Miracle-dz/UniFAR-FTUs huggingface dataset:
Time-dependent Canonical Correlation Analysis for Multilevel Time Series

Canonical Correlation Analysis is a technique in multivariate data analysis for finding linear projections that maximize the correlation between two groups of variables. The correlations are typically defined without accounting for the serial correlations between observations, a typical setting for time series data. To understand the coupling dynamics and temporal variations between the two time-varying sources, we introduce the time-dependent canonical correlation analysis (TDCCA), a method for inferring time-dependent canonical vectors from multilevel time series data. A convex formulation of the problem is proposed, which leverages the singular value decomposition (SVD) characterization of all solutions of the CCA problem. We use simulated datasets to validate the proposed algorithm. Moreover, we propose a novel measure, canonical correlation variation as another way to assess the dynamic pattern of brain connections and we apply it to a real resting state fMRI dataset to study the aging effects on brain connectivity. Additionally, we explore our proposed method in a task-related fMRI to detect the temporal dynamics due to different motor tasks. We show that, compared to extant methods, the TDCCA-based approach not only detect temporal changes but also improves feature extraction. Together, this paper contributes broadly to new computational methodologies in understanding multilevel time series.
Ontoloji Tabanlı Çok-etmenli Sistemlerin Model Güdümlü Geliştirilmesi

Çok-etmenli Sistemler’in (ÇES) geliştirilmesinde mümkün olan en yüksek soyutlama seviyesinde çalışmak ÇES’lerin iç karmaşıklıklarından, dağıtık yapılarından ve açık bilgi modellerine (ontolojiler) dayalı geliştirilmelerinden dolayı gün geçtikçe önem kazanmaktadır. Yazılım geliştirme odağını koddan modellere çeviren ve farklı soyutlama seviyelerindeki modelleri kullanarak yazılım geliştirmedeki karmaşıklığı azaltmayı hedefleyen Model Güdümlü Geliştirme (MGG) yaklaşımının ÇES yazılımlarının geliştirilmesinde de uygun bir alternatif olduğu görülmektedir. Bu durumdan hareketle bu bildiride ontoloji tabanlı ÇES’lerin model güdümlü geliştirilmesi için gereken sürece ait adımlar tanımlanmıştır ve uygulanacak yöntem üzerinde durulmuştur. ÇES sistemlerinin platforma özgü seviyede modellenmesi için gereken süreç adımı somutlaştırılarak yazılım araçları ile nasıl desteklendiği yine bu bildiride anlatılmıştır.
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A very simple classi cation of PfaÆan systems of dimension 2 in 5 variables is given. It is used to show that any controllable driftless system with 3 inputs and 5 states is 0at and can be put into multi-input chained form by dynamic feedback and coordinate change.
Low ICU Burnout in a Safety Net Hospital

Objectives: Burnout tends to be high in ICU settings. Stressors include serious patient illness, round-the-clock acute events, and end-of-life (nonbeneficial) care. We report on an ICU with very low burnout scores. We sought to understand factors that might be responsible for these favorable outcomes. Design: We compared ICU scores on burnout and its predictors with scores in non-ICU providers, merging scores in four ICUs (burn, medical, surgical, and pediatrics). Analyses included descriptive statistics, as well as general estimating equations to assess odds of burnout in ICU clinicians versus non-ICU clinicians. Setting: Annual wellness survey performed in October 2017 at Hennepin Healthcare System, an integrated system of care that includes an urban safety net hospital in Minneapolis, MN. Subjects: Six-hundred seventy-nine providers (physicians and advanced practice providers). Interventions: None. Measurements and Main Results: Annual surveys are performed using the validated Mini-Z 10 item wellness instrument. The Mini-Z assesses stress, satisfaction, and burnout, as well as known predictors including work control, chaos, teamwork, values alignment, and electronic medical record-related stress. Response rate in ICUs was 70% (64% elsewhere). Ten percentage of ICU clinicians reported burnout versus 37% of other providers (p = 0.015). ICUs were characterized as having lower chaos, less stress, and very high teamwork and values alignment between clinicians and leaders. Odds of burnout were four times lower in ICU clinicians (odds ratio, 0.24; 95% CI, 0.06–0.96; p = 0.043). Of all Hennepin Healthcare System providers, those with values not aligned with leaders had 3.28 times the odds of burnout (95% CI, 1.92–5.59; p < 0.001). Conclusions: Low burnout can be present in a busy, safety net ICU. Explicitly aligning values between clinicians and leaders may hold promise as a remediable worklife factor for producing these favorable results.
Experimental Study on the Effect of Different Injection Rates of Biopolymer Extracted from Natural Waste Material (Coconut Residue) as Drag Reducing Agent (DRA) in Water Injection Well: Formation Permeability Reduction

Applying drag reducing agent (DRA) into water injection system has improved injection capacity of wells by reducing the friction that occurs inside the well tubing. However, the synthetic polymer that is widely used in the industry have raised the environmental concern as its chemical content are believed to be harmful to the environment. This has generate the idea of using natural biopolymer DRA instead of the synthetic polymer. In this study, grated coconut residue has been chosen as the source of biopolymer to produce the natural DRA due to its high content of cellulose. Many studies were conducted to understand the behavior and optimize the performance of DRA when it is being applied in the pipeline. However, the effect of DRA on the reservoir formation has been less studied, especially in the near wellbore zone. A water injection system using core flood equipment was used in this work. Injection rates were varied so that the relationship between permeability reduction and the rates could be established. It is found that low injection rate of 1cc/min gives more permeability reduction compared to high injection rates at 5cc/min, while synthetic DRA solution gives more permeability reduction compared to natural DRA solution.
Saliency-Guided Deep Neural Networks for SAR Image Change Detection

Change detection is an important task to identify land-cover changes between the acquisitions at different times. For synthetic aperture radar (SAR) images, inherent speckle noise of the images can lead to false changed points, which affects the change detection performance. Besides, the supervised classifier in change detection framework requires numerous training samples, which are generally obtained by manual labeling. In this paper, a novel unsupervised method named saliency-guided deep neural networks (SGDNNs) is proposed for SAR image change detection. In the proposed method, to weaken the influence of speckle noise, a salient region that probably belongs to the changed object is extracted from the difference image. To obtain pseudotraining samples automatically, hierarchical fuzzy C-means (HFCM) clustering is developed to select samples with higher probabilities to be changed and unchanged. Moreover, to enhance the discrimination of sample features, DNNs based on the nonnegative- and Fisher-constrained autoencoder are applied for final detection. Experimental results on five real SAR data sets demonstrate the effectiveness of the proposed approach.
Linking Climate Change and Mortality in Piñon-Juniper Woodlands, from Leaf to Ecosystem

As global climate changes, the Southwestern US is predicted to experience more frequent and intense drought events. Extreme droughts can drive decreases in both physiological and ecosystem function, and can result in widespread tree mortality. Piñonjuniper (PJ) woodlands are a prevalent ecosystem in the region, co-dominated by two tree species, piñon (Pinus edulis) and juniper (Juniperus monosperma). Drought-induced piñon mortality has occurred over the past few decades, coinciding with outbreaks of a piñon-specific bark beetle. Piñon and juniper have different hydraulic strategies (isohydry and anisohydry, respectively) that should affect the way each species responds to drought. In this dissertation, I used PJ woodlands to quantify both physiological and ecosystem effects of drought and mortality, and the ways in which they interact. First, I focused on a PJ woodland where all of the large piñon had been girdled to simulate drought-related mortality. I looked for evidence of competitive release in both species by measuring photosynthetic and hydraulic parameters in the girdled plot vs. an intact control plot. I did not find evidence of competitive release in response to piñon mortality, likely due to the multi-year drought that followed the girdling event. I next examined whether hydraulic strategy affected piñon and juniper responses to two components of
Hybrid Beamforming for Relay-Aided mmWave Backhaul links

Massive multiple-input multiple-output (MIMO) and millimeter-wave (mmWave) technologies have emerged as a promising solution to enhance the backhaul wireless link in 5G Heterogeneous networks (HetNets). These mmWave backhaul links, however, are very susceptible to a significant path loss due to the blockage of the line-ofsight and massive antenna arrays may not be sufficient to alleviate such losses. To this end, relays are usually deployed to provide alternative routes that help boost links with high path loss. In this paper, therefore, we consider using relay base stations (RBS) in mmWave backhaul links between small cell base stations (SBS) and a macro-cell base station (MBS). It is assumed that the SBSs, the RBSs, and the MBS are all equipped with massive antenna arrays employing hybrid analog and digital beamforming. The analog beamformers are based on the selection of fixed multi-beams using a constrained eigenbeamforming scheme while the digital beamformers are based on the maximum ratio transmission and maximum ratio combining (MRT/MRC) schemes that maximize the transmit and receive SINRs of the effective channels created by the actual channel and the analog beamformer. The performance evaluation in terms of the beampatterns and the ergodic channel capacity shows that the proposed HBF scheme achieves near-optimal performance with only 4 RF chains and requires considerably less computational complexity.
Study the effect of khazra iron nano chelate fertilizer foliar application on two rapeseed varieties

An experiment was conducted in Al-zafraniya station / Baghdad – Iraq, during the winter season 2017 - 2018 in order to study the effect of khazra iron nano chelate fertilizer foliar application on two rapeseed varieties for increasing yield and yield components. The experiment designed by randomized complete blocks design (RCBD) for three replicates. The first factor included two rapeseed varieties (Brassica napus var. oleifera and Brassica napus L. var. pactol) and second factor included khazra iron nano chelated in three levels 0, 5 (kg.ha-1) (0.5 kg nano iron fertilizers per 1000-liter water) and 10 (kg.ha-1) (1 kg nano iron fertilizers per 1000-liter water). Means of the treatments was compared with a significant difference in the use of the least significant difference (LSD) at the probability level (p ≤ 0.05). Results showed that effect of khazra iron nano chelate foliar application, rapeseed varieties and interaction of them had significant effects on yield, biological yield and total dry biomass, which were 1298.6 (kg.ha-1), 7900 (kg.ha-1), and 7288 (kg.ha-1) respectively, at level 10 (kg.ha-1) for rapeseed (Brassica napus var. oleifera). While rapeseed (Brassica napus L. var. pactol) only had biological yield and total dry biomass which were 4260 (kg.ha-1) and 4460 (kg.ha-1) respectively. This is due to the genetic nature of each plant species. According to the results, rapeseed (Brassica napus var. oleifera) with 10 kg. ha-1 khazra iron nano chelate fertilizer foliar application had the highest values at most of evaluated characters.
Photopic and scotopic flicker sensitivity of a rod monochromat

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EXPLORING PEER -TO-PEER DATA MINING

The emerging widespread use of Peer-to-Peer computing is making the P2P Data Mining a natural choice when data sets are distributed over such kind of systems. The huge amount of data stored within the nodes of P2P networks and the bigger and bigger number of applications dealing with them as p2p file-sharing, p2p chatting, p2p electronic commerce etc., is moving the spotlight on this challenging field. In this paper we give an overview of two different approaches for implementing primitives for P2P Data Mining, trying then to show differences and similarities. The first one is based on the definition of Local algorithms; the second one relies on the Newscast model of computation.
Cellulase and Xylanase Immobilized on Chitosan Magnetic Particles for Application in Coconut Husk Hydrolysis

Cellulase and xylanase were covalently immobilized on chitosan magnetic particles. They were employed as catalysts to produce reducing sugar from coconut husk individually and simultaneously. Fourier Transform Infrared Spectroscopy (FT-IR) and Bradford’s protein analysis confirmed that the enzymes were covalently immobilized on the support. Cellulase from Aspergillus niger and Trichoderma reesei immobilized on chitosan magnetic microparticles yielded 0.352 g/L and 0.316 g/L of reducing sugar respectively, while immobilized xylanase from Trichoderma longibachterium yielded 0.432 g/L. The simultaneous use of cellulase and xylanase produced 0.8034 g/L of sugar, and the addition of glutaraldehyde as a cross-linking agent increased the amount of reducing sugar. Enzyme could maintain its activity at 91% for up to five cycles. Using nanosized particles resulted in a sugar yield of 0.49 g/l and 54.7 % of its activity maintained after five cycles.
Would Uber Help to Reduce Traffic Congestion

: This research explores the effects of Uber entry on New York City’s traffic. The two major questions I am trying to answer that might be of vital importance to transportation authorities are 1) does Uber substitute public transits? 2) does an introduce of Uber slow down average travel speed? After Uber was first introduced in year 2009, there are continuous debates on distinguishing its impact on traffic (Rayle et al., 2014; Li et al., 2016; Schaller, 2018; Castiglione et al., 2018). Considering that Uber is relatively new, relevant traffic data such as congestion indices are in general unavailable, which appears as a common limitation in previous analysis. In this research, I use monthly number of public transit trips in NYC to estimate a substitution effect of Uber on public transit ridership. To measure its direct impact on road traffic, I use Average Travel Speed generated from NYC yellow cab trips as a proxy for the citywide Average Travel Speed. A further application of monthly number of vehicles crossing nine major bridges and tunnels is used to capture a trend of traffic volume in NYC. The final dataset comprises 133 observations range from January 2008 to January 2018. Perceiving that Uber was introduced to NYC on May 2011 and was suspended on issuance of new vehicle licenses starting from August 2018, I use a regression discontinuity (RD) design and set the two events as cutoff points in the model. Additional use of Google Trend helps to more precisely determine the cutoff point. The regression results suggest that after Uber was introduced to NYC, 1) number of public transit trips has increased by about 3%; 2) average travel speed has decreased by . 127 mph; and 3) traffic volume was not affected. an urban area-year level panel dataset consisting of 957 observations over 87 urban areas starting from year 2003 to 2014. The regression result suggests that entry of has improved traffic by reducing TTI by 0.19%, CSI by 0.3% and Daily Vehicle Hours of delay by about 1.2%. Though their results are statistically significant, we observe that 57 out of
Experimental design applied to the inactivation of Escherichia coli in water through electrooxidation with Ti/IrO2 anodes

An electrooxidation process using Ti/IrO2 anodes was studied for the inactivation of Escherichia coli (E.coli) through an experimental design. This statistical methodology was used to evaluate the influence of electrooxidation parameters electrolyte support (NaCl and NaHCO3) and current density, on E.coli inactivation. The electrolyte support significantly influenced the response variable, in contrast to the current density that showed no significant effect. Microorganism inactivation was more efficient with an increase in NaCl concentration and a decrease in NaHCO3 concentration. The experimental design allowed to determine the optimum values for the electrooxidation under the conditions studied, obtaining a high NaCl concentration (0.45 mol/L) and low NaHCO3 concentration (0.35 mol/ L), in combination with low current intensity (6 mA/cm), for an E.coli inactivation. The predicted values of the empirical model were consistent with the experimental values. 84 Katherin Castro-Ríos et al.
Event-Triggered Distributed H∞ Constrained Control of Physically Interconnected Large-Scale Partially Unknown Strict-Feedback Systems

In this paper, an event-triggered distributed <inline-formula> <tex-math notation="LaTeX">${ {\mathcal {H}}_{\infty }}$ </tex-math></inline-formula> constrained control algorithm is designed for physically interconnected large-scale partially unknown strict-feedback systems with constrained-input and external disturbance. The advantage of both physical interconnection and communication is synchronously exploited for the scheme. First, an event-triggered feedforward control policy is proposed to transform control of physically interconnected large-scale systems into equivalent event-triggered control of decoupled multiagent systems. Then, an event-triggering condition and an event-triggered feedback control algorithm are designed to learn the optimal control policy and the disturbance policy in the worst case. The algorithm eliminates identifier, actor, and disturber neural networks and also relaxes the persistent excitation condition. It guarantees that the closed-loop dynamics is stabilized and the cost function is converged to the bounded <inline-formula> <tex-math notation="LaTeX">${\mathcal {L}}_{2}$ </tex-math></inline-formula>-gain optimal value while the Zeno phenomenon is excluded. Finally, the effectiveness of the proposed algorithm is verified through simulation results of event-triggered distributed control of a physically interconnected constrained-torques multimobile robot system.
A Hepta-band Antenna Loaded with E-shaped Slot for S/C/X-band Applications

A compact planar multiband antenna operating at 3.1 (S-band) /4.7/6.4/7.6 (C-band) /8.9/10.4/11.8 GHz (X-band) is presented. The proposed Microstrip Patch Antenna (MSPA) consists of a rectangular radiator in which an E-shaped slot is etched out and a microstrip feed line. The E-shaped slot modifies the total current path thereby making the antenna to operate at seven useful bands. No external impedance matching circuit is used and the impedance matching at these bands are solely achieved by using a rectangular microstrip feed line of length 10mm (L6) and width 2mm (W10). The antenna has a compact dimension of and exhibits S 11 <-10dB bandwidth of about 6.45% (3.2-3.0GHz), 8.5% (4.9-4.5GHz), 7.6% (6.7-6.2GHz), 3.9% (7.8-7.5GHz), 5.7% (9.1-8.6GHz), 1.2% (10.44-10.35GHz) and 2.2% (11.87-11.62GHz). The simulation analysis of the antenna is carried out by using HFSS v.13. 0
Reflection on the research on and implementation of biorefinery systems – a systematic literature review with a focus on feedstock

Te concept of sustainably processing biomass into a spectrum of products and bioenergy is thousands of years old. Large‐scale utilization of such processes existed in the 19th century and the term biorefinery first appeared in the late 20th century. This review addresses how different feedstock classes, products, and regions have been described in the scientific literature on the development of biorefineries (1999–2017). The results of a systematic literature review were compared with information included in two reports on the implementation of biorefineries to identify similarities and differences between theory (described in the literature) and practice (carried out in industry). The number of scientific articles per year on this topic has increased over the last decade, particularly in the fields of natural sciences and engineering. The first authors of these articles are primarily affiliated with North American and European institutions. References to lignocellulosic raw materials (especially residues, but also dedicated crops) in the feedstock are by far the most frequent and those to aquatic biomass have also increased over time. Most of the products mentioned are related to energy products (mainly fuels), and the heterogeneous class of chemicals was also strongly represented. We examined two reports on existing biorefineries in Europe and identified differences between the roles of food‐crop‐based biorefineries and lignocellulosic‐feedstock biorefineries in terms of research and practice. New studies on the practical implementation of biorefineries should be carried out, with particular attention to these discrepancies. © 2019 The Authors. Biofuels, Bioproducts and Biorefining published by Society of Chemical Industry and John Wiley & Sons, Ltd.