Miracle-dz/UniFAR-FTUs huggingface dataset:
A Hybrid Metaheuristic Algorithm for Multi-Objective Runway Scheduling Problem in Simulation-based Optimization

Even a moderately sized real-life runway scheduling problem tends to be too complex to be solved by analytical methods where the proposed mathematical models for this problem all belong to the complexity class of NP-Hard in a strong sense. Therefore, it is only possible to solve practical runway scheduling problems using mathematical programming methods by making a large number of simplifications and assumptions. As a result, most of the analytical models proposed in the literature suffer from too much abstraction and, in turn, not much applicability in practice. However, simulation-based methods have the capability to characterize complex and stochastic real-life runway systems in detail, as well as cope with several constraints and multiple objectives, which are important factors in practice. With a simulation-based optimization (SbO) approach where a discrete event simulation model is integrated with an optimization algorithm, the search for Pareto-optimal solutions can be done conveniently. Due to its large and unstructured search space, finding exact Pareto-optimal solutions to such multi-objective optimization problem is computationally intractable; given that such solutions need to be found in a reasonable timeframe, metaheuristic algorithms are the best option to pursue. In this study, a hybrid metaheuristic algorithm based on scatter search (SS), which takes advantage of the structural details of the problem, and uses a dynamic update mechanism to produce high-quality solutions and a rebuilding strategy to promote diversity, is proposed and presented. SS-based multi-objective evolutionary algorithms seem to be a promising research direction due to its efficiency and effectiveness in finding a set of non-dominated solutions in a SbO framework with multiple objectives. The experimental results that evaluate the proposed hybrid metaheuristic algorithm’s performance in terms of both diversity of solutions and their proximity to the Pareto frontier are also presented.
Modeling Classifier for Code Mixed Cross Script Questions

With a boom in the internet, the social media text had been increasing day by day and the user generated content (such as tweets and blogs) in Indian languages are written using Roman script due to various socio-cultural and technological reasons. A majority of these posts are multilingual in nature and many involve code mixing where lexical items and grammatical features from two languages appear in one sentence. Focusing on this current multilingual scenario, code-mixed cross-script (i.e., non-native script) data gives rise to a new problem and presents serious challenges to automatic Question Answering (QA) and for this question classification will be required which is an important step towards QA. This paper proposes an approach to handle cross script question classification as it is an important task of question analysis which detects the category of the question.
A Smart Electrostatic Sensor for Online Condition Monitoring of Power Transmission Belts

Online condition monitoring of power transmission belts is essential to keep industrial belt-driven equipment functioning smoothly and reliably. This paper presents a smart electrostatic sensor that monitors belt motion through detection of static charge on the belt. A theoretical model is established using the method of moments for calculation of induced charge on strip-shaped electrodes placed in the vicinity of a belt moving both axially and transversely. The sensor unit converts the induced charge into proportional voltage signals using charge amplifiers and measures belt speed and vibration through cross correlation and spectral analysis, respectively. The performance of the smart electrostatic sensor is validated against a photoelectric rotary encoder and a laser displacement sensor. Comparative experimental results show that the belt speed can be measured with a relative error within ±2% over the range 1.7–15.5 m/s. The electrostatic sensor is capable of measuring the frequencies of transverse vibration accurately. Although absolute displacement cannot be measured due to the uncertain level of charge on the belt, the measurement results of relative vibration magnitudes for different modes and at different belt speeds are reasonably accurate.
Making the Transition: An Exploratory Study of Academic Achievement, Involvement, Adjustment, and Satisfaction of Transfer Students at UCLA.

In fall 1995, a study was conducted of students who transferred from California community colleges to the University of California at Los Angeles (UCLA) to determine their academic performance and to explore the nature of their adjustment. Two transfer populations were examined: students enrolled in the Transfer Alliance Program (TAP), a program run by UCLA to prepare community college students to transfer in as juniors, and traditional transfer students with no prior TAP experience. Questionnaires were administered to 868 students who had transferred to UCLA in fall 1993, soliciting information on students' characteristics, community college experiences, and UCLA experiences. Based on responses from 49 TAP and 201 non-TAP students, the study found that 77% of TAP and 59% of non-TAP students were of traditional college age (i.e., younger than 24). In addition, while the mean grade point average (GPA) of TAP students was significantly higher in high school than non-TAP students and slightly higher at their community college, it was slightly lower at UCLA. TAP students were also more likely to have had positive experiences with academic counselors at community colleges and were more likely to approach faculty outside of class. Non-TAP students were more likely to use academic tutoring and to have higher levels of academic involvement. Contains 33 references. Appendixes provide a list of colleges participating in TAP, a description of TAP, the survey instrument and cover letters, and tables of survey responses. (HAA) *********************************************************************** * Reproductions supplied by EDRS are the best that can be made from the original document. *************************************************************AAAAAAA * Making the Transition: An Exploratory. Study of Academic Achievement, Involvement, Adjustment, and Satisfaction of Transfer Students at UCLA
An Efficient and Lightweight Source Privacy Protecting Scheme for Sensor Networks Using Group Knowledge

Providing source privacy is a critical security service for sensor networks. However, privacy preserving in sensor networks is a challenging task, particularly due to the limited resources of sensor nodes and the threat of node capture attack. On the other hand, existing works use either random walk path or fake packets injection, both incurring tremendous overhead. In this work, we propose a new approach, which separates the sensor nodes into groups. The source packet is randomly forwarded within and between the groups with elaborate design to ensure communication anonymity; furthermore, members of each group exchange encrypted traffic of constant packet length to make it difficult for the adversary to trace back. One salient feature of the proposed scheme is its flexibility of trading transmission for higher anonymity requirement. We analyze the ability of our proposed scheme to withstand different attacks and demonstrate its efficiency in terms of overhead and functionality when compared to existing works.
primers4clades: a web server that uses phylogenetic trees to design lineage-specific PCR primers for metagenomic and diversity studies

Primers4clades is an easy-to-use web server that implements a fully automatic PCR primer design pipeline for cross-species amplification of novel sequences from metagenomic DNA, or from uncharacterized organisms, belonging to user-specified phylogenetic clades or taxa. The server takes a set of non-aligned protein coding genes, with or without introns, aligns them and computes a neighbor-joining tree, which is displayed on screen for easy selection of species or sequence clusters to design lineage-specific PCR primers. Primers4clades implements an extended CODEHOP primer design strategy based on both DNA and protein multiple sequence alignments. It evaluates several thermodynamic properties of the oligonucleotide pairs, and computes the phylogenetic information content of the predicted amplicon sets from Shimodaira–Hasegawa-like branch support values of maximum likelihood phylogenies. A non-redundant set of primer formulations is returned, ranked according to their thermodynamic properties. An amplicon distribution map provides a convenient overview of the coverage of the target locus. Altogether these features greatly help the user in making an informed choice between alternative primer pair formulations. Primers4clades is available at two mirror sites: http://maya.ccg. unam.mx/primers4clades/and http://floresta.eead. csic.es/primers4clades/. Three demo data sets and a comprehensive documentation/tutorial page are provided for easy testing of the server's capabilities and interface.
Treatments for gestational diabetes: a systematic review and meta-analysis

Objective To investigate the effectiveness of different treatments for gestational diabetes mellitus (GDM). Design Systematic review, meta-analysis and network meta-analysis. Methods Data sources were searched up to July 2016 and included MEDLINE and Embase. Randomised trials comparing treatments for GDM (packages of care (dietary and lifestyle interventions with pharmacological treatments as required), insulin, metformin, glibenclamide (glyburide)) were selected by two authors and double checked for accuracy. Outcomes included large for gestational age, shoulder dystocia, neonatal hypoglycaemia, caesarean section and pre-eclampsia. We pooled data using random-effects meta-analyses and used Bayesian network meta-analysis to compare pharmacological treatments (ie, including treatments not directly compared within a trial). Results Forty-two trials were included, the reporting of which was generally poor with unclear or high risk of bias. Packages of care varied in their composition and reduced the risk of most adverse perinatal outcomes compared with routine care (eg, large for gestational age: relative risk0.58 (95% CI 0.49 to 0.68; I2=0%; trials 8; participants 3462). Network meta-analyses suggest that metformin had the highest probability of being the most effective treatment in reducing the risk of most outcomes compared with insulin or glibenclamide. Conclusions Evidence shows that packages of care are effective in reducing the risk of most adverse perinatal outcomes. However, trials often include few women, are poorly reported with unclear or high risk of bias and report few outcomes. The contribution of each treatment within the packages of care remains unclear. Large well-designed and well-conducted trials are urgently needed. Trial registration number PROSPERO CRD42013004608.
Siblingrivalry: online autotuning through local competitions

Modern high performance libraries, such as ATLAS and FFTW, and programming languages, such as PetaBricks, have shown that autotuning computer programs can lead to significant speedups. However, autotuning can be burdensome to the deployment of a program, since the tuning process can take a long time and should be re-run whenever the program, microarchitecture, execution environment, or tool chain changes. Failure to re-autotune programs often leads to widespread use of sub-optimal algorithms. With the growth of cloud computing, where computations can run in environments with unknown load and migrate between different (possibly unknown) microarchitectures, the need for online autotuning has become increasingly important. We present SiblingRivalry, a new model for always-on online autotuning that allows parallel programs to continuously adapt and optimize themselves to their environment. In our system, requests are processed by dividing the available cores in half, and processing two identical requests in parallel on each half. Half of the cores are devoted to a known safe program configuration, while the other half are used for an experimental program configuration chosen by our self-adapting evolutionary algorithm. When the faster configuration completes, its results are returned, and the slower configuration is terminated. Over time, this constant experimentation allows programs to adapt to changing dynamic environments and often outperform the original algorithm that uses the entire system.
Three Weeks of Overload Training Increases Resting Muscle Sympathetic Activity

Purpose Overload training is hypothesized to alter autonomic regulation, although interpretations using indirect measures of heart rate variability are conflicting. The aim of the present study was to examine the effects of overload training on muscle sympathetic nerve activity (MSNA), a direct measure of central sympathetic outflow, in recreational endurance athletes. Methods Measurements of heart rate variability, cardiac baroreflex sensitivity (BRS), MSNA (microneurography), and sympathetic BRS were obtained in 17 healthy triathletes and cyclists after 1 wk of reduced training (baseline) and again after 3 wk of either regular (n = 7) or overload (n = 10) training. Results After training, the changes (&Dgr;) in peak power output (10 ± 10 vs −12 ± 9 W, P < 0.001), maximal heart rate (−2 ± 4 vs −8 ± 3 bpm, P = 0.006), heart rate variability (SD of normal-to-normal intervals, 27 ± 31 vs −3 ± 25 ms; P = 0.04), and cardiac BRS (7 ± 6 vs −2 ± 8 ms·mm Hg−1, P = 0.02) differed between the control and overload groups. The change in MSNA burst frequency (−2 ± 2 vs 4 ± 5 bursts per minute, P = 0.02) differed between groups. Across all participants, the changes in resting MSNA and peak power output were correlated negatively (r = −0.51, P = 0.04). No between-group differences in resting heart rate or blood pressure were observed (all P > 0.05). Conclusions Overload training increased MSNA and attenuated increases in cardiac BRS and heart rate variability observed with regular training. These results support neural adaptations after overload training and suggest that increased central sympathetic outflow may be linked with decreased exercise performance.
The clinical importance of white matter hyperintensities on brain magnetic resonance imaging: systematic review and meta-analysis

Objectives To review the evidence for an association of white matter hyperintensities with risk of stroke, cognitive decline, dementia, and death. Design Systematic review and meta-analysis. Data sources PubMed from 1966 to 23 November 2009. Study selection Prospective longitudinal studies that used magnetic resonance imaging and assessed the impact of white matter hyperintensities on risk of incident stroke, cognitive decline, dementia, and death, and, for the meta-analysis, studies that provided risk estimates for a categorical measure of white matter hyperintensities, assessing the impact of these lesions on risk of stroke, dementia, and death. Data extraction Population studied, duration of follow-up, method used to measure white matter hyperintensities, definition of the outcome, and measure of the association of white matter hyperintensities with the outcome. Data synthesis 46 longitudinal studies evaluated the association of white matter hyperintensities with risk of stroke (n=12), cognitive decline (n=19), dementia (n=17), and death (n=10). 22 studies could be included in a meta-analysis (nine of stroke, nine of dementia, eight of death). White matter hyperintensities were associated with an increased risk of stroke (hazard ratio 3.3, 95% confidence interval 2.6 to 4.4), dementia (1.9, 1.3 to 2.8), and death (2.0, 1.6 to 2.7). An association of white matter hyperintensities with a faster decline in global cognitive performance, executive function, and processing speed was also suggested. Conclusion White matter hyperintensities predict an increased risk of stroke, dementia, and death. Therefore white matter hyperintensities indicate an increased risk of cerebrovascular events when identified as part of diagnostic investigations, and support their use as an intermediate marker in a research setting. Their discovery should prompt detailed screening for risk factors of stroke and dementia.
Structural Brain Connectivity and Treatment Improvement in Mood Disorder.

BACKGROUND The treatment of depressive episodes is well established, with clearly demonstrated effectiveness of antidepressants and psychotherapies. However, more than one-third of depressed patients do not respond to treatment. Identifying the brain structural basis of treatment-resistant depression could prevent useless pharmacological prescriptions, adverse events, and lost therapeutic opportunities. METHODS Using diffusion magnetic resonance imaging, we performed structural connectivity analyses on a cohort of 154 patients with mood disorder (MD) - and 77 sex- and age-matched healthy control (HC) participants. To assess illness improvement, the MD patients went through two clinical interviews at baseline and at 6-month follow-up and were classified based on the Clinical Global Impression-Improvement score into improved or not-improved. First, the threshold-free network-based statistics was conducted to measure the differences in regional network architecture. Second, nonparametric permutations tests were performed on topological metrics based on graph theory to examine differences in connectome organization. RESULTS The threshold-free network-based statistics revealed impaired connections involving regions of the basal ganglia in MD patients compared to HC. Significant increase of local efficiency and clustering coefficient was found in the lingual gyrus, insula and amygdala in the MD group. Compared with the not-improved, the improved displayed significantly reduced network integration and segregation, predominately in the default-mode regions, including the precuneus, middle temporal lobe and rostral anterior cingulate. CONCLUSIONS This study highlights the involvement of regions belonging to the basal ganglia, the fronto-limbic network and the default mode network, leading to a better understanding of MD disease and its unfavorable outcome.
Perceived early-life maternal care and the cortisol response to repeated psychosocial stress.

BACKGROUND In the past decade, a body of animal and human research has revealed a profound influence of early-life experiences, ranging from variations in parenting behaviour to severe adversity, on hypothalamic-pituitary-adrenal axis regulation in adulthood. In our own previous studies, we have shown how variations in early-life parental care influence the development of the hippocampus and modify the cortisol awakening response. METHODS In the present study, we investigated the influence of early-life maternal care on cortisol, heart rate and subjective psychological responses to the repeated administration of a psychosocial laboratory stressor in a population of 63 healthy young adults. Low, medium and high early-life maternal care groups were identified using the Parental Bonding Instrument. RESULTS Controlling for the effect of sex, we found an inverted u-shaped relation between increasing levels of maternal care and cortisol stress responsivity. Specifically, overall and stress-induced cortisol levels went from below normal in the low maternal care, to normal in the medium care, back to below normal in the high maternal care groups. We found no group differences with respect to heart rate and subjective psychological stress measures. Whereas low and high maternal care groups exhibited similarly low endocrine stress responses, their psychological profiles were opposed with increased levels of depression and anxiety and decreased self-esteem in the low care group. LIMITATIONS Sex was unequally distributed among maternal care groups, whereby the number of men with low maternal care was too small to allow introducing sex as a second between-group variable. CONCLUSION We discuss the potential significance of this dissociation between endocrine and psychological parameters with respect to stress vulnerability and resistance for each maternal care group.
Neocortical Self-Structuration as a Basis for Learning

Neocortical connectivity displays striking regulari- ties that self-organize via epigenetic interactions with activity. We construe this self-structuration as a process of spatiotemporal pattern formation in a simple neural network model. Starting from random connections, ordered "synfire-chain" structures and wave-like correlations emerge simultaneously and reinforce each other through cooperation, while the global stability of the net- work is maintained by competition. Out of broad diffuse contacts and low stochastic firing, select synapses are strengthened and spike correlations increase. We suggest that the concurrent growth of multiple chains defines a mesoscopic scale of neural organization, and view the ontogenetic development of the nerv- ous system as a "self-made tapestry" of synfire patterns, which could provide the basis for compositionality and learning.
DC-DC converters: From discrete towards fully integrated CMOS

Monolithic integration of electronic systems is one of the major techniques to reduce cost, size and power consumption in state-of-the-art consumer applications. Integration of transceivers and other mixed-signal building blocks has proven to be a very successful approach to build low cost, compact and portable systems [1]. Remarkably a certain building block remains discrete in commercial applications: the switched-power supply. This paper will demonstrate how recent research efforts cleared the path to develop fully integrated DC-DC converters in standard CMOS.
An algorithm for tracking multiple targets

An algorithm for tracking multiple targets in a cluttered environment is developed. The algorithm is capable of initiating tracks, accounting for false or missing reports, and processing sets of dependent reports. As each measurement is received, probabilities are calculated for the hypotheses that the measurement came from previously known targets in a target file, or from a new target, or that the measurement is false. Target states are estimated from each such data-association hypothesis, using a Kalman filter. As more measurements are received, the probabilities of joint hypotheses are calculated recursively using all available information such as density of unknown targets, density of false targets, probability of detection, and location uncertainty. This branching technique allows correlation of a measurement with its source based on subsequent, as well as previous, data. To keep the number of hypotheses reasonable, unlikely hypotheses are eliminated and hypotheses with similar target estimates are combined. To minimize computational requirements, the entire set of targets and measurements is divided into clusters that are solved independently. In an illustrative example of aircraft tracking, the algorithm successfully tracks targets over a wide range of conditions.