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This study aims to drive the induction machine system with a low switching frequency.

An unconventional inverter control strategy – field-oriented predictive control (FOPC) – is presented. The strategy limits current distortion by setting a boundary circle. The voltage vector, which could keep current trajectories in boundary, is selected to obtain a low switching frequency.

A dual simulation step technique is developed to investigate the influence of sampling frequency on current distortion control and switching frequency. Current control distortion can be improved, i.e. reduced, by increasing the sampling frequency; however, the switching frequency will also increase. Such a law is discovered by the dual simulation step technique and finally verified by experiments.

A new predictive control method, FOPC, is derived from the rotor filed coordinate machine model and presented in this paper. FOPC circumvents derivative calculations, and thus avoids high-frequency noise amplification.

As a “unicorn” devoted to the rural market, Huitongda has gone through a major evolution since its establish-ment in 2010 from a rural home appliance distributor, a supply chain platform, an O2O service platform to an industry Internet platform of the rural e-commerce ecosystem, based on its deep understanding of the pain points in the rural market and operational experiences. After 2017, as the platform scaled with more vendors, Huitongda was no longer satisfied with selling a single product from urban to rural areas, but was committed to promoting the two-way flow of diverse commodities between urban and rural areas. It also set out to promote employment by entering the rural human resource market, expanding the single-industry O2O service platform to a complete multi-industry ecosystem. In 2018, with a service network covering over 17,000 townships across 20 Chinese provinces, Huitongda's sales reached RMB 35 billion yuan, enabling over 500,000 rural dwellers to start their own businesses or to find employment.

However, the depth, breadth and complexity of the rural industry Internet gradually multiplied, as more member stores joined the business ecosystem with more valuable commodities and services. As a rural industry Internet network owner, how could Huitongda better tap into digitalization in order to support its industry Internet business model and the huge network? How can it further widen the network boundaries to drive more business innovations and maximize network value?

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.

The purpose of this paper is to investigate the effects of Sn-Ag-x leveling layers on the mechanical properties of SnBi solder joints. Four Sn-Ag-x (Sn-3.0Ag-0.5Cu, Sn-0.3Ag-0.7Cu, Sn-0.3Ag-0.7Cu-0.5 Bi-0.05Ni and Sn-3.0Ag-3.0 Bi-3.0In) leveling layers were coated on Cu pads to prepare SnBi/Sn-Ag-x/Cu solder joints. The microstructure, hardness, shear strength and fracture morphology of solder joints before and after aging were studied.

The interfacial brittleness of the SnBi low-temperature solder joint is a key problem affecting its reliability. The purpose of this study is to improve the mechanical properties of the SnBi solder joint.

Owing to the addition of the leveling layers, the grain size of the ß-Sn phase in the SnBi/Sn-Ag-x/Cu solder joint is significantly larger than that in the SnBi/Cu eutectic solder joint. Meanwhile, the hardness of the solder bulk in the SnBi/Cu solder joint shows a decrease trend because of the addition of the leveling layers. The SnBi/Cu solder joint shows obvious strength drop and interfacial brittle fracture after aging. Through the addition of the Sn-Ag-x layers, the brittle failure caused by aging is effectively suppressed. In addition, the Sn-Ag-x leveling layers improve the shear strength of the SnBi/Cu solder joint after aging. Among them, the SnBi/SACBN/Cu solder joint shows the highest shear strength.

This work suppresses the interfacial brittleness of the SnBi/Cu solder joint after isothermal aging by adding Sn-Ag-x leveling layers on the Cu pads. It provides a way to improve the mechanical performances of the SnBi solder joint.

The purpose of this research note is to investigate the implications of supply chain management of e‐business for the macroeconomic phenomenon of business cycles.

The paper provides a list of propositions, which form the broad basis of an empirical research agenda, to explore and investigate the mechanisms through which supply chain innovations can influence business cycle.

Economic research literature has pointed out that there are linkages between inventory investment and business cycle fluctuation. Given that the e‐business supply chain management drastically alters inventory investment across a range of industries, it is likely to affect the behaviour of economic fluctuation.

This research has the potential to contribute to a better‐informed formulation of economic policies at national and global level.

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.

The purpose of this research study is to improve sentiment analysis (SA) at the aspect level, which is accomplished through two independent goals of aspect term and opinion extraction and subsequent sentiment classification.

The proposed architecture uses neighborhood and dependency tree-based relations for target opinion extraction, a domain–ontology-based knowledge management system for aspect term extraction, and deep learning techniques for classification.

The authors use different deep learning architectures to test the proposed approach of both review and aspect levels. It is reported that Vanilla recurrent neural network has an accuracy of 83.22%, long short-term memory (LSTM) is 89.87% accurate, Bi-LSTM is 91.57% accurate, gated recurrent unit is 65.57% accurate and convolutional neural network is 82.33% accurate. For the aspect level analysis, ρ_aspect comes out to be 0.712 and Δ²_aspect is 0.384, indicating a marked improvement over previously reported results.

This study suggests a novel method for aspect-based SA that makes use of deep learning and domain ontologies. The use of domain ontologies allows for enhanced aspect identification, and the use of deep learning algorithms enhances the accuracy of the SA task.

This study aims to establish a model for rapid and accurate emotion recognition in restaurant online reviews, thus advancing the literature and providing practical insights into electronic word-of-mouth management for the industry.

This study elaborates a hybrid model that integrates deep learning (DL) and a sentiment lexicon (SL) and compares it to five other models, including SL, random forest (RF), naïve Bayes, support vector machine (SVM) and a DL model, for the task of emotion recognition in restaurant online reviews. These models are trained and tested using 652,348 online reviews from 548 restaurants.

The hybrid approach performs well for valence-based emotion and discrete emotion recognition and is highly applicable for mining online reviews in a restaurant setting. The performances of SL and RF are inferior when it comes to recognizing discrete emotions. The DL method and SVM can perform satisfactorily in the valence-based emotion recognition.

These findings provide methodological and theoretical implications; thus, they advance the current state of knowledge on emotion recognition in restaurant online reviews. The results also provide practical insights into intelligent service quality monitoring and electronic word-of-mouth management for the industry.

This study proposes a superior model for emotion recognition in restaurant online reviews. The methodological framework and steps are elucidated in detail for future research and practical application. This study also details the performances of other commonly used models to support the selection of methods in research and practical applications.