Miracle-dz/UniFAR-FTUs huggingface dataset:
Depression predicts increased incidence of adverse health outcomes in older Mexican Americans with type 2 diabetes.

OBJECTIVE To examine the separate and combined effects of depression and diabetes on the incidence of adverse health outcomes among older Mexican Americans. RESEARCH DESIGN AND METHODS Longitudinal data from the Hispanic Established Population for the Epidemiologic Study of the Elderly (EPESE) survey were used to examine the main effects and interaction effects of diabetes and depressive symptoms (measured with the Center for Epidemiologic Study of Depression) or clinical diagnostic criteria (measured with the Composite International Diagnostic Interview Depression Module) on the development of macrovascular complications (including cardiovascular disease, stroke, and kidney disease), microvascular complications (including nephropathy, neuropathy, retinopathy, and amputations), functional disability, and mortality over 7 years in a sample of 2,830 Mexican Americans aged >or=65 years. RESULTS The interaction of diabetes and depression was found to be synergistic, predicting greater mortality, greater incidence of both macro- and microvascular complications, and greater incidence of disability in activities of daily living, even when controlling for sociodemographic characteristics such as sex, age, education, acculturation, and marital status. Importantly, this interaction was found to predict not only greater incidence but also earlier incidence of adverse events in older adults. CONCLUSIONS Whether a marker for underlying disease severity, an indicator of diminished self-care motivation, or the result of physiologic changes, the interaction of depression and diabetes has a synergistic effect on the health of older Mexican Americans, increasing the risk for poor outcomes. This is of particular clinical importance because although depression is often underrecognized in older adults, effective treatment is available and can result in improved medical outcomes.
Significant factors contributing to the successful inclusion of students with moderate to severe disabilities into general K-2 classrooms

Bonni Rubin-Sugarman SIGNFTCANT FACTORS CONTRIBUTING TO THSUCCESSFUL INCLUSION OF STUDENTS WITH MODERATE TO SEVERE DISABILITIES INTO GENERAL K-2 CLASSROOMS 1997 Dr. Margaret M. Shuff Masters Degree Of Learning Disabilities Since 1975 a federal law has made the local school district responsible for the education of ALL children living within its geographical boundaries, This law, originally known as the Education of All Handicapped Children Act (PL 94-142), and now known as IDEA or The Individuals with Disabilities Education Act, guarantees that children with disabilities will have a free and appropriate public education in the least restrictive environment. This free and appropnate public education would afford children with disabilities the opportunity, to the maxdmum extent possible, to be educated iu their neighborhood school alongside of their non-disabled peers. The purpose of this study was to examine the strategies / practices used regularly by successful kindergarten, first, and second grade teachers who have included children with moderate to severe disabilities in their general education classrooms in hopes of determining how to best support teachers providing inclusive placements for primary students. It examined the supports made available to those teachers, and it investigated
Current-steering pre-emphasis transmitter with continuously tuned line terminations for optimum impedance match and maximum signal drive range

A 24-segment current-steering transmitter with 3-tap pre-emphasis and linear auto-tuned active line terminations is presented. A linearized resistor-MOSFET termination structure for continuous impedance tuning is proposed to maintain an accurate output voltage level (σ=1%) and good impedance matching (σ=2%). This enables larger signal drive strengths thanks to more efficient supply utilization. The circuit is implemented in 0.16 μm CMOS technology for data rates up to 6 Gbps and consumes 5-43 mA from a 1.8V supply for 50-1200 mVppd output signal.
High-Order Mismatched Disturbance Compensation for Motion Control Systems Via a Continuous Dynamic Sliding-Mode Approach

A new continuous dynamic sliding-mode control (CDSMC) method is proposed for high-order mismatched disturbance attenuation in motion control systems using a high-order sliding-mode differentiator. First, a new dynamic sliding surface is developed by incorporating the information of the estimates of disturbances and their high-order derivatives. A CDSMC law is then designed for a general motion control system with both high-order matched and mismatched disturbances, which can attenuate the effects of disturbances from the system output. The proposed control method is finally applied for the airgap control of a MAGnetic LEViation (MAGLEV) suspension vehicle. Simulation results show that the proposed method exhibits promising control performance in the presence of high-order matched and mismatched disturbances.
Learning a Multi-Subject Model of the Central Nervous SystemControl Using Neural

This paper is focused on the identiication of Central Nervous System (CNS) controller models by means of Time-Delay Neural Networks (TDNNs). The CNS controls the hemodynamical system by generating the regulating signals for the blood vessels and the heart. The functioning of the CNS is of high complexity and not yet fully understood. This makes the use of modelling methodologies that are not based on a priori structural knowledge, such as those based on neural networks, potentially useful for this problem. In a previous work 1], TDNNs were used to learn and predict the dynamic behavior of the system under study for a single subject. In this paper, a more extensive study is presented, in which the performance of TDNNs when applied to infer CNS control models is assessed by using data from ve diierent subjects. Both multi-subject models and individual models for each subject have been extracted and evaluated. In addition, the use of Markovian techniques has been investigated for automatically setting the connguration of the input layer in TDNNs, and a signiicant improvement in prediction performance has been obtained.
Wavelet based Technique for Super Resolution Image Reconstruction

The super resolution means the quality of the image to the extent of the maximum capability of the technology referring it. The image capturing devices have their hardware limitations to reach to the perfection. A technique for Reconstruction of super resolution image using low resolution natural color image has been developed. The presented technique identifies local features of low resolution image and then enhances its resolution appropriately. It is noticed that the higher PSNR is observed for the developed technique than the existing methods.
A DUAL INTERVAL APPROACH FOR THE ALIGNMENT OF GEOMETRIES IN VECTOR DATA SETS

Today, a growing availability of geo datasets can be observed, together with an increasing accessibility of the data via web-services. In spite of this favourable situation, there are still some problems left on the way to true interoperability, which are due to the heterogeneity of the data sets: even when data relate to the same phenomena in reality, there are still geometric discrepancies left. The reasons for this are manifold: different underlying data model, different acquisition time, different acquisition type and finally different human operators who performed the measurements. In order to allow for a true interoperability a geometric adaptation of corresponding objects is necessary. Only then, overlay and buffer operations can be executed, as well as geometric calculations. In the paper, an approach for matching and adaptation of geometric objects is presented, which leads to one consolidated data set.The adaptation process starts with finding corresponding objects in both data sets using matching techniques. These corresponding objects serve as “anchor objects” according to which the adaptation of the rest of the data can be performed using rubber sheeting. The adaptation consists of two different adaptation operations, namely the well-known ICP-algorithm and the Dual Interval Adaptation (DIA), that allow for a global adaptation of whole objects, and local adaptation of parts of objects, respectively.
On the bias of adaptive first-order recursive smoothing

In signal processing, first-order recursive smoothing is often used to determine the mean of a nonstationary random variable. In order to find a better compromise between the tracking speed and the variance of the estimate, adaptive smoothing factors have been proposed, e.g., for single-channel background noise power spectral density estimators. In this paper, the bias of recursive smoothing using adaptive smoothing functions is investigated. For adaptive functions that do not depend on the estimated mean an analytical derivation of the bias is given. For adaptive functions having a dependence on the recursively estimated mean, an iterative procedure is proposed which allows to approximately determine the bias with a sufficiently high precision.
Data‐driven Local Coordinate Systems for Image‐Based Rendering

Image‐based representations of an object profit from known geometry. The more accurate this geometry is known, the better corresponding pixels in the different images can be aligned, which leads to less artifacts and better compression performance. For opaque objects the per‐pixel data can then be interpreted as a sampling of the BRDF at the respective surface point. In order to parameterize this sampled data a coordinate frame has to be defined. In previous work this coordinate frame was either the global frame or a local frame derived from the base geometry. Both approaches lead to misalignments between sample vectors: Features of basically very similar BRDFs will be shifted to different regions in the sample vector leading to poor compression performance. In order to improve alignment between the sampled BRDFs in image‐based rendering, we propose an optimization algorithm which determines consistent coordinate frames for every sample point on the object surface. This way we efficiently align the features even of anisotropic reflection functions and reconstruct approximate local coordinate frames without performing an explicit 3D‐reconstruction. The optimization is calculated efficiently by exploiting the Fourier‐shift theorem for spherical harmonics. In order to deal with different materials in a scene, the technique is combined with a clustering algorithm. We demonstrate the utility of our method by applying it to BTFs and 6D surface reflectance fields.
Cost-Minimizing Online VM Purchasing for Application Service Providers with Arbitrary Demands

Recent years witness the proliferation of Infrastructure-as-a-Service (IaaS) cloud services, which provide on-demand resources (CPU, RAM, disk) in the form of virtual machines (VMs) for hosting applications/services of third parties. Given the state-of-the-art IaaS offerings, it is still a problem of fundamental importance how the Application Service Providers (ASPs) should rent VMs from the clouds to serve their application needs, in order to minimize the cost while meeting their job demands over a long run. Cloud providers offer different pricing options to meet computing requirements of a variety of applications. However, the challenge facing an ASP is how these pricing options can be dynamically combined to serve arbitrary demands at the optimal cost. In this paper, we propose an online VM purchasing algorithm based on the Lyapunov optimization technique, for minimizing the long-term-averaged VM rental cost of an ASP with time-varying and delay-tolerant workloads, while bounding the maximum response delay of its jobs. In stark contrast with the existing studies, the proposed algorithm enables an ASP to optimally decide the amount of reserved, on-demand and spot instances to purchase simultaneously. Rigorous analysis shows that our algorithm can achieve a time-averaged resource cost close to the offline optimum. Trace-driven simulations further verify the efficacy of our algorithm.
Semi-automatic generation of interactive exercises related to search algorithms

We have developed and use in our Department the Artificial Intelligence Teaching System (AITS). AITS is an adaptive and intelligent tutoring system that teaches AI aspects and uses AI techniques for personalized learning and assessment of the students. In this paper, we present the way that AITS can help students in learning search algorithms and the learning approaches and activities it offers. Also, we present a tool developed to assist tutors to create exercises in a semi-automatic way. It is an assistant tool to be embedded into the AITS system that produces different types of interactive exercises related to AI search algorithms. The tool takes as input a number of parameters regarding the desired exercise and it creates semi-automatically the interactive exercise based on the corresponding parameters. The tool aims at helping tutors in creating and updating the teaching material. Results gathered from an evaluation study are quite promising.
A Traac-balanced Adaptive Wormhole Routing Scheme for Two-dimensional Meshes 1

In this paper, we have analyzed several issues that are involved in developing low latency adaptive wormhole routing schemes for two-dimensional meshes. It is observed that along with adaptivity, balanced distribution of traac has a signiicant impact on the system performance. Motivated by this observation, we have developed a new fully adaptive routing algorithm called positive-rst-negative-rst for two-dimensional meshes. The algorithm uses only two virtual channels per physical channel creating two virtual networks. The messages are routed positive-rst in one virtual network and negative-rst in the other. Because of this combination, the algorithm distributes the system load uniformly throughout the network and is also fully adaptive. It is shown that the proposed algorithm results in providing better performance in terms of the average network latency and throughput when compared with the previously proposed routing algorithms.
Mixing Compression and CA Encryption

We consider the enciphering of a data stream while it is being compressed by a dictionary algorithm. This scheme has to be compared to the classical encryption after compression methods used in classical security protocols. Actually, most cryptanalysis techniques exploit patterns found in the plaintext to crack the cipher and compression techniques reduce these attacks. Our scheme is based on a LZ compression algorithm in which a Vernam cipher has been included. We first present an experiment with a pseudo-random sequence generated by a simple linear feedback shift register before considering pseudo-random sequences generated by cellular automata. We make some remarks on the security by trying to measure the randomness of our method with some statistical tests. Such a scheme could be employed to increase the speed of some security protocols and to decrease the computing power for mobile devices.
Structural issues in quantum gravity

A discursive, non-technical, analysis is made of some of the basic issues that arise in almost any approach to quantum gravity, and of how these issues stand in relation to recent developments in the field. Specific topics include the applicability of the conceptual and mathematical structures of both classical general relativity and standard quantum theory. This discussion is preceded by a short history of the last twenty-five years of research in quantum gravity, and concludes with speculations on what a future theory might look like.
A Model-Driven Architecture Approach to the Efficient Identification of Services on Service-Oriented Enterprise Architecture

efficient development of loosely-coupled and interoperable sets of services. Existing design approaches do not always take full advantage of the value and importance of the engineering invested in existing legacy systems. This paper proposes an approach to define the key services from such legacy systems effectively. The approach focuses on defining these services based on a Model-Driven Architecture approach supported by guidelines over a wide range of possible service types.
Disodium gadoxetate uptake in progressive familial intrahepatic cholestasis type I: Enhancing our understanding of the cholestatic disease

Hepatocyte-specific magnetic resonance imaging (MRI) contrast agents are commonly used to depict anatomic hepatobiliary lesions and are also useful in characterizing the kinetics of hepatocyte uptake and excretion. We report a case of a 13-year old female with progressive familial intrahepatic cholestasis (PFIC) type 1 who demonstrated decreased uptake and excretion of gadoxetate disodium contrast material. This case illustrates the challenge of imaging children with cholestasis using hepatobiliary-specific contrast agents; we propose an alternative explanation for the delayed excretion that may be related to the underlying genetic defect of this child.
Simulations of a Variable Friction Device for Multihazard Mitigation

AbstractThis paper investigates the performance of a novel semiactive damping device at mitigating nonsimultaneous multihazard loads. The device, termed modified friction device (MFD), has been previously proposed by the authors. It consists of a variable friction system based on automotive drum brake technology. The device has been demonstrated in a laboratory environment, and its dynamic behavior has been modeled. This model is used to conduct numerical simulations on two representative structures, one short building located in Japan and the other tall building located in Boston, MA. Simulated hazards include wind, blast, and seismic loads. Various control cases are considered, including semiactive control under five different sets of control weights, and passive viscous and passive friction to benchmark performance. Results show that the semiactive control cases outperforms all of the other cases for the vast majority of hazards and performance indices, provided that the right control weights are utilized.
Fusion of Multisubject Hemodynamic and Event-Related Potential Data Using Independent Component Analysis

Functional magnetic resonance imaging (fMRI) data provides spatially localized subcentimeter information about blood flow and oxygenation secondary to neuronal activation, but with temporal resolution on the order of seconds. Event-related potential (ERP) studies provide millimeter resolution measurements of the electric changes induced by neuronal activity, but spatial information is not well localized and suffers from an ill-posed inverse problem since there are much fewer sensors than solutions. Combining or fusing these two techniques thus has the potential to provide simultaneous higher temporal and high spatial resolution. Localization of the brain's response to infrequent, task-relevant target 'oddball' stimuli in humans has remained challenging due to the lack of a single imaging technique with good spatial and temporal resolution. In this paper, we use independent component analysis to fuse ERP and fMRI modalities to identify, for the first time in humans, the dynamics of the auditory oddball response with high spatiotemporal resolution across the entire brain. The results illuminate a new era of brain research utilizing the precise temporal information in ERPs and the high spatial resolution of fMRI
Border control: selectivity of chloroplast protein import and regulation at the TOC-complex

Plants have evolved complex and sophisticated molecular mechanisms to regulate their development and adapt to their surrounding environment. Particularly the development of their specific organelles, chloroplasts and other plastid-types, is finely tuned in accordance with the metabolic needs of the cell. The normal development and functioning of plastids require import of particular subsets of nuclear encoded proteins. Most preproteins contain a cleavable sequence at their N terminal (transit peptide) serving as a signal for targeting to the organelle and recognition by the translocation machinery TOC–TIC (translocon of outer membrane complex–translocon of inner membrane complex) spanning the dual membrane envelope. The plastid proteome needs constant remodeling in response to developmental and environmental factors. Therefore selective regulation of preprotein import plays a crucial role in plant development. In this review we describe the diversity of transit peptides and TOC receptor complexes, and summarize the current knowledge and potential directions for future research concerning regulation of the different Toc isoforms.
7 Nudging Individuals Toward Healthier Food Choices with the 4 P's Framework for Behavior Change

D espite millions of dollars being spent on nutritional and wellness education, people eat more and eat more fattening foods than they did 20 years ago, with rates of obesity skyrocketing as a result. In contrast to entreating people to marshal their limited energy toward self-control by providing them with more information, we present a framework that leverages principles of behavioral economics, psychology, and marketing to restructure the environment in ways that (1) maximize the benefits arising from sporadic efforts to achieve health goals and (2) minimize the willpower needed to make healthy choices. We propose the 4 P’s Framework for Behavior Change, a comprehensive framework that integrates research findings to suggest ways of making desired behaviors like healthy eating less taxing. The framework can be applied by planners in any organizational context. This chapter is structured as follows. First, we discuss five psychological barriers to making healthy choices. Next, we present the 4 P’s Framework for Behavior Change: possibilities, process, persuasion, and person. Finally, we apply the framework in an organizational case study at Google.
CHARACTERISTIC BOUNDARY CONDITIONS FOR THE NUMERICAL SOLUTION OF EULER EQUATIONS BY THE DISCONTINUOUS GALERKIN METHODS

We present artificial boundary conditions for the numerical simulation of nonlinear Euler equations with the discontinuous Galerkin (DG) finite element method. The construction of the proposed boundary conditions is based on characteristic analysis which follows the Euler equations and are applied for boundaries with arbitrary shape and orientation. Numerical experiments demonstrate that the proposed boundary treatment enables to convect out of the computational domain complex flow features with little distortion. In addition, it is shown that small-amplitude acoustic disturbances could be convected out of the computational domain, with no significant deterioration of the overall accuracy of the method.
Inclusion of children experiencing parental separation and divorce in mainstream early childhood development in Zimbabwe

ABSTRACT This study examined teachers’ strategies for inclusion of children experiencing parental separation and divorce in mainstream early childhood development (ECD) in Zimbabwe. Entrenched in international research findings on best practices in inclusion and support for children experiencing parental separation and divorce in mainstream education, this descriptive study drew on a purposive sample of 23 mainstream ECD teachers. Thematic data analysis, which involved identification of themes founded on commonalities and patterns evident in document analysis, observational field notes and interview data, was utilized. Despite participants’ inadequate preparation to manage children with behavioural challenges and motivate active participation and involvement of all children in mainstream pedagogy, they held positive dispositions towards inclusion. Participants institutionalized collaborative structures and cultures, structured child-centred activities, support systems, emotional support, educational support and behavioural support to include children experiencing parental separation and divorce in mainstream classrooms. This study is a springboard for future studies on the subject.