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Introduction Type 2 diabetes mellitus (T2DM) is a powerful risk factor for cardiovascular disease (CVD), conferring a greater relative risk in women than men. We sought to examine sex differences in cardiometabolic risk factors and management in the contemporary cohort represented by the Glycemia Reduction Approaches in Diabetes: A Comparative Effectiveness Study (GRADE).Research design and methods GRADE enrolled 5047 participants (1837 women, 3210 men) with T2DM on metformin monotherapy at baseline. The current report is a cross-sectional analysis of baseline data collected July 2013 to August 2017.Results Compared with men, women had a higher mean body mass index (BMI), greater prevalence of severe obesity (BMI≥40 kg/m2), higher mean LDL cholesterol, greater prevalence of low HDL cholesterol, and were less likely to receive statin treatment and achieve target LDL, with a generally greater prevalence of these risk factors in younger women. Women with hypertension were equally likely to achieve blood pressure targets as men; however, women were less likely to receive ACE inhibitors or angiotensin receptor blockers. Women were more likely to be divorced, separated or widowed, and had fewer years of education and lower incomes.Conclusions This contemporary cohort demonstrates that women with T2DM continue to have a greater burden of cardiometabolic and socioeconomic risk factors than men, particularly younger women. Attention to these persisting disparities is needed to reduce the burden of CVD in women.Trial registration number ClinicalTrials.gov (NCT01794143)

ABSTRACTWhat happens to newly built resilience capabilities when the pandemic is over? Using the concept of exaptation, we investigate how supply chain organizations have repurposed supply chain resilience capabilities post-pandemic. In particular, we examine the degree of ambidexterity capabilities to identify the exaptation potential from the newly acquired supply chain resilience capabilities during a disruptive event. In this paper, we (1) adopt a framework that depicts four types of different exaptation potential for supply resilience based on the management constructs of exploitation and exploration capabilities and (2) use the results from a related survey among 447 supply chain managers in Australia to subsequently analyse the exaptation potentials post COVID-19. The integration of the exaptation potential into supply chain literature opens a new chapter on how resilience capabilities are utilized, and we found that the majority of supply chains are able to simultaneously pursue and develop exploitative and exploratory capabilities.

Abstract Coffee is a universal beverage that drives a multi‐industry market on a global basis. Today, the sustainability of coffee production is threatened by accelerated climate changes. In this work, we propose the implementation of genomic‐assisted breeding for climate‐smart coffee in Coffea canephora. This species is adapted to higher temperatures and is more resilient to biotic and abiotic stresses. After evaluating two populations, over multiple harvests, and under severe drought weather condition, we dissected the genetic architecture of yield, disease resistance, and quality‐related traits. By integrating genome‐wide association studies and diallel analyses, our contribution is four‐fold: (i) we identified a set of molecular markers with major effects associated with disease resistance and post‐harvest traits, while yield and plant architecture presented a polygenic background; (ii) we demonstrated the relevance of nonadditive gene actions and projected hybrid vigor when genotypes from different geographically botanical groups are crossed; (iii) we computed medium‐to‐large heritability values for most of the traits, representing potential for fast genetic progress; and (iv) we provided a first step toward implementing molecular breeding to accelerate improvements in C. canephora. Altogether, this work is a blueprint for how quantitative genetics and genomics can assist coffee breeding and support the supply chain in the face of the current global changes.

The ability to recover from disruptions is important for organizations and supply chains. Empirical data were used to investigate factors that affect supply chain recovery from disruptions, including collaboration, visibility, flexibility, analytical orientation, and supply chain risk management. A literature review was conducted to build an online questionnaire that was applied to manufacturing firms in Brazil. This work’s statistical method includes confirmatory factor analysis and structural equation modeling. Our results indicate that a package of resilience capabilities - collaboration, flexibility, visibility, and analytical orientation - positively affect supply chain resilience. Improving such capabilities, therefore, will allow supply chains to recover better from disruptions. It was also discovered, however, that supply chains do not recover from disruptions by way of supply chain risk management alone. Mutual impacts also exist between the group of resilience capabilities and supply chain risk management.

A habilidade em se recuperar de interrupções é um importante fator para as organizações e suas cadeias de suprimentos. Dados empíricos foram utilizados para investigar fatores que impactam a recuperação das cadeias de suprimentos em face de interrupções, como a colaboração, visibilidade, flexibilidade, orientação analítica e a gestão de riscos. Uma revisão de literatura foi conduzida para desenvolver o questionário que foi aplicado em indústrias de transformação do Brasil. Os métodos estatísticos utilizados incluem a análise fatorial confirmatória e a modelagem de equações estruturais. Os resultados indicam que o pacote de capabilidades em resiliência – colaboração, flexibilidade, visibilidade e orientação analítica – impacta positivamente a resiliência das cadeias de suprimentos. Assim, aprimorar essas capabilidades permitirá uma recuperação mais adequada de interrupções. Também se constatou que a gestão de riscos, sozinha, não favorece a recuperação de interrupções. Adicionalmente, existe um impacto mútuo entre as capabilidades em resiliência e a gestão de riscos.

Artificial intelligence (AI) is one of the most disruptive technologies of our time. Interest in the use of AI for urban innovation continues to grow. Particularly, the rise of smart cities—urban locations that are enabled by community, technology, and policy to deliver productivity, innovation, livability, wellbeing, sustainability, accessibility, good governance, and good planning—has increased the demand for AI-enabled innovations. There is, nevertheless, no scholarly work that provides a comprehensive review on the topic. This paper generates insights into how AI can contribute to the development of smarter cities. A systematic review of the literature is selected as the methodologic approach. Results are categorized under the main smart city development dimensions, i.e., economy, society, environment, and governance. The findings of the systematic review containing 93 articles disclose that: (a) AI in the context of smart cities is an emerging field of research and practice. (b) The central focus of the literature is on AI technologies, algorithms, and their current and prospective applications. (c) AI applications in the context of smart cities mainly concentrate on business efficiency, data analytics, education, energy, environmental sustainability, health, land use, security, transport, and urban management areas. (d) There is limited scholarly research investigating the risks of wider AI utilization. (e) Upcoming disruptions of AI in cities and societies have not been adequately examined. Current and potential contributions of AI to the development of smarter cities are outlined in this paper to inform scholars of prospective areas for further research.

This paper employs the network perspective to study patterns and structures of intra-organizational learning networks. The theoretical background draws from cognitive theories, theories of homophily and proximity, theories of social exchange, the theory of generalized exchange, small-worlds theory, and social process theory. The levels of analysis applied are actor, dyadic, triadic, and global. Confirmatory social network analysis (exponential random graph modeling) was employed for data analysis. Findings suggest: (1) central actors in the learning network are experienced and hold senior positions in the organizational hierarchy; (2) evidence of homophily (in terms of gender, tenure, and hierarchical level relations) and proximity (in terms of geographical and departmental distances) in learning relationships; (3) learning relationships are non-reciprocal; and (4) transitivity and high local clustering with sparse inter-cluster ties are significant for intra-organizational learning networks. [ABSTRACT FROM AUTHOR]

Purpose ? Recently, the demise of the dot.com mania, coupled with slow economic growth has caused organizations to cut costs in an attempt to improve efficiency and the bottom line. Discontinuing or suspending knowledge management efforts and disbanding the chief knowledge officers' (CKOs) role is one common response from most organizations faced with these cost and efficiency pressures. The purpose of this paper is to describe why firms choose to cut knowledge management efforts and point to the deleterious long-term effects of this course of action. Design/methodology/approach ? The approach is based on discussions with executives. Findings ? The paper highlights three common reasons why firms choose to cut knowledge management efforts, namely: knowledge management is seen as a luxury, not a necessity; knowledge management is subsumed under information technology methods; and investment in knowledge management does not offer immediate results. Moreover, the paper argues that cutting knowledge management efforts does more harm than good for a corporation in the long run. Originality/value ? The paper describes techniques that CKOs should employ to gain support of their executive peers. [ABSTRACT FROM AUTHOR]

Abstract Aims In this multicentre study, we compared cardio‐pulmonary exercise test (CPET) parameters between heart failure (HF) patients classified as New York Heart Association (NYHA) class I and II to assess NYHA performance and prognostic role in mild HF. Methods and results We included consecutive HF patients in NYHA class I or II who underwent CPET in three Brazilian centres. We analysed the overlap between kernel density estimations for the per cent‐predicted peak oxygen consumption (VO2), minute ventilation/carbon dioxide production (VE/VCO2) slope, and oxygen uptake efficiency slope (OUES) by NYHA class. Area under the receiver‐operating characteristic curve (AUC) was used to assess the capacity of per cent‐predicted peak VO2 to discriminate between NYHA class I and II. For prognostication, time to all‐cause death was used to produce Kaplan–Meier estimates. Of 688 patients included in this study, 42% were classified as NYHA I and 58% as NYHA II, 55% were men, and mean age was 56 years. Median global per cent‐predicted peak VO2 was 66.8% (IQR 56–80), VE/VCO2 slope was 36.9 (31.6–43.3), and mean OUES was 1.51 (±0.59). Kernel density overlap between NYHA class I and II was 86% for per cent‐predicted peak VO2, 89% for VE/VCO2 slope, and 84% for OUES. Receiving‐operating curve analysis showed a significant, albeit limited performance of per cent‐predicted peak VO2 alone to discriminate between NYHA class I vs. II (AUC 0.55, 95% CI 0.51–0.59, P = 0.005). Model accuracy for probability of being classified as NYHA class I (vs. NYHA class II) across the spectrum of the per cent‐predicted peak VO2 was limited, with an absolute probability increment of 13% when per cent‐predicted peak VO2 increased from 50% to 100%. Overall mortality in NYHA class I and II was not significantly different (P = 0.41), whereas NYHA class III patients displayed a distinctively higher death rate (P

Digital transformation has far-reaching effects on business and society. While the literature to date has mainly considered the positive opportunities associated with digital innovations at the consumer interface in terms of products and services, the impact on asset-intensive organisations has not yet been examined in detail. Because asset-intensive organisations have unique requirements, a focused approach is essential. This study provided an in-depth analysis of digital transformation efforts in asset-intensive organisations by interviewing elite informants in the field. Our results provide an explanatory model for the digital transformation of asset-intensive organisations from a dynamic process perspective. Our results also allowed us to uncover the dark side of digital transformation, and we theorise on its implications.

Little is known about Nima‐related kinase (NEKs), a widely conserved family of kinases that have key roles in cell‐cycle progression. Nevertheless, it is now clear that multiple NEK family members act in networks, not only to regulate specific events of mitosis, but also to regulate metabolic events independently of the cell cycle. NEK5 was shown to act in centrosome disjunction, caspase‐3 regulation, myogenesis, and mitochondrial respiration. Here, we demonstrate that NEK5 interacts with LonP1, an AAA+ mitochondrial protease implicated in protein quality control and mtDNA remodeling, within the mitochondria and it might be involved in the LonP1‐TFAM signaling module. Moreover, we demonstrate that NEK5 kinase activity is required for maintaining mitochondrial mass and functionality and mtDNA integrity after oxidative damage. Taken together, these results show a new role of NEK5 in the regulation of mitochondrial homeostasis and mtDNA maintenance, possibly due to its interaction with key mitochondrial proteins, such as LonP1.

COVID-19 severity and mortality risk are greater for older adults whereas economic impact is deeper for younger adults. Using the Health Belief Model (HBM) as a framework, this study used a web-based survey to examine how perceived COVID-19 susceptibility and severity and perceived efficacy of recommended health behaviors varied by age group and were related to the adoption of health behaviors. Proportional odds logistic regression was used to examine the relationship between age group and perceived COVID-19 susceptibility, severity, impact, and health behavior efficacy and adoption. Structural equation modeling based on HBM constructs examined the relationships between health beliefs and behaviors. Data from 820 participants (Ontario, Canada) were analyzed (age: 42.7, 16.2 years; 79% women). Middle-aged and older adults reported greater concerns about the personal risk of hospitalization and mortality, economic impact, and social impact of COVID-19 than young adults. Middle-aged adults also reported greatest concern for other age groups. Adoption and perceived efficacy of health behaviors was similar across age groups with few exceptions. Both middle-aged and older-adults were more likely to perceive their own and each other's age groups as responding adequately to COVID-19 compared to young adults. Structural equation modeling indicated perceived benefits of health behaviors were the primary driver of behavior uptake, with socioeconomic factors and perceived severity and susceptibility indirectly associated with uptake through their influence on perceived benefits. Overall, these results suggest adoption of health behaviors is very high with few differences between age groups, despite differences in perceived impact of COVID-19. Public health communications should focus on the benefits of health behaviors to drive adoption.

Artificial intelligence (AI) is a powerful technology with an increasing popularity and applications in areas ranging from marketing to banking and finance, from agriculture to healthcare and security, from space exploration to robotics and transport, and from chatbots to artificial creativity and manufacturing. Although many of these areas closely relate to the urban context, there is limited understanding of the trending AI technologies and their application areas—or concepts—in the urban planning and development fields. Similarly, there is a knowledge gap in how the public perceives AI technologies, their application areas, and the AI-related policies and practices of our cities. This study aims to advance our understanding of the relationship between the key AI technologies (n = 15) and their key application areas (n = 16) in urban planning and development. To this end, this study examines public perceptions of how AI technologies and their application areas in urban planning and development are perceived and utilized in the testbed case study of Australian states and territories. The methodological approach of this study employs the social media analytics method, and conducts sentiment and content analyses of location-based Twitter messages (n = 11,236) from Australia. The results disclose that: (a) digital transformation, innovation, and sustainability are the most popular AI application areas in urban planning and development; (b) drones, automation, robotics, and big data are the most popular AI technologies utilized in urban planning and development, and; (c) achieving the digital transformation and sustainability of cities through the use of AI technologies—such as big data, automation and robotics—is the central community discussion topic.