This paper deals with a few of the properties of metals under uniform stress distribution. Consideration is then given to non‐uniform stress conditions, and the importance of the…
Abstract
This paper deals with a few of the properties of metals under uniform stress distribution. Consideration is then given to non‐uniform stress conditions, and the importance of the ductility of alloys after the limit of proportionality is passed. Particular reference is made to the light alloys used for primary aircraft structures and, finally, a tentative explanation is put forward to explain the manner in which the basic atomic structure, the metal lattice, reacts to stress conditions.
Ansumalini Panda, Srinivas Subbarao Pasumarti and Suvarna Hiremath
Need of the Study: Digitalisation, machine learning, and artificial intelligence (AI) is changing at a swift pace, significantly uplifting the role of information technology. The…
Abstract
Need of the Study: Digitalisation, machine learning, and artificial intelligence (AI) is changing at a swift pace, significantly uplifting the role of information technology. The present human resource (HR) aspect transpires AI-based resolution, are gradually more effective with HR process, time-consumption and a complex tasks surrounded by the HRM functionalities.
Purpose: This study attempts to investigate the adoption and diffusion of human resource management (HRM) with the phenomenon of AI-based applications. Hence, this study has emphasised the predictors of AI adoption like competitive pressure, performance expectancy, top management support, strategic partner, employee champion, etc. Moreover, how the AI predictors are connected with HR practices. The research sample focused on 207 HR managers and senior managers from various industries.
Methodology: This study is based on a quantitative research technique encompassing mean, standard deviation, exploratory factor analysis (EFA), Confirmatory Factor Analysis (CFA), Average Variance Extracted (AVE), and Dependent Variable (DV).
Findings: The study’s empirical findings show that higher performance expectations and higher management support are both major predictors of AI adoption. In contrast, competitive pressure did not show a significant relationship with such an intention, and the ‘employee champion’ role has a negative impact on AI adoption.
Implications: AI diffusion and implementation show a significant research gap. In previous studies, adoption in HRM was overlooked. The study’s results provide a comprehensive picture of the situation. The framework and a major contribution to the study of the phenomenon in relation to its possible role in AI’s effectiveness and quality in HRM. The research inspires a debate among service providers, policy-makers, and stakeholders, and builds an efficient workplace.
Details
Keywords
Garima Goel and Saumya Ranjan Dash
This paper aims to investigate the moderating role of government policy interventions amid the early spread of novel coronavirus (COVID-19) (January–May 2020) on the investor…
Abstract
Purpose
This paper aims to investigate the moderating role of government policy interventions amid the early spread of novel coronavirus (COVID-19) (January–May 2020) on the investor sentiment and stock returns relationship.
Design/methodology/approach
This paper uses panel data from a sample of 53 countries to examine the impact of investor sentiment, measured by the financial and economic attitudes revealed by the search (FEARS) index (Da et al., 2015) on the stock return.
Findings
The moderating role of government policy response indices with the FEARS index on the global stock returns is further explored. This paper finds that government policy responses have a moderating role in the sentiment and stock returns relationship. The effect holds true even when countries are split based on five classifications, i.e. cultural distance, health standard, government effectiveness, social well-being and financial development. The results are robust to an alternative measure of pandemic search intensity, quantile regression and two measures of stock market activity, i.e. conditional volatility and exchange traded fund returns.
Research limitations/implications
The sample period of this study encompasses the early spread phase (January–May 2020) of the novel COVID-19 spread.
Originality/value
This paper provides some early evidence on whether the government policy interventions are helpful to mitigate the impact of investor sentiment on the stock market. The paper also helps to shed better insights on the role of different country characteristics for the sentiment and stock return relationship.
Details
Keywords
Lin‐Chih Chen and Cheng‐Jye Luh
This study aims to present a new web page recommendation system that can help users to reduce navigational time on the internet.
Abstract
Purpose
This study aims to present a new web page recommendation system that can help users to reduce navigational time on the internet.
Design/methodology/approach
The proposed design is based on the primacy effect of browsing behavior, that users prefer top ranking items in search results. This approach is intuitive and requires no training data at all.
Findings
A user study showed that users are more satisfied with the proposed search methods than with general search engines using hot keywords. Moreover, two performance measures confirmed that the proposed search methods out‐perform other metasearch and search engines.
Research limitations/implications
The research has limitations and future work is planned along several directions. First, the search methods implemented are primarily based on the keyword match between the contents of web pages and the user query items. Using the semantic web to recommend concepts and items relevant to the user query might be very helpful in finding the exact contents that users want, particularly when the users do not have enough knowledge about the domains in which they are searching. Second, offering a mechanism that groups search results to improve the way search results are segmented and displayed also assists users to locate the contents they need. Finally, more user feedback is needed to fine‐tune the search parameters including α and β to improve the performance.
Practical implications
The proposed model can be used to improve the search performance of any search engine.
Originality/value
First, compared with the democratic voting procedure used by metasearch engines, search engine vector voting (SVV) enables a specific combination of search parameters, denoted as α and β, to be applied to a voted search engine, so that users can either narrow or expand their search results to meet their search preferences. Second, unlike page quality analysis, the hyperlink prediction (HLP) determines qualified pages by simply measuring their user behavior function (UBF) values, and thus takes less computing power. Finally, the advantages of HLP over statistical analysis are that it does not need training data, and it can target both multi‐site and site‐specific analysis.
Details
Keywords
Fattane Zarrinkalam and Mohsen Kahani
The purpose of this paper is to propose a novel citation recommendation system that inputs a text and recommends publications that should be cited by it. Its goal is to help…
Abstract
Purpose
The purpose of this paper is to propose a novel citation recommendation system that inputs a text and recommends publications that should be cited by it. Its goal is to help researchers in finding related works. Further, this paper seeks to explore the effect of using relational features in addition to textual features on the quality of recommended citations.
Design/methodology/approach
In order to propose a novel citation recommendation system, first a new relational similarity measure is proposed for calculating the relatedness of two publications. Then, a recommendation algorithm is presented that uses both relational and textual features to compute the semantic distances of publications of a bibliographic dataset from the input text.
Findings
The evaluation of the proposed system shows that combining relational features with textual features leads to better recommendations, in comparison with relying only on the textual features. It also demonstrates that citation context plays an important role among textual features. In addition, it is concluded that different relational features have different contributions to the proposed similarity measure.
Originality/value
A new citation recommendation system is proposed which uses a novel semantic distance measure. This measure is based on textual similarities and a new relational similarity concept. The other contribution of this paper is that it sheds more light on the importance of citation context in citation recommendation, by providing more evidences through analysis of the results. In addition, a genetic algorithm is developed for assigning weights to the relational features in the similarity measure.
Details
Keywords
Jinxiang Zeng, Shujin Cao, Yijin Chen, Pei Pan and Yafang Cai
This study analyzed the interdisciplinary characteristics of Chinese research studies in library and information science (LIS) measured by knowledge elements extracted through the…
Abstract
Purpose
This study analyzed the interdisciplinary characteristics of Chinese research studies in library and information science (LIS) measured by knowledge elements extracted through the Lexicon-LSTM model.
Design/methodology/approach
Eight research themes were selected for experiment, with a large-scale (N = 11,625) dataset of research papers from the China National Knowledge Infrastructure (CNKI) database constructed. And it is complemented with multiple corpora. Knowledge elements were extracted through a Lexicon-LSTM model. A subject knowledge graph is constructed to support the searching and classification of knowledge elements. An interdisciplinary-weighted average citation index space was constructed for measuring the interdisciplinary characteristics and contributions based on knowledge elements.
Findings
The empirical research shows that the Lexicon-LSTM model has superiority in the accuracy of extracting knowledge elements. In the field of LIS, the interdisciplinary diversity indicator showed an upward trend from 2011 to 2021, while the disciplinary balance and difference indicators showed a downward trend. The knowledge elements of theory and methodology could be used to detect and measure the interdisciplinary characteristics and contributions.
Originality/value
The extraction of knowledge elements facilitates the discovery of semantic information embedded in academic papers. The knowledge elements were proved feasible for measuring the interdisciplinary characteristics and exploring the changes in the time sequence, which helps for overview the state of the arts and future development trend of the interdisciplinary of research theme in LIS.
Details
Keywords
Haihua Chen, Yunhan Yang, Wei Lu and Jiangping Chen
Citation contexts have been found useful in many scenarios. However, existing context-based recommendations ignored the importance of diversity in reducing the redundant issues…
Abstract
Purpose
Citation contexts have been found useful in many scenarios. However, existing context-based recommendations ignored the importance of diversity in reducing the redundant issues and thus cannot cover the broad range of user interests. To address this gap, the paper aims to propose a novelty task that can recommend a set of diverse citation contexts extracted from a list of citing articles. This will assist users in understanding how other scholars have cited an article and deciding which articles they should cite in their own writing.
Design/methodology/approach
This research combines three semantic distance algorithms and three diversification re-ranking algorithms for the diversifying recommendation based on the CiteSeerX data set and then evaluates the generated citation context lists by applying a user case study on 30 articles.
Findings
Results show that a diversification strategy that combined “word2vec” and “Integer Linear Programming” leads to better reading experience for participants than other diversification strategies, such as CiteSeerX using a list sorted by citation counts.
Practical implications
This diversifying recommendation task is valuable for developing better systems in information retrieval, automatic academic recommendations and summarization.
Originality/value
The originality of the research lies in the proposal of a novelty task that can recommend a diversification context list describing how other scholars cited an article, thereby making citing decisions easier. A novel mixed approach is explored to generate the most efficient diversifying strategy. Besides, rather than traditional information retrieval evaluation, a user evaluation framework is introduced to reflect user information needs more objectively.
Details
Keywords
Lydie Myriam Marcelle Amelot, Ushad Subadar Agathee and Yuvraj Sunecher
This study constructs time series model, artificial neural networks (ANNs) and statistical topologies to examine the volatility and forecast foreign exchange rates. The Mauritian…
Abstract
Purpose
This study constructs time series model, artificial neural networks (ANNs) and statistical topologies to examine the volatility and forecast foreign exchange rates. The Mauritian forex market has been utilized as a case study, and daily data for nominal spot rate (during a time period of five years spanning from 2014 to 2018) for EUR/MUR, GBP/MUR, CAD/MUR and AUD/MUR have been applied for the predictions.
Design/methodology/approach
Autoregressive integrated moving average (ARIMA) and generalized autoregressive conditional heteroskedasticity (GARCH) models are used as a basis for time series modelling for the analysis, along with the non-linear autoregressive network with exogenous inputs (NARX) neural network backpropagation algorithm utilizing different training functions, namely, Levenberg–Marquardt (LM), Bayesian regularization and scaled conjugate gradient (SCG) algorithms. The study also features a hybrid kernel principal component analysis (KPCA) using the support vector regression (SVR) algorithm as an additional statistical tool to conduct financial market forecasting modelling. Mean squared error (MSE) and root mean square error (RMSE) are employed as indicators for the performance of the models.
Findings
The results demonstrated that the GARCH model performed better in terms of volatility clustering and prediction compared to the ARIMA model. On the other hand, the NARX model indicated that LM and Bayesian regularization training algorithms are the most appropriate method of forecasting the different currency exchange rates as the MSE and RMSE seemed to be the lowest error compared to the other training functions. Meanwhile, the results reported that NARX and KPCA–SVR topologies outperformed the linear time series models due to the theory based on the structural risk minimization principle. Finally, the comparison between the NARX model and KPCA–SVR illustrated that the NARX model outperformed the statistical prediction model. Overall, the study deduced that the NARX topology achieves better prediction performance results compared to time series and statistical parameters.
Research limitations/implications
The foreign exchange market is considered to be instable owing to uncertainties in the economic environment of any country and thus, accurate forecasting of foreign exchange rates is crucial for any foreign exchange activity. The study has an important economic implication as it will help researchers, investors, traders, speculators and financial analysts, users of financial news in banking and financial institutions, money changers, non-banking financial companies and stock exchange institutions in Mauritius to take investment decisions in terms of international portfolios. Moreover, currency rates instability might raise transaction costs and diminish the returns in terms of international trade. Exchange rate volatility raises the need to implement a highly organized risk management measures so as to disclose future trend and movement of the foreign currencies which could act as an essential guidance for foreign exchange participants. By this way, they will be more alert before conducting any forex transactions including hedging, asset pricing or any speculation activity, take corrective actions, thus preventing them from making any potential losses in the future and gain more profit.
Originality/value
This is one of the first studies applying artificial intelligence (AI) while making use of time series modelling, the NARX neural network backpropagation algorithm and hybrid KPCA–SVR to predict forex using multiple currencies in the foreign exchange market in Mauritius.
Details
Keywords
Byung-Won On, Gyu Sang Choi and Soo-Mok Jung
The purpose of this paper is to collect and understand the nature of real cases of author name variants that have often appeared in bibliographic digital libraries (DLs) as a case…
Abstract
Purpose
The purpose of this paper is to collect and understand the nature of real cases of author name variants that have often appeared in bibliographic digital libraries (DLs) as a case study of the name authority control problem in DLs.
Design/methodology/approach
To find a sample of name variants across DLs (e.g. DBLP and ACM) and in a single DL (e.g. ACM), the approach is based on two bipartite matching algorithms: Maximum Weighted Bipartite Matching and Maximum Cardinality Bipartite Matching.
Findings
First, the authors validated the effectiveness and efficiency of the bipartite matching algorithms. The authors also studied the nature of real cases of author name variants that had been found across DLs (e.g. ACM, CiteSeer and DBLP) and in a single DL.
Originality/value
To the best of the authors knowledge, there is less research effort to understand the nature of author name variants shown in DLs. A thorough analysis can help focus research effort on real problems that arise when the authors perform duplicate detection methods.