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Constraint-based diagnosis algorithms for multiprocessors A. Petri, P. Urban, J. Altmann, M. Dal Cin, E. Selenyi, K. Tilly, A. Pataricza In the latest years, new ideas appeared in system level diagnosis of multiprocessor systems. In contrary to the traditional diagnosis models (like PMC, BGM, etc.) which use strictly graph-oriented methods to determine the faulty components in a system, these new theories prefer AI-based algorithms, especially CSP methods. Syndrome decoding, the basic problem of self-diagnosis, can be easily transformed into constraints between the state of the tester and the tested components. Therefore, the diagnosis algorithm can be derived from a special constraint solving algorithm. The "benign" nature of the constraints (all their variables, representing the fault states of the components, have a very limited domain; the constraints are simple and similar to each other) reduces the algorithm's complexity so it can be converted to a powerful distributed diagnosis method with a minimal overhead. Experimental algorithms (using both centralized and distributed approach) were implemented for a Parsytec GC massively parallel multiprocessor system.

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Background Despite the availability of thousands of health apps worldwide, when considering those addressing children’s first 1000 days of life, most apps fail to consider the continuity between the prenatal and postnatal stages, and their joint impact on maternal and child health. The reliability, quality, and effectiveness of these apps are largely unknown, and the provided content seems questionable in terms of completeness, updating, and trustworthiness. Objective This study evaluates available Italian pregnancy and postnatal care apps to highlight the main gaps to be overcome and the resulting future challenges to be met in this mobile health–related field. Methods A systematic search was conducted on the Apple App Store and Google Play Store, and basic information was collected for all identified apps. After deduplication and further selection based on the exclusion criteria, an in-depth analysis of each app was performed by two researchers independently. A 71-item six-domain questionnaire about the desirable features of apps was used to assess information, functionalities, and technical features, while the Mobile Application Rating Scale (MARS) was employed for app quality evaluation. Results From an initial sample of 684 apps, 22 were deeply analyzed. Most apps did not fulfill the expectations, as just one achieved 50% of all desirable aspects. Postnatal care and counselling for both the mother and child was the least accomplished domain. Moreover, the quality of app information was generally rated more negatively than the quality of their functionality and esthetic features. The lacking aspects were information about methods for postpartum family planning and birth spacing (1/22, 5%) and immunization (2/22, 9%). Conclusions The identified gaps could serve as a basis for designing and implementing increasingly high-quality, targeted, and effective apps for pregnancy and postnatal health care, which provide comprehensive, reliable, and evidence-based information, as well as appropriate esthetic and functional characteristics, with relevant implications in terms of maternal and newborn health prevention and promotion.