Microstructural analysis of multilayered titanium aluminide sheets fabricated by hot rolling and heat treatment

This study is concerned with the microstructural analysis of multilayered or bulk Ti aluminide sheets fabricated by the self-propagating high-temperature synthesis (SHS) reaction using hot rolling and heat treatment. Multilayered Ti/Al sheets were prepared by stacking thin Ti and Al sheets alternately, and a good Ti/Al interfacial bonding was achieved after rolling at 500 °C. When these sheets were held at 1000 °C, spheroidal TiAl₃ phases were formed by the SHS reaction at Ti/Al interfaces and inside Al layers. Microstructural analysis on the hot-rolled, multilayered Ti/TiAl₃ sheets revealed that intermetallic phases such as TiAl₂, TiAl, and Ti₃Al were formed at Ti/TiAl₃ interfaces due to interaction between Ti and TiAl₃ and that pores formed in the TiAl₃ layer were significantly reduced during hot rolling. When multilayered Ti/Ti aluminide sheets were heat treated at 1000 °C, Ti₃Al, TiAl, and TiAl₂ were grown as Ti and TiAl₃ were consumed. As the heat treatment proceeded, TiAl grew further, eventually leading to the fabrication of multilayered sheets composed of Ti₃Al and TiAl. Bulk Ti aluminide sheets, having a lamellar structure of Ti₃Al and TiAl, instead of multilayered sheets, were also fabricated successfully by heat treatment at 1400 °C. This fabrication method of the bulk sheets had several advantages over the method by hot forging or rolling of conventional cast Ti aluminides. From these findings, an idea to fabricate multilayered or bulk Ti aluminide sheets by hot rolling and heat treatment is suggested as an economical and continuous fabrication method, and the formation and growth mechanisms of interfacial phases are elucidated in this study.     

Establishing a limit of recognition for a vapor sensor array

Organic vapor analysis with microsensor arrays relies principally on two output parameters: the response pattern, which provides qualitative information, and the response sensitivity, which determines the limit of detection (LOD). The latter is used to define the operating limit in the low-concentration range, under the implicit assumption that, if a vapor can be detected, it can be identified and differentiated from other vapors on the basis of its response pattern. In this study, the performance of an array of four polymer-coated surface acoustic wave vapor sensors was explored using calibrated response data from 16 solvent vapors in Monte Carlo simulations coupled with pattern recognition analysis. The statistical modeling revealed that the ability to recognize a vapor from its response pattern decreases with decreasing vapor concentration, as expected, but also that the concentration at which errors in vapor recognition become excessive is well above the calculated LOD in most cases, despite the LOD being based on the least sensitive sensor in the array. These results suggest the adoption of a limit of recognition (LOR), defined as the concentration below which a vapor can no longer be reliably recognized from its response pattern, as an additional criterion for evaluating the performance of multisensor arrays. A generalized method for estimating the LOR is presented, as well as a means for improving the LOR via residual error analysis.     

Supplemental methionine and time of supplementation effects on ruminal fermentation, digesta kinetics, and in situ dry matter and neutral detergent fiber disappearance in cattle

Two experiments were conducted to study the effects of methionine supplementation on ruminal fermentation and digesta kinetics. In Exp. 1, nine ruminally cannulated beef heifers (average initial BW = 527 kg) in a crossover design were fed low-quality grass hay and cottonseed meal with or without 11.4 g of supplemental methionine (polysaccharide-coated). Particulate and fluid kinetics, rate of DM and NDF disappearance, ruminal VFA and NH3 N concentrations, and pH were not altered (P > .10) by supplemental methionine; however, ruminal purine concentration was greater (P < .05) in methionine-supplemented heifers than in unsupplemented heifers. In Exp. 2, 12 ruminally cannulated Holstein steers (average initial BW = 622 kg) grazing a fescue pasture were allotted to one of three groups: no supplemental methionine (CON) or 11.4 g of supplemental methionine fed at 0700 (AM) or at 1200 (PM). Forage intake, particulate kinetics, ruminal fluid kinetics, pH, VFA, and NH3 N concentrations were not altered (P > .10) by supplemental methionine or supplementation time. In situ rate of DM and NDF disappearance was greater (P < .05) in supplemented steers than in CON steers; AM steers exhibited faster (P < .05) rates than PM steers. Overall, methionine supplementation of low-quality forage increased ruminal purine concentration but did not alter in situ fermentation or digesta passage, whereas supplementation at 0700, but not at 1200, of steers grazing fescue forage increased rate of NDF fermentation.     

Superior–Subordinate Relationships in Korean Civil Engineering Companies

The Leader–Member Exchange (LMX) relationship quality between superiors and subordinates in Korean civil engineering companies were empirically examined for superiors’ feedback-seeking behaviors. The results showed that for superiors, affect, loyalty, and contribution toward subordinates were positively related to seeking negative as well as positive feedback from subordinates. From subordinates’ point of view, affect, contribution, and professional respect toward their superiors were positively related to superiors’ negative feedback seeking, but affect was negatively related to superiors’ positive feedback seeking. It was also found that superiors and subordinates were not consensual in LMX and superiors’ feedback-seeking behaviors. For example, for superiors, all of the four LMX dimensions were positively related to superiors’ asking subordinates directly for feedback, whereas for subordinates, none of the LMX dimensions were significantly related to superiors’ asking subordinates directly for feedback. These and other findings are discussed in detail, and implications for the findings are provided.     

Machine Learning-Based Two-Stage Data Selection Scheme for Long-Term Influenza Forecasting

One popular strategy to reduce the enormous number of illnesses and deaths from a seasonal influenza pandemic is to obtain the influenza vaccine on time. Usually, vaccine production preparation must be done at least six months in advance, and accurate long-term influenza forecasting is essential for this. Although diverse machine learning models have been proposed for influenza forecasting, they focus on short-term forecasting, and their performance is too dependent on input variables. For a country’s long-term influenza forecasting, typical surveillance data are known to be more effective than diverse external data on the Internet. We propose a two-stage data selection scheme for worldwide surveillance data to construct a long-term forecasting model for influenza in the target country. In the first stage, using a simple forecasting model based on the country’s surveillance data, we measured the change in performance by adding surveillance data from other countries, shifted by up to 52 weeks. In the second stage, for each set of surveillance data sorted by accuracy, we incrementally added data as input if the data have a positive effect on the performance of the forecasting model in the first stage. Using the selected surveillance data, we trained a new long-term forecasting model for influenza and perform influenza forecasting for the target country. We conducted extensive experiments using six machine learning models for the three target countries to verify the effectiveness of the proposed method. We report some of the results.     

An Enhanced Jacobi Precoder for Downlink Massive MIMO Systems

Linear precoding methods such as zero-forcing (ZF) are near optimal for downlink massive multi-user multiple input multiple output (MIMO) systems due to their asymptotic channel property. However, as the number of users increases, the computational complexity of obtaining the inverse matrix of the gram matrix increases. For solving the computational complexity problem, this paper proposes an improved Jacobi (JC)-based precoder to improve error performance of the conventional JC in the downlink massive MIMO systems. The conventional JC was studied for solving the high computational complexity of the ZF algorithm and was able to achieve parallel implementation. However, the conventional JC has poor error performance when the number of users increases, which means that the diagonal dominance component of the gram matrix is reduced. In this paper, the preconditioning method is proposed to improve the error performance. Before executing the JC, the condition number of the linear equation and spectrum radius of the iteration matrix are reduced by multiplying the preconditioning matrix of the linear equation. To further reduce the condition number of the linear equation, this paper proposes a polynomial expansion precondition matrix that supplements diagonal components. The results show that the proposed method provides better performance than other iterative methods and has similar performance to the ZF.     

Novel preparation and high electrical performance effect of Mn-doped ultra-high surface area activated carbon (USAC) as an additive for Ni hybrid capacitors

This paper reports the synthesis of various molar concentrations of manganese (Mn)-doped Ultra-High Surface area Activated Carbon (USAC) additives and their efficient use as cathode materials for supercapacitors. We synthesized the nanoparticles via a novel and facile dip-coating process and characterized them in detail by various analytical techniques. The SEM, EDAX, and XPS results showed that the Mn ions were successfully substituted on the USAC additives’ layered structure without any structural changes. The long cyclic stability of the as-prepared Mn-doped USAC additives was tested as a cathode material for supercapacitors at different current densities. The detailed experimental results showed that the Mn dopant content crucially determines the electrochemical performances of the USAC additives. Electrochemical measurements showed that the MnCEP-S600HTT with 0.10 mol% molar concentration of Mn dopant gives the best cycling performances. It delivers a discharge capacity of 262.9 mAh g^?1 after 100 cycles. Further increasing the current density to 1000 mA g^?1 allowed it to still maintain 253.6 mAh g^?1 after 200 cycles. We confirmed that the structure of Mn-doped USAC additives is an important pole to improve the structural stability and electrochemical properties.