Adecco (Switzerland)

The current uncertain, dynamic scenario calls for a systemic perspective when referring to organizational complexity and behavior. Our research contributes to the analysis of organizational complexity through multidimensional behavioral mapping. Our method uses machine learning tools to detect the interconnections between the different behaviors of a person in his/her operating context. First, the research project dealt with prototyping a model to read the organizational behavior, the related detection tool, and a data analysis methodology. It used machine learning tools and ended with a data visualization phase. We set our model to read the organizational behavior by comparing the literature benchmark theories with our field experience. The model was organized around 4 areas and 16 behaviors. These were the basis for singling out the indicators and the questionnaire items. The data analysis methodology aimed at detecting the interconnections between behaviors. We designed it by joining univariate analysis with a multivariate technique based on the application of machine learning tools. This led to a high-resolution network map through three specific steps: (a) creating a multidimensional topology based on a Kohonen Map (a type of unsupervised learning artificial neural network) to geometrically represent behavioral relationships; (b) implementing k-means clustering for identifying which areas of the map have behavior similarity or affinity factors; and (c) locating people and the various identified clusters within the map. The research highlighted the validity of machine learning tools to detect the multidimensionality of organizational behavior. Therefore, we could delineate the networking of the observed elements and visualize an otherwise unattainable complexity through multimedia and interactive reporting. Application in the field of research consisted of the design and development of a prototype integrated with our LMS platform via a plugin. Field experimentation confirmed the effectiveness of the method for creating professional growth and development paths. Furthermore, this experimentation allowed us to obtain significant data by applying our model to several sectors, namely pharmaceutical, TLC, banking, automotive, machinery, and services.

View Video Presentation: https://doi.org/10.2514/6.2021-1513.vid This study examines the aerodynamic characteristics of the preliminary design shapes of the Yamato’s “PUPA™ Heavy” concept, a dual-mode detachable cargo pod with a 400kg-class payload, designed to be carried either by being attached onto cargo eVTOL (electrical vertical take-off and landing) aircrafts or loaded onto ground transports together with load units that the company is currently using. Because of its use on the ground, the external dimensions of the pod in ground transportation mode should be defined exactly by the standard of the conventional load units. Under this restriction, there exists a tradeoff between aerodynamics and load volume. In this study, CFD-based assessment of the aerodynamic issues of the preliminary design shapes which keep a balance between aerodynamics and load volume, in addition with the design of the hinged tail section which makes those factor balance feasible.

Information technology has become essential in supporting growth and sustainability of many types of organizations, including healthcare organizations. These organizations can improve its processes and patient flow. However, IT in the hospital sector can be considered underdeveloped when compared to other sectors. For this reason, an analysis of the healthcare system is useful, mainly with the goal of minimizing the cost of healthcare and maximizing the quality of the patient’s processes. In this paper, our aim was to present a framework showing the applicability of IT self-servicing app in patient’s treatment. The framework is structured by five types of self-servicing IT apps and eight patient’s processes steps, settled in a Polyclinic Cardiology. Our results affirm that self-servicing IT apps are applicable both for medical staff and for the patient’s satisfaction and treatment, from the “information help desk” step to the "patient check-out". Our work could contribute for implementations and suggestions in future research. In fact, in a increasingly digitalized world, the healthcare sector has a great growth and improvement potential to receive thanks to IT. http://dx.doi.org/10.18226/23185279.v4iss3p167

The protective effect of the breast-milk may be partly attributed to the characteristics of the intestinal microbiota provided by this mode of nutrition. The microbiota of formula-fed infants differs from that of breast-fed infants. Although the main bifidogenic effect of breast-milk has been attributed to the non-protein fraction, it is now recognized that it is due to a complex set of inter-acting factors, among which the type and level of protein (standard formula 2.5 g; human milk 1.8 g/100 Kcal). Aims: To evaluate if a formula with an adapted protein profile, enabling an amino acid profile more similar to human milk (consistent with the international recommendations) may provide similar growth and intestinal microbiota compared to human milk. Subjects and Methods: Non-randomized parallel study in healthy newborn infants less than 28 d of age (21 breast-fed, 27 bottle-fed). The bottle-fed infants received ad libitum a NAN formula with adapted protein profile (1.8 g/100 kcal), and were followed until the age of 120 d. The breast-fed infants were exclusively breast-fed from birth to 60 ± 3 d of age. Anthropometric data and fecal bacteria counts (Bifidobacteria, Lactobacilli and Enterobacteria; evaluated by FISH and/or by plating techniques) were measured. Results: The test formula with adapted protein profile provided satisfactory growth up to 120 d compared to the Euro-Growth data, and favored the bifidobacteria population to reach a level similar to that of the breast-fed infants at 60d. Fecal bifidobacteria counts in bottled-fed vs. breast-fed infants obtained by FISH and culture plates were 8.6 ± 0.6 vs. 8.8 ± 0.8 (p = 0.33) and 7.2 ± 3.3 vs. 7.8 ± 3.2 log cfu/g of feces (p = 0.53), respectively. Likewise, the other bacterial populations were not affected by the mode of feeding. Conclusion: Infants fed a starter formula with adapted protein profile had normal growth and intestinal microbiota similar to that of breast-fed infants.