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The fifth Sustainable Development Goal of the United Nations targets achieving gender equality by 2030, but recent progress has been sluggish. Gender inequalities in the labor markets may have been exacerbated by the Covid-19 recession. This paper aims to investigate the effects of the Covid-19 pandemic on gender inequalities in the labor markets by...

Facial expression recognition (FER) is essential for discerning human emotions and is applied extensively in big data analytics, healthcare, security, and user experience enhancement. This paper presents an empirical study that evaluates four existing deep learning models—VGG16, DenseNet, ResNet50, and GoogLeNet—utilizing the Facial Expression Recognition 2013 (FER2013) dataset. The dataset contains seven distinct emotional expressions: angry, disgust, fear, happy, neutral, sad, and surprise. Each model underwent rigorous assessment based on metrics including test accuracy, training duration, and weight file size to test their effectiveness in FER tasks. ResNet50 emerged as the top performer with a test accuracy of 69.46%, leveraging its residual learning architecture to effectively address challenges inherent in training deep neural networks. Conversely, GoogLeNet exhibited the lowest test accuracy among the models, suggesting potential architectural constraints in FER applications. VGG16, while competitive in accuracy, demonstrated lengthier training times and a larger weight file size (512MB), highlighting the inherent balance between model complexity and computational efficiency.

<jats:title>Abstract</jats:title><jats:p>This research unveils to predict consumer ad preferences by detecting seven basic emotions, attention and engagement triggered by advertising through the analysis of two specific physiological monitoring tools, electrodermal activity (EDA), and Facial Expression Analysis (FEA), applied to video advertising, offering a twofold contribution of significant value. First, to identify the most relevant physiological features for consumer preference prediction. We integrated a statistical module encompassing inferential and exploratory analysis tools, which identified emotions such as Joy, Disgust, and Surprise, enabling the statistical differentiation of preferences concerning various advertisements. Second, we present an artificial intelligence (AI) system founded on machine learning techniques, encompassing k‐Nearest Neighbors, Support Vector Machine, and Random Forest (RF). Our findings show that the RF technique emerged as the top performer, boasting an 81% Accuracy, 84% Precision, 79% Recall, and an F1‐score of 81% in predicting consumer preferences. In addition, our research proposes an eXplainable AI module based on feature importance, which discerned Attention, Engagement, Joy, and Disgust as the four most pivotal features influencing consumer ad preference prediction. The results indicate that computerized intelligent systems based on EDA and FEA data can be used to predict consumer ad preferences based on videos and effectively used as supporting tools for marketing specialists.</jats:p>

The origins of neutrino masses is one of the biggest mysteries in modern physics since they are beyond the realm of the Standard Model. As massive particles, neutrinos undergo flavor oscillations throughout their propagation. In this paper we show that when a neutrino oscillates from a flavor state {\alpha} to a flavor state \b{eta}, it follows three possible paths consistent with the Quantum Yang- Baxter Equations. These trajectories define the transition probabilities of the oscillations. Moreover, we define a probability matrix for flavor transitions consistent with the Quantum Yang-Baxter Equations, and estimate the values of the three neutrino mass eigenvalues within the framework of the triangular formulation.

The cosmological constant is normally introduced as an additional term entering the Einstein–Hilbert (EH) action. In this letter, we demonstrate that, instead, it appears naturally from the standard EH action as an invariant term emerging from spacetime symmetries. We then demonstrate that the same constraint emerging from this invariant suppresses the short wavelength modes and it favors the long wavelength ones. In this way, inside the proposed formulation, the observed value for the vacuum energy density is obtained naturally from the zero-point quantum fluctuations.

The mutual information method has demonstrated to be very useful for deriving the potential order parameter of a system. Although the method suggests some constraints which help to define this quantity, there is still some freedom in the definition. The method then results inefficient for cases where we have order parameters with a large number of constants in the expansion, which happens when we have many degenerate vacuums. Here, we introduce some additional constraints based on the existence of broken symmetries, which help us to reduce the arbitrariness in the definitions of the order parameter in the proposed mutual information method.

Theories and models try to describe the complexity of how people embrace and make use of innovations. The determinants of behavior concerning battery electric vehicles (BEVs) have traditionally revolved around factors such as vehicles’ price and range. Surprisingly, despite being small and one of the wealthiest territories in the world, Macau SAR faces challenges in BEV market penetration, underscoring the need to explore alternative causes of behavioral intention. To explain the adoption of technologies, this paper focuses on technology show-off (TS), which combines Rogers’ (2003) concepts of visibility and trialability of technology in a single construct as a determinant of behavioral intention. Therefore, when individuals perceive technology as more visible and available for trial, their behavioral intention to adopt it strengthens. Using structural equation modeling for quantitative analysis, this study examines the significance of TS in terms of the intention to adopt battery electric vehicles. The findings highlight the salience of the technology show-off construct in explaining behavioral intention, underscoring its significance in the context of modern society’s characteristics. This study contributes to advances in understanding technology acceptance and highlights the importance of incorporating experiential aspects (such as TS) into the traditional technology acceptance models.

<jats:p>Detecting emotions is a growing field aiming to comprehend and interpret human emotions from various data sources, including text, voice, and physiological signals. Electroencephalogram (EEG) is a unique and promising approach among these sources. EEG is a non-invasive monitoring technique that records the brain’s electrical activity through electrodes placed on the scalp’s surface. It is used in clinical and research contexts to explore how the human brain responds to emotions and cognitive stimuli. Recently, its use has gained interest in real-time emotion detection, offering a direct approach independent of facial expressions or voice. This is particularly useful in resource-limited scenarios, such as brain–computer interfaces supporting mental health. The objective of this work is to evaluate the classification of emotions (positive, negative, and neutral) in EEG signals using machine learning and deep learning, focusing on Graph Convolutional Neural Networks (GCNN), based on the analysis of critical attributes of the EEG signal (Differential Entropy (DE), Power Spectral Density (PSD), Differential Asymmetry (DASM), Rational Asymmetry (RASM), Asymmetry (ASM), Differential Causality (DCAU)). The electroencephalography dataset used in the research was the public SEED dataset (SJTU Emotion EEG Dataset), obtained through auditory and visual stimuli in segments from Chinese emotional movies. The experiment employed to evaluate the model results was “subject-dependent”. In this method, the Deep Neural Network (DNN) achieved an accuracy of 86.08%, surpassing SVM, albeit with significant processing time due to the optimization characteristics inherent to the algorithm. The GCNN algorithm achieved an average accuracy of 89.97% in the subject-dependent experiment. This work contributes to emotion detection in EEG, emphasizing the effectiveness of different models and underscoring the importance of selecting appropriate features and the ethical use of these technologies in practical applications. The GCNN emerges as the most promising methodology for future research.</jats:p>

Social Media Influencer (SMI) marketing represents a contemporary addition to the arsenal of digital advertising tools. Digital Content Creators are individuals who regularly share a variety of content, including visuals, audio recordings, and updates, across multiple social media platforms to shape consumers' perceptions of a brand and its products. The focus of this study is to examine how the credibility aspects of social media influencers (expertise, attractiveness, and trustworthiness) influence purchase intention and brand intimacy while also considering the mediating role of consumer engagement. This study used a quantitative, cross-sectional design with convenience sampling targeting social media-active individuals. Data were collected via a questionnaire distributed through email and social media, selecting participants who followed influencers. To gather data, 250 participants were engaged in an online questionnaire distributed via Google Forms. The findings indicate that the credibility dimensions of SMIs, particularly their attractiveness and trustworthiness, positively influence brand intimacy and purchase intention. Furthermore, consumer engagement serves as a critical mediator, connecting the authenticity of social media influencers with purchase intention and brand intimacy. In line with these results, it becomes evident that consumer engagement indirectly influences influencer credibility (trustworthiness and attractiveness), purchase intention, and brand intimacy. Notably, expertise does not exert any discernible impact on either brand intimacy or purchase intention. This study's outcomes provide valuable insights for marketing managers, underscoring the significance of partnering with influencers who possess a high level of trust within their respective marketing niches.

The potential of blockchain technology extends beyond cryptocurrencies and has the power to transform various sectors, including accounting and auditing. Its integration into auditing practices presents opportunities and challenges, and auditors must navigate new standards and engage with clients effectively. Blockchain technology provides tamper-proof record-keeping and fraud prevention, enhancing efficiency, transparency, and security in domains such as finance, insurance, healthcare, education, e-voting, and supply chain management. This paper conducts a bibliometric analysis of blockchain technology literature to gain insights into the current state and future directions of blockchain technology in auditing. The study identifies significant research themes and trends using keyword and citation analysis. The Vosviewer software was used to analyze the data and visualize the results. Findings reveal significant growth in blockchain research, particularly from 2021 onwards, with China emerging as a leading contributor, followed by the USA, India, and the UK. This study provides valuable insights into current trends, key contributors, and global patterns in blockchain technology research within auditing practices, and future research may explore thematic areas in greater depth.

Consumer neuroscience analyzes individuals’ preferences through the assessment of physiological data monitoring, considering brain activity or other bioinformation to assess purchase decisions. Traditional marketing tactics include customer surveys, product evaluations, and comments. For product or brand marketing and mass production, it is important to understand consumer neurological responses when seeing an ad or testing a product. In this work, we use the bi-clustering method to reduce EEG noise and automatic machine learning to classify brain responses. We analyze a neuromarketing EEG dataset that contains EEG data from product evaluations from 25 participants, collected with a 14 channel Emotiv Epoch + device, while examining consumer items. Four components comprised the research methodology. Initially, the Welch Transform was used to filter the EEG raw data. Second, the best converted signal biclusterings are used to train different classification models. Each biclustering is evaluated with a separate classifier, considering F1-Score. After that, the H2O.ai AutoML library is used to select the optimal biclustering and models. Instead of traditional procedures, two thresholds are used. First-threshold values indicate customer satisfaction. Low values of the second threshold reflect consumer dissatisfaction. Values between the first and second criteria are classified as uncertain values. We outperform the state of the art with a 0.95 F1-Score value.

We demonstrate that the flavor oscillation when a neutrino travels through spacetime, is equivalent to permanent changes on the vacuum state condition perceived by the same particle. This can be visualized via the Quantum Yang Baxter equations (QYBE). From this perspective, the neutrino never breaks the symmetry of the ground state because it never selects an specific vacuum condition. Then naturally the Higgs mechanism cannot be the generator of the neutrino masses. The constraints emerging from this model predict a normal mass hierarchy and some specific values for the mass eigenvalues once we fix the mixing angles. Interestingly, the model suggests that the sum of the mix angles is equal to $\pi/2$.

After the Covid-19 Pandemic crisis in international economic relations it became evident that climate-smart aspects should be considered when re-establishing a new international trade order. International organizations have proclaimed that this momentum should be used to include climate-smart trade and investment provisions to enable sustainable development. It has been acknowledged that trade has an important role to play in the global response to climate change, providing economies with tools to draw on in their efforts to mitigate climate change and to adapt to its consequences. In this paper we focus the analysis on investigating the digital and sustainable component of trade facilitation measures applied in Western Balkans countries. To evaluate the importance of trade facilitation measures and their digital and sustainable components we apply standard gravity model with the data from UN Global Survey on digital and sustainable trade facilitation. The results show that trade facilitation measures are important for improving and increasing trade among the Western Balkans countries. Especially, measures connected to improving transparency procedures in trade and measures for alleviating trade formalities are most significant for increasing bilateral trade among Western Balkans countries. With a lower level of importance are the measures for improving cross-border paperless trade between these countries.

We demonstrate that black hole evaporation can be modeled as a process where one symmetry of the system is spontaneously broken continuously. We then identify three free parameters of the system. The sign of one of the free parameters governs whether the particles emitted by the black hole are fermions or bosons. The present model explains why the black hole evaporation process is so universal. Interestingly, this universality emerges naturally inside certain modifications of gravity.

Intended as an economic and development hub, the Hengqin Cooperation Zone aims to foster collaboration and integration between mainland China, Hong Kong, and Macao, serving as a platform for economic development and innovation among the three regions. The zone's development has increased demand for financial services, often offered through fintech. There is, however, a lack of interoperability between the fintech services currently used in Macao and Hengqin. This may hinder Macao users' adoption of the technology. Thus, our research objective is to identify the factors determining Macao residents' adoption of fintech services in the area and provide insights for service providers, developers, and policymakers. A framework based on the Technology Acceptance Model (TAM) and Theory of Planned Behavior (TPB) was used for this purpose. The responses of 103 Macao residents provided evidence that ease of use significantly and positively impacts the usefulness of the technology. This in turn influences attitudes towards fintech usage. Subjective norms and perceived behavioral control positively impact fintech adoption intentions. The fintech industry and the governments of Macao and Hengqin can work on improving technology's ease of use and usefulness. They can also promote them to Macao users, and provide the resources required for better access to fintech in the zone

There are many systematic reviews on predicting stock. However, each reveals a different portion of the hybrid AI analysis and stock prediction puzzle. The principal objective of this research was to systematically review the existing systematic reviews on Artificial Intelligence (AI) models applied to stock market prediction to provide valuable inputs for the development of strategies in stock market investments. Keywords that would fall under the broad headings of AI and stock prediction were looked up in Scopus and Web of Science databases. We screened 69 titles and read 43 systematic reviews, including more than 379 studies, before retaining 10 for the final dataset. This work revealed that support vector machines (SVM), long short-term memory (LSTM), and artificial neural networks (ANN) are the most popular AI methods for stock market prediction. In addition, the time series of historical closing stock prices are the most commonly used data source, and accuracy is the most employed performance metric of the predictive models. We also identified several research gaps and directions for future studies. Specifically, we indicate that future research could benefit from exploring different data sources and combinations, while we also suggest comparing different AI methods and techniques, as each may have specific advantages and applicable scenarios. Lastly, we recommend better evaluating different prediction indicators and standards to reflect prediction models’ actual value and impact.