In recent years, the rapid development of artificial intelligence has raised a series of ethical issues. How to balance AI innovation and AI governance has attracted wide attention from society. Some principle-based and practical approaches have already emerged. However, these strategies mainly face problems of time, speed, and cost. They fail to fundamentally balance dynamic factors and the need for explainability. This study proposes an explainable and adaptive AI governance approach from the perspective of time to balance AI governance and AI innovation. Causal models have causal explanation capabilities. They meet the requirements of responsible innovation. They can improve governance speed and reduce governance and innovation costs. Taking Bayesian networks as an example, they have explainability and dynamic adaptation ability. Bayesian networks can be used as a governance step before a regulatory sandbox. Bayesian networks can implement a human-assisted regulatory approach. This approach can combine the EU Artificial Intelligence Act with scholars’ recommendations on quantifiable risk levels. The explainable and adaptive AI governance approach has three important meanings for AI governance. First, it can provide an inclusive governance path. Second, it can supplement static governance strategies. Third, it can automatically generate explainable value alignment schemes.

Increasing e-waste demands design approaches beyond repair and recycling. This study explores adaptability, upgradability, and flexibility, as proposed to mitigate product obsolescence. Processes and guidelines are examined from legislative, academic, and industrial perspectives, complemented by a workshop (n=16). Applications show potential yet remain scarce and uncover rebound effects. A common framework for adaptable design is suggested. Future research is recommended to evaluate it, expand guidelines empirically and address barriers like consumer perceptions and business model challenges.

This study proposes an animal selection protocol for adaptability using machine learning models to analyse variables related to genotype–environment interaction in cows raised in the Ñeembucú wetlands of Paraguay. The objective is to optimise selection and improve reproductive efficiency by addressing adaptive traits related to specific environments. Machine learning enabled the identification of key physiological variables associated with environmental adaptability that influence body condition in cows, including phosphatase, cholesterol, phosphorus, hair length, creatinine, haematocrit, creatine phosphokinase, haemoglobin, body temperature and calcium. The gradient boosting machine model was selected for its superior performance based on root mean square error and mean absolute error indicators. Results indicated that low concentrations of phosphatase and creatine phosphokinase, along with shorter hair length, positively affect body condition score. Likewise, body temperature dynamics were reflected in the response variable. Higher levels of haematocrit and haemoglobin showed a positive influence on body condition score. Based on the identified influential variables, a selection protocol for adaptability in breeding cows is proposed.

Aphis gossypii Glover (Hemiptera: Aphididae) is a significant pest of Capsicum annuum (Solanales: Solanaceae) and exhibits intraspecific differentiation within populations. To investigate the adaptability of Hap3 and Hap17 A. gossypii to various C. annuum varieties, including ‘Lvzhou101’ (LZ), ‘Lashen’ (LA), ‘Saierweilvtianjiao’ (SE), ‘Haimaihongri’ (HM), ‘Chaotianjiao’ (CT), and ‘Luosijiangjun’ (LS), we employed life tables to analyse growth and population parameters post-feeding and conducted petri dish host choice experiments to assess the host plant preference of A. gossypii. Survival rates of A. gossypii varied significantly across C. annuum varieties. Notably, Hap3 and Hap17 thrived on ‘LZ’ but failed to establish populations on ‘LA’. The net reproductive rate (R0), average generation time (T), and intrinsic rate of increase (rm) differed markedly between Hap3 and Hap17 across C. annuum varieties. Feeding on ‘LZ’ resulted in a significantly higher R0 value (26.49) for Hap3 relative to other varieties. The T (7.60 days) and rm (0.27) values for Hap3 on ‘SE’ were superior to those observed on other C. annuum varieties. These findings indicate that ‘SE’ is the optimal host for Hap3 growth, while ‘LZ’ best supports Hap17. Both haplotypes exhibited the lowest adaptability to ‘LA’. Therefore, the utilisation capacity of A. gossypii populations on C. annuum demonstrates differentiation, and the resistance levels among C. annuum varieties to A. gossypii vary. This differentiation can inform targeted management strategies for aphid infestations on pepper crops.

Additive main effects and multiplicative interactive effect stability model (AMMI) was used in the present study to understand the impact of season × genotype interaction (SGI) on pod yield and its attributing traits. A total of 86 determinate growth habit type French bean germplasm were evaluated in randomized block design with two replications in three different seasons. Significant variability was observed for genotypes, seasons and SGI. The component ‘seasons’ contributed more than 50% of variability to pod yield, pod number per plant and days to flowering (DFL), and ‘genotypes’ accounted more than 50% of phenotypic variation for pod length and pod width. The SGI signals were observed for pod yield per plant, number of pods per plant, pod weight and DFL, and SGI accounted for more than 20% phenotypic variability for all traits. We identified IIHR-155 and IIHR-11 as the promising genotypes across three seasons based on their position on AMMI biplots, stability indices combined with high trait mean, estimates of best linear unbiased prediction and minimal crossover interaction. The results from the present study are highly useful for utilization in crop improvement programmes to evolve the season-specific varieties and varieties with wide adaptability in French bean.

The role of capital in measuring resilience is investigated. Focusing on the current short-run and potential long-run growth paths of the economic system, we propose new indexes to separately measure adaptability and resistance to shocks, which are the essence of a system’s resilience. Capital dynamics during the transition and along the balanced growth path are used here instead of employment to represent the evolution of the size and composition of the economy. Our indexes measure adaptability and resistance by comparing the two capital growth rates. They are built by mimicking the average and variance of the difference in growth rates. In this new setting, investment and depreciation flows play an important role in explaining what the partial index of adaptability reveals. The available data on the USA and Spanish capital allow us to empirically compute the indexes and draw conclusions about their ability to resist shocks and absorb their effects. We conclude that the US economy is more adaptable and has a greater capacity to absorb impacts than the Spanish economy, but it is less resistant to disturbances.

Climate change (CC) is a major threat to biodiversity, increasing species extinction risk. Assessments of its possible impacts on species are crucial for designing conservation strategies. Here, we adjusted a global trait-based approach to the national level and apply it to Uruguay (South America) to evaluate the CC vulnerability of its herpetofauna. A total of 112 species were assessed in a scenario of CC projections for 2050 with regard to three dimensions of vulnerability: sensitivity, low adaptive capacity and exposure. We conducted the assessment through an expert elicitation process based on the Delphi method. We found that most local species (64.6% amphibians; 100% reptiles) were highly sensitive to CC. Among them, seven amphibians (14.6%) and seven reptiles (10.9%) were identified as highly vulnerable to CC. Important gaps in the life-history traits of the species were found that should guide future research. The structured expert consultation process allowed us to gather more and better information than if it had only been based on published sources. Our study identified challenges associated with changing the scale from global to national that might be used for similar assessments in other countries.

Understanding how and why exotic species use their habitats is crucial for defining effective conservation strategies. We aimed to investigate habitat use by an exotic population of squirrel monkeys living in an Atlantic Forest fragment and identify factors associated with their habitat preferences. Over 6 months of scan sampling observations, we collected data on native and exotic plants consumed by the squirrel monkeys, food availability, and interactions between the squirrel monkeys and the native common marmosets. We also georeferenced the estimated centroid point of the study group during each scan. Squirrel monkeys used Secondary Old Forest habitats more often than the other habitats available. The consumption of native and exotic plants and the association with common marmoset appear to have influenced the habitat use of the exotic squirrel monkeys; however, the choice habitat did not demonstrate to be associated with food availability. The exotic squirrel monkeys preferred to use less disturbed habitats to consume a high amount of food (often associated with the common marmoset), potentially optimizing their food intake. Our findings demonstrated the adaptive success of an exotic primate in its non-natural habitat and the key role of the plant community in maintaining this population.

Dairy cattle breeding has historically focused on relatively small numbers of elite bulls as sires of sons. In recent years, even if generation intervals were reduced and more diverse sires of sons could have been selected, genomic selection has not fundamentally changed the fact that a large number of individuals are being analyzed. However, a relatively small number of elite bulls are still siring those animals. Therefore inbreeding-derived negative consequences in the gene pool have brought concern. The detrimental effects of non-additive genetic changes such as inbreeding depression and dominance have been widely disseminated while seriously affecting bioeconomically important parameters because of an antagonistic relationship between dairy production and reproductive traits. Therefore, the estimation of benefits and limitations of inbreeding and variance of the selection response deserves to be evaluated and discussed to preserve genetic variability, a significant concern in the selection of individuals for reproduction and production. Short-term strategies for genetic merit improvement through modern breeding programs have severely lowered high-producing dairy cattle fertility potential. Since the current selection programs potentially increase long-term costs, genetic diversity has decreased globally as a consequence. Therefore, a greater understanding of the potential that selection programs have for supporting long-term genetic sustainability and genetic diversity among dairy cattle populations should be prioritized in managing farm profitability. The present review provides a broad approach to current inbreeding-derived problems, identifying critical points to be solved and possible alternative strategies to control selection against homozygous haplotypes while maintaining sustained selection pressure. Moreover, this manuscript explores future perspectives, emphasizing theoretical applications and critical points, and strategies to avoid the adverse effects of inbreeding in dairy cattle. Finally, this review provides an overview of challenges that will soon require multidisciplinary approaches to managing dairy cattle populations, intending to combine increases in productive trait phenotypes with improvements in reproductive, health, welfare, linear conformation, and adaptability traits into the foreseeable future.

Over the last few years, the concept of agility has become increasingly popular in organizations. Companies are hoping to foster speed, adaptability, and innovation by rolling out an agile strategy, implementing agile methods, and creating an agile mind-set among leaders and employees. There is, however, much ambiguity about what the concept of agility entails and how it can be successfully implemented in an organization. The purpose of this practice-focused paper is to organize the extant agility literature for readers by giving an overview of different definitions of agility; outlining evidence-based factors that contribute to agility at the individual, team, and organizational levels; and describing three practical examples at a large German car company. Finally, the authors suggest steps that organizations can take to increase agility in their workforce.