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This study explores the extent of undergraduate students' engagement in interdisciplinary learning experiences across their academic journey and its potential correlation with elevated levels of self-efficacy in learning. Furthermore, the research investigates how the clarity of career decisions and future goals contributes to the perception of relevance, value and alignment of interdisciplinary learning experiences among undergraduate students.

Data were collected using a self-report questionnaire in a longitudinal survey administered annually to undergraduate students at a university in northern Taiwan over four waves from 2018 to 2021. The sample analyzed for this study consisted of 123 undergraduate students who willingly and continuously participated in the research throughout the specified period.

The results showed that self-efficacy within interdisciplinary learning experiences could be classified into three clusters: high efficacy, moderate efficacy and fluctuating efficacy. The determinants influencing these clusters include career decisions and years spent in university. Undergraduate students who have determined their career decisions and are in their latter two years of undergraduate studies demonstrate higher self-efficacy in interdisciplinary learning. Conversely, students who have yet to determine their career decisions exhibit a fluctuating pattern of self-efficacy across the three interdisciplinary learning categories.

Two key limitations of this research include a small sample size and a confined university-specific context, potentially constraining the applicability of the results to a broader population.

This study contributes to the interdisciplinary learning experience in higher education by explaining the significance of undergraduates' self-efficacy and career-related factors. Whereas most research has focused on the effects of self-efficacy, this study investigated the factors that influence undergraduates' self-efficacy.

The ‘managed stock’ market in Taiwan is neglected by the authorities and general investors. In this paper, we explore the link between financial trait and stock price changes in this special market.

Overall, we analyze and discuss managerial implications for institutional investors, general investors and the authorities as well.

The purpose of this paper is to prepare, characterise and evaluate nano-emulsions of copolymers of various compositions as eco-friendly binders for flexographic ink industry.

Various nano-emulsions of copolymers were prepared using styrene (St), butyl acrylate (BuAc), acrylic acid (AA) and acrylamide (AAm) monomers by means of a conventional seeded emulsion polymerisation technique, using K2S2O8 as the initiator. The characterisation of the prepared emulsions was performed using Fourier transform infrared (FT-IR), thermogravimetric analysis (TGA), gel permeation chromatography (GPC) and transmission electron microscopy (TEM). A selection of copolymers was formulated with pigments and additional ingredients, as water-based flexographic inks. The inks were characterised for their viscosity, pH, degree of dispersion, water resistance and colour density.

It was found that the low viscosity of the prepared copolymers may reduce the film thickness of the flexographic inks and may also increase the spreading of the ink on the surface. As a result, stable modified polyacrylate-based latex with improved physico-mechanical properties were obtained. The prepared latexes were showed improving and enhancing in water resistance; gloss values, and the print density that ranged from 2.06 to 2.51 and the maximum gloss values (39 and 48) were also obtained. Also, these binders provide excellent adhesion properties for both the pigment particles and the base paper.

This study focuses on the preparation of new water-based copolymer nanoparticles and their use as eco-friendly binders for flexographic ink industry.

The ink formulations developed could find use in industrial-scale printing.

Eco-friendly environment ink formulations for printing on paper substrates are novel.

This study examines the operational and relational governances as antecedents of cooperation commitment in buyer–supplier exchanges. It also assesses the impact of cooperation commitment on operational performance.

Path analysis was performed on the data collected from manufacturers.

The results of this study show that both operational and relational governances exert impact on cooperation commitment, which, in turn, is associated with operational performance improvement.

First, this is the first study employing the reciprocity theory to theorize the conceptual framework of the governance antecedents of cooperation commitment and operations excellence effect. Second, the study highlights how the research framework can enrich the reciprocity theory in exploring the mechanisms of the operational and relational governances of buyer–supplier exchanges and their impact on the commitment to the cooperation. Third, this study extends the reciprocity theory to examine in detail how cooperation commitment exerts impact on the operational performance.

A core challenge in background subtraction (BGS) is handling videos with sudden illumination changes in consecutive frames. In our pilot study published in, Sakkos:SKIMA 2019, we tackle the problem from a data point-of-view using data augmentation. Our method performs data augmentation that not only creates endless data on the fly but also features semantic transformations of illumination which enhance the generalisation of the model.

In our pilot study published in SKIMA 2019, the proposed framework successfully simulates flashes and shadows by applying the Euclidean distance transform over a binary mask generated randomly. In this paper, we further enhance the data augmentation framework by proposing new variations in image appearance both locally and globally.

Experimental results demonstrate the contribution of the synthetics in the ability of the models to perform BGS even when significant illumination changes take place.

Such data augmentation allows us to effectively train an illumination-invariant deep learning model for BGS. We further propose a post-processing method that removes noise from the output binary map of segmentation, resulting in a cleaner, more accurate segmentation map that can generalise to multiple scenes of different conditions. We show that it is possible to train deep learning models even with very limited training samples. The source code of the project is made publicly available at https://github.com/dksakkos/illumination_augmentation