Effect of mould inoculation on formation of chunky graphite in heavy section spheroidal graphite cast iron parts

The manufacturing process of heavy section ductile iron castings is strongly influenced by the risk of graphite degeneration under slow cooling rates. Appearance of this kind of defect is commonly linked to significant reductions in the mechanical properties of large castings. Studies on the effect of inoculation on chunky graphite formation in heavy sections have led to contradictory results in the literature and this triggered the present work. New experimental data are presented on the effect of mould inoculation on chunky graphite appearance during solidification of nodular irons which clearly demonstrate that mould inoculation increases the risk of chunky graphite formation in heavy sections. This is in agreement with some previous works which are reviewed, and it is suggested that the contradiction with other results could relate to the fact that these latter works dealt with chill casting

Effect of antimony on the eutectic reaction of heavy section spheroidal graphite castings

There is a strong demand for heavy section castings made of spheroidal graphite with a fully ferritic matrix, e.g. for manufacturing hubs for windmills. Such castings with slow solidification process are prone to graphite degeneration that leads to a dramatic decrease of the mechanical properties of the cast parts. Chunky graphite is certainly the most difficult case of graphite degeneracy, though it has long been known that the limited and controlled addition of antimony may help eliminate it. The drawback of this remedy is that too large Sb additions lead to other forms of degenerate graphite, and also that antimony is a pearlite promoter. As part of an investigation aimed at mastering low level additions to cast iron melts before casting, solidification of large blocks with or without Sb added was followed by thermal analysis. Comparison of the cooling curves and of the microstructures of these different castings gives suggestions to understand the controlling nucleation and growth mechanisms for chunky graphite cells

Enhanced Support for High Intensity Users of the Criminal Justice System – an evaluation of mental health nurse input into Integrated Offender Management Services in the North East of England

The current UK Government’s focus on the development of services to manage and support offenders with mental health problems has resulted in a number of innovative project developments. This research examines a service development in the North East of England which co-located Mental Health nurses with two Integrated Offender Management teams. While not solving all problems, the benefits of co-location were clear – although such innovations are now at risk from government changes which will make Integrated Offender Management the responsibility of new providers without compelling them to co-operate with health services

Analysis of 5G-TSN Integration to Support Industry 4.0

Time Sensitive Networking (TSN) is becoming the standard Ethernet-based technology for converged networks of Industry 4.0 due to its capacity to support deterministic latency requirements. However, it cannot provide the required flexibility to support mobile industrial applications required for the factories of the future. This could be enabled through the integration of wireless technologies in factories, and in particular of 5G and Beyond networks since they have been designed to support ultra-reliable and low-latency communications. This has triggered significant interest to integrate 5G and TSN networks, and first frameworks for such integration have been defined. However, the work is at early stages and the solutions to effectively integrate the two networks so that 5G can support TSN QoS levels are yet to be designed. This paper discusses current research and standardization work on 5G-TSN integration, and quantifies for a closed loop control application the 5GS bridge delay. The paper uses an example based on 5G-ACIA [1] to discuss open technical and research challenges to effectively integrate 5G and TS

The wideband backend at the MDSCC in Robledo. A new facility for radio astronomy at Q- and K- bands

The antennas of NASA's Madrid Deep Space Communications Complex (MDSCC) in Robledo de Chavela are available as single-dish radio astronomical facilities during a significant percentage of their operational time. Current instrumentation includes two antennas of 70 and 34 m in diameter, equipped with dual-polarization receivers in K (18 - 26 GHz) and Q (38 - 50 GHz) bands, respectively. We have developed and built a new wideband backend for the Robledo antennas, with the objectives (1) to optimize the available time and enhance the efficiency of radio astronomy in MDSCC; and (2) to tackle new scientific cases impossible to that were investigated with the old, narrow-band autocorrelator. The backend consists of an IF processor, a FFT spectrometer (FFTS), and the software that interfaces and manages the events among the observing program, antenna control, the IF processor, the FFTS operation, and data recording. The whole system was end-to-end assembled in August 2011, at the start of commissioning activities, and the results are reported in this paper. Frequency tunings and line intensities are stable over hours, even when using different synthesizers and IF channels; no aliasing effects have been measured, and the rejection of the image sideband was characterized. The first setup provides 1.5 GHz of instantaneous bandwidth in a single polarization, using 8192 channels and a frequency resolution of 212 kHz; upgrades under way include a second FFTS card, and two high-resolution cores providing 100 MHz and 500 MHz of bandwidth, and 16384 channels. These upgrades will permit simultaneous observations of the two polarizations with instantaneous bandwidths from 100 MHz to 3 GHz, and spectral resolutions from 7 to 212 kHz.Comment: 9 pages, 8 figures. Accepted to Astronomy and Astrophysic

Connectivity, neutral theories and the assessment of species vulnerability to global change in temperate estuaries

One of the main adaptation strategies to global change scenarios, aiming to preserve ecosystem functioning and biodiversity, is to maximise ecosystem resilience. The resilience of a species metapopulation can be improved by facilitating connectivity between local populations, which will prevent demographic stochasticity and inbreeding. The objective of this investigation is to estimate the degree of connectivity among estuarine species along the north-eastern Iberian coast, in order to assess community vulnerability to global change scenarios. To address this objective, two connectivity proxy types have been used based upon genetic and ecological drift processes: 1) DNA markers for the bivalve cockle (Cerastoderma edule) and seagrass Zostera noltei, and 2) the decrease in the number of species shared between two sites with geographic distance; neutral biodiversity theory predicts that dispersal limitation modulates this decrease, and this has been explored in estuarine plants and macroinvertebrates. Results indicate dispersal limitation for both saltmarsh plants and seagrass beds community and Z. noltei populations; this suggests they are especially vulnerable to expected climate changes on their habitats. In contrast, unstructured spatial pattern found in macroinvertebrate communities and in C. edule genetic populations in the area suggests that estuarine soft-bottom macroinvertebrates with planktonic larval dispersal strategies may have a high resilience capacity to moderate changes within their habitats. Our findings can help environmental managers to prioritise the most vulnerable species and habitats to be restored

Changes in Social and Clinical Determinants of COVID-19 Outcomes Achieved by the Vaccination Program: A Nationwide Cohort Study

Background: The objective of this study was to assess changes in social and clinical determinants of COVID-19 outcomes associated with the first year of COVID-19 vaccination rollout in the Basque population. Methods: A retrospective study was performed using the complete database of the Basque Health Service (n = 2,343,858). We analyzed data on age, sex, socioeconomic status, the Charlson comorbidity index (CCI), hospitalization and intensive care unit (ICU) admission, and COVID-19 infection by Cox regression models and Kaplan–Meier curves. Results: Women had a higher hazard ratio (HR) of infection (1.1) and a much lower rate of hospitalization (0.7). With older age, the risk of infection fell, but the risks of hospitalization and ICU admission increased. The higher the CCI, the higher the risks of infection and hospitalization. The risk of infection was higher in high-income individuals in all periods (HR = 1.2–1.4) while their risk of hospitalization was lower in the post-vaccination period (HR = 0.451). Conclusion: Despite the lifting of many control measures during the second half of 2021, restoring human mobility patterns, the situation could not be defined as syndemic, clinical determinants seeming to have more influence than social ones on COVID-19 outcomes, both before and after vaccination program implementation

Digging into acceptor splice site prediction : an iterative feature selection approach

Feature selection techniques are often used to reduce data dimensionality, increase classification performance, and gain insight into the processes that generated the data. In this paper, we describe an iterative procedure of feature selection and feature construction steps, improving the classification of acceptor splice sites, an important subtask of gene prediction. We show that acceptor prediction can benefit from feature selection, and describe how feature selection techniques can be used to gain new insights in the classification of acceptor sites. This is illustrated by the identification of a new, biologically motivated feature: the AG-scanning feature. The results described in this paper contribute both to the domain of gene prediction, and to research in feature selection techniques, describing a new wrapper based feature weighting method that aids in knowledge discovery when dealing with complex datasets