Industry 4.0: a supply chain innovation perspective

The Fourth Industrial Revolution – also known as Industry 4.0 (i4.0) – comprises the digitalisation of the industrial sector. This paper uses the theoretical lens of supply chain innovation (SCI) to investigate the implications of i4.0 on supply chain management. For these purposes, the method of structured content analysis is applied to more than 200 use cases of i4.0-enabled SCI introduced by both established and startup companies. i4.0-enabled SCI manifests along three dimensions: process, technology, and business architecture. The key findings of this study can be summarised as follows: first, i4.0-enabled SCI extends the initial focus on productivity improvements in SC processes towards scalability and flexibility. Second, extant i4.0 solutions rely mostly on analytics and smart things while omitting smart people technology and the human-centric approach associated with the i4.0 paradigm. Third, established companies adopt i4.0 merely to sustain their existing business architectures while startup companies radically change their operating models, relying heavily on data analytics and the platform economy. Consequently, established companies pursue a problem-driven, engineering-based approach to SCI while startup companies follow an ‘asset-light’, business-driven approach. Lastly, there are two distinct approaches to digitalising operational SC processes: platform-based crowdsourcing of standard processes and on-demand provision of customised services.     

Heat transport in dilute mixtures of3He in superfluid4He

We report new measurements of the effective thermal conductivity K_eff and relaxation time τ in dilute mixtures of³He in superfluid⁴He, with molar concentrationsX≤10⁻³. The temperature range extended fromT≈1.4 K toT _λ. Both K_cff and τ are found to agree with theoretical predictions, in contrast to previous experiments where significant differences were observed. A new thermal conductivity cell design was used which almost completely eliminates extraneous volumes and surfaces, and the earlier results are explained in relation to these design changes.     

Introduction of a nonlinearity measure for principal component models

Although principal component analysis (PCA) is an important tool in standard multivariate data analysis, little interest has been devoted to assessing whether the underlying relationship within a given variable set can be described by a linear PCA model or whether nonlinear PCA must be utilized. This paper addresses this deficiency by introducing a nonlinearity measure for principal component models. The measure is based on the following two principles: (i) the range of recorded process operation is divided into smaller regions; and (ii) accuracy bounds are determined for the sum of the discarded eigenvalues. If this sum is within the accuracy bounds for each region, the process is assumed to be linear and vice versa. This procedure is automated through the use of cross-validation. Finally, the paper shows the utility of the new nonlinearity measure using two simulation studies and with data from an industrial melter process.     

Cr-Mo solid solutions forced by high-energy ball milling

Mixtures of Cr and Mo elemental powders, with the nominal compositions Cr₂₅Mo₇₅, Cr₅₀Mo₅₀, and Cr₇₅Mo₂₅, are processed by high-energy ball milling at ambient temperature. Milling is observed to force the mixing of the immiscible bcc elements Cr and Mo into solid solutions. The lattice parameter of these solid solutions, measured by X-ray diffraction (XRD), displays the expected positive deviation from Vegard’s law. These deviations are compared to the ones predicted by Eshelby’s inclusion model for dilute alloys. The conventional Williamson-Hall approach is shown to fail to determine the grain size in as-milled samples, probably due to the high density of dislocations. Annealing at 700 °C for 10 hours under argon leads to a large reduction in structural defect density, without inducing any significant decomposition. The mixing measured in Cr-Mo is discussed in the broader context of the mechanical mixing forced by ball milling in moderately immiscible systems.     

Interdiffusion behavior of Hg in HgTe/CdTe superlattices grown on Cd0.96Zn0.04 Te (211)B substrates by molecular beam epitaxy

Double-crystal x-ray rocking curve (DCRC) and secondary-ion mass-spectroscopy (SIMS) measurements have been performed to investigate the effect of rapid thermal annealing on the interdiffusion behavior of Hg in HgTe/CdTe superlattices grown on Cd_0.96Zn_0.04Te (211)B substrates by molecular beam epitaxy. The sharp satellite peaks of the DCRC measurements on a 100-period HgTe/CdTe (100Å/100Å) superlattice show a periodic arrangement of the superlattice with high-quality interfaces. The negative direction of the entropy change obtained from the diffusion coefficients as a function of the reciprocal of the temperature after RTA indicates that the Hg diffusion for the annealed HgTe/CdTe superlattice is caused by an interstitial mechanism. The Cd and the Hg concentration profiles near the annealed HgTe/CdTe superlattice interfaces, as measured by SIMS, show a nonlinear behavior for Hg, originating from the interstitial diffusion mechanism of the Hg composition. These results indicate that a nonlinear interdiffusion behavior is dominant for HgTe/CdTe superlattices annealed at 190°C and that the rectangular shape of HgTe/CdTe superlattices may change to a parabolic shape because of the intermixing of Hg and Cd due to the thermal treatment.     

Measurement of Energy Expenditure in Elite Athletes Using MEMS-Based Triaxial Accelerometers

Fitness development and performance assessment of elite athletes requires an understanding of many physiological factors, many of these are direct and indirect measures of athlete energy expenditure. Many methods are physiological factor assessments and require the athlete to be constrained by laboratory equipment or periodic interruption of activity to take measurements such as blood samples are required to be taken. This paper presents a method that is entirely ambulatory and noninvasive, using microelectromechanical systems (MEMS) accelerometers. The commonly used output of commercial accelerometer-based devices (known as "counts") cannot discriminate activity intensity for the activities of interest. This, in conjunction with variability in output from different systems and lack of commonality across manufacturers, limits the usefulness of commercial devices. This paper identifies anthropometric and kinematic sources of inter-athlete variability in accelerometer output, leading to an alternate energy expenditure estimator based mainly on step frequency modified by anthropometric measures. This energy expenditure estimator is more robust and not influenced by many sources of variability that affect the currently used estimator. In this system, low-power signal processing was implemented to extract both the energy estimator and other information of physiological and statistical interest