Conspiracy theories on Twitter: emerging motifs and temporal dynamics during the COVID-19 pandemic

The COVID-19 pandemic resulted in an upsurge in the spread of diverse conspiracy theories (CTs) with real-life impact. However, the dynamics of user engagement remain under-researched. In the present study, we leverage Twitter data across 11 months in 2020 from the timelines of 109 CT posters and a comparison group (non-CT group) of equal size. Within this approach, we used word embeddings to distinguish non-CT content from CT-related content as well as analysed which element of CT content emerged in the pandemic. Subsequently, we applied time series analyses on the aggregate and individual level to investigate whether there is a difference between CT posters and non-CT posters in non-CT tweets as well as the temporal dynamics of CT tweets. In this regard, we provide a description of the aggregate and individual series, conducted a STL decomposition in trends, seasons, and errors, as well as an autocorrelation analysis, and applied generalised additive mixed models to analyse nonlinear trends and their differences across users. The narrative motifs, characterised by word embeddings, address pandemic-specific motifs alongside broader motifs and can be related to several psychological needs (epistemic, existential, or social). Overall, the comparison of the CT group and non-CT group showed a substantially higher level of overall COVID-19-related tweets in the non-CT group and higher level of random fluctuations. Focussing on conspiracy tweets, we found a slight positive trend but, more importantly, an increase in users in 2020. Moreover, the aggregate series of CT content revealed two breaks in 2020 and a significant albeit weak positive trend since June. On the individual level, the series showed strong differences in temporal dynamics and a high degree of randomness and day-specific sensitivity. The results stress the importance of Twitter as a means of communication during the pandemic and illustrate that these beliefs travel very fast and are quickly endorsed.

Nonlinear Characteristics of the Effect of Manufacturing Servitization on Consumer Business Performance

Servitization is an important trend in the transformation and upgrading of the manufacturing industry, but whether it can significantly improve enterprise performance is the key to the transformation. Based on the sample of Chinese A-share listed companies from 2011 to 2019, we analyze the business scope of 2502 annual reports to identify the service level of consumer goods manufacturing enterprises. The results show the following. (1) The “service performance” curve shows obvious nonlinear trends and heterogeneity in different industries and different performance conditions. The curve between servitization and return on assets tends to show a positive “U” shape, but the relationship between servitization and revenue per employee obviously shows an inverted “U” shape. (2) Manufacturing enterprises with relatively low technical complexity and relatively high industry competition will reach the inflection point of service performance “U” curve more quickly and get rid of “service trap” more easily. (3) The automobile manufacturing industry invests in software development and other fields that are not related to its own advantages, which violates the correlation law of the industrial value chain, leading to the coexistence of “service trap” and “principle-agent dilemma.” The clothing and electrical appliances industries are more likely to fall into the “service trap” because they face the challenge of “Internet + manufacturing” transformation. The beverage and wine manufacturing industry has induced a “service spillover” effect, which is mainly due to its low technical complexity and service based on the industrial chain. It is proposed that manufacturing enterprises explore business growth points from the perspective of industrial value chain extension and strengthen upstream product R&D and terminal e-commerce services.

The impacts of inward knowledge transfer and absorptive capacity on the turnover of host country nationals in MNE subsidiaries: a multilevel modeling approach

Purpose This study aims to find how the turnover of host country nationals (HCNs) would be affected by the knowledge transfer from a headquarter to a subsidiary. Knowledge transfer in a multinational corporation (MNC) has been discussed as a critical factor in the MNC’s success. Because HCNs are essential to synergizing with a new knowledge inflow during this knowledge transfer process, their turnover entails negative consequences such as knowledge loss. Design/methodology/approach This paper empirically tests the unbalance between knowledge received (KR) and absorptive capacity (AC) as the most critical organizational predictor by using the secondary longitudinal records and survey data of 4,915 employees. Multilevel survival analysis is used to calculate the individuals’ turnover hazard. Findings While finding that the primary effect of transferred knowledge is to reduce turnover, the study demonstrates the unbalance between a subsidiary’s AC and KR increases the likelihood of HCNs’ turnover within the organization. The authors also recognize the possibility of nonlinear trends of KR and AC on the turnover hazard. Originality/value The authors answer how knowledge transfer shapes a subsidiary’s work environment to prevent or increase turnover, which has been barely examined for HCNs who comprise the crucial demographic group in knowledge transfer. To enhance the originality further, this study empirically observes the actual turnover of HCNs with a conceptually comprehensive view incorporating both learning and political approaches.

The Impact of Vibrational Entropy on the Segregation of Cu to Antiphase Boundaries in Fe3Al

We performed a quantum mechanical study of segregation of Cu atoms toward antiphase boundaries (APBs) in Fe3Al. The computed concentration of Cu atoms was 3.125 at %. The APBs have been characterized by a shift of the lattice along the ⟨001⟩ crystallographic direction. The APB energy turns out to be lower for Cu atoms located directly at the APB interfaces and we found that it is equal to 84 mJ/m2. Both Cu atoms (as point defects) and APBs (as extended defects) have their specific impact on local magnetic moments of Fe atoms (mostly reduction of the magnitude). Their combined impact was found to be not just a simple sum of the effects of each of the defect types. The Cu atoms are predicted to segregate toward the studied APBs, but the related energy gain is very small and amounts to only 4 meV per Cu atom. We have also performed phonon calculations and found all studied states with different atomic configurations mechanically stable without any soft phonon modes. The band gap in phonon frequencies of Fe3Al is barely affected by Cu substituents but reduced by APBs. The phonon contributions to segregation-related energy changes are significant, ranging from a decrease by 16% at T = 0 K to an increase by 17% at T = 400 K (changes with respect to the segregation-related energy difference between static lattices). Importantly, we have also examined the differences in the phonon entropy and phonon energy induced by the Cu segregation and showed their strongly nonlinear trends.

Artificial Neural Networks for Pyrolysis, Thermal Analysis, and Thermokinetic Studies: The Status Quo

Artificial neural networks (ANNs) are a method of machine learning (ML) that is now widely used in physics, chemistry, and material science. ANN can learn from data to identify nonlinear trends and give accurate predictions. ML methods, and ANNs in particular, have already demonstrated their worth in solving various chemical engineering problems, but applications in pyrolysis, thermal analysis, and, especially, thermokinetic studies are still in an initiatory stage. The present article gives a critical overview and summary of the available literature on applying ANNs in the field of pyrolysis, thermal analysis, and thermokinetic studies. More than 100 papers from these research areas are surveyed. Some approaches from the broad field of chemical engineering are discussed as the venues for possible transfer to the field of pyrolysis and thermal analysis studies in general. It is stressed that the current thermokinetic applications of ANNs are yet to evolve significantly to reach the capabilities of the existing isoconversional and model-fitting methods.

Effect of de-trending climatic parameters on temporal changes of reference evapotranspiration in the eastern Himalayan region of Sikkim, India

Reference Evapotranspiration (ET0) is an essential factor in irrigation scheduling, climate change studies, and drought assessment. The study's main objective was to identify the influences of detrending input climatic parameters (CPs) on ET0 using linear and nonlinear approaches throughout 1980–2015 in Gangtok, East Sikkim, India. The benchmark values of ET0 were calculated using the global standard FAO56 Penman–Montieth equation. The ET0-related CPs included for the analysis are maximum temperature (Tmax), minimum temperature (Tmin), maximum relative humidity (RHmax), minimum relative humidity (RHmin), and sunshine duration (SSH). The linear and nonlinear trends in various CPs affect ET0 change. Linearly detrended series was obtained by linear regression method whereas, nonlinearly detrended series was obtained using Complete Ensemble Empirical Mode Decomposition with Adaptive Noise method. Twenty-three scenarios, including the original scenario, 11 scenarios in Group1 (CPs de-trended linearly), and 11 scenarios in Group2 (CPs de-trended nonlinearly) were generated. Influences of Tmax and SSH were more substantial than the influences of other CPs for both Group1 and Group2. This results in the SSH masked the weak influences of other CPs. The effects of the trends in CPs, especially of SSH and Tmax, were clearly shown. The ET0 values decreased significantly during 1980–2015; however, no significant decreasing trend was observed in the case of SSH, during the same period. The nonlinear detrending gave closer results to the benchmark values as compared to linear detrending because of non-monotone variations of the ET0 and CPs. Therefore, the results from nonlinear detrending were more plausible as compared to linear detrending. The diminishing trend of ET0 prompted an overall alleviation in dry spell, hence there would be a somewhat lower risk of water use in the study region.