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The purpose of this paper is to explore the synergic effect of wild turmeric (Curcuma Aromatica Salisb.) and holy basil (Ocimum Tenuiflorum L.) combination herbal extracts treatment on the moisture management properties of cotton, lyocell and micro-denier single jersey knitted fabrics and the factors affecting it, which is intended for the development of healthcare apparel products.

The pre-treated single jersey knitted fabrics of cotton, lyocell and micro-denier polyester fabrics were given finishing treatment with the wild turmeric (Curcuma Aromatica Salisb.) and holy basil (Ocimum Tenuiflorum L.) combination herbal extract proportions of 100%:0%, 75%:25%,50%:50%; 25%:75% and 0%:100%. The D-optimal factorial design developed using Design Expert software was used for the study. The finishing treatments were carried out using the pad−dry−cure method. The aim of the work is to find out the influence of combination herbal extract proportion, textile material and their interaction effect on the moisture management properties.

The ANOVA results revealed that the overall moisture management properties of single jersey knitted fabrics are influenced by the material type, combination herbal extract proportion and the interaction between material type and the combination herbal extracts proportion. The overall moisture management properties of combination herbal extracts treated cotton single jersey fabrics are found to be better than that of lyocell and micro-denier polyester fabrics due to their excellent accumulative one-way transport capability after the finishing treatment. Among the combination herbal extract proportions, 50:50 per cent combination herbal extract proportion was found to be better than other proportions.

The study on the moisture management properties of combination herbal extracts of wild turmeric (Curcuma Aromatica Salisb.) and holy basil (Ocimum Tenuiflorum L.) is a novel attempt to explore the synergic effect of active constituents in both the herbs.

The case looks at the Sethusamudram Shipping Canal Project from its inception in the year 1860 to 2012 when the Pachauri Committee was about to submit a report on the latest canal alignment (4A) as suggested by the Supreme Court. It takes the reader through a series of developments starting from the initial proposals and alignments to formation of Sethusamudram Corporation Limited and highlights the impact of National Environmental Engineering Research Institute Report, Tsunami Detailed Project Report, and Subramaniam Swamy Report on various issues including environmental, political, religious, security and legal. The case brings out multi-dimensional aspects involved in an Indian infrastructure project and gives both students and the faculty an opportunity to explore the complexities faced by the Indian decision makers in today's context.

This paper aims to propose a novel way of using textual clustering as a feature selection method. It is applied to identify the most important keywords in the profile classification. The method is demonstrated through the problem of sick-leave promoters on Twitter.

Four machine learning classifiers were used on a total of 35,578 tweets posted on Twitter. The data were manually labeled into two categories: promoter and nonpromoter. Classification performance was compared when the proposed clustering feature selection approach and the standard feature selection were applied.

Radom forest achieved the highest accuracy of 95.91% higher than similar work compared. Furthermore, using clustering as a feature selection method improved the Sensitivity of the model from 73.83% to 98.79%. Sensitivity (recall) is the most important measure of classifier performance when detecting promoters’ accounts that have spam-like behavior.

The method applied is novel, more testing is needed in other datasets before generalizing its results.

The model applied can be used by Saudi authorities to report on the accounts that sell sick-leaves online.

The research is proposing a new way textual clustering can be used in feature selection.

Learning orientation is critical in new product development. However, research has disregarded how learning orientation operates via the potential mediator to influence new product performance. The purpose of this study is to examine the mediating role of ambidextrous capability in the relationship between learning orientation and new product performance.

The empirical study uses a questionnaire approach designed to collect data for testing research hypotheses. This study tests the hypotheses using structural equation model in a sample of 336 firms in Taiwan.

The findings indicate that learning orientation relates positively to ambidextrous capability and new product performance. Ambidextrous capability, in turn, relates positively to new product performance. The results also support the argument that ambidextrous capability plays a mediating role in learning orientation and new product performance.

The value of this study is to identify ambidextrous capability as the potential mediator in the relationship between learning orientation and new product performance. The results enrich the understanding of learning orientation in new product project teams and suggest important implications for new product development and future research.

Reorganising unstructured academic abstracts according to a certain logical structure can help scholars not only extract valid information quickly but also facilitate the faceted search of academic literature. This study aims to build a high-performance model for identifying of the functional structures of unstructured abstracts in the social sciences.

This study first investigated the structuring of abstracts in academic articles in the field of social sciences, using large-scale statistical analyses. Then, the functional structures of sentences in the abstract in a corpus of more than 3.5 million abstracts were identified from sentence classification and sequence tagging by using several models based on either machine learning or a deep learning approach, and the results were compared.

The results demonstrate that the functional structures of sentences in abstracts in social science manuscripts include the background, purpose, methods, results and conclusions. The experimental results show that the bidirectional encoder representation from transformers exhibited the best performance, the overall F1 score of which was 86.23%.

The data set of annotated social science abstract is generated and corresponding models are trained on the basis of the data set, both of which are available on Github (https://github.com/Academic-Abstract-Knowledge-Mining/SSCI_Abstract_Structures_Identification). Based on the optimised model, a Web application for the identification of the functional structures of abstracts and their faceted search in social sciences was constructed to enable rapid and convenient reading, organisation and fine-grained retrieval of academic abstracts.

Healthcare networks are becoming ubiquitous, yet it is unclear how hospitals with varying quality capabilities would fare by being affiliated with large healthcare networks. The purpose of this paper is to first consider the deductive configuration perspective and distinguish high and low quality hospitals by using clinical and experiential quality as two dimensions of quality capability. Next, it examines the impact of healthcare network size on operating costs of hospitals. Additionally, the paper investigates the interaction effect of hospital demand and healthcare network size on operating costs.

The paper uses a dataset that was created by combining five separate sources. Cluster analysis technique is used to classify hospitals into four groups – holistic quality leaders (high clinical and experiential quality capability), experiential quality focusers (low clinical quality capability and high experiential quality capability), clinical quality focusers (high clinical capability and low experiential quality capability), and quality laggards (low clinical and experiential quality capability). The authors test the research hypotheses by means of regression analyses after controlling for several contextual characteristics.

The results show that affiliation with large healthcare networks reduces operating costs for quality laggards, but increases these costs for experiential quality focusers and clinical quality focusers. The hypothesized positive relationship between healthcare network size and costs is not supported for holistic quality leaders. The authors find that clinical quality focusers and holistic quality leaders can complement higher utilization levels in their operations due to increased demand and healthcare network size to reduce their operating costs per day.

There has been increasing evidence suggesting that hospitals must carefully manage both clinical and experiential quality. By focusing on both clinical and experiential quality, unlike experiential quality focusers and clinical quality focusers, holistic quality leaders are not adversely affected by the size of their network. The results suggest that experiential quality focusers and clinical quality focusers should either embrace holistic quality management or restrict the size of their networks to maintain their quality level and to reduce coordination costs.

This study aims to introduce a multilevel micro-foundational perspective on supply chain (SC) ambidexterity, grounded in organizational learning and adaptation research. It investigates the interplay of contextual factors, strategic orientation and a bundle of supply chain management practices to foster ambidextrous performance.

Leveraging a blend of perceptual and objective data and measures, this study explores the intricacies of macro and micro factors at multiple levels, offering empirical support for the research framework. The interrelationships among these factors are scrutinized through three analytical approaches: selection, interaction and system forms of interdependence analysis.

First, the authors offer empirical support for their conceptual model, illustrating that ambidexterity behavior and outcomes in the SC emanate from intricate interactions between macro and micro factors across various levels. Second, the authors present robust empirical evidence endorsing a system/gestalt form of interdependence analysis in capturing SC ambidexterity and performance. This analytical approach effectively captures the complementarity and contradictory interdependence among the opposing poles of efficiency and responsiveness.

The organizational and SC activity configuration faces numerous paradoxical tensions, such as profitability versus sustainability. This study offers valuable insights into establishing an ambidextrous system capable of navigating and addressing these paradoxical situations.

The purpose of this paper is to examine the relationship between quality ambidexterity (QAMB), competitive strategies (cost leadership, differentiation, and focus), and firm performance in Palestinian industry, and to analyze the combination of quality exploitation (QEI) and quality exploration (QER) (QAMB) associated with the different levels of each competitive strategy.

Using data collected through a survey of 205 Palestinian industrial firms, the study conducted structural equation modeling to test the proposed relationships. Additional statistical analyses were applied to the combinations of QEI and QER for each competitive strategy.

The results show a positive and significant relationship between QAMB and three competitive strategies, and between competitive strategies and financial performance, focus strategy excepted. Balanced combination with similar levels of QEI and QER is found to be more suitable for higher levels of competitive strategies implementation, whereas an excess of QER over QEI is associated with lower levels of strategies implementation.

Although Palestine has two regions, the West Bank and the Gaza Strip, all survey respondents were from the West Bank. The data used in this study come from the industrial sector only.

This study is the first empirical test to examine the impact of QAMB on financial performance through competitive strategies. The study results may help managers to implement QEI and QER practices in order to allocate resources effectively and ultimately improve financial performance.

The purpose of this paper is to understand the similarities and differences between the Twitter users who tweeted on journal articles in psychology and political science disciplines.

The data were collected from Web of Science, Altmetric.com, and Twitter. A total of 91,826 tweets with 22,541 distinct Twitter user profiles for psychology discipline and 29,958 tweets with 10,478 distinct Twitter user profiles for political science discipline were used for analysis. The demographics analysis includes gender, geographic location, individual or organization user, academic or non-academic background, and psychology/political science domain knowledge background. A machine learning approach using support vector machine (SVM) was used for user classification based on the Twitter user profile information. Latent Dirichlet allocation (LDA) topic modeling was used to discover the topics that the users discussed from the tweets.

Results showed that the demographics of Twitter users who tweeted on psychology and political science are significantly different. Tweets on journal articles in psychology reflected more the impact of scientific research finding on the general public and attracted more attention from the general public than the ones in political science. Disciplinary difference in term of user demographics exists, and thus it is important to take the discipline into consideration for future altmetrics studies.

From this study, researchers or research organizations may have a better idea on who their audiences are, and hence more effective strategies can be taken by researchers or organizations to reach a wider audience and enhance their influence.

This study investigates the effect of ring and compact spun yarns such as Sussen Elite and Com4 spun yarn on the physical and comfort characteristics of single jersey, rib and plain interlock knitted fabrics. The physical characteristics such as fabric aerial density, tightness factor, spirality and pilling behaviour were studied and statistically analyzed using Multivariable ANOVA analysis. The comfort characteristics such as thermal insulation behaviour (TIV), water vapour permeability, wicking and air permeability were studied and reported. The test results showed that compact spun yarn knitted fabrics such as Sussen Elite and Com4 yarn fabrics demonstrated higher thermal insulation behaviour in all the knitted structures when compared to ring spun yarn knitted fabrics. The low stress mechanical characteristics such as shear and compressional behaviour of ring and compact spun yarn knitted fabrics were also reported.