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The preponderance of studies that rely on self-report for both independent (e.g. stressors) and dependent (e.g. well-being) variables is often deplored, as it creates problems of common method variance, which may lead to inflated, or even spurious, correlations and predictions. It is sometimes suggested that alternative measures should yield more “objective” information on the phenomena under investigation. We discuss this issue with regard to: (a) observational measures of working conditions; (b) physiological measures of strain; and (c) event-based “self-observation” on a micro-level. We argue that these methods are not necessarily “objective.” Like self-report, they are influenced by a plethora of factors; and measurement artifacts can easily be produced. All this can make their interpretation quite difficult, and the conclusion that lack of convergence with self-report automatically invalidates self-report is not necessarily warranted. Especially with regard to physiological measures, one has to keep in mind that they refer to a different response level that follows its own laws and is only loosely coupled with psychological responses. Therefore, replacement is not a promising way to get more reliable estimates of stressor-strain relationships. We argue instead that each method contains both substantive and error variance, and that a combination of various methods seems more auspicious. After discussing advantages and pitfalls of observational, physiological, and self-observational measures, respectively, we report empirical examples from our own research on each of these methods, which are meant to illustrate both the advantages and the problems associated with them. They strengthen the overall conclusion that there is no “substitute” for self-report (which often is necessary to be able to interpret data from other methods, most notably physiological ones). They also illustrate that collecting such data is quite cumbersome, and that a number of conditions have to be carefully considered before using them, and we report some problems we encountered in this research. Altogether, we conclude that self-report measures, if carefully constructed, are better than their reputation, but that the optimal way is to complement them with other measures.

The aim of this chapter is to provide a literature review on daily recovery during non-work time. Specifically, next to discussing theories that help us understand the process of recovery, we will clarify how recovery and its potential outcomes have been conceptualized so far. Consequently, we present empirical findings of diary studies addressing the activities that may facilitate or hinder daily recovery. We will pay special attention to potential mechanisms that may underlie the facilitating or hindering processes. Owing to the limited research on daily recovery, we will review empirical findings on predictors and outcomes of a related construct, namely need for recovery. We conclude with an overall framework from which daily recovery during non-work time can be understood. In this framework, we claim that daily recovery is an important moderator in the process through which job characteristics and their related strain may lead to unfavorable states on a daily basis.

This paper examines the relationship between different manufacturing strategies and Industry 4.0's (I4.0) critical success factors (CSFs) and technology adoption.

For that, the authors surveyed 165 practitioners from different manufacturers. Participants provided information about the levels of product customization and production volume in their companies. They also indicated the adoption level of I4.0 technologies and CSFs. Using multivariate data techniques, the authors identified four clusters of different manufacturing strategies and two readiness levels based on the establishment of I4.0 CSFs. The adoption level of I4.0 technologies was then cross compared among clusters to identify which technologies are more likely to be supported.

The findings indicate that, in low-readiness companies, the adoption level of I4.0 technologies does not significantly differ between manufacturing strategies. However, when companies present a higher I4.0 readiness, the adoption of I4.0 technologies seem to vary according to the existing manufacturing strategy.

This study sheds light on the influence that manufacturing strategies may have on the digital transformation of companies, highlighting which strategies are more likely to offer a context to successfully adopt I4.0 technologies. The identification of these relationships helps to define the expectation regarding the company's digital transformation, determining coherent benchmarks and allowing managers to anticipate potential issues.

Sarcasm is a linguistic expression that usually carries the opposite meaning of what is being said by words, thus making it difficult for machines to discover the actual meaning. It is mainly distinguished by the inflection with which it is spoken, with an undercurrent of irony, and is largely dependent on context, which makes it a difficult task for computational analysis. Moreover, sarcasm expresses negative sentiments using positive words, allowing it to easily confuse sentiment analysis models. This paper aims to demonstrate the task of sarcasm detection using the approach of machine and deep learning.

For the purpose of sarcasm detection, machine and deep learning models were used on a data set consisting of 1.3 million social media comments, including both sarcastic and non-sarcastic comments. The data set was pre-processed using natural language processing methods, and additional features were extracted and analysed. Several machine learning models, including logistic regression, ridge regression, linear support vector and support vector machines, along with two deep learning models based on bidirectional long short-term memory and one bidirectional encoder representations from transformers (BERT)-based model, were implemented, evaluated and compared.

The performance of machine and deep learning models was compared in the task of sarcasm detection, and possible ways of improvement were discussed. Deep learning models showed more promise, performance-wise, for this type of task. Specifically, a state-of-the-art model in natural language processing, namely, BERT-based model, outperformed other machine and deep learning models.

This study compared the performance of the various machine and deep learning models in the task of sarcasm detection using the data set of 1.3 million comments from social media.

Because of the bioaccumulation effect, organophosphorus pesticides cause long-term damage to mammals, even at small concentrations. The ability to perturb the phospholipid bilayer structure as well as the overstimulation of cholinergic receptors makes them hazardous to humans. Therefore, there is a need for a quick and inexpensive detection of organophosphorus pesticides for agricultural and household use. As organophosphorus pesticides are acetylcholinesterase (AChE) inhibitors, biosensors using this mechanism hold a great promise to meet these requirements with a fraction of reagents and time used for measurement comparing to laboratory methods. This study aims to manufacture AChE-coated, screen-printed carbon electrodes applicable in such amperometric biosensors.

AChE enzyme, known for catalytic activity for the hydrolysis of acetylthiocholine (ATCh), could be used to obtain electrochemically active thiocholine from acetylthiocholine chloride in aqueous solutions. Using Malathion’s inhibitory effect towards AChE, pesticides’ presence can be detected by reduction of anodic oxidation peaks of thiocholine in cyclic voltammetry.

The conducted research proved that it is possible to detect pesticides using low-cost, simple-to-manufacture screen-printed graphite (GR) electrodes with an enzymatic (AChE) coating. Investigated electrodes displayed significant catalytic activity to the hydrolysis of ATCh. Owing to inhibition effect of the enzyme, amperometric response of the samples decreased in pesticide-spiked solution, allowing determination of organophosphorus pesticides.

Printed electronics has grown significantly in recent years as well as research focused on carbon-based nanocomposites. Yet, the utilization of carbon nanocomposites in screen-printed electronics is still considered a novelty in the market. Biosensors have proved useful not only in laboratory conditions but also in home applications, as glucometers are a superior solution for glucose determination for personal use. Although pesticides could be detected accurately using chromatography, spectroscopy, spectrometry or spectrophotometry, the market lacks low-cost, disposable solutions for pesticide detection applicable for household use. With biosensing techniques and electric paths screen-printed with GR or graphene nanocomposites, this preliminary research focuses on meeting these needs.

The purpose of this paper is to investigate, from a thorough review of the literature, the role of metaverse-based quality 4.0 (MV-based Q4.0) in achieving manufacturing resilience (MFGRES). Based on a categorization of MV-based Q4.0 enabler technologies and MFGRES antecedents, the paper provides a conceptual framework depicting the relationship between both areas while exploring existing knowledge in current literature.

The paper is structured as a comprehensive systematic literature review (SLR) at the intersection of MV-based Q4.0 and MFGRES fields. From the Scopus database up to 2023, a final sample of 182 papers is selected based on the inclusion/exclusion criteria that shape the knowledge base of the research.

In light of the classification of reviewed papers, the findings show that artificial intelligence is especially well-suited to enhancing MFGRES. Transparency and flexibility are the resilience enablers that gain most from the implementation of MV-based Q4.0. Through analysis and synthesis of the literature, the study reveals the lack of an integrated approach combining both MV-based Q4.0 and MFGRES. This is particularly clear during disruptions.

This study has a significant impact on managers and businesses. It also advances knowledge of the importance of MV-based Q4.0 in achieving MFGRES and gaining its full rewards.

This paper makes significant recommendations for academics, particularly those who are interested in the metaverse concept within MFGRES. The study also helps managers by illuminating a key area to concentrate on for the improvement of MFGRES within their organizations. In light of this, future research directions are suggested.

This paper examines how medical practice, like all other productive activities, has been subject to the transformative elements of the forces and the relations of production involving class struggle and intra-class conflict. It will explore changes in the relations of production of medical practice which have been catalyzed by powerful productive forces. The current period of medical production involves the transformation of simple commodity production into a transitional stage of capitalist production with the seemingly unbounded growth of the medical productive forces. This development was precipitated by the intervention of capital as a whole, to restrict the drain on their variable capital through the placement of units of financial capital into the management of medical production, using the leverage of access to patients. In response, physicians have consolidated and centralized their practices to create enterprises with market power to limit the extraction of surplus by financial capital, and by their own employment of productive labor to extract surplus from hired physician labor and other clinical workers. Rationalization of the production of medical service commodities, and the sharing of surplus generated from exploitation of an expanded labor force by managed care financial capital and their capitalist partners owning medical enterprises, constitutes the contemporary relations of production. The contradictions of this mode of medical production and the potential for its reproduction will be analyzed.