher education workforce in Bangladesh, where the data in the public domain are neither sufficient nor robust.
Interethnic Differences in Antigen-Presenting Cell Activation and TLR Responses in Malian Children during Plasmodium falciparum Malaria

The Fulani ethnic group from West Africa is relatively better protected against Plasmodium falciparum malaria as compared to other sympatric ethnic groups, such as the Dogon. However, the mechanisms behind this lower susceptibility to malaria are largely unknown, particularly those concerning innate immunity. Antigen-presenting cells (APCs), and in particular dendritic cells (DCs) are important components of the innate and adaptive immune systems. Therefore, in this study we investigated whether APCs obtained from Fulani and Dogon children exhibited differences in terms of activation status and toll-like receptor (TLR) responses during malaria infection. Lower frequency and increased activation was observed in circulating plasmacytoid DCs and BDCA-3+ myeloid DCs of infected Fulani as compared to their uninfected counterparts. Conversely, a higher frequency and reduced activation was observed in the same DC subsets obtained from peripheral blood of P. falciparum-infected Dogon children as compared to their uninfected peers. Moreover, infected individuals of both ethnic groups exhibited higher percentages of both classical and inflammatory monocytes that were less activated as compared to their non-infected counterparts. In line with APC impairment during malaria infection, TLR4, TLR7 and TLR9 responses were strongly inhibited by P. falciparum infection in Dogon children, while no such TLR inhibition was observed in the Fulani children. Strikingly, the TLR-induced IFN-γ release was completely abolished in the Dogon undergoing infection while no difference was seen within infected and non-infected Fulani. Thus, P. falciparum infection is associated with altered activation status of important APC subsets and strongly inhibited TLR responses in peripheral blood of Dogon children. In contrast, P. falciparum induces DC activation and does not affect the innate response to specific TLR ligands in Fulani children. These findings suggest that DCs and TLR signalling may be of importance for the protective immunity against malaria observed in the Fulani.
Aircraft engine health monitoring using density modelling and extreme value statistics

Current practice in the operation and maintenance of an aircraft fleet requires analysis of data obtained from in-service engines in order to identify engine deterioration and provide preventative maintenance. Typically large quantities of engine vibration and performance data are available from various engine-mounted sensors. The analysis of such data requires techniques for modelling these multivariate data allowing fleet specialists to establish profiles of engine behaviour under different operating conditions. Additionally, such techniques can be used to identify precursors of engine events to avoid loss of engine service.
The Senses Considered as One Perceptual System

ABSTRACT J. J. Gibson (1966) rejected many classical assumptions about perception but retained 1 that dates back to classical antiquity: the assumption of separate senses. We suggest that Gibson's retention of this assumption compromised his novel concept of perceptual systems. We argue that lawful, 1:1 specification of the animal–environment interaction, which is necessary for perception to be direct, cannot exist in individual forms of ambient energy, such as light, or sound. We argue that specification exists exclusively in emergent, higher order patterns that extend across different forms of ambient energy. These emergent, higher order patterns constitute the global array. If specification exists exclusively in the global array, then direct perception cannot be based upon detection of patterns that are confined to individual forms of ambient energy and, therefore, Gibson's argument for the existence of several distinct perceptual systems cannot be correct. We argue that the senses function as a single, irreducible perceptual system that is sensitive exclusively to patterns in the global array. That is, rather than distinct perceptual systems there exists only 1 perceptual system.
Is That a Jaguar?: Segmenting Ancient Maya Glyphs via Crowdsourcing

Crowdsourcing is popular in multimedia research to obtain image annotation and segmentation data at scale. In the context of analysis of cultural heritage materials, we propose a novel crowdsourced task, namely the segmentation of ancient Maya hieroglyph-blocks by non-experts. This is a task that is highly perceptual and thus potentially feasible even though the crowd is not likely to have prior specialized knowledge about hieroglyphics. Based on a new data set of glyph-block line drawings for which ground-truth segmentation exists, we study how non-experts perceive glyph blocks (e.g. whether they see closed contours as a separate glyph, or how they combine visual components under plausible hypotheses of the number of glyphs present in a block.) Using Amazon Mechanical Turk as platform, we perform block-based and worker-based objective analyses to assess the difficulty of glyph blocks and the performance of workers. The results suggest that a crowdsourced approach is promising for glyph-blocks of moderate degrees of complexity.
On the Expressiveness of Linda Coordination Primitives

We introduce a process algebra containing the coordination primitives of Linda (asynchronous communication via a shared data space, read operation, nonblocking test operators on the shared space). We compare two possible semantics for the output operation: the former, that we call ordered, defines the output as an operation that returns when the message has reached the shared data space; the latter, that we call unordered, returns just after sending the message to the tuple space. The process algebra under the ordered semantics is Turing powerful, as we are able to program any random access machine. The main result of the paper is that the process algebra under the unordered semantics is not Turing powerful. This result is achieved by resorting to a net semantics in terms of contextual nets (P/T nets with inhibitor and read arcs) and by showing that there exists a deadlock-preserving simulation of such nets by finite P/T nets, a formalism where termination is decidable.
Rendimiento académico en educación superior y su asociación con la participación activa en la plataforma Moodle

This study analyzes the association between quantity and type of access to the Moodle platform in a subject and the academic results of all students (N = 229) enrolled in their first year of psychology studies. Analysis of variance, Pearson’s correlations and multiple regression analysis were used. The results show that the use of active teaching-learning strategies is associated with better academic results. These findings underscore the importance of active student involvement, as opposed to merely accessing available information.
Migration and the Genetic Covariance between Habitat Preference and Performance

Studies of the genetic covariance between habitat preference and performance have reported conflicting outcomes ranging from no covariance to strong covariance. The causes of this variability remain unclear. Here we show that variation in the magnitude of genetic covariance can result from variability in migration regimes. Using data from walking stick insects and a mathematical model, we find that genetic covariance within populations between host plant preference and a trait affecting performance on different hosts (cryptic color pattern) varies in magnitude predictably among populations according to migration regimes. Specifically, genetic covariance within populations is high in heterogeneous habitats where migration between populations locally adapted to different host plants generates nonrandom associations (i.e., linkage disequilibrium) between alleles at color pattern and host preference loci. Conversely, genetic covariance is low in homogeneous habitats where a single host exists and migration between hosts does not occur. Our results show that habitat structure and patterns of migration can strongly affect the evolution and variability of genetic covariance within populations.
Mark-sweep or copying?: a "best of both worlds" algorithm and a hardware-supported real-time implementation

Copying collectors offer a number of advantages over their mark-sweep counterparts. First, they do not have to deal with mark stacks and potential mark stack overflows. Second, they do not suffer from unpredictable fragmentation overheads since they inherently compact the heap. Third, the tospace invariant maintained by many copying collectors allows for incremental compaction and provides the basisfor efficient real-time implementations. Unfortunately, however, standard copying collectors depend on two semispaces and therefore need at least twice as much memory as required for the maximum amount of live data.In this paper, we introduce a novel mark-compact algorithm that combines the elegance and simplicity of Baker's copying algorithm with the memory efficiency of mark-sweep algorithms. Furthermore, we present a hardware-supported implementation for real-time applications in the framework of an object-based RISC architecture.Measurements of Java programs on an FPGA-based prototype show that our novel mark-compact algorithm outperforms a corresponding copying collector in every respect. It requires far less memory space for real-time behavior and, at the same time, reduces the overall runtime overhead under typical operating conditions.
Adaptive synchronization of an uncertain complex dynamical network

This note further investigates the locally and globally adaptive synchronization of an uncertain complex dynamical network. Several network synchronization criteria are deduced. Especially, our hypotheses and designed adaptive controllers for network synchronization are rather simple in form. It is very useful for future practical engineering design. Moreover, numerical simulations are also given to show the effectiveness of our synchronization approaches.