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Found 6374 publications
Positive Motherhood at Work: Factors Supporting a Sustainable Workforce Through Reintegration After Returning from Maternity Leave
Publication Name: International Journal of Innovative Research and Scientific Studies
Publication Date: 2025-03-04
Volume: 8
Issue: 2
Page Range: 3789-3796
Description:
This study explores sustainability through the successful reintegration of women into the workforce following maternity leave. It aims to identify the key factors that contribute to positive motherhood at work and their role in building a sustainable workforce. The research is based on a systematic literature review using Scopus-indexed sources from 2006 to 2024, focusing on "maternity leave" and "wellbeing." The study applies a grounded theory approach to analyze existing organizational policies and workplace practices that influence female employees' reintegration. The findings highlight the necessity of a supportive work environment that facilitates the balance between career and motherhood. Key factors affecting reintegration include organizational policies, workplace culture, and supervisor support. The study also identifies gaps in existing research and the need for further empirical studies on reintegration practices in different industries and countries. Successful reintegration of women after maternity leave is essential for fostering a resilient and inclusive workforce. Organizations that implement supportive measures enhance employee retention, satisfaction, and overall workplace sustainability. The study's insights will inform primary research on reintegration policies in commercial banks across multiple countries. The findings will serve as a foundation for developing a model and best practices to improve the reintegration of women post-maternity leave. This research has broader implications for policymakers, HR professionals, and organizations aiming to promote gender equality and workforce sustainability.
Open Access: Yes
Chatbot as a Corporate Communication Tool: Best Practice of a Hungarian HR Services Company
Publication Name: Journal of Ecohumanism
Publication Date: 2024-08-31
Volume: 3
Issue: 6
Page Range: 849-858
Description:
The advent of cutting-edge technologies has changed the way humans communicate. Corporate communication also has to face the challenges and take advantage of the opportunities stemming from the evolution of infocommunication technologies. The paper aims to present the best practice of a Hungarian certified company providing full HR services regarding the use of a chatbot software application as a corporate communication tool. The company applies its award-winning chatbot to communicate with its temporary workforce in five different languages. The paper is a single-case study based on secondary and primary research. The empirical research is carried out by means of an in-depth interview with the examined company’s representative to get details about the successful application of the chatbot software. The results reveal that the company was able to successfully integrate a rule-based chatbot into its corporate communications. The use of the chatbot has brought a number of benefits, e.g. time-and cost-effectiveness, satisfaction from both the temporary workforce and the internal staff. The paper concludes that even the most basic type of chatbot software can be applied effectively if the users are informed and well-prepared in advance, meanwhile the knowledge base of the chatbot software is appropriately determined and constantly updated.
Open Access: Yes
Generative AI-driven transition to circular and responsible supply chains: Unpacking the dynamics of eco-centric design intelligence and ethical responsiveness
Publication Name: Technological Forecasting and Social Change
Publication Date: 2026-04-01
Volume: 225
Issue: Unknown
Page Range: Unknown
Description:
The study focuses on understanding how the use of generative Artificial Intelligence (AI) can beneficially result in circular supply chain transformation while embedding design intelligence, ethical intelligence, and predictive intelligence within socio-technical systems. This study proposes and validates a model that integrates generative eco-design intelligence, predictive circular supply chain planning, and ethical generative AI awareness, which collectively affect circular supply chain resilience and socio-environmental value realization, mediated by Sustainable process reconfiguration capability and AI-enabled stakeholder co-creation. To test the hypothesis, data were collected from 264 professionals in supply chain and technology-related industries in the USA. As the findings suggest, generative eco-design intelligence, predictive circular supply chain planning, and ethical generative AI awareness significantly enhance sustainable process reconfiguration capability, which drives AI-enabled stakeholder co-creation. A serial mediation model indicates that Generative AI capabilities affect circular supply chain resilience and socio-environmental value realization via sustainable process reconfiguration capability and AI-enabled stakeholder co-creation. To our surprise, the regenerative policy ambidexterity negatively moderates the relationship between AI-enabled stakeholder co-creation and the realization of socio-environmental value. The results provide actionable advice for managers implementing generative AI in sustainable supply chains. Instead of focusing solely on algorithmic efficiency, if an organization can develop reconfiguration capability and engage stakeholders, it would generate systemic sustainability benefits.
Open Access: Yes
Displacement-based failure analysis of metal matrix syntactic foam
Publication Name: Wit Transactions on Engineering Sciences
Publication Date: 2019-01-01
Volume: 124
Issue: Unknown
Page Range: 161-173
Description:
Metal matrix syntactic foams are being used more and more widely thanks to their relative density as well as their perfect energy-absorbing characteristics. Multiple studies concern the economical production of these materials – particularly, how energy absorption as a physical characteristic can be increased. Many studies examined the effects of material choice as well as cell size and wall thickness of reinforcing materials on compressive strength. However, there are only estimated models about descriptions concerning decaying processes of samples from different material composition and geometrical variation. In this study, we introduced an “in situ” examination in order to model the decaying process. We upsetted aluminium-ceramic composite foams cyclically. We reconstructed the geometry of the sample with microcomputed tomography (μCT) technology and digital image processing at certain specified points of the compression test. During the complete decaying process, the process was evaluated with volume change and number of broken hollow particles, as well as elementary particle displacements, orientation and sphericity of the reinforced material.
Open Access: Yes
DOI: 10.2495/MC190161
Carotenoid quantification of Cucurbita spp. by spectrophotometry, high-performance liquid chromatography and photoacoustics
Publication Name: Acta Scientiarum Polonorum Technologia Alimentaria
Publication Date: 2019-01-01
Volume: 18
Issue: 2
Page Range: 143-152
Description:
Background. Photoacoustic spectroscopy (PAS) is a tool for the rapid and non-destructive identification of materials even without contact. In recent years, there have been several works concerning the applicability of PAS in food analytical measurements. The intention of this work is to identify whether there is a correlation between total carotenoid and the β-carotene content of pumpkin and squash measured by high-performance liquid chromatography (HPLC), spectrophotometry (SP) and PAS. Material and methods. 'Crown prince F1', 'Veenas F1', 'Atlas F1' and 'Apollo F1' (SAKATA) were used as experimental materials. The samples were measured in a fresh state and in a lyophilised condition with HPLC, SP and PAS. Results. The results of SP show that total carotene content varies according to the species and variety. Lyophilisation resulted in lower, although varying carotene content compared to the raw form. Typical PA spectra of pumpkins were determined (300-550 nm), normalized to the carbon black powder. At 17 Hz the amplitude and carotene content shows direct proportionality in the range investigated. Photoacoustic (PA) signal and carotenoid content of pumpkin samples gave a linear correlation (R2 = 0.9821). Conclusion. The measurement of PA spectra gives reliable information about the total carotene content of pumpkin and squash samples. These findings may allow the use of PAS as a fast tool for the carotenoid determination in squashes and give the possibility of instead for the results to be used for the evaluation of squash varieties currently used for industrial processing in functional food development.
Open Access: Yes
ReGAIN: a reinforcement-enhanced generative AI framework for intelligent intrusion detection in IoT networks
Publication Name: Complex and Intelligent Systems
Publication Date: 2026-04-01
Volume: 12
Issue: 4
Page Range: Unknown
Description:
The advent of the Internet of Things (IoT) enables billions of devices in wide-ranging domains such as healthcare, industry, and smart cities to interconnect with each other, but these connections make the network vulnerable to advanced cyber threats too. Current intrusion detection methods have failed to provide effective detection capabilities mainly because of issues such as extremely imbalanced data distributions, low classification accuracy, or static and manually tuned hyperparameters that do not generalize well in dynamic IoT settings. These challenges are exacerbated by unique IoT constraints, including limited device resources and dynamic attack patterns, which further complicate effective detection. To address these challenges, in this study we present a Reinforcement-enhanced Generative Artificial Intelligence (ReGAIN) framework for intelligent intrusion detection in IoT networks. In this approach, we use a generative autoencoder for data balancing to generate realistic minority class instances in the latent feature space, and meanwhile to obtain stable and unbiased learning of the model. This paper introduces a novel Pointer-Attention Dual Network (PAD-Net) that employs a Dual Attention Network (DANet) and a Pointer Network (PtrNet) to enhance spatial attention and inter-feature relationships. We also propose Reinforcement-enhanced PAD-Net (RePAD-Net), which leverages reinforcement learning to automatically optimize key hyperparameters at each training step, further enhancing generalization ability and robustness. The intrusion detection task in this study is a multi-class classification problem, where different types of attacks are distinguished from each other. Experimental results demonstrate that PAD-Net and RePAD-Net achieve notable improvements of 3.79% and 8.79% in accuracy, 3.79% and 8.78% in recall, 2.79% and 9.01% in F1-score, 3.79% and 8.83% in Mathews correlation coefficient, and 3.94% and 9.11% in Cohen’s Kappa, respectively, along with significant reductions in log loss of 47.42% and 70.96% and hamming loss of 24.33% and 56.37% compared with baseline models such as naive bayes, gradient boosting, densely connected network, long short term memory, hybrid models, DANet and PtrNet. Additionally, 10-fold cross validation is applied to validate the results of proposed models. These findings confirm that our proposed ReGAIN framework, which is able to alleviate data imbalance and improve learning generalization, can dramatically enhance the reliability of detection performance under complex IoT intrusion environments.
Open Access: Yes
Sustainable development and common commercial policy
Publication Name: Acta Juridica Hungarica
Publication Date: 2012-12-01
Volume: 53
Issue: 4
Page Range: 334-344
Description:
No description provided
Open Access: Yes
Effects of Vinasse and zinc complex on the yield, crude protein, and gluten of winter wheat
Publication Name: Bio Web of Conferences
Publication Date: 2024-08-23
Volume: 125
Issue: Unknown
Page Range: Unknown
Description:
The primary goal of agricultural production is to produce adequate quantity and quality of crops. One crucial aspect of this is providing the appropriate nutrients to plants. In recent times, there has been a growing emphasis on replenishing micronutrients beside macronutrients, as essential microelements, although in smaller quantities, are indispensable for the cultivation of our crops. In our three-year small-plot experiments, the effect of two foliar fertilizers, Vinasse, which is a byproduct of alcohol production, and a zinc complex on the yield, crude protein, and gluten content of winter wheat, was investigated. The effects of these formulations when applied as foliar fertilizers separately and together, at doses of 50, 100, 250 and 500 l/ha for Vinasse and 0.5 kg/ha for zinc complex, were examined. Based on the results of the small-plot experiments set up in the fall of 2020 and 2021 and harvested in the summer of 2021 and 2022, it can be concluded that using Vinasse + zinc complex treatments a higher yield and better content indicators were achieved compared to the control plots. The highest dose of Vinasse (500 l/ha) + zinc complex (0.5 kg/ha) had the greatest positive effect on yield values.
Open Access: Yes
Curve Trajectory Model for Human Preferred Path Planning of Automated Vehicles
Publication Name: Automotive Innovation
Publication Date: 2024-02-01
Volume: 7
Issue: 1
Page Range: 59-70
Description:
Automated driving systems are often used for lane keeping tasks. By these systems, a local path is planned ahead of the vehicle. However, these paths are often found unnatural by human drivers. In response to this, this paper proposes a linear driver model, which can calculate node points reflective of human driver preferences and based on these node points a human driver preferred motion path can be designed for autonomous driving. The model input is the road curvature, effectively harnessed through a self-developed Euler-curve-based curve fitting algorithm. A comprehensive case study is undertaken to empirically validate the efficacy of the proposed model, demonstrating its capacity to emulate the average behavioral patterns observed in human curve path selection. Statistical analyses further underscore the model's robustness, affirming the authenticity of the established relationships. This paradigm shift in trajectory planning holds promising implications for the seamless integration of autonomous driving systems with human driving preferences.
Open Access: Yes
