Miracle-dz/UniFAR-FTUs huggingface dataset:
Reliability of Packet Transmissions in Vehicular Networks

The current road transport system has large problems with safety and efficiency, and these problems do not only cost the society enormous amounts of money and affect the every day life of people, but might in the long run also have devastating consequences for the environment and the global climate. Future intelligent transportation systems, where vehicular communication systems play a key role, are envisioned to alleviate these problems and allow for a safer and more efficient coordination of vehicles. In particular, wireless communication is expected to increase the situational awareness in complex and accident-prone scenarios such as intersections, where the ability to coordinate the traffic flow otherwise would be limited by the range and quality of each vehicle’s on-board sensors. However, by relying on wireless communication another form of uncertainty is introduced as the information exchange between nodes suffer from both packet drops and random latencies. This means that before deploying such systems we need to fully understand and be able to handle these uncertainties. This thesis focuses on reliability of packet transmissions in vehicular networks and the main goal is to better understand the performance of vehicular communication systems in different scenarios typical for the vehicular environment, both to gain insights on how to design better communication systems and to understand what uncertainties a control system might have to deal with. The overview part of the thesis provides some background on vehicular communication systems and stochastic geometry which can be used to quantify the impact of interference and derive analytical key performance metrics for this type of networks. In the appended papers we present a general procedure to analytically determine the reliability of packet transmissions for a selected link as well as system wide throughput in intersections. We provide a model repository that can be used to model different MAC protocols, as well as different propagation conditions typical to both urban and rural environments, and the generality and flexibility of the model makes it applicable to both 5G D2D and IEEE 802.11p communication. Furthermore, we study a centralized intersection crossing coordination scenario where vehicles approaching the intersection communicate with a central coordinator. We show how tools from stochastic geometry can be used to analyze the communication performance in this scenario and provide design guidelines that guarantees a certain communication performance (i.e., QoS) while minimizing the use of system resources. This type of results can for example be used to study how far away from the intersection a centralized controller can expect to have information available from all vehicles given certain communication parameters, QoS requirements, vehicle densities and velocities.
Sharp estimates for bubbling solutions of a fourth order mean field equation

We consider a sequence of multi-bubble solutions uk of the following fourth order equation uk = ρk h(x)e uk ∫ he uk in , uk = uk = 0 on ∂ , (∗) where h is a C2,β positive function, is a bounded and smooth domain in R4, and ρk is a constant such that ρk ≤C . We show that (after extracting a subsequence), limk→+∞ ρk = 32σ3m for some positive integer m ≥ 1, where σ3 is the area of the unit sphere in R4. Furthermore, we obtain the following sharp estimates for ρk : ρk −32σ3m =c0 m ∑ j=1 2 k, j  ∑ l = j G4(p j ,pl )+ R4(p j ,p j )+ 1 32σ3 log h(p j )   + o   m ∑ j=1 2 k, j   where c0 >0, log 64 4 k, j = max x∈Bδ(p j ) uk(x)−log( ∫ heuk ) and uk →32σ3 m ∑ j=1 G4(·, p j ) in C4 loc( \{p1, . . . , pm}). This yields a bound of solutions as ρk converges to 32σ3m from below provided that m ∑ j=1  ∑ l = j G4(p j , pl ) + R4(p j , p j ) + 1 32σ3 log h(p j )   > 0. The analytic work of this paper is the first step toward computing the LeraySchauder degree of solutions of equation (∗). Mathematics Subject Classification (2000): 35B40 (primary); 35B45, 35J40 (secondary). Received December 12, 2006; accepted in revised form November 13, 2007. 600 CHANG-SHOU LIN AND JUNCHENG WEI
Deep learning for medical image segmentation – using the IBM TrueNorth neurosynaptic system

Deep convolutional neural networks have found success in semantic image segmentation tasks in computer vision and medical imaging. These algorithms are executed on conventional von Neumann processor architectures or GPUs. This is suboptimal. Neuromorphic processors that replicate the structure of the brain are better-suited to train and execute deep learning models for image segmentation by relying on massively-parallel processing. However, given that they closely emulate the human brain, on-chip hardware and digital memory limitations also constrain them. Adapting deep learning models to execute image segmentation tasks on such chips, requires specialized training and validation. In this work, we demonstrate for the first-time, spinal image segmentation performed using a deep learning network implemented on neuromorphic hardware of the IBM TrueNorth Neurosynaptic System and validate the performance of our network by comparing it to human-generated segmentations of spinal vertebrae and disks. To achieve this on neuromorphic hardware, the training model constrains the coefficients of individual neurons to {-1,0,1} using the Energy Efficient Deep Neuromorphic (EEDN)1 networks training algorithm. Given the ∼1 million neurons and 256 million synapses, the scale and size of the neural network implemented by the IBM TrueNorth allows us to execute the requisite mapping between segmented images and non-uniform intensity MR images >20 times faster than on a GPU-accelerated network and using <0.1 W. This speed and efficiency implies that a trained neuromorphic chip can be deployed in intra-operative environments where real-time medical image segmentation is necessary.
Numerical simulations of laser-plasma interaction with “nanostructured targets”

In this contribution we report on Particle-In-Cell simulations of ultra-intense laser interaction with nanostructured foam targets. Modern-day ultra-intense laser facilities are able to provide ultra short (~30 fs) laser pulses with a very high contrast (as high as ~10). Although at relativistic laser intensities (I > ~10 W/cm ) any material becomes a plasma in a few laser cycles, the short duration and the high contrast of the laser pulse might allow the nanostructure of a target to survive long enough to influence the interaction The study of ultra-intense laser interaction with nanostructured targets has attracted significant attention recently[1,2,3] (e.g. grating targets have been used to study plasmonic effects at high field intensities, arrays of nanowires have been irradiated to obtain extreme plasma temperatures.). In this contribution we are particularly interested in random nanostructures with a very low average density. This kind of target is one of the very few available options to obtain nearcritical plasmas for Ti:Sapphire laser system. A recent experimental campaign performed at GIST PW-class laser facility [4] has shown that targets consisting in a solid foil coupled with a near-critical foam layer are particularly attractive for laser-driven ion acceleration, leading to higher ion energies and a higher total number of particles with respect to simple flat foils. This effect is attributed to the very efficient laser-target coupling in the near-critical plasma [5]. However a foam target consists of nanoparticles (~10nm radius) aggregated in larger structures, whose scalelength is close to the laser wavelength. Thus with high contrast laser systems, the nanostructure of the foam might play a role during the laser-plasma interaction. In previous numerical simulations, foam targets have been modeled with a simple uniform near-critical plasma.We present results of 3D PIC simulations (performed with open-source piccante[6] code) of laser interaction with a realistic model of nanostructured foam targets, based on the well known Diffusion Limited Aggregation[7] algorithm. These more realistic simulations lead to a better agreement with the experimental results if compared with simulations performed with uniform near-critical plasmas, highlighting also the role played by the nanostructure and suggesting possible strategies to improve the properties of the accelerated ions.
Observation on therapeutic effect of Kanglaite Injection combined with transcatheter arterial chemoembolization ( TACE ) in treating mid-late stage liver cancer

[Abstract] Objective: Explore therapeutic efficacy of Kanglaite Injection (KLT) combined with TACE in treating mid-late stage liver cancer. Methods: Grouping differences comparison in symptom improvement, blood biochemical indexes, and short-term therapeutic effect between single TACE (25 cases in control group) and KLT+TACE (31 cases in treatment group). Results: Treatment group was significantly better than control group in clinical symptom improvement, tumor growth inhibition, and reduction of chemotherapeutic toxic side reaction. Conclusion: KLT combined with TACE in treating mid-late stage liver cancer can apparently raise therapeutic effect, improve symptoms, and reduce toxic side reaction of TACE.
The Role of Multisensory Data for Automatic Segmentation of Manipulation Skills

Due to the improvement of technology and the decline in the cost of robots, there is an increasing push to place robots in homes. However, robots placed in unstructured environments such as the home need to deal with the uncertainties of the environment. A multisensory representation of the world is important for robust interaction with the environment and recent work in the area show that using multiple sensory modalities can be learned through data collected through exploration and is beneficial for manipulation [12, 13, 10, 6]. Take for example a lamp similar to the one seen in Fig. 1. While the robot could rely on visual information to determine if the light has been turned on, it can also utilize touch to detect the change in pressure and sound to hear the click of the switch. This allows the robot to naturally develop contingency cases (e.g. light bulb is broken). Furthermore, a robot could adapt its control schemes to the environment by using feedback on each of its sensor modalities (e.g. pull until a particular force, hears a click, or sees light). This also shows how skills can be represented as low-level primitives (e.g. pull, grasp), which connect naturally to the different sensory modalities. For a robot to quickly to learn about the different sensory modalities and utilize them for manipulation, we use learning from demonstration (LfD) [2] to gather data of various manipulation skills. From these demonstrations, we use segmentation to separate the skills into low-level primitives, which are each individually modeled, similar to work from [9, 7, 8, 11, 3]. This allows the robot to adapt its actions and repeat and/or select different segments to successfully complete a task. While prior work autonomously segment trajectories, they do not utilize all three sensory modalities (i.e. visual, haptic, and audio). Furthermore, any combination of modalities (typically visual and proprioceptive), use carefully crafted features spaces (e.g. contact features for pick and place) to guide segmentation. The closest work that uses all sensory input is Kappler et al. [6], where they expand the notion of Associated Skill Memories (ASM) to include multimodal information about the effects of the actions. The sensory inputs include visual, haptic, and auditory information. However, this work does not segment the trajectory automatically. Instead, they rely on a provided manipulation graph. Unlike prior work, we Fig. 1: Our experimental platform, with the four different objects it interacted with. From left-to-right they are a lamp, a pasta jar, a plastic drawer set, and a breadbox
UNIVERSIDADE FEDERAL DO CEARÁ FACULDADE DE FARMÁCIA, ODONTOLOGIA E ENFERMAGEM PROGRAMA DE PÓS-GRADUAÇÃO EM ODONTOLOGIA MESTRADO EM ODONTOLOGIA NADINE LUÍSA GUIMARÃES ALBUQUERQUE EFEITO DA INCORPORAÇÃO DE MICROPARTÍCULAS POLIMÉRICAS CARREGADAS COM CATEQUINA NAS PROPRIEDADES FÍSICO-QUÍMIC

The aim of this in vitro study was to evaluate the performance of polymeric microparticles loaded with Epigallocatechin-3-gallate (EGCG) on the physicochemical properties of a twostep etch-and-rinse adhesive system. First, the degree of conversion (%DC) was evaluated by FT-IR spectrophotometry and release assay of adhesives to evaluate the performance of EGCG loaded PLGA microparticles was realized (Experiment 1). For release assay, aliquots were collected of each samples and quantified in terms of EGCG release at pre-defined times by means of UV-Vis Spectrophotometer. In Experiment 2, forty-five molars were divided into 5 groups (n=9) according to the rewetting solution used (distilled water, 0.1% EGCG aqueous solution and 1.0% microparticles aqueous solution (PLGA50:50/EGCG)) and the Adper Single Bond 2 adhesive system used (containing 0.1% free EGCG, 1.0% EGCG loaded PLGA microparticles or in original form as control). Five 1-mm-thick increments of composite resin were build up and light-cured for 40 s individually. The teeth were stored in distilled water at 37oC for 24 h. After storage, they were longitudinally sectioned in both directions to obtain bonded sticks with a cross-sectional area of approximately 1.0 mm2. Each bonded stick was testing to a tensile force of 0.5 mm/min in the universal testing machine. %DC and μTBS values were statistically analyzed with ANOVA, with significance level of 5%. There was no statistically significant difference between the DC means after PLGAmicroparticles loaded with EGCG incorporated (p>0.05). In relation to release assay, the 1.0% PLGA50:50/EGCG group presented better results, achieving the highest release in quantitative terms, being the elect to be used in bond strength test (Experiment 2). After 24 h of storage, there was no statistically significant difference between the mean bond strength values of the tested groups (p>0.05). The incorporation of the polymeric microparticles loaded with EGCG did not interfere in the adhesive degree of conversion. The adhesive system loaded microparticles EGCG incorporated in its composition was able to release EGCG. However, the flavonoid epigallocatechin-3-gallate (EGCG) had no effect when incorporated into etch-and-rinse adhesive system or applied as dentin pretreatment, on free and microencapsulated forms, in the immediate bond strength.
Restructuring of Logistics Processes : Case Study of Cross-Docking Operations at Warehouse AZ 1 of Grupo Luís Simões

Organizations involved in supply chain management face increasing pressures to reduce inventories and lead-times and to improve global efficiency. Cross-docking operations are being increasingly adopted as part of a strategy to concentrate inventories and to reduce the number of stock-keeping locations, a trend that requires flexible and well-coordinated flows of goods and information along the logistic chain. The present work is intended to improve the efficiency of processes associated with crossdocking operations in a logistics center of a major logistic operator in Portugal. And, for that end, a simulation model of the operations that take place at the facility was developed with the purpose of testing and evaluating alternatives in managing cross-docking operations. These include, dock-door assignment, truck scheduling and temporary storage space allocation. Results revealed that the best scenario consisted in the combination of a temporary storage policy which minimizes the distance travelled within the facility, an exclusive service mode with alternated doors for loading and unloading with a preferential dock door assignment policy and a first-come-first-served truck scheduling method.
Effect of experimentally induced climate change on the seed bank of aMediterranean shrubland

del Cacho,M. (corresponding author, mowtse@yahoo.es), Saura-Mas, S. (s.sauramas@creaf.uab.es) & Lloret, F. (Francisco.Lloret@uab.cat): CREAF-Ecology Unit, Universitat Autònoma de Barcelona, Edifici C, Bellaterra, Barcelona, Spain Estiarte, M. (m.estiarte@creaf.uab.cat), Peñuelas, J. (Josep.Penuelas@uab.cat): Global Ecology Unit CSIC-CEAB-CREAF, Universitat Autònoma de Barcelona, Edifici C, Bellaterra, Barcelona, Spain Abstract
ROBUST DAMPING CONTROL DESIGN FOR UPFC USING MIXED H 2 / H ∞ TECHNIQUE

Power systems such as the other industrial plants contain different kinds of uncertainties which should be considered in controller design procedure. For this reason, the idea of robust mixed H2/H∞ control was used for designing of Unified Power Flow Controller (UPFC) Power Oscillation Damping (POD) controller. This newly developed design strategy combines the advantage of the H2 and H∞ control synthesizes and gives a powerful multi-objectives design addressed by the Linear Matrix Inequality (LMI) techniques. To achieve decentralization, using the Schauder fixed point theorem the synthesis and analysis of Multi-Input Multi-output (MIMO) control system is translated into set of equivalent Multi-Input Single-Output (MISO) control system. The proposed mixed H2/H∞ controller has a decentralized scheme and advantage of a decentralized controller design is reduction in the controller complexity and suitability for practical implementation. The effectiveness of the proposed control strategy was evaluated under operating conditions for damping low frequency oscillations in comparison with the classical controller to demonstrate its robust performance through nonlinear time simulation and some performance indices.
POTENTIAL OF PAENIBACILLUS AND TRICHODERMA ISOLATES AGAINST COLLETOTRICHUM FALCATUM CAUSING RED ROT DISEASE IN SUGARCANE

Twenty bacterial and 27 Trichoderma isolates obtained from sugarcane ecosystem were identified at species level based on ITS-rDNA sequencing. Phylogenetic analysis based on 16S rDNA gene sequencing of bacterial isolates resulted in four groups, viz. Paenibacillus alvei, Alcaligenes faecalis, Pseudomonas fluorescenes and uncultured bacterium. Also, sequencing ITS region of rDNA (ITS1, 2 and 5.8S rDNA) in Trichoderma isolates led to clustering into four taxa, viz. T. asperellum, T. harzianum, T. aureoviride and T. atroviride. All the fungal and bacterial antagonistic isolates were screened against C. falcatum in vitro and effective ones were selected. The sequence results revealed that four effective bacterial antagonistic isolates (BA2, BA8, BA10 and BA19) have high similarity with Paenibacillus alvei. Among the effective Trichoderma isolates, T1, T3 and T18 showed similarity with T. asperellum while T17, T22 and T20 were confirmed as T. harzianum. The performance of all these antagonistic isolates was evaluated individually, in comparison with fungicide alone and in combination with best bacterial antagonist against red rot under pot culture and field conditions. Among the bacterial and fungal antagonists, P. alvei (BA2) and T. harzianum (T20) stand superior to other isolates as they augmented percent germination and plant survival against soil borne inoculum of red rot significantly as compared to pathogen inoculated plots without treatment. However, the combination of bacterial antagonist and fungicide was found to be the best under field conditions. Hence, a combination of P. alvei and fungicide could be recommended for red rot management by conventional means whereas the best antagonistic isolates could be selected for molecular analysis of suppression of C. falcatum and characterization of antifungal proteins.
Improving Students' Daily Life Stress Forecasting using LSTM Neural Networks

Accurately forecasting stress may enable people to make behavioral changes that could improve their future health. For example, accurate stress forecasting might inspire people to make changes to their schedule to get more sleep or exercise, in order to reduce excessive stress tomorrow night. In this paper, we examine how accurately the previous $N$-days of multi-modal data can forecast tomorrow evening's high/low binary stress levels using long short-term memory neural network models (LSTM), logistic regression (LR), and support vector machines (SVM). Using a total of 2,276 days, with 1,231 overlapping 8-day sequences of data from 142 participants (including physiological signals, mobile phone usage, location, and behavioral surveys), we find the LSTM significantly outperforms LR and SVM with the best results reaching 83.6% using 7 days of prior data. Using time-series models improves the forecasting of stress even when considering only subsets of the multi-modal data set, e.g., using only physiology data. In particular, the LSTM model reaches 81.4% accuracy using only objective and passive data, i.e., not including subjective reports from a daily survey.
The Significance of Domestic Tourism in Durban , South Africa

Domestic tourism is the cornerstone of economic development processes in most cities andDurban is no exception since it is the centre of attraction in the province of KwaZulu-Natal. The main aim of the study was to examine the significance of domestic tourism in Durban. A qualitative study was underaken, in which purposive sampling was used. In-depth, semi-structured interviews were conducted, and the data was analysed using thematic analysis. Study findings highlighted the importance of tourism for job creation, poverty alleviation, and infrastructure development. Potential challenges that arise for domestic tourists are pricing and transportation. More needs to be done to invest extra effort into marketing the city more efficiently to all tourists, but especially to domestic tourists in all provinces of South Africa, since domestic tourism offers a reliable market throughout the year.
Extending the Linked Data Cloud with Multilingual Lexical Linked Data

A lot of information that is already available on the Web, or retrieved from local information systems and social networks, is structured in data silos that are not semantically related. Semantic technologies make it apparent that the use of typed links that directly express their relations are an advantage for every application that can reuse the incorporated knowledge about the data. For this reason, data integration, through reengineering (e.g., triplify) or querying (e.g., D2R), is an important task in order to make information available for everyone. Thus, in order to build a semantic map of the data, we need knowledge about data items itself and the relation between heterogeneous data items. Here we present our work of providing Lexical Linked Data (LLD) through a meta-model that contains all the resources and gives the possibility to retrieve and navigate them from different perspectives. After giving the definition of Lexical Linked Data, we describe the existing datasets we collected and the new datasets we included. Here we describe their format and show some use cases where we link lexical data, and show how to reuse and inference semantic data derived from lexical data. Different lexical resources (MultiWordNet, EuroWordNet, MEMODATA Lexicon, the Hamburg Methaphor Database) are connected to each other towards an Integrated Vocabulary for LLD that we evaluate and present. Received 10 June 2013; Accepted 16 June 2013
The Value of Business Group Affiliation for Political Connections: Preferential Lending in Brazil

1356 Abstract: Political connections are a worldwide phenomenon, but little is known about the capability differentials in using them. We argue that business group affiliates are capable of extracting more value from political connections than non-affiliates. We use political contributions to proxy for firm connections. A selection-corrected fixed effects model was fitted using a panel sample of 778 firm-election observations for three major elections in Brazil. We find support that group affiliation moderates the effect of political connections on preferential lending. We also find that local business groups are more capable of using their political capability than foreign groups.