Search Results (55)

This study investigates how persuasion, trust, and empathy from Human Resources (HR) managers affect the acceptance of nudging practices in workplace, especially when these interventions are meant to be ethical and supportive. Based on the theory of advisory nudge, the research connects ideas from Human Resource Management and ethical marketing. A quantitative method was applied using a structured questionnaire answered by 733 HR professionals in European companies. The model was tested with PLS-SEM, and results confirmed strong influence of supervisor’s persuasion and empathy on HR professionals’ perception of nudges as ethical and autonomy-enhancing. The findings also showed that empathy plays important role in how HR professionals experience the intention behind soft interventions, with gender-based differences being significant. Additional analyses with IPMA and MGA confirmed the strategic importance of trust and emotional intelligence in organizational settings. The results help to understand when a persuasive act is seen as ethical guidance and when it is not, offering theoretical and practical insights both for HR leadership and marketing communication. The study suggests future research to explore different types of nudging and include variables such as organizational culture or HR professionals’ values, to better understand the ethical acceptance of influence at work. Full article

This study introduces two distinct post-processing strategies to address systematic biases in sea surface temperature (SST) numerical forecasts, thereby enhancing SST predictive accuracy. The first strategy implements a spatiotemporal four-dimensional multi-grid analysis (4D-MGA) scheme within a three-dimensional variational (3D-Var) data assimilation framework. The second strategy establishes a hybrid deep learning architecture integrating empirical orthogonal function (EOF) analysis, empirical mode decomposition (EMD), and a backpropagation (BP) neural network (designated as EE–BP). The 4D-MGA strategy dynamically corrects systematic biases through a temporally coherent extrapolation of analysis increments, leveraging its inherent capability to characterize intrinsic temporal correlations in model error evolution. In contrast, the EE–BP strategy develops a bias correction model by learning the systematic biases of the SST numerical forecasts. Utilizing a satellite fusion SST dataset, this study conducted bias correction experiments that specifically addressed the daily SST numerical forecasts with 7-day lead times in the Kuroshio region south of Japan during 2017, systematically quantifying the respective error reduction potentials of both strategies. Quantitative verification reveals that EE–BP delivers enhanced predictive skill across all forecast horizons, achieving 18.1–22.7% root–mean–square error reduction compared to 1.2–9.1% attained by 4D-MGA. This demonstrates deep learning’s unique advantage in capturing nonlinear bias evolution patterns. Full article

The increasing global emphasis on sustainability necessitates the integration of environmentally responsible practices within supply chains. This study explores the impact of green supply chain management practices (GSCMPs) on firm sustainable performance in Pakistan’s manufacturing and service industries. Unlike prior research, which primarily focuses on the direct impact of GSCMPs, this study advances knowledge by incorporating green technological innovation (GTI) and green managerial innovation (GMI) as mediators and green organizational culture (GOC) as a moderator. The study looks at survey data from 480 industry professionals and uses partial least squares structural equation modeling (PLS-SEM) and multi-group analysis (MGA). It discovers that GSCMPs greatly enhance sustainability outcomes, especially when green innovations are used. Furthermore, the impact of GSCMPs is more pronounced in the manufacturing sector, emphasizing the role of regulatory pressures and technological advancements. This study makes a significant contribution to the literature by integrating post-pandemic sustainability challenges, highlighting industry-specific dynamics, and providing actionable strategies to enhance green supply chain adoption in emerging markets. The study provides applicable strategies for managers and policymakers to embed sustainability deeper into corporate strategies, ensuring resilience and competitive advantages in evolving global markets. Full article

Arable land is fundamental to agricultural production and a crucial component of ecosystems. However, its complex texture and distribution in remote sensing images make it susceptible to interference from other land cover types, such as water bodies, roads, and buildings, complicating accurate identification. Building on previous research, this study proposes an efficient and lightweight CNN-based network, U-MGA, to address the challenges of feature similarity between arable and non-arable areas, insufficient fine-grained feature extraction, and the underutilization of multi-scale information. Specifically, a Multi-Scale Adaptive Segmentation (MSAS) is designed during the feature extraction phase to provide multi-scale and multi-feature information, supporting the model’s feature reconstruction stage. In the reconstruction phase, the introduction of the Multi-Scale Contextual Module (MCM) and Group Aggregation Bridge (GAB) significantly enhances the efficiency and accuracy of multi-scale and fine-grained feature utilization. The experiments conducted on an arable land dataset based on GF-2 imagery and a publicly available dataset show that U-MGA outperforms mainstream networks (Unet, A2FPN, Segformer, FTUnetformer, DCSwin, and TransUnet) across six evaluation metrics (Overall Accuracy (OA), Precision, Recall, F1-score, Intersection-over-Union (IoU), and Kappa coefficient). Thus, this study provides an efficient and precise solution for the arable land recognition task, which is of significant importance for agricultural resource monitoring and ecological environmental protection. Full article

Deep forgery detection plays a crucial role in addressing the challenges posed by the rapid spread of deeply generated content that significantly erodes public trust in online information and media. Deeply forged images typically present subtle but significant artifacts in multiple regions, such as in the background, lighting, and localized details. These artifacts manifest as unnatural visual distortions, inconsistent lighting, or irregularities in subtle features that break the natural coherence of the real image. To address these features of forged images, we propose a novel and efficient deep image forgery detection method that utilizes Multi-Graph Attention (MGA) techniques to extract global and local features and minimize accuracy loss. Specifically, our method introduces an interactive dual-channel encoder (DIRM), which aims to extract global and channel-specific features and facilitate complex interactions between these feature sets. In the decoding phase, one of the channels is processed as a block and combined with a Dynamic Graph Attention Network (PDGAN), which is capable of recognizing and amplifying forged traces in local information. To further enhance the model’s ability to capture global context, we propose a global Height–Width Graph Attention Module (HWGAN), which effectively extracts and associates global spatial features. Experimental results show that the classification accuracy of our method for forged images in the GenImage and CIFAKE datasets is comparable to that of the optimal benchmark method. Notably, our model achieves 97.89% accuracy on the CIFAKE dataset and has the lowest number of model parameters and lowest computational overhead. These results highlight the potential of our method for deep forgery image detection. Full article

This study examines the impact of Customer-based Brand Equity (CBBE) on passengers’ Willingness to Pay Premium (WPP) for airline tickets, comparing low-cost and flag airlines. The research is prompted by the competitive nature of the industry and the need to comprehend passenger preferences, focusing on brand image, brand awareness, and service attributes as key variables influencing CBBE. The survey data collected from 489 recent travelers were analyzed through Partial Least Squares Structural Equation Modelling (PLS-SEM) and Multigroup Analysis (MGA), generating two quantitative analyses: first, the model was analyzed for airlines in general, and second, a multi-group analysis was performed to understand how the model behaves through price tiers. The findings indicate the significant influence of the chosen variables on both CBBE and WPP. A distinguishing factor lies in the differentiation between low-cost and flag airlines, revealing differing impacts on CBBE and WPP. This research contributes to the branding literature by expanding CBBE’s application to services, especially in the airline sector. It also builds on existing knowledge of WPP’s importance in service industries. Segmenting airline price tiers offers actionable insights for management strategies. In conclusion, this study augments the knowledge of CBBE, providing valuable managerial implications, guiding brand-tailored strategies to increase passengers’ willingness to pay premium. Full article

The segmentation of medical images, particularly for brain tumors, is essential for clinical diagnosis and treatment planning. In this study, we proposed MMAformer, a Multiscale Modality-Aware Transformer model, which is designed for segmenting brain tumors by utilizing multimodality magnetic resonance imaging (MRI). Complementary information between different sequences helps the model delineate tumor boundaries and distinguish different tumor tissues. To enable the model to acquire the complementary information between related sequences, MMAformer employs a multistage encoder, which uses a cross-modal downsampling (CMD) block for learning and integrating the complementary information between sequences at different scales. In order to effectively fuse the various information extracted by the encoder, the Multimodal Gated Aggregation (MGA) block combines the dual attention mechanism and multi-gated clustering to effectively fuse the spatial, channel, and modal features of different MRI sequences. In the comparison experiments on the BraTS2020 and BraTS2021 datasets, the average Dice score of MMAformer reached 86.3% and 91.53%, respectively, indicating that MMAformer surpasses the current state-of-the-art approaches. MMAformer’s innovative architecture, which effectively captures and integrates multimodal information at various scales, offers a promising solution for tackling complex medical image segmentation challenges. Full article

In many European countries the consumption of tropical fruit is constantly growing, and people are increasingly turning to diets rich in fruit and vegetables. In this context, mango is considered a super-food for its nutritional medium-high energy value. Produced mainly in developing countries, tropical fruits animate an interesting international market. Production in Mediterranean countries is also growing and is increasingly requested in European markets. The aim of this work is to investigate the factors that drive the inclination to purchase fresh mango and mango food and drinks in Italy and Brazil in order to observe consumer preferences in the two countries. The personal experiences, motivations and choices of consumers regarding fresh mango and mango-based products were taken into consideration. Through an online survey, a semi-structured questionnaire was administered in Italy and Brazil which led to a total sample of 453 participants. The data were statistically analyzed, and a PLS-SEM model was used to empirically examine the factors influencing the consumption of fresh mango and mango food and drinks. The research hypotheses are all supported. For a comparison between the two countries, a multigroup analysis (PLS-MGA) was performed. In Italy, consumers are attentive to the quality and safety of the fruit; they choose the point of sale where they buy fresh mango or mango foods because they trust the seller to guarantee the fruit’s origin and transformation. In Brazil, new consumer trends are emerging especially in gastronomy; since they are local foods, they are considered safe, sustainable and healthy by consumers. The study addresses a little-explored topic and aims to enrich the debate on consumer orientations, preferences and reasons for buying mango and mango products. Full article

Tax compliance and avoidance are critical issues for governments and businesses worldwide, especially as businesses often use legal methods to minimize taxes, which can impact public revenue and equity within the tax system. This study focuses on understanding the factors influencing tax avoidance behaviors among SMEs in Greece’s tourism sector, a sector that has received limited research attention. To this end, a quantitative cross-sectional design was employed, using a structured questionnaire to explore potential factors influencing tax avoidance behavior. Data were collected from 534 SME managers and analyzed using Structural Equation Modeling (SEM) to assess the impact of key factors and their interrelationships, including coercive power, digital transformation, tax knowledge, firm performance, and perceived fairness, on tax avoidance. In addition, corporate social responsibility (CSR) was included as a moderator variable, while coercive power and digital transformation were assessed as mediators. Furthermore, Multi-Group Analysis (MGA) was conducted to explore the differences between small and medium enterprises, as well as different ownership structures. The results indicate that all key determinants, except perceived fairness, are significantly and positively related to tax avoidance intention. Additionally, it was revealed that coercive power increases tax avoidance through firm performance and tax knowledge, while digital transformation mediates the influence of firm performance on tax avoidance by curtailing avoidance intentions. While CSR mitigates the negative influence of coercive power, digital transformation has a dual role: that of promoting transparency and strategic efforts to reduce the tax burden. These findings have important policy implications, as policymakers seek to promote digital adoption and enhance CSR engagement while formulating specific regulatory strategies to reduce tax avoidance among SMEs. Full article

Utilizing time-series data from ship trajectories to forecast their subsequent movement is crucial for enhancing the safety within maritime traffic environments. The application of deep learning techniques, leveraging Automatic Identification System (AIS) data, has emerged as a pivotal area in maritime traffic studies. Within this domain, the precise forecasting of ship trajectories stands as a central challenge. In this study, we propose the multi-gated attention encoder decoder (MGAED) network, a model based on an encoder–decoder structure specialized for predicting ship trajectories in canals. The model employs a long short-term memory network (LSTM) as an encoder, combined with multiple Gated Recurrent Units (GRUs) and an attention mechanism for the decoder. Long-term dependencies in time-series data are captured through GRUs, while the attention mechanism is used to strengthen the model’s ability to capture key information, and a soft threshold residual structure is introduced to handle sparse features, thus enhancing the model’s generalization ability and robustness. The efficacy of our model is substantiated by an extensive evaluation against current deep learning benchmarks. Through comprehensive comparison experiments with existing deep learning methods, our model shows significant improvements in prediction accuracy, with an at least 9.63% reduction in the mean error (MAE) and an at least 20.0% reduction in the mean square error (MSE), providing a new solution to improve the accuracy and efficiency of ship trajectory prediction. Full article

Streptococcus pyogenes, commonly known as Group A Streptococcus (GAS), is a significant human pathogen responsible for a wide range of diseases, from mild pharyngitis to severe conditions such as necrotizing fasciitis and toxic shock syndrome. The increasing antibiotic resistance, especially against macrolide antibiotics, poses a challenge to the effective treatment of these infections. This paper reviews the current state and mechanisms of antibiotic resistance in S. pyogenes. Furthermore, molecular targets for developing anti-virulence agents, which aim to attenuate virulence rather than killing it outright, are explored. This review specifically focuses on virulence regulators, proteins that coordinate the expression of multiple virulence factors in response to environmental signals, playing a crucial role in the pathogen’s ability to cause disease. Key regulatory systems, such as RopB, Mga, CovRS, and the c-di-AMP signaling system, are discussed for their roles in modulating virulence gene expression. Additionally, potential molecular target sites for the development of anti-virulence agents are suggested. By concentrating on these regulatory pathways, it is proposed that anti-virulence strategies could enhance the effectiveness of existing antibiotics and reduce the selective pressure that drives the development of resistance. Full article

With increasing worries about the environment, there is a rising focus on saving energy in various industries. In the e-mobility industry of electric motors, permanent magnet synchronous motors (PMSMs) are widely utilized for saving energy due to their high-efficiency motor technologies. However, challenges like environmental degradation from rare earth development and difficulties in controlling magnetic field fluctuations persist. To address these issues, active research focuses on the wound field synchronous motor (WFSM), known for its ability to regulate field current efficiently across various speeds and operating conditions. Nevertheless, compared with other synchronous motors, the WFSM tends to exhibit relatively lower efficiency and torque density. Because the WFSM involves winding both the rotor and the stator, it results in increased copper and iron losses. In this article, a model that enhances torque density by inserting permanent magnets (PMs) into the rotor of the basic WFSM is proposed. This proposed model bolsters the d axis magnetic flux, thereby enhancing the motor’s overall performance while addressing environmental concerns related to rare-earth materials and potentially reducing manufacturing costs when compared with those of the PMSM. The research methodology involves a comprehensive sensitivity analysis to identify key design variables, followed by sampling using optimal Latin hypercube design (OLHD). A surrogate model is then constructed using the kriging interpolation technique, and the optimization process employs a micro-genetic algorithm (MGA) to derive the optimal model configuration. The algorithm was performed to minimize the use of PMs when the same torque as that of the basic WFSM is present, and to reduce torque ripple. Error assessment is conducted through comparisons with finite element method (FEM) simulations. The optimized permanent-magnet-assisted WFSM (PMa-WFSM) model improved efficiency by 1.08% when it was the same size as the basic WFSM, and the torque ripple decreased by 5.43%. The proposed PMa-WFSM derived from this article is expected to be suitable for use in the e-mobility industry as a replacement for PMSM. Full article

Food waste is an issue throughout the food supply chain from production to consumption, especially in the later stages, such as retailing and final consumption. For the future of the developing world, changes in farming and retail practices are crucial. This study introduces a digital system for managing agricultural waste in Thailand that aims to encourage farmers and food retailers to sell their excess agricultural materials. The study’s objectives are as follows: (1) to explore factors that affect users’ behavioral intention to utilize an agriculture waste trading platform; (2) to compare the behavioral differences between farmers and retailers regarding their intention to use a digital platform for sustainable agriculture. Data were gathered from 570 fruit and vegetable sellers and farmers across five provinces in the northeastern region of Thailand. Structural equation modeling (SEM) was used to analyze the relationships between constructs based on the modified Unified Theory of Acceptance and Use of Technology (UTAUT2), and multigroup analysis (MGA) was employed to analyze differences in path coefficients across groups. The key findings revealed that social influence (SI) had a more significant impact on retailers compared to farmers, while facilitating conditions (FC), habits (HB), and privacy (PR) were necessary for both groups. Unlike retailers, farmers were also motivated by hedonic motivation (HM) from using the platform. Explicitly, retailers’ behavioral intentions were influenced by a more significant number of factors than those of farmers. This research suggests that policymakers should develop targeted marketing campaigns leveraging social influence for retailers, improve platform usability and security, and create incentives for habitual use to enhance platform adoption. Additionally, policymakers should promote engaging features for farmers, provide comprehensive education and training, and advocate for supportive policies and financial incentives. Strategic actions to facilitate the transition toward a circular economy will improve the environmental sustainability and economic resilience of the agri-food sector. Full article

The pregnane X receptor (PXR) is a nuclear hormone receptor that plays a pivotal role in regulating gene expression in response to various ligands, particularly xenobiotics. In this context, the aim of this study was to shed light on the ligand affinity and functions of four NR1J1 paralogs identified in the marine mussel Mytilus galloprovincialis, employing a dual-luciferase reporter assay. To achieve this, the activation patterns of these paralogs in response to various toxins, including freshwater cyanotoxins (Anatoxin-a, Cylindrospermopsin, and Microcystin-LR, -RR, and -YR) and marine algal toxins (Nodularin, Saxitoxin, and Tetrodotoxin), alongside natural compounds (Saint John’s Wort, Ursolic Acid, and 8-Methoxypsoralene) and microalgal extracts (Tetraselmis, Isochrysis, LEGE 95046, and LEGE 91351 extracts), were studied. The investigation revealed nuanced differences in paralog response patterns, highlighting the remarkable sensitivity of MgaNR1J1γ and MgaNR1J1δ paralogs to several toxins. In conclusion, this study sheds light on the intricate mechanisms of xenobiotic metabolism and detoxification, particularly focusing on the role of marine mussel NR1J1 in responding to a diverse array of compounds. Furthermore, comparative analysis with human PXR revealed potential species-specific adaptations in detoxification mechanisms, suggesting evolutionary implications. These findings deepen our understanding of PXR-mediated metabolism mechanisms, offering insights into environmental monitoring and evolutionary biology research. Full article

Chatbots are computer programs that mimic human conversation using text or voice or both. Users’ acceptance of chatbots is highly influenced by their persona. Users develop a sense of familiarity with chatbots as they use them, so they become more approachable, and this encourages them to interact with the chatbots more readily by fostering favorable opinions of the technology. In this study, we examine the moderating effects of persona traits on students’ acceptance and use of chatbot technology at higher educational institutions in the UK. We use an Extended Unified Theory of Acceptance and Use of Technology (Extended UTAUT2). Through a self-administrated survey using a questionnaire, data were collected from 431 undergraduate and postgraduate computer science students. This study employed a Likert scale to measure the variables associated with chatbot acceptance. To evaluate the gathered data, Structural Equation Modelling (SEM) coupled with multi-group analysis (MGA) using SmartPLS3 were used. The estimated Cronbach’s alpha highlighted the accuracy and legitimacy of the findings. The results showed that the emerging factors that influence students’ adoption and use of chatbot technology were habit, effort expectancy, and performance expectancy. Additionally, it was discovered that the Extended UTAUT2 model did not require grades or educational level to moderate the correlations. These results are important for improving user experience and they have implications for academics, researchers, and organizations, especially in the context of native chatbots. Full article