An Exploratory Study of Field Failures

Field failures, that is, failures caused by faults that escape the testing phase leading to failures in the field, are unavoidable. Improving verification and validation activities before deployment can identify and timely remove many but not all faults, and users may still experience a number of annoying problems while using their software systems. This paper investigates the nature of field failures, to understand to what extent further improving in-house verification and validation activities can reduce the number of failures in the field, and frames the need of new approaches that operate in the field. We report the results of the analysis of the bug reports of five applications belonging to three different ecosystems, propose a taxonomy of field failures, and discuss the reasons why failures belonging to the identified classes cannot be detected at design time but shall be addressed at runtime. We observe that many faults (70%) are intrinsically hard to detect at design-time

A Ka-Band Receiver Front-End With Noise Injection Calibration Circuit for CubeSats Inter-Satellite Links

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Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

<b>Enteropathogenic bacterial contamination of a latosol following application of organic fertilizer

Poultry manure is used as fertilizer in natura, but little is known about whether it contaminates the soil with pathogenic organisms. The aim of this study was to assess the effects of organic, organomineral and mineral fertilizers on soil contamination by enteric pathogens, using poultry manure as the organic fertilizer. Manure was applied in field experiments at rates of 7.0 ton. ha-1 (maize crop, 2008/2009), 8.0 ton. ha-1 (wheat crop, 2009) and 14 ton. ha-1 (maize crop, 2010/2011). Organomineral fertilizer was applied at the same rates but was comprised of 50% manure and 50% mineral fertilizer. At 30 and 70 days after fertilization, the organic fertilizer and the upper 0-5 cm layer of the soil were tested for the presence of helminth eggs and larvae and enteropathogenic bacteria. Fecal and non-fecal coliforms (Escherichia coli and Clostridium perfringes) were found in the organic fertilizer, but neither Salmonella spp. nor enteroparasites were detected. The population of enteropathogenic bacteria in the soil was similar among the treatments for all crops at both evaluation times. The population of thermotolerant coliforms in the organic fertilizer was larger than the maximum level allowed in Brazil, but neither the organic or nor the organomineral fertilizer contaminated the soil