The Peter Principle Revisited: A Computational Study

In the late sixties the Canadian psychologist Laurence J. Peter advanced an apparently paradoxical principle, named since then after him, which can be summarized as follows: {\it 'Every new member in a hierarchical organization climbs the hierarchy until he/she reaches his/her level of maximum incompetence'}. Despite its apparent unreasonableness, such a principle would realistically act in any organization where the mechanism of promotion rewards the best members and where the mechanism at their new level in the hierarchical structure does not depend on the competence they had at the previous level, usually because the tasks of the levels are very different to each other. Here we show, by means of agent based simulations, that if the latter two features actually hold in a given model of an organization with a hierarchical structure, then not only is the Peter principle unavoidable, but also it yields in turn a significant reduction of the global efficiency of the organization. Within a game theory-like approach, we explore different promotion strategies and we find, counterintuitively, that in order to avoid such an effect the best ways for improving the efficiency of a given organization are either to promote each time an agent at random or to promote randomly the best and the worst members in terms of competence.Comment: final version published on Physica A, 10 pages, 4 figures, 1 table (for on-line supplementary material see the link: http://www.ct.infn.it/cactus/peter-links.html

Effects of a weight management program delivered by social media on weight and metabolic syndrome risk factors in overweight and obese adults: A randomised controlled trial

Introduction: The aim of this project was to evaluate the effectiveness of using social media to augment the delivery of, and provide support for, a weight management program delivered to overweight and obese individuals during a twenty four week intervention. Methods: Participants randomly divided into either one of two intervention groups or a control group. The two intervention groups were instructed to follow identical weight-management program. One group received the program within a Facebook group, along with a support network with the group, and the other intervention group received the same program in a booklet. The control group was given standard care. Participants' weight and other metabolic syndrome risk factors were measured at baseline and at weeks 6, 12, 18 and 24. Results: The Facebook Group reported a 4.8% reduction in initial weight, significant compared to the CG only (p = 0.01), as well as numerically greater improvements in body mass index, waist circumference, fat mass, lean mass, and energy intake compared to the Pamphlet Group and the Control Group. Conclusions: These results demonstrate the potential of social media to assist overweight and obese individuals with respect to dietary and physical activity modifications for weight management, and justify further research into the inclusion of social media in clinical weight management programs. It is anticipated that social media will provide an invaluable resource for health professionals, as a low maintenance vehicle for communicating with patients, as well as a source of social support and information sharing for individuals undergoing lifestyle modifications

Soft SUSY Breaking Grand Unification: Leptons vs Quarks on the Flavor Playground

We systematically analyze the correlations between the various leptonic and hadronic flavor violating processes arising in SUSY Grand Unified Theories. Using the GUT-symmetric relations between the soft SUSY breaking parameters, we assess the impact of hadronic and leptonic flavor observables on the SUSY sources of flavor violation.Comment: 39 pages, 10 figure

Alteration of immune function in women collegiate soccer players and college students

The purpose of this study was to monitor the stress-induced alteration in concentrations of salivary immunoglobulin (S-IgA) and cortisol and the incidence of upper respiratory tract infections (URTI) over the course of a 9-week competitive season in college student-athletes and college students. The subjects consisted of 14 NCAA Division III collegiate female soccer athletes (19.8 ± 1.0 years, mean ± SD) and 14 female college students (22.5 ± 2.6 years). Salivary samples were collected for 9 weeks during a competitive soccer season. S-IgA and cortisol concentrations were determined by enzyme linked immunosorbent assay (ELISA). A training and performance questionnaire was given to the subjects every week, to record the subjects’ session rating of perceived exertion (RPE) for all the training, load, monotony and strain, as well as any injuries or illnesses experienced. The between groups ANOVA procedure for repeated measures showed no changes in salivary concentrations of IgA and cortisol. Chisquare analysis showed that during the 9-week training season injury and illness occurred at a higher rate among the soccer players. There was a significant difference at baseline between soccer and control SIgA levels (p ≤ 0.05). Decreased levels of SIgA and increases in the indices of training (load, strain and monotony) were associated with an increase in the incidence of illness during the 9-week competitive soccer season

Dissipative Kerr solitons in optical microresonators

This chapter describes the discovery and stable generation of temporal dissipative Kerr solitons in continuous-wave (CW) laser driven optical microresonators. The experimental signatures as well as the temporal and spectral characteristics of this class of bright solitons are discussed. Moreover, analytical and numerical descriptions are presented that do not only reproduce qualitative features but can also be used to accurately model and predict the characteristics of experimental systems. Particular emphasis lies on temporal dissipative Kerr solitons with regard to optical frequency comb generation where they are of particular importance. Here, one example is spectral broadening and self-referencing enabled by the ultra-short pulsed nature of the solitons. Another example is dissipative Kerr soliton formation in integrated on-chip microresonators where the emission of a dispersive wave allows for the direct generation of unprecedentedly broadband and coherent soliton spectra with smooth spectral envelope.Comment: To appear in "Nonlinear optical cavity dynamics", ed. Ph. Grel

Deep learning enables spatial mapping of the mosaic microenvironment of myeloma bone marrow trephine biopsies

Bone marrow trephine biopsy is crucial for the diagnosis of multiple myeloma. However, the complexity of bone marrow cellular, morphological, and spatial architecture preserved in trephine samples hinders comprehensive evaluation. To dissect the diverse cellular communities and mosaic tissue habitats, we developed a superpixel-inspired deep learning method (MoSaicNet) that adapts to complex tissue architectures and a cell imbalance aware deep learning pipeline (AwareNet) to enable accurate detection and classification of rare cell types in multiplex immunohistochemistry images. MoSaicNet and AwareNet achieved an area under the curve of >0.98 for tissue and cellular classification on separate test datasets. Application of MoSaicNet and AwareNet enabled investigation of bone heterogeneity and thickness as well as spatial histology analysis of bone marrow trephine samples from monoclonal gammopathies of undetermined significance (MGUS) and from paired newly diagnosed and post-treatment multiple myeloma. The most significant difference between MGUS and newly diagnosed multiple myeloma (NDMM) samples was not related to cell density but to spatial heterogeneity, with reduced spatial proximity of BLIMP1+ tumor cells to CD8+ cells in MGUS compared with NDMM samples. Following treatment of multiple myeloma patients, there was a reduction in the density of BLIMP1+ tumor cells, effector CD8+ T cells, and T regulatory cells, indicative of an altered immune microenvironment. Finally, bone heterogeneity decreased following treatment of MM patients. In summary, deep-learning based spatial mapping of bone marrow trephine biopsies can provide insights into the cellular topography of the myeloma marrow microenvironment and complement aspirate-based techniques