Hitachi (Germany)

OBJECTIVES: To evaluate whether real-time elastography, a new, non-invasive method for the diagnosis of breast cancer, improves the differentiation and characterization of benign and malignant breast lesions. METHODS: Real-time elastography was carried out in 108 potential breast tumor patients with cytologically or histologically confirmed focal breast lesions (59 benign, 49 malignant; median age, 53.9 years; range, 16-84 years). Tumor and healthy tissue were differentiated by measurement of elasticity based on the correlation between tissue properties and elasticity modulus. Evaluation was performed using the three-dimensional (3D) finite element method, in which the information is color-coded and superimposed on the B-mode ultrasound image. A second observer evaluated the elastography images, in order to improve the objectivity of the method. The results of B-mode scan and elastography were compared with those of histology and previous sonographic findings. Sensitivities and specificities were calculated, taking histology as the gold standard. RESULTS: B-mode ultrasound had a sensitivity of 91.8% and a specificity of 78%, compared with sensitivities of 77.6% and 79.6% and specificities of 91.5% and 84.7%, respectively, for the two observers evaluating elastography. Agreement between B-mode ultrasound and elastography was good, yielding a weighted kappa of 0.67. CONCLUSIONS: Our initial clinical results suggest that real-time elastography improves the specificity of breast lesion diagnosis and is a promising new approach for the diagnosis of breast cancer. Elastography provides additional information for differentiating malignant BI-RADS (breast imaging reporting and data system) category IV lesions.

We report here a detailed study on the surface topology of well-known ordered mesoporous silica (SBA-15, MCM-41, and KIT-6) and a series of nanocast Co 3O 4, Co 3O 4/CoFe 2O 4 composites by high resolution scanning electron microscopy (HR-SEM). Images of the MCM-41 structure were obtained at a resolution of the pore size, as well as a real space image of the gyroid silica surface of KIT-6 for two different aging temperatures, clearly revealing the differences of the aging procedures. By using the low voltage HR-SEM technique with extremely high resolution, we could very clearly show the influence of the template properties on the structure of the nanocast metal oxides.

Nowadays, reconfiguration and adaptation by means of optimal re-parameterization in Industrial Cyber-Physical Systems (ICPSs) is one of the bottlenecks for the digital transformation of the manufacturing industry. This article proposes a cloud-to-edge-based ICPS equipped with machine learning techniques. The proposed reasoning module includes a learning procedure based on two reinforcement learning techniques, running in parallel, for updating both the data-conditioning and processing strategy and the prediction model. The presented solution distributes computational resources and analytic engines in multiple layers and independent modules, increasing the smartness and the autonomy for monitoring and control the behavior at the shop floor level. The suitability of the proposed solution, evaluated in a pilot line, is endorsed by fast time response (i.e., 0.01 s at the edge level) and the appropriate setting of optimal operational parameters for guaranteeing the desired quality surface roughness during macro- and micro-milling operations.

Abstract The electrochemical conversion of CO 2 into commodity chemicals or fuels is an attractive reaction for sustainable CO 2 utilization. In this context, the application of gas diffusion electrodes is promising due to efficient CO 2 mass transport. Herein, a scalable and reproducible method is presented for polytetrafluoroethylene (PTFE)‐bound copper gas diffusion electrodes (GDEs) via the dry‐pressing method and compositional parameters are emphasized to alter such electrodes. The assembly of the catalytic layer plays a critical role in the electrode performance, as elevated bulk hydrophobicity coupled with good surface wettability is observed to offer highest performance in 0.5 m KHCO 3 . With optimized electrodes, formate, CO, and H 2 are obtained at a current density of 25 mA cm −2 as main products in 1 m KOH in faradaic efficiencies (FEs) of 27%, 30%, and 36%. At 200 mA cm −2 , an altered product composition with ethylene (33% FE) and ethanol (9% FE) along with H 2 (33% FE) is observed. In addition, n ‐propanol is observed with 7% faradaic efficiency. The results indicate that the composition of the GDE has a severe influence on the electrode performance and setting proper hydrophobicity gradients within the electrode is key toward developing a successful electrochemical CO 2 reduction.

The genetics underlying heterosis, the difference in performance of crosses compared with midparents, is hypothesized to vary with relatedness between parents. We established a unique germplasm comprising three hybrid wheat sets differing in the degree of divergence between parents and devised a genetic distance measure giving weight to heterotic loci. Heterosis increased steadily with heterotic genetic distance for all 1903 hybrids. Midparent heterosis, however, was significantly lower in the hybrids including crosses between elite and exotic lines than in crosses among elite lines. The analysis of the genetic architecture of heterosis revealed this to be caused by a higher portion of negative dominance and dominance-by-dominance epistatic effects. Collectively, these results expand our understanding of heterosis in crops, an important pillar toward global food security.

High Temperature Nickel based alloy 617 (A 617) is considered as a candidate material for the 700 °C power plants due to its combination of creep strength and good fabricability. The alloy has been investigated in numerous publicly sponsored and privately financed programmes with regard to its use in USC boilers. Based on the gained experience, the material has been tailored to fit the specific purpose of USC boilers. The first development step resulted in a modified version of Alloy 617 labeled Alloy 617 B, with creep strength at 700 °C about 25% higher than that listed by ASME and VdTÜV. A second step focused on the definition of welding procedures and heat treatment, demonstrating that crack formation in large welded components can be avoided by suitable post-weld heat treatment. This paper will briefly describe the effect of boron and heat-treatment on the mechanical behaviour of Alloy 617 within the scope of the German USC boiler programmes.

Abstract The energy sector in the European market has been changing significantly over the last years. European Union (EU) energy strategy includes the EU low-carbon roadmap milestone, which indicates for 2020, a 20% reduction in carbon emissions, and a 20% EU-wide share for renewables, and by 2030 a 40% reduction in carbon emissions and 30% EU-wide share for renewables. The increased renewable energy sources (RES) penetration and their intermittent energy production have led to the emerging need for energy storage technologies. Especially in the European energy market, large-scale energy balancing with sustainable technologies with product flexibility and cost-effective operation are being investigated. The carbon capture and utilization (CCU) concept, as a means for low-carbon sustainable industries, is integrated in the energy storage technologies. The present paper addresses the integration of power to fuel concept in the energy storage sector with simultaneous emission reduction. Grid management, the scale, and the efficient operation of electrolyzers are the basis for the implementation of Power to Fuel technology. The use of surplus and/or low-cost electricity via water electrolysis to commute captured CO2 from industrial plants to versatile energy carriers such as methane and methanol is being investigated in the present paper. Shadow operation of fossil fuel power plants under minimum load conditions leads to optimized energy dispatch of the power plants and provides product flexibility in terms of electricity, grid services, and chemical production. The produced fuels can be used in highly efficient and well-established power systems and further used in the transportation sector or for covering heat demands. The energy efficiency of the different processes is presented and a comparison is made in terms of cost effective energy storage solutions via the simultaneous grid management optimization, the reduction of carbon dioxide, and the production of valuable chemicals. The cross-sectorial concept of the Power to Fuel is presented for Steel and Power industry for the case of methane and methanol production. A review of the U.S. and European markets is made for the application of Power to Fuel.

This paper presents an approach for image classification based on an ensemble of convolutional neural networks and the application to a real case study of an industrial welding process. The ensemble consists of five convolutional neural networks, whose outputs are combined through a voting policy. In order to select appropriate network parameters (i.e., the number of convolutional layers and layers hyperparameters) and voting policy, an efficient search process was carried out by using an evolutionary algorithm. The proposed method is applied and validated in a case study focused on detecting misalignment of metal sheets to be joined through submerged arc welding process. After selecting the most convenient setup, the ensemble outperforms other seven strategies considered in a comparison in several metrics, while maintaining an adequate computational cost.

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Oxyfuel combustion is one of the promising technologies to enable CCS for new and existing coal-fired power plants. For retrofit applications, oxyfuel is an attractive option because it does not have major impact on the boiler-turbine steam cycle. This paper presents a case study for retrofitting oxyfuel combustion technology in large state-of-the-art power plants that are originally commissioned and operated in air-fired mode. The overall process design for the modified power plant is outlined; necessary modifications of relevant components are explained. The paper also discusses results of experiments and numerical calculations on combustion behavior in the furnace. Retrofit measures ensure that the power stations still can run under both air-fired and oxyfuel-fired conditions if required by regulations/market conditions. This provides additional operational and commercial benefits for the operator of the plant and reduces the technical risk of implementing new components and processes not yet proven in the power sector.

One of the most important operations during the manufacturing process of a pressure vessel is welding. The result of this operation has a great impact on the vessel integrity; thus, welding inspection procedures must detect defects that could lead to an accident. This paper introduces a computer vision system based on structured light for welding inspection of liquefied petroleum gas (LPG) pressure vessels by using combined digital image processing and deep learning techniques. The inspection procedure applied prior to the welding operation was based on a convolutional neural network (CNN), and it correctly detected the misalignment of the parts to be welded in 97.7% of the cases during the method testing. The post-welding inspection procedure was based on a laser triangulation method, and it estimated the weld bead height and width, with average relative errors of 2.7% and 3.4%, respectively, during the method testing. This post-welding inspection procedure allows us to detect geometrical nonconformities that compromise the weld bead integrity. By using this system, the quality index of the process was improved from 95.0% to 99.5% during practical validation in an industrial environment, demonstrating its robustness.

The potential of big data to support businesses has been demonstrated in financial services, manufacturing, and telecommunications. Here, we report on efforts to enter a new data era in plant breeding by collecting genomic and phenotypic information from 12,858 wheat genotypes representing 6575 single-cross hybrids and 6283 inbred lines that were evaluated in six experimental series for yield in field trials encompassing ~125,000 plots. Integrating data resulted in twofold higher prediction ability compared with cases in which hybrid performance was predicted across individual experimental series. Our results suggest that combining data across breeding programs is a particularly appropriate strategy to exploit the potential of big data for predictive plant breeding. This paradigm shift can contribute to increasing yield and resilience, which is needed to feed the growing world population.

A high-conductivity insulated gate bipolar transistor (IGBT) (HiGT) with a double diffused MOS structure and an n-type hole-barrier layer surrounding a p-layer (planar HiGT) is presented. The hole-barrier layer prevents the holes from flowing into the p-layer and stores them in the n-layer. The planar HiGT provides a better tradeoff between collector-emitter saturation voltage [VcE(sat)] and turn-off loss than conventional IGBTs, regardless of the injection efficiency of the p-layer on the collector side, while it maintains a high blocking voltage by controlling the sheet carrier concentration of the hole-barrier layer. The planar HiGT has a tough short-circuit capability of more than 10 mus at 125degC, with a saturation current similar to that of conventional IGBTs.

This paper investigates the capability and behaviors of two different tooth-concentrated winding topologies for traction electric machine applications. The well-known conventional 12-teeth/10-poles winding and also the new 18-teeth/10-poles winding with low-magnetomotive force (MMF) harmonic contents are investigated. Using these winding types, two permanent-magnet (PM) machines are designed and analyzed, and their performances have been compared. Considering the main machine parameters, such as the electromagnetic torque, losses, field-weakening capability, thermal behavior, and also the noise and vibrations characteristics, the obtained results show significant advantages for the new machine over the conventional design. For the new 18-teeth/10-poles PM machine design, a prototype machine is built and several measurements results for the electromagnetic torque and machine efficiency are given.

Purpose The purpose of this paper is to outline some key aspects such as material systems used, phenomenological and statistical process modeling, techniques applied to monitor the process and optimization approaches reported. All these need to be taken into account for the ongoing development of the SLM technique, particularly in health care applications. The outcomes from this review allow not only to summarize the main features of the process but also to collect a considerable amount of investigation effort so far achieved by the researcher community. Design/methodology/approach This paper reviews four significant areas of the selective laser melting (SLM) process of metallic systems within the scope of medical devices as follows: established and novel materials used, process modeling, process tracking and quality evaluation, and finally, the attempts for optimizing some process features such as surface roughness, porosity and mechanical properties. All the consulted literature has been highly detailed and discussed to understand the current and existing research gaps. Findings With this review, there is a prevailing need for further investigation on copper alloys, particularly when conformal cooling, antibacterial and antiviral properties are sought after. Moreover, artificial intelligence techniques for modeling and optimizing the SLM process parameters are still at a poor application level in this field. Furthermore, plenty of research work needs to be done to improve the existent online monitoring techniques. Research limitations/implications This review is limited only to the materials, models, monitoring methods, and optimization approaches reported on the SLM process for metallic systems, particularly those found in the health care arena. Practical implications SLM is a widely used metal additive manufacturing process due to the possibility of elaborating complex and customized tridimensional parts or components. It is corroborated that SLM produces minimal amounts of waste and enables optimal designs that allow considerable environmental advantages and promotes sustainability. Social implications The key perspectives about the applications of novel materials in the field of medicine are proposed. Originality/value The investigations about SLM contain an increasing amount of knowledge, motivated by the growing interest of the scientific community in this relatively young manufacturing process. This study can be seen as a compilation of relevant researches and findings in the field of the metal printing process.

Free-flow electrophoresis (FFE) exploits differences in the overall charge of bio-particles separating cells, organelles, macromolecules, ions, etc. according to their distinct electrophoretic mobility and isoelectric point (pI) values. Indeed, around a neutral pH organelles usually exhibit a negative surface charge, migrating in an electric field from the cathode toward the anode. Since its introduction more than five decades ago by Barrollier et al., Z. Naturforsch. 1958, 13b, 745-755 and Hannig, Z. Anal. Chem. 1961, 181, 244-254, FFE has become an established analytical and preparative separation method for the isolation of a variety of organelles. Particularly, in sophisticated, multistep separating processes to separate subpopulations of organelles, it has gained, meanwhile, a position as a versatile technology and essential element. Relying on the distinct surface charges instead of buoyant densities of cell organelles, the FFE technology is best supporting a preceding centrifugation-based fractionation of subcellular compartments in the second dimension. In the following review, the two-step isolation and purification of subpopulations of classic animal and plant cell organelles will be mainly exemplified.

As a major energy consumer, steel plants can help stabilize the power grid by shifting production from periods with high demand. Electric arc furnaces can be operated at different power levels, affecting the energy efficiency, the duration of melting tasks, and the rate of electrode degradation, which has previously been neglected. We thus propose a new mixed-integer linear programming (MILP) formulation for optimal scheduling under time-of-use electricity pricing that captures the tradeoffs involved. It relies on the resource-task network (RTN) for modeling processing tasks with variable electrode mass depletion and replacement tasks that regenerate the mass. Results for an industrial case study show that the high-power mode, which allows for faster execution and to fit more tasks in low-price periods but is the least energy-efficient and consumes the largest mass of electrode, is mostly avoided. It indicates that electrode replacement plays an important role in total cost minimization.

Situated in the near of Schwarze Pumpe power station southeast of Berlin, Germany, Vattenfall operates a 30 MWth pilot plant in order to investigate the Oxyfuel process. Hitachi Power Europe delivered a DST-burner (DST-Brenner®) for the indirect firing system of the plant which was installed without modifications to the pressure part of the boiler. The combustion behavior during Oxyfuel operation was investigated and optimal burner settings for different operation points were established. The results show that the burner can be operated in a wide range of oxygen concentrations in the oxidant gas flow. The burner is characterized by low emissions of carbon monoxide and nitrogen oxides. The high flame stability was found to be unaffected by operational influences such as fluctuations in the coal mass flow, coal quality or soot blowing. Due to the high flame stability the change-over from air blown to Oxyfuel combustion can be realized very easily and is automated. During Oxyfuel operation measurements of temperatures and flue gas species in the flame were performed. The measurement values were compared with the values of the combustion modeling by computational fluid dynamics. The good agreement between both measurement and modeling shows the suitability of the applied models to predict the combustion behavior under Oxyfuel conditions.

The partial pressure of carbon dioxide in the flue gas of an Oxyfuel combustion process is significantly increased in comparison with conventional air-blown firing. Depending on the moisture content of the fuel and the type of flue gas recirculation (either wet or dry), the partial pressure of water vapor varies for Oxyfuel atmospheres. The calculation of the heat transfer by radiation in a furnace requires an accurate modeling of the optical properties of the flue gas. In order to reduce the computational effort in engineering calculations, the band radiation of the gaseous combustion products is approximated as a weighted sum of one clear and one or more gray gases. The partial pressures of carbon dioxide and water vapor of an Oxyfuel atmosphere exceed the range of published weighting factors and absorption coefficients. These have been developed for air-blown combustion with a high concentration of non-radiating nitrogen in the flue gas. New parameters for a weighted sum of one clear and four gray gases were determined in order to allow for the higher concentration of carbon dioxide in the flue gas. A fixed ratio between carbon dioxide and water vapor is no longer suitable for the calculation of the gas emissivity. Therefore, polynomials of the molar ratio of both radiating flue gas species represent the coefficients of the modified model. The emissivities calculated by this model are compared to emissivity data generated by the exponential wide band model. The heat transfer by radiation for a simplified, exemplary furnace is calculated for relevant atmospheres by computational fluid dynamics (CFD) software FLUENT using different models. The results show the suitability of the new model parameters to calculate the gas emissivity of Oxyfuel atmospheres with variable fractions of carbon dioxide and water vapor.

This paper addresses the problem faced by large electricity consumers to simultaneously determine the optimal day-ahead electricity procurement and the optimal energy-aware production schedule. The inherent uncertainty of the problem, due to the bidding process in the day-ahead market, is dealt with by means of the stochastic programming modeling framework. In particular, a two-stage problem is formulated with the aim of establishing the optimal bidding strategy and the optimal production schedule hedging against price uncertainty. The optimal integrated solution is defined to minimize the overall cost and to control the risk of high cost scenarios due to uncertain price peaks. The stochastic model is solved with a scenario-decomposition approach. Extensive numerical experiments have been carried out to assess the performance of the proposed decision approach. The results collected when considering an industrial relevant case-study show the superiority of the proposed methodology in comparison with a deterministic approach.