The imperative need to develop guidelines to manage human versus machine intelligence

Machine intelligence is increasingly entering roles that were until recently dominated by human intelligence. As humans now depend upon machines to perform various tasks and operations, there appears to be a risk that humans are losing the necessary skills associated with producing competitively advantageous decisions. Therefore, this research explores the emerging area of human versus machine decision-making. An illustrative engineering case involving a joint machine and human decision-making system is presented to demonstrate how the outcome was not satisfactorily managed for all the parties involved. This is accompanied by a novel framework and research agenda to highlight areas of concern for engineering managers. We offer that the speed at which new human-machine interactions are being encountered by engineering managers suggests that an urgent need exists to develop a robust body of knowledge to provide sound guidance to situations where human and machine decisions conflict. Human-machine systems are becoming pervasive yet this research has revealed that current technological approaches are not adequate. The engineering insights and multi-criteria decision-making tool from this research significantly advance our understanding of this important area

The imperative need to develop guidelines to manage human versus machine intelligence

Machine intelligence is increasingly entering roles that were until recently dominated by human intelligence. As humans now depend upon machines to perform various tasks and operations, there appears to be a risk that humans are losing the necessary skills associated with producing competitively advantageous decisions. Therefore, this research explores the emerging area of human versus machine decision-making. An illustrative engineering case involving a joint machine and human decision-making system is presented to demonstrate how the outcome was not satisfactorily managed for all the parties involved. This is accompanied by a novel framework and research agenda to highlight areas of concern for engineering managers. We offer that the speed at which new human-machine interactions are being encountered by engineering managers suggests that an urgent need exists to develop a robust body of knowledge to provide sound guidance to situations where human and machine decisions conflict. Human-machine systems are becoming pervasive yet this research has revealed that current technological approaches are not adequate. The engineering insights and multi-criteria decision-making tool from this research significantly advance our understanding of this important area

Studies on the relationship of weather on Fall armyworm damage in maize (Zea mays L.) under different growing environments

Fall armyworm is a recently occurring invasive pest in India, the most important defoliator causing drastic damage to maize production. Hence, the present study aimed to understand the temporal infestation level of Fall armyworms on maize (Zea mays L.) with weather patterns. Field experiments were conducted during Summer (February-May) and Rainy seasons, 2022 (August-December) at Agro Climate Research Centre, Tamil Nadu Agricultural University, Coimbatore. Three different growing environments (GE1, GE2 and GE3) were created by providing staggered sowing. Regression models were developed for per cent leaf damage against three-days lagged (LT3) and seven-day lagged (LT7) weather variables. Results showed that irrespective of growing environments, weather variables showed negative correlation (Tmax: r = -0.57, -0.81*, -0.31; SSH: -0.30, -0.48, -0.39; Tmean: -0.49, -0.23, -0.30; and SR: -0.48, -0.94*, -0.40) during summer season whereas same variables (i.e Tmax =0.62*, 0.41, 0.33; SSH = 0.09, 0.68*, 0.24; Tmean = 0.29, 0.32, 0.44; and SR=0.13, 0 .67*, 0.26 ) showed a positive correlation with PLD. Rainfall exhibits positive relation (0.06, 0.54, 0.53) and negative correlation (-0.64*, -0.10, -0.02) during summer and rainy season, respectively. Among the regression models, LT7 model had higher R2 (0.65 and 0.76) than LT3 (0.57 and 0.68) during summer and rainy seasons, respectively. These models had good regression values of 0.56 and 0.70 during Rainy and Summer, respectively. It was concluded that Tmax (32.9 °C), Tmin (23.7 °C), Tmean (28.3 °C), RH-I (85.6%), RH-II (56.4%), SSH (4.1), SR (274.6 cal cm-2 m-2), afternoon cloud cover (4.8 okta) and weekly total rainfall (10.2 mm) were very conducive for the greater leaf damage

Stable Isotopic Evidence for Methane Seeps in Neoproterozoic Postglacial Cap Carbonates

The Earth's most severe glaciations are thought to have occurred about 600 million years ago, in the late Neoproterozoic era. A puzzling feature of glacial deposits from this interval is that they are overlain by 1–5-m-thick 'cap carbonates' (particulate deep-water marine carbonate rocks) associated with a prominent negative carbon isotope excursion. Cap carbonates have been controversially ascribed to the aftermath of almost complete shutdown of the ocean ecosystems for millions of years during such ice ages—the 'snowball Earth' hypothesis. Conversely, it has also been suggested that these carbonate rocks were the result of destabilization of methane hydrates during deglaciation and concomitant flooding of continental shelves and interior basins. The most compelling criticism of the latter 'methane hydrate' hypothesis has been the apparent lack of extreme isotopic variation in cap carbonates inferred locally to be associated with methane seeps. Here we report carbon isotopic and petrographic data from a Neoproterozoic postglacial cap carbonate in south China that provide direct evidence for methane-influenced processes during deglaciation. This evidence lends strong support to the hypothesis that methane hydrate destabilization contributed to the enigmatic cap carbonate deposition and strongly negative carbon isotopic anomalies following Neoproterozoic ice ages. This explanation requires less extreme environmental disturbance than that implied by the snowball Earth hypothesis

Assessing the Health of Richibucto Estuary with the Latent Health Factor Index

The ability to quantitatively assess the health of an ecosystem is often of great interest to those tasked with monitoring and conserving ecosystems. For decades, research in this area has relied upon multimetric indices of various forms. Although indices may be numbers, many are constructed based on procedures that are highly qualitative in nature, thus limiting the quantitative rigour of the practical interpretations made from these indices. The statistical modelling approach to construct the latent health factor index (LHFI) was recently developed to express ecological data, collected to construct conventional multimetric health indices, in a rigorous quantitative model that integrates qualitative features of ecosystem health and preconceived ecological relationships among such features. This hierarchical modelling approach allows (a) statistical inference of health for observed sites and (b) prediction of health for unobserved sites, all accompanied by formal uncertainty statements. Thus far, the LHFI approach has been demonstrated and validated on freshwater ecosystems. The goal of this paper is to adapt this approach to modelling estuarine ecosystem health, particularly that of the previously unassessed system in Richibucto in New Brunswick, Canada. Field data correspond to biotic health metrics that constitute the AZTI marine biotic index (AMBI) and abiotic predictors preconceived to influence biota. We also briefly discuss related LHFI research involving additional metrics that form the infaunal trophic index (ITI). Our paper is the first to construct a scientifically sensible model to rigorously identify the collective explanatory capacity of salinity, distance downstream, channel depth, and silt-clay content --- all regarded a priori as qualitatively important abiotic drivers --- towards site health in the Richibucto ecosystem.Comment: On 2013-05-01, a revised version of this article was accepted for publication in PLoS One. See Journal reference and DOI belo

Cognitive Dysfunction in Adult Chd With Different Structural Complexity

OBJECTIVE: We carried out a cross-sectional study to assess cognitive function in a sample of adult CHD patients, within the Functioning in Adult Congenital Heart Disease study London. The association between cognitive functioning and disease complexity was examined. METHODS: A total of 310 patients participated in this study. Patients were classified into four structural complexity groups – tetralogy of Fallot, transposition of the great arteries, single ventricle, and simple conditions. Each patient underwent neuropsychological assessment to evaluate cognitive function, including memory and executive function, and completed questionnaires to assess depression and anxiety. RESULTS: Among all, 41% of the sample showed impaired performance (>1.5 SD below the normative mean) on at least three tests of cognitive function compared with established normative data. This was higher than the 8% that was expected in a normal population. The sample exhibited significant deficits in divided attention, motor function, and executive functioning. There was a significant group difference in divided attention (F=5.01, p=0.002) and the mean total composite score (F=5.19, p=0.002) between different structural complexity groups, with the simple group displaying better cognitive function. CONCLUSION: The results indicate that many adult CHD patients display impaired cognitive function relative to a healthy population, which differs in relation to disease complexity. These findings may have implications for clinical decision making in this group of patients during childhood. Possible mechanisms underlying these deficits and how they may be reduced or prevented are discussed; however, further work is needed to draw conclusive judgements