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This paper aims to present a compact, broadband substrate integrated waveguide (SIW) three-way power divider with improved isolation based on six-port SIW coupler.

The power coupling among the three output ports occurs due to short openings in the narrow walls of the central SIW channel. Performance improvement in the isolation and return loss among ports is achieved using matching posts placed at the input and output ends of the coupling region. This enhances the coupling between TE₁₀ and TE₃₀ modes. The input matching ports enhance the return loss, whereas the isolation is alleviated by both the input and output matching posts. The bandwidth enhancement is achieved by optimizing the outer SIW channel widths.

The measured fractional bandwidth of 27.3% with over 15 dB of isolation and return loss is achieved. The coupling length is 1.55 λ_g at the centre frequency. The power divider achieves better than 15 dB isolation between non-adjacent output ports. The measured reflection and isolation coefficients are in close agreement with simulated results over 8.2 to 10.8 GHz.

Isolation between the adjacent and non-adjacent ports is an important parameter as the reflections from these ports will interfere with signals from other ports reducing the fractional bandwidth of the power divider and affecting the overall performance of the transmitters and receivers.

The authors present the enhancement of isolation between the output non-adjacent ports by optimizing the SIW channel width and matching post in the coupling region to reduce the reflected signals from non-adjacent ports entering into other ports. To the author’s knowledge, this is the only SIW three-way power divider paper showing non-adjacent port isolation among six-port couplers based three-way power dividers.

This paper aims to investigate the combined effect of economic policy uncertainty (EPU) and the firm life cycle on the degree of accrual-based earnings management of publicly traded companies in the USA and Brazilian stock markets.

The EPU index used was the one developed by Baker et al. (2016), the Kothari et al. (2005) model was used in the main analysis to obtain the discretionary accruals and the classification of firms into different life cycles was based on the Dickinson (2011) approach, which relies on the sign of operating, investment and financing cash flows. The methodology includes correlation matrix and panel regression with fixed effects.

The overall results for the USA sample suggest that economic policy uncertainty does have a heterogeneous influence on the firms’ accrual earnings management conditional on their life cycle where firms in the introduction, growth and decline stages decrease this practice when policy uncertainty increases. For the Brazilian case, in general, there is no combined effect between these variables. These contrasting findings can be associated with either the different underlying characteristics of both stock markets or the reduced sample size for the emerging market analyzed.

This research contributes to the earnings management literature examining how policy uncertainty is related to accruals manipulation under different life cycle stages and institutional environments. It is also one of the first studies to explore this conditioning effect.

This study investigated the relationship between generalised trust and psychological well-being in college students, considering the social support obtained from their social networks via Twitter and face-to-face (FTF) interactions. Initially, the authors planned to collect data at the beginning of the first semester in 2019 for fine-tuning the model as a pilot study, and in 2020 for the main study. However, due to the coronavirus disease 2019 (COVID-19) pandemic, the data helped authors to analyse changes in young people's psychological situation before and during the pandemic in Japan.

The study conducted a self-report survey targeting college students in the Kanto region in Japan. Data were collected from mid-May to the end of June 2019, as well as in early to mid-June 2020, with 304 and 584 responses, respectively. The collected data were analysed using structural equation modelling and a multiple regression analysis.

The findings using the 2019 data set indicated that (a) students mostly used Twitter for information gathering and sharing of hobbies, and they received both informatics and emotional support from Twitter, and from FTF interactions; (b) there were direct positive effects of generalised trust and social skills on their psychological well-being; and (c) students with lower levels of generalised trust tended to interact with very intimate individuals using Twitter to obtain social support, which did not have any effects on their improvement of psychological well-being. From the 2020 data set, the authors also found that, like 2019, generalised trust and social skills had direct effects on the improvement of psychological well-being. Additionally, we observed that students spent more time using Twitter and received more emotional support from it, as most people tried not to meet other people in person due to the first State of Emergency in Japan. Similarly, the authors found that in 2019, only social support from very intimate partners via FTF communication had slightly significant effects on improving their psychological well-being, whereas in 2020, their expectation for social networks via FTF had decreased their levels of psychological well-being, but their social support from Twitter had slightly significant effects on their improvement of psychological well-being. One of the main reasons for this might be due to the challenge of meeting with others in person, and therefore, social support from Twitter partially played a role that traditionally was only beneficial through FTF communication.

We understand that this is one of the few social psychological studies on social media that collected data both before and during the COVID-19 pandemic. It provides unique evidence in demonstrating how the COVID-19 pandemic has changed college students communication behaviours.

Purpose – Social relations play an important role in a real community. Interaction patterns reveal relations among actors (such as persons, groups, firms), which can be merged to produce valuable information such as a network structure. This paper aims to present a new approach to extract inter‐firm networks from the web for further analysis. Design/methodology/approach – In this study extraction of relations between a pair of firms is obtained by using a search engine and text processing. Because names of firms co‐appear coincidentally on the web, an advanced algorithm is proposed, which is characterised by the addition of keywords (“relation keywords”) to a query. The relation keywords are obtained from the web using a Jaccard coefficient. Findings – As an application, a network of 60 firms in Japan is extracted including IT, communication, broadcasting, and electronics firms from the web and comprehensive evaluations of this approach are shown. The alliance and lawsuit relations are easily obtainable from the web using the algorithm. By adding relation keywords to named pairs of firms as a query, It is possible to collect target pages from the top of web pages more precisely than by only using the named pairs as a query. Practical implications – This study proposes a new approach for extracting inter‐firm networks from the web. The obtained network is useful in several ways. It is possible to find a cluster of firms and characterise a firm by its cluster. Business experts often make such inferences based on firm relations and firm groups. For that reason the firm network might enhance inferential abilities on the business domain. Also we might use obtained networks to recommend business partners based on structural advantages. The authors' intuition is that extracting a social network might provide information that is only recognisable from the network point of view. For example, the centrality of each firm is identified only after generating a social network. Originality/value – This study is a first attempt to extract inter‐firm networks from the web using a search engine. The approach is also applicable to other actors, such as famous persons, organisations or other multiple relational entities.

Stock market has always been lucrative for various investors. But, because of its speculative nature, it is difficult to predict the price movement. Investors have been using both fundamental and technical analysis to predict the prices. Fundamental analysis helps to study structured data of the company. Technical analysis helps to study price trends, and with the increasing and easy availability of unstructured data have made it important to study the market sentiment. Market sentiment has a major impact on the prices in short run. Hence, the purpose is to understand the market sentiment timely and effectively.

The research includes text mining and then creating various models for classification. The accuracy of these models is checked using confusion matrix.

Out of the six machine learning techniques used to create the classification model, kernel support vector machine gave the highest accuracy of 68%. This model can be now used to analyse the tweets, news and various other unstructured data to predict the price movement.

This study will help investors classify a news or a tweet into “positive”, “negative” or “neutral” quickly and determine the stock price trends.

The purpose of this paper is to focus on perfect trajectory tracking control of 2 DOF non‐holonomic mobile robots in which the guidance and control commands are imposed through independent driver wheels. Model‐based nonlinear controllers for these robots with unknown parameters require estimation of a specified set of the robot parameters. The effects of the proposed model dynamics in both local and global coordinate systems are fully examined on the parameter estimation and tracking performance.

Design of suitable feedback linearization (FL) controllers for trajectory tracking control of wheeled mobile robots (WMRs) is first reviewed. A FL controller whose parameters are tuned using fuzzy computations (fuzzy if‐then rules) is then developed. In the line of the other contributions of the paper, a pure fuzzy controller that is merely based on fuzzy if‐then rules is proposed to trajectory tracking control of the mobile robots.

Use of global dynamics for designing a suitable FL control system leads to a perfect compensation for initial off‐tracks. Furthermore, the estimated parameters are unbiased because the corresponding regressor/signal matrix indicates a high rank of persistent excitation. Fuzzy tuning of the controller instead of keeping the gains fixed makes the overall system more robust against measurement noises while upper bounds and fluctuations of the input torques are both remarkably reduced. The pure fuzzy controller is naturally independent of the robot dynamics and therefore, the necessity of parameter estimation algorithm is removed.

The paper provides some new nonlinear controllers for WMRs, in order to make perfect trajectory tracking and initial off‐tracks compensation.

The purpose of this paper is to advance supply chain network theory by applying theoretical and empirical developments in complex network literature to the context of supply chains as complex adaptive systems. The authors synthesize these advancements to gain an understanding of the network properties underlying efficient supply chains. To develop a suitable theory of supply chain networks, the authors look to mirror the properties of complex network models with real‐world supply chains.

The authors review complex network literature drawn from multiple disciplines in top scientific journals. From this interdisciplinary review a series of propositions are developed around supply chain complexity and adaptive phenomena.

This paper proposes that the structure of efficient supply chains follows a “scale‐free” network. This proposal emerges from arguments that the key properties of efficient supply chains are a short characteristic path length, a high clustering coefficient and a power law connectivity distribution.

The authors' discussion centres on applying advances found in recent complex network literature. Hence, the need is noted to empirically validate the series of propositions developed in this paper in a supply chain context.

If efficient supply chains resemble a scale‐free network, then managers can derive a number of implications. For example, supply chain resilience is derived by the presence of hub firms. To reduce the vulnerability of supply chains to cascading failures, it is recognized that managers could build in redundancy, undertake a multi‐sourcing strategy or intermediation between hub firms.

This paper advances supply chain network theory. It offers a novel understanding of supply chains as complex adaptive systems and, in particular, that efficient and resilient supply chain systems resemble a scale‐free network. In addition, it provides a series of propositions that allow modelling and empirical research to proceed.

Fat‐tailed distributions have been found in many financial and economic variables ranging from forecasting returns on financial assets to modeling recovery distributions in bankruptcies. They have also been found in numerous insurance applications such as catastrophic insurance claims and in value‐at‐risk measures employed by risk managers. Financial applications include:

Sequel movies are very popular; however, there are limited studies on sequel movie revenue prediction. The purpose of this paper is to propose a sentiment analysis based model for sequel movie revenue prediction and to propose a missing value imputation method for the sequel revenue prediction dataset.

A sequel of a successful movie will most likely also be successful. Therefore, we propose a supervised learning approach in which data are created from sequel movies to predict the box-office revenue of an upcoming sequel. The algorithms used in the prediction are multiple linear regression, support vector machine and multilayer perceptron neural network.

The results show that using four sequel movies in a franchise to predict the box-office revenue of a fifth sequel achieved better prediction than using three sequels, which was also better than using two sequel movies.

The model produced will be beneficial to movie producers and other stakeholders in the movie industry in deciding the viability of producing a movie sequel.

Previous studies do not give priority to sequel movies in movie revenue prediction. Additionally, a new missing value imputation method was introduced. Finally, sequel movie revenue prediction dataset was prepared.