Mobile setup for synchrotron based in situ characterization during thermal and plasma-enhanced atomic layer deposition

We report the design of a mobile setup for synchrotron based in situ studies during atomic layer processing. The system was designed to facilitate in situ grazing incidence small angle x-ray scattering (GISAXS), x-ray fluorescence (XRF), and x-ray absorption spectroscopy measurements at synchrotron facilities. The setup consists of a compact high vacuum pump-type reactor for atomic layer deposition (ALD). The presence of a remote radio frequency plasma source enables in situ experiments during both thermal as well as plasma-enhanced ALD. The system has been successfully installed at different beam line end stations at the European Synchrotron Radiation Facility and SOLEIL synchrotrons. Examples are discussed of in situ GISAXS and XRF measurements during thermal and plasma-enhanced ALD growth of ruthenium from RuO4 (ToRuS™, Air Liquide) and H2 or H2 plasma, providing insights in the nucleation behavior of these processes

Towards the identification of metabolite markers of nipple pain and inflammation in human milk

Background: Human milk is considered the best source of nutrition for all newborns as it contains important growth, developmental and immunological factors. The WHO (2003) recommends exclusive breastfeeding for the first six months of age, with complementary breastfeeding up to two years and beyond. However, some women experience complications of the breast that lead to early cessation of breastfeeding, which can adversely affect the well being of the developing infant and her own health. Nipple pain is the most commonly cited reason for weaning in the first week postpartum. Nipple pain is also linked to mastitis from milk stasis and possible bacterial infection, although the influence of bacteria is still largely unknown. However, it is known that the presence of bacteria and fungi along with their metabolites contribute to the composition of the milk as the baby receives it. Metabolomics is increasingly being utilised in the dairy industry to determine spoilage as a result of teat trauma and mastitis. Given the current diagnostic application of metabolomics in clinical medicine uses blood and urine samples, it has been proposed as a potential tool for detecting biomarkers and determining compositional changes in human milk. Measuring the composition of milk from human mothers experiencing persistent nipple pain, with or without evidence of trauma, and identifying the influence of this condition on endogenous and exogenous metabolites may determine the relationship between milk composition and nipple pain. Aims: The aims of this study were to source the appropriate human and bovine milk samples; to identify and quantify bacterial and fungal species using traditional culture and microscopy techniques; to measure the effect of nipple pain on the paracellular pathway of the breast by measuring the sodium and potassium concentration and ratio in the milk; to optimise GC-MS methodology for the measurement of milk metabolites; and to use untargeted metabolomics to identify compositional differences in the metabolite profile in human milk from mothers presenting with nipple pain compared to healthy control mothers. Results: Two groups were recruited; a control group of mothers not experiencing nipple pain (n=22 samples) and a group of mothers experiencing persistent nipple pain during breastfeeding (n=11 samples); mothers with unilateral nipple pain supplied a milk sample from their affected and non-affected breast (n=4). The nipple pain group (n=11) was divided into two subgroups; persistent nipple pain without evidence of trauma (PG) (n=6) and persistent nipple pain with evidence of trauma (TG) (n=5). Additionally 9 bovine samples were collected, 3 from healthy cows (control), 4 from cows presenting with mastitis and 2 from a single storage vat, to be used as positive controls throughout the study. All 42 samples were tested for the presence of microbial and fungal species, sodium and potassium concentrations and ratio were determined and untargeted metabolomics analysis of the milk metabolome was performed. Overall there was no significant difference in microbe content between the human control and nipple pain group (1, 623 CFU/ml vs. 1, 503 CFU/ml); the TG subgroup had the highest colony count of 2, 778 CFU/ml. The bovine mastitis group had a higher colony count than the bovine control group, 2, 173 CFU/ml vs. 473 CFU/ml. Coagulase negative staphylococcus ssp. were the most frequently isolated microorganisms and was found in 91% of human milk samples and 100% of bovine milk samples. Staphylococcus aureus were identified in one human milk sample from a mother in the PG subgroup and in one bovine sample from a cow suffering from untreated mastitis as well as both pooled bovine vat samples. Streptococcus ssp. And yeast were only found in bovine samples. The TG subgroup had the highest Na+ concentration of the human milk samples (8.04 ± 2.40 mM), significantly highly than the control group (4.32 ± 1.18 mM; p Untargeted metabolomic analysis found compositional differences between the human control and nipple pain groups, in particular samples from the TG subgroup. Compositional variations between milk from the control and nipple pain subgroups was identified using principal component analysis and PC4 best represented the differences in metabolite composition between the groups. This result is consistent with the subtlety of the nipple pain condition. A list of the most influential metabolites based on their correlation loadings (explained within 50-100% of the model) was determined. The most influential metabolites with respect to the TG milk samples were included isoleucine, proline, galactose and some as yet unidentified metabolites. Conclusion: As nipple pain is often a precursor to mastitis the results from this study will form a basis for further development using metabolomics as a tool for more efficient detection and treatment of breast infection and inflammation within the nipple and breast

Immigration amnesties in the southern EU member states - a challenge for the entire EU?

The question of how to proceed with illegal immigrants arriving in the southern EU member states is one of the pressing policy issues for the EU. In our article we will provide a thorough analysis of immigration policy and immigration amnesties from an economist’s perspective. In particular, we are interested in answering questions such as why (at all) some states legalize irregular immigrants and what effects unilateral policy measures in this field have in an economic union such as the EU. While most of the work in the area of immigration amnesties focuses on the single country case we extend this scenario to the case in which the legalizing country is part of a federation and spillover effects between different states may occur. Several interesting aspects will be considered in this context, in particular, potential changes of the policy mix between internal and external enforcement on the one hand and legalization on the other hand when a federal setting is considered instead of a single country.illegal migration, immigration policy, regularization, amnesties, enforcement, interregional transfers, European Union

Framework for a Perceptive Mobile Network using Joint Communication and Radar Sensing

In this paper, we develop a framework for a novel perceptive mobile/cellular network that integrates radar sensing function into the mobile communication network. We propose a unified system platform that enables downlink and uplink sensing, sharing the same transmitted signals with communications. We aim to tackle the fundamental sensing parameter estimation problem in perceptive mobile networks, by addressing two key challenges associated with sophisticated mobile signals and rich multipath in mobile networks. To extract sensing parameters from orthogonal frequency division multiple access (OFDMA) and spatial division multiple access (SDMA) communication signals, we propose two approaches to formulate it to problems that can be solved by compressive sensing techniques. Most sensing algorithms have limits on the number of multipath signals for their inputs. To reduce the multipath signals, as well as removing unwanted clutter signals, we propose a background subtraction method based on simple recursive computation, and provide a closed-form expression for performance characterization. The effectiveness of these methods is validated in simulations.Comment: 14 pages, 12 figures, Journal pape

Functional and formal component design for an electric motorbike “Sound Module”

Nowadays, new technologies allow creating new advances in the society through the innovation or improvement of existent products. This project intends to design a sound module that will be incorporated in an electric motorbike. As every motorbike has a different inside structure, the study will be carried out considering that the module’s volume may be adapted depending on the motorbike. The electric motorbike market is still in its development stage, and the studied topic in the project seems to be currently in research by many automotive enterprises, as the noise limit rulemakings in the city are a burning issue that has been already accomplished by 4-wheel vehicles. The design and study aims to contribute in the decrease of accidents due to the lack of noise of this type of vehicles. This will be accomplished with a selection of elements that combined, will allow the citizens to discern the presence of the electric vehicle and act in consequence

Security Analysis of Mobile Payments: Direct Carrier Billing

Payments are a compensation for a product or a service received. The funds are transferred from one party (consumer) to another (merchant). Mobile payments are a particular form of electronic payment where a mobile device serves as the key instrument to initiate, authorize or complete a payment. The payment methods have been continuously changing to adjust to cashless trends. Seeking to reach a larger number of customers has promoted the development of different solutions to provide means of payment. With an increasing number of mobile subscribers, mobile solutions such as carrier billing, SMS-based payments, and mobile wallets are gaining importance, permeating different markets, such as public transportation, digital content, advertisements and charity. This thesis investigates and analyses mobile payment solutions. The main purpose is, primarily, to identify and describe the security protocols that occur during the payment transaction. Subsequently, to distinguish the mechanisms utilised to identify and authenticate consumers and the mechanisms providing integrity to the payment data. Additionally, to recognize the possible security threats overlooked during the design and deployment of payment solutions. The analysis and tests carried out showed opportunity areas for the service providers to improve the security level of their services. We found vulnerabilities that jeopardise the integrity and authenticity of transactions from the merchant and consumer sides. The major vulnerabilities found lead to conclude that despite the development of protocols and technologies to strengthen security, an appropriate analysis is required to design and develop secure solutions. Neglecting security requirements in exchange for simplicity could come at a high price for the parties involved in mobile payments, specially, in direct carrier billing

Age-Related Differences in Multimodal Information Processing and Their Implications for Adaptive Display Design.

In many data-rich, safety-critical environments, such as driving and aviation, multimodal displays (i.e., displays that present information in visual, auditory, and tactile form) are employed to support operators in dividing their attention across numerous tasks and sources of information. However, limitations of this approach are not well understood. Specifically, most research on the effectiveness of multimodal interfaces has examined the processing of only two concurrent signals in different modalities, primarily in vision and hearing. Also, nearly all studies to date have involved young participants only. The goals of this dissertation were therefore to (1) determine the extent to which people can notice and process three unrelated concurrent signals in vision, hearing and touch, (2) examine how well aging modulates this ability, and (3) develop countermeasures to overcome observed performance limitations. Adults aged 65+ years were of particular interest because they represent the fastest growing segment of the U.S. population, are known to suffer from various declines in sensory abilities, and experience difficulties with divided attention. Response times and incorrect response rates to singles, pairs, and triplets of visual, auditory, and tactile stimuli were significantly higher for older adults, compared to younger participants. In particular, elderly participants often failed to notice the tactile signal when all three cues were combined. They also frequently falsely reported the presence of a visual cue when presented with a combination of auditory and tactile cues. These performance breakdowns were observed both in the absence and presence of a concurrent visual/manual (driving) task. Also, performance on the driving task suffered the most for older adult participants and with the combined visual-auditory-tactile stimulation. Introducing a half-second delay between two stimuli significantly increased response accuracy for older adults. This work adds to the knowledge base in multimodal information processing, the perceptual and attentional abilities and limitations of the elderly, and adaptive display design. From an applied perspective, these results can inform the design of multimodal displays and enable aging drivers to cope with increasingly data-rich in-vehicle technologies. The findings are expected to generalize and thus contribute to improved overall public safety in a wide range of complex environments.PhDIndustrial and Operations EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/133203/1/bjpitts_1.pd

Advanced document data extraction techniques to improve supply chain performance

In this thesis, a novel machine learning technique to extract text-based information from scanned images has been developed. This information extraction is performed in the context of scanned invoices and bills used in financial transactions. These financial transactions contain a considerable amount of data that must be extracted, refined, and stored digitally before it can be used for analysis. Converting this data into a digital format is often a time-consuming process. Automation and data optimisation show promise as methods for reducing the time required and the cost of Supply Chain Management (SCM) processes, especially Supplier Invoice Management (SIM), Financial Supply Chain Management (FSCM) and Supply Chain procurement processes. This thesis uses a cross-disciplinary approach involving Computer Science and Operational Management to explore the benefit of automated invoice data extraction in business and its impact on SCM. The study adopts a multimethod approach based on empirical research, surveys, and interviews performed on selected companies.The expert system developed in this thesis focuses on two distinct areas of research: Text/Object Detection and Text Extraction. For Text/Object Detection, the Faster R-CNN model was analysed. While this model yields outstanding results in terms of object detection, it is limited by poor performance when image quality is low. The Generative Adversarial Network (GAN) model is proposed in response to this limitation. The GAN model is a generator network that is implemented with the help of the Faster R-CNN model and a discriminator that relies on PatchGAN. The output of the GAN model is text data with bonding boxes. For text extraction from the bounding box, a novel data extraction framework consisting of various processes including XML processing in case of existing OCR engine, bounding box pre-processing, text clean up, OCR error correction, spell check, type check, pattern-based matching, and finally, a learning mechanism for automatizing future data extraction was designed. Whichever fields the system can extract successfully are provided in key-value format.The efficiency of the proposed system was validated using existing datasets such as SROIE and VATI. Real-time data was validated using invoices that were collected by two companies that provide invoice automation services in various countries. Currently, these scanned invoices are sent to an OCR system such as OmniPage, Tesseract, or ABBYY FRE to extract text blocks and later, a rule-based engine is used to extract relevant data. While the system’s methodology is robust, the companies surveyed were not satisfied with its accuracy. Thus, they sought out new, optimized solutions. To confirm the results, the engines were used to return XML-based files with text and metadata identified. The output XML data was then fed into this new system for information extraction. This system uses the existing OCR engine and a novel, self-adaptive, learning-based OCR engine. This new engine is based on the GAN model for better text identification. Experiments were conducted on various invoice formats to further test and refine its extraction capabilities. For cost optimisation and the analysis of spend classification, additional data were provided by another company in London that holds expertise in reducing their clients' procurement costs. This data was fed into our system to get a deeper level of spend classification and categorisation. This helped the company to reduce its reliance on human effort and allowed for greater efficiency in comparison with the process of performing similar tasks manually using excel sheets and Business Intelligence (BI) tools.The intention behind the development of this novel methodology was twofold. First, to test and develop a novel solution that does not depend on any specific OCR technology. Second, to increase the information extraction accuracy factor over that of existing methodologies. Finally, it evaluates the real-world need for the system and the impact it would have on SCM. This newly developed method is generic and can extract text from any given invoice, making it a valuable tool for optimizing SCM. In addition, the system uses a template-matching approach to ensure the quality of the extracted information

Monitoring and Failure Recovery of Cloud-Managed Digital Signage

Digitaal signage kasutatakse laialdaselt erinevates valdkondades, nagu näiteks transpordisüsteemid, turustusvõimalused, meelelahutus ja teised, et kuvada teavet piltide, videote ja teksti kujul. Nende ressursside usaldusväärsus, vajalike teenuste kättesaadavus ja turvameetmed on selliste süsteemide vastuvõtmisel võtmeroll. Digitaalse märgistussüsteemi tõhus haldamine on teenusepakkujatele keeruline ülesanne. Selle süsteemi rikkeid võib põhjustada mitmeid põhjuseid, nagu näiteks vigased kuvarid, võrgu-, riist- või tarkvaraprobleemid, mis on üsna korduvad. Traditsiooniline protsess sellistest ebaõnnestumistest taastumisel hõlmab sageli tüütuid ja tülikaid diagnoose. Paljudel juhtudel peavad tehnikud kohale füüsiliselt külastama, suurendades seeläbi hoolduskulusid ja taastumisaega.Selles väites pakume lahendust, mis jälgib, diagnoosib ja taandub tuntud tõrgetest, ühendades kuvarid pilvega. Pilvepõhine kaug- ja autonoomne server konfigureerib kaugseadete sisu ja uuendab neid dünaamiliselt. Iga kuva jälgib jooksvat protsessi ja saadab trace’i, logib süstemisse perioodiliselt. Negatiivide puhul analüüsitakse neid serverisse salvestatud logisid, mis optimaalselt kasutavad kohandatud logijuhtimismoodulit. Lisaks näitavad ekraanid ebaõnnestumistega toimetulemiseks enesetäitmise protseduure, kui nad ei suuda pilvega ühendust luua. Kavandatud lahendus viiakse läbi Linuxi süsteemis ja seda hinnatakse serveri kasutuselevõtuga Amazon Web Service (AWS) pilves. Peamisteks tulemusteks on meetodite kogum, mis võimaldavad kaugjuhtimisega kuvariprobleemide lahendamist.Digital signage is widely used in various fields such as transport systems, trading outlets, entertainment, and others, to display information in the form of images, videos, and text. The reliability of these resources, availability of required services and security measures play a key role in the adoption of such systems. Efficient management of the digital signage system is a challenging task to the service providers. There could be many reasons that lead to the malfunctioning of this system such as faulty displays, network, hardware or software failures that are quite repetitive. The traditional process of recovering from such failures often involves tedious and cumbersome diagnosis. In many cases, technicians need to physically visit the site, thereby increasing the maintenance costs and the recovery time. In this thesis, we propose a solution that monitors, diagnoses and recovers from known failures by connecting the displays to a cloud. A cloud-based remote and autonomous server configures the content of remote displays and updates them dynamically. Each display tracks the running process and sends the trace and system logs to the server periodically. These logs, stored at the server optimally using a customized log management module, are analysed for failures. In addition, the displays incorporate self-recovery procedures to deal with failures, when they are unable to create connection to the cloud. The proposed solution is implemented on a Linux system and evaluated by deploying the server on the Amazon Web Service (AWS) cloud. The main result of the thesis is a collection of techniques for resolving the display system failures remotely

Norm Origin and Development in Cyberspace: Models of Cybernorm Evolution

Social norm theory seeks to explain such informal constraints on human behavior. While numerous areas of academia employ social norm theory, scholars have yet to apply it directly to the study of the Internet. This Article traces norm origin and development in cyberspace and presents a corresponding theory of “cybernorms”; a theory which explains informal constraints on human behavior in cyberspace