High frequency components of the AE signal as useful features for bond/fibre break discriminators

Abstract not available

Attraction, recruitment and retention of high performing sales professionals case study: Lindstr��m

The goal of this thesis is to gain more in-depth understanding of employer branding and offer suggestions on how this knowledge could be utilized in the case company. More in detail, the purpose of this research is to provide tools for improving Lindstr��m���s organizational attractiveness and boosting the recruitment and retention of the segment of high-performing sales professionals. A strategy for reaching this particular segment has not been previously drawn and HR-managers believe strongly that it would be very beneficial for the company���s development and growth. The topic of this research is very current for Lindstr��m, but also contributes on general level as companies are competing against each other in attracting, recruiting and retention of skilled workforce in the times of labor shortage. The research is conducted with qualitative methods and the data collection includes primary data through interviews as well as secondary data in the form of analysis on previous research, websites, recruitment material and discussions with Lindstr��m���s HR department. This research provides a good basis for broader examination on the topic and presents development suggestions for the identified challenges. Based on the key findings Lindstr��m���s HR department was advised to increase firm���s visibility, broaden recruitment channels, provide more hands-on knowledge about the sales positions and investigate their possibilities of developing sales reward systems.

Relationship between hyperacute blood pressure and outcome after ischemic stroke: data from the VISTA Collaboration

Background and Purpose���High blood pressure (BP) is associated independently with poor outcome after acute ischemic stroke, although in most analyses ���baseline��� BP was measured 24 hours or more postictus, and not during the hyperacute period. Methods���Analyses included 1722 patients in hyperacute trials (recruitment 8 hours) from the Virtual Stroke International Stroke Trial Archive (VISTA) Collaboration. Data on BP at enrolment and after 1, 2, 16, 24, 48, and 72 hours, neurological impairment at 7 days (NIHSS), and functional outcome at 90 days (modified Rankin scale) were assessed using logistic regression models, adjusted for confounding variables; results are for 10-mm Hg change in BP. Results���Mean time to enrolment was 3.7 hours (range 1.0 to 7.9). High systolic BP (SBP) was significantly associated with increased neurological impairment (odds ratio, OR 1.06, 95% confidence interval, 95% CI 1.01 to 1.12), and poor functional outcome; odds ratios for both increased with later BP measurements made at up to 24 hours poststroke. Smaller (versus larger) declines in SBP over the first 24 hours were significantly associated with poor NIHSS scores (OR 1.16, 95% CI 1.05 to 1.27) and functional outcome (OR 1.23, 95% CI 1.13 to 1.34). A large variability in SBP was also associated with poor functional outcome. Conclusions���High SBP and large variability in SBP in the hyperacute stages of ischemic stroke are associated with increased neurological impairment and poor functional outcome, as are small falls in SBP over the first 24 hours.

Sparse time-frequency data analysis : a multi-scale approach

In this work, we further extend the recently developed adaptive data analysis method, the Sparse Time-Frequency Representation (STFR) method. This method is based on the assumption that many physical signals inherently contain AM-FM representations. We propose a sparse optimization method to extract the AM-FM representations of such signals. We prove the convergence of the method for periodic signals under certain assumptions and provide practical algorithms specifically for the non-periodic STFR, which extends the method to tackle problems that former STFR methods could not handle, including stability to noise and non-periodic data analysis. This is a significant improvement since many adaptive and non-adaptive signal processing methods are not fully capable of handling non-periodic signals. Moreover, we propose a new STFR algorithm to study intrawave signals with strong frequency modulation and analyze the convergence of this new algorithm for periodic signals. Such signals have previously remained a bottleneck for all signal processing methods. Furthermore, we propose a modified version of STFR that facilitates the extraction of intrawaves that have overlaping frequency content. We show that the STFR methods can be applied to the realm of dynamical systems and cardiovascular signals. In particular, we present a simplified and modified version of the STFR algorithm that is potentially useful for the diagnosis of some cardiovascular diseases. We further explain some preliminary work on the nature of Intrinsic Mode Functions (IMFs) and how they can have different representations in different phase coordinates. This analysis shows that the uncertainty principle is fundamental to all oscillating signals.

Identifying wind power ramp causes from multivariate datasets: a methodological proposal and its application to reanalysis data.

Forecasting abrupt variations in wind power generation (the so-called ramps) helps achieve large scale wind power integration. One of the main issues to be confronted when addressing wind power ramp forecasting is the way in which relevant information is identified from large datasets to optimally feed forecasting models. To this end, an innovative methodology oriented to systematically relate multivariate datasets to ramp events is presented. The methodology comprises two stages: the identification of relevant features in the data and the assessment of the dependence between these features and ramp occurrence. As a test case, the proposed methodology was employed to explore the relationships between atmospheric dynamics at the global/synoptic scales and ramp events experienced in two wind farms located in Spain. The achieved results suggested different connection degrees between these atmospheric scales and ramp occurrence. For one of the wind farms, it was found that ramp events could be partly explained from regional circulations and zonal pressure gradients. To perform a comprehensive analysis of ramp underlying causes, the proposed methodology could be applied to datasets related to other stages of the wind-topower conversion chain.

Genetic diversity and connectivity remain high in Holothuria polii (Delle Chiaje 1823) across a coastal lagoon-open sea environmental gradient

Coastal lagoons represent habitats with widely heterogeneous environmental conditions, particularly as regards salinity and temperature,which fluctuate in both space and time. These characteristics suggest that physical and ecological factors could contribute to the genetic divergence among populations occurring in coastal lagoon and opencoast environments. This study investigates the genetic structure of Holothuria polii at a micro-geographic scale across theMar Menor coastal lagoon and nearbymarine areas, estimating the mitochondrial DNA variation in two gene fragments, cytochrome oxidase I (COI) and 16S rRNA (16S). Dataset of mitochondrial sequences was also used to test the influence of environmental differences between coastal lagoon andmarine waters on population genetic structure. All sampled locations exhibited high levels of haplotype diversity and low values of nucleotide diversity. Both genes showed contrasting signals of genetic differentiation (non-significant differences using COI and slight differences using 16S, which could due to different mutation rates or to differential number of exclusive haplotypes. We detected an excess of recent mutations and exclusive haplotypes, which can be generated as a result of population growth. However, selective processes can be also acting on the gene markers used; highly significant generalized additive models have been obtained considering genetic data from16S gene and independent variables such as temperature and salinity.

High Frequency Class DE Converter Using a Multilayer Coreless PCB Transformer

In modern power electronics equipment, it is desirable to design a low profile, high power density, and fast dynamic response converter. Increases in switching frequency reduce the size of the passive components such as transformers, inductors, and capacitors which results in compact size and less requirement for the energy storage. In addition, the fast dynamic response can be achieved by operating at high frequency. However, achieving high frequency operation while keeping the efficiency high, requires new advanced devices, higher performance magnetic components, and new circuit topology. These are required to absorb and utilize the parasitic components and also to mitigate the frequency dependent losses including switching loss, gating loss, and magnetic loss. Required performance improvements can be achieved through the use of Radio Frequency (RF) design techniques. To reduce switching losses, resonant converter topologies like resonant RF amplifiers (inverters) combined with a rectifier are the effective solution to maintain high efficiency at high switching frequencies through using the techniques such as device parasitic absorption, Zero Voltage Switching (ZVS), Zero Current Switching (ZCS), and a resonant gating. Gallium Nitride (GaN) device technologies are being broadly used in RF amplifiers due to their lower on- resistance and device capacitances compared with silicon (Si) devices. Therefore, this kind of semiconductor is well suited for high frequency power converters. The major problems involved with high frequency magnetics are skin and proximity effects, increased core and copper losses, unbalanced magnetic flux distribution generating localized hot spots, and reduced coupling coefficient. In order to eliminate the magnetic core losses which play a crucial role at higher operating frequencies, a coreless PCB transformer can be used. Compared to the conventional wire-wound transformer, a planar PCB transformer in which the windings are laid on the Printed Board Circuit (PCB) has a low profile structure, excellent thermal characteristics, and ease of manufacturing. Therefore, the work in this thesis demonstrates the design and analysis of an isolated low profile class DE resonant converter operating at 10 MHz switching frequency with a nominal output of 150 W. The power stage consists of a class DE inverter using GaN devices along with a sinusoidal gate drive circuit on the primary side and a class DE rectifier on the secondary side. For obtaining the stringent height converter, isolation is provided by a 10-layered coreless PCB transformer of 1:20 turn���s ratio. It is designed and optimized using 3D Finite Element Method (FEM) tools and radio frequency (RF) circuit design software. Simulation and experimental results are presented for a 10-layered coreless PCB transformer operating in 10 MHz.

���Step by step���: high frequency short-distance epizoochorous dispersal of aquatic macrophytes

Aquatic macrophytes can successfully colonise and re-colonise areas separated by space and time. The mechanisms underlying such ���mobility��� are not well understood, but it has often been hypothesised that epizoochory (external dispersal) plays an important role. Yet, there is only limited, and mostly anecdotal, evidence concerning successful epizoochorous dispersal of aquatic macrophytes, particularly in the case of short-distance dispersal. Here we examine in situ and ex situ dispersal of aquatic macrophytes, including three invasive alien species. A high frequency of Lemna minor Linnaeus dispersal was observed in situ, and this was linked to bird-mediated epizoochory. We concluded that wind had no effect on dispersal. Similarly, in an ex situ examination Lemna minuta Kunth and Azolla filiculoides Lamarck, were found to be dispersed with a high frequency by mallard ducks (Anas platyrhynchos). No dispersal was measured for Elodea nuttalli (Planchon) H. St. John. It is concluded that short-distance or ���stepping-stone��� dispersal via bird-mediated epizoochory can occur with high frequencies, and therefore can play an important role in facilitating colonisation, range expansion and biological invasion of macrophytes.

Abusing data dominance in the digital market. The application of merger control and article 102 TFEU to data-related conducts.

The internet and digital technologies revolutionized the economy. Regulating the digital market has become a priority for the European Union. While promoting innovation and development, EU institutions must assure that the digital market maintains a competitive structure. Among the numerous elements characterizing the digital sector, users��� data are particularly important. Digital services are centered around personal data, the accumulation of which contributed to the centralization of market power in the hands of a few large providers. As a result, data-driven mergers and data-related abuses gained a central role for the purposes of EU antitrust enforcement. In light of these considerations, this work aims at assessing whether EU competition law is well-suited to address data-driven mergers and data-related abuses of dominance. These conducts are of crucial importance to the maintenance of competition in the digital sector, insofar as the accumulation of users��� data constitutes a fundamental competitive advantage. To begin with, part 1 addresses the specific features of the digital market and their impact on the definition of the relevant market and the assessment of dominance by antitrust authorities. Secondly, part 2 analyzes the EU���s case law on data-driven mergers to verify if merger control is well-suited to address these concentrations. Thirdly, part 3 discusses abuses of dominance in the phase of data collection and the legal frameworks applicable to these conducts. Fourthly, part 4 focuses on access to ���essential��� datasets and the indirect effects of anticompetitive conducts on rivals��� ability to access users��� information. Finally, Part 5 discusses differential pricing practices implemented online and based on personal data. As it will be assessed, the combination of an efficient competition law enforcement and the auspicial adoption of a specific regulation seems to be the best solution to face the challenges raised by ���data-related dominance���.

Inequality of income and poverty persistence in the Municipality of Bologna: a cross-sectional and longitudinal analysis through administrative data

This work is part of a project promoted by Emilia-Romagna that aims at encouraging research activities in order to support the innovation strategies of the regional economic system through the exploitation of new data sources. To gain this scope, a database containing administrative data is provided by the Municipality of Bologna. This is achieved by linking data from the Register Office of the Municipality and fiscal data coming from the tax returns submitted to the Revenue Agency and released by the Ministry of Economy and Finance for the period 2002-2017. The main purpose of the project is the analysis of the medium term financial and distributional trends of income of the citizens residing in the Municipality of Bologna. Exploiting this innovative source of data allow us to analyse the dynamics of income at municipal level, overcoming the lack of information in official survey-based statistic. We investigate these trends by building inequality indicators and by examining the persistence of in-work poverty. Our results represent an important informative element to improve the effectiveness and equity of welfare policies at the local level, and to guide the distribution of economic and social support and urban redevelopment interventions in different areas of the Municipality.

Interpretable Convolutional Neural Networks for Decoding and Analyzing Neural Time Series Data

Machine learning is widely adopted to decode multi-variate neural time series, including electroencephalographic (EEG) and single-cell recordings. Recent solutions based on deep learning (DL) outperformed traditional decoders by automatically extracting relevant discriminative features from raw or minimally pre-processed signals. Convolutional Neural Networks (CNNs) have been successfully applied to EEG and are the most common DL-based EEG decoders in the state-of-the-art (SOA). However, the current research is affected by some limitations. SOA CNNs for EEG decoding usually exploit deep and heavy structures with the risk of overfitting small datasets, and architectures are often defined empirically. Furthermore, CNNs are mainly validated by designing within-subject decoders. Crucially, the automatically learned features mainly remain unexplored; conversely, interpreting these features may be of great value to use decoders also as analysis tools, highlighting neural signatures underlying the different decoded brain or behavioral states in a data-driven way. Lastly, SOA DL-based algorithms used to decode single-cell recordings rely on more complex, slower to train and less interpretable networks than CNNs, and the use of CNNs with these signals has not been investigated. This PhD research addresses the previous limitations, with reference to P300 and motor decoding from EEG, and motor decoding from single-neuron activity. CNNs were designed light, compact, and interpretable. Moreover, multiple training strategies were adopted, including transfer learning, which could reduce training times promoting the application of CNNs in practice. Furthermore, CNN-based EEG analyses were proposed to study neural features in the spatial, temporal and frequency domains, and proved to better highlight and enhance relevant neural features related to P300 and motor states than canonical EEG analyses. Remarkably, these analyses could be used, in perspective, to design novel EEG biomarkers for neurological or neurodevelopmental disorders. Lastly, CNNs were developed to decode single-neuron activity, providing a better compromise between performance and model complexity.

Inflammation markers in healthy and periodontitis patients: a preliminary data screening

Advances in diagnostic research are moving towards methods whereby the periodontal risk can be identified and quantified by objective measures using biomarkers. Patients with periodontitis may have elevated circulating levels of specific inflammatory markers that can be correlated to the severity of the disease. The purpose of this study was to evaluate whether differences in the serum levels of inflammatory biomarkers are differentially expressed in healthy and periodontitis patients. Twenty-five patients (8 healthy patients and 17 chronic periodontitis patients) were enrolled in the study. A 15 mL blood sample was used for identification of the inflammatory markers, with a human inflammatory flow cytometry multiplex assay. Among 24 assessed cytokines, only 3 (RANTES, MIG and Eotaxin) were statistically different between groups (p<0.05). In conclusion, some of the selected markers of inflammation are differentially expressed in healthy and periodontitis patients. Cytokine profile analysis may be further explored to distinguish the periodontitis patients from the ones free of disease and also to be used as a measure of risk. The present data, however, are limited and larger sample size studies are required to validate the findings of the specific biomarkers.

Nanochromics: old materials, new structures and architectures for high performance devices

Due to the development of nanoscience, the interest in electrochromism has increased and new assemblies of electrochromic materials at nanoscale leading to higher efficiencies and chromatic contrasts, low switching times and the possibility of color tuning have been developed. These advantages are reached due to the extensive surface area found in nanomaterials and the large amount of organic electrochromic molecules that can be easily attached onto inorganic nanoparticles, as TiO2 or SiO2. Moreover, the direct contact between electrolyte and nanomaterials produces high ionic transfer rates, leading to fast charge compensation, which is essential for high performance electrochromic electrodes. Recently, the layer-by-layer technique was presented as an interesting way to produce different architectures by the combination of both electrochromic nanoparticles and polymers. The present paper shows some of the newest insights into nanochromic science.

Effect of alpha prime due to 475 degrees C aging on fracture behavior and corrosion resistance of DIN 1.4575 and MA 956 high performance ferritic stainless steels

The 475 degrees C embrittlement in stainless steels is a well-known phenomenon associated to alpha prime (alpha`) formed by precipitation or spinodal decomposition. Many doubts still remain on the mechanism of alpha` formation and its consequence on deformation and fracture mechanisms and corrosion resistance. In this investigation, the fracture behavior and corrosion resistance of two high performance ferritic stainless steels were investigated: a superferritic DIN 1.4575 and MA 956 superalloy were evaluated. Samples of both stainless steels (SS) were aged at 475 degrees C for periods varying from 1 to 1,080 h. Their fracture surfaces were observed using scanning electron microscopy (SEM) and the cleavage planes were determined by electron backscattering diffraction (EBSD). Some samples were tested for corrosion resistance using electrochemical impedance spectroscopy (EIS) and potentiodynamic polarization. Brittle and ductile fractures were observed in both ferritic stainless steels after aging at 475 degrees C. For aging periods longer than 500 h, the ductile fracture regions completely disappeared. The cleavage plane in the DIN 1.4575 samples aged at 475 degrees C for 1,080 h was mainly {110}, however the {102}, {314}, and {131} families of planes were also detected. The pitting corrosion resistance decreased with aging at 475 degrees C. The effect of alpha prime on the corrosion resistance was more significant in the DIN 1.4575 SS comparatively to the Incoloy MA 956.

UV-Derivative Spectrophotometric and Stability-Indicating High-Performance Liquid Chromatographic Methods for Determination of Simvastatin in Tablets

A stability-indicating high-performance liquid chromatographic (HPLC) and a second-order derivative spectrophotometric (UVDS) analytical methods were validated and compared for determination of simvastatin in tablets. The HPLC method was performed with isocratic elution using a C18 column and a mobile phase composed of methanol:acetonitrile:water (60:20:20, v/v/v) at a flow rate of 1.0 ml/min. The detection was made at 239 nm. In UVDS method, methanol and water were used in first dilution and distilled water was used in consecutive dilutions and as background. The second-order derivative signal measurement was taken at 255 nm. Analytical curves showed correlation coefficients > 0.999 for both methods. The quantitation limits (QL) were 2.41 mu g/ml for HPLC and 0.45 mu g/ml for UVDS, respectively. Intra and inter-day relative standard deviations were < 2.0 %. Statistical analysis with t- and F-tests are not exceeding their critical values demonstrating that there is no significant difference between the two methods at 95 % confidence level.