Applications Residual Control Charts Based on Variable Limits

The main purpose of this paper is to verify the stability of a productive process in the presence of the effects of autocorrelation and volatility, in order to capture these characteristics by a joint forecast model which produces residuals that are evaluated by a control chart based on variable control limits. The methodology employed will be the joint estimation of the residuals by ARIMA – ARCH models and the conditional standard deviation from residuals to establish the chart control limits. The joint AR (1)-ARCH (1) model shows that an appropriate forecasting model brings a great contribution to the performance of residual control charts in monitoring the stability of industrial variables using just one chart to monitor mean and variance together.info:eu-repo/semantics/publishedVersio

TRY plant trait database – enhanced coverage and open access

Plant traits—the morphological, anatomical, physiological, biochemical and phenological characteristics of plants—determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait‐based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits—almost complete coverage for ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait–environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Análise energética em sistema de produção de suínos com aproveitamento dos desejos como biofertilizante em pastagem Energy analysis in a swine production system with use of manure as biofertilizer in pasture

O objetivo deste trabalho foi estimar a quantidade de energia para a produção de suínos, em ciclo completo, e o balanço energético do sistema com reaproveitamento dos resíduos gerados como biofertilizante, em área de pastagem, utilizando dados médios de cinco ciclos, em granja comercial no município de Diamantina - MG. Quantificou-se o coeficiente energético de cada componente envolvido no processo produtivo de suínos terminados, tratamento dos resíduos e produção de pastagem de Brachiaria decumbens, nas formas de ração, trabalho humano, energia elétrica, máquinas e equipamentos, combustíveis e lubrificantes, instalações, produção de suínos vivos e produção de Brachiaria decumbens. A quantidade média de energia para produzir 1 kg de suíno vivo foi de 53,35 MJ. De toda energia empregada no sistema, 76,03% (1.067.106,07 MJ) referem-se às entradas e 23,97% (331.400 MJ) às saídas, resultando em coeficiente de eficiência energética de 0,31. A energia transformada em suínos para abate correspondeu a 55,58% (184.200 MJ) das saídas, ao passo que a pastagem de Brachiaria decumbens assumiu o valor de 44,42% (147.200 MJ).<br>This work objective was to esteem the amount of energy employed in a complete cycle swine production and the energy balance of the system with utilization of the generated manure as biofertilizer in pasture area, by using five cycles' average data, in a commercial farm in Diamantina municipal district - MG Brazil. The energy coefficient of each involved component was quantified in the productive process of finished swine, residues treatment and Brachiaria decumbens pasture production, in the form of ration, human labor, electric power, machines and equipments, fuel and lubricants, buildings, finished swine production and Brachiaria decumbens production. The average quantity of energy to produce 1 kg of alive swine was of 53.35 MJ. Of total employed energy in the system 76.03% (1,067,106.07 MJ) refers to the inputs and 23.97% (331,400 MJ) refers to the outputs, resulting in an energy efficiency coefficient of 0.31. The energy converted in swine for abate corresponded to 55.58% (184,200 MJ) of the outputs, while the pasture of Brachiaria decumbens reached a value of 44.42% (147,200 MJ)

European 1: a globally important clonal complex of Mycobacterium bovis.

Item does not contain fulltextWe have identified a globally important clonal complex of Mycobacterium bovis by deletion analysis of over one thousand strains from over 30 countries. We initially show that over 99% of the strains of M. bovis, the cause of bovine tuberculosis, isolated from cattle in the Republic of Ireland and the UK are closely related and are members of a single clonal complex marked by the deletion of chromosomal region RDEu1 and we named this clonal complex European 1 (Eu1). Eu1 strains were present at less than 14% of French, Portuguese and Spanish isolates of M. bovis but are rare in other mainland European countries and Iran. However, strains of the Eu1 clonal complex were found at high frequency in former trading partners of the UK (USA, South Africa, New Zealand, Australia and Canada). The Americas, with the exception of Brazil, are dominated by the Eu1 clonal complex which was at high frequency in Argentina, Chile, Ecuador and Mexico as well as North America. Eu1 was rare or absent in the African countries surveyed except South Africa. A small sample of strains from Taiwan were non-Eu1 but, surprisingly, isolates from Korea and Kazakhstan were members of the Eu1 clonal complex. The simplest explanation for much of the current distribution of the Eu1 clonal complex is that it was spread in infected cattle, such as Herefords, from the UK to former trading partners, although there is evidence of secondary dispersion since. This is the first identification of a globally dispersed clonal complex M. bovis and indicates that much of the current global distribution of this important veterinary pathogen has resulted from relatively recent International trade in cattle.1 augustus 201

European 1: A globally important clonal complex of Mycobacterium bovis

Please help us populate SUNScholar with the post print version of this article. It can be e-mailed to: [email protected] have identified a globally important clonal complex of Mycobacterium bovis by deletion analysis of over one thousand strains from over 30 countries. We initially show that over 99% of the strains of M. bovis, the cause of bovine tuberculosis, isolated from cattle in the Republic of Ireland and the UK are closely related and are members of a single clonal complex marked by the deletion of chromosomal region RDEu1 and we named this clonal complex European 1 (Eu1). Eu1 strains were present at less than 14% of French, Portuguese and Spanish isolates of M. bovis but are rare in other mainland European countries and Iran. However, strains of the Eu1 clonal complex were found at high frequency in former trading partners of the UK (USA, South Africa, New Zealand, Australia and Canada). The Americas, with the exception of Brazil, are dominated by the Eu1 clonal complex which was at high frequency in Argentina, Chile, Ecuador and Mexico as well as North America. Eu1 was rare or absent in the African countries surveyed except South Africa. A small sample of strains from Taiwan were non-Eu1 but, surprisingly, isolates from Korea and Kazakhstan were members of the Eu1 clonal complex. The simplest explanation for much of the current distribution of the Eu1 clonal complex is that it was spread in infected cattle, such as Herefords, from the UK to former trading partners, although there is evidence of secondary dispersion since. This is the first identification of a globally dispersed clonal complex M. bovis and indicates that much of the current global distribution of this important veterinary pathogen has resulted from relatively recent International trade in cattle

TRY plant trait database, enhanced coverage and open access

Plant traits-the morphological, ahawnatomical, physiological, biochemical and phenological characteristics of plants-determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait-based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits-almost complete coverage for 'plant growth form'. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait-environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives