Miracle-dz/UniFAR-FTUs huggingface dataset:
Synthesis of Pt–Containing Metals Alloy and Hybrid Nanowires and Investigation of Electronic Structure Using Synchrotron-Based X-Ray Absorption Techniques

Platinum–containing bimetallic nanomaterials have shown different electronic structures from their monometallic counterparts, bringing exotic properties to various catalytic and magnetic applications. The synthesis and shape–dependent properties of bimetallic cubes, truncated cubes, dendrites, and hollow structures, have been intensively studied. One dimensional (1D) Pt–containing nanostructure has attracted special interest, although synthetic challenges in the fine control over morphology arising from unfavorable anisotropic growth of symmetric cubic lattice structures from Pt metal and alloys (Ahmadi et al., 1996; Cozzoli & Manna, 2005; Xia et al., 2003). The electronic structure of bimetallic nanowires is associated with their heterogeneous structural diversity strongly, such as phase segregation (Zhong & Maye, 2001), intermetallic alloy (Casado-Rivera et al., 2004), random alloy (Shibata, et al., 2002) and near surface alloy (NSA) (Greeley & Mavrikakis, 2006; Knudsen, et al., 2007) (See Scheme). Therefore, detailed studies of bimetallic heterogeneous structures are essential for their applications. The structural characterization of bimetallic nanomaterials is not trivial, partly due to the complexity of heterogeneous nature imposed by nanoscale system. X–ray absorption fine spectroscopy (XAS) is a well established tool for investigating the element–resolved structure of bimetallic nanomaterials, since the local environment and electronic properties of atoms of each resonant element can be studied separately by tuning the X–ray energy to the absorbing edge of each metal. XAS has been successfully used to study bonding habit, geometry, electronic and surface structure of many bimetallic nanoparticles (e.g., Pt/Ru, Pt/Au, Pd/Au and Pt/Ir) (Nashner et al., 1997). When the short range order information extracted from XAS analysis is combined with the knowledge of the long range order and average compositional distribution obtained by complementary techniques, actual heterogeneous structure and electronic state of nanomaterials can be quantitatively analyzed. In this chapter, we intend to focus on our recent results on the synthesis and electronic structures of ultrathin PtAu alloy nanowires, Pt/Au phase–segregated hybrid nanowires.
A software assistant for the design of realistic software phantoms

Segmentation and quantification of medical image data are difficult problems in image analysis. Especially, an accurate and robust assessment of quantitative parameters is a key issue in many fields, and can have a direct impact on diagnosis and treatment planning. To this end, physical and software phantom data sets have become an integral tool during the design, implementation, and optimization of new algorithms. Unfortunately, a common research resource has not been established until now for many applications. In this work we propose a software assistant for the development of realistic software phantoms. Our aim is an easy-to-use tool with an intuitive user interface. Furthermore, we provide a software for researchers including a common basis of reference data, which facilitates a standardized and objective validation of performance and limitations of own developments, as well as the comparison of different methods. The fundamental phantom design focuses on objects that can be incorporated into a given background. This can either be a homogeneous artificial background, or a volunteer or patient data set. For each phantom the exact ground truth of the investigated object is available, which provides us with an excellent tool for the generation of realistic data sets. Several experiments are carried out for a number of different applications including software phantoms of small, hyperintense brain lesions, as well as software phantoms of liver metastases.
Incremental Learning with Support Vector Machines and Fuzzy Set Theory

Over the past few years, a considerable number of studies have been made on Support Vector Machines (SVMs) in many domains to improve classification or prediction. However, SVMs request high computational time and memory when the datasets are large. Although incremental learning techniques are viewed as one possible solution developed to reduce the computation complexity of the scalability problem, few studies have considered that some examples close to the decision hyperplane other than support vectors (SVs) might contribute to the training process. Consequently, we propose a novel algorithm by improving Syed’s incremental learning method based on fuzzy set theory. At each learning step, SVs and potential informative examples, called candidate examples (CEs), are added to the next incremental learning step. We expect to achieve better accuracy and less execution time than other methods. In this ongoing study, the proposed algorithm would be investigated on five standard machine learning benchmark datasets to demonstrate the effectiveness of the method.
Stochastic Evolution via Graph Grammars

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés. Stochastic Evolution via Graph Grammars Vadim A. Malyshev
Modelling Thermal Effects of Battery Cells inside Electric Vehicle Battery Packs

Introduction: The poster presents a methodology to account for thermal effects on battery cells to improve the typical thermal performances in a pack through heating calculations generally performed under the operating condition assumption. The aim is to analyse the issues based on battery thermophysical characteristics and their impact on the electrical state of battery cells(Khan, Mulder et al. 2013, Khan, Andreasen et al. 2014, Khan et al. 2014, Khan, Mulder et al. 2014, Khan, Nielsen et al. 2014). Based on this analysis, we derive strategies in achieving the goal, and then propose a battery thermal management system with cell-level thermal controls. Computational Methods: To achieve this, a 3D FEM model of a simplified battery pack is solved in COMSOL Multiphysics with the time varying heat source with different flow rates and in two different cell orientations. The Heat equation is used to model the pack and the classical cooling media e.g. Air and Liquid for the battery pack is implemented. Results: At the end of the discharge, Figure indicates lower temperatures (around 25°C) at the front and higher temperatures (around 30°C) at the rear of the module. Therefore, Figure emphasises the need for a sufficient inlet air velocity for a proper thermal management of the cells located in the rear of the module.
Robust Place Recognition and Loop Closing in Laser-Based SLAM for UGVs in Urban Environments

Robust place recognition plays a key role for the long-term autonomy of unmanned ground vehicles (UGVs) working in indoor or outdoor environments. Although most of the state-of-the-art that approaches for place recognition are vision-based, visual sensors lack adaptability in environments with poor or dynamically changing illumination. In this paper, a 3-D-laser-based place recognition algorithm is proposed to accomplish loop closure detection for simultaneous localization and mapping. An image model named bearing angle (BA) is adopted to convert 3-D laser points to 2-D images, and then ORB features extracted from BA images are utilized to perform scene matching. Since the computational cost for matching a query BA image with all the BA images in a database is too high to meet the requirement of performing real-time place recognition, a visual bag of words approach is used to improve search efficiency. Furthermore, a speed normalization algorithm and a 3-D geometry-based verification algorithm are proposed to complete the proposed place recognition algorithm. Experiments were conducted on two self-developed UGV platforms to verify the performance of the proposed method.
Nonlinear Responses and Optical Limiting Behavior of Ag Nanoparticle Suspension

In this study, the nonlinear optical properties and optical limiting performance of the silver nanoparticles (AgNPs) in distilled water are investigated. The nonlinear absorption coefficient of the colloid is measured by the Z-scan technique. The optical limiting behavior of the AgNP suspension is investigated under exposure to nanosecond laser pulses at 532 nm. The results show that nonlinear scattering can increase the performance of the optical limiting. A theoretical analysis is suggested investigating the observed nonlinear behavior of the AgNPs. It will be shown that nonlinear light scattering occurs at high beam intensity due to induced refractive mismatch between the silver nanoparticles and water. The experimental Z-scan data is fitted with the proposed theoretical model and this has allowed extracting the values of nonlinear absorption coefficient, linear and nonlinear scattering coefficients for the AgNP suspension.
Analysis of Spectral Features of EEG signal in Brain Tumor Condition

Abstract The scalp electroencephalography (EEG) signal is an important clinical tool for the diagnosis of several brain disorders. The objective of the presented work is to analyze the feasibility of the spectral features extracted from the scalp EEG signals in detecting brain tumors. A set of 16 candidate features from frequency domain is considered. The significance on the mean values of these features between 100 brain tumor patients and 102 normal subjects is statistically evaluated. Nine of the candidate features significantly discriminate the brain tumor case from the normal one. The results encourage the use of (quantitative) scalp EEG for the diagnosis of brain tumors
Exchange Rate Pass-Through and the Inflation Environment in Industrialized Countries: An Empirical Investigation

This paper investigates the question of whether a transition to a low-inflation environment, induced by a shift in monetary policy, results in a decline in the degree of pass-through of exchange rate movements to consumer prices. It differs from previous empirical work in its focus on the identification of changes in the inflation environment and its use of a panel-data approach. Evidence from a panel-data set of 11 industrialized countries over the period from 1977 to 2001, supports the hypothesis that exchange rate pass-through declines with a shift to a low-inflation environment brought about by a change in the monetary policy regime. More specifically, the results suggest that pass-through to import, producer, and consumer price inflation declined following the inflation stabilization that occurred in many industrialized countries in the early 1990s but did not decline following a similar episode in the 1980s. Several potential explanations for this finding are discussed, including the possibility that changes in the monetary policy regimes implemented in the 1990s were perceived as more credible than those carried out in the 1980s, and the possibility that the credibility of the new monetary policy regimes was acquired over time.
An empirical test for a zone of canalization in thermal reaction norms

Theoretical models on the evolution of phenotypic plasticity predict a zone of canalization where reaction norms cross, and genetic variation is minimized in the environment a population most frequently encounter. Empirical tests of this prediction are largely missing, in particular for life‐history traits. We addressed this prediction by quantifying thermal reaction norms of three life‐history traits (somatic growth rate, age and size at maturation) of a Norwegian population of Daphnia magna and testing for the occurrence of an intermediate temperature (Tm) at which genetic variance in the traits is minimized. Size at maturation changed relatively little with temperature compared to the other traits, and there was no genetic variance in the shape of the reaction norm. Consequently, age at maturation and somatic growth rate were strongly negatively correlated. Both traits showed a strong genotype–environment interaction, and the estimated Tm was 14 °C for both age at maturation and growth rate. This value of Tm corresponds well with mean summer temperatures experienced by the population and suggests that the population has evolved under stabilizing selection in temperatures that fluctuate around this mean temperature. These results suggest local adaptation to temperature in the studied population and allow predicting evolutionary trajectories of thermal reaction norms under changing thermal regimes.
An energy-efficient opportunistic routing protocol in delay tolerant networks

In delay tolerant networks (DTN), connectivity between nodes is not guaranteed and a message is delivered to a destination node from a source node via opportunistic contacts between intermediate nodes. In DTN, nodes are operated using battery and thus, an energy-efficient operation is crucial, especially for disaster environment. In this paper, we consider PRoPHET protocol as a base protocol since it is the standardized DTN protocol and thus, is regarded as one of the representative DTN routing protocol. Then, we propose an energy-efficient opportunistic routing protocol in DTN, where messages are forwarded based on node's remaining battery and delivery predictability in PRoPHET protocol, analyze the performance of the proposed scheme, and compare the performance with PRoPHET and PRoPHET with periodic sleep in detail, from the aspect of delivery ratio, overhead ratio, delivery latency, and ratio of alive nodes. Simulation results show that the proposed protocol has better delivery probability and overhead ratio, in spite of small increase of delivery latency.
A voyage on medical image segmentation algorithms

Image segmentation is the process of subdividing an image into eloquent regions that are consistent and homogeneous in some characteristics. Image segmentation is indeed a vital process in the early diagnosis of abnormalities and treatment planning. The segmentation algorithms are employed to extract the anatomical structures and anomalies from medical images. The segmentation algorithms can be categorized into three generations. The first generation algorithms are based on threshold, seed point selection and edge tracing methods. The second generation algorithms incorporate uncertainty and optimization models and the third generation algorithms considers the prior information in segmentation process. This review work discusses and conceptualizes the various segmentation algorithms, which are in correlation with medical images and adduce the result of some of the significant algorithms in each generation. Moreover, the proposed work does spell out the pros and cons of the algorithms for computer aided analysis. In extension, this literature review indeed paves an ample platform to the researchers for better understanding of various segmentation techniques and its characteristics for medical images.
Replacement of Cereal with Low Starch Fibrous By-Products on Nutrients Utilization and Methane Emissions in Dairy Goats

Feeding systems for dairy ruminants need to ensure high intake of energy to achieve maximum milk production potential. This might be accomplished by raising the dietary concentration of cereal grain. Increasing the concentration of starch in diets can lead to undesirable ruminal fermentation, and to prevent it, the partial replacement of cereal grain with low starch by-product feeds is recommended. The purpose of the present study was to compare the effect of fed two mixed diets to dairy goats differing in the type of carbohydrate (starch vs. easily degradable fiber). Energy and nitrogen balance, short chain fatty acids in rumen liquor and milk performance in dairy goats during mid lactation were determined. Enteric methane (CH4) emissions and CH4 production from manure were determined as well. Ten multiparous Muciano-Granadina goats were assigned to two isoenergetic and isoproteic diets (19.1 MJ/kg dry matter (DM) and 18.1% of CP, DM basis) in a crossover design. One group was fed a mixed ration with 21.9% of starch (HS diet) and the other (LS diet) with 7.0% of starch. HS diet had 36% of barley (as source of starch) and it was replaced with soy hulls and corn gluten feed in LS diet (as potentially digestible fiber). No differences were observed for dry matter intake in both diets (2.05 kg/d, on average). A significant increase of ruminal acetic acid was found for low starch diet (66.4 and 56.6 mol/100 mol for LS and HS diet, respectively). No significant effect was found among diets for enteric CH4 emissions (28.5 g/d, on average). Manure derived maximum potential yield was (Bo) higher in HS diet, with 5.9 L CH4/kg OM vs. 0.28 L CH4/kg OM for LS diet, probably associated with the low ADF digestibility. Differences among diets were found for milk production (2.4 vs. 2.2 kg/d for HS and LS, respectively), and greater milk fat was observed with LS diet compared with HS (6.4% vs. 5.5%, respectively).
Applications of community detection techniques to brain graphs: Algorithmic considerations and implications for neural function

The human brain can be represented as a graph in which neural units such as cells or small volumes of tissue are heterogeneously connected to one another through structural or functional links. Brain graphs are parsimonious representations of neural systems that have begun to offer fundamental insights into healthy human cognition, as well as its alteration in disease. A critical open question in network neuroscience lies in how neural units cluster into densely interconnected groups that can provide the coordinated activity that is characteristic of perception, action, and adaptive behaviors. Tools that have proven particularly useful for addressing this question are community detection approaches, which can be used to identify communities or modules in brain graphs: groups of neural units that are densely interconnected with other units in their own group but sparsely interconnected with units in other groups. In this paper, we describe a common community detection algorithm known as modularity maximization, and we detail its applications to brain graphs constructed from neuroimaging data. We pay particular attention to important algorithmic considerations, especially in recent extensions of these techniques to graphs that evolve in time. After recounting a few fundamental insights that these techniques have provided into brain function, we highlight potential avenues of methodological advancements for future studies seeking to better characterize the patterns of coordinated activity in the brain that accompany human behavior. This tutorial provides a naive reader with an introduction to theoretical considerations pertinent to the generation of brain graphs, an understanding of modularity maximization for community detection, a resource of statistical measures that can be used to characterize community structure, and an appreciation of the utility of these approaches in uncovering behaviorally-relevant network dynamics in neuroimaging data.
In Vitro Schistosomicidal Activity of Phytol and Tegumental Alterations Induced in Juvenile and Adult Stages of Schistosoma haematobium

There is renewed interest in natural products as a starting point for discovery of drugs for schistosomiasis. Recent studies have shown that phytol reveals interesting in vivo and in vitro antischistosomal properties against Schistosoma mansoni adult worms. Here, we report the in vitro antischistosomal activity of phytol against Schistosoma haematobium juvenile and adult worms and alterations on the tegumental surface of the worms by means of scanning electron microscopy. The assay, which was carried out with 6 concentrations (25, 50, 75, 100, 125, and 150 μg/ml) of phytol, has shown a promising activity in a dose and time-dependent manner. There was a significant decline in the motility of the worms and a mortality rate of 100% was found at 48 hr after they had been exposed to phytol in the concentration of 150 μg/ml. Male worms were more susceptible. On the ultrastructural level, phytol also induced tegumental peeling, disintegration of tubercles and spines in addition to morphological disfiguring of the oral and ventral suckers. This report provides the first evidence that phytol is able to kill S. haematobium of different ages, and emphasizes that it is a promising natural product that could be used for development of a new schistosomicidal agent.
Compression and Aggregation for Optimizing Information Transmission in Distributed CNN

Modern deep learning has significantly improved the performance and has been used in a variety of applications. Due to the heavy processing cost, major platforms for deep learning have been migrated from commodity computers to the cloud where have huge amount of resources. However, the above situation leads to the slowdown of response time due to severe congestion of the network traffic. To alleviate the overconcentration of data traffic and power consumption, many researchers have paid attention to edge computing. We tackle with the parallel processing model using Deep Convolutional Neural Network (DCNN) employed on multiple devices, and the size reduction of network traffic among the devices. We propose a technique that compresses the intermediate data and aggregates common computation %used in the classification in AlexNet for video recognition. Our experiments demonstrate that Zip loss-less compression reduces the amount of data by up to 1/24, and HEVC lossy compression reduces the amount of data by 1/208 with only 3.5\% degradation of the recognition accuracy. Moreover, aggregation of common calculation reduces the amount of computation for 30 DCNNs by 90\%.
Mobile crowd sensing based on CICN

In this paper, we propose a Mobile Crowd Sensing application based on Community Information-Centric Networking to collect opportunistic sensing data in limited area where it restricts radius of Bluetooth Low Energy beacon. The application has more valuable features by comparing with common Mobile Crowd Sensing one. First is to support data integrity and utilize simple communication model according to IP-less communication through common CICN features. Secondly, to gather sensing data by using a name including BLE beacon Identifier. The application inherently supports user privacy and data integrity. We deployed several environmental sensors with Raspberry Pis and BLE beacons together in different places across a building. The developed MCS application collects sensing data as the user walks around in a smart building and draws building sensor map. We show implementation result as well as simple analysis work.