Quality costs and Industry 4.0: inspection strategy modelling and reviewing

Inspection strategy (IS) is a key component impacting quality costs. Although often considered an infexible output of initial quality plans, it may require revisions given the dynamic quality situation of the manufacturing system. It is from this background that the present study aims to model and compare diferent IS based on the cost of quality (CoQ) approach for a case study in the automotive manufacturing industry. While many computational inspection strategy models (ISMs) are available in the literature, most of them face application challenges and struggle to incorporate real-world data. The present study addresses this gap by developing a model that not only represents a real testing station in a manufacturing line but also uses historical production data. Additionally, in relation to model inputs, this study explores the challenges and opportunities of acquiring reliable quality cost estimates in the Industry 4.0 context. Among the main contributions of this work, the developed CoQ-based ISM can be used as a decision-making aiding tool for inspection revision and improvement, while conclusions about quality cost data collection in the industrial digitalization context can help advance the CoQ approach in practiceFCT|FCCN (b-on

Decision-making framework for implementing safer human-robot collaboration workstations: system dynamics modeling

Human-Robot Collaboration (HRC) systems are often implemented seeking for reducing risk of Work-related Musculoskeletal Disorders (WMSD) development and increasing productivity. The challenge is to successfully implement an industrial HRC to manage those factors, considering that non-linear behaviors of complex systems can produce counterintuitive effects. Therefore, the aim of this study was to design a decision-making framework considering the key ergonomic methods and using a computational model for simulations. It considered the main systemic influences when implementing a collaborative robot (cobot) into a production system and simulated scenarios of productivity and WMSD risk. In order to verify whether the computational model for simulating scenarios would be useful in the framework, a case study in a manual assembly workstation was conducted. The results show that both cycle time and WMSD risk depend on the Level of Collaboration (LoC). The proposed framework helps deciding which cobot to implement in a context of industrial assembly process. System dynamics were used to understand the actual behavior of all factors and to predict scenarios. Finally, the framework presented a clear roadmap for the future development of an industrial HRC system, drastically reducing risk management in decision-making.This work was supported by European Structural and Investment Funds in the FEDER component, through the Operational Competitiveness and Internationalization Programme (COMPETE 2020) [Project n◦ 39479; Funding Reference: POCI-01-0247-FEDER-39479] and by FCT - Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/202

The ER luminal binding protein (BiP) mediates an increase in drought tolerance in soybean and delays drought-induced leaf senescence in soybean and tobacco

The ER-resident molecular chaperone BiP (binding protein) was overexpressed in soybean. When plants growing in soil were exposed to drought (by reducing or completely withholding watering) the wild-type lines showed a large decrease in leaf water potential and leaf wilting, but the leaves in the transgenic lines did not wilt and exhibited only a small decrease in water potential. During exposure to drought the stomata of the transgenic lines did not close as much as in the wild type, and the rates of photosynthesis and transpiration became less inhibited than in the wild type. These parameters of drought resistance in the BiP overexpressing lines were not associated with a higher level of the osmolytes proline, sucrose, and glucose. It was also not associated with the typical drought-induced increase in root dry weight. Rather, at the end of the drought period, the BiP overexpressing lines had a lower level of the osmolytes and root weight than the wild type. The mRNA abundance of several typical drought-induced genes [NAC2, a seed maturation protein (SMP), a glutathione-S-transferase (GST), antiquitin, and protein disulphide isomerase 3 (PDI-3)] increased in the drought-stressed wild-type plants. Compared with the wild type, the increase in mRNA abundance of these genes was less (in some genes much less) in the BiP overexpressing lines that were exposed to drought. The effect of drought on leaf senescence was investigated in soybean and tobacco. It had previously been reported that tobacco BiP overexpression or repression reduced or accentuated the effects of drought. BiP overexpressing tobacco and soybean showed delayed leaf senescence during drought. BiP antisense tobacco plants, conversely, showed advanced leaf senescence. It is concluded that BiP overexpression confers resistance to drought, through an as yet unknown mechanism that is related to ER functioning. The delay in leaf senescence by BiP overexpression might relate to the absence of the response to drought

Can the understory affect the Hymenoptera parasitoids in a Eucalyptus plantation?

The understory in forest plantations can increase richness and diversity of natural enemies due to greater plant species richness. The objective of this study was to test the hypothesis that the presence of the understory and climatic season in the region (wet or dry) can increase the richness and abundance of Hymenoptera parasitoids in Eucalyptus plantations, in the municipality of Belo Oriente, Minas Gerais State, Brazil. In each eucalyptus cultivation (five areas of cultivation) ten Malaise traps were installed, five with the understory and five without it. A total of 9,639 individuals from 30 families of the Hymenoptera parasitoids were collected, with Mymaridae, Scelionidae, Encyrtidae and Braconidae being the most collected ones with 4,934, 1,212, 619 and 612 individuals, respectively. The eucalyptus stands with and without the understory showed percentage of individuals 45.65% and 54.35% collected, respectively. The understory did not represent a positive effect on the overall abundance of the individuals Hymenoptera in the E. grandis stands, but rather exerted a positive effect on the specific families of the parasitoids of this order

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

Heterogeneous contributions of change in population distribution of body mass index to change in obesity and underweight NCD Risk Factor Collaboration (NCD-RisC)

From 1985 to 2016, the prevalence of underweight decreased, and that of obesity and severe obesity increased, in most regions, with significant variation in the magnitude of these changes across regions. We investigated how much change in mean body mass index (BMI) explains changes in the prevalence of underweight, obesity, and severe obesity in different regions using data from 2896 population-based studies with 187 million participants. Changes in the prevalence of underweight and total obesity, and to a lesser extent severe obesity, are largely driven by shifts in the distribution of BMI, with smaller contributions from changes in the shape of the distribution. In East and Southeast Asia and sub-Saharan Africa, the underweight tail of the BMI distribution was left behind as the distribution shifted. There is a need for policies that address all forms of malnutrition by making healthy foods accessible and affordable, while restricting unhealthy foods through fiscal and regulatory restrictions

Processing of soybean products by semipurified plant and microbial α-Galactosidases

Galactooligosaccharides (GO) are responsible for intestinal disturbances following ingestion of legume-derived products. Enzymatic reduction of GO level in these products is highly desirable to improve their acceptance. For this purpose, plant and microbial semipurified α-galactosidases were used for GO hydrolysis in soybean flour and soy molasses. α-Galactosidases from soybean germinating seeds, Aspergillus terreus, and Penicillium griseoroseum presented maximal activities at pH 4.0−5.0 and 45−65 °C. The KM,app values determined for raffinose by the soybean, A. terreus, and P. griseoroseum α-galactosidases were 3.44, 19.39, and 20.67 mM, respectively. The enzymes were completely inhibited by Ag+ and Hg2+, whereas only soybean enzyme was inhibited by galactose. A. terreus α-galactosidase was more thermostable than the enzymes from the other two sources. This enzyme maintained about 100% of its original activity after 3 h at 60 °C. The microbial α-galactosidases were more efficient for reducing GO in soybean flour and soy molasses than soybean enzyme