Miracle-dz/UniFAR-FTUs huggingface dataset:
A system dynamics model of financial flow in supply chains: a case study

The article discusses the operational and financial relationships among the channel members of a supply chain comprising of a manufacturing company and a distributor. This research simulates the financial system that enables a more accurate diagnosis of disaggregated metrics “Cash to Cash”, considering different interactions between material flow and the financial flow of the two links, manufacturer and distributor. This model considers the feedback loops between material flow models and financial models without which some interactions are lost during simulation. The proposed diagnostic method which incorporates an eclectic process re-engineering practices and state of the art of dynamic simulation with the implementation of advanced techniques of sensitivity and dynamic optimization models those are applied on the concept of stocks and flows. This methodology is used in order to analyze and improve business strategies by generating policies which help to improve cash flow of the company. To validate our model, a case study illustrating the improvement of different metrics of the supply chain is considered here. The results show that the companies have to invest in technology in order to generated strategic decision to enhance their financial metrics.
Rehabilitation use in multiple sclerosis: Do illness representations matter?

Multidisciplinary rehabilitation improves illness outcomes and is recommended in clinical guidelines for multiple sclerosis (MS). However, many people with MS do not make use of rehabilitation. We do not know much about the barriers to the use of rehabilitation in MS, but in other patient groups, illness representations have proven to be predictors of service utilization. Therefore, the aim of our study was to explore whether, in patients with MS, illness representations are associated with self‐reports of rehabilitation use in the past and the intention to use rehabilitation in the future, beyond sociodemographic and illness‐related factors.
Storage, Computation, and Communication: A Fundamental Tradeoff in Distributed Computing

We consider a MapReduce-like distributed computing system. We derive a lower bound on the communication cost for any given storage and computation costs. This lower bound matches the achievable bound we proposed recently. As a result, we completely characterize the optimal tradeoff between the storage, the computation, and the communication. Our result generalizes the previous one by Li et at. to also account for the number of computed intermediate values.
Autonom omkörning i motorvägsmiljö med Model Predictive Control

An important step towards making road transportation safer around the world is the development of autonomous vehicles. In this paper a controller for performing autonomous overtaking at highway speeds using Model Predictive Control (MPC) is designed. The MPC framework is designed and tested in a simulated environment in order to evaluate the performance of the controller. Four different MPC frameworks are developed for generating paths for autonomous overtaking and a Proportional Integral Derivative (PID) controller is formulated for a general comparison. The four MPC frameworks all plan trajectories by introducing constraints; they differ in the way they formulate said constraints. From the simulations we conclude that MPC is a better controller choice than PID for the application of controlling autonomous vehicles because of the usability of MPC, even though they might be equally fast. The benefits and drawbacks of the MPC implementations and their characteristics are discussed, and we conclude that the preferred implementation is a linear sloped edge dynamic constraint where a disallowed area around the leading vehicle is explicitly defined outside of the MPC framework.
Sex-Specific Differences in Essential Lipid Requirements of Daphnia magna

Sex-specific differences in nutritional requirements may crucially influence the performances of the sexes, which may have implications for sexual reproduction and thus is of great ecological and evolutionary interest. In the freshwater model species Daphnia magna, essential lipid requirements have been extensively studied. Dietary deficiencies in sterols and polyunsaturated fatty acids (PUFA) have been shown to constrain somatic growth and parthenogenetic reproduction of female Daphnia. In contrast, nutrient requirements of male Daphnia have not been studied yet. Supplementation experiments were conducted to investigate differences in sterol (cholesterol) and PUFA (eicosapentaenoic acid, EPA) requirements between female and male D. magna. Thresholds for sterol-limited juvenile growth were higher in females than in males, suggesting that females are more susceptible to dietary sterol deficiencies than males. Sex-specific differences in maximum somatic growth rates were evident primarily in the presence of dietary EPA; females could not exploit their generally higher growth potential in the absence of dietary PUFA. However, the thresholds for EPA-limited growth did not differ between sexes, suggesting that both sexes have similar dietary EPA requirements during juvenile growth. During a life history experiment, the gain in body dry mass was higher in females than in males, irrespective of food treatment. In both sexes, the gain in body dry mass increased significantly upon EPA supplementation, indicating that both sexes benefited from dietary EPA supply also later in life. However, the positive effects of EPA supplementation were most pronounced for female reproduction-related traits (i.e. clutch sizes, egg dry masses, and total dry mass investment in reproduction). The high maternal investment in reproduction resulted in a depletion of nutrients in female somata. In contrast, the comparatively low paternal investment in reproduction allowed for the accumulation of nutrients in male somata. We conclude that males are generally less susceptible to dietary nutrient deficiencies than females, because they can rely more on internal body stores. Our data suggest that the performances of the sexes are differentially influenced by lipid-mediated food quality, which may have consequences for sexual reproduction and thus the production of resting eggs and the maintenance of Daphnia populations.
Pentacene devices: Molecular structure, charge transport and photo response

In this feature article, we will discuss to which extend the peculiar growth properties of pentacene on metallic contacts and on gate dielectrics contribute to the device performance of organic thin film transistors. The early growth state of pentacene monolayers is reviewed, as well as the molecular structure of the so called thin film phase. Then, the relation of structural defect densities to trap densities is discussed. The spatially resolved photo response of a pentacene transistor will be presented in the context of injection barriers and contact homogeneity. (© 2008 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)
A Novel Aerodynamic Shape Optimization Approach for Three-Dimensional Turbulent Flows

A novel gradient-based computational tool for efficient three-dimensional aerodynamic shape optimization is presented. An integrated approach is applied to geometry parameterization and mesh movement based on B-spline surfaces and volumes. Grid refinement and redistribution techniques are introduced to enable the fitting of B-spline volumes to grids appropriate for computing turbulent flows. The three-dimensional Reynolds-averaged Navier-Stokes equations are solved using a parallel Newton-Krylov-Schur flow analysis algorithm that makes use of Summation-by-Parts (SBP) operators and Simultaneous Approximation Terms (SATs) at block interfaces and boundaries. Gradients are evaluated using the discrete-adjoint method. The performance of the algorithm is demonstrated through a series of optimization studies, including a pitching moment coefficient optimization in subsonic flow and lift-constrained drag minimizations in transonic flow.
Classifying Object Manipulation Actions based on Grasp-types and Motion-Constraints

In this work, we address a challenging problem of fine-grained and coarse-grained recognition of object manipulation actions. Due to the variations in geometrical and motion constraints, there are different manipulations actions possible to perform different sets of actions with an object. Also, there are subtle movements involved to complete most of object manipulation actions. This makes the task of object manipulation action recognition difficult with only just the motion information. We propose to use grasp and motion-constraints information to recognise and understand action intention with different objects. We also provide an extensive experimental evaluation on the recent Yale Human Grasping dataset consisting of large set of 455 manipulation actions. The evaluation involves a) Different contemporary multi-class classifiers, and binary classifiers with one-vs-one multi- class voting scheme, b) Differential comparisons results based on subsets of attributes involving information of grasp and motion-constraints, c) Fine-grained and Coarse-grained object manipulation action recognition based on fine-grained as well as coarse-grained grasp type information, and d) Comparison between Instance level and Sequence level modeling of object manipulation actions. Our results justifies the efficacy of grasp attributes for the task of fine-grained and coarse-grained object manipulation action recognition.
A review of Sentiment Analysis in Spanish

Sentiment analysis is an area of research that shows an upward trend, especially in the last two years due to large-scale production of opinions and comments from users active over the Web and social networks, in general. Companies and organizations are interested in knowing of their reputation in social networks, blogs, wikis, and other Web sites. Until now, the vast majority of the work involves systems in English. For this reason, the scientific community is interested in studies in other languages. This paper provides a brief overview of the current state of sentiment analysis in Spanish language
Clinical efficacy of PVP and PKP in the treatment of OVCFs after bilateral resection of ovarian cancer

The clinical efficacy of percutaneous vertebroplasty (PVP) and percutaneous kyphoplasty (PKP) in the treatment of osteoporotic vertebral compression fractures after bilateral resection of ovarian cancer was investigated. Eighty-six patients with osteoporotic vertebral compression fractures after bilateral resection of ovarian cancer admitted to the Second People's Hospital of Hefei from September, 2015 to August, 2016 were selected and randomly divided into control group (n=43) and observation group (n=43). The control group was treated with PVP, while the observation group received PKP. The operation time, fluoroscopy times, bone cement volume and leakage rate of patients in the two groups were recorded; the postoperative pain of patients was compared using Short-form McGill Pain Questionnaire; the changes in height of injured vertebra and Cobb angle of patients in two groups were compared; the efficacy of patients in the two groups was compared in accordance with Oswestry dysfunction index (ODI) and Japanese Orthopedic Association (JOA) low back pain scoring; and the quality of life was compared. The fluoroscopy times and bone cement leakage were significantly less in observation group than those in control group (P<0.05). After operation, the scores of ODI, MPQ and JOA in the two groups were significantly improved (P<0.05). The postoperative height of injured vertebra and the Cobb angle of patients in two groups were significantly different than those before the operation (P<0.05). The quality of life of patients in the observation group was higher than that in the control group one year after operation (P<0.05). In conclusion, PKP and PVP are effective in the treatment of osteoporotic vertebral compression fractures after bilateral resection of ovarian cancer; however, PKP is more conducive to lumbar stability and maintenance of intervertebral height thus greatly correcting the kyphosis, which is beneficial to improving the quality of life of patients.
An evaluation of performance enhancements to particle swarm optimisation on real-world data

Swarm Computation is a relatively new optimisation paradigm. The basic premise is to model the collective behaviour of self-organised natural phenomena such as swarms, flocks and shoals, in order to solve optimisation problems. Particle Swarm Optimisation (PSO) is a type of swarm computation inspired by bird flocks or swarms of bees by modelling their collective social influence as they search for optimal solutions. In many real-world applications of PSO, the algorithm is used as a data pre-processor for a neural network or similar post processing system, and is often extensively modified to suit the application. The thesis introduces techniques that allow unmodified PSO to be applied successfully to a range of problems, specifically three extensions to the basic PSO algorithm: solving optimisation problems by training a hyperspatial matrix, using a hierarchy of swarms to coordinate optimisation on several data sets simultaneously, and dynamic neighbourhood selection in swarms. Rather than working directly with candidate solutions to an optimisation problem, the PSO algorithm is adapted to train a matrix of weights, to produce a solution to the problem from the inputs. The search space is abstracted from the problem data. A single PSO swarm optimises a single data set and has difficulties where the data set comprises disjoint parts (such as time series data for different days). To address this problem, we introduce a hierarchy of swarms, where each child swarm optimises one section of the data set whose gbest particle is a member of the swarm above in the hierarchy. The parent swarm(s) coordinate their children and encourage more exploration of the solution space. We show that hierarchical swarms of this type perform better than single swarm PSO optimisers on the disjoint data sets used. PSO relies on interaction between particles within a neighbourhood to find good solutions. In many PSO variants, possible interactions are arbitrary and fixed on initialisation. Our third contribution is a dynamic neighbourhood selection: particles can modify their neighbourhood, based on the success of the candidate neighbour particle. As PSO is intended to reflect the social interaction of agents, this change significantly increases the ability of the swarm to find optimal solutions. Applied to real-world medical and cosmological data, this modification is and shows improvements over standard PSO approaches with fixed neighbourhoods.
The influence of hyperlinks on reading on the Web: an empirical approach

We increasingly spend a vast amount of time on the Web and much of that time is spent reading. One of the main differences between reading non-Web based text and reading on the Web is the presence of hyperlinks within the text, linking various related Web content and webpages together. Some researchers and commentators have claimed that hyperlinks hinder reading because they are a distraction that may have a negative effect on the reader’s ability to process the text. However, very few controlled experiments have been conducted to verify these claims. In the experiments documented here we utilise eye tracking as a new methodology for examining how we read hyperlinked text. During reading we move our eyes in order to bring new information into our fovea where the highest visual acuity is present. There is a well-documented tight link between when and where we look and what we process. By measuring eye movements,we can gain insights into the ongoing cognitive processing that is occurring during a task. Eye movements have been used extensively to help us to understand the cognitive processing that occurs during reading, but there has been very little research into how our reading differs when we read information on the Web.Therefore, in this thesis we examine the influences of hyperlinks on reading on the Web
Towards Robust Interpretability with Self-Explaining Neural Networks

Most recent work on interpretability of complex machine learning models has focused on estimating a posteriori explanations for previously trained models around specific predictions. Self-explaining models where interpretability plays a key role already during learning have received much less attention. We propose three desiderata for explanations in general – explicitness, faithfulness, and stability – and show that existing methods do not satisfy them. In response, we design self-explaining models in stages, progressively generalizing linear classifiers to complex yet architecturally explicit models. Faithfulness and stability are enforced via regularization specifically tailored to such models. Experimental results across various benchmark datasets show that our framework offers a promising direction for reconciling model complexity and interpretability.
Study of Bacterial Community Composition and Correlation of Environmental Variables in Rambla Salada, a Hypersaline Environment in South-Eastern Spain

We studied the bacterial community in Rambla Salada in three different sampling sites and in three different seasons and the effect of salinity, oxygen, and pH. All sites samples had high diversity and richness (Rr > 30). The diversity indexes and the analysis of dendrograms obtained by DGGE fingerprint after applying Pearson's and Dice's coefficient showed a strong influence of sampling season. The Pareto-Lorenz (PL) curves and Fo analysis indicated that the microbial communities were balanced and despite the changing environmental conditions, they can preserve their functionality. The main phyla detected by DGGE were Bacteroidetes (39.73%), Proteobacteria (28.43%), Firmicutes (8.23%), and Cyanobacteria (5.14%). The majority of the sequences corresponding to uncultured bacteria belonged to Bacteroidetes phylum. Within Proteobacteria, the main genera detected were Halothiobacillus and Roseovarius. The environmental factors which influenced the community in a higher degree were the salinity and oxygen. The bacteria belonging to Bacteroidetes and Proteobacteria were positively influenced by salinity. Nevertheless, bacteria related to Alpha- and Betaproteobacteria classes and phylum Firmicutes showed a positive correlation with oxygen and pH but negative with salinity. The phylum Cyanobacteria were less influenced by the environmental variables. The bacterial community composition of Rambla Salada was also studied by dilution-to-extinction technique. Using this method, 354 microorganisms were isolated. The 16S sequences of 61 isolates showed that the diversity was very different to those obtained by DGGE and with those obtained previously by using classic culture techniques. The taxa identified by dilution-to-extinction were Proteobacteria (81.92%), Firmicutes (11.30%), Actinobacteria (4.52%), and Bacteroidetes (2.26%) phyla with Gammaproteobacteria as predominant class (65.7%). The main genera were: Marinobacter (38.85%), Halomonas (20.2%), and Bacillus (11.2%). Nine of the 61 identified bacteria showed less than 97% sequence identity with validly described species and may well represent new taxa. The number of bacteria in different samples, locations, and seasons were calculated by CARD-FISH, ranging from 54.3 to 78.9% of the total prokaryotic population. In conclusion, the dilution-to-extinction technique could be a complementary method to classical culture based method, but neither gets to cultivate the major taxa detected by DGGE. The bacterial community was influenced significantly by the physico-chemical parameters (specially the salinity and oxygen), the location and the season of sampling.
A Distributed Second-Order Algorithm You Can Trust

Due to the rapid growth of data and computational resources, distributed optimization has become an active research area in recent years. While first-order methods seem to dominate the field, second-order methods are nevertheless attractive as they potentially require fewer communication rounds to converge. However, there are significant drawbacks that impede their wide adoption, such as the computation and the communication of a large Hessian matrix. In this paper we present a new algorithm for distributed training of generalized linear models that only requires the computation of diagonal blocks of the Hessian matrix on the individual workers. To deal with this approximate information we propose an adaptive approach that - akin to trust-region methods - dynamically adapts the auxiliary model to compensate for modeling errors. We provide theoretical rates of convergence for a wide class of problems including L1-regularized objectives. We also demonstrate that our approach achieves state-of-the-art results on multiple large benchmark datasets.
Decentralized Firmware Attestation for In-Vehicle Networks

Today's vehicles are equipped with a large number of Electronic Control Units (ECUs), which control everything from heating to steering and braking. Due to the increasing complexity and inter-dependency of these units, it has become essential for an ECU to be able to ensure the integrity of the firmware running on other ECU's to guarantee its own correct operation. Existing solutions for firmware attestation uses a centralized approach which means a single point of failure. In this article, we propose and investigate a decentralized firmware attestation scheme for the automotive domain. The basic idea of this scheme is that each ECU can attest the state of those ECU's on which it depends. Two flavors of ECU attestation i.e. parallel and serial solution were designed, implemented and evaluated. The two variants were compared in terms of both detection performance (i.e., the ability to identify unauthorized firmware modifications) and timing performance. Our results show that the proposed scheme is feasible to implement and that the parallel solution showed a significant improvement in timing performance over the serial solution.
Multi-factor motivation model in software engineering environments

Software applications are developed differently based on each organisation’s needs and requirements. Software projects are fundamentally based on three considerations (time, cost and quality), each of which is affected by organisational factors. Both Project Management and Software Engineering have emphasised the role of organisational structure on the quality of the deliverable software applications, recognising that organisational structure influences flexibility, reporting relationships and conflict management during the software development lifecycle. This paper reports on a qualitative study which highlights the impact of three organisational structures on software development processes in public organisations and proposes a new organisational model. Semi-structured interviews were conducted with three types of participants, a within-case analysis performed to identify themes, and as a result, a new organisational model was proposed and validated through further interviews. Outcomes showed that combining two existing structures (Functional and Matrix) into one new structure “Independent Project Management Office” (IPMO) would help to overcome administrative obstacles and conflicts in the public sector. The IPMO structure would augment the flexibility and interaction level among software development stakeholders from the perspective of organisations’ leaders. Re-Evaluating Media Richness Theory in Software Development Settings: 8.14. 2 Comparative Study between Agile and Waterfall (Full paper accepted in Journal of Computer and Communications JCC, December 2014)