Miracle-dz/UniFAR-FTUs huggingface dataset:
Combined forecasting of regional logistics demand optimized by a Genetic Algorithm

Accurate prediction of regional logistics demand was the premise for scientific decision-making. On the basis of analyzing Trend Extrapolation, Grey System Method and Regression Method, two weight value determination methods that based on Method of Arithmetic Means and Validity Method were compared. Then, the idea of using a Genetic Algorithm to optimize weight values was put forward. The predicting outcomes of turnover volume of freight transport in region A turned out that the Genetic Algorithm can minimize the Sum of Squared Errors.
Latent space model for analysis of conventions

This paper propose a new approach to predict spreading behavior of conventions. Conventions in our case are verbal i.e. phrases used by many people for a new purpose regarding a social issue. We study usage of some conventions in Twitter popularized among Persian speaking users. We show that the number of tweets that contain a convention phrase in a period has a bell shaped curve. We use the latent space model to calculate the distance matrix for a convention in order to understand its spreading behavior. We first calculate the distance matrices of the conventions and utilize them to estimate the distance matrix for new conventions.
Differentiating between Encoding and Processing during Diagnostic Reasoning : An Eye tracking study

When finding a best explanation for observed symptoms a multitude of information has to be integrated and matched against explanations stored in memory. Although assumptions about ongoing memory processes can be derived from the process models, little process data exists that would allow to sufficiently test these assumptions. In order to explore memory processes in diagnostic reasoning, 29 participants were asked to solve a visual reasoning task (the Black Box paradigm) where critical information had to be retrieved from memory. This study focused on differentiating between processes that take place during the encoding and the evaluation of symptom information by comparing eye movement measures (the number of fixation and fixation duration per dwell). Results will be discussed in light of existing theories on sequential diagnostic reasoning. Further, it will be discussed to which extent eye movements can be informative about memory processes underlying sequential diagnostic
Assessing the Effects of Fiscal Shocks

This paper investigates the response of real wages and hours worked to an exogenous shock in fiscal policy. We identify this shock with the dynamic response of government purchases and tax rates to an exogenous increase in military purchases. The fiscal shocks that we isolate are characterized by highly correlated increases in government purchases, tax rates and hours worked as well as persistent declines in real wages. We assess the ability of standard Real Business Cycle models to account for these facts. They can-but only under the assumption that marginal income tax rates are constant, a standard assumption in the literature. Once we abandon this counterfactual assumption, RBC models cannot account for the facts. We argue that our empirical findings pose a challenge to a wide class of business cycle models.
Vulnerability to sleep deprivation : a drift diffusion model perspective

Most of us have experienced sleep deprivation (SD) every now and then, and know from experience that lack of sleep can adversely affect cognitive function and vigilance. The neurophysiology of sleep and lack thereof, its association with diseases and general well being has been studied extensively for over a century. The lack of sleep has enormous economic, health and life cost. The loss of performance and attention after SD can lead to industrial and transportation accidents, medical errors and lapses in security. While the decline in performance with SD is well established, it was recently (2004) observed that this decline in performance varies significantly between subjects, with some subjects remaining relatively unaffected while others show considerable decline in performance. This subject specific vulnerability towards SD remains stable in time and shows trait like features, i.e., the relative ranking of individuals according to subject specific performance (on some behavioral task) is maintained over time irrespective of sleep history. This suggests a stable neuro-cognitive basis for between-subjects differences in performance. Being able to predict an individual’s vulnerability to performance decline when sleep deprived is therefore of considerable interest. The Psychomotor Vigilance Task (PVT) is a sustained-attention, simple onechoice reaction time (RT) task that measures the speed with which subjects respond to a visual stimulus. It is a proven assay for evaluating vigilance. We use the PVT to evaluate and quantify the degradation of performance with SD on three large independent data sets collected from two different labs over an extended period of time. Instead of looking at the RTs using summary statistics as it is traditionally done, we used the drift diffusion model (DDM) which is a powerful model of perceptual decision making with strong neuro-behavioral underpinnings. Using DDM we tried to address three fundamental questions: (1) How are subjects vulnerable to SD differentially affected compared to resistant subjects. (2) Can these differences measured prior to SD predict performance
End-to-end delay guarantees for multiple-channel schedulers

Consider a network in which adjacent nodes exchange messages via multiple communication channels. Multiple channels between adjacent nodes are desirable due to their cost effectiveness and improved fault-tolerance. We consider the problem of providing deterministic quality of service guarantees in this network. We show that any scheduling protocol designed for a single channel can be converted into a multiple-channel scheduling protocol without significantly increasing the delay at the scheduling node. However, because there are multiple channels between adjacent nodes, the packets of a flow may be reordered. This in turn significantly increases the upper bound on the end-to-end delay of the flow. We show how this increase in delay can be avoided through the use of efficient sorting techniques.
An analytic center cutting plane algorithm for finding equilibrium points

We present a variant of the analytic center cutting plane algorithm proposed by Goffin et al. (1996) to approximately solve equi- librium problems as proposed by Blum and Oettli (1994), which include as particular problems the variational inequalities problem, the Nash equilibria problem in non-cooperative games, the convex minimization problem, and the fixed point problem. Furthermore, we analyze the convergence and complexity of the modified algorithm.
An automatic wavelet-based nonlinear image enhancement technique for aerial imagery

Recently we proposed a wavelet-based dynamic range compression algorithm to improve the visual quality of digital images captured in the high dynamic range scenes with non-uniform lighting conditions. The fast image enhancement algorithm which provides dynamic range compression preserving the local contrast and tonal rendition is a very good candidate in aerial imagery applications such as image interpretation for defense and security tasks. This algorithm can further be applied to video streaming for aviation safety. In this paper the latest version of the proposed algorithm which is able to enhance aerial images so that the enhanced images are better then direct human observation, is presented. The results obtained by applying the algorithm to numerous aerial images show strong robustness and high image quality.
Trends in Breast Cancer Stage and Mortality in Michigan (1992–2009) by Race, Socioeconomic Status, and Area Healthcare Resources

The long-term effect of socioeconomic status (SES) and healthcare resources availability (HCA) on breast cancer stage of presentation and mortality rates among patients in Michigan is unclear. Using data from the Michigan Department of Community Health (MDCH) between 1992 and 2009, we calculated annual proportions of late-stage diagnosis and age-adjusted breast cancer mortality rates by race and zip code in Michigan. SES and HCA were defined at the zip-code level. Joinpoint regression was used to compare the Average Annual Percent Change (AAPC) in the median zip-code level percent late stage diagnosis and mortality rate for blacks and whites and for each level of SES and HCA. Between 1992 and 2009, the proportion of late stage diagnosis increased among white women [AAPC = 1.0 (0.4, 1.6)], but was statistically unchanged among black women [AAPC = −0.5 (−1.9, 0.8)]. The breast cancer mortality rate declined among whites [AAPC = −1.3% (−1.8,−0.8)], but remained statistically unchanged among blacks [AAPC = −0.3% (−0.3, 1.0)]. In all SES and HCA area types, disparities in percent late stage between blacks and whites appeared to narrow over time, while the differences in breast cancer mortality rates between blacks and whites appeared to increase over time.
Quark Confinement and the Hadron Spectrum

These lectures contain an introduction to the following topics: 1) Phenomenology of the hadron spectrum; 2) The static Wilson loop in perturbative and in lattice QCD. Confinement and the flux tube formation; 3) Non static properties: effective field theories and relativistic corrections to the quarkonium potential; 4) The QCD vacuum: minimal area law, Abelian projection and dual Meissner effect, stochastic vacuum.
Sequential haplotype scan methods for association analysis

Multi‐locus association analyses, including haplotype‐based analyses, can sometimes provide greater power than single‐locus analyses for detecting disease susceptibility loci. This potential gain, however, can be compromised by the large number of degrees of freedom caused by irrelevant markers. Exhaustive search for the optimal set of markers might be possible for a small number of markers, yet it is computationally inefficient. In this paper, we present a sequential haplotype scan method to search for combinations of adjacent markers that are jointly associated with disease status. When evaluating each marker, we add markers close to it in a sequential manner: a marker is added if its contribution to the haplotype association with disease is warranted, conditional on current haplotypes. This conditional evaluation is based on the well‐known Mantel‐Haenszel statistic. We propose two permutation based methods to evaluate the growing haplotypes: a haplotype method for the combined markers, and a summary method that sums conditional statistics. We compared our proposed methods, the single‐locus method, and a sliding window method using simulated data. We also applied our sequential haplotype scan algorithm to experimental data for CYP2D6. The results indicate that the sequential scan procedure can identify a set of adjacent markers whose haplotypes might have strong genetic effects or be in linkage disequilibrium with disease predisposing variants. As a result, our methods can achieve greater power than the single‐locus method, yet is much more computationally efficient than sliding window methods. Genet. Epidemiol. 2007. © 2007 Wiley‐Liss, Inc.
Recommendations for improving serum creatinine measurement: a report from the Laboratory Working Group of the National Kidney Disease Education Program.

BACKGROUND Reliable serum creatinine measurements in glomerular filtration rate (GFR) estimation are critical to ongoing global public health efforts to increase the diagnosis and treatment of chronic kidney disease (CKD). We present an overview of the commonly used methods for the determination of serum creatinine, method limitations, and method performance in conjunction with the development of analytical performance criteria. Available resources for standardization of serum creatinine measurement are discussed, and recommendations for measurement improvement are given. METHODS The National Kidney Disease Education Program (NKDEP) Laboratory Working Group reviewed problems related to serum creatinine measurement for estimating GFR and prepared recommendations to standardize and improve creatinine measurement. RESULTS The NKDEP Laboratory Working Group, in collaboration with international professional organizations, has developed a plan that enables standardization and improved accuracy (trueness) of serum creatinine measurements in clinical laboratories worldwide that includes the use of the estimating equation for GFR based on serum creatinine concentration that was developed from the Modification of Diet in Renal Disease (MDRD) study. CONCLUSIONS The current variability in serum creatinine measurements renders all estimating equations for GFR, including the MDRD Study equation, less accurate in the normal and slightly increased range of serum creatinine concentrations [<133 micromol/L (1.5 mg/dL)], which is the relevant range for detecting CKD [<60 mL.min(-1). (1.73 m2)(-1)]. Many automated routine methods for serum creatinine measurement meet or exceed the required precision; therefore, reduction of analytical bias in creatinine assays is needed. Standardization of calibration does not correct for analytical interferences (nonspecificity bias). The bias and nonspecificity problems associated with some of the routine methods must be addressed.
Solutions for Missing Data in Structural Equation Modeling

Many times in both educational and social science research it is impossible to collect data that is complete. When administering a survey, for example, people may answer some questions and not others. This missing data causes a problem for researchers using structural equation modeling (SEM) techniques for data analyses. Because SEM and multivariate methods require complete data, several methods have been proposed for dealing with these missing data.What follows is a review of several methods currently used, a description of strengths and weaknesses of each method, and a proposal for future research. Methods for Dealing with Missing Data Listwise Deletion Listwise deletion is an ad hoc method of dealing with missing data in that it deals with the missing data before any substantive analyses are done. It is considered the easiest and simplest method of dealing with missing data (Brown, 1983). It involves removing incomplete cases (record with missing data on any variable) from the dataset. This means the researcher removes all the records that have missing data on any variable. Depending on the sample size and number of variables this can result in a great reduction in the sample size available for data analysis. Listwise deletion assumes that the data are missing completely at random (MCAR). Data are missing completely at random when the probability of obtaining a particular pattern of missing data is not dependant on the values that are missing and when the probability of obtaining the missing data pattern in the sample is not dependant on the observed data (Rubin, 1976). An advantage in using listwise deletion is that all analyses are calculated with the same set of cases. Pairwise Deletion
Seasonal Adjustment and the Detection of Business Cycle Phases

To date, there has been little investigation of the impact of seasonal adjustment on the detection of business cycle expansion and recession regimes. We study this question both analytically and through Monte Carlo simulations. Analytically, we view the occurrence of a single business cycle regime as a structural break that is later reversed, showing that the effect of the linear symmetric X-11 filter differs with the duration of the regime. Through the use of Markov switching models for regime identification, the simulation analysis shows that seasonal adjustment has desirable properties in clarifying the true regime when this is well underway, but it distorts regime inference around turning points, with this being especially marked after the end of recessions and when the one-sided X-11 filter is employed.
Support of Managerial Decision Making by Transductive Learning

Transductive inference has been introduced as a novel paradigm towards building predictive classification models from empirical data. Such models are routinely employed to support decision making in, e.g., marketing, risk management and manufacturing. To that end, the characteristics of the new philosophy are reviewed and their implications for typical decision problems are examined. The paper’s objective is to explore the potential of transductive learning for corporate planning. The analysis reveals two main factors that govern the applicability of transduction in business settings, decision scope and urgency. In a similar fashion, two major drivers for its effectiveness are identified and empirical experiments are undertaken to confirm their influence. The results evidence that transductive classifiers are well superior to their inductive counterparts if their specific application requirements are fulfilled.
A Unified Approach for the Optimal PMU Location for Power System State Estimation

Phasor measurement units (PMUs) are considered as a promising tool for future monitoring, protection and control of power systems. In this paper, a unified approach is proposed in order to determine the optimal number and locations of PMUs to make the system measurement model observable and thereby can be used for power system state estimation. The PMU placement problem is formulated as a binary integer linear programming (BILP), in which the binary decision variables (0, 1) determine whether to install a PMU at each bus, while preserving the system observability and lowest system metering economy. The proposed approach integrates the impacts of both existing conventional power injection/flow measurements (if any) and the possibility of single or multiple PMU loss into the decision strategy of the optimal PMU allocation. Unlike other available techniques, the network topology remains unaltered for the inclusion of conventional measurements, and therefore the network connectivity matrix is built only once based on the original network topology. The mathematical formulation of the problem maintains the original bus ordering of the system under study, and therefore the solution directly points at the optimal PMU locations. Simulations using Matlab are conducted on a simple testing seven-bus system, as well as on different IEEE systems (14-bus, 30-bus, 57-bus, and 118-bus) to prove the validity of the proposed method. The results obtained in this paper are compared with those published before in literature.
Automatic Generalized Nonparametric Regression via Maximum Likelihood

A relatively recent development in nonparametric regression is the representation of spline-based smoothers as mixed model fits. In particular, generalized nonparametric regression (e.g. smoothingwith a binary response) corresponds to fitting a generalized linear mixedmodel. Automation, or data-driven smoothing parameter selection, can be achieved via (restricted) maximum likelihood estimation of the variance component in the model. However, multidimensional integrals arise in the likelihood. This paper describes and compares some approaches to this problem in the context of nonparametric regression.
Associates with Etiquette: Meta-Communication to Make Human-Automation Interaction more Natural, Productive and Polite

Several different architectures for adaptive automation systems have now been demonstrated. Yet users remain ambivalent them—as our own work on the U.S. Air Force’s Rotorcraft Pilot’s Associate (RPA) reveals. Thus, we argue, research should be shifting away from how adaptive interfaces and automation can be made to work, and onto how they should behave in conjunction with human users. We find it useful to think about behavioral details and roles and responsibilities in terms of various ‘etiquettes’ for human-machine interactions. Results from RPA suggest that one aspect of establishing acceptable etiquette may be more and better inclusion of ‘meta-communication’ on the part of the system—the ability to report and accept feedback on the system’s perceptions and intentions. We discuss these results in the broader context of etiquette recommendations for adaptive automation systems.
Quantifying the Aerosol Indirect Effect Using Ground-Based Remote Sensors and Models

The effect of aerosols on cloud microphysical and radiative properties (the “indirect effect”) has the greatest uncertainty of all known climate-forcing mechanisms. Increases in aerosol concentrations result in higher concentrations of cloud condensation nuclei (CCN), increased cloud droplet concentrations, and smaller droplet sizes (Twomey 1974). A possible secondary effect is the suppression of rainfall. Together, these effects generate more reflective clouds, which in theory create a radiative forcing estimated on the global scale to range from 0.0 Wm to -4.8 Wm. Numerous observational studies identify situations where the connection between the aerosol and cloud microphysics is clear, while in other situations no connection appears to exist. However, the conclusions differ widely. For example, some in situ studies identify the indirect effect in stratiform clouds but not in cumuliform. Other studies clearly find the effect in cumuliform clouds. Because the indirect effect is defined as a climatic forcing, it is necessary to extend our scope beyond in situ measurements and evaluate it on the regional or global scale using ground-based or satellite remote sensing. Satellite remote sensing efforts have been widely used in biomass burning regions and provide evidence of the indirect effect beyond the limited environment of ship track or other in situ experiments. However, they also show variation in the strength of the indirect effect that cannot be explained by simple formulations such as Twomey’s.
The relationship between supplier management and firm's performance: A multidimensional perspective

This paper presents an empirical study which examines the relationships between supplier management practices and firm's performance. Specifically, this study focuses on three supplier management practices, namely strategic long-term relationship, supplier assessment, and logistics integration, and tests their effects on four different operational performance measures, namely quality, delivery, flexibility, and cost. The data were drawn from 232 manufacturing firms in Australia. The findings demonstrate the unique effect of different supplier management practices on different dimensions of performance, and they provide practical insights for managers to understand the effectiveness as well as the limitation of different supplier management practices in enhancing different types of performance in the firm's operations.
Visual abstraction improvement of interactive dot map

The distribution of multiclass discrete data in geographic space is a research hotspot in the field of geographic-related visualization. As a basic visual presentation of such data, the advantages of dot maps are perceptual intuition and abundant details, but there is also the problem of poor readability due to the points overlap. The approach of density estimation by resolution is proposed in this paper to optimize dot maps, and to flexibly adjust sampling parameters of the current resolution, so as to show the details to the maximum extent and maintain the relative density characteristics of various types of property. In order to compensate the missing discrete features caused by sampling, a series of interactive tools are used to effectively improve the accuracy of visual analysis and assist the overall visual representation. Finally, the effectiveness of this approach is proved through case analysis and user research.