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Statistical quality control charts cannot indicate explicitly whether there is any special disturbance in the manufacturing process. pattern recognition scheme can solve this problem. The simultaneous control of two or more variables is necessary when the quality of the product depends on the joint effect of these variables. Studies the combination patterns of random and random, shift and cycle, trend and cycle, and trend and shift for two variables. Proposes a T² control chart and uses simulation to determine pattern diagnostic characteristics for these combinations. The pattern diagnostic characteristics studied are window size, zone boundary, and zone representation. The results indicate that window size 20 is appropriate for these particular parameters, equal probability and the highest percentage alternative are adopted as the zone boundary and the zone representation, respectively. The sensitivity analysis of the pattern parameters indicates the pattern diagnostic is robust for changes in the parameter values.

The subject of part‐time work is one which has become increasingly important in industrialised economies where it accounts for a substantial and growing proportion of total employment. It is estimated that in 1970, average annual hours worked per employee amounted to only 60% of those for 1870. Two major factors are attributed to explaining the underlying trend towards a reduction in working time: (a) the increase in the number of voluntary part‐time employees and (b) the decrease in average annual number of days worked per employee (Kok and de Neubourg, 1986). The authors noted that the growth rate of part‐time employment in many countries was greater than the corresponding rate of growth in full‐time employment.

Comparative studies have confirmed that the current types of cohabitation (defined as living together as a couple without being married) and the meanings attached to them differ across Europe. This variation could reflect differences in the levels of progress or the stages countries have reached in a common developmental process, as suggested by the theory of the Second Demographic Transition and Kiernan’s stage model of cohabitation. However, it may also indicate that countries are on different developmental paths, as suggested by path dependency theories. To examine whether changes in the prevalence of cohabitation follow a common script, this study analyses types of cohabitation within emerging family formation patterns over cohorts and across countries.

For this purpose, sequence methodology is applied to analyze cohort-specific family trajectories in France, western Germany, Norway, and Italy. In particular, using data from the German National Educational Panel Study (NEPS) and the Generation and Gender Survey (GGS), patterns of union status and co-residence with (own) children between ages 15 and 35 among the 1935–1969 birth cohorts (for Germany, among the 1940–1974 birth cohorts) are compared.

Our findings provide some support for the claim that there were common patterns of change. However, also country-specific variations in family trajectory patterns were detected, which suggests that general processes of change were mediated through country-specific institutions (path dependencies). The empirical evidence for convergence as well as for divergence indicates that both theoretical strands add to our understanding of the spread of cohabitation in European countries.

This study aims to explore the higher education expansion phenomenon and to what extent that expansion could reshape the pattern of gender.

We selected Taiwan's higher education as a research target. First, we describe the gender diversity patterns in the humanity, social science and STEM programs according to Trow's definition. Second, we identify discrepancies in gender in the major programs according to different indices. Becker’s D, Blau index, GPI, trend analysis and multiple comparisons were used to transform the comparison series data.

First, to assess system-wide gender diversity, it was shown that in the elite stage, the D is more sensitive than the Blau index in plotting trends. Second, neither the D nor the Blau index showed sensitivity in humanity and social science programs in the post-mass stage. Third, the GPI more accurately detects differences in gender patterns in the mass and universal stages.

This study illuminates gender diversity patterns with indices transformation in higher education. Considering higher education expansion is a global phenomenon, the fitted indices can be used to detect gender diversity issues in wider higher education settings.

This study provides an example for addressing the issue using different indices to identify previous and future trends in the higher education system. The study suggests alternative interpretations for gender diversity in various programs that will assist in modifying related gender policies in higher education.

Reports the highlights of a study designed to describe certain relationships between sales trends and advertising and price patterns for nine Canadian grocery markets from 1958 to 1967. Uses the results of the study to illustrate several characteristics of modern competition and marketing, their implications for current thinking about the examine role of advertising and the applicability of the product life cycle in this context.

The purpose of this paper is to identify salient topic categories and outline their evolution patterns and temporal trends in microblogs on a public health emergency across different stages. Comparisons were also examined to reveal the similarities and differences between those patterns and trends on microblog platforms of different languages and from different nations.

A total of 459,266 microblog entries about the Ebola outbreak in West Africa in 2014 on Twitter and Weibo were collected for nine months after the inception of the outbreak. Topics were detected by the latent Dirichlet allocation model and classified into several categories. The daily tweets were analyzed with the self-organizing map technique and labeled with the most salient topics. The investigated time span was divided into three stages, and the most salient topic categories were identified for each stage.

In total, 14 salient topic categories were identified in microblogs about the Ebola outbreak and were summarized as increasing, decreasing, fluctuating or ephemeral types. The topical evolution patterns of microblogs and temporal trends for topic categories vary on different microblog platforms. Twitter users were keen on the dynamics of the Ebola outbreak, such as status description, secondary events and so forth, while Weibo users focused on background knowledge of Ebola and precautions.

This study revealed evolution patterns and temporal trends of microblog topics on a public health emergency. The findings can help administrators of public health emergencies and microblog communities work together to better satisfy information needs and physical demands by the public when public health emergencies are in progress.

Quality control charts are statistical process control tools aimed at monitoring a (manufacturing) process to detect any deviations from normal operation and to aid in process diagnosis and correction. The information presented on the chart is a key to the successful implementation of a quality process correction system. Pattern recognition methodology has been pursued to identify unnatural behaviour on quality control charts. This approach provides the ability to utilize patterning information of the chart and to track back the root causes of process deviation, thus facilitating process diagnosis and maintenance. Presents analysis and development of a statistical pattern recognition system for the explicit identification of unnatural patterns on control charts. Develops a set of statistical pattern recognizers based on the likelihood ratio approach and on correlation analysis. Designs and implements a training algorithm to maximize the probability of identifying unnatural patterns, and presents a classification procedure for real‐time operation. Demonstrates the system performance using a set of newly defined measures, and obtained results based on extensive experiments illustrate the power and usefulness of the statistical approach for automating unnatural pattern detection on control charts.

The purpose of this paper is to develop a control chart pattern recognition methodology for monitoring the weekly customer complaints of outsourced information technology-enabled service (ITeS) processes.

A two-step methodology is used to classify the processes as having natural or unnatural variation based on past 20 weeks' customer complaints. The step one is to simulate data on various control chart patterns namely natural variation, upward shift, upward trend, etc. Then a deep learning neural network model consisting of two dense layers is developed to classify the patterns as of natural or unnatural variation.

The validation of the methodology on telecom vertical processes has correctly detected unnatural variations in two terminated processes. The implementation of the methodology on banking and financial vertical processes has detected unnatural variation in one of the processes. This helped the company management to take remedial actions, renegotiate the deal and get it renewed for another period.

This study provides valuable information on controlling information technology-enabled processes using pattern recognition methodology. The methodology gives a lot of flexibility to managers to monitor multiple processes collectively and avoids the manual plotting and interpretation of control charts.

The application of control chart pattern recognition methodology for monitoring service industry processes are rare. This is an application of the methodology for controlling information technology-enabled processes. This study also demonstrates the usefulness of deep learning techniques for process control.

This study aims to investigate the research productivity in terms of publications count of the top four premiers Indian Institute of Management (IIM) institutions and to explore the current research trends.

Bibliometric techniques were employed to assess the performance in terms of research productivity of authors affiliated with IIMs. The Elsevier Scopus database was selected as a tool to extract the prospective publications data limiting the time frame for 2010–2021. The IIM-Ahmedabad, IIM-Bangalore, IIM-Calcutta and IIM-Lucknow have been selected for the study. The harvested data were analyzed by using the standard bibliometric indicators and scientometric parameters to measure the research landscape such as average growth rate, compound average growth rate, relative growth rate, doubling time, degree of collaboration, collaborative index, collaborative coefficient and modified collaborative coefficient. VOSviewer 1.6.17, BibExcel and Microsoft Excel were used for data analysis and visualization.

The research productivity of selected four IIMs has shown an upward trend during the study period from 2010–2021 and accrued 4,397 publications with an average of 366 publications per year. The authorship patterns demonstrate the collaborative trends as most of the publications were produced by the multiple-authors (81.03%). IIM-Ahmedabad has produced the maximum number of publications (32.20%). The research productivity of IIMs has come out in collaboration with the 125 nations across the world and the USA, the UK, Canada, Germany and China are the front runners with IIMs in the collaborative network. The high magnitude and density of collaboration are evident from the calculated mean values of the degree of collaboration (0.82). The mean values of the collaborative index (2.64), collaborative coefficient (0.51) and modified collaborative coefficient (0.51) demonstrated a positive trend, but indicate the fluctuation in the collaborative pattern as time proceeds.

The study is limited to the publications data indexed in the Scopus database, therefore the outcome may not be generalized across other databases available in the public domain like Web of Science (WoS), PubMed, Dimensions and Google Scholars.

The findings of the study may aid academics and library professionals in identifying research trends, collaboration networks and evaluating other academic and research institutions by using the current advancement in data analysis.

The present study is the first effort to evaluate the research productivity of IIMs. The expanding literature will make an important contribution to identifying patterns and evaluating current research trends on a worldwide scale.

Despite extensive academic research related to digital technologies (DT), their integration into architecture, engineering and construction (AEC) projects lags in practice. This paper aims to discover DT deployment patterns and emerging trends in real-life AEC projects.

A case study methodology was adopted, including individual case analyses and comparative multiple-case analyses.

The results revealed the temporal distribution of DT in practical AEC projects, specific DT products/software, major project types integrated with digital solutions, DT application areas and project stages and associated project performance. Three distinct patterns in DT adoption have been observed, reflecting the evolution of DT applications, the progression from single to multiple DT integration and alignment with emerging industry requirements. The DT adoption behavior in the studied cases has been examined using the technology-organization-environment-human (TOE + H) framework. Further, eight emerging trend streams for future DT adoption were identified, with “leveraging the diverse features of certain mature DT” being a shared recognition of all studied companies.

This research offers actionable insights for AEC companies, facilitating the development of customized DT implementation roadmaps aligned with organizational needs. Policymakers, industry associations and DT suppliers may leverage these findings for informed decision-making, collaborative educational initiatives and product/service customization.

This research provides empirical evidence of applicable products/software, application areas and project performance. The examination of the TOE + H framework offers a holistic understanding of the collective influences on DT adoption. The identification of emerging trends addresses the evolving demands of the AEC industry in the digital era.