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The purpose of this paper is to provide a general picture for describing the formed tribofilm, including chemical and physical aspects in the micro-scale and the nano-scale. In a previous study, the durability of zinc dialkyl dithiophosphate (ZDDP) tribofilms on cylinder liner samples has been investigated in a tribometer model system by using fresh and aged fully formulated oils and replacing them with PAO8 without additives. Analyses of the derived tribofilms by means of X-ray photoelectron spectroscopy and scanning electron microscopy could give some hints about the underlying mechanisms of the tribofilm build-up and wear performance, but a final model has not been achieved.

Thus, characterisation of these tribofilms by means of focused ion beam-transmission electron microscopy (FIB-TEM) and energy dispersive X-ray spectroscopy is presented and a concluding model of the underlying mechanisms of tribofilm build-up is discussed in this paper.

For tribotests running first with fresh fully formulated engine oil, a rather homogeneous ZDDP-like tribofilm is found underneath a carbon rich tribofilm after changing to non-additivated PAO8. However, when the tests run first with aged fully formulated engine oil, no ZDDP-like tribofilm has been found after changing to non-additivated PAO8, but a wear protective carbon rich tribofilm.

The obtained results provide insights into the structure and durability of tribofilms. Carbon-based tribofilms are built up on the basis of non-additivated PAO8 because of the previously present ZDDP tribofilms, which suggests an alternative way to reducing the consumption of antiwear additives.

The railway signal equipment failure diagnosis is a vital element to keep the railway system operating safely. One of the most difficulties in signal equipment failure diagnosis is the uncertainty of causality between the consequence and cause for the accident. The traditional method to solve this problem is based on Bayesian Network, which needs a rigid and independent assumption basis and prior probability knowledge but ignoring the semantic relationship in causality analysis. This paper aims to perform the uncertainty of causality in signal equipment failure diagnosis through a new way that emphasis on mining semantic relationships.

This study proposes a deterministic failure diagnosis (DFD) model based on the question answering system to implement railway signal equipment failure diagnosis. It includes the failure diagnosis module and deterministic diagnosis module. In the failure diagnosis module, this paper exploits the question answering system to recognise the cause of failure consequences. The question answering is composed of multi-layer neural networks, which extracts the position and part of speech features of text data from lower layers and acquires contextual features and interactive features of text data by Bi-LSTM and Match-LSTM, respectively, from high layers, subsequently generates the candidate failure cause set by proposed the enhanced boundary unit. In the second module, this study ranks the candidate failure cause set in the semantic matching mechanism (SMM), choosing the top 1st semantic matching degree as the deterministic failure causative factor.

Experiments on real data set railway maintenance signal equipment show that the proposed DFD model can implement the deterministic diagnosis of railway signal equipment failure. Comparing massive existing methods, the model achieves the state of art in the natural understanding semantic of railway signal equipment diagnosis domain.

It is the first time to use a question answering system executing signal equipment failure diagnoses, which makes failure diagnosis more intelligent than before. The EMU enables the DFD model to understand the natural semantic in long sequence contexture. Then, the SMM makes the DFD model acquire the certainty failure cause in the failure diagnosis of railway signal equipment.

Today’s society interest in mobile photography drives consumers’ and brands’ growing usage of Instagram. This paper aims to address the consequences of excessive use of Instagram on the negative feeling of losing information when not connected and the emotional fatigue resulting from an overcharge with new information. The mediating role of addiction between Instagram overuse and the two outcomes is also analyzed.

Data from 342 active Instagram users were used to test the proposed model, applying the partial least square equation modeling method (SmartPLS 3).

Addiction partially mediates the impact of overuse on emotional fatigue and instastress. Addiction to Instagram was mainly due to respondents’ lack of control over the time spent on it resulting in incapability to reduce its usage.

Social networking site managers, educators, families and public institutions should promote an adequate use of Instagram, making users (especially the young) aware of the potential threats of its excessive usage. The control on the amount of time devoted to Instagram is a key factor for detaining overuse and addiction, as well as avoiding the negative outcomes analyzed in this research.

The findings contribute to the extant knowledge on the negative side of the digitization of the individual, as little is known about it to the best of the authors’ knowledge.

La presente investigación aborda las consecuencias del uso excesivo de Instagram relacionadas con el sentimiento negativo de pérdida de información, cuando no se está conectado, y la fatiga emocional derivada de la sobrecarga de información. El estudio también analiza el papel mediador de la adicción entre el uso excesivo de Instagram y ambas consecuencias.

Para testar el modelo propuesto se utilizó información de 342 usuarios activos de Instagram, aplicándose el método de ecuaciones estructurales por mínimos cuadrados parciales (SmartPLS 3).

La adicción media parcialmente el impacto que el uso excesivo de Instagram tiene sobre (i) la fatiga emocional y (ii) el Instastress. La adicción a Instagram se deriva principalmente de la falta de control del tiempo invertido en su uso, lo que da como resultado su incapacidad para reducir dicho uso.

Los responsables de los medios sociales, los educadores, las familias y las instituciones públicas deberían promover un uso adecuado de Instagram, haciendo que los usuarios (especialmente los más jóvenes) sean conscientes de las amenazas potenciales de su uso excesivo. El control sobre la cantidad de tiempo dedicado a Instagram es un factor clave para detener el uso excesivo y la adicción, así como para evitar los resultados negativos analizados en esta investigación.

Los resultados contribuyen a ampliar el conocimiento sobre el lado negativo de la digitalización del individuo, dada la escasa literatura al respecto.

Adicción, Uso excesivo, Instastress, Fatiga emocional, Redes sociales, Instagram

Artículo de investigación