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This paper aims to analyze the performance of artificial neural networks with filling methods in predicting the minimum fluidization velocity of different biomass types for bioenergy applications.

An extensive literature review was performed to create an efficient database for training purposes. The database consisted of experimental values of the minimum fluidization velocity, physical properties of the biomass particles (density, size and sphericity) and characteristics of the fluidization (monocomponent experiments or binary mixture). The neural models developed were divided into eight different cases, in which the main difference between them was the filling method type (K-nearest neighbors [KNN] or linear interpolation) and the number of input neurons. The results of the neural models were compared to the classical correlations proposed by the literature and empirical equations derived from multiple regression analysis.

The performance of a given filling method depended on the characteristics and size of the database. The KNN method was superior for lower available data for training and specific fluidization experiments, like monocomponent or binary mixture. The linear interpolation method was superior for a wider and larger database, including monocomponent and binary mixture. The performance of the neural model was comparable with the predictions of the most well-known correlations from the literature.

Techniques of machine learning, such as filling methods, were used to improve the performance of the neural models. Besides the typical comparisons with conventional correlations, comparisons with three main equations derived from multiple regression analysis were reported and discussed.

In recent decades, human resource management (HRM) in health organizations has faced several problems associated with employees' efficiency and happiness, which has been particularly exacerbated after the pandemic crisis. In this scenario, this study seeks to analyze nurses' turnover intention by comparing Portuguese public and private healthcare organizations. As determining factors, transformational leadership, perceived organizational support and organizational commitment were considered.

A survey was digitally applied to 277 nurses from Portuguese public and private healthcare organizations.

Results suggested that there are differences in nurses' turnover intentions: there is a greater likelihood of nurses in the private sector planning to leave the healthcare organizations the nurses work for when compared to public hospital nurses. Furthermore, nurses in public hospitals perceive lower levels of transformational leadership, organizational support and organizational commitment than those in the private sector. The underlying cause as to the intention of leaving the public sector resides in normative commitment. On the other hand, lower affective commitment explains the intention to abandon the private sector.

This study is relevant for human resource managers and administrators in public and private hospitals since it enables a diagnosis of the situation, as well as a definition of the most appropriate policies for each of the sectors as a strategy to attract and retain health professionals.

This study is significant as the study provides a better understanding of the reasons which lead nurses to consider leaving the organization where the nurses work and the difference between nursing professionals in public and private hospitals.

This study aims to assess the factors influencing the effective implementation of extended producer responsibility (EPR) practices within the context of sustainable accounting goals and circular economy principles. It seeks to provide insights into the significance of EPR policies for manufacturing industries striving to enhance their environmental, social and governance performance while ensuring sustainable accounting of their business operations.

The methodology proposed in this article is based on the decision-making trial and evaluation laboratory (DEMATEL) technique. This approach formulates a structural framework for evaluating influential elements among critical recognized factors. By using DEMATEL, the study examines the interconnectedness between assessed factors through a cause-and-effect diagram, facilitating the integration of EPR into sustainable accounting practices.

The research findings reveal that the most impactful contributors to sustainable accounting practices of EPR within the framework of sustainable development goals and circular economy are producers, consumers, eco-design, public awareness and the support of local authorities. These findings underscore the importance of considering these factors in implementing EPR and advancing sustainable accounting practices.

This paper contributes to the literature by proposing a DEMATEL-based model for evaluating the factors affecting the implementation of EPR within the context of sustainable accounting goals and circular economy principles.

The language of participative theatre can be considered immersive in the treatment of its dialectics where participants engage fully with their dichotomies and value systems through physical and psychological exploratory processes as they commit themselves to transformation.

The use of drama as an intervention for challenging recurring mental models of oppressive narratives is used extensively in experiential psychotherapy and as a socio-psychological integrative tool. This experiential methodology allows for an organic development and expression of themes and motifs by encouraging a participant to develop a deeper awareness of how he/she interprets their identity and that of the community in which they function.

This paper aims to review the implications of applying drama-based interventions as positive psychotherapeutic devices to facilitate self-reflection and active-constructive responding in enabling a rendering of positive patterns of thought and purposeful movement towards emotional and physical well-being.

Research on the principles of positive psychology suggests that positive emotions lead to therapeutic change. Nurturing positive emotions which are immanent in spirituality, creativity and optimistic perseverance through autonomy and self-regulation enable individual potential to come to meaningful fruition.

The paper conceptualizes psychodrama as a framing technique in enabling reflexive action in identity transformation and well-being.

Brewer's spent grain (BSG) is the main by-product of the brewing industry, holding significant potential for biomass applications. The purpose of this paper was to determine the effective thermal conductivity (k_eff) of BSG and to develop an Artificial Neural Network (ANN) to predict k_eff, since this property is fundamental in the design and optimization of the thermochemical conversion processes toward the feasibility of bioenergy production.

The experimental determination of k_eff as a function of BSG particle diameter and heating rate was performed using the line heat source method. The resulting values were used as a database for training the ANN and testing five multiple linear regression models to predict k_eff under different conditions.

Experimental values of k_eff were in the range of 0.090–0.127 W m⁻¹ K⁻¹, typical for biomasses. The results showed that the reduction of the BSG particle diameter increases k_eff, and that the increase in the heating rate does not statistically affect this property. The developed neural model presented superior performance to the multiple linear regression models, accurately predicting the experimental values and new patterns not addressed in the training procedure.

The empirical correlations and the developed ANN can be utilized in future work. This research conducted a discussion on the practical implications of the results for biomass valorization. This subject is very scarce in the literature, and no studies related to k_eff of BSG were found.

This research used a temporal approach to operationalize employee engagement, capturing subjective/objective time of the day and day of the week to analyze the dynamic patterns of employees’ daily/weekly well-being, basic needs satisfaction, and situational work motivation under the integrated framework of self-determination theory.

Multi-level data was collected using the survey structure outlined under the day reconstruction methodology (DRM) with samples of Canadian part-time working undergraduate students and full-time US corporate employees (1980 work episodes reported by 321 participants).

Multi-level confirmatory factorial analysis results supported the measurement invariance for within-person variables in all the working episodes across the US and Canada samples. Structural equation modeling path analysis results, using the within-person variables, captured the daily temporal patterns that employees’ well-being (vitality and positive affect), basic psychological needs (autonomy and relatedness), and situational autonomous motivation started at a high level and decreased with both subjective and objective time of the day. Negative affect showed asymmetric daily and weekly temporal patterns compared to positive affect. A few indirect paths were found, including one from the subjective time of the day to employee well-being (vitality and affect) via situational autonomous motivation and another one from the day of the week to vitality and positive affect via relatedness needs satisfaction and situational autonomous motivation.

The socio-cultural and business impacts of work scheduling practices and implications for theory-driven, evidence-based organizational development practices were discussed together with the research limitations.

Results on how the variations in self-regulation during the performance of different work tasks in a single work event help practitioners to connect repeated situational motivational change patterns to effective supervision. HR business partner can also utilize such findings to shape evidence-based practice to improve employee engagement.

This research is one of the few pioneer studies to look into how temporal factors, such as work scheduling, affect employees' well-being through the dynamic understanding of the mediated path model from time to employee well-being via psychological engagement conditions such as motivation and needs satisfaction.

Although the fitness switching costs scale (FSCS) was shown to have sound psychometric properties, the length of the 54-item may impose burdens on survey participants and present methodological and analytic challenges for researchers and practitioners. Therefore, the present study shortened and validated two versions of the FSCS, namely the 33-item FSCS (FSCS-33) and the 11-item FSCS (FSCS-11).

In Study 1 (n = 411), the most useful items from the FSCS for the FSCS-33 and FSCS-11 were identified using item response theory (IRT). Study 2 (n = 391) and Study 3 (n = 400) assessed the psychometric properties of the FSCS-33 and FSCS-11, respectively, using partial least squares structural equation modeling.

The FSCS-33 and FSCS-11 demonstrated strong reliability and validity in assessing switching costs in fitness centers.

The psychometrically sound short-form scales provide researchers and practitioners with convenient and accurate means of measuring switching costs in fitness centers.

This study delves into the intricate relationship between specific positive and negative emotions experienced by tourists during their vacations and the corresponding emotion regulation strategies they employ. Drawing from emotion regulation theory, we examine the nuanced impact of various strategies on tourists' emotional experiences, thereby advancing our understanding of emotion dynamics in the context of tourism.

Data were gathered through an online survey and travel diaries, subsequently analyzed using linear mixed-effects models.

Our findings underscore that emotion regulation strategies exert a significant influence on both positive and negative emotions. Furthermore, we identified that different strategies correlate uniquely with specific emotions. For instance, the deployment of Expressive Suppression, Savoring, and Stimulus Control strategies notably amplifies the intensity of joy.

This study recommends that tourism managers design experiences that evoke positive emotions through curated sensory cues, storytelling, and stress-free service offerings. Tourism managers should prioritize stress-free services, guide tourists in expressing themselves, and train service providers to manage emotions effectively, thus promoting positive emotional interactions and improving overall customer satisfaction.

Theoretically, this research enriches the emotion regulation literature by contextualizing it within the tourism domain, highlighting the differential effects of regulation strategies on diverse emotional experiences. From a practical standpoint, these insights can guide practitioners in crafting targeted marketing strategies and empower tourists with knowledge to select optimal strategies for enhancing their emotional well-being during vacations.

Ensuring the early detection of structural issues in aircraft is crucial for preserving human lives. One effective approach involves identifying cracks in composite structures. This paper employs experimental modal analysis and a multi-variable Gaussian process regression method to detect and locate cracks in glass fiber composite beams.

The present study proposes Gaussian process regression model trained by the first three natural frequencies determined experimentally using a roving impact hammer method with crystal four-channel analyzer, uniaxial accelerometer and experimental modal analysis software. The first three natural frequencies of the cracked composite beams obtained from experimental modal analysis are used to train a multi-variable Gaussian process regression model for crack localization. Radial basis function is used as a kernel function, and hyperparameters are optimized using the negative log marginal likelihood function. Bayesian conditional probability likelihood function is used to estimate the mean and variance for crack localization in composite structures.

The efficiency of Gaussian process regression is improved in the present work with the normalization of input data. The fitted Gaussian process regression model validates with experimental modal analysis for crack localization in composite structures. The discrepancy between predicted and measured values is 1.8%, indicating strong agreement between the experimental modal analysis and Gaussian process regression methods. Compared to other recent methods in the literature, this approach significantly improves efficiency and reduces error from 18.4% to 1.8%. Gaussian process regression is an efficient machine learning algorithm for crack localization in composite structures.

The experimental modal analysis results are first utilized for crack localization in cracked composite structures. Additionally, the input data are normalized and employed in a machine learning algorithm, such as the multi-variable Gaussian process regression method, to efficiently determine the crack location in these structures.

The purpose of this study is to explore how the personal traits of the informal entrepreneurs influence their formalization decisions.

This study adopted a qualitative approach using a multicase design in which 28 informal entrepreneurs situated in Kampala district, Uganda, were engaged. An interview guide, recorders and note books were used in data collection.

The results indicate that the traits of informal and semiformal entrepreneurs are distinct. Informal entrepreneurs have been noted to be more courageous and resilient, while their semiformal counterparts have greater passion for their businesses. It is thus observed that the formalization prospects are higher for the semiformal entrepreneurs than for their informal counterparts. Entrepreneurs that would be willing to formalize their businesses are discouraged by distance, technology and the cost of involving middlemen. Whereas the resilient entrepreneurs are noted to work through these challenges, the passive ones in both the informal and semiformal categories will not formalize their businesses by giving such excuses.

This study contributes to the extant literature on informal entrepreneurship by providing initial empirical evidence on how the personal traits of the entrepreneurs influence their formalization decisions specifically.