Suchergebnisse

This paper sought to aver Cloud Computing as a Suitable Alternative to the Traditional On-Premise ERP and Massive Data Storage based on the information from the institutions that implement ERP system, government and private organisations that consider the adoption of cloud ERP and professionals comments from cloud technology. In this complete range, the genuine number of detriments of on premise ERP deployment in today technology arena and subsidies of Cloud Based ERP adoption were sought. The researcher, after getting the above information conducted as survey about this census to establish the potential of Cloud Computing based ERP as a suitable and alternative use of the On-Premise ERP and Massive Data Storage. The study had three objectives including examining the extent at which On-premise and Cloud ERP is being adopted and establishing whether people are likely to adopt Cloud based ERP, determine the factors affecting Cloud computing System adoption, indicating the factors that are important for them to adopt or not adopt the Cloud computing Based ERP

OBJECTIVE: To assess the public's preferences regarding potential privacy threats from devices or services storing health-related personal data. MATERIALS AND METHODS: A pan-European survey based on a stated-preference experiment for assessing preferences for electronic health data storage, access, and sharing. RESULTS: We obtained 20 882 survey responses (94 606 preferences) from 27 EU member countries. Respondents recognized the benefits of storing electronic health information, with 75.5%, 63.9%, and 58.9% agreeing that storage was important for improving treatment quality, preventing epidemics, and reducing delays, respectively. Concerns about different levels of access by third parties were expressed by 48.9% to 60.6% of respondents.On average, compared to devices or systems that only store basic health status information, respondents preferred devices that also store identification data (coefficient/relative preference 95% CI = 0.04 [0.00-0.08], P = 0.034) and information on lifelong health conditions (coefficient = 0.13 [0.08 to 0.18], P < 0.001), but there was no evidence of this for devices with information on sensitive health conditions such as mental and sexual health and addictions (coefficient = -0.03 [-0.09 to 0.02], P = 0.24). Respondents were averse to their immediate family (coefficient = -0.05 [-0.05 to -0.01], P = 0.011) and home care nurses (coefficient = -0.06 [-0.11 to -0.02], P = 0.004) viewing this data, and strongly averse to health insurance companies (coefficient = -0.43 [-0.52 to 0.34], P < 0.001), private sector pharmaceutical companies (coefficient = -0.82 [-0.99 to -0.64], P < 0.001), and academic researchers (coefficient = -0.53 [-0.66 to -0.40], P < 0.001) viewing the data. CONCLUSIONS: Storing more detailed electronic health data was generally preferred, but respondents were averse to wider access to and sharing of this information. When developing frameworks for the use of electronic health data, policy makers should consider approaches that both highlight the benefits to the individual and minimize the perception of privacy risks. ; This study has received funding from the European Commission's Seventh Framework Programme for research, technological development, and demonstration under grant agreement No. 285635 (Public perception of security and privacy: Assessing knowledge, Collecting evidence, Translating research into action).

Context The use of camera traps in ecological research has grown exponentially over the past decade, but questions remain about the effect of camera-trap settings on ecological inference. The delay-period setting controls the amount of time that a camera trap is idle between motion-activated triggers. Longer delay periods may potentially extend battery life, reduce data-storage requirements, and shorten data-analysis time. However, they might result in lost data (i.e. missed wildlife detections), which could bias ecological inference and compromise research objectives. Aims We aimed to examine the effect of the delay period on (1) the number of camera-trap triggers, (2) detection and site-occupancy probabilities for eight mammalian species that varied in size, movement rate and commonness and (3) parameter estimates of habitat-based covariates from the occupancy models for these species. Methods We deployed 104 camera traps for 4 months throughout an extensive urban park system in Cleveland, Ohio, USA, using a spatially random design. Using the resultant data, we simulated delay periods ranging from 10s to 60min. For each of these delay periods and for each of our eight focal species, we calculated the number of camera-trap triggers and the parameter estimates of hierarchical Bayesian occupancy models. Key results A simulated increase in the delay period from 10s to 10min decreased the number of triggers by 79.6%, and decreased detection probability and occupancy probability across all species by 1.6% and 4.4% respectively. Further increases in the delay period (i.e. from 10 to 60min) resulted in modest additional reductions in the number of triggers and detection and occupancy probabilities. Variation in the delay period had negligible effects on the qualitative interpretations of habitat-based occupancy models for all eight species. Conclusions Our results suggest that delay-period settings ranging from 5 to 10min can drastically reduce data-storage needs and analysis time without compromising inference resulting from occupancy modelling for a diversity of mammalian species. Implications Broadly, we provide guidance on designing camera-trap studies that optimally trade-off research effort and potential bias, thereby increasing the utility of camera traps as ecological research tools.

Industrie 4.0 und maschinelles Lernen (ML) versprechen hohes Potenzial der Effizienzsteigerung für die Produktion. Die Etablierung in der Praxis bedarf kleiner, umsetzungsfähiger Beispiele um den Nutzen aufzuzeigen. Dieser Beitrag beschreibt das Vorgehen für die Vernetzung einer Maschine zur nicht-isothermen Glasumformung und anschließender Verwertung der Daten mittels ML. Das übergeordnete Ziel dabei ist, die Optimierung der Prozessparameter durch eine Prognose der Qualität der Endprodukte zu erreichen.
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Industry 4.0 has become a widely understood term. Establishment in practice requires small, implementable examples to show concrete potenzials. This article describes the procedure for networking a machine for non-isothermal glass forming and subsequent utilization of the data by means of machine learning (ML). The overall goal is to optimize the process parameters by predicting the quality of the end products.

The article examines the legal regulation of using cloud technologies for information storage. The author analyses the legal acts of Ukraine, the European Union, the USA, and self-governing organisations that unite intermediaries of cloud information systems regulating relations in data storage and using cloud technologies for user information processing. The author considers the provisions on the definition of cloud computing and cloud storage from the point of view of law. The author analyses specific regulations governing legal relations in the storage field and the use of information in cloud storage. The main functions are analysed: information storage creation of a multi-level database; collection of data and their systematisation, storage of big data and their processing; information exchange; support of other services; collection of data and their distribution by applications; and economic function. It is noted that the legal regulation of information storage in cloud storage is complex.