Comparing artificial intelligence and human coaching goal attainment efficacy.

The history of artificial intelligence (AI) is filled with hype and inflated expectations. Notwithstanding, AI is finding its way into numerous aspects of humanity including the fast-growing helping profession of coaching. Coaching has been shown to be efficacious in a variety of human development facets. The application of AI in a narrow, specific area of coaching has also been shown to work. What remains uncertain, is how the two compare. In this paper we compare two equivalent longitudinal randomised control trial studies that measured the increase in clients' goal attainment as a result of having received coaching over a 10-month period. The first study involved human coaches and the replication study used an AI chatbot coach. In both studies, human coaches and the AI coach were significantly more effective in helping clients reach their goals compared to the two control groups. Surprisingly however, the AI coach was as effective as human coaches at the end of the trials. We interpret this result using AI and goal theory and present three significant implications: AI coaching could be scaled to democratize coaching; AI coaching could grow the demand for human coaching; and AI could replace human coaches who use simplistic, model-based coaching approaches. At present, AI's lack of empathy and emotional intelligence make human coaches irreplicable. However, understanding the efficacy of AI coaching relative to human coaching may promote the focused use of AI, to the significant benefit of society.

Probabilistic dose prediction using mixture density networks for automated radiation therapy treatment planning.

We demonstrate the application of mixture density networks (MDNs) in the context of automated radiation therapy treatment planning. It is shown that an MDN can produce good predictions of dose distributions as well as reflect uncertain decision making associated with inherently conflicting clinical tradeoffs, in contrast to deterministic methods previously investigated in the literature. A two-component Gaussian MDN is trained on a set of treatment plans for postoperative prostate patients with varying extents to which rectum dose sparing was prioritized over target coverage. Examination on a test set of patients shows that the predicted modes follow their respective ground truths well, both spatially and in terms of their dose-volume histograms. A special dose mimicking method based on the MDN output is used to produce deliverable plans and thereby showcase the usability of voxel-wise predictive densities. Thus, this type of MDN may serve to support clinicians in managing clinical tradeoffs and has the potential to improve the quality of plans produced by an automated treatment planning pipeline.

Interactions and Transport in Highly Concentrated LiTFSI-based Electrolytes.

To elucidate what properties control and practically limit ion transport in highly concentrated electrolytes (HCEs), the viscosity, ionic conductivity, ionicity, and transport numbers were studied for nine model electrolytes and connected to the rate capability in Li-ion battery (LIB) cells. The electrolytes employed the LiTFSI salt in three molar ratio concentrations; 1 : 2, 1 : 4, and 1 : 16 (LiTFSI:X) vs. solvents (X) with different permittivities; tert-butyl methyl ether (MTBE), tetrahydrofuran (THF) and propylene carbonate (PC). While the low polarity MTBE creates liquid electrolytes, ion-pairing limits the ionic conductivity despite extremely low viscosities. For the less concentrated 1 : 16 LiTFSI:MTBE and 1 : 16 LiTFSI:THF electrolytes the ionic diffusivities decrease with increased temperature, a sign of aggregation, but still their ionic conductivities and LIB performance increase. In general, the low ionic conductivity and high viscosity both limit the use of HCEs in LIBs, and no compensating mechanism seems to be present.

Active Commuting: Workplace Health Promotion for Improved Employee Well-Being and Organizational Behavior.

Objective: This paper describes a behavior change intervention that encourages active commuting using electrically assisted bikes (e-bikes) for health promotion in the workplace. This paper presents the preliminary findings of the intervention's impact on improving employee well-being and organizational behavior, as an indicator of potential business success. Method: Employees of a UK-based organization participated in a workplace travel behavior change intervention and used e-bikes as an active commuting mode; this was a change to their usual passive commuting behavior. The purpose of the intervention was to develop employee well-being and organizational behavior for improved business success. We explored the personal benefits and organizational co-benefits of active commuting and compared these to a travel-as-usual group of employees who did not change their behavior and continued taking non-active commutes. Results: Employees who changed their behavior to active commuting reported more positive affect, better physical health and more productive organizational behavior outcomes compared with passive commuters. In addition, there was an interactive effect of commuting mode and commuting distance: a more frequent active commute was positively associated with more productive organizational behavior and stronger overall positive employee well-being whereas a longer passive commute was associated with poorer well-being, although there was no impact on organizational behavior. Conclusion: This research provides emerging evidence of the value of an innovative workplace health promotion initiative focused on active commuting in protecting and improving employee well-being and organizational behavior for stronger business performance. It considers the significant opportunities for organizations pursuing improved workforce well-being, both in terms of employee health, and for improved organizational behavior and business success.