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A methodology of integrating the functions of engineering design and manufacturing in a production environment is described. A hybrid approach using quality function deployment (QFD) and multi‐objective optimisation techniques is developed. The purpose of this methodology is to alleviate the problem of uni‐directional flow of information and to eliminate the subjective decision making of the QFD approach.

This chapter elaborates on the usefulness of embracing complexity theory, modeling outcomes rather than directionality, and modeling complex rather than simple outcomes in strategic management. Complexity theory includes the tenet that most antecedent conditions are neither sufficient nor necessary for the occurrence of a specific outcome. Identifying a firm by individual antecedents (i.e., noninnovative vs. highly innovative, small vs. large size in sales or number of employees, or serving local vs. international markets) provides shallow information in modeling specific outcomes (e.g., high sales growth or high profitability) – even if directional analyses (e.g., regression analysis, including structural equation modeling) indicate that the independent (main) effects of the individual antecedents relate to outcomes directionally – because firm (case) anomalies almost always occur to main effects. Examples: a number of highly innovative firms have low sales while others have high sales and a number of noninnovative firms have low sales while others have high sales. Breaking-away from the current dominant logic of directionality testing – null hypothesis significance testing (NHST) – to embrace somewhat precise outcome testing (SPOT) is necessary for extracting highly useful information about the causes of anomalies – associations opposite to expected and “statistically significant” main effects. The study of anomalies extends to identifying the occurrences of four-corner strategy outcomes: firms doing well in favorable circumstances, firms doing badly in favorable circumstances, firms doing well in unfavorable circumstances, and firms doing badly in unfavorable circumstances. Models of four-corner strategy outcomes advance strategic management beyond the current dominant logic of directional modeling of single outcomes.

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.

The purpose of this paper is to explore four women principals’ experiences with power in the course of their daily leadership. The data used in this exploration was collected through in‐depth interviews, conducted from a phenomenological perspective, during the second and third years of a three‐year study on the leadership experiences of the four principals. The thematic findings which emerged from this data included empowerment, positive power, traditional power and negative power, and are discussed in relation to three lenses of power: dominance or “power over”, facilitation or “power through”, and as energy and competence or “power with”. The four principals’ experiences were remarkable in that they were extensively engaged in interpreting, experiencing and using power as “power through” and “power with” rather than as “power over”. The findings from this research serve as examples of ways in which power is enacted by women leaders within traditional organizational settings, and the potential of their actions to positively transform school organizations and the experiences of those who work within them.

Purpose – The purpose of this chapter is to bring data to suggest that group processes have a biological base, lodged in human neurology as it evolved over the last 7 million years.

Design/methodology/approach – The method for discovering the neurological basis of group processes is labelled evolutionary sociology, and this method revolves around: (1) cladistic analysis of traits of distant ancestors to humans and the great apes, with whom humans share a very high proportion of genes, (2) comparative neurology between the great apes and humans that can inform us about how the brains of humans were rewired from the structures shared by the last common ancestor to humans and apes, and (3) ecological analysis of the habitats and niches that generated selection pressures on the neurology of apes and hominins.

Findings – A key finding is that most of the interpersonal processes that drive group processes are neurologically based and evolved before the brain among hominins was sufficiently large to generate systems of symbols organized in cultural texts remotely near the human measure. There is, then, good reason to study the neurological basis of behavior because neurology explains more about the dynamics of interpersonal behavior than does culture, which was a very late arrival to the hominin line.

Research implications – One implication of these findings is that social scientific analysis of interpersonal processes and group dynamics can no longer assume that groups are solely a constructed process, mediated by culture and social structure. There were powerful selection pressures during the course of hominin evolution to increase hominin sociality and especially group formation, which required considerable rewiring of the basic ape brain. Since groups are not “natural” to apes in general and even to an evolved ape-like humans, it is important to discover how humans ever became group-organizing animals. The answer resides in the dramatic enhancing of emotions in hominins and humans, which shifts attention away from the neocortex to the older subcortical areas of the brain. Once this shift is made, theorizing and research, as well as public views on human sociality, need to be recast as, first, an evolved biological trait and, only second, as a most tenuous and fragile of a big-brained animal using language and culture to construct its social world.

Originality/value – The value of this kind of analysis is to liberate sociology and the social sciences in general from simplistic views that, because humans have language and can use language to construct culture and social structures, the underlying biology and neurology of human action is not relevant to understanding the social world. Indeed, just the opposite is the case: to the extent that social scientists insist upon a social constructionists research agenda, they will fail to conceptualize and perform research on more fundamental forces in the social world, including group dynamics.

In this work, minimum energy based interpolation has been done for an optimal well path and effects of energy and fitting coefficients have been studied. An optimal well path also maintains the given radius of curvature and satisfies drilling requirements. Interactive incremental design concept has been used in this work which, controls length and other geometric properties of well path. In the first part of the research, whole well segment has been taken for optimization. Geometric constraints are put to satisfy the drilling requirements. In the second part of the research only the curved portion of well segment has been taken for optimization. Geometric perspective of the well has been considered in this formulation.

Urban typogenetics investigates the use of machine intelligence for the evaluation of performance measures as a decision support system (DSS) with a focus on urban aesthetics evaluation. This framework allows designers to address performance measures, urban measures and aesthetic criteria in an adaptive, interactive generative design approach. The purpose of this paper is to provide an understanding of the structure and the nature of the framework and the application of human-in-the-loop design systems to urban design.

Significant literature reviewed lead to the identification of an application potential in the decision-making process. This potential is situated around the use of AI for the evaluation of subjective performance criteria in a DSS. Recognising that the key decisions about urban aesthetics are based on the individual evaluation of the designer, an HITL approach for computational design software to support creative decisions is presented in this paper.

Urban typogenetics for interactive generative urban design allows the exploration of complex design spaces by using a human-in-the-loop design system in the context of urban aesthetics. Hybrid aesthetic evaluation allows the designer to analyse morphological features and urban aesthetics during exploratory search and reveal hidden aspects of the urban context by visualisation of the results of the aesthetic evaluation. Integrating performance measures and urban aesthetics in urban typogenetics addresses major criteria of urban design at the beginning of the creative process.

The use of a broad interactive approach to typogenetic design in an application to urban scenarios is a novel conceptual approach to the design of urban configurations. The suggested adaptive mechanism would allow the user of a typogenetic tool to subjectively evaluate solutions by sight and reason about aesthetic, social and cultural implication of the reviewed design solutions.

This paper aims to study the interlinkages between cryptocurrency and the stock market by characterizing their connectedness and the effects of the COVID-19 crisis on their relations.

The author employs a quantile vector autoregression (QVAR) to identify the connectedness of nine indicators from January 1, 2018, to December 31, 2021, in an effort to examine the relationships between cryptocurrency and stock markets.

The results demonstrate that the pandemic shocks appear to have influences on the system-wide dynamic connectedness. Dynamic net total directional connectedness implies that Bitcoin (BTC) is a net short-duration shock transmitter during the sample. BTC is a long-duration net receiver of shocks during the 2018–2020 period and turns into a long-duration net transmitter of shocks in late 2021. Ethereum is a net shock transmitter in both durations. Binance turns into a net short-duration shock transmitter during the COVID-19 outbreak before receiving net shocks in 2021. The stock market in different areas plays various roles in the short run and long run. During the COVID-19 pandemic shock, pairwise connectedness reveals that cryptocurrencies can explain the volatility of the stock markets with the most severe impact at the beginning of 2020.

Insightful knowledge about key antecedents of contagion among these markets also help policymakers design adequate policies to reduce these markets' vulnerabilities and minimize the spread of risk or uncertainty across these markets.

The author is the first to investigate the interlinkages between the cryptocurrency and the stock market and assess the influences of uncertain events like the COVID-19 health crisis on the dynamic interlinkages between these two markets.

本學術論文擬透過找出加密貨幣與股票市場兩者相互關聯之特徵,來探討這個聯繫;文章亦擬探究2019冠狀病毒病全球大流行對這相互關聯的影響。

作者以分量向量自我迴歸法、來找出2018年1月1日至2021年12月31日期間九個指標的關聯,藉此探討加密貨幣與股票市場之間的關係。

研究結果顯示,全球大流行的驚愕,似對全系統動態關聯產生了影響。動態總淨值定向關聯暗示了就我們的樣本而言,比特幣是一個純短期衝擊發送器。比特幣在2018年至 2020年期間是一個衝擊的長期純接收器,並進而於2021年年底成為一個衝擊的長期純發送器。以太坊則為短期以及長期之純衝擊發送器。幣安在2019冠狀病毒病爆發期間,在2021年接收純衝擊前、成為一個純短期衝擊發送器。位於不同地區的股票市場,無論在短期抑或長期而言均扮演各種不同的角色。在2019冠狀病毒病全球大流行的驚愕期間,成對的關聯顯示了加密貨幣可以以2020年年初最嚴重的影響去解釋和說明股票市場的波動。

研究結果使我們能深入認識有關的市場之間不同情緒和看法的蔓延所帶來的影響的主要先例,這些知識、亦能幫助決策者制定適當的政策,以減少有關的市場的弱點,並把這些市場間的風險和不確定性的散播減到最低。

作者是首位研究加密貨幣與股票市場之間的相互關聯的學者,亦是首位學者、去評估像2019冠狀病毒病健康危機的不確定事件,會如何影響有關的兩個市場之間的動態相互關聯。

Machine learning (ML), and deep learning in particular, is gaining traction across a myriad of real-life applications. Portfolio management is no exception. This paper provides a systematic literature review of deep learning applications for portfolio management. The findings are likely to be valuable for industry practitioners and researchers alike, experimenting with novel portfolio management approaches and furthering investment management practice.

This review follows the guidance and methodology of Linnenluecke et al. (2020), Massaro et al. (2016) and Fisch and Block (2018) to first identify relevant literature based on an appropriately developed search phrase, filter the resultant set of publications and present descriptive and analytical findings of the research itself and its metadata.

The authors find a strong dominance of reinforcement learning algorithms applied to the field, given their through-time portfolio management capabilities. Other well-known deep learning models, such as convolutional neural network (CNN) and recurrent neural network (RNN) and its derivatives, have shown to be well-suited for time-series forecasting. Most recently, the number of papers published in the field has been increasing, potentially driven by computational advances, hardware accessibility and data availability. The review shows several promising applications and identifies future research opportunities, including better balance on the risk-reward spectrum, novel ways to reduce data dimensionality and pre-process the inputs, stronger focus on direct weights generation, novel deep learning architectures and consistent data choices.

Several systematic reviews have been conducted with a broader focus of ML applications in finance. However, to the best of the authors’ knowledge, this is the first review to focus on deep learning architectures and their applications in the investment portfolio management problem. The review also presents a novel universal taxonomy of models used.

The purpose of this paper is to describe how and why to shift away from bad science practices now dominant in research in marketing to good science practices.

The essay includes details in theory construction and the use of symmetric tests to illustrate bad science practices. In contrast, the essay includes asymmetric case-based asymmetric theory construction and testing to illustrate good science practices.

Researchers in marketing science should not report null hypothesis significance tests. They should report somewhat precise outcome tests, avoid using multiple regression analysis (MRA) and do use Boolean-algebra-based algorithms to predict cases of interest.

Given the widespread dominance of bad science practices (e.g. MRA and structural equation modeling), the inclusion of both bad and good science practices may be necessary during the transition years of 2015–2025 (e.g. Ordanini et al., 2014).

Good science practices fit reality much closer than bad science practices. Asymmetric modeling includes recognizing the separate models are necessary for positive vs negative outcomes because the antecedents of each often differ.

This essay presents details of why and how researchers need to embrace a new research paradigm that is helpful for ending bad science practices that are now dominant in research in marketing.