Sex Transm Dis

Background:To address sexual and reproductive health (SRH) needs of young minority urban males, we developed and evaluated Project Connect Baltimore (Connect), which was adapted from a program with demonstrated effectiveness among young females. The objectives were to determine 1) the feasibility of Connect as adapted for young minority men, 2) whether the program increased SRH knowledge and resource sharing of youth-serving professionals (YSPs) working with young men, and 3) whether the program improved awareness and use of resources for young minority men in Baltimore City, an urban environment with high rates of STDs.Methods:Connect developed a clinic referral guide for male youth-friendly resources for SRH. YSPs working with partners and organizations serving young minority men were trained to use Connect materials and pre-, immediate-, and three-month post-training surveys were conducted to evaluate program effects. A before-after evaluation study was conducted among young men attending five urban Connect clinics where STD/HIV rates are high, recruiting young men in repeated cross-sectional surveys from April 2014 to September 2017.Results:235 YSPs were trained to use Connect materials, including a website, a paper-based pocket guide, and information regarding SRH for young men. These professionals demonstrated increased knowledge about SRH for young men at immediate post-test (60.6% to 86.7%, p<0.05), and reported more sharing of websites for SRH (23% to 62%, p<0.05) from pre- to three-month post-training. 169 young minority men were surveyed and reported increased awareness of Connect over three and a half years (4% to 11%, p=0.015), although few young men reported using the website to visit clinics.Conclusion:Project Connect Baltimore increased knowledge of SRH needs among youth-serving professionals and sharing of SRH resources by these professionals with young men. This program also demonstrated increases in awareness of SRH resources among young minority urban men.20192020-03-01T00:00:00ZCC999999/ImCDC/Intramural CDC HHS/United StatesH25 PS003796/PS/NCHHSTP CDC HHS/United StatesH25 PS005095/PS/NCHHSTP CDC HHS/United States30652988PMC6631304729

Fitness-to-Drive Screening Measure©: Patterns and Trends for Canadian Users

Background: The Fitness-to-Drive Screening Measure© (FTDS) is an online screening tool that enables proxy raters (caregivers, family members, and friends) to identify at-risk older adult drivers via 54 driving-related items. This study aimed to identify areas in need of improvement for the FTDS by identifying the patterns and trends of Canadian users and providing recommendations to increase the usage, reach, and potential impact of the FTDS as a health promotion tool. Methods: We used monthly Google Analytics reports to calculate descriptive statistics for web page and session specific variables. Variables were separated into Year 1 and Year 2 and were compared using the independent sample t-test. Results: Patterns were identified for session and web page specific variables; for example, users spent less than the recommended 20 min to complete the FTDS. There was only a significant decrease in the number of French speaking users (t (22) = .01, p \u3c .05) from Year 1 to Year 2. Conclusion: Canadians across the country are able to easily access and use the FTDS for screening older adult drivers in its current format. However, implementing suggested recommendations (e.g., short form FTDS) may increase the overall usage, utility, and/or reach of the FTDS, and, as such, may yield additional benefits to potential users

Professional reputation and identity in the online world

The interface between new entrants to professional disciplines, professional reputation management and social media usage is an under-researched and little understood phenomenon. A small-scale study on social media usage conducted with new social workers and teachers, working in the Irish context, revealed interesting in-sights into the complexities of reputation management for new professionals and the particular issues related to development of their professional reputations raised by online interactions, relationships and behaviour. Key messages for professionals and professional educators emerge from the findings outlined in this article

DeapSECURE Computational Training for Cybersecurity: Third-Year Improvements and Impacts

The Data-Enabled Advanced Training Program for Cybersecurity Research and Education (DeapSECURE) was introduced in 2018 as a non-degree training consisting of six modules covering a broad range of cyberinfrastructure techniques, including high performance computing, big data, machine learning and advanced cryptography, aimed at reducing the gap between current cybersecurity curricula and requirements needed for advanced research and industrial projects. By its third year, DeapSECURE, like many other educational endeavors, experienced abrupt changes brought by the COVID-19 pandemic. The training had to be retooled to adapt to fully online delivery. Hands-on activities were reformatted to accommodate self-paced learning. In this paper, we describe and assess the third and fourth years of the project and compare them with the first half of the project, which implemented in-person instruction. We also indicate major improvements and present future opportunities for this training program to advance the cybersecurity field

An online innovation platform to promote collaboration and sustainability in short food supply chains

A sustainable Short Food Supply Chain (SFSC) requires collaboration among all actors, which nowadays is facilitated by information and communication technologies (ICT). However, not all SFSC stakeholders network with others in this way, and it is not clear what will draw them to ICT interaction. A simple, user-friendly website, the SMARTCHAIN Innovation Platform, evolving since March 2019, may facilitate interaction and cooperation among SFSC stakeholders. This article presents the Platform's development and evaluates its efficacy and impact by analysing data from Google Analytics (GA) and other sources. Primary Platform features promote communication and information sharing: these are the 1) Innovation Hubs in 9 European countries, 2) Inventories including 150 SFSC innovations and 50 SFSC initiatives, 3) Resources databases of Publications and Weblinks, and 4) Training section. GA showed that visitors to the Platform increased slowly in the 16 months since its start, and the number of page views increased with the amount of time on the Platform. The most visited page of the Platform was the information-providing Innovation Inventory. Most Platform users were in partner countries of the SMARTCHAIN project, but not all Innovation Hubs had high numbers of users. Most users arrived at the Platform by direct link, but LinkedIn was the most important originating social network. Taken together, these data suggest growth potential for an easy-to-use website that provides useful and up-to-date information but little inclination for SFSC stakeholders to use an online Platform for communication

Knowledge discovery with recommenders for big data management in science and engineering communities

Recent science and engineering research tasks are increasingly becoming dataintensive and use workflows to automate integration and analysis of voluminous data to test hypotheses. Particularly, bold scientific advances in areas of neuroscience and bioinformatics necessitate access to multiple data archives, heterogeneous software and computing resources, and multi-site interdisciplinary expertise. Datasets are evolving, and new tools are continuously invented for achieving new state-of-the-art performance. Principled cyber and software automation approaches to data-intensive analytics using systematic integration of cyberinfrastructure (CI) technologies and knowledge discovery driven algorithms will significantly enhance research and interdisciplinary collaborations in science and engineering. In this thesis, we demonstrate a novel recommender approach to discover latent knowledge patterns from both the infrastructure perspective (i.e., measurement recommender) and the applications perspective (i.e., topic recommender and scholar recommender). In the infrastructure perspective, we identify and diagnose network-wide anomaly events to address performance bottleneck by proposing a novel measurement recommender scheme. In cases where there is a lack of ground truth in networking performance monitoring (e.g., perfSONAR deployments), it is hard to pinpoint the root-cause analysis in a multi-domain context. To solve this problem, we define a "social plane" concept that relies on recommendation schemes to share diagnosis knowledge or work collaboratively. Our solution makes it easier for network operators and application users to quickly and effectively troubleshoot performance bottlenecks on wide-area network backbones. To evaluate our "measurement recommender", we use both real and synthetic datasets. The results show our measurement recommender scheme has high performance in terms of precision, recall, and accuracy, as well as efficiency in terms of the time taken for large volume measurement trace analysis. In the application perspective, our goal is to shorten time to knowledge discovery and adapt prior domain knowledge for computational and data-intensive communities. To achieve this goal, we design a novel topic recommender that leverages a domain-specific topic model (DSTM) algorithm to help scientists find the relevant tools or datasets for their applications. The DSTM is a probabilistic graphical model that extends the Latent Dirichlet Allocation (LDA) and uses the Markov chain Monte Carlo (MCMC) algorithm to infer latent patterns within a specific domain in an unsupervised manner. We evaluate our scheme based on large collections of the dataset (i.e., publications, tools, datasets) from bioinformatics and neuroscience domains. Our experiments result using the perplexity metric show that our model has better generalization performance within a domain for discovering highly-specific latent topics. Lastly, to enhance the collaborations among scholars to generate new knowledge, it is necessary to identify scholars with their specific research interests or cross-domain expertise. We propose a "ScholarFinder" model to quantify expert knowledge based on publications and funding records using a deep generative model. Our model embeds scholars' knowledge in order to recommend suitable scholars to perform multi-disciplinary tasks. We evaluate our model with state-of-the-art baseline models (e.g., XGBoost, DNN), and experiment results show that our ScholarFinder model outperforms state-ofthe-art models in terms of precision, recall, F1-score, and accuracy.Includes bibliographical references (pages 113-124)

Plano de comunicação online: space2business

Esta versão contém as críticas e sugestões dos elementos do júri.Nota: 14 valoreseconomia da partilha é um conceito ambíguo, mas cada vez mais na moda nos dias de hoje. As plataformas online baseadas neste modelo apresentam inúmeras vantagens, seja para quem está disposto a partilhar os seus bens, seja para quem usufrui dessa mesma partilha. O sucesso alcançado tornou a economia da partilha num tema em ebulição, sendo que já existem plataformas que permitem chamar táxis, reservar quartos de hotéis ou até reservar quartos de casas particulares. Nesse sentido, foi criada a empresa space2business, cujo modelo de negócio assenta na reserva de espaços de trabalho, também essa uma área de negócio em evolução, especialmente a categoria de co-working. Dado que se trata de uma startup fundada apenas em 2015, o lançamento da marca e respetiva gestão surge como o primeiro passo para a estabilização da empresa. Para o efeito, a revisão de literatura aborda temas genéricos, como a comunicação integrada de marketing e o marketing digital, mas também as boas práticas em cada um dos canais digitais, especialmente no website (desktop e mobile), blog, publicidade online, redes sociais e email. Após a revisão de literatura, foi traçado um plano de comunicação online, visando a promoção da marca no meio digital. Terminada a sua elaboração, deu-se a implementação do plano, com utilização prevista para um ano. Este projeto abrange apenas o período de teste de um mês. Finalmente, debruçando-se sobre a análise de resultados, este projeto regista as ações que tiveram sucesso e sugere propostas de melhoria, para o período anual, ou seja, os onze meses seguintes ao período de teste. Dentro de todas as táticas executadas na implementação do plano nesse período, o Facebook Ads apresentou-se como a ferramenta mais valiosa, beneficiando assim a página de Facebook da empresa. Em contrapartida, o Google AdWords obteve um desempenho bastante aquém do esperado.Sharing economy is an ambiguous concept. However, is getting more and more trendy nowadays. Online platforms based on this model present several advantages, either to individuals willing to share their goods or to those that take benefits from that same share. The achieved success turned sharing economy in a matter in hand. There are already platforms that allow people to call taxis, to book hotels or to accommodate in private homes. In this context, space2business was created. Its business model lays in workspace booking, which is also a business area that has been evolving fast, especially the co-working category. Since it is a startup funded in 2015, its brand lauching emerges as the first step to the company stabilization. To achieve these goals, the literature review focus on generic themes, like integrated marketing communication and digital marketing, but also the good practices in each digital channel, especially on website (desktop and mobile), blog, online ads, social networks and email. After the literature review, an online communication plan was outlined, aiming the brand promotion in the digital mean. Then, the plan was implemented with an expected usage for the period of a year. This project covers the trial period of the first month. Finally, supported by the results analysis, this project registers the actions that succeed and suggests improvement proposals to the total period of its usage, the eleven months after the trial period. Within all the tactics developed during the execution of the plan, Facebook Ads was the most valuable tool and even the Facebook page of the company took some advantages from its performance. In contrast, Google AdWords didn’t achieve the expected results

Estimation of High-Resolution Evapotranspiration in Heterogeneous Environments Using Drone-Based Remote Sensing

Evapotranspiration (ET) is a key element of hydrological cycle analysis, irrigation demand, and for better allocation of water resources in the ecosystem. For successful water resources management activities, precise estimate of ET is necessary. Although several attempts have been made to achieve that, variation in temporal and spatial scales constitutes a major challenge, particularly in heterogeneous canopy environments such as vineyards, orchards, and natural areas. The advent of remote sensing information from different platforms, particularly the small unmanned aerial systems (sUAS) technology with lightweight sensors allows users to capture high-resolution data faster than traditional methods, described as “flexible in timing”. In this study, the Two Source Energy Balance Model (TSEB) along with high-resolution data from sUAS were used to bridge the gap in ET issues related to spatial and temporal scales. Over homogeneous vegetation surfaces, relatively low spatial resolution information derived from Landsat (e.g., 30 m) might be appropriate for ET estimate, which can capture differences between fields. However, in agricultural landscapes with presence of vegetation rows and interrows, the homogeneity is less likely to be met and the ideal conditions may be difficult to identify. For most agricultural settings, row spacing can vary within a field (vineyards and orchards), making the agricultural landscape less homogenous. This leads to a key question related to how the contextual spatial domain/model grid size could influence the estimation of surface fluxes in canopy environments such as vineyards. Furthermore, temporal upscaling of instantaneous ET at daily or longer time scales is of great practical importance in managing water resources. While remote sensing-based ET models are promising tools to estimate instantaneous ET, additional models are needed to scale up the estimated or modeled instantaneous ET to daily values. Reliable and precise daily ET (ETd) estimation is essential for growers and water resources managers to understand the diurnal and seasonal variation in ET. In response to this issue, different existing extrapolation/upscaling daily ET (ETd) models were assessed using eddy covariance (EC) and sUAS measurements. On the other hand, ET estimation over semi-arid naturally vegetated regions becomes an issue due to high heterogeneity in such environments where vegetation tends to be randomly distributed over the land surface. This reflects the conditions of natural vegetation in river corridors. While significant efforts were made to estimate ET at agricultural landscapes, accurate spatial information of ET over riparian ecosystems is still challenging due to various species associated with variable amounts of bare soil and surface water. To achieve this, the TSEB model with high-resolution remote sensing data from sUAS were used to characterize the spatial heterogeneity and calculate the ET over a natural environment that features arid climate and various vegetation types at the San Rafael River corridor

Towards a capability maturity model for a cyber range

This work describes research undertaken towards the development of a Capability Maturity Model (CMM) for Cyber Ranges (CRs) focused on cyber security. Global cyber security needs are on the rise, and the need for attribution within the cyber domain is of particular concern. This has prompted major efforts to enhance cyber capabilities within organisations to increase their total cyber resilience posture. These efforts include, but are not limited to, the testing of computational devices, networks, and applications, and cyber skills training focused on prevention, detection and cyber attack response. A cyber range allows for the testing of the computational environment. By developing cyber events within a confined virtual or sand-boxed cyber environment, a cyber range can prepare the next generation of cyber security specialists to handle a variety of potential cyber attacks. Cyber ranges have different purposes, each designed to fulfil a different computational testing and cyber training goal; consequently, cyber ranges can vary greatly in the level of variety, capability, maturity and complexity. As cyber ranges proliferate and become more and more valued as tools for cyber security, a method to classify or rate them becomes essential. Yet while a universal criteria for measuring cyber ranges in terms of their capability maturity levels becomes more critical, there are currently very limited resources for researchers aiming to perform this kind of work. For this reason, this work proposes and describes a CMM, designed to give organisations the ability to benchmark the capability maturity of a given cyber range. This research adopted a synthesised approach to the development of a CMM, grounded in prior research and focused on the production of a conceptual model that provides a useful level of abstraction. In order to achieve this goal, the core capability elements of a cyber range are defined with their relative importance, allowing for the development of a proposed classification cyber range levels. An analysis of data gathered during the course of an expert review, together with other research, further supported the development of the conceptual model. In the context of cyber range capability, classification will include the ability of the cyber range to perform its functions optimally with different core capability elements, focusing on the Measurement of Capability (MoC) with its elements, namely effect, performance and threat ability. Cyber range maturity can evolve over time and can be defined through the Measurement of Maturity (MoM) with its elements, namely people, processes, technology. The combination of these measurements utilising the CMM for a CR determines the capability maturity level of a CR. The primary outcome of this research is the proposed level-based CMM framework for a cyber range, developed using adopted and synthesised CMMs, the analysis of an expert review, and the mapping of the results