Miracle-dz/UniFAR-FTUs huggingface dataset:
Energy efficient path planning techniques for UAV-based systems with space discretization

Unmanned Aerial Vehicles are miniature air-crafts that have proliferated in many military and civil applications. Their affordability allows for tasks to be held with not just one but a fleet of UAVs. One of the problems that arise with the use of multi-UAVs is the multi-UAV path planning and assignment problem. We propose three algorithms that aim at assigning energy efficient trajectories for a fleet of UAVs. Our optimal path planning solution (OPP) is formulated using a Mixed Integer Linear Programming model (MILP). We also propose two other heuristic solutions that are greedy in nature; namely, Greedy Least Cost (GLC) and First Detect First Reserve (FDFR). To aid with collision avoidance, we adopt the concept of space discretization, and present a more realistic view of the space a UAV occupies. The comparative study of our proposed solutions reveals insightful trade-offs between energy consumption and complexity.
Effective Handling of Urgent Jobs - Speed Up Scheduling for Computing Applications

A queue is required when a service provider is not able to handle jobs arriving over the time. In a highly flexible and dynamic environment, some jobs might demand for faster execution at run-time especially when the resources are limited and the jobs are competing for acquiring resources. A user might demand for speed up (reduced wait time) for some of the jobs present in the queue at run time. In such cases, it is required to accelerate (directly sending the job to the server) urgent jobs (requesting for speed up) ahead of other jobs present in the queue for an earlier completion of urgent jobs. Under the assumption of no additional resources, such acceleration of jobs would result in slowing down of other jobs present in the queue. In this paper, we formulate the problem of Speed Up Scheduling without acquiring any additional resources for the scheduling of on-line speed up requests posed by a user at run-time and present algorithms for the same. We apply the idea of Speed Up Scheduling to two different domains -Web Scheduling and CPU Scheduling. We demonstrate our results with a simulation based model using trace driven workload and synthetic datasets to show the usefulness of Speed Up scheduling. Speed Up provides a new way of addressing urgent jobs, provides a different evaluation criteria for comparing scheduling algorithms and has practical applications.
The Land that Lean Manufacturing Forgot? Management Practices in Transition Countries

We have conducted the first survey on management practices in transition countries. We found that Central Asian transition countries, such as Uzbekistan and Kazakhstan, have on average very poor management practices. Their average scores are below emerging countries such as Brazil, China and India. In contrast, the central European transition countries such as Poland and Lithuania operate with management practices that are only moderately worse than those of western European countries such as Germany. Since we find these practices are strongly linked to firm performance, this suggests poor management practices may be impeding the development of Central Asian transition countries. We find that competition, multinational ownership, private ownership and human capital are all strongly correlated with better management. This implies that the continued opening of markets to domestic and foreign competition, privatisation of state-owned firms and increased levels of workforce education should promote better management, and ultimately faster economic growth.
Boolean Modelling Reveals New Regulatory Connections between Transcription Factors Orchestrating the Development of the Ventral Spinal Cord

We have assembled a network of cell-fate determining transcription factors that play a key role in the specification of the ventral neuronal subtypes of the spinal cord on the basis of published transcriptional interactions. Asynchronous Boolean modelling of the network was used to compare simulation results with reported experimental observations. Such comparison highlighted the need to include additional regulatory connections in order to obtain the fixed point attractors of the model associated with the five known progenitor cell types located in the ventral spinal cord. The revised gene regulatory network reproduced previously observed cell state switches between progenitor cells observed in knock-out animal models or in experiments where the transcription factors were overexpressed. Furthermore the network predicted the inhibition of Irx3 by Nkx2.2 and this prediction was tested experimentally. Our results provide evidence for the existence of an as yet undescribed inhibitory connection which could potentially have significance beyond the ventral spinal cord. The work presented in this paper demonstrates the strength of Boolean modelling for identifying gene regulatory networks.
Accurate prediction of RNA nucleotide interactions with backbone k-tree model

MOTIVATION Given the importance of non-coding RNAs to cellular regulatory functions, it would be highly desirable to have accurate computational prediction of RNA 3D structure, a task which remains challenging. Even for a short RNA sequence, the space of tertiary conformations is immense; existing methods to identify native-like conformations mostly resort to random sampling of conformations to achieve computational feasibility. However, native conformations may not be examined and prediction accuracy may be compromised due to sampling. State-of-the-art methods have yet to deliver satisfactory predictions for RNAs of length beyond 50 nucleotides. RESULTS This paper presents a method to tackle a key step in the RNA 3D structure prediction problem, the prediction of the nucleotide interactions that constitute the desired 3D structure. The research is based on a novel graph model, called a backbone k-tree, to tightly constrain the nucleotide interaction relationships considered for RNA 3D structures. It is shown that the new model makes it possible to efficiently predict the optimal set of nucleotide interactions (including the non-canonical interactions in all recently revealed families) from the query sequence along with known or predicted canonical basepairs. The preliminary results indicate that in most cases the new method can predict with a high accuracy the nucleotide interactions that constitute the 3D structure of the query sequence. It thus provides a useful tool for the accurate prediction of RNA 3D structure. AVAILABILITY AND IMPLEMENTATION The source package for BkTree is available at http://rna-informatics.uga.edu/index.php? f=software&p=BkTree. CONTACT lding@uga.edu or cai@cs.uga.edu SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
The Sprouting Capacity of 8–21-Year-Old Poplars and Some Practical Implications

We investigated the sprouting capacity of poplar stumps in ten 8–21-year old stands growing on former farmland in Sweden situated between 55°N and 60°N. Seven of the stands were planted with the clone OP-42 (Populus maximowiczii Henry × Populus trichocarpa Torr. and Gray), one with black cottonwood (Populus trichocarpa Torr. and Gray) and two with unidentified clones. The poplars’ mean age was 17 years (range 8–21); six of the stands were growing on clay soils, two on tills and two on loam. The studied sprouts were 1–7 years old. Stump sprouting was observed in all studied stands. The number of sprouts per living stump decreased as sprout age increased. The mean dry mass of all sprouts stump−1 was 16.1 ± 14.0 (range 3.3–37.2) kg. A biomass equation was constructed for estimating sprout biomass from the sprouts’ diameter at 10 cm above the ground (D10). The mean total sprout weight per hectare for sprouts amounted to 16.9 ± 14.6 (range 1.2–41.3) tons ha−1 when calculated for the actual living stumps in the studied areas.
GENERATING HOT GAS IN SIMULATIONS OF DISK-GALAXY MAJOR MERGERS

We report on the merger-induced generation of a shock-heate d g s wind and formation of a remnant gas halo in simulations of colliding disk galaxies. The simulations use cosmologically motivated initial conditions and include the effects of radiative cooling, star formation, s tellar feedback and the non-adiabatic heating of gas. The non-adiabatic heating, i.e. shocks, generated in the fin al merger forces gas out of the central region of the merger remnant and into the dark-matter halo. We demonstrat e th the amount of heating depends on the size of the progenitor disk galaxy as well as the initial orbit the ga laxies are placed on. Based upon these dependencies, we motivate a possible recipe for including this effect in se mi-analytic models of galaxy formation. Subject headings: galaxies:evolution — formation — interactions — ISM — metho ds:N-body simulations
Assessment of inversion models for sea surface wind speed retrieval from Sentinel-1 data

Among the different available wind sources, i.e. in situ measurements, numeric weather models, the retrieval of wind speed from Synthetic Aperture Radar (SAR) data is one of the most widely used methods, since it can give high wind resolution cell. For this purpose, one can find two principal methods: via electromagnetic (EM) models and empirical (EP) ones. In both approaches, the Geophysical Model Functions (GMFs) are used to describe the relation of radar scattering, wind speed, and the geometry of observations. By knowing radar scattering and geometric parameters, it is possible to invert the GMFs to retrieve wind speed. In this paper, wind speed retrieved by the two different models is compared together and evaluated with measured data. Based on the comparisons, some ideas will be proposed to improve the performance of wind speed retrieval.
What Code Implements Such Service? A Behavior Model Based Feature Location Approach

Enterprises today are keen to unlock new business values of their legacy services towards new trends (e.g., cloud and mobile). To accelerate such process, automatic feature location techniques can enable developers to rapidly locate/understand implementations of certain services (e.g., services to expose, transform or improve). Existing feature location techniques [1-3, 5-10, 32] provide a good foundation but have several key limitations: limited leverage of description sources, less considerations of internal behaviors, and ineffectiveness for the identification of service-relevant code entries. To address these limitations, we propose a behavior model based feature location approach and implement a tool named BMLocator. In the offline phase, BMLocator applies Natural Language Processing (NLP) techniques and static code analysis to extract “behavior models” of code units via considering multiple information sources. While in the online phase, given a service description, BMLocator first extracts its behavior model and then recommends service-relevant code units/entries by matching its behavior model with code units under analysis. Through evaluations with public service requests of open-source projects (e.g., Tomcat and Hadoop), we show that the approach is more effective in recommending service-relevant code entries (e.g., most of entries are prioritized as the first ones) than existing techniques (i.e., TopicXP[37], CVSSearch[6]).
The Impact of Board Structure on Information Security Breaches

This paper investigates the association between the board structure of a firm and the possibility of information security breaches. Building on the agency theory and resource dependence theory, we hypothesize that the board structure could affect the guidance and advice capability of the board on the executives’ decision of information security management. Our results show that the board size and the number of independent directors could increase the possibility of security breaches while the average and heterogeneity of age/tenure could reduce it. Our findings shed lights on the crucial role played by the board when managing information security risks in organizations.
Robust Matrix Completion via Joint Schatten p-Norm and lp-Norm Minimization

The low-rank matrix completion problem is a fundamental machine learning problem with many important applications. The standard low-rank matrix completion methods relax the rank minimization problem by the trace norm minimization. However, this relaxation may make the solution seriously deviate from the original solution. Meanwhile, most completion methods minimize the squared prediction errors on the observed entries, which is sensitive to outliers. In this paper, we propose a new robust matrix completion method to address these two problems. The joint Schatten p-norm and ℓp-norm are used to better approximate the rank minimization problem and enhance the robustness to outliers. The extensive experiments are performed on both synthetic data and real world applications in collaborative filtering and social network link prediction. All empirical results show our new method outperforms the standard matrix completion methods.
Generalized Gompertz-power series distributions

In this paper, we introduce the generalized Gompertz-power series class of distributions which is obtained by compounding generalized Gompertz and power series distributions. This compounding procedure follows same way that was previously carried out by Silva et al. (2013) and Barreto-Souza et al. (2011) in introducing the compound class of extended Weibull-power series distribution and the Weibull-geometric distribution, respectively. This distribution contains several lifetime models such as generalized Gompertz, generalized Gompertz-geometric, generalized Gompertz-poisson, generalized Gompertz-binomial distribution, and generalized Gompertz-logarithmic distribution as special cases. The hazard rate function of the new class of distributions can be increasing, decreasing and bathtub-shaped. We obtain several properties of this distribution such as its probability density function, Shannon entropy, its mean residual life and failure rate functions, quantiles and moments. The maximum likelihood estimation procedure via a EM-algorithm is presented, and sub-models of the distribution are studied in details.
Cloud based mental state monitoring system for suicide risk reconnaissance using wearable bio-sensors

Development of wireless body- and bio-sensor network opens a new horizon of healthcare especially to measure the vital physical signs of personnel for disease diagnosis and patient monitoring. And the cloud computing technology has enabled patient monitoring service dynamically scalable and ubiquitously accessible. The success of suicide risk reconnaissance is depends on effective prediction of mental states. This paper proposes a suicide risk scouting prototype by predicting mental states in cloud environment. In this system, patients' real-time vital diseases symptoms are collected through wireless body area network (WBAN) and then analyzed the collected data in healthcare cloud platform with patient's historical repository of diseases, habits, rehabilitations and genetics. Here, the mental statuses of patients have been modeled as the discrete set of states of hidden Markov model (HMM), where WBANs annotations and stored facts of patients in cloud are considered as the observations of HMM. Subsequently, the Viterbi, a machine learning algorithm has been applied to generate the most probable mental state sequence to monitor suicide risk of mentally disordered patients. Finally, the proposed system is validated by deploying this model on mental patients dataset.
SENSITIVITY ANALYSIS OF NONLINEAR BEHAVIOR WITH DISTORTED PROBABILITY

In this paper, we propose a sensitivity‐based analysis to study the nonlinear behavior under nonexpected utility with probability distortions (or “distorted utility” for short). We first discover the “monolinearity” of distorted utility, which means that after properly changing the underlying probability measure, distorted utility becomes locally linear in probabilities, and the derivative of distorted utility is simply an expectation of the sample path derivative under the new measure. From the monolinearity property, simulation algorithms for estimating the derivative of distorted utility can be developed, leading to gradient‐based search algorithms for the optimum of distorted utility. We then apply the sensitivity‐based approach to the portfolio selection problem under distorted utility with complete and incomplete markets. For the complete markets case, the first‐order condition is derived and optimal wealth deduced. For the incomplete markets case, a dual characterization of optimal policies is provided; a solvable incomplete market example with unhedgeable interest rate risk is also presented. We expect this sensitivity‐based approach to be generally applicable to optimization problems involving probability distortions.
Microbial aetiology and sensitivity of asymptomatic bacteriuria among ante-natal mothers in Mulago hospital, Uganda.

BACKGROUND Asymptomatic bacteriuria in pregnancy is associated with potential urinary and obstetric complications. However the prevalence aetiology and antimicrobial sensitivity patterns of asymptomatic bacteriurea among women attending ante-natal care in our Hospital is not known. OBJECTIVE To determine the prevalence and identify the aetiological agents associated with asymptomatic bacteriurea in antenatal mothers in Mulago Hospital. We also intended to determine the anti-microbial sensitivity patterns of the common uropathogen in this population METHODS We performed culture and anti-microbial sensitivity tests on urine samples from 218 consecutive ante-natal mothers in Mulago Hospital. All participants did not have any clinical symptoms attributable to urinary tract infection. RESULTS Twenty nine (13.3%) of the samples had significant bacterial growth and E.coli was the commonest isolate (51.2%). There was a high level (20-62%) of anti-bacterial resistance to the commonly used antibiotics. CONCLUSION Asymptomatic bacteriuria is common among ante-natal mothers in Mulago. E. Coli that is resistant to the most commonly used antibiotics is the commonest isolate. "
Protein Structures and Optimal Folding from a Geometrical Variational Principle

A novel approach, validated by an analysis of barnase and chymotrypsin inhibitor, is introduced to elucidate the paramount role played by the geometry of the protein backbone in steering the folding to the native state. It is found that native states of proteins, compared with compact artificial backbones, have an exceedingly large number of conformations with a given amount of structural overlap with them; moreover, the density of overlapping conformations, at a given overlap, of unrelated proteins of the same length are nearly equal. These results suggest an extremality principle underlying protein evolution, which, in turn, is shown to be possibly associated with the emergence of secondary structures.
Do Supervisors Affect the Valuation of Public Goods?

Systematic supervision procedures have been proposed to improve contingent valuation surveying, particularly in developing countries. Surprisingly, the CV literature does not say much about the potential effects of supervision even though there is evidence of interviewer effects and social desirability issues that can bias results. This paper investigates the effects of interview supervision on the valuation of public services, using split-sample treatments to include a test of scope of a nested good and to assess the effect of interview supervision on reported WTP. Results suggest that supervisors can be used to improve quality with no effect on WTP estimates.
Myeloid cell expressed proprotein convertase FURIN attenuates inflammation

The proprotein convertase enzyme FURIN processes immature pro-proteins into functional end- products. FURIN is upregulated in activated immune cells and it regulates T-cell dependent peripheral tolerance and the Th1/Th2 balance. FURIN also promotes the infectivity of pathogens by activating bacterial toxins and by processing viral proteins. Here, we evaluated the role of FURIN in LysM+ myeloid cells in vivo. Mice with a conditional deletion of FURIN in their myeloid cells (LysMCre-fur(fl/fl)) were healthy and showed unchanged proportions of neutrophils and macrophages. Instead, LysMCre-fur(fl/fl) mice had elevated serum IL-1β levels and reduced numbers of splenocytes. An LPS injection resulted in accelerated mortality, elevated serum pro-inflammatory cytokines and upregulated numbers of pro-inflammatory macrophages. A genome-wide gene expression analysis revealed the overexpression of several pro-inflammatory genes in resting FURIN-deficient macrophages. Moreover, FURIN inhibited Nos2 and promoted the expression of Arg1, which implies that FURIN regulates the M1/M2-type macrophage balance. FURIN was required for the normal production of the bioactive TGF-β1 cytokine, but it inhibited the maturation of the inflammation-provoking TACE and Caspase-1 enzymes. In conclusion, FURIN has an anti-inflammatory function in LysM+ myeloid cells in vivo.
Life-course socioeconomic status and DNA methylation of genes regulating inflammation.

BACKGROUND In humans, low socioeconomic status (SES) across the life course is associated with greater diurnal cortisol production, increased inflammatory activity and higher circulating antibodies for several pathogens, all suggesting a dampened immune response. Recent evidence suggests that DNA methylation of pro-inflammatory genes may be implicated in the biological embedding of the social environment. METHODS The present study examines the association between life-course SES and DNA methylation of candidate genes, selected on the basis of their involvement in SES-related inflammation, in the context of a genome-wide methylation study. Participants were 857 healthy individuals sampled from the EPIC Italy prospective cohort study. RESULTS Indicators of SES were associated with DNA methylation of genes involved in inflammation. NFATC1, in particular, was consistently found to be less methylated in individuals with low vs high SES, in a dose-dependent manner. IL1A, GPR132 and genes belonging to the MAPK family were also less methylated among individuals with low SES. In addition, associations were found between SES and CXCL2 and PTGS2, but these genes were consistently more methylated among low SES individuals. CONCLUSIONS Our findings support the hypothesis that the social environment leaves an epigenetic signature in cells. Although the functional significance of SES-related DNA methylation is still unclear, we hypothesize that it may link SES to chronic disease risk.
The fundamental plane of ellipticals: I. The dark matter connection

We show that the small scatter around the fundamental plane (FP) of massive elliptical galaxies can be used to derive important properties about their dark and luminous matter. The central velocity dispersion σ 0 (appearing in the fundamental plane) is linked to photometric, dynamical and geometrical properties of (luminous and dark) matter. We find that, inside the effective radius R e , the matter traced by the light must largely dominate over the dark matter (DM) in order to keep the ellipticals close enough to the FP. This recalls analogous findings for spiral galaxies. In particular we also find that cuspy DM distributions, as predicted by numerical simulations in ACDM cosmology, are unable to explain the very existence of the FP; in fact, according to this theory, the structural properties of dark and luminous matter are so interwoven that a curved surface is predicted in the log-space (σ 0 , R e , L), rather than a plane. To agree with the FP implies that CDM haloes have values in the range of 5-9 for the concentration parameter c (i.e. values significantly lower than the current predictions). Assuming a more heuristic approach and allowing for cored DM haloes, we find that the small intrinsic scatter of the FP yields: (i) an average value of about 0.3 for the dark-to-light-traced mass ratio, (ii) a mass-to-light of the matter traced by the light increasing with spheroidal luminosity: M s p h /L r L 0 . 2 r (Gunn-r band), with a value of 5.3 at L * r ≡ 2.7 x 10 1 0 L r O .