Multiple ratiometric nanothermometry using semiconductor BiFeO3 nanowires and quantitative validation of thermal sensitivity

Here, we report a very sensitive, non-contact, ratio-metric, and robust luminescence-based temperature sensing using a combination of conventional photoluminescence (PL) and negative thermal quenching (NTQ) mechanisms of semiconductor BiFeO3 (BFO) nanowires. Using this approach, we have demonstrated the absolute thermal sensitivity of ~ 10 mK−1 over the 300–438 K temperature range and the relative sensitivity of 0.75% K−1 at 300 K. Further, we have validated thermal sensitivity of BFO nanowires quantitatively using linear regression and analytical hierarchy process (AHP) and found close match with the experimental results. These results indicated that BFO nanowires are excellent candidates for developing high‐performance luminescence-based temperature sensors. Graphical abstract

Mathematical modeling and estimation for next wave of COVID-19 in Poland

We investigate the problem of mathematical modeling of new corona virus (COVID-19) in Poland and tries to predict the upcoming wave. A Gaussian mixture model is proposed to characterize the COVID-19 disease and to predict a new / future wave of COVID-19. This prediction is very much needed to prepare for medical setup and continue with the upcoming program. Specifically, data related to the new confirmed cases of COVID-19 per day are considered, and then we attempt to predict the data and statistical activity. A close match between actual data and analytical data by using the Gaussian mixture model shows that it is a suitable model to present new cases of COVID-19. In addition, it is thought that there are N waves of COVID-19 and that information for each future wave is also present in current and previous waves as well. Using this concept, predictions of a future wave can be made.

Physical modeling of sand columns application in recharge reservoir to prevent seawater intrusion

This study aims to provide visual evidence by the physical simulation to demonstrate the sand column performance of a recharge reservoir to control seawater encroachment and confirm some previous studies. In this analysis, a two-dimensional sand tank illustrates the sand column's role in overcoming seawater intrusion. Besides using dyes, the sand tank is also fitted with sensors to observe the length of seawater penetration. Furthermore, the simulation using SEAWAT numerical modeling is used as a reference in this analysis. The criteria analyzed were the number of sand columns, the reservoir water level, and the isochlors concentration. The results revealed a reasonably close match between physical and computational modeling. It was also found that the more sand columns and the higher the reservoir water level, resulted in the decrease of seawater penetration length that occurred. Physical and computational modeling findings indicated that the optimal results are derived using three sand columns with an RMSE value of 0.76. The seawater infiltration length decreased to 84.72% relative to sand column-free conditions at a reservoir water level of 15.0 cm.

Numerical and Empirical Studies on Friction Stir Welding of Yellow Brass 405-20

Friction stir welding (FSW) is an eco-friendly and solid-state joining technology. Due to this reason, industries are keenly adopting this joining process in their various applications e.g., automobile, aerospace, marine, etc. Several materials have already been welded by FSW including aluminum, copper, steel, alloys of these materials, plastics, composites, and list are still going on. Few researchers have welded the brass using FSW. In this research, yellow brass 405-20 is welded with FSW for the very first time. Thermal distribution during FSW of brass was recorded via both simulations and experiments. Moreover, ultimate tensile strength was also measured numerically with its validation from its empirical counterpart. Finally, hardness was measured numerically in the form of compressive strength of welded brass, and it was also validated experimentally. Three aspects of validated simulations were never studied for brass 405-20 before and finally a good and close match was found between results from both simulations and experiments.

The coordination effect of B2B digital process capabilities on competitive performance: balancing or complementing

PurposeThe purpose of this paper is to investigate the consequences of two strategies of coordinating the online procurement capability and the online channel management capability on competitive performance.Design/methodology/approachA research model is presented to examine the performance impacts of these two coordination strategies, namely the balancing strategy (achieving a close match relationship) and the complementing strategy (maintaining the synergy effect), and tested using firm-level data collected from 196 manufacturing firms in China. Garen's two-stage econometric technique was used to identify the impacts of two coordination strategies on competitive performance.FindingsOur study discusses and compares two different coordination strategies of mitigating the operational tensions across processes and deploying resource configurations for improving competitive performance. Our results show that while the balancing strategy can mitigate the risks resulted, the complementing strategy does not create synergistic effects on the focal firms' competitive performance.Originality/valueThe results extend our understanding of the nature of B2B digital process coordination both in IS management and supply chain operations.

Multiple Ratiometric Nanothermometry using Semiconductor BiFeO3 Nanowires and Quantitative Validation of Thermal Sensitivity

Here, we report a very sensitive, non-contact, ratio-metric, and robust temperature sensing using a combination of conventional and negative thermal quenching (NTQ) mechanisms of semiconductor BiFeO3 (BFO) nanowires. Using this approach, we have demonstrated the absolute thermal sensitivity of ~10x10^-3 K^-1 over the 300 K - 438 K temperature range and the relative sensitivity of 0.75% K^-1 at 300 K. Further, we have validated thermal sensitivity of BFO nanowires quantitatively using linear regression and analytical hierarchy process (AHP) and found close match with the experimental results. These results indicated that BFO nanowires are excellent candidates for developing high‐performance luminescence based temperature sensors.

Multiple Ratiometric Nanothermometry using Semiconductor BiFeO3 Nanowires and Quantitative Validation of Thermal Sensitivity

Here, we report a very sensitive, non-contact, ratio-metric, and robust temperature sensing using a combination of conventional and negative thermal quenching (NTQ) mechanisms of semiconductor BiFeO3 (BFO) nanowires. Using this approach, we have demonstrated the absolute thermal sensitivity of ~10x10^-3 K^-1 over the 300 K - 438 K temperature range and the relative sensitivity of 0.75% K^-1 at 300 K. Further, we have validated thermal sensitivity of BFO nanowires quantitatively using linear regression and analytical hierarchy process (AHP) and found close match with the experimental results. These results indicated that BFO nanowires are excellent candidates for developing high‐performance luminescence based temperature sensors.

Registers or surveys – does the type of microdata matter for empirical analyses of consumption behavior?

In recent years it has come into focus whether longitudinal microdata on consumption derived from administrative registers can constitute an attractive supplement to survey data. This paper explores the consistency between register-imputed and survey-based consumption figures at the household level for Denmark over the period 2002–15. Moreover, it presents estimated consumer demand functions based on the two types of microdata for the same households. The paper finds no significant differences between the marginal propensities to consume out of income estimated on the basis of the two data sources. Furthermore, it demonstrates a close match between total private consumption in the national-accounts statistics and the register-based consumption microdata aggregated over all households.

Mathematical Modeling and Estimation for Next Wave of COVID19 in Poland

We investigate the problem of mathematical modeling of new corona virus (Covid19) in Poland and tries to predict the upcoming wave. A Gaussian mixture model is proposed to characterize the COVID-19 disease and to predict a new / future wave of COVID-19. This prediction is very much needed to prepare for medical setup and continue with the upcoming program. Specifically, data related to the new confirmed cases of COVID-19 per day are considered, and then we attempt to predict the data and statistical activity. A close match between actual data and analytical data by using the Gaussian mixture model shows that it is a suitable model to present new cases of COVID-19. In addition, it is thought that there are N waves of COVID-19 and that information for each future wave is also present in current and previous waves as well. Using this concept, predictions of a future wave can be made.

Further Astrochemical Insights From Bond Strengths of Small Molecules Containing Atoms From the First Three Rows of the Periodic Table

The atoms contributing to the strongest “single bonds” on the periodic table do not continue to produce the strongest “double bonds” or “triple bonds.” In fact, the opposite appears to be the case. This quantum chemical examination of nominal X = Y and X ≡ Y bonds in model molecules of atoms from the first three rows of the periodic table shows that the strongest “double bond” is in formaldehyde once the astrophysically-depleted Be and B atoms are removed from consideration. The strongest “triple bond” is a close match between acetylene and N2. However, these results indicate that astrophysical regions containing a high abundance of hydride species will likely be areas where inorganic oxide formation is favored. Those where H2 molecules have already been dissociated will favor organic/volatile astrochemistry.