Miracle-dz/UniFAR-FTUs huggingface dataset:
Returning to the Financial Services Workforce after an Extended Child-Rearing Leave: A Descriptive Study Regarding the Influence of Mentorship on Feelings of Identity and Self-Efficacy

Objectives: To explore the experiences of women in the United States that have returned to the financial services workforce after an extended child-rearing leave and their descriptions of the transition back to the workforce, specifically regarding feelings of self-efficacy and identity and the influence of mentorship. Study design/setting: This study utilized a qualitative descriptive research methodology to capture descriptions through semi-structured inter-views and a focus group. Data were gathered by means of extensive one-on-one interviews with women throughout the country and an extensive focus group discussion. All study participants were currently working in the financial services sector after a minimum one-year absence due to child-rearing. Principal findings: The findings imply that multiple factors influence the experiences of women transitioning back into the workforce summarized by five primary themes: 1) challenges, pressure and feelings of identity and self-efficacy, 2) merging personal and professional identities, 3) re-inventing professional competencies and self-efficacy, 4) organizational intent to provide connection, goal-setting and path development through mentorship and 5) the unique value of mothers in a historically male-dominated industry and the importance of mentorship to support them. Conclusions: The findings suggest that women returning to the financial services industry after an extended child-rearing find mentorship beneficial by: differentiating and/or merging professional and personal identities, combating imposter syndrome, balancing pressure from both home and work, highlighting value despite resume gap and minimizing self-doubt, heightening sense of belongingness in a historically male-dominated industry and developing a strategic career and development path to meet desired professional goals.
Unlocking Antioxidant Potential: Comparative Analysis of Unlocking Antioxidant Potential: Comparative Analysis of Sembada 188 and Keladi Rice as Functional Ingredients in Novel Sembada 188 and Keladi Rice as Functional Ingredients in Novel Food Production Food Production

Rice ( Oryza sativa L.) is an essential crop that is a dietary staple for more than half of the world ' s population. There is currently a movement in consumer behaviour toward healthier food consumption, which is fuelling interest in functional diets like coloured rice. Sembada 188 and Keladi rice are underutilised in Brunei Darussalam and are expected to have functional qualities. A comparison of antioxidant properties between Sembada 188 and Keladi rice was performed in this study. This research produced puffed rice treats prepared by steaming rice, washing, drying for 6 e 8 h at 60 (cid:1) C, deep frying for 25 s at 180 (cid:1) C, and then mixing with Nypa fruticans Wurmb sugar. The result showed that Keladi rice has high anti-radical activity with 92.62% RSA detected, which has the potential to be a functional ingredient. Due to high-temperature treatment, the production process has decreased in % RSA in Keladi rice by 6.83%. The addition of Nypa fruticans Wurmb sugar has increased the cumulative antioxidants value in Sembada 188 rice signi ﬁ cantly (p < 0.05) by 43.9% whilst signi ﬁ cantly (p < 0.05) decreased in Keladi from 92.62% to 85.79%. These ﬁ ndings indicate that the underutilised Keladi and Sembada 188 rice can contribute to antioxidant properties in novel food production.
I nternational J ournal of Agriculture and Biosciences

Tamarix L is the largest genus within the family Tamaricaceae. In this study, we try to simulate the phylogenetic relationships of Tamarix species using the nuclear internal transcribed spacer (ITS) sequences and projecting them to Algeria. The internal transcribed spacer (ITS) sequences were obtained from the GenBank to identify the relationships among Tamarix taxa. The phylogenetic trees were constructed using Neighbour-Joining (NJ) and Maximum Parsimony (MP) inference methods and were found to have very identical topologies. The phylogenetic analyses divide the Tamarix species into two clades. The results of this study revealed that this genus constituted a monophyletic group. This study reveals that phylogenetic analysis of the Tamarix genus in Algeria is not based solely on molecular analyzes. The substitution of a bio-morphological analysis proves complementary in order to effectively distinguish the different species, especially hybrids.
SURFACE at Syracuse University SURFACE at Syracuse University

Mundlak (1978) shows that the fixed effects estimator is equivalent to the random effects estimator in the one-way error component model once the random individual effects are modeled as a linear function of all the averaged regressors over time. In the spirit of Mundlak, this paper shows that this result also holds for the two-way error component model once this individual and time effects are modeled as linear functions of all the averaged regressors across time and across individuals. Woolridge (2021) also shows that the two-way fixed effects estimator can be obtained as a pooled OLS with the regressors augmented by the time and individual averages and calls it the two-way Mundlak estimator. While Mundlak (1978) used GLS rather than OLS on this augmented regression, we show that both estimators are equivalent for this augmented regression. This extends B al tagi’s (2006) results from the one-way to the two-way error component model. The F test suggested by Mundlak (1978) to test for this correlation between the random effects and the regressors generate a Hausman (1978) type test that is easily generalizable to the two-way Mundlak regression. In fact, the resulting F-tests for the two-way error component regression are related to the Hausman type tests proposed by Kang (1985) for the two-way error component model.
The simulated sea ice thermal microwave emission at window and sounding frequencie

Abstract The impacts of the sea-ice characteristics distribution, roughness, temperature and thermal conductivity on an on-ice moving trough in the Fram Strait on 7March 2002 are investigated. The situation is simulated with the mesoscale transport and fluid model METRAS and the named characteristics are varied within the range of observational uncertainty. The test cases are evaluated against aircraftmeasurements performed within the ‘Fram Strait Cyclone Experiment 2002’. The model’s sensitivity on the changes in sea-ice characteristics is quantified by statistical means. The strongest impacts on the near-ground temperature are found from sea-ice temperature, manifesting as an overall bias, and the positioning of the sea-ice edge, manifesting as a phase error. Only higher than natural homogenization of the sea-ice cover leads to some reduction of the amplitude error. A reduction of the sea-ice surface roughness is performed by applying an unrealistically small roughness length of z0 = 1 mm. This reduces the negative wind speed bias, enhances the advection of contrasting air masses and improves the frontal sharpness. The thermal conductivity has the smallest influence. The lateral forcing taken from ‘European Centre for Medium-Range Weather Forecasts’ (ECMWF) reanalyses shows the strongest effect on the limited area model performance.
Future Generation Computer Systems ( ) – Future Generation Computer Systems Scheduling Highly Available Applications on Cloud Environments

Available online xxxx Keywords: Cloud scheduling Multi-objective scheduling Meta-heuristics Nonlinear-programming High available systems a b s t r a c t Cloud computing is becoming a popular solution for storing data and executing applications due to its on-demand pay-per-use policy that allows access to virtually unlimited resources. In this frame applications such as those oriented towards Web 2.0 begin to be migrated on cloud systems. Web 2.0 applications are usually composed of several components that run indefinitely and need to be available to end users throughout their execution life cycle. Their availability strongly depends on the number of resource failures and on the variation in user hit rate. These problems are usually solved through scaling. A scaled application can span its components on several nodes. Hence if one or more nodes fail it could become unavailable. Therefore we require a method of ensuring the application's functionality despite the number of node failures. In this paper we propose to build highly available applications, i.e., systems with low downtimes, by taking advantage of the component based architecture and of the application scaling property. We present a solution to finding the optimal number of component types needed on nodes so that every type is present on every allocated node. Furthermore nodes cannot exceed a maximum threshold and the total running cost of the applications needs to be minimized. A sub-optimal solution is also given. Both solutions rely on genetic algorithms to achieve their goals. The efficiency of the sub-optimal algorithm is studied with respect to its success rate, i.e., probability of the schedule to provide highly available applications in case all but one node fail. Tests performed on the sub-optimal algorithm in terms of node load, closeness to the optimal solution and success rate prove the algorithm's efficiency.
DeCovarT, a R package for a robust deconvolution of cell mixture in transcriptomic samples using a multivariate Gaussian generative framework

Transcriptomic analyses have contributed greatly to a better understanding of the biological processes involved in the evolution of complex and versatile diseases. However, bulk transcriptomic analyses ignore the heterogenous contribution of diverse cell populations to samples heterogeneity. Thus, computational deconvolution methods have been developed to analyse the cellular composition of tissues. However, the performance of these algorithms is limited in distinguishing between cell populations with very similar expression profiles, and we hypothesised that integrating the covariance between genes could enhance the performance of deconvolution algorithms for closely related cell populations. We therefore developed a new deconvolution algorithm, DeCovarT, which takes into account the transcriptomic network structure of each cell population. To do so, we represented the set of transcriptomic interactions as a multivariate Gaussian distribution, assuming a sparse network structure deduced from the precision matrix returned by the gLasso algorithm. Next, we reconstruct the overall mixing profile by a generative model, in which we show, under reasonable assumptions, that the law describing the overall expression profile conditional on the cell ratios and purified expression profiles also follows a multivariate Gaussian distribution. The maximum likelihood estimate (MLE) of the associated function, i.e. the cell ratios optimising the probability of observing the observed transcriptomic distribution, is estimated in our paper by first reparametrising the log-likelihood function into an unconstrained version and then optimising it by consecutive iterations of the Levenberg-Marquardt algorithm. This allows us to obtain an estimator that respects the simplex constraint and to derive the corresponding asymptotic confidence bands. In addition to the introduction of a new statistical modelling paradigm, we plan in our presentation to briefly review the standard optimisation methods implemented in R with their specific features and main restrictions. Notably, we benchmarked them on a toy example that highlights strong behavioural differences in the context of constrained optimisation.
Determining Task Optimal Modular Robot Assembly Con(cid:12)gurations

A \modular" robotic system consists of standardized joint and link units that can be assembled into a number of di(cid:11)erent kinematic con(cid:12)gurations in order to meet di(cid:11)erent task requirements. Given a predetermined set of modules, this paper considers the problem of (cid:12)nding an \optimal" module assembly con(cid:12)g-uration for a speci(cid:12)c task. We formulate the solution as a discrete optimization procedure. The formulation is based on an assembly incidence matrix representation of a modular assembly con(cid:12)guration and a general task-oriented objective function that can incorporate many realistic task criteria. Genetic algorithms (GA) are employed to solve this optimization problem, and a canonical method to represent a modular assembly in terms of genetic strings is introduced. An example involving a 3-DOF manipulator con(cid:12)guration is presented to demonstrate the feasibility of this approach.
Sustainable Storm Water Management by Predicting Climate Change Using Fuzzy Neural Network and GIS

Analysis of urban climate changing is the basis for the implementation of storm water management measurements. Climate tensions such as changing precipitation patterns, fluctuations in temperature, and extreme events are already affecting water resources. For instance, precipitation pattern will be changed due to more water vapor in the atmosphere. Hence, it will not be evenly distributed. Some places will see more rain, others will get less snow. However, climate changes, such as the amount, timing, and intensity of rain events, in combination with land development, can significantly affect the amount of storm water runoff that needs to be managed. Firstly, this essay will be discussed about the prediction of climate change using a fuzzy neural network (FNN) and it shows the accuracy of this method for anticipating storm water. Secondly, based on the results of the first phase, it determines the critical area for preparing storm water systems with the application of GIS tools and technology.
A TIME-FRACTIONAL

A fractional time derivative is introduced into Burger’s equation to model losses of nonlinear waves. This term amounts to a time convolution product, which greatly penalizes the numerical modeling. A diﬀusive representation of the fractional derivative is adopted here, replacing this nonlocal operator by a continuum of memory variables that satisfy local-in-time ordinary diﬀer-ential equations. Then a quadrature formula yields a system of local partial diﬀerential equations, well-suited to numerical integration. The determination of the quadrature coeﬃcients is crucial to ensure both the well-posedness of the system and the computational eﬃciency of the diﬀusive approximation. For this purpose, optimization with constraint is shown to be a very eﬃcient strategy. Strang splitting is used to solve successively the hyperbolic part by a shock-capturing scheme, and the diﬀusive part exactly. Numerical experiments are proposed to assess the eﬃciency of the numerical modeling, and to illustrate the eﬀect of the fractional attenuation on the wave propagation.
Future Generation Computer Systems

Federated Learning (FL) represents the de-facto standard paradigm for enabling distributed learning over multiple clients in real-world scenarios. Despite the great strides reached in terms of accuracy and privacy awareness, the real adoption of FL in real-world scenarios, in particular in industrial deployment environments, is still an open thread. This is mainly due to privacy constraints and to the additional complexity stemming from the set of hyperparameters to tune when employing AI techniques on bandwidth, computing, and energy-constrained nodes. Motivated by these issues, we focus on scenarios where participating clients are characterised by highly heterogeneous computing capabilities and energy budgets proposing EneA-FL, an innovative scheme for serverless smart energy management. This novel approach dynamically adapts to optimise the training process while fostering seamless interaction between Internet of Things (IoT) devices and edge nodes. In particular, the proposed middleware provides a containerised software module that efficiently manages the interaction of each worker node with the central aggregator. By monitoring local energy budget, computational capabilities
Intraoperative neurophysiologic monitoring.

Intraoperative neurophysiologic monitoring (IONM) describes a variety of procedures used to monitor the integrity of neural pathways during high-risk neurosurgical, orthopedic, and vascular surgeries. It involves the detection of electrical signals produced by the nervous system in response to sensory or electrical stimuli to provide information about the functional integrity of neuronal structures.
GPU accelerated biochemical network simulation

Motivation: Mathematical modelling is central to systems and synthetic biology. Using simulations to calculate statistics or to explore parameter space is a common means for analysing these models and can be computationally intensive. However, in many cases, the simulations are easily parallelizable. Graphics processing units (GPUs) are capable of efficiently running highly parallel programs and outperform CPUs in terms of raw computing power. Despite their computational advantages, their adoption by the systems biology community is relatively slow, since differences in hardware architecture between GPUs and CPUs complicate the porting of existing code. Results: We present a Python package, cuda-sim, that provides highly parallelized algorithms for the repeated simulation of biochemical network models on NVIDIA CUDA GPUs. Algorithms are implemented for the three popular types of model formalisms: the LSODA algorithm for ODE integration, the Euler–Maruyama algorithm for SDE simulation and the Gillespie algorithm for MJP simulation. No knowledge of GPU computing is required from the user. Models can be specified in SBML format or provided as CUDA code. For running a large number of simulations in parallel, up to 360-fold decrease in simulation runtime is attained when compared to single CPU implementations. Availability: http://cuda-sim.sourceforge. net/ Contact: christopher.barnes@imperial.ac. uk; m.stumpf@imperial.ac. uk Supplementary information: Supplementary data are available at Bioinformatics online.
MOODS: fast search for position weight matrix matches in DNA sequences

Summary: MOODS (MOtif Occurrence Detection Suite) is a software package for matching position weight matrices against DNA sequences. MOODS implements state-of-the-art online matching algorithms, achieving considerably faster scanning speed than with a simple brute-force search. MOODS is written in C++, with bindings for the popular BioPerl and Biopython toolkits. It can easily be adapted for different purposes and integrated into existing workflows. It can also be used as a C++ library. Availability: The package with documentation and examples of usage is available at http://www.cs. helsinki.fi/group/pssmfind. The source code is also available under the terms of a GNU General Public License (GPL). Contact: janne.h. korhonen@helsinki.fi
Along-Dip Segmentation of the Slip Behavior and Rheology of the 1 Copiapó Ridge Subducted in North-Central Chile 2 3

44 We studied the along-dip influence of the Copiapó ridge subduction in the Atacama region, North-Central 45 Chile by building a new seismicity catalog, including similar events and non-volcanic tremors (NVTs). We 46 also obtained a 3-D tomographic model for Pand S-waves velocity (and the implied Vp/Vs ratio). We 47 identify down-dip segmentation involving 4 distinct segments: a locked seismogenic zone hosting ordinary 48 seismicity and clusters of similar events; a transition zone with NVTs and low seismicity; an aseismic zone 49 with slow-slip events; and a deep zone with abundant intraslab seismicity. The velocity models show 50 differences among these zones, with low velocity anomalies of Vp and Vs coinciding with aseismic slip 51 zones, indicating the possible presence of fluids. Due to the spatial distribution along-strike and along-dip of 52 the aseismic zones, we propose that these differences in seismogenic behavior are generated by subduction 53 of the heterogeneous seamounts associated with the Copiapó ridge. 54 55 Plain Language Summary 56 Several studies suggest that subduction of large bathymetric features such as seamounts and fracture zones 57 can produce aseismic slip in the subduction zones. We investigate the possible influence of the subduction of 58 the Copiapó ridge in north-central Chile. Our results show down-dip zones with distinct seismic and aseismic 59 behavior. The seismic zones are distinguished by ordinary seismicity and high coupling values, while the 60 aseismic zones are characterized by low coupling values, clusters of similar events, non-volcanic tremor and 61 slow-slip events. In addition, by means of a three-dimensional tomography model, we suggest that fluids are 62 present in the aseismic zones which can help to produce aseismic slip. All these consistent observations 63 exhibit a significant spatial correlation with the Copiapó ridge. Therefore we propose these differences in the 64 seismic behavior are due to the subduction of the Copiapó ridge. 65 66 67 68 69 70 7
Climate, shocks, weather and maize intensification decisions in rural Kenya: Food Security in a Changing Environment

We explore how climate, climate risk and weather affect maize intensification among smallholders in Kenya. We find that they all play an important role in maize intensification choice. The economic implications of this choice are also analyzed. We find that the share of maize area planted to hybrid seeds contributes positively to expected crop income, without increasing exposure to income variability or downside risk. The promotion of maize intensification is potentially a valuable adaptation strategy to support the well-being of smallholder farmers.
Meshing Roundtable ( IMR 24 ) Recurrent Neural Networks for Geometric Problems

We introduce a new architecture using Recurrent Neural Networks (RRNs) to learn approximate solutions to three geometric problems – finding planar convex hulls, computing Delaunay triangulations, and the planar Travelling Salesman Problem – using training examples alone. We hope our results on these tasks will encourage a broader exploration of neural learning for discrete geometric problems, including mesh generation. c © 2015 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of organizing committee of the 24th International Meshing Roundtable (IMR24).
Implementation of Kalman Filters Optimal Sensorimotor Integration in Recurrent Cortical Networks : A Neural

[PDF] [Full Text] [Abstract] , June 15, 2003; 23 (12): 4821-4825. J. Neurosci. C. D. Mah and F. A. Mussa-Ivaldi Generalization of Object Manipulation Skills Learned without Limb Motion [PDF] [Full Text] [Abstract] , March 1, 2004; 91 (3): 1158-1170. J Neurophysiol J. B. Dingwell, C. D. Mah and F. A. Mussa-Ivaldi Experimentally Confirmed Mathematical Model for Human Control of a Non-Rigid Object [PDF] [Full Text] [Abstract] , May 23, 2007; 27 (21): 5744-5756. J. Neurosci. S. Deneve, J.-R. Duhamel and A. Pouget Implementation of Kalman Filters Optimal Sensorimotor Integration in Recurrent Cortical Networks: A Neural [PDF] [Full Text] [Abstract] , September 1, 2007; 98 (3): 1075-1082. J Neurophysiol D. A. Kistemaker, A. J. Van Soest and M. F. Bobbert Equilibrium Point Trajectories Equilibrium Point Control Cannot be Refuted by Experimental Reconstruction of [PDF] [Full Text] [Abstract] , October 26, 2007; 318 (5850): 606-610. Science K. Kording Decision Theory: What "Should" the Nervous System Do?