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The wind turbine (WT) is a complex system subjected to wind disturbances. Because the aerodynamics is nonlinear, the control is thus challenging. For the variation of wind speed when rated power is delivered at rated wind speed, the power is limited to the rate by the pitching of the blades of the turbine. This paper aims to address pitch control with the WT benchmark model. The possible use of appropriate adaptive controller design that modifies the control action automatically identifying any change in system parameters is explored.

To deal with pitch control problem when wind speed exceeds the rated wind speed of the WT, six digital self-tuning controller (STC) with different structures such as proportional integral (PI), proportional derivative (PD), Dahlin’s, pole placement, deadbeat and Takahashi has been taken herein. The system model is identified as a second-order autoregressive exogenous (ARX) model by three techniques for comparison: recursive least square method (RLS), RLS with exponential forgetting and RLS with adaptive directional forgetting identification methods. A comparative study of three identification methods, six adaptive controllers with the conventional PI controller and sliding mode controller (SMC), are shown.

As per the results, the best improvement in control of the output power by pitching in full load region of benchmark model is achieved by self-tuning PD controller based on RLS with adaptive directional forgetting method. The adaptive control design has a future in WT control applications.

A comparative study of identification methods, six adaptive controllers with the conventional PI controller and SMC, are shown here. As per the results, the best improvement in control of the output power by pitching in the full load region of the benchmark model has been achieved by self-tuning PD controller. The best identification method or the system is RLS with an adaptive directional forgetting method. Instead of a step input response design for the controllers, the controller design has been carried out for the stochastic wind and the performance is adjudged by the normalized sum of square tracking error (NSSE) index. The validation of the proposed self-tuning PD controller has been shown in comparison to the conventional controller with Monte-Carlo analysis to handle model parameter alteration and erroneous measurement issues.

During everyday work, individuals often engage in unplanned conversations that help them develop relationships, share information and coordinate tasks. Unfortunately, the work-from-home mandates issued due to the pandemic have reduced the frequency of unplanned conversations among employees. So, as businesses reopen, organizations are considering post-pandemic workplace solutions that can facilitate unplanned conversations. To aid these efforts and move research and theory on unplanned conversations forward, this study proposes and tests a multi-factor model of the antecedents of unplanned conversations.

This study adopted a multi-methodological approach and collected data using automated sensing technology, structured observations and cross-sectional survey methods. A total of 5,297 unplanned conversations among 61 employees were recorded using the custom mobile application and structured observations. Cross-sectional survey data about these employees' work contexts were also collected.

The study results showed that the model significantly predicted the frequency of unplanned conversations. Notably, technical expertise, perceived time pressure and team psychological safety significantly predicted the frequency of unplanned conversations. The study findings have both theoretical and practical significance.

Previous research studies have primarily focused on the influence of workplace designs on unplanned conversations. However, this study demonstrates that several other factors facilitate unplanned conversations. This research theorizes and empirically tests the relationship between unplanned conversations and several individual, team and organizational factors.

This study seeks to build a financial uncertainty index for Saudi Arabia. This index serves as a leading indicator of Saudi economic activity and helps to describe economic fluctuations and forecast economic trends.

This study adopts an extension of the Jurado et al. (2015) procedure by combining financial uncertainty factors with their net spillover effects on GDP and inflation to construct an aggregate financial uncertainty index. The authors consider 13 monthly financial variables for Saudi Arabia from January 2010 to June 2021.

The empirical results show that the constructed financial uncertainty estimates are good leading indicators of economic activity. The robustness analysis suggests that the authors’ proposed financial uncertainty estimators outperform the alternative estimates used by other existing approaches to estimate the financial conditions index.

To the best of the authors’ knowledge, this is the first attempt at constructing a financial uncertainty index for Saudi Arabia. This study extends the empirical literature, from which the authors propose a novel conceptual framework for building a financial uncertainty index by combining the approach of Jurado et al. (2015) and the time-varying connectedness network approach proposed by Antonakakis et al. (2020)

The purpose of this study is to examine the propagation of consumer and business confidence in the euro area with a particular focus on the global financial crisis (GFC), the European sovereign debt crisis (ESDC) and the COVID-19-induced Great Lockdown.

The authors apply Diebold and Yilmaz’s connectedness framework and the improved method based on the time-varying parameter vector autoregressive model.

The authors find that although the evolution of business confidence marked the GFC and the ESDC the role of consumer confidence (mainly in those countries with stricter containment and closure measures) increased in the COVID-19-induced crisis.

The findings are related to the different origins of the examined crisis periods, and the analysis of their interrelationship is a very relevant topic for future research.

This study aims at exploring the nexus between herding behavior and the spillover effect in G7 and BRICS stock markets.

The authors used the dynamic connectedness approach TVP-VAR model of Antonakakis et al. (2019) to capture the spillovers across different markets. Moreover, to explore herding behavior, the authors used a modified version of the CSAD measure of Chang et al. (2000) including extreme market movements. Finally, to study the link between these two phenomena, the authors estimated a DCC-GARCH model.

The results show that herding behavior exists in the American market and some BRICS markets. Furthermore, spillover between G7 and BRICS increases in times of crisis. Moreover, the authors find a dynamic conditional correlation between herding behavior and spillovers both in the short and long run. The authors conclude that in times of crisis, the transmission of shocks between markets is more frequent, fuelling uncertainty and pushing investors to suppress their own beliefs and follow the general market trends.

This paper uses the TVP-VAR model to explore the spillover effect and the DCC-GARCH model to explore the connectedness between herding behavior and the spillover effect in G7 and BRICS countries in both the short and long run.

This paper aims to propose a learning exponential jerk trajectory planning to suppress the residual vibrations of industrial robots.

Based on finite impulse response filter technology, a step signal with a proper amplitude first passes through two linear filters and then performs exponential filter shaping to obtain an exponential jerk trajectory and cancel oscillation modal. An iterative learning strategy designed by gradient descent principle is used to adjust the parameters of exponential filter online and achieve the maximum vibration suppression effect.

By building a SCARA robot experiment platform, a series of contrast experiments are conducted. The results show that the proposed method can effectively suppress residual vibration compared to zero vibration shaper and zero vibration and derivative shaper.

The idea of the adopted iterative leaning strategy is simple and reduces the computing power of the controller. A cheap acceleration sensor is available because it just needs to measure vibration energy to feedback. Therefore, the proposed method can be applied to production practice.

This paper investigates the static and dynamic directional return spillovers and dependence among green investments, carbon markets, financial markets and commodity markets from January 2013 to September 2020.

This study employed both the quantile vector autoregression (QVAR) and time-varying parameter VAR (TVP-VAR) technique to examine the magnitude of static and dynamic directional spillovers and dependence of markets.

Results show that the magnitude of connectedness is extremely higher at quantile levels (q = 0.05 and q = 0.95) compared to those in the mean of the conditional distribution. This connotes that connectedness between green bonds and other assets increases with shock size for both negative and positive shocks. This further indicates that return shocks spread at a higher magnitude during extreme market conditions relative to normal periods. Additional analyses show the behavior of return transmission between green bond and other assets is asymmetric.

The findings of this study offer significant implications for portfolio investors, policymakers, regulatory authorities and investment community in terms of carefully assessing the unique characteristics offered by each markets in terms of return spillovers and dependence and diversifying the portfolios.

The study, first, uses a relatively new statistical technique, the QVAR advanced by Ando et al. (2018), to capture upper and lower tails’ quantile price connectedness and directional spillover. Therefore, the results possess adequate power against departure from mean-based conditional connectedness. Second, using a portfolio of green investments, carbon markets, financial markets and commodity markets, the uniqueness of this study lies in the examination of the static and dynamic dependence of the markets examined.

This study aims to conduct a systematic literature review to investigate the mental health problems during COVID-19 and the role of nutrition in minimizing mental and health-related issues during COVID-19.

A literature search was done electronically on April–May 2022 in the databases Google Scholar, PubMed and Cochrane Library, reviewing all the articles published in English. There were no limitations for the study (such as study design, region or any time frame). The quality assessment was done. The beginning database search picked out a total of 654 articles, 47 in PubMed, 575 in Google Scholar, 22 in Cochrane Library and 10 records from other sources. A total of 565 (duplicates found 89) were found after removing the duplicated articles, after reading the title and abstracts were further decreased to 88 full-text articles. These 88 studies went for full-text analysis, which excluded 56 studies and generated a final 32 articles for systemic analysis. The quality of the included study for the systematic review was assessed in two ways: one is evidence-based and another one on the JBI checklist.

People in social isolation and home quarantine suffer from severe anxiety, stress, depression, loneliness, anger and panic attack. During COVID-19, the vital role of diet and nutrients in mental health has been acknowledged and helps mitigate COVID-19 infection. Many studies showed stress and anxiety due to increased unhealthy eating and lifestyle practices.

This review will explain the interlink between diet and mental health because what we eat and think is interconnected with the gut–brain axis. The dietary elements and psychobiotic help in improving the immune system and psychological distress during the pandemic. This paper describes the role of different nutrients, psychobiotics and phytochemicals, to minimize mental and health issues during the COVID-19 pandemic. This paper also contains a balanced diet plan to withstand COVID-19.

This paper aims to investigate how the Japanese media conveyed the country’s foreign aid policy and analyse how framing biases in the news differ depending on which language (either Japanese or English) was used in the broadcasts.

This study uses a qualitative single case-study design and conducts a content analysis. The study uses news videos about the fifth Tokyo International Conference on African Development aired on YouTube by the Japanese media using Japanese and English.

The findings reveal subtle but notable differences in the patterns of the framing biases in the Japanese media’s news aired in Japanese intended for the domestic audience, and in the news on the same topic broadcast in English to the international audience.

The limitation of the study is the rather small data set used for the single case study of one event.

Framing biases could lead the general public in a monolingual society to a more skewed view of their government’s policy and its activities abroad. This could be an obstacle to developing a common ground for global issues and cross-border policy agendas.

The study explores an under-researched function of language in international affairs. It highlights how the mass media in a non-English-speaking country uses a dual approach to framing news while addressing different audiences. To the best of the author’s knowledge, the context that this paper deals with is novel because there are limited studies on the nexus between the influence of language choices and media logic in the field of international business.

The goal of this study is to explore the consequences of flexibility i-deals in work and non-work domains of employees.

A matched supervisor–subordinate sample context drawn from a security organization in Chile (N = 3,624 subordinates matched with 107 supervisors) was used which is an unrepresented context in research on i-deals.

Results from multi-level analyses reveal that the interaction of performance motivation of subordinates and flexibility i-deals matters to performance motivation of subordinates. In turn, performance motivation reduces turnover intentions and work–family conflict of subordinates.

The findings from multi-level structural equation modelling supported our hypotheses and offered interesting implications for the i-deals literature and practitioners. Our findings highlight (1) the importance of being performance driven to obtain flexibility i-deals and (2) the enabling role of performance-oriented supervisors.

Flexibility i-deals act as mechanism that translate the impact of performance motivation on key work and non-work outcomes and can be considered as important HR tools for employees and managers.

This research highlights the importance of performance motivation to obtain i-deals and emphasises that the motivation of supervisors is key to enable these deals. Furthermore, the context of this research, which is a security organisation, is important as research to date has been conducted in Western and corporate settings.