Presented article takes a study done by researchers Davari & Strutton in the US in 2014 and replicated the same approach and methodology in evaluating how green marketing mix elements (product, price, promotion, place) influence brand associations, grand loyalty, perceived brand quality, and brand trust, in the context of retail chain stores in Czechia. The reason for this is the fact that the issue of reconciling pro-environmental beliefs of consumers with their real behavior is still topical. Businesses need to be careful with their green claims and focus on authentic green marketing in order to attract and retain the trust of environmentally conscious consumers in the long term. The research employs quantitative data analysis, drawing data from the survey, which was run online for five weeks and collected 4700 responses. The respondents are people who live in Czechia and have shopped in one of five stores at least during the last month. The reason for focusing on the Czechia is primarily the fact that green marketing is basically only on the rise here, while greenwashing still remains a significant problem. Six hypothesis were formulated, and linear regression analysis was used to test them. Key findings of the research revealed that green products and promotions positively influence brand associations and perceived brand quality, while green promotions significantly enhance brand loyalty and trust. Additionally, there was observed influence of consumers´ environmental concerns and consideration of future consequences significantly moderating the relationship between green marketing and brand equity. The findings provide insight for businesses to integrate green marketing strategies to increase brand trust, loyalty, and perceived quality while environmentally conscious consumers.

Indonesia ranks as the second-largest source of plastic garbage in marine areas, behind China. This is a critical problem that emphasises the need for synergistic endeavors to safeguard the long-term viability of marine ecosystems. The objective of this work is to examine the implementation of the Penta Helix model in the management of marine plastic trash. For this purpose, a Systematic Literature Review (SLR) was carried out, utilizing scholarly papers sourced from the Science Direct, Scopus, and Web of Science databases. The analysis centred on evaluating the Penta Helix model as a cooperative framework for tackling plastic waste management in the marine environments of Indonesia and China. The results suggest that the Penta Helix methodology successfully enables the amalgamation of many interests and resources, making a valuable contribution to the mitigation of plastic pollution in the waters of both nations. In order to advance a more comprehensive and sustainable approach to plastic waste management, this multidisciplinary plan brings together stakeholders from government, academia, business, civil society, and the media. Under this framework, the government is responsible for formulating laws, guidelines, and programs to decrease the use of disposable plastics and improve waste management infrastructure, all while guaranteeing adherence to environmental constraints. Simultaneously, the industrial and academic sectors are responsible for creating sustainable technology and pioneering business strategies, while civil society, in collaboration with the media, has a crucial role in increasing public consciousness regarding the destructive effects of plastic trash. This comprehensive strategy emphasizes the need of synergistic endeavors in tackling the intricate issues of marine plastic contamination.

In this paper advanced Sentiment Analysis techniques were applied to evaluate public opinions reported by rail users with respect to four major European railway companies, i.e., Trenitalia and Italo in Italy, SNCF in France and Renfe in Spain. Two powerful language models were used, RoBERTa and BERT, to analyze big amount of text data collected from a social platform dedicated to customers reviews, i.e., TrustPilot. Data concerning the four European railway companies were first collected and classified into subcategories related to different aspects of the railway sector, such as train punctuality, quality of on-board services, safety, etc. Then, the RoBERTa and BERT models were developed to understand context and nuances of natural language. This study provides a useful support for railways companies to promote strategies for improving their service.

Brain tumors are a primary factor causing cancer-related deaths globally, and their classification remains a significant research challenge due to the variability in tumor intensity, size, and shape, as well as the similar appearances of different tumor types. Accurate differentiation is further complicated by these factors, making diagnosis difficult even with advanced imaging techniques such as magnetic resonance imaging (MRI). Recent techniques in artificial intelligence (AI), in particular deep learning (DL), have improved the speed and accuracy of medical image analysis, but they still face challenges like overfitting and the need for large annotated datasets. This study addresses these challenges by presenting two approaches for brain tumor classification using MRI images. The first approach involves fine-tuning transfer learning cutting-edge models, including SEResNet, ConvNeXtBase, and ResNet101V2, with global average pooling 2D and dropout layers to minimize overfitting and reduce the need for extensive preprocessing. The second approach leverages the Vision Transformer (ViT), optimized with the AdamW optimizer and extensive data augmentation. Experiments on the BT-Large-4C dataset demonstrate that SEResNet achieves the highest accuracy of 97.96%, surpassing ViT’s 95.4%. These results suggest that fine-tuning and transfer learning models are more effective at addressing the challenges of overfitting and dataset limitations, ultimately outperforming the Vision Transformer and existing state-of-the-art techniques in brain tumor classification.

Conversion of the ocean’s vertical thermal energy gradient to electricity via OTEC has been demonstrated at small scales over the past century. It represents one of the planet’s most significant (and growing) potential energy sources. As described here, all living organisms need to derive energy from their environment, which heretofore has been given scant serious consideration. A 7th Law of Thermodynamics would complete the suite of thermodynamic laws, unifying them into a universal solution for climate change. 90% of the warming heat going into the oceans is a reasonably recoverable reserve accessible with existing technology and existing economic circumstances. The stratified heat of the ocean’s tropical surface invites work production in accordance with the second law of thermodynamics with minimal environmental disruption. TG is the OTEC improvement that allows for producing two and a half times more energy. It is an endothermic energy reserve that obtains energy from the environment, thereby negating the production of waste heat. This likewise reduces the cost of energy and everything that relies on its consumption. The oceans have a wealth of dissolved minerals and metals that can be sourced for a renewable energy transition and for energy carriers that can deliver ocean-derived power to the land. At scale, 31,000 one-gigawatt (1-GW) TG plants are estimated to displace about 0.9 W/m² of average global surface heat into deep water, from where, at a depth of 1000 m, unconverted heat diffuses back to the surface and is available for recycling.

This review focuses on ferrites, which are gaining popularity with their unique properties like high electrical resistivity, thermal stability, and chemical stability, making them suitable for versatile applications both in industry and in biomedicine. This review is highly indicative of the importance of synthesis technique in order to control ferrite properties and, consequently, their specific applications. While synthesizing the materials with consideration of certain properties that help in certain methods of preparation using polyol route, green synthesis, sol-gel combustion, or other wise to tailor make certain properties shown by ferrites, this study also covers biomedical applications of ferrites, including magnetic resonance imaging (MRI), drug delivery systems, cancer hyperthermia therapy, and antimicrobial agents. This was able to inhibit the growth of all tested Gram-negative and positive bacteria as compared with pure ferrite nanoparticles without Co, Mn or Zn doping. In addition, ferrites possess the ability to be used in environmental remediation; such as treatment of wastewater which makes them useful for high-surface-area and adsorption capacity due heavy metals and organic pollutants. A critical analysis of functionalization strategies and possible applications are presented in this work to emphasize the capability of nanoferrites as an aid for the advancement both biomedical technology and environmental sustainability due to their versatile properties combined with a simple, cost effective synthetic methodology.