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1 – 10 of over 18000Ark Rukhaiyar, Bhagya Jayant, Kunal Dahiya, Rahul Kumar Meena and Ritu Raj
In this study the comparison is presented for the variation in cross-sectional shape along the height of the building model. For this purpose Model B and Model C are having the…
Abstract
Purpose
In this study the comparison is presented for the variation in cross-sectional shape along the height of the building model. For this purpose Model B and Model C are having the considerable variation and Model A result can be easily predicted on the basis of the result of Model B and C while Model X is considered for the validation purposes only and it is well established that the results are within the allowable limit. This paper aims to discuss these wind generated effects in the tall building model.
Design/methodology/approach
Computational Fluid Dynamics (CFD) in ANSYS: CFX is used to investigate the wind effects on varying cross-sectional shape along the height of the building model.
Findings
From pressure contours, it was observed that shape and size of the face is independent of the pressure distribution. It is also observed that pressure distribution for the windward face (A) was less than the magnitude of the leeward face for both models. The leeward face and lateral faces had similar pressure distribution. Also slight changes in pressure distribution were observed at the periphery of the models.
Originality/value
This study has been performed to analyse and compare the wind effect on tall buildings having varying cross sections with variation of different cross sections along the height. Most of the studies done in the field of tall buildings are concentrated to one particular cross-sectional shape while the present study investigates wind effects for combination of two types of cross sections along the height. This analysis is performed for wind incidence angles ranging from 0° to 90° at an interval of 30°. Analysis of wind flow characteristics of two models, Models B and C will be computed using CFD. These two models are the variation of Model A which is a combination of two types of cross section that is square and plus. Square and plus cross-sectional heights for Model B are 48 m and 144 m, respectively. Similarly, square and plus cross-sectional heights for Model C are 144 m and 48 m, respectively. The results are interpreted using pressure contours and streamlines, and comparative graphs of drag and lift forces are presented.
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Gyeongcheol Cho, Sunmee Kim, Jonathan Lee, Heungsun Hwang, Marko Sarstedt and Christian M. Ringle
Generalized structured component analysis (GSCA) and partial least squares path modeling (PLSPM) are two key component-based approaches to structural equation modeling that…
Abstract
Purpose
Generalized structured component analysis (GSCA) and partial least squares path modeling (PLSPM) are two key component-based approaches to structural equation modeling that facilitate the analysis of theoretically established models in terms of both explanation and prediction. This study aims to offer a comparative evaluation of GSCA and PLSPM in a predictive modeling framework.
Design/methodology/approach
A simulation study compares the predictive performance of GSCA and PLSPM under various simulation conditions and different prediction types of correctly specified and misspecified models.
Findings
The results suggest that GSCA with reflective composite indicators (GSCAR) is the most versatile approach. For observed prediction, which uses the component scores to generate prediction for the indicators, GSCAR performs slightly better than PLSPM with mode A. For operative prediction, which considers all parameter estimates to generate predictions, both methods perform equally well. GSCA with formative composite indicators and PLSPM with mode B generally lag behind the other methods.
Research limitations/implications
Future research may further assess the methods’ prediction precision, considering more experimental factors with a wider range of levels, including more extreme ones.
Practical implications
When prediction is the primary study aim, researchers should generally revert to GSCAR, considering its performance for observed and operative prediction together.
Originality/value
This research is the first to compare the relative efficacy of GSCA and PLSPM in terms of predictive power.
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Tandralee Chetia, Dhayalan Rajaram and Kumaran G. Sreejalekshmi
Flapping-wing vehicles show various advantages as compared to fixed wing vehicles, making flapping-wing vehicles' study necessary in the current scenario. The present study aims…
Abstract
Purpose
Flapping-wing vehicles show various advantages as compared to fixed wing vehicles, making flapping-wing vehicles' study necessary in the current scenario. The present study aims to provide guidelines for fixing geometric parameters for an initial engineering design by a simple aerodynamic and flight dynamic parametric study.
Design/methodology/approach
A mathematical analysis was performed to understand the aerodynamics and flight dynamics of the micro-air vehicle (MAV). Only the forces due to the flapping wing were considered. The flapping motion was considered to be a combination of the pitching and plunging motion. The geometric parameters of the flapping wing were varied and the aerodynamic forces and power were observed. Attempts were then made to understand the flight stability envelope of the MAV in a forward horizontal motion in the vertical plane with similar parametric studies as those conducted in the case of aerodynamics.
Findings
From the aerodynamic study, insights were obtained regarding the interaction of design parameters with the aerodynamics and feasible ranges of values for the parameters were identified. The flapping wing was found to have neutral static stability. The flight dynamic analysis revealed the presence of an unstable oscillatory mode, a stable fast subsidence mode and a neutral mode, in the forward flight of the MAV. The presence of unstable modes highlighted the need for active control to restore the MAV to equilibrium from its unstable state.
Research limitations/implications
The study does not take into account the effects of control surfaces and tail on the aerodynamics and flight dynamics of the MAV. There is also a need to validate the results obtained in the study through experimental means which shall be taken up in the future.
Practical implications
The parametric study helps us to understand the extent of the impact of the design parameters on the aerodynamics and stability of the MAV. The analysis of both aerodynamics and dynamic stability provides a holistic picture for the initial design. The study incorporates complex mathematical equations and simplifies such to understand the aerodynamics and flight stability of the MAV from an engineering perspective.
Originality/value
The study adds to already existing knowledge on the design procedures of a flapping wing.
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Mohammadreza Tavakoli Baghdadabad
We propose a risk factor for idiosyncratic entropy and explore the relationship between this factor and expected stock returns.
Abstract
Purpose
We propose a risk factor for idiosyncratic entropy and explore the relationship between this factor and expected stock returns.
Design/methodology/approach
We estimate a cross-sectional model of expected entropy that uses several common risk factors to predict idiosyncratic entropy.
Findings
We find a negative relationship between expected idiosyncratic entropy and returns. Specifically, the Carhart alpha of a low expected entropy portfolio exceeds the alpha of a high expected entropy portfolio by −2.37% per month. We also find a negative and significant price of expected idiosyncratic entropy risk using the Fama-MacBeth cross-sectional regressions. Interestingly, expected entropy helps us explain the idiosyncratic volatility puzzle that stocks with high idiosyncratic volatility earn low expected returns.
Originality/value
We propose a risk factor of idiosyncratic entropy and explore the relationship between this factor and expected stock returns. Interestingly, expected entropy helps us explain the idiosyncratic volatility puzzle that stocks with high idiosyncratic volatility earn low expected returns.
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This paper aims to consider ways to visually model data generated by qualitative case studies, pointing out a need for visualizations that depict both synchronic relations across…
Abstract
Purpose
This paper aims to consider ways to visually model data generated by qualitative case studies, pointing out a need for visualizations that depict both synchronic relations across representations and how those relations change diachronically. To develop an appropriate modeling approach, the paper critically examines Max Boisot’s I-Space model, a conceptual model for understanding the interplay among knowledge assets used by a population. I-Space maps information in three dimensions (abstraction, codification and diffusion). It is not directly adoptable for case study methodology due to three fundamental disjunctures: in theory, methodology and unit of analysis. However, it can be adapted for qualitative research by substituting analogues for abstraction, codification and diffusion.
Design/methodology/approach
Using an example from early-stage technology entrepreneurship, this paper first reviews network, flow and matrix models used to systematically visualize case study data. It then presents Boisot’s I-Space model and critiques it from the perspective of qualitative workplace studies. Finally, it adapts the model using measures that have been used in qualitative case studies.
Findings
This paper notes three limitations of the I-Space model when applied to empirical cases of workplace learning. Its theory of information does not account well for how people use representations synchronically for learning. It is a conceptual framework, and the tentative attempts to use it for mapping representations have been used in workshops, not for systematically collected data. It does not adequately bound a case for analysis. Thus, it can be applied analogically but not directly for mapping representations in qualitative case studies.
Practical implications
This paper identifies a possible way to develop I-Space for strategically mapping representations in qualitative case studies, using measures analogous to the I-Space axes to reflect observable behavior.
Originality/value
In providing a methodological critique for one model of knowledge management, this paper also develops criteria for appropriate modeling of meaningful artifacts in the context of qualitative studies of workplaces.
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Marija Vuković and Snježana Pivac
Investors' behavior in financial markets is often under the influence of various psychological and cognitive factors, as well as personality characteristics. This research…
Abstract
Purpose
Investors' behavior in financial markets is often under the influence of various psychological and cognitive factors, as well as personality characteristics. This research explores which behavioral factors and personality traits affect investment decisions and, consequently, investment performance.
Design/methodology/approach
A survey analysis was conducted on a sample of 310 investors in Croatia. Partial least squares structural equation modeling was used to obtain the results.
Findings
Overconfidence heuristic, prospect theory elements, emotions and stability and plasticity (as big two personality dimensions) positively affect investment decisions, while herding has a negative effect. Investment decisions, observed through the preference for long-term investments, consequently have a positive effect on the investment performance satisfaction.
Originality/value
This research proposes a unique comprehensive model of the effect of numerous different cognitive and psychological behavioral factors on investment decisions. Furthermore, the influence of investment decisions on investment performance is observed simultaneously. Understanding human behavior based on their personal characteristics can help investors to make better investment decisions. Advisors can learn from human behavior and guide their clients in the right direction when it comes to stock investment. Scientists will be able to replicate the model with other data and make comparative analyses.
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Armin Mahmoodi, Leila Hashemi and Milad Jasemi
In this study, the central objective is to foresee stock market signals with the use of a proper structure to achieve the highest accuracy possible. For this purpose, three hybrid…
Abstract
Purpose
In this study, the central objective is to foresee stock market signals with the use of a proper structure to achieve the highest accuracy possible. For this purpose, three hybrid models have been developed for the stock markets which are a combination of support vector machine (SVM) with meta-heuristic algorithms of particle swarm optimization (PSO), imperialist competition algorithm (ICA) and genetic algorithm (GA).All the analyses are technical and are based on the Japanese candlestick model.
Design/methodology/approach
Further as per the results achieved, the most suitable algorithm is chosen to anticipate sell and buy signals. Moreover, the authors have compared the results of the designed model validations in this study with basic models in three articles conducted in the past years. Therefore, SVM is examined by PSO. It is used as a classification agent to search the problem-solving space precisely and at a faster pace. With regards to the second model, SVM and ICA are tested to stock market timing, in a way that ICA is used as an optimization agent for the SVM parameters. At last, in the third model, SVM and GA are studied, where GA acts as an optimizer and feature selection agent.
Findings
As per the results, it is observed that all new models can predict accurately for only 6 days; however, in comparison with the confusion matrix results, it is observed that the SVM-GA and SVM-ICA models have correctly predicted more sell signals, and the SCM-PSO model has correctly predicted more buy signals. However, SVM-ICA has shown better performance than other models considering executing the implemented models.
Research limitations/implications
In this study, the data for stock market of the years 2013–2021 were analyzed; the long length of timeframe makes the input data analysis challenging as they must be moderated with respect to the conditions where they have been changed.
Originality/value
In this study, two methods have been developed in a candlestick model; they are raw-based and signal-based approaches in which the hit rate is determined by the percentage of correct evaluations of the stock market for a 16-day period.
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Anish Khobragade, Shashikant Ghumbre and Vinod Pachghare
MITRE and the National Security Agency cooperatively developed and maintained a D3FEND knowledge graph (KG). It provides concepts as an entity from the cybersecurity…
Abstract
Purpose
MITRE and the National Security Agency cooperatively developed and maintained a D3FEND knowledge graph (KG). It provides concepts as an entity from the cybersecurity countermeasure domain, such as dynamic, emulated and file analysis. Those entities are linked by applying relationships such as analyze, may_contains and encrypt. A fundamental challenge for collaborative designers is to encode knowledge and efficiently interrelate the cyber-domain facts generated daily. However, the designers manually update the graph contents with new or missing facts to enrich the knowledge. This paper aims to propose an automated approach to predict the missing facts using the link prediction task, leveraging embedding as representation learning.
Design/methodology/approach
D3FEND is available in the resource description framework (RDF) format. In the preprocessing step, the facts in RDF format converted to subject–predicate–object triplet format contain 5,967 entities and 98 relationship types. Progressive distance-based, bilinear and convolutional embedding models are applied to learn the embeddings of entities and relations. This study presents a link prediction task to infer missing facts using learned embeddings.
Findings
Experimental results show that the translational model performs well on high-rank results, whereas the bilinear model is superior in capturing the latent semantics of complex relationship types. However, the convolutional model outperforms 44% of the true facts and achieves a 3% improvement in results compared to other models.
Research limitations/implications
Despite the success of embedding models to enrich D3FEND using link prediction under the supervised learning setup, it has some limitations, such as not capturing diversity and hierarchies of relations. The average node degree of D3FEND KG is 16.85, with 12% of entities having a node degree less than 2, especially there are many entities or relations with few or no observed links. This results in sparsity and data imbalance, which affect the model performance even after increasing the embedding vector size. Moreover, KG embedding models consider existing entities and relations and may not incorporate external or contextual information such as textual descriptions, temporal dynamics or domain knowledge, which can enhance the link prediction performance.
Practical implications
Link prediction in the D3FEND KG can benefit cybersecurity countermeasure strategies in several ways, such as it can help to identify gaps or weaknesses in the existing defensive methods and suggest possible ways to improve or augment them; it can help to compare and contrast different defensive methods and understand their trade-offs and synergies; it can help to discover novel or emerging defensive methods by inferring new relations from existing data or external sources; and it can help to generate recommendations or guidance for selecting or deploying appropriate defensive methods based on the characteristics and objectives of the system or network.
Originality/value
The representation learning approach helps to reduce incompleteness using a link prediction that infers possible missing facts by using the existing entities and relations of D3FEND.
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Majid Ghasemy and Lena Frömbling
Guided by the affective events theory (AET), the purpose of this paper was to explore the impact of interpersonal trust in peers, as an affective work event, on two affect-driven…
Abstract
Purpose
Guided by the affective events theory (AET), the purpose of this paper was to explore the impact of interpersonal trust in peers, as an affective work event, on two affect-driven behaviors (i.e. job performance and organizational citizenship behavior toward individuals [OCBI]) via positive affect during the Covid-19 pandemic, particularly in the Asia–Pacific region.
Design/methodology/approach
This study is quantitative in approach, and longitudinal survey study in design. The authors collected data from lecturers in 2020 at the beginning, at the end and two months after the first Covid-19 lockdown in Malaysia. Then, the authors utilized the efficient partial least squares (PLSe2) estimator to investigate the relationships between the variables, while also considering gender as a control variable.
Findings
The findings show that positive affect fully mediates the relationship between interpersonal trust in peers and job performance and partially mediates the relationship between interpersonal trust in peers and OCBI. Given that gender did not demonstrate any significant relationships with interpersonal trust in peers, positive affect, job performance and OCBI, the recommended policies can be universally developed and applied, irrespective of the gender of academics.
Originality/value
This research contributes originality by integrating the widely recognized theoretical framework of AET and investigating a less explored context, specifically the Malaysian higher education sector during the challenging initial phase of the Covid-19 pandemic. Furthermore, the authors adopt a novel and robust methodological approach, utilizing the efficient partial least squares (PLSe2) estimator, to thoroughly examine and validate the longitudinal theoretical model from both explanatory and predictive perspectives.
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