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The purpose of the paper is to build a real-time integrated turboprop take-off model which fully takes the interaction between diverse parts of aircraft into consideration. Turboprops have the advantage of short take-off distance derived from propeller-wing interaction. Traditional turboprop take-off model is inappropriate because interactions between diverse parts of aircrafts are not fully considered or longer calculation time is required. To make full use of the advantage of short take-off distance, a real-time integrated take-off model is needed for analysing flight performance and developing an integrated propeller-engine-aircraft control system.

A new integrated three-degree-of-freedom take-off model is developed, which takes a modified propeller model, a wing model and the predominant propeller-wing interaction into account. The propeller model, based on strip theory, overcomes the shortage that the strip theory does not work if the angle of propeller axis and inflow velocity is non-zero. The wing model uses the lifting line method. The proposed propeller-wing interaction model simplifies the complex propeller-wing flow field. Simulations of ATR42 take-off model are conducted in the following three modes: propeller-wing interaction is ignored; influence of propeller on wing is considered only; and propeller-wing interaction is considered.

Comparison of take-off distances and flight parameters shows that propeller-wing interaction has a vital impact on take-off distance and flight parameters of turboprops.

The real-time integrated take-off model provides time-history flight parameters, which plays an important role in an integrated propeller-engine-aircraft control system to analyse and improve flight performance.

The real-time integrated take-off model is more precise because propeller-wing interaction is considered. Each calculation step costs less than 20 ms, which meets real-time calculation requirements. The modified propeller model overcomes the shortage of strip theory.

The traditional numerical methods to predict the interaction between the wing and propeller are too complex and time-consuming for computation to a certain extent. Therefore, they are not applicable for a real-time integrated turboprop aircraft model. This paper aims to present a simplified model capable of high-precision and real-time computing.

A wing model based on the lifting line theory coupled with a propeller model based on the strip theory is used to predict the propeller-wing interaction. To meet the requirement of real-time computing, a novel decoupling parameter is presented to replace lifting line model (LLM) applied for wings with a simplified fitting model (FM).

The comparison between the LLM and the simplified FM demonstrates that the results of the FM have a good agreement with the results of the LLM, which means that the simplified FM has the advantages of both high-accuracy and real-time computation.

After simplification, the propeller-wing interaction model is suitable for a real-time integrated turboprop aircraft model.

A novel decoupling parameter is presented to replace LLM applied for wings with a simplified FM, which has the advantages of both high-accuracy and real-time computation.

Concerning the radiation effects on the three-dimensional (3D) packaging in space environment, this study aims to investigate the influence of the total dose effect on the transmission characteristics of high-frequency electrical signals using experimental and simulation methods.

This work carries out the irradiation test of the specimens and measures their S21 parameters before and after irradiation. A simulation model describing the total dose effect was built based on the experimental test results. And, the radiation hardening design is evaluated by the simulation method.

The experimental results demonstrate that the S21 curve of the interconnection decreases with the increase of the irradiation dose, indicating that the total dose effect leads to the decline of its signal transmission characteristics. According to the simulation results, decreasing the height of the through silicon via (TSV), increasing the radius of the TSV, reducing the length of Si and increasing the number of grounded through silicon via have positive effects on improving the radiation resistance of the interconnection structure.

This work investigates the effect of radiation on the transmission characteristics of interconnection structures for 3D packaging and proposes the hardening design methods. It is meaningful for improving the reliability of 3D packaging in space applications.

This paper gives a review of the finite element techniques (FE) applied in the area of material processing. The latest trends in metal forming, non‐metal forming and powder metallurgy are briefly discussed. The range of applications of finite elements on the subjects is extremely wide and cannot be presented in a single paper; therefore the aim of the paper is to give FE users only an encyclopaedic view of the different possibilities that exist today in the various fields mentioned above. An appendix included at the end of the paper presents a bibliography on finite element applications in material processing for the last five years, and more than 1100 references are listed.

This study aims to use a thermal elastohydrodynamic lubrication (EHL) algorithm incorporating an Eyring flow model to solve a steady-state contact in simple sliding motion.

A theoretical model was used to investigate the effect of starvation on the surface dimple phenomenon by gradually reducing the thickness of the inlet oil layer.

The increase in the starvation degree reduces the dimple depth, film thickness, the pressure peak and the temperature rise. Under the severe starvation condition, the dimple is eliminated so that the EHL contact becomes partly parched. In elliptical results, for the same starvation parameters, the oil replenishment is stronger than that in circular contact.

This paper fulfils an exploration to study how the oil starvation influences the surface dimple phenomenon.

The purpose of this paper is to identify and examine the major forces that are changing the way biopharmaceutical medications are purchased, distributed, and sold throughout the supply chain. This will become important as healthcare reform moves forward, and logistics will be transformed in this industry.

Multiple interviews with key informants at each level of the value chain were combined with manifest text analysis from practitioner articles to derive key insights into the primary change drivers influencing the future of the biopharmaceutical supply chain.

The research discovered radical shifts in the structure of the biopharmaceutical supply chain. Future research into biopharmaceutical supply chain practices will need to explore three primary issues: How will supply chain member compensation influence the power of parties within the network? How will the role of supply chain intermediaries change the landscape of medication delivery to the end customer? What impact will the role of regulatory constraints on product pedigree and proliferation have on this network? The relationship between these forces is mediated by operations strategy concerning inventory policy, supply chain visibility, and desired service levels.

The research was based on multiple interviews with a convenience sample, as well as text analysis from practitioner articles. These findings are an initial step to guide future more in‐depth research for this dynamic and contextually rich supply chain environment that impacts consumers in every country in the world.

The paper adds insights into the pharmaceutical supply chain, examining this from multiple perspectives.

The purpose of this paper is to develop a methodology for knowledge discovery in emergency response service databases based on police occurrence reports, generating information to help law enforcement agencies plan actions to investigate and combat criminal activities.

The developed model employs a methodology for knowledge discovery involving text mining techniques and uses latent Dirichlet allocation (LDA) with collapsed Gibbs sampling to obtain topics related to crime.

The method used in this study enabled identification of the most common crimes that occurred in the period from 1 January to 31 December of 2016. An analysis of the identified topics reaffirmed that crimes do not occur in a linear manner in a given locality. In this study, 40 per cent of the crimes identified in integrated public safety area 5, or AISP 5 (the historic centre of the city of RJ), had no correlation with AISP 19 (Copacabana – RJ), and 33 per cent of the crimes in AISP 19 were not identified in AISP 5.

The collected data represent the social dynamics of neighbourhoods in the central and southern zones of the city of Rio de Janeiro during the specific period from January 2013 to December 2016. This limitation implies that the results cannot be generalised to areas with different characteristics.

The developed methodology contributes in a complementary manner to the identification of criminal practices and their characteristics based on police occurrence reports stored in emergency response databases. The generated knowledge enables law enforcement experts to assess, reformulate and construct differentiated strategies for combating crimes in a given locality.

The production of knowledge from the emergency service database contributes to the government integrating information with other databases, thus enabling the improvement of strategies to combat local crime. The proposed model contributes to research on big data, on the innovation aspect and on decision support, for it breaks with a paradigm of analysis of criminal information.

The originality of the study lies in the integration of text mining techniques and LDA to detect crimes in a given locality on the basis of the criminal occurrence reports stored in emergency response service databases.

The purpose of this paper is to develop a method for the discovery of knowledge in emergency response databases based on police incident reports, generating information that identifies local criminal demands that allow the selection of the appropriate policing strategies portfolio to solve the problem.

The developed model uses a methodology for the discovery of knowledge involving text mining techniques using Latent Dirichlet Allocation (LDA) integrated with the ELECTRE I multicriteria method.

The developed method allowed the identification of the most common criminal demands that occurred from January 1 to December 31, 2016, in the policing areas studied. One of the crimes does not occur homogeneously in a particular locality. In this study, it was initially observed that 40 per cent of the crimes identified in the Integrated Public Safety Area 5, or AISP-5, (historical city center of RJ) had no correlation with AISP-19 (Copacabana - RJ), and 33 per cent of crimes crimes in AISP-19 were not identified in AISP-5. This finding guided the second part of the method that sought to identify which portfolio of policing strategies would be most appropriate for the identified demands. In this sense, using the ELECTRE I method, eight different scenarios were constructed where it can be identified that for each specific criminal demand set there is a set of policing strategies to be applied.

The collected data represent the social dynamics of neighbourhoods in the central and southern zones of the city of Rio de Janeiro during the specific period from January 2013 to December 2016. This limitation implies that the results cannot be generalised to areas with different characteristics.

The developed methodology contributes in a complementary way to the identification of criminal practices and their characteristics based on reports of police occurrences stored in emergency response databases. The knowledge generated through the identification of criminal demands allows law enforcement decision makers to evaluate and choose among the available policing strategies, which best suit the reality they study, and produce the reduction of criminal indices.

It is possible to infer that by choosing appropriate strategies to combat local crime, the proposed model will increase the population’s sense of safety through an effective reduction in crime.

The originality of the study lies in the integration of text mining techniques, LDA and the ELECTRE I method for detecting crime in a given location based on crime reports stored in emergency response databases, enabling identification and choice, from customized policing strategies to particular criminal demands.

Digital resources such as smart applications reviews and online feedback information are important sources to seek customers’ feedback and input. This paper aims to help government entities gain insights on the needs and expectations of their customers. Towards this end, we propose an aspect-based sentiment analysis hybrid approach that integrates domain lexicons and rules to analyse the entities smart apps reviews. The proposed model aims to extract the important aspects from the reviews and classify the corresponding sentiments. This approach adopts language processing techniques, rules, and lexicons to address several sentiment analysis challenges, and produce summarized results. According to the reported results, the aspect extraction accuracy improves significantly when the implicit aspects are considered. Also, the integrated classification model outperforms the lexicon-based baseline and the other rules combinations by 5% in terms of Accuracy on average. Also, when using the same dataset, the proposed approach outperforms machine learning approaches that uses support vector machine (SVM). However, using these lexicons and rules as input features to the SVM model has achieved higher accuracy than other SVM models.

Sharing and disseminating debunking information are critical to correcting rumours and controlling disease when dealing with public health crises. This study investigates the factors that influence social media users' debunking information sharing behaviour from the perspective of persuasion. The authors examined the effects of argument adequacy, emotional polarity, and debunker's identity on debunking information sharing behaviour and investigated the moderating effects of rumour content and target.

The model was tested using 150 COVID-19-related rumours and 2,349 original debunking posts on Sina Weibo.

First, debunking information that contains adequate arguments is more likely to be reposted only when the uncertainty of the rumour content is high. Second, using neutral sentiment as a reference, debunking information containing negative sentiment is shared more often regardless of whether the government is the rumour target, and information containing positive sentiment is more likely to be shared only when the rumour target is the government. Finally, debunking information published by government-type accounts is reposted more often and is enhanced when the rumour target is the government.

The study provides a systematic framework for analysing the behaviour of sharing debunking information among social media users. Specifically, it expands the understanding of the factors that influence debunking information sharing behaviour by examining the effects of persuasive cues on debunking information sharing behaviour and the heterogeneity of these effects across various rumour contexts.