Background:This study was conceived to analyze how exercise and weight management psychosocial variables, derived from several health behavior change theories, predict weight change in a short-term intervention.

Background: Changes in body image and subjective well-being variables (e.g. self-esteem) are often reported as outcomes of obesity treatment. However, they may, in turn, also influence behavioral adherence and success in weight loss. The present study examined associations among obesity treatment-related variables, i.e., change in weight, quality of life, body image, and subjective well-being, exploring their role as both mediators and outcomes, during a behavioral obesity treatment.

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The objective was to compare measures from dual-energy X-ray absorptiometry (DXA), bioelectrical impedance analysis (BIA) and anthropometry with a reference four-compartment model to estimate fat mass (FM) and fat-free mass (FFM) changes in overweight and obese women after a weight-loss programme. Forty-eight women (age 39.8^5.8 years; weight 79·2^11·8 kg; BMI 30·7^3·6 kg/m 2 ) were studied in an out-patient weight-loss programme, before and after the 16-month intervention. Women attended weekly meetings for the first 4 months, followed by monthly meetings from 4 to 12 months. Body composition variables were measured by the following techniques: DXA, anthropometry (waist circumference-based model; Antrform), BIA using Tanita (TBF-310) and Omron (BF300) and a reference four-compartment model. Body weight decreased significantly (23·3 (SD 3·1) kg) across the intervention. At baseline and after the intervention, FM, percentage FM and FFM assessed by Antrform, Tanita, BF300 and DXA differed significantly from the reference method (P#0·001), with the exception of FFM assessed by Tanita (baseline P¼ 0·071 and after P¼0·007). DXA significantly overestimated the change in FM and percentage FM across weight loss (2 4·5 v. 23·3 kg; P, 0·001 and 23·7 v. 22·0 %; P,0·001, respectively), while Antrform underestimated FM and percentage FM (22·8 v. 2 3·3 kg; P¼ 0·043 and 2 1·1 v. 2 2·0 %; P¼ 0·013) compared with the four-compartment model. Tanita and BF300 did not differ (P. 0·05) from the reference model in any body composition variables. We conclude that these methods are widely used in clinical settings, but should not be applied interchangeably to detect changes in body composition. Furthermore, the several clinical methods were not accurate enough for tracking body composition changes in overweight and obese premenopausal women after a weight-loss programme.

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The technological evolution emerges a unified (Industrial) Internet of Things network, where loosely coupled smart manufacturing devices build smart manufacturing systems and enable comprehensive collaboration possibilities that increase the dynamic and volatility of their ecosystems. On the one hand, this evolution generates a huge field for exploitation, but on the other hand also increases complexity including new challenges and requirements demanding for new approaches in several issues. One challenge is the analysis of such systems that generate huge amounts of (continuously generated) data, potentially containing valuable information useful for several use cases, such as knowledge generation, key performance indicator (KPI) optimization, diagnosis, predication, feedback to design or decision support. This work presents a review of Big Data analysis in smart manufacturing systems. It includes the status quo in research, innovation and development, next challenges, and a comprehensive list of potential use cases and exploitation possibilities.

We present measurements of the resistivity ρx,x of URu2Si2 high-quality single crystals in pulsed high magnetic fields up to 81 T at a temperature of 1.4 K and up to 60 T at temperatures down to 100 mK. For a field H applied along the magnetic easy-axis c, a strong sample-dependence of the low-temperature resistivity in the hidden-order phase is attributed to a high carrier mobility. The interplay between the magnetic and orbital properties is emphasized by the angle-dependence of the phase diagram, where magnetic transition fields and crossover fields related to the Fermi surface properties follow a 1/cos θ-law, θ being the angle between H and c. For H c, a crossover defined at a kink of ρx,x, as initially reported in [Shishido et al., Phys. Rev. Lett. 102, 156403 (2009)], is found to be strongly sample-dependent: its characteristic field µ0H* varies from ≃ 20 T in our best sample with a residual resistivity ratio RRR = ρx,x(300K)/ρx,x(2K) of 225 to ≃ 25 T in a sample with a RRR of 90. A second crossover is defined at the maximum of ρx,x at the sample-independent characteristic field µ0H LT ρ,max ≃ 30 T. Fourier analyzes of Shubnikov-de Haas oscillations show that H LT ρ,max coincides with a sudden modification of the Fermi surface, while H * lies in a regime where the Fermi surface is smoothly modified. For H a, i) no phase transition is observed at low temperature and the system remains in the hidden-order phase up to 81 T, ii) quantum oscillations surviving up to 7 K are related to a new and almost-spherical orbit -for the first time observed here -at the frequency F λ ≃ 1400 T and associated with a low effective mass m * λ = (1 ± 0.5) · m0, where m0 is the free electron mass, and iii) no Fermi surface modification occurs up to 81 T.