Ab initio and DFT calculations reveal a very strong ferromagnetic exchange of the order of 200 cm(-1) in an endohedral radical hetero-metallo-fullerene molecule Gd2@C79N. Calculations performed on the anisotropic Dy2@C79N molecule reveal that very strong Dy-radical exchange not only quenches the QTM effects but also immensely enhances the barrier height for magnetization reversal.

The intentional yaw misalignment of leading, upwind turbines in a wind farm, termed wake steering, has demonstrated potential as a collective control approach for wind farm power maximization. The optimal control strategy and the resulting effect of wake steering on wind farm power production are in part dictated by the power degradation of the upwind yaw misaligned wind turbines. In the atmospheric boundary layer, the wind speed and direction may vary significantly over the wind turbine rotor area, depending on atmospheric conditions and stability, resulting in freestream turbine power production which is asymmetric as a function of the direction of yaw misalignment and which varies during the diurnal cycle. In this study, we propose a model for the power production of a wind turbine in yaw misalignment based on aerodynamic blade elements, which incorporates the effects of wind speed and direction changes over the turbine rotor area in yaw misalignment. The proposed model can be used for the modeling of the angular velocity, aerodynamic torque, and power production of an arbitrary yaw misaligned wind turbine based on the incident velocity profile, wind turbine aerodynamic properties, and turbine control system. A field experiment is performed using multiple utility-scale wind turbines to characterize the power production of yawed freestream operating turbines depending on the wind conditions, and the model is validated using the experimental data. The resulting power production of a yaw misaligned variable speed wind turbine depends on a nonlinear interaction between the yaw misalignment, the atmospheric conditions, and the wind turbine control system.

PurposeThis paper compares impact of Industry 4.0 / emerging information and communication Technologies (ICTs), for example, Internet of things (IOT), machine learning, artificial intelligence (AI), robotics and cloud computing, on 22 organisational performance indicators under nine combinations of Lean Six Sigma (LSS) and quality management systems (QMS).Design/methodology/approachSurvey of 105 Indian organisations was done about their experience of using QMS, Lean Six Sigma and emerging ICTs. Respondents included both manufacturing and service enterprises of different scales and sectors. The responses collected were compared, and statistically significant difference among them was evaluated using chi-square test.FindingsThe study confirmed statistically significant difference among 20 organisational performance indicators under different combinations of QMS, LSS and ICTs. These indicators include quality performance, delivery performance, sales turnover, inventory level and so forth. However, for two indicators, namely, absenteeism and throughput, significant difference in responses was not established.Research limitations/implicationsAll possible combinations of QMS, LSS, only LSS tools and ICTs were not studied because of either theoretical impossibility (e.g. using LSS without LSS tools) or practically rare situations (e.g. organisations using ICTs and LSS without QMS). Furthermore, the impact from different sequences of implementing QMS, LSS and ICTs can be studied.Practical implicationsUsing this study, practitioners can identify which LSS, Quality System and ICT combination results in best performance and quick success. On theoretical front, the study confirms impact of LSS and QMS on organisational performance.Originality/valueThis study evaluates organisational performance under several possible combinations of QMS, LSS, and emerging ICTs, which was so far unexplored.

Purpose Critical success factors (CSF) for lean six sigma (LSS) using quality 4.0 are not researched so far. This study aims to bridge this gap. It also validates CSF already identified for LSS under conventional technologies. Design methodology approach Empirical research using the questionnaire method is conducted. Construct of the questionnaire is checked using Cronbach’s alpha and responses received are analysed using t-test and exploratory factor analysis. Findings In total, 20 factors are evaluated for LSS success. It includes 7 factors related to quality 4.0 technologies and 13 related to the conventional set-up. All 7 quality 4.0 related factors were found critical; whereas, under traditional set-up, 11 factors out of 13 were found critical. Practical implications The study will help enterprises in the fast and effective adoption of quality 4.0 and seamless integration with LSS. The post-Covid-19 business scenario is expected to be information technology focussed. The findings of this study will be useful in these circumstances. Consultants and practitioners can prioritise their efforts based on newly identified CSF. The new revelation about CSF for LSS in quality 4.0 enriches theory as well. Social implications Developing skills based on newly identified CSF will help people in remaining employable in the era of automation, robotics and artificial intelligence which is otherwise ill-famed for destroying jobs. Originality value CSF for LSS using quality 4.0 is a new contribution. It differentiates CSF established earlier for conventional technologies. Moreover, many CSF are common for LSS and industry 4.0, therefore this study will also help in smoother adoption of industry 4.0/quality 4.0 in organisations.

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